# embedding_metadata

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**Summary:**
```python
TFTransfoXLLMHeadModel: A transformer-based language modeling model head implementation utilizing TensorFlow backend.

Key Features:
  - Initializes with 'tf' backend support

Implementation Details:
  - Requires TensorFlow (TF) backend support during initialization

Dependencies:
  - TensorFlow (TF)
```

**Contextual Code Summary:**
```python
class TFTransfoXLLMHeadModel(metaclass=DummyObject):
    _backends = ["tf"]  # Specifies the supported backends for this model head

    def __init__(self, *args, **kwargs):  # Initializes a new instance of TFTransfoXLLMHeadModel
        requires_backends(self, ["tf"])  # Verifies that the 'tf' backend is explicitly set during initialization
```

This summary highlights the key aspects of the class, including its purpose, implementation details, and dependencies. The contextual code snippet provides additional context on how the class is instantiated and verified.


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**Summary**

The `TFTransfoXLMainLayer` class is the main entry point of a transformation layer in a machine learning pipeline. It inherits from `DummyObject` and uses TensorFlow (`tf`) as its backend.

**Key Details**

* Primary purpose: Serve as the main transformation layer for TFTransfoXL.
* Implementation:
	+ Requires TensorFlow (tf) as a backend using the `requires_backends` function.
	+ Initializes with variant arguments (`*args`, **kwargs`).
* Notable dependencies:
	+ tensorflow (tf)
	+ DummyObject (基类, not implemented)

**External Dependencies**

* tensorflow (tf)


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**Summary:**

*   Class: `TFTransfoXLModel`
*   Purpose: Represents a transformable XL Model using TensorFlow backend
*   Primary purpose: Implements the model's initialization process

**Implementation Details:**

*   Required backends: Only supported by TensorFlow (`tf` backend)

**Key Method Signature:**
```python
def __init__(self, *args, **kwargs):
```

This class initializes a TFTransformable XL Model with support for the TensorFlow backend. The `requires_backends` function ensures that only the TensorFlow backend is used during model initialization.

Notable Dependencies:
*   `DummyObject`: Used as the metaclass for this class
*   TensorFlow library (`tf`)


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**TFTransfoXLPreTrainedModel Class Summary**

The `TFTransfoXLPreTrainedModel` class is an implementation of the TF backend for the Hugging Face Transformers library. It inherits from `DummyObject`.

**Key Features:**

* Primary purpose: Pre-trained model for transformer-based architectures using the TF backend.
* Key implementation details:
	+ Requires TensorFlow as a backend, ensuring its deployment on compatible environments.
	+ Utilizes the `requires_backends` function to validate required backends during initialization.

**Notable Dependencies and Requirements:**

- **External Dependency:** TensorFlow (required backend)
- **Implemented Interface:** `DummyObject` (metaclass)


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**Summary:**

`TFDistilBertForMaskedLM` is a masked language model built using the TensorFlow backend (`[tf]`). The class initializes with a `DummyObject` metaclass and requires the TF backend for runtime.

**Key Implementation Details:**

* **Class Signature:** `TFDistilBertForMaskedLM(*args, **kwargs)`
* **_backend Requirements:** `[tf]`

**Notable Dependencies:**

* External Dependency: `tf`
* Implemented Interface: `_backends` (inherits a list of required backends)


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**Summary:**

`TFDistilBertForMultipleChoice` is a pre-trained language model based on DistilBERT and BERT architectures, utilizing the TensorFlow backend.

```python
**Class Overview**
-----------------

*   `TFDistilBertForMultipleChoice`
*   Meta-class: `DummyObject`

**Key Implementation Details**
------------------------------

*   Primary Functionality: Supports multiple-choice reasoning
*   Engineered using DistilBERT and BERT architecture

**Notable Dependencies/Requirements**
--------------------------------------

*   Backend: TensorFlow (`tf`)

**Notes:**

This class requires the `tf` backend to be available.
```

I applied the following guidelines:
1.  Focused on providing a clear summary of the class purpose and functionality.
2.  Highlighted key implementation details, including primary functionality and notable dependencies.
3.  Preserved exact class name (`TFDistilBertForMultipleChoice`) in the summary.


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**Summary**

`TFDistilBertForQuestionAnswering` is a pre-trained question answering model using the DistilBERT architecture, optimized for TensorFlow backend. The class initializes with the required TensorFlow backend and no additional arguments.

**Key Details**

*   `requires_backends`: Verifies that the instance only supports TensorFlow (TF) backend.
*   `_backends = ["tf"]`: Specifies the supported backends.

**Context**: This code is likely part of a larger library or framework for building question answering models. The use of DistilBERT architecture suggests efficient features extraction capabilities, making it suitable for large-scale deployment applications.


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**Summary**

`TFDistilBERT Model for Sequence Classification`

* `_backends` specification: Requires TensorFlow (TF) backend
* Primary purpose: Implementation of the `TFDistilBertForSequenceClassification` model using TensorFlow (TF)
	+ Utilizes `metaclass=DummyObject`
	+ Applies `requires_backends` function to specify required backends (`tf`) for this class
* Key implementation detail:
	+ No specific algorithm or pattern details are provided in this snippet, implying that the actual implementation is likely contained within other methods or classes.


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**TFDistilBertForTokenClassification Class Summary**

**Main Purpose and Functionality**
The `TFDistilBertForTokenClassification` class is a PyTorch implementation of the DistilBERT model for token classification tasks. It utilizes the TensorFlow backend.

**Key Implementation Details**

* Utilizes the `DummyObject` metaclass.
* Requires the `tf` backend to function.

**Notable Dependencies and Requirements**

* Inherently uses TensorFlow (`tf`) as its backend.
* Expects the presence of related classes/functions from the `requires_backends` method ( implementation details not shown in this snippet).


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**Summary:**

`TFDistilBertMainLayer` class:
=====================================

*   Primary purpose: Main layer of the DistilBERT model implemented using TensorFlow (`tf`) backend.

**Implementation Details:**

*   The `__init__` method initializes the layer with `tf` as a required backend.
*   The `requires_backends` function ensures that the necessary backend is available before initializing the instance.

**Notable Dependencies and Requirements:**

*   TensorFlow (`tf`)
*   `DummyObject` metaclass


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**Class Summary: `TFDistilBertModel`**

**Purpose and Functionality:** The `TFDistilBertModel` class is a wrapper around the BERT model using DistilBERT architecture. It is designed to work with the TensorFlow backend.

**Key Implementation Details:**

* Initializes the model instance, ensuring it requires the TensorFlow backend (`requires_backends(self, ["tf"])`) for running.
 

**Notable Dependencies and Requirements:**
- TensorFlow backend (backend engine)

This summary captures the core functionality and essential details of the `TFDistilBertModel` class.


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**TFDistilBertPreTrainedModel Class Summary**

*   **Primary Purpose**: Instantiated model for TFDistilBert, a pre-trained variant of Distillation BERT.

**Key Implementation Details**

*   Uses the "tf" backend for compilation.
*   Requires the "tf" backend in its constructor invocation (`requires_backends` method).

**Notable Dependencies and Requirements**

*   Inherits from `DummyObject` metaclass.
*   External dependency: "tf" (TensorFlow backend).


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**Summary:**

`TFDPRContextEncoder` is a class that provides context encoding for the TFDPR inference backend. It initializes an instance with the required `tf` backend.

**Key Details:**

- **Class Inheritance:** Uses `DummyObject` as its metaclass.
- **Backend Requirements:** Requres the `"tf"` backend to be provided at instantiation time.
- **Method Signatures:**
  - `__init__(*args, **kwargs)`: Initializes the instance with required backends.

**Notable Dependencies:**

- `DummyObject`: Used as the metaclass for the class.
- `tf`: Required backend dependency.


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**Class Summary:**

`TFDPRPretrainedContextEncoder`
=====================================

A class using the TensorFlow backend for pre-trained context encoding.

**Key Implementation Details:**
- Initializes with `*args` and **`kwargs`, enforcing `tf` backend requirement via `requires_backends`.

**Notable Dependencies/Requirements:**
- External Dependency: TensorFlow (`tf`) backend.


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**Summary**

The `TFDPRPretrainedQuestionEncoder` class is a custom encoder designed for question-answering tasks. It inherits from `DummyObject`, using TensorFlow (TF) as its backend.

**Key Details:**

*   Implementing TFDPRPretrainedQuestionEncoder requires the TF backend.
*   Uses custom class with metaclass "DummyObject"


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**Summary:**

TFDPR Pretrained Reader Class (tf)

* A class utilizing the `DummyObject` metaclass, specifically designed to leverage the `tf` backend.
* Initializes with `args` and `kwargs`, enforcing the requirement of the `tf` backend.

**Key Takeaways:**

* Primary purpose: Leverages the `tf` backend for TFDPR (TensorFlow DistilBERT Pretraining) functionality.
* Notable Details:
	+ Method Signature: `__init__` initializes with `args` and `kwargs`, while enforcing the `tf` backend requirement using `requires_backends`.
	+ Backend Reference: `_backends = ["tf"]` explicitly specifies the use of the `tf` backend.

**Implementation Focus:**

* The class primarily leverages the `tf` backend, making it a suitable choice for applications requiring TFDPR functionality with TensorFlow.
* The `requires_backends` method ensures that the required `tf` backend is available during initialization.


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**TFDPR Question Encoder Class Summary**

* **Class Overview**: `TFDPRQuestionEncoder` is a custom encoder class using the TF backend for natural language processing tasks.
* **Primary Functionality**: Encodes questions for Turing Fluency Difficulty Prediction (TFDPR) models.
	+ Initializes with required backends (`tf`)
* **Key Implementation Details**:
	+ Requires `tf` backend for initialization
	+ Utilizes `requires_backends` function to validate backend configuration
* **Notable Dependencies**:
	+ `DummyObject` metaclass
	+ `tf` backend library (unspecified version)


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**Summary:**

TFDPRReader Class - Reads TFDPR data using TensorFlow backend.

**Key Details:**

- Purpose: Reads from "tf" backend
- Primary Method: `__init__` initializes reader with required "tf" backend

**Notable Implementation Highlights:**

- `requires_backends` method ensures "tf" backend is available
- Exact backend used: ["tf"]

**Dependencies/Requirements:**

- TensorFlow (TF) backend
- DummyObject metaclass


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**Summary:**
```python
TFElectraForMaskedLM Class Summary:

 Purpose:
  - A masked language model (MLM) implementation of TFElectra with 'tf' backend support.

 Key Implementation Details:
  - Requires TensorFlow ('tf') backend for initialization.

 Notable Dependencies and Requirements:
  - TensorFlow (external dependency)
```
**Breakdown:**

* **Purpose**: Implements the TFElectra mask language model with support for TensorFlow backend.
* **Key Implementation Detail**: Requirements checking using `requires_backends` method to ensure 'tf' backend is available during initialization.
* **Notable Dependencies and Requirements**: Dependents on TensorFlow (TF) library as a required external dependency.


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**Class Summarization: TFElectraForMultipleChoice**

*   **Purpose**: This class implements the TickerForMultipleChoice functionality with TensorFlow backend support (`"tf"`).
*   **Key Implementation Details**:
    *   The `__init__` method initializes the object, requiring TensorFlow (`"tf"`) as a backend.
    *   It checks if the required backends are provided using the `requires_backends` method.
*   **Notable Dependencies/Requirements**:
    *   Expects at least TensorFlow (`"tf"`) to be installed and configured.

(Note: The `DummyObject` metaclass is not explicitly analyzed as it is used for class definition purposes, not explicitly altering behavior or implementation details.)


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**Summary:**

The `TFElectraForPreTraining` class is a customization of the Hugging Face model for pre-training using TensorFlow (TF) as its backend.

*   **Main Purpose and Functionality:** Customizes Hugging Face model for pre-training, utilizing TF backend.
*   **Key Implementation Details:** 
    *   Requires TensorFlow backend using `requires_backends` function.
    *   Utilizes a list of supported backends (`_backends = ["tf"]`).
*   **Notable Dependencies or Requirements:**
    *   Uses Hugging Face library for model customization.
    *   Requires TensorFlow (TF) as its underlying backend.


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**Concise Summary:**

* Class Name: `TEElectraForQuestionAnswering`
* Purpose: A Question Answering model built on top of TFElectra architecture.

**Key Implementation Details:**

* Uses `DummyObject` class as metaclass
* Requires TF backend (tf) by default

**Notable Dependencies:**

* External dependency: tf (TF library)
* Interfaces:
	+ `requires_backends(self, ["tf"])`: Ensures use of specific backends


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**Summary**

The `TFElectraForSequenceClassification` class is a wrapper for the Hugging Face Transformers library's Electra model. It provides a convenient interface using the TensorFlow backend (`_backends = ["tf"]`). The constructor initializes the object, ensuring that the required `TF` backend is present.

**Key Implementation Details**

* Initializes with `requires_backends` to ensure presence of `TF` backend
* Uses Hugging Face Transformers' Electra model

**Notable Dependencies/Requirements**

* TensorFlow (`_backends = ["tf"]`)
* Hugging Face Transformers library (`class TFElectraForSequenceClassification`)


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**Summary:**

The `TFElectraForTokenClassification` class is a machine learning model implementation using the Transformers library for token classification. Specifically, it utilizes the Electra algorithm behind the scenes, focusing on TensorFlow (`"tf"` backend).

**Key Details:**

* The class is specialized to work with only the "tf" backend.
* It requires the "tf" backend to be available at initialization.

**Implementations and Dependencies:**
No specific implementation details are provided. However, it assumes the presence of an `DummyObject` metaclass and an existing implementation in Transformers (`transformers`) library for Electra algorithm.
 
 **Requirements/Meta-Dependencies:** 
 None explicitly mentioned but relies on availability of the `"tf"` backend.

The "dummy" class metaclass is specific to this project, but no information about it is provided here. In a real-world code base analysis this summary would be expanded with additional features


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**Class Summmary: `TFElectraModel`**

* **Purpose:** A variant of the Electra model for natural language processing tasks with TensorFlow as the backend.
* **Primary Functionality:** Initializes and validates Electra model parameters using the `requires_backends` function.
 
**Key Implementation Details:**
- Uses the `DummyObject` metaclass.
- Defines `_backends` attribute with a single value `"tf"`, indicating TensorFlow is the required backend.

**Dependencies/Requirements:**

* `requires_backends` function, referenced by exact name (`dummy_object_requires_backends`)


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**T FElectra Pre-Trained Model Class Summary**

*   **Purpose and Functionality:** TFElectraPreTrainedModel is a class representing a pre-trained electra model for TensorFlow backend.

    The model uses the `DummyObject` metaclass and serves as the base class for Electra models. It requires the `tf` backend to operate.


*   **Key Implementation Details:**

    *   The `__init__` method takes variable arguments (`args`) and keyword arguments (`kwargs`). It also checks which backends are required (specifically, `["tf"]`) using the `requires_backends` function.

        ```python
def requires_backends(self, backedends):
    """Checks if required backends are implemented."""
```

*   **Notable Dependencies or Requirements:** The `tf` backend is explicitly required for TFElectraPreTrainedModel operations.

            *   
            *   Related Classes/Functions: `DummyObject`, `requires_backends`, `"tf"`. (Note that the class name `TFElectraPreTrainedModel` uses camel case, whereas the code snippet suggests using `T FElectra Pre-Trained Model` instead. These two naming conventions may be inconsistent. If it's necessary for your analysis or summary to match the original code exactly, you can correct this.)


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**TFEncoderDecoderModel Class Summary**

**Main Purpose and Functionality:**
The `TFEncoderDecoderModel` class is an encoder-decoder model designed to be backed by TensorFlow (TF) backend.

**Key Implementation Details:**

* Uses the `DummyObject` metaclass.
* Verifies that only TF backend is required at initialization (`requires_backends(self, ["tf"])`).

**Notable Dependencies and Requirements:**

* TensorFlow (TF) backend (`_backends = ["tf"]`)
 

This summary provides a clean and concise overview of the class's purpose, key features, and dependencies.


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**Summary:**

`TFEsmForMaskedLM` class with `tf` backend support.

*   Initializes context using `requires_backends` function to validate `self` object against `["tf"]`.
*   Metaclass is set as `DummyObject`.

**Key Details:**

*   Backend framework: TensorFlow (`"tf"`)
*   Method Signatures:
    *   `__init__(self, *args, **kwargs)`

**Notable Dependencies and Requirements:**

*   External dependency on `tf`: `requires_backends` function
*   No other explicitly mentioned dependencies or interfaces.


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**Class Summary:**

`TEFesmForSequenceClassification`

*   A PyTorch-based sequence classification model using TFEsM (Transformers for Few-shot Machine Learning) implementation.

**Key Implementation Details:**

*   Initializes the model with `tf` backend, which supports TensorFlow.
*   Verifies that `tf` is one of the required backends using the `requires_backends` function.

**Notable Dependencies and Requirements:**

*   PyTorch
*   TensorFlow (`tf`) as a required backend

**Additional Notes:**

*   The model is subclassed with `DummyObject`, indicating its use case might involve customization or modification for specific tasks.


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**Summary**

The `TFEsmForTokenClassification` class is a token classification model implemented using TensorFlow. Its primary purpose is to perform token classification tasks.

**Key Features**

* Metaclass: `DummyObject`
* Requires backend "tf"
* Uses TensorFlow ( `_backends = ["tf"]` )

**Implementation Details**

* Implementation of the model itself not provided in this code chunk

The class requires a TensorFlow backend to be instantiated (`requires_backends(self, ["tf"])`).


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**Summary:**

Creates a model for TensorFlow backend (`TFEsmModel`) that inherits from `DummyObject`. The model initializes with optional arguments and checks if the required `TFEsm` backend is present.

**Key Details:**

- **Inheritance**: Inheriting from `DummyObject`
- **Backend Check**: Verifies presence of `tf` backend
- **Primary Method**: `__init__`

**Notable Dependencies:**

- None explicitly listed, but implies presence of `TFEsm` class and `requires_backends` function.


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**Summary:**

`TFEsmPreTrainedModel`: A pre-trained model class using the `tf` backend.

**Key Details:**

- Initializes with one or more backends (`requires_backends` checks for `["tf"]`)
- Uses the `DummyObject` metaclass
 

Note: No key algorithms, patterns or design choices were found in this snippet.


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**Summary:**

The `TFFlaubertForMultipleChoice` class is a multiple-choice question model that leverages the `tf` backend. Its primary purpose is to provide a platform for generating and evaluating multiple-choice questions.

**Key Implementation Details:**

*   The class uses the `DummyObject` metaclass, indicating its unique structure.
*   It has an `_backends` attribute specifying the supported backend (`"tf"`).
*   The `__init__` method requires the `"tf"` backend to be established.

**Notable Dependencies and Requirements:**

*   Requires the `"tf"` backend (`requires_backends(self, ["tf"])`) to function.


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**Class Summary: TFFlaubertForQuestionAnsweringSimple**

The `TFFlaubertForQuestionAnsweringSimple` class is a machine learning model designed for question-answering tasks. It is a variant of the FLaubert algorithm, utilizing the TensorFlow backend.

**Key Implementation Details:**

* Initializes with `tf` backend support.
* Requires the `tf` backend at instantiation.

**Notable Dependencies and Requirements:**

* `tf`: The TensorFlow framework is a primary dependency for this class.
* `DummyObject` (metaclass): Used to define the metaclass for this class.


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**Class Summary**

`TFFlaubertForSequenceClassification`: A machine learning model class for sequence classification using TensorFlow (`tf`) backend.

**Key Functionality and Implementation Details:**

- Initializes the model with support for TensorFlow backend.
- Verifies that the required TensorFlow backend is used.

`requires_backends()` function is not shown in this snippet, but it is assumed to be a utility method that checks if the specified backends are installed or configured. 

No notable dependencies or interfaces are mentioned explicitly in this code chunk. However, the model class inherits from `DummyObject`, which might involve additional setup or configuration.

**Method Signatures:**

- `__init__`: Initializes the model with optional arguments (`*args` and `**kwargs`).


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**Summary:**

* **Class:** `TFFlaubertForTokenClassification`
* **Purpose:** A token classification model for deep learning tasks
* **Functionality:** Instantiates the TensorFlow backend and initializes the model

**Key Implementation Details:**

* Uses the `DummyObject` metaclass
* Required backend is TensorFlow (`tf`)
* Initializes with optional arguments `args` and `kwargs`

**Notable Dependencies or Requirements:**

* TensorFlow backend ( TF )


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**Summary:**

This class defines the `TFFlaubertModel` model using the `DummyObject` metaclass. It initializes with TensorFlow (`tf`) backend requirements.

**Key Details:**

* Primary purpose: Initializes TFFlaubertModel model with specified backends.
* Key implementation:
	+ Requires TensorFlow backend (`requires_backends(self, ["tf"])`)
* Notable dependencies:
	+ `DummyObject` metaclass
	+ TensorFlow library


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**Summary:**

### Class Overview
`TFFlaubertPreTrainedModel` is a class model that inherits from `DummyObject` with the `_backends` metadata set to `["tf"]`. It requires the `tf` backend for initialization.

### Primary Purpose:
It is a pre-trained model specifically designed for use with the TensorFlow (TF) backend.

### Key Implementation Details:

*   **Initialization:** The `__init__` method initializes the model by checking if the required `tf` backend is available using the `requires_backends` function.
    *   Method Signature: `__init__(self, *args, **kwargs)`
    *   Functionality: Verifies availability of the TF backend and throws an error if it's not present.

### Dependencies/Requirements:

*   **Backend:** TensorFlow (TF) is the required backend for this model to function correctly.
    *   Reference Class/Index: `DummyObject` (inherited), `requires_backends`
    *   External Dependency: TensorFlow library


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**Summary:**

Class `TFFlaubertWithLMHeadModel`:

*   Primary purpose: Implement a NLP model with TensorFlow backend (`_backends = ["tf"]`).
*   Implementation details:
    *   Initializes the model with variable arguments and keyword arguments, enforcing `tf` backend requirement using `requires_backends`.
*   Notable dependencies:
    +   External dependency: TensorFlow (backend specified in `_backends`).
*   Inherited or implemented interfaces: None explicitly mentioned.

Let me know if you'd like any further analysis!


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**Class Summary:**

`TFFunnelBaseModel` - A base class for models that rely on TensorFlow backends.

**Key Functionality:**

* Initializes the model with support for only TensorFlow (`tf`) backend.
* Verifies that the required backend is specified at initialization.

**Notable Implementation Details:**

* Uses `metaclass=DummyObject` which allows it to be properly registered.
* Calls `requires_backends(self, ["tf"])` in the constructor to ensure the correct backend is used.
* The method signature of `requires_backends(self, ["tf"])` indicates that a specific backend is expected.

**Dependencies and Requirements:**

* TensorFlow (`tf`) - the primary backend supported by this class.


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**Summary**

The `TFFunnelForMaskedLM` class is a TensorFlow-based implementation of the Masked Language Model (MLM) tunnel mechanism. It ensures the required backend (`"tf"` ) is present and initializes the object with various arguments.

*   **Purpose**: Facilitate interaction between masked language models and TensorFlow-specific APIs.
*   **Key Implementation Details**:
    *   Requires at least one of the backend "tf" in its constructor.
    *   Initializes with variable arguments (`*args` and `**kwargs`).
*   **Notable Dependencies or Requirements**:
    *   TensorFlow backend ("tf").
    *   `DummyObject` metaclass.

This summary focuses on the key aspects of the class, providing clarity on its primary purpose, crucial implementation details, and relevant dependencies.


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**Class Summary:**

`TFFunnelForMultipleChoice`

Creates a tunnel for multiple choice functionality using the TensorFlow backend.

**Methods and Details:**

* `__init__(self, *args, **kwargs)`:
	+ Requires the "tf" backend to be specified
	+ Checks if all required backends are present

**Notable Dependencies:**
- `DummyObject` metaclass
- `requires_backends` function (not shown in this code chunk)
- TensorFlow ("tf") backend


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**Class Summary: TFFunnelForPreTraining**

This class, `TFFunnelForPreTraining`, serves as a parameter funnel for pre-training workflows utilizing TensorFlow (TF) backend support through the `TFF` framework.

### Key Implementation Details

- **Backend Requirements**: The class relies solely on the "tf" backend.
  
### Notable Dependencies and Requirements
 
- `_backends` dictionary: specifies supported backends
- `requires_backends` function: ensures compliance with specified backends


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**Summary:**

*   Class Name: `TFFunnelForQuestionAnswering`
*   Purpose: Implements a question-answering processing pipeline using TensorFlow (TF) as the backend.

**Detailed Analysis:**

### Key Features and Functionality:

*   Uses the `DummyObject` metaclass.
*   Expects TF as its primary backend.
*   Initializes with variable arguments (`*args`, `**kwargs`) but has a hard requirement for the "tf" backend being present via `requires_backends`.

### Implementation Insights:

*   The constructor utilizes `requires_backends` to enforce the presence of the required TF backend, ensuring this class is used in a valid configuration.
*   Note that no specific initialization or preparation steps are executed during class instantiation; it's prepared but not set up.

### Notable Dependencies and Requirements:

*   **Backend**: TensorFlow (TF)
*   **Interface Implemented**: An abstract interface for backend requirements is acknowledged through `requires_backends`.


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**Class Summary: TFFunnelForSequenceClassification**

### Purpose and Functionality
TFFunnelForSequenceClassification is a machine learning model designed for sequence classification tasks using the TensorFlow backend.

### Key Implementation Details
- Uses the `"tf"` backend.
- Requires any additional arguments to be passed through the `requires_backends` method, ensuring proper backend requirements are met.

### Notable Dependencies and Requirements
- Requires the `"tf"` backend to function.


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**Summary:**

`TFFunnelForTokenClassification` class: A meta-class that defines a funnel for token classification tasks using the TensorFlow backend.

**Main Purpose and Functionality:**
Implementing a funnel for token classification tasks with support for the TensorFlow backend.

**Key Implementation Details:**

* Uses `tf` as the primary backend.
* Initializes with required dependencies.

**Notable Dependencies or Requirements:**

* TensorFlow (`tf`)
 

**Metaclass:** `DummyObject`


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**Class Sumary:**

The `TFFunnelModel` class is a data model that utilizes TensorFlow (`"tf"` backend) and initializes with optional arguments.

**Key Implementation Details:**

* Initializes with `tf` backend using the `requires_backends` method.

**Notable Dependencies/Requirements:**

* Expects `"tf"` backend to be passed in during initialization.


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**Class Summary: TFFunnelPreTrainedModel**

*   **Purpose:** The `TFFunnelPreTrainedModel` class implements a pre-trained model that utilizes the TensorFlow backend.

*   **Key Implementation Details:**
    *   Initializes with TF backend support (`requires_backends(["tf"])`)
    *   `DummyObject` metaclass ensures proper object creation

*   **Notable Dependencies/Requirements:**

    *   References the "tf" backend
    *   Does not explicitly list any external dependencies or interfaces


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**TFGPT2DoubleHeads Model Class Summary**

### Main Purpose and Functionality
The `TFGPT2DoubleHeadsModel` class is an implementation of the TFGPT2 model using the TensorFlow backend.

### Key Implementation Details
- The model requires the TensorFlow backend (`tf`) to be provided during initialization.
- Initializes with `*args`, `**kwargs` for flexibility in model configuration.

### Notable Dependencies or Requirements
- **Backend:** `tf` (TensorFlow)
- **Interfaces/Methods:** `requires_backends(self, ["tf"])` ( Verifies required backend availability)

### Class Signature and Properties
- Class Name: `TFGPT2DoubleHeadsModel`
- Metaclass: `DummyObject`
- Requires TensorFlow backend (`_backends = ["tf"]`)


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**Summary:**

`TFGPT2ForSequenceClassification` is a PyTorch-based neural network model for sequence classification. It uses the TF backend and initializes with a variable number of arguments (`*args`, `**kwargs`). The `__init__` method enforces the use of the TF backend.

**Key Details:**

- **Model Class:** `TFGPT2ForSequenceClassification`
- **Backend:** `tf`
- **Interfaces/Methods:** `requires_backends`

**Notable Dependencies:**

- `DummyObject` (metaclass)
- `tf` (backend)

Note: The `dummy_object` metaclass is likely a custom implementation for this project, which might not be widely used or referenced outside of the specific context.


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**Summary:**

*   Class: `TFGPT2LMHeadModel`
*   Main Purpose: Implements a transformer-based language model head for TFGPT2 with support for TensorFlow backend.
*   Primary Functionality: 
    +   Initializes the model with required backends (currently only supported by "tf")
*   Key Implementation Details:
    +   `__init__` method initializes the model, requiring TensorFlow backend
*   Notable Dependencies and Requirements:
    +   Relies on `requires_backends` function for backend validation
    +   Requires TensorFlow backend implementation (`_backends = ["tf"]`)


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**Summary:**

`TFGPT2MainLayer` class:
 Primary purpose: Implements the main layer for the TFGPT2 model.


*   Initializes the TFGPT2 model with a single backend ("tf").
*   Ensures that required backends are present (`requires_backends`).


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**Class Summary**

`TFGPT2Model`
================

### Purpose and Functionality

The `TFGPT2Model` class is an implementation of the GPT-2 model using TensorFlow backend.

### Key Implementation Details

* Initializes with `require_backends` to ensure use of TensorFlow.
* Method signatures and interfaces are not explicitly defined in this snippet, but it inherits from a metaclass (`DummyObject`) indicating a potential focus on abstracted base classes or generic implementations.

### Dependencies and Requirements

* External dependencies:
	+ TensorFlow
* Related classes/functions:
	+ `requires_backends` (function)
	+ `DummyObject` (metaclass)


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**Summary:**

The `TFGPT2PreTrainedModel` class is a pre-trained model for transformer-based language processing. 

**Key Details:**

* `__init__` method initializes the model with supports TF backend.
* The model requires access to the `tf` backend.

**Notable Dependencies:**

* `dummy_object` metaclass
* `tf` backend


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**Summary:**

The `TFGPTJForCausalLM` class is a custom transformer architecture implementation for causal language models. It inherits from the `DummyObject` metaclass and has the following key characteristics:

* Primary purpose: Implementation of TFGPTJ (Transformer, Few-Shot Prompt Tampering, Joint) model variant for causal language modeling.

**Key Implementation Details:**

* The class requires TensorFlow (`tf`) backend to be specified.
* The `__init__` method initializes the instance with variable arguments and keyword arguments.

**Notable Dependencies or Requirements:**

* `DummyObject`: metaclass used for instantiation
* `requires_backends`: function that enforces the use of a specific backend (TF in this case)
* TensorFlow (TF) library


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**Summary:**

`TFGPTJForQuestionAnswering` Class Summary:

*   **Main Purpose:** Instantiation of the TFGPTJ model for question answering tasks using TensorFlow backend.
*   **Initialization:**
    *   Requires the `tf` backend for initialization.
    *   Accepts arbitrary arguments and keyword arguments (`*args` and `**kwargs`).


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**TFPJTJForSequenceClassification Class Summary**

**Main Purpose and Functionality:**
The `TFPJTJForSequenceClassification` class is an implementation of a tensor flow-based sequence classification model. It belongs to the TFP (TensorFlow Probability) library, which provides probabilisticmachine learning primitives.

**Key Implementation Details:**

*   The `__init__` method initializes the model and requires the "tf" backend using the `requires_backends` function.
    *     Method Signatures:
        - `__init__(*args, **kwargs)`
    *   `_backends = ["tf"]`: specifies that this class supports the TensorFlow backend.

**Notable Dependencies or Requirements:**

*   The TFP (`TensorFlow Probability`) library
*   External dependencies: TensorFlow


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**TFGPTJModel Class Summary**

*   **Purpose**: A machine learning model class specifically designed for TFGPTJ algorithm ('tf' backend).

**Key Implementation Details:**

*   The `__init__` method checks if the required 'tf' backend is provided during initialization.
*   It utilizes a custom `requires_backends` function, which is not shown in this snippet.

**Notable Dependencies and Requirements:**

*   Exactly one 'tf' backend is required.


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**Concise Summary:**

`TFGPTJPreTrainedModel`

*   Class that inherits from `DummyObject`.
*   Exclusive backend support for TensorFlow (`_backends = ["tf"]`).
*   Initializes the model, requiring TensorFlow as a backend.

**Key Implementation Details:**

*   Requires an instance with `requires_backends(self, ["tf"])`.

**Notable Dependencies/Requirements:**

*   Exclusive backend dependency on TensorFlow.
   


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**Summary:**

The `TFGroupViTModel` class is a custom TensorFlow model that inherits from the `DummyObject` metaclass. It requires at least the "tf" backend and initializes with additional arguments.

**Key Details:**

* Primary purpose: Instantiation of a TensorFlow model.
* Important implementation:
	+ Initializes the model with `requires_backends`.
* Notable dependencies:
	+ Backends required: `tf`
	+ Interface implemented: None (no inheritance or interface implementation specified).

Note: The `DummyObject` metaclass is not shown in this code snippet, but it's implied to be a custom metaclass used for the model.


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**TFGroupViTPreTrainedModel Class Summary**

* **Purpose and Functionality**: Pre-trained model variant for TF backend support.
*   
    Main purpose: To provide pre-trained models compatible with the `tf` backend.

**Key Implementation Details**:

   - Checks if the required TF backend is present (`requires_backends()`).
* 
   - Method Signature: `__init__(self, *args, **kwargs)` (initializer)

**Notable Dependencies or Requirements**:

   - Backend Support: `tf`
   - Required Backend Check Function: `requires_backends()`
   - Inherited Interface: None
   - Implemented Interface: None

**Summary Summary**


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**TFGroupViTTextModel Class Summary**

* **Main Purpose and Functionality**: A custom text model class utilizing the TensorFlow (TF) backend.

* **Key Implementation Details**:
	+ Initializes with `*args` and `**kwargs`.
	+ Requires TF backend using the `requires_backends` method.
	+ Uses a `DummyObject` metaclass.

* **Notable Dependencies or Requirements**:
	+ External dependency: TensorFlow (TF).
	+ Inherited/implemented interface: None.


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**Summary:**

`TFGroupViTVisionModel` class:
```markdown
*   Class: TFGroupViTVisionModel (inherits from DummyObject meta-class)
*   PRIMARY PURPOSE: Models a custom vision processing pipeline using TensorFlow backend.

    -   Backends: Supports only TensorFlow backend ('tf')
```
**Implementation Details:**

*   Initialization process: Requires TensorFlow backend for instance creation (`requires_backends` function)

```python
def __init__(self, *args, **kwargs):
    requires_backends(self, ["tf"])
```

**Dependencies/Requirements:**

*   Inherited Interface: `DummyObject`
*   External Dependency: 
    -   TensorFlow (`'tf'`)


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**TFHubertForCTC Class Summary**

*   **Purpose:** Implements the TFHubertForCTC neural network model, utilizing TensorFlow backend (`"tf"`).

    *   The primary purpose is to provide an environment for training this specific type of CTC (Constrained Conditional Random Fields) loss function on top of a Multitask Fast R-CNN and Fast RPN for Object Detection Model.
*   **Key Implementation Details:**
    *   Initializes the model with TensorFlow backend using `requires_backends(self, ["tf"])`.
*   **Dependencies/Requirements:**

    *   *`DummyObject`*: Used as the metaclass for this class.

        No external dependencies required (`None`).


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**Summary**

`TFHubertModel`
===============

*   Main purpose: Implemented model using TF backend for Hubert-style text embeddings.
*   Primary function:
    *   Initializes the model with the `tf` backend.

Implementation Details
---------------------

*   Algorithm/Pattern: Uses the TF backend.
*   Key Method Signatures:
    *   `__init__(self, *args, **kwargs)`: Initializes the model with provided arguments.
*   Dependencies:
    *   External dependency on TF (backend).
        


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**TFHubertPreTrainedModel Summary**

*   **Main Purpose and Functionality**: The `TFHubertPreTrainedModel` class is a TensorFlow-based model derived from the HUBERT model. It allows developers to utilize pre-trained knowledge representations for various NLP tasks.
*   **Key Implementation Details**: 
    *   Implements `DummyObject` metaclass, which suggests that this model does not have its own metaclass and may rely on an external or base class for definitions and behaviors.
    *   Requires the `tf` backend to be available during initialization.
*   **Notable Dependencies and Requirements**:
    *   External dependency: The `tf` (TensorFlow) library, which indicates that this model requires TensorFlow as a dependency to function correctly.

Overall, the `TFHubertPreTrainedModel` provides an interface for utilizing pre-trained knowledge representations in NLP tasks using the HUBERT model, while relying on external dependencies, particularly the TensorFlow backend.


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**Class Summary:**

`TFIdeficsForVisionText2Text`

*   **Purpose:** This class implements a text-to-text transformation using TensorFlow as the backend.
*   **Key Functionality:** It initializes with any number of arguments (`args`) and keyword-only arguments (`kwargs`), ensuring that the required `tf` backend is specified during initialization.

**Implementation Details:**

*   The `__init__` method leverages the `requires_backends` function to enforce the use of the `tf` backend, passing it as a list argument.
*   The class defaults to using TensorFlow (`_backends = ["tf"]`) as the backend engine.

**Dependencies and Requirements:**

*   Used **Backend:** TensorFlow (`"tf"`), leveraging its capabilities for text-to-text transformation.

Note: Since `DummyObject` is not specified, I assume it's a custom metaclass used for this class. For the summary's sake, I've omitted the reference to `DummyObject`.


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**Summary:**

The `TFIdeficsModel` class is a Python model class that utilizes the TensorFlow backend. It inherits from a `DummyObject` metaclass.

**Key Details:**

* Primary purpose: Establishes a connection to the TensorFlow backend.
* Notable implementation:
	+ Requires TensorFlow (`"tf"` backend) for initialization (`requires_backends` method).
	+ Uses the `__init__` method to accept variable arguments and keyword arguments.

**Notable Dependencies:**

* External dependency: TensorFlow (specified in the `_backends` attribute).
* Inherited/interface:
	+ Implies inheritance from a dummy metaclass (`DummyObject`) for instantiation purposes only.


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**TFIdefics Pre-Trained Model Class Summary**

### Overview

The `TFIdeficsPreTrainedModel` class is a pre-trained model implementation using the TensorFlow backend.

### Main Purpose and Functionality

Defines a pre-trained model architecture with the primary purpose of providing a trained model inference function.

* **Primary Class:** `TFIdeficsPreTrainedModel`
* **Backend:** TensorFlow
* **Purpose:** Pre-trained model inference

### Key Implementation Details

* Uses the `tf` backend.
* Requires the `tf` backend for initialization.

### Notable Dependencies or Requirements

* Relies on the `DummyObject` metaclass.
* Depends on the `requires_backends` function for setting up required backends.


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**TFLayoutLMForMaskedLM Class Summary**

### Purpose and Functionality
The `TFLayoutLMForMaskedLM` class is an implementation of the LayoutLM model using TensorFlow backend. It extends the `DummyObject` metaclass.

### Key Implementation Details
- Initializes with `*args` and `**kwargs`, requiring the TensorFlow backend.
- Uses the `_requires_backends` function to validate backend compatibility.

### Notable Dependencies
- Extends `DummyObject` metaclass.
- Utilizes TensorFlow backend (`"tf"`).


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**TFLayoutLMForQuestionAnswering Class Summary**

**Main Purpose:** The `TFLayoutLMForQuestionAnswering` class is a machine learning model implementation designed for question answering tasks.

**Key Implementation Details:**

* **Initialization Method**: The `__init__` method initializes the model with the required backend (`tf`) and validates input parameters.
	+ **Method Signature**: `def __init__(self, *args, **kwargs):`
* **Backend Configuration**: The `_backends` attribute is set to `["tf"]`, indicating that the model uses TensorFlow as its primary backend.

**Notable Dependencies and Requirements:**

* **Related Classes/Functions**: `requires_backends(self, ["tf"])` references the `DummyObject` metaclass.
* **External Dependency**: TensorFlow (`tf`) is required for this class.


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**TFLayoutLMForSequenceClassification Class Summary**

**Purpose:** Pre-trained model for sequence classification tasks using TensorFlow (TF) backend.

**Main Functionality:**

- Inherit from `DummyObject` metaclass.
- Supported backend: TF with requirement checking in the constructor (`requires_backends`).


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**TFLayoutLMForTokenClassification Class Summary**

### Main Purpose and Functionality
The `TFLayoutLMForTokenClassification` class is a PyTorch-based language model for token classification tasks.

### Key Implementation Details

* Initializes with optional arguments (`*args`, `**kwargs`) using the `__init__` method.
* Requires the `tf` backend during instantiation through the `requires_backends` function.

### Notable Dependencies and Requirements
The class relies on the following dependencies:

* `tf`: The TensorFlow backend library.
* `DummyObject`: A metaclass used to define the class structure.

### Code Chunk
```python
class TFLayoutLMForTokenClassification(metaclass=DummyObject):
    _backends = ["tf"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, ["tf"])
```
### Context
This class is likely used for token classification tasks in natural language processing (NLP) and can be integrated with various backend frameworks.


key: chroma:document
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Here is a concise and focused summary:

**TFLayoutLMMainLayer**

*Class*

Primary purpose: A main layer for Tensorflow (TF) backend in the TFLayout LM model.

Key implementation details:
- Initializes with `tf` backend, ensuring compatibility.

Notable dependencies:
- TensorFlow (TF)
- `requires_backends` function from the class namespace

(This summary uses precise technical terms and preserves class names exactly.)


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**Summary:**

`TFLayoutLMModel` is a class for `layout-lm` models using TensorFlow backend.

*   Main purpose and functionality:
    - Class for `layout-lm` models with TensorFlow implementation.
    - Uses the `dummyobject` metaclass, but no further information about it is provided.
    - Has a specific backend (`tf`) supported by this class.
*   Key implementation details:
    - The class initializes with variable length arguments and keyword-only arguments (`self`, **kwargs).
    - Calls `requires_backends` function to enforce support for the 'tf' backend.
        *   This function is not defined within the given code snippet, but it's essential for ensuring compatibility.
*   Notable dependencies or requirements:
    - Uses `DummyObject` metaclass.
    - Required backend: TensorFlow (`tf`).


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**Summary:**

The `TFLayoutLMPreTrainedModel` class is a PyTorch model implementation of the Layout-LM (Lightweight Language Model) architecture. It inherits from a meta-class (`DummyObject`) and has an initialization method that enforces the "tf" backend.

**Key Details:**

* Primary purpose: Implementing the Layout-LM model in TensorFlow.
* Implementation details:
	+ Initializes with "*args" and "**kwargs" for flexibility.
	+ Ensures the "tf" backend is required using `requires_backends()`.

**Dependencies/Requirements:**

* Backend: "tf" (TensorFlow)
* Interfaces/inherited classes not explicitly listed, but assumed to be part of PyTorch API.


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**Summary:**

The `TFLayoutLMv3ForQuestionAnswering` class is an implementation of the Hugging Face model for question answering using TensorFlow backends. The primary purpose is to provide a pre-trained transformer-based model with various layout models.

**Key Details:**

- **Method Signature:** `__init__(*args, **kwargs)`
- **Backend Requirements:** Implements the `requires_backends` method to enforce the use of the TensorFlow backend.

**Notable Dependencies/Requirements:**

- `DummyObject` as a metaclass
- `tf` as a required back-end for the model
- Related dependencies not explicitly listed in this snippet.


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**TFLayoutLMv3ForSequenceClassification Class Summary**

The TFLayoutLMv3ForSequenceClassification class is a PyTorch-based model for sequence classification tasks. It utilizes the TensorFlow backend (`backend="tf"`).

**Primary Purpose**
Create an instance of the TFLayoutLMv3ForSequenceClassification model, requiring the TensorFlow backend.

**Key Implementation Details**

* Uses `requires_backends` function to ensure the use of the TensorFlow backend.
* Initializes the model with arbitrary arguments `args` and keyword arguments `kwargs`.

**Notable Dependencies/Requirements**

* External dependency: TensorFlow (`backend="tf"`)
* Inherited/introduced interfaces: `DummyObject` (metaclass) 

Note: The provided summary only includes the requested information, without adding unnecessary details. It aims to preserve the precision and accuracy of the original code while providing a concise overview of the TFLayoutLMv3ForSequenceClassification class.


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**Summary:** 

`TFLayoutLMv3ForTokenClassification` is a model class utilizing the TensorFlow backend for token classification tasks.

*   Primary purpose: Token classification with LayoutLMv3 model.
*   Key implementation details:
    *   Method signature: `__init__(self, *args, **kwargs)`.
*   Notable dependencies and requirements:
    *   Backend: TensorFlow (`tf`).
    *   Required backend check: `requires_backends(self, ["tf"])`.


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**TFLayoutLMv3Model Class Summary**

### Main Purpose and Functionality
The `TFLayoutLMv3Model` class is a PyTorch-based implementation of the Layernorm-based Transformer (LayNet) for Multilingual Visual Question Answering (MQAVA). It utilizes TensorFlow (TF) backend.

### Key Implementation Details

- Initializes the model with optional arguments (`*args` and `**kwargs`)
- Requires the 'tf' backend to be installed
- Uses `requires_backends` function to verify if required backends are present
- Applies the TFLayoutLMv3Model architecture on top of a transformer encoder

### Notable Dependencies or Requirements

- PyTorch library for dynamic computation graphs and modular code organization.
- TensorFlow (TF) backend, which requires additional installation (`requires_backends`).


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**Summary:**
`TFLayoutLMv3PreTrainedModel`: A TensorFlow-based implementation of the Layout LM model pre-trained for multilingual text generation and layout tasks.

**Key Details:**

- **Primary Purpose:** Provides a pre-trained model with support for multilingual text generation and layout tasks.
- **Implementation Details:**
  - Uses the `tf` backend.
  - Requires the `tf` backend for initialization.
  
**Dependencies and Requirements:**

- **External Dependencies:**
  - TensorFlow library (`tf`)
- **Interfaces/Classes:**
  - Inherits from `DummyObject` metaclass.


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Here is a concise summary of the code chunk:

**TFLEDForConditionalGeneration Class Summary**

This class implements the `TFLEDForConditionalGeneration` framework using the TensorFlow backend.

**Key Details:**

* Initializes with optional arguments (`args` and `kwargs`)
* Requires the TensorFlow backend for operation
* Inherently defines available backends (`_backends = ["tf"]`)

**Notable Dependencies/Requirements:**

* `requires_backends` function (specifically, one that supports "tf" engine)
* TensorFlow library (backend)


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Here is a concise and focused summary of the given code chunk:

**Summary:**

* `TFLEDModel` class with `DummyObject` metaclass
* Primary purpose: Utilizes TensorFlow backend (`_backends = ["tf"]`)
* Initialization checks if required backend is present (`requires_backends(self, ["tf"])`)

**Key Implementation Details:**

* Custom metaclass (`metaclass=DummyObject`)
* Backend usage check in `__init__` method

**Notable Dependencies and Requirements:**

* External dependency on TensorFlow library
* Required backend initialization function (`requires_backends`)


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**TFLEDPreTrainedModel Class Summary**

**Main Purpose and Functionality**
The `TFLEDPreTrainedModel` class is a pre-trained model based on TensorFlow (TF) backend. Its primary purpose is to provide a pre-trained model for text classification tasks.

**Key Implementation Details**
The class uses the `requires_backends` function to ensure that it runs on a TF backend, and it initializes with additional arguments (`*args` and `**kwargs`).

**Notable Dependencies or Requirements**
- Requires the TensorFlow (TF) backend.
 
 **Code is concise but lacks explicit method signature and interface information. It should be rewritten as per best practices for readability and maintainability.**

Here's how it would look like after applying our summary techniques:

```python
class TFLEDPreTrainedModel(metaclass=DummyObject):
    """
    A pre-trained model based on TensorFlow (TF) backend.
    Provides a text classification model.

    :param object
    """

    _backends = ["tf"]
    
    def __init__(self, 
                 input_ids,
                 attention_mask,
                 **kwargs,
                 ):
        """
        Initializes the TFLEDPreTrainedModel instance.
        
        :param input_ids:
            Input IDs for the model.
        
        :param attention_mask:
            Attention mask for the model.
        
        :param object
        """

        self.__class__.requires_backends(["tf"])

```


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**Summary:**

The `TFLongformerForMaskedLM` class is an implementation of the Longformer architecture for masked language modeling using TensorFlow. It creates a backend-aware token sequence model.

**Key Details:**

* Primary purpose: Builds a Longformer-based model for masked language modeling.
* Implementation details:
	+ Uses the TensorFlow backend (`tf`).
	+ Requires the `tf` backend for initialization.
* Notable dependencies and requirements:
	+ The `DummyObject` metaclass is used, but its implementation is not shown.
	+ No explicit external dependencies are listed, as they seem to be managed by the metaclass or implied through the use of TensorFlow.


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**Summary:**

`TFLongformerForMultipleChoice` is a class that inherits from `DummyObject` and uses the TensorFlow backend. It initializes with variables `*args` and `**kwargs`. The constructor requires the presence of the Tensorflow backend (`requires_backends(self, ["tf"])`). 

**In-depth Analysis:**

- **Purpose:** This class seems to be designed for implementing a Longformer-based model for multiple-choice questions using TensorFlow.
- **Key Implementation Details:**
  - Uses the `DummyObject` metaclass.
  - Depends on the Tensorflow backend (`requires_backends(self, ["tf"])`).
- **Notable Dependencies or Requirements:** 
  - TensorFlow (backend-specific implementation)


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**Summary:**

The `TFLongformerForQuestionAnswering` class is a longformer-based question answering model implemented with TensorFlow backend.

**Key Details:**

* Primary purpose: Implement TF Longformer for QA
* Important implementation:
	+ Requires TF backend (`requires_backends(self, ["tf"])`)
	+ Uses metaclass `DummyObject`
* Dependencies/Requirements:
	+ TensorFlow (`_backends = ["tf"]`)


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**Summary**

The `TFLongformerForSequenceClassification` class is a meta-class subclass that inherits from None (due to `_backends` attribute being used for metaclass functionality).

**Key Details**

* Primary purpose: Implementing the Longformer model architecture for sequence classification tasks using TensorFlow as the backend.
* Method Signatures:
	+ `requires_backends(self, ["tf"]):` Verifies if the required backend is available.

**Notable Dependencies/Requirements**

* Backend: TensorFlow (TF)
* No explicit dependencies listed, but inherits from `metaclass=DummyObject`.


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**Summary:**

`TFLongformerForTokenClassification` is a class utilizing the `tf` backend for token classification tasks. It initializes with support for `"tf"` backend and enforces its availability at instantiation.

**Key Implementation Details:**

- Inheritance: Utilizes the `DummyObject` metaclass.
- Initialization Requirements: Verifies the presence of `"tf"` backend (`requires_backends`, ["tf"]).


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**T FLongformer Model Class Summary**

*   **Purpose**: A TensorFlow-based implementation of the Longformer model.

**Key Implementation Details:**

*   The `__init__` method initializes the model with support for only the "tf" backend.
*   It requires a valid "tf" backend to be explicitly specified.

**Notable Dependencies and Requirements:**

*   Relies on the `DummyObject` metaclass.
*   Requires a TensorFlow backend, specifically `"tf"`.

```markdown
Class: TFLongformerModel
 Purpose: Longformer model implementation in TensorFlow
 Backends: ["tf"]
```

This concise summary captures the essential details about the provided class chunk, including its purpose, key implementation detail, and notable dependencies.


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**Summary**

The `TFLongformerPreTrainedModel` class inherits from `DummyObject`. It is a pre-trained model based on the Longformer architecture, specific to TensorFlow backend. 

**Key Implementation Details:**

* Initializes with one or more additional positional arguments (*args) and keyworded arguments (**kwargs)
* Requires TensorFlow as a supported backend
* Implements a custom meta-class (`DummyObject`) for the class definition

**Notable Dependencies/Requirements:**

* Related classes/functions: `_backends`, `requires_backends`, and `DummyObject`
* External dependencies: TensorFlow (TF)


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**Summary:**

The `TFLxmertForPreTraining` class is a machine learning model designed for pre-training using the TFL library. It inherits from a dummy object metaclass.

*   **Purpose**: Pre-trains a language modeling task using the xMERT algorithm.
*   **Key Implementation Details**:
    *   Requires TensorFlow (TFL) backend, specifically version "tf".
    *   Inheriting from DummyObject metaclass is evident but no information on it's usage is provided in code snippet.
*   **Notable Dependencies or Requirements**: TFL (TensorFlow) library.

This class can be used for pre-training language models by providing the necessary backends and configurations. However, without further context, we cannot determine if the model is trained correctly or achieves desired results for the task at hand.


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Here is a concise and focused summary of the provided class code chunk:

**Class Summary: TFLxmertMainLayer**

* Purpose: A main layer for XMERT (eXplainable Model-based TEsting with RTCA) using TensorFlow as the backend.
* Functionality:
	+ Initializes the layer with a requirement for the `tf` backend.
	+ Ensures compatibility of required backends with the specified `tf` backend.

Key Implementation Details:

*   Backends: Supports only TensorFlow (`"tf"`).

Notable Dependencies and Requirements:

*   Requires the `tf` backend.
*   Inherently uses `DummyObject` as a metaclass.


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**Summary:**

The `TFLxmertModel` class is a concrete implementation of an XMERT model using the TensorFlow backend. It initializes with input arguments and ensures compatibility with the "tf" backend.

**Implementation Details:**

* Ensures backend compatibility via the `requires_backends` method.
* Uses the `DummyObject` metaclass.

**Notable Dependencies:**

* `tf`: TensorFlow backend
* `DummyObject`: Metaclass used to define the class

This summary highlights the key aspects of the class code, providing a concise and focused overview for developers to quickly understand its functionality.


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**Summary**

`TFLxmertPreTrainedModel` class creates a model using the TensorFlow backend. It is an instance of `DummyObject`.

**Main Purpose**
Instantiates a pre-trained Xmert model compatible with TensorFlow.

**Key Implementation Details**
- Backend configuration: `"tf"`
- Initialization function: `__init__`
  - Requires TensorFlow for implementation
  - Accepts variable arguments and keyword arguments

**Notable Dependencies/Requirements**

- Related classes/functions: `DummyObject`, `requires_backends`
- External dependencies:
  - TensorFlow (`"tf"`)


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**Summary:**

The `TFLxmertVisualFeatureEncoder` class is a visual feature encoder designed for TFL (Tensorflow-Lite) implementation. It uses the "tf" backend.

**Key Details:**

- **Initialization:** The constructor takes variable arguments (`*args`) and keyword arguments (`**kwargs`). It requires the "tf" backend to be present.
- **Backend Dependency:** `reQUIRES_BACKENDS(self, ["tf"])`

**External Dependencies:**
 
- TFL (Tensorflow-Lite) Backend


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**Concise Summary:**

`TFMarianModel`

*   **Class Type:** `tf.keras.Model`
*   **Primary Purpose:** Build and configure Marian Model with TensorFlow backend.

**Key Implementation Details:**

*   Initializes with variable arguments (`args`) and keyword arguments (`kwargs`).
*   Requires the "tf" backend using the `requires_backends` function.
*   Inherits from TensorFlow's Keras Model class (`metaclass=DummyObject`).

**Notable Dependencies/Requirements:**

*   TensorFlow (`"tf"`): Backend dependency for building the Marian Model.


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**TFMarianMTModel Class Summary**

*   **Purpose**: The `TFMarianMTModel` class represents a Marian MT model integration using the TensorFlow backend, utilizing the `DummyObject` metaclass.

    This class enables the utilization of pre-trained models derived from the Hugging Face Model Hub's MarianMT repository via the TensorFlow framework. It enforces the backend-specific configuration and initialization process for seamless compatibility.
        
        **Key Implementation Details**: 
*   `requires_backends(self, ["tf"])`: Ensures that only the specified backend (`tensorflow` or `tf`) is allowed.

    It checks for backend support and configures internal state according to given backend requirements. This approach enhances maintainability by decoupling the model-specific code from a multitude of frameworks.
        
       **Notable Dependencies**: 
*   None

    No external libraries are explicitly referenced; however, it relies on the Hugging Face Model Hub's MarianMT repository for its pre-trained models.

    Note that any imported modules or components required by this class will ideally be defined at a higher abstraction level within the model implementation.


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**Concise Summary**

`TFMarianPreTrainedModel`

* A PyTorch model class implementing TFMarian pre-training algorithm (`metaclass=DummyObject`).
* Requires TensorFlow backend (`_backends = ["tf"]`).

**Key Details**

* Initializes with `requires_backends(self, ["tf"])`, ensuring the TF backend is available.

**Notable Dependencies/Requirements**

* TensorFlow (TF) backend
* PyTorch framework


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**Summary:**

`TFMBartForConditionalGeneration` is a conditional generation model class built with TensorFlow (`tf`) backend. It requires the `tf` backend for initialization.

**Key Points:**

* Back-end requirement: `tf`
* Model type: Conditional Generation
* Initialization requirements: `[TFMBartForConditionalGeneration]`

**Note:** The exact implementation and key details about this class are likely to be found in its parent model or other related classes, which may not be shown here.


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**TFMBartModel Class Summary**

*   **Purpose and Functionality:**
    *   A variant of the BART model for text-to-text transformations.
    *   Supports only TensorFlow backend.

**Key Implementation Details:**

*   Initializes the model with TensorFlow backend using `requires_backends`.
*   Checks if TensorFlow backend is available at instantiation time (`__init__` method).

**Notable Dependencies and Requirements:**

*   TensorFlow backend (required).
*   `_DummyObject` metaclass.


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**Summary:**

`TFMBartPreTrainedModel`: A PyTorch model class utilizing the `DummyObject` metaclass and relying on the TensorFlow backend (`tf`) for computation.

**Key Features:**

* **Initialize**: Takes variable-length argument list and keyword-argument dictionary
* **Required Backend**: Requires a compatible TensorFlow backend (`tf`) for computation

**Implementation Details:**

* Utilizes `DummyObject` metaclass, which is not explicitly provided in the code snippet


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**TFMistralForCausalLM Class Summary**

### Main Purpose and Functionality
The `TFMistralForCausalLM` class is a custom transformer model for causal language models. It uses the TensorFlow backend.

### Key Implementation Details

* Initializes the model with required backends using `requires_backends`.

### Notable Dependencies or Requirements

* TensorFlow (`tf`) backend:
	+ Inherited interface: `DummyObject`
	+ Implemented interfaces: None
	+ External dependency: None


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**TFMistralForSequenceClassification Class Summary**

* **Main Purpose and Functionality:** A sequence classification model using the TFMistral library with TensorFlow backend.
 
* **Key Implementation Details:**
	+ Requires the TensorFlow backend for initialization.
 
* **Notable Dependencies or Requirements:**
	+ ` DummyObject` metaclass
	+ `tf` (TensorFlow)
	+ `requires_backends` function to verify required backends


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**TFMistralModel Class Summary**

### Purpose and Functionality

The `TFMistralModel` class is an abstract model implementation that utilizes the TensorFlow backend (`"tf"`). It inherits from a metaclass `DummyObject`, which likely provides additional functionality.

### Key Implementation Details

* The `__init__` method checks if the required backend "tf" is provided. If not, it calls the `requires_backends` function.
	+ `requires_backends(self, ["tf"])`: A function call to verify the presence of a specific backend.

### Notable Dependencies or Requirements

* TensorFlow (`"tf"`): The primary backend dependency for this model.

Note: Since no method signatures or interfaces are provided in the given code chunk, they are not included in the summary.


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**TFMistral Pre-Trained Model Class Summary**

**Main Purpose and Functionality:**
This class defines a pre-trained model implementation (`TFMistralPreTrainedModel`) using the `DummyObject` metaclass. It is designed to be used with the TensorFlow backend.

**Key Implementation Details:**
- Initializes the model with supported backends (specifically, TensorFlow) using the `requires_backends` function.
- Uses a variable `_backends` to store the list of supported backends.

**Notable Dependencies or Requirements:**

*   Tensorflow (backend)
*   DummyObject metaclass

No specific dependencies are listed in this code snippet. However, to utilize this class, one would need to have the required backend (Tensorflow) installed and configured within their application.


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**Summary:**

`TFMobileBertForMaskedLM` is an implementation of a masked language model using the TensorFlow backend specifically designed for mobile devices.

**Key Details:**

- Primary purpose: Masked Language Modeling for Mobile Devices
- Important features: `dummy_object` metaclass, requires `tf` backend

**Implementation:**

- Initialization with optional arguments (`*args`, `**kwargs`)
- Backend configuration verification using `requires_backends`

**Dependencies:**

- External dependency on TensorFlow (`"tf"` backend)


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**Summary:**

*Class: `TFMobileBertForMultipleChoice`
---------------------------

*   **Purpose:** This class implements the `TFMobileBertForMultipleChoice` model using TensorFlow backends.
*   **Key Features:**
    +   Uses TensorFlow (`tf`) backend.
    +   Initializes with variable arguments and keyword arguments (`args`, `kwargs`).

**Implementation Details:**

*   Requires TensorFlow backend.

**Notable Dependencies/Requirements:**

*   Relies on the TensorFlow library for model implementation.


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**Summary:**

`TFMobileBertForNextSentencePrediction` is a class for next sentence prediction using mobile BERT model. The primary purpose is to pretrain BERT Model on a task that can be used to predict the likelihood of "next" in a sequence.

**Key Implementation Details:**

* Requires `tf` backend.
* Inherits from `DummyObject` metaclass.
* Utilizes `requires_backends` function for backend setup.

**Notable Dependencies and Requirements:**

* `_backends = ["tf"]`
* External Dependency: `tf` library.


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**Class Summary: TFMobileBERTForPreTraining**

*   **Purpose and Functionality:** A PyTorch implementation of the MobileBERT model for pre-training tasks.

    Class `TFMobileBertForPreTraining` is built on top of TensorFlow. It's primarily used for pre-training MobileBERT, a lightweight variant of BERT.


*   **Key Implementation Details:**
    *   The `__init__` method initializes the class with required backends and performs validation for supported backends using `requires_backends`.
        ```python
def __init__(self, *args, **kwargs):
    requires_backends(self, ["tf"])
```
*   **Notable Dependencies or Requirements:**

    The class depends on PyTorch and TensorFlow with the `"tf"` backend specified.
        ```
_class TFMobileBertForPreTraining(metaclass=DummyObject):_
       `_backends = ["tf"]`

This indicates that this particular implementation of MobileBERT requires access to TensorFlow backend.


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**TFMobileBertForQuestionAnswering Class Summary**

### Class Overview
The `TFMobileBertForQuestionAnswering` class is a machine learning model built on top of TensorFlow (TF) backend.

### Primary Purpose
The primary purpose of this class is to provide pre-trained, mobile-friendly BERT model for question-answering tasks.

### Key Implementation Details

* Uses the `DummyObject` metaclass.
* Requires the TF backend (`requires_backends(self, ["tf"])`).

### Notable Dependencies and Requirements

* TensorFlow (TF) backend:
	+ External dependency: TensorFlow library.


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**Class Summary**

`TFMobileBertForSequenceClassification`

This class implements a sequence classification model using the TFMobileBert architecture, utilizing the TensorFlow backend. The primary purpose of this class is to provide a pre-trained model for sentence classification tasks.

**Key Implementation Details:**

* Utilizes the `DummyObject` metaclass
* Requires TensorFlow as a backend (`tf`)
* Accepts arbitrary number of arguments and keyword arguments in the constructor

This summary focuses on preserving exact class name (`TFMobileBertForSequenceClassification`) and key implementation details, while omitting unnecessary information.


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**Summary:**

`TFMobileBertForTokenClassification` class:

* Primary purpose: Token classification model using MobileBERT, optimized for TensorFlow backend.
* Implementation details:
	+ Requires TensorFlow backend (`requires_backends`, ["tf"]); uses `DummyObject` metaclass.
* Dependencies:
	+ TensorFlow (backend dependency).


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**Summary:**

`TFMobileBertMainLayer` is a main layer class for mobile inference using TFBackend. It initializes with one of two supported backends (`"tf"`). 

**Key Implementation Details:**

* Requiress backend initialization with `tf` as required backend.

**Notable Dependencies/Requirements:**

* `DummyObject` metaclass
* `requires_backends` function


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**Concise Summary:**

*   Class Name: `TFMobileBertModel`
*   Description: A class implementing the MobileBERT model using the TensorFlow backend.

**Key Details:**
-   **Initialization:** The class requires the `"tf"` backend and is initialized with arbitrary arguments (`*args` and `**kwargs`).

**Notable Dependencies/ Requirements:**
-   Requires the `tf` backend.
-   References the `requires_backends()` function.


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**Summary:**

`TFMobileBertPreTrainedModel`: A pre-trained model class for leveraging the MobileBERT architecture on TensorFlow backend. 

**Key Implementation Details:**
- Initializes with `tf` backend support.

**Notable Dependencies and Requirements:**
- Requires the `tf` backend for runtime.
- Inherits from `DummyObject` metaclass. 

**Class Signature:**
```python
class TFMobileBertPreTrainedModel(metaclass=DummyObject):
    def __init__(self, *args, **kwargs):
        requires_backends(self, ["tf"])
```

This class is designed to work with the TensorFlow backend ( `_backends = ["tf"]` ). The `__init__` method initializes the model and verifies that the `tf` backend is required.


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**Summary**

TFMobileViTForImageClassification class:
-----------------

### Main Purpose and Functionality
Context-specific instance creation for `TFMobileViTForImageClassification` model using the `tf` backend.

### Key Implementation Details

*   Initializes the model with required backends: `tf`
*   Uses `requires_backends` function to validate that the specified backend is available.

### Notable Dependencies and Requirements
*   TensorFlow (`tf`) backend

### Class Information
*   Metaclass: `DummyObject`
*   `_backends`: Supported backends: ["tf"]
```


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**TF Mobile ViT Semantic Segmentation Model Class Summary**

### Class Definition and Purpose
The `TFMobileViTForSemanticSegmentation` class is a deep learning model-based class that uses the TensorFlow backend.

### Primary Functionality
Its main purpose is to enable semantic segmentation tasks using the Mobile ViT architecture.

### Implementation Details

*   The model requires the TensorFlow backend (`tf`).
*   The `_backends` attribute is set to `["tf"]`, indicating compatibility only with the TensorFlow framework.
*   The constructor function (`__init__`) initializes the model instance and enforces the use of the specified backend using a custom `requires_backends` function.

**No additional implementation details are provided.**

This summary provides an overview of the essential class definition, purpose, and key implementation details in a concise and focused manner.


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**Summary:**

`TFMobileViTModel` is a PyTorch-based computer vision model (specifically Mobile Vision IT) class that initializes with the TensorFlow backend.

**Key Details:**

* **Backend:** Uses TensorFlow (`_backends = ["tf"]`)
* **Initialization:** Verifies presence of required TensorFlow backend (`requires_backends(self, ["tf"])`)
 

Note: The `DummyObject` metaclass and the `(TFMobileViTModel)` class usage are not detailed here as they seem specific to framework implementation details or internal mechanics that don't directly contribute to the model's functionality.


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**TF Mobile VIP Pre-Trained Model Class Summary**

**Primary Purpose:**
The `TFMobileViTPreTrainedModel` class is a pre-trained model implementation for TensorFlow (TF) using the ViT architecture.

**Key Implementation Details:**

* Initialization method (`__init__`) requires the `tf` backend to be present.
* The `_backends` attribute specifies that the model supports only the `tf` backend.

**Notable Dependencies and Requirements:**

* Related classes/functions: None
* External dependencies:
	+ TensorFlow (TF): `"tf"`
	+ DummyObject metaclass: "DummyObject"

**Contextual Note:**
This class is part of a pre-trained model framework, likely used for natural language processing or computer vision tasks.


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**TFMPNetForMaskedLM Class Summary**

```markdown
# Main Purpose and Functionality
TFMPNetForMaskedLM is a neural network model designed for masked language modeling tasks.

### Implementation Details

*   The model uses the TensorFlow backend (tf).
*   The `__init__` method initializes the model with required backends, checking if 'tf' is supported.

### Notable Dependencies/Requirements

*   TensorFlow (`tf`)
```

Note: Since the class inherits from a dummy object and requires backend support, additional implementation details might be missing. A more comprehensive analysis would require access to the full codebase or additional context.


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**TFMPNetForMultipleChoice Class Summary**

**Main Purpose and Functionality:**
This class implements the TFMPNet model using the TensorFlow backend for multiple-choice tasks. The primary purpose is to support machine learning modeling.

**Key Implementation Details:**

* Uses the `tf` backend (requiring it at initialization).
* Initializes with variable arguments (`self, *args, **kwargs`) with no specific interface defined.

**Notable Dependencies and Requirements:**

* TensorFlow backend (`requires_backends(self, ["tf"])`)
* No other external dependencies listed.


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**Class Summary:**

`TFMPNetForQuestionAnswering`: A question-answering network architecture utilizing TensorFlow backend (`"tf"`).

**Key Details:**

* Initializes with optional arguments and requires the `"tf"` backend.

**Notable Dependencies/Requirements:**

* `requires_backends(self, ["tf"])`: Explicitly specifies the `"tf"` backend as a requirement.
 
This summary focuses on the class's purpose, key implementation details, and dependencies/requirements.


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**Summary**

The `TFMPNetForSequenceClassification` class is a machine learning model utilizing the TensorFlow backend. Its primary purpose is for sequence classification tasks.

**Key Implementation Details:**

- Initializes with `requires_backends` check to ensure TensorFlow (tf) is available.

**Notable Dependencies/Requirements:**

- TensorFlow (`tf`)
- `DummyObject` metaclass


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**Summary:**

`TFMPNetForTokenClassification`:

*   Primary purpose: Implementation of the `TFMPNetForTokenClassification` class, likely a PyTorch model for token classification tasks.

**Key Implementation Details:**

*   Uses the `"tf"` backend (specifically TensorFlow).
*   Initializes with arbitrary arguments and keyword arguments.
*   Requires the `"tf"` backend to be specified upon instantiation (`requires_backends(self, ["tf"])`).

**Notable Dependencies/Requirements:**

*   Relies on the `DummyObject` metaclass (`metaclass=DummyObject`).
*   No external dependencies listed.


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**Summary:**

TFMPNet Main Layer Class

* Purpose: A meta-class based main layer of TFMP Net framework using the TensorFlow backend (`tf`).
* Primary purpose: Defines the main entry point for the TFMP Net architecture.

**Key Implementation Details:**

* **Backend Requirements**: Requires TensorFlow as a backend (`requires_backends(self, ["tf"])`).
* **Initialization**: Initializes the class with variable arguments and keyword arguments using `self.__init__(*args, **kwargs)`.

Note: The implementation of ` DummyObject` and its used method `requires_backends(self, backend_list)` are not shown in this snippet.


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**TFMPNetModel Class Summary**

* **Main Purpose and Functionality**: 
  - An instance of the `TFMPNetModel` class is a neural network model built using the TensorFlow backend.

      The primary purpose is to initialize the model with the provided arguments (`self`) while ensuring that the required backend ("tf") is installed.


* **Key Implementation Details**:
  - The `requires_backends` function is used to verify that the necessary backend is available before proceeding with object initialization.
  - Interface requirements are referenced through an explicit backend selection.

      
 
    External Dependencies: 
       TensorFlow


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**TFMPNetPreTrainedModel Class Summary**

**Class Purpose:** A pre-trained model variant for TFMP (TensorFlow Multi-modal Fusion) network architecture.

**Primary Functionality:**
Instantiates a `TFMPNet` instance with TensorFlow backend.

**Key Implementation Details:**

* **`requires_backends` method**: Verifies that the backend is set to "tf".
* **Class Variable `_backends`:** Specifies supported backends for the model.
* **`__init__` Method:** Initializes the model instance, requiring the "tf" backend.

**Notable Dependencies and Requirements:**

* TensorFlow library (`tf`)


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**Summary**

`TFMT5EncoderModel`: 

This class represents a model for encoding TFMT5 format data. It inherits from `DummyObject` metaclass and is backed by the TensorFlow backend (`tf`). The `requires_backends` method checks if the required backend (`tf`) is present in the constructor arguments.

**Key Details**

*   `__init__`: Initializes the model with optional arguments.
*   Requires `tf` backend: `_backends = ["tf"]`

**Dependencies/Requirements**

*   **Backend Dependencies:** TensorFlow (`tf`)
*   **Inherited Interfaces:** None

This summary aims to provide a clear and concise overview of the class's purpose, key implementation details, and dependencies.


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**Summary**

`TFMT5ForConditionalGeneration`: A class for conditional generation using the TFMT5 algorithm with TensorFlow backend.

**Key Implementation Details**

- Implemented in TensorFlow (`tf`) backend
- Initializes with `*args` and `**kwargs`

**Dependencies/Requirements**

- Requires TensorFlow backend (`requires_backends(self, ["tf"])`)
 
Note: `_DummyObject metaclass` usage is not explicitly documented due to the lack of information on its purpose. For comprehensive understanding, additional context or documentation should be consulted.


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**Summary**

The `TFMT5Model` class is an implementation of a machine learning model using the TensorFlow backend. It requires only the TensorFlow backend to function and initializes with optional arguments.

**Key Points:**

* **Main Purpose**: Develops a machine learning model using TensorFlow (TFS).
* **Notable Implementation Details**:
	+ Initializes with variable arguments (`*args`, `**kwargs`) but ensures that the TensorFlow backend is required.
	+ Sets _backends attribute to ["tf"] indicating the backend(s) supported by this class.
* **Dependencies**: Relies on TensorFlow as its primary backend.


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**Summary:**

`TFOpenAIGPTDoubleHeadsModel`: A TFOpenAI model using the TensorFlow backend. This class meets the requirements of an OpenGPT model with double heads architecture.

**Key Implementation Details:**

* Uses `tf` backend.
* Requires `requires_backends` method to be implemented, which ensures the presence of a specified set of backends (in this case, 'tf').

**Notable Dependencies/ Requirements:**

* Inherited from `DummyObject` metaclass.
* Utilizes external dependency on TensorFlow (`tf`) for its backend implementation.


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**Class Summary: TFOpenAIGPTForSequenceClassification**

* **Purpose:** A PyTorch-based implementation of OpenAI's GPT model with sequence classification capabilities.

* **Key Implementation Details:**
	+ Uses the TensorFlow backend (`"tf"`).
	+ Requires TensorFlow as a dependency for instantiation.
	+ Utilizes the `requires_backends` function to validate backend compatibility.

* **Notable Dependencies:**
	+ TensorFlow

* **Inherited/Implemented Interfaces:** None.


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**Summary:**

`TFOpenAIGPTLMHeadModel`

*   A class implementing the OpenAI GPT-LM Head Model using TensorFlow backend (`self._backends = ["tf"]`).

    *   Initializes with `*args` and `**kwargs`.
    
    *   Requires the `tf` backend (`requires_backends(self, ["tf"])`) to be present.


**Implementation Details:**

*   Uses `DummyObject` metaclass.

*   Does not include any notable methods or key algorithm implementations in this snippet. Further examination may reveal key methods such as `__call__()`, prediction logic, or hyperparameter tuning scripts.


**Notable Dependencies and Requirements:**

*   Relies on TensorFlow backend (`self._backends = ["tf"]`).
*   Requires the `dummy_object_metaclass.py` module (referenced by the metaclass name).


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**Summary:**

`TFOpenAIGPTMainLayer` class:

*   Purpose: A main layer for OpenAI GPT model using TensorFlow backend (`backend = "tf"`).
*   Implementation:
    *   Initializes with `args` and `kwargs`, enforcing presence of at least one TF backend (`requires_backends(self, ["tf"])`).

**Implementation Details:**

- **`__init__`:**
    - Method signature: `__init__(self, *args, **kwargs)`.
- **`requirements`:**
    - Requires the presence of a TensorFlow backend (`requires_backends(self, ["tf"])`).


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**Class Summary:**

`TFOpenAIGPTModel`

*   **Purpose:** A class implementing the OpenAI GPT model for TensorFlow backend.

**Methods and Key Details:**

*   `__init__`: Initializes the model, requiring TensorFlow as a backend. 

**Dependencies and Requirements:**

*   TensorFlow (`tf`) is required.
*   The `DummyObject` metaclass is used.


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**Class Summary: TFOpenAIGPTPreTrainedModel**

*   **Main Purpose and Functionality:** This class represents a pre-trained OpenAI GPT model using the TensorFlow backend.

### Key Implementation Details:

*   Initializes with `requires_backends` to check if TensorFlow is available.
    *   Method Signature: `requires_backends(self, ["tf"])`
*   Uses the "tf" backend as specified in `_backends`.

### Notable Dependencies or Requirements:

*   TensorFlow (TF) is a required dependency.

No other dependencies or interfaces are mentioned.


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**Summary:**

The `TFOPTForCausalLM` class implements a meta-class using `DummyObject`, specifying that it only supports the TensorFlow backend (`_backends = ["tf"]`). The `__init__` method requires the TensorFlow backend to be present when initializing an instance, ensuring compatibility.

**Key Details:**

* `[metaclass=DummyObject]`: Specifies the custom metaclass used for this class.
* `_backends = ["tf"]`: Restricts compatible backends to TensorFlow.
* `[self, *args, **kwargs]`: Accepts arbitrary arguments and keyword arguments in the `__init__` method.

**Notable Dependencies/Requirements:**

* `DummyObject`: Custom metaclass used for this class.
* TensorFlow (TF): Required backend for instantiation.


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**Summary:**

The `TFOPTModel` class is an abstract model that utilizes the TensorFlow [TFOPT](https://arxiv.org/abs/1807.06267) backend for optimization tasks.

**Key Highlights:**

*   Primary purpose: Enables use of TFOPT algorithm within the PyTorch ecosystem.
*   Implementation details:
    *   `requires_backends` method checks if the required backend (tf) is available before instantiation.
    *   `_backends` class attribute specifies the supported backends for this model (`["tf"]`).
*   Dependencies/Requirements:
    *   TensorFlow (TFOPT backend).


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**Summary:**

`TFOPTPreTrainedModel` is a model class using the `tf` backend, requiring TensorFlow installation.

**Key Details:**

* Primary purpose: Pre-trained model with TFOPT optimization
* Important method signature: `__init__(*args, **kwargs)`
* Requiring backend: `requires_backends(self, ["tf"])`
* Backend dependency: `tf`

**No notable dependencies or interfaces inherited/implemented.**


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**Summary:**

```markdown
# TFPEGASUSForConditionalGeneration Class

The `TFPegasusForConditionalGeneration` class is a neural network engine that utilizes the TensorFlow backend for conditional generation tasks. Its primary purpose is to execute conditional generation models.

## Key Features:

* Uses TensorFlow (`tf`) as its backend.
* Requires `tf` backend installation.

## Implementation Details:

- The class uses a meta-class (`DummyObject`) to manage its configuration.
```

Let me know if you'd like any further analysis or modifications.


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**Summary:**

### TFPegasusModel Class Summary

The `TFPegasusModel` class is an instance of `DummyObject`, designed for TensorFlow (TF) backends. The primary purpose of this model is to provide an interface for building and managing machine learning models.

**Key Implementation Details:**

- **Initialization**: The `__init__` method initializes the model with the specified arguments (`args`) and keyword arguments (`kwargs`).
- **Backend Requirements**: The `requires_backends` function is used to specify the required backends for this model, which in this case is only TensorFlow (TF).

**Notable Dependencies:**

- **DummyObject metaclass**: Used as the metaclass for this class.
- **tf backend**: Only compatible with TensorFlow.

### Method Signatures and Interfaces:

* `__init__(self, *args, **kwargs)` initializes the model instance.
* `requires_backends(self, ["tf"])` specifies the required backends.


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**TFPegasus Pre-Trained Model Class Summary**

### Purpose and Functionality
The `TFPegasusPreTrainedModel` class represents a pre-trained model for the TFPegasus library. It inherits from `DummyObject` and serves as a base class for specific models.

### Key Implementation Details

* The model is restricted to only support TensorFlow (`tf`) backend.
* In the `__init__` method, it checks if the required Tensorflow backend is available using the `requires_backends` function with the argument `["tf"]`.

### Notable Dependencies and Requirements
- `DummyObject`: Inherited metaclass for the model class.
- `requirements_backends(name)` function: Used to verify availability of specific backends.

```markdown
# TFPegasus Pre-Trained Model Class

## Purpose
Represent a pre-trained model in TFPegasus library.

## Functionality
 - Supports only TensorFlow backend (`tf`).
 - Verifies requirement for Tensorflow backend using `requires_backends` function.
```


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**Code Summary**

`TFRagModel` is a customized metaclass for RAG system modeling with TensorFlow backend. It initializes models that require the "tf" backend.

### Key Points

#### Class Information
- `Class Name`: `TFRagModel`
- `Parent/Metaclass`: Uses `DummyObject`

#### Constructor Information
- **Primary Purpose**: Initializes new instances of `TFRagModel` classes.
- **Key Method**: `__init__(*args, **kwargs)`
  - Requires "tf" backend to be installed.

### Key Design Choices

#### Backend Requirements
- Backend name: "tf"

### Notable Dependencies

#### External Dependencies
- TensorFlow (TF) library


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**Summary**

The `TFRagPreTrainedModel` class is a pre-trained model built on top of TensorFlow backend. 

*   Primary purpose: To create a pre-trained model using the TFRag framework.

**Key Implementation Details**

*   Uses a single supported backend (`tf`) with the `DummyObject` metaclass.
    *   The `requires_backends(self, ["tf"])` method ensures that only the TensorFlow backend is supported for this class.

**Notable Dependencies or Requirements**

*   Related interfaces:
    +   `requires_backends(class_name, [required_backend])`: Verifies that a specific backend is required for a given class.
*   External dependencies:
    +   None explicitly mentioned; however, it is assumed to use the TensorFlow library (`tf`).


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**Summary**

The `TFRagSequenceForGeneration` class is a fragment of code that implements a sequence for generating ragged data with TensorFlow backend support.

**Key Features:**

- Uses TensorFlow (`"tf"` ) as the primary backend.
    
    - Requires `"tf"` backend implementation in its `__init__` method.


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**Concise Summary:**

The `TFRagTokenForGeneration` class is a generated token for TensorFlow (TF) based fragments. It utilizes the `DummyObject` metaclass and initializes with a requirement to use the `"tf"` backend.

**Key Details:**

* **Primary Purpose:** Generation of TF-based fragments.
* **Implementation Details:**
	+ Requires the `"tf"` backend using the `requires_backends` function.
* **Notable Dependencies or Requirements:**
	+ Related Classes/Functions: `DummyObject`, `requires_backends`
	+ External Dependencies:
		- TensorFlow (TF) library


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Here is a concise and focused summary of the class code chunk:

**TFRegNetForImageClassification**

*   **Purpose:** A neural network model for image classification using TensorFlow backend.

*   **Key Implementation Details:**
    *   Uses `tf` as its primary backend.
    *   Verifies that the required backend is present in the environment (i.e., TensorFlow) through the `requires_backends` function.
    *   Initializes with variable arguments (`args`) and keyword arguments (`kwargs`).
  
*   **Notable Dependencies:**
    *   TensorFlow backend requirement.

Note that some implementation details, such as the internal ` DummyObject` metaclass and the `requires_backends` function, are not included in this summary to maintain a focus on the core functionality of the class.


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**Summary:**

The `TFRegNetModel` class is an implementation of the RegNet model in TensorFlow (TF). 

**Keys:**
- **Purpose:** Implements RegNet model in TF.
- **Impl Details:**
  - Uses only the TensorFlow backend (`tf`).
- **Dependencies/Req:**
  - Requires the `tf` backend and checks for its availability at initialization.


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**Summary: TFRegNet Pre-Trained Model**

This class represents a pre-trained model for the TFRegNet architecture. It inherits from `DummyObject` and is tied to the TensorFlow backend.

**Implementation Details:**

* Initially requires the TensorFlow backend to function (`tf`).

No additional implementation details are provided in this initial definition. The model's pre-training process and usage would be defined through separate methods or subclasses, likely defined elsewhere in the codebase.


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**TFRemBertForCausalLM Class Summary**

### Purpose and Functionality
The `TFRemBertForCausalLM` class is a custom transformer-based language model designed for causal language modeling tasks. It utilizes the TensorFlow backend.

### Primary Method Signature
```python
__init__(self, *args, **kwargs)
```
Initializes the model with `tf` backend.

### Key Implementation Details

*   Uses the `DummyObject` metaclass.
*   Requires the `tf` backend for initialization.
*   Has a custom `__init__` method to oversee the backend setup process.

### Notable Dependencies and Requirements

*   **Backend:** TensorFlow (`tf`)
*   **Interface Inheritance:** None (uses custom metaclass)


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**Class Summary:**

`TFRemBertForMaskedLM`

*   Main Purpose and Functionality: Implementation of the `TFRemBert` model for masked language modeling tasks using the TensorFlow backend.

*   Key Implementation Details:
    *   Algorithm/Pattern: BERT (Bidirectional Encoder Representations from Transformers) model structure.
    *   Method Signatures: None explicitly mentioned, but it inherits from parent models and provides customized functionality.

*   Notable Dependencies or Requirements:
    *   Backends: TensorFlow (`self._backends = ["tf"]`)
    *   Parent Interfaces: Inherited from `TFRemBert` (not shown in this snippet)

**Class Structure**

This class leverages the `DummyObject` metaclass and defines a constructor that requires the TensorFlow backend to be available for initialization. It inherits the BERT model structure and provides tailored functionality, making it suitable for masked language modeling tasks on TensorFlow platforms.


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**TFRemBertForMultipleChoice Class Summary**

**Main Purpose and Functionality:**
The `TFRemBertForMultipleChoice` class is a model implementation for multiple-choice type question answering tasks using the BERT library.

**Key Implementation Details:**

* **Backend Support:** The class uses the "tf" backend (TensorFlow).
* **Initialization:** Requires TensorFlow as a backend, which allows the model to initialize with the required dependencies.

**Notable Dependencies or Requirements:**

* `DummyObject`: Metaclass used for the class definition.
* `requires_backends`: Function that ensures required backends are available before initialization.


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**Class Summary: `TFRemBertForQuestionAnswering`**

**Main Purpose and Functionality**
---------------------------

The `TFRemBertForQuestionAnswering` class is a BERT-based question answering model implemented in TensorFlow (`tf`). It is designed to work with the `dummy_object` metaclass.

**Key Implementation Details**
----------------------------

* The model requires TensorFlow as a backend (`_backends = ["tf"]`).
* The `__init__` method checks if the required backend is available, throwing an error if not.

**Notable Dependencies and Requirements**
-----------------------------------------

* **Backends**: Requires TensorFlow (`tf`) for implementation.
* **Interfaces**: Implements the `DummyObject` metaclass with a custom backend (`_backends = ["tf"]`).
 
Note: More detailed implementation or model specifications would require additional context or information about the parent class, which is not provided in the snippet.


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**TFRemBERT Sequence Classification Model**

*   **Class Summary**: An implementation of the TF RemBERT model for sequence classification tasks, utilizing the TensorFlow backend.
*   **Primary Purpose**: The TFRemBertForSequenceClassification class is designed to perform sequence classification on input sequences using a modified BERT-like architecture.

        ```
    class TFRemBertForSequenceClassification(metaclass=DummyObject):
    _backends = ["tf"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, ["tf"])
        # Additional initialization details not provided in this snippet
```

**Key Implementation Details:**

*   *Modified BERT-like architecture*: The class leverages a BERT-inspired model with modifications tailored for sequence classification.
*   *Dependencies on 'tf' backend*: The Initialize function verifies the presence of the TensorFlow backend, ensuring compatibility and setup.

Note that no additional method signatures or interfaces are highlighted as they are not detailed in this code chunk.


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**Class Summary: `TFRemBertForTokenClassification`**

**Main Purpose:** A token classification model utilizing BERT architecture.

**Primary Functionality:** Extends BERT's functionality to perform token-level classification tasks.

**Key Implementation Details:**

*   **Model Architecture**: Utilizes the `tf` backend, indicating TensorFlow as the primary framework for this implementation.
*   **Initialization Requirements**: Requires specifying the `tf` backend during instantiation (`requires_backends(self, ["tf"])`).

**External Dependencies:**

*   Relies on `DummyObject` as a metaclass (inherited or implemented).
*   Implicitly depends on TensorFlow (`tf`) for its functionality.

**Other Notable Details:** This class does not explicitly define a constructor; instead, it utilizes Python's default argument behavior to achieve initialization capabilities.


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**TFRemBertModel Class Summary**

**Main Purpose and Functionality:**
The `TFRemBertModel` class is a machine learning model that leverages the Transformers library to implement a BERT-based architecture.

**Key Implementation Details:**

* Initializes with the specified backend (`tf`) using the `requires_backends` method.
* Requires the presence of at least one argument (not used) and keyword arguments (not used).

**Notable Dependencies or Requirements:**

* Related classes: `DummyObject`, `Transformers`.
* External dependencies:
	+ TensorFlow (`tf`)
* Implemented interfaces:
	+ None (custom metaclass)


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**TF RemBERT Pre-Trained Model Summary**

### Purpose and Functionality

The `TFRemBertPreTrainedModel` class represents a pre-trained model for natural language processing tasks using the RemBERT architecture. It employs the TensorFlow backend.

### Key Implementation Details

*   **Initialization**: The `__init__` method initializes the model by ensuring the presence of the Tensorflow backend.

### Notable Dependencies or Requirements

*   Relevant classes: None
*   External dependencies:
    *   TensorFlow (backend)
        [tf]
    *   DummyObject metaclass
        [DummyObject]


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**TFResNetForImageClassification Class Summary**

**Main Purpose and Functionality**
===========================================================

*   A TensorFlow (TF) implementation of ResNet for image classification.

**Key Implementation Details**
-------------------------------

*   **`__init__` Method**: Initializes the model with required backend, `tf`, and accepts arbitrary arguments (`*args`) and keyword arguments (`**kwargs`).

**Notable Dependencies or Requirements**
-----------------------------------------

*   TensorFlow (TF) backend is required (`requires_backends(self, ["tf"])`).


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**Class Summary**

`TFResNetModel`: A neural network model built using TensorFlow as the backend.

**Key Features and Implementation**

* Uses TensorFlow backend (`class _backends = ["tf"]`)
* Requires `tf` backend for initialization

**Notable Dependencies/Requirements**

* TensorFlow (backend)

**Additional Notes**

* Inherited from `DummyObject` metaclass.


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**TFResNetPreTrainedModel Summary**

The `TFResNetPreTrainedModel` class is a pre-trained neural network model for ResNet architecture, built using the TensorFlow backend.

### Key Implementation Details

* Initializes with `requires_backends` function to enforce `tf` backend usage.
* Utilizes `DummyObject` metaclass.

### Notable Dependencies and Requirements
* TensorFlow (`tf`) framework.


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**Summary:**

`TFRobertaForCausalLM` is a class that utilizes TensorFlow backend (`"tf"`). The `requires_backends` method ensures the use of the `"tf"` backend in its implementation.

**Key Details:**

- **Main Purpose:** Implement a causal language model using Hugging Face's RoBERTa model with TensorFlow.
- **Implementation Detail:** Verifies that the `"tf"` backend is available before initialization.
  
**Notable Dependencies/Requirements:**

- `DummyObject` (metaclass)
- `requires_backends` method
- TensorFlow (`"tf"`)


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**Class Summary: `TFRobertaForMaskedLM`**

* **Main Purpose**: A pre-trained transformer model (`Roberta`) optimized for masked language modeling tasks (`maskLM`) using the TensorFlow backend.

**Key Implementation Details:**

* **Initialization**: The model requires either the TensorFlow backend and accepts additional arguments/keywords.

**Notable Dependencies or Requirements:**

* External Backend: TensorFlow (`tf`)
* Parent Class/Object: `DummyObject` (metaclass)
* Sibling Class/Object: Not applicable in this snippet.


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**Summary:**

`TFRobertaForMultipleChoice` is a configuration class for the `Roberta` model with support for multiple-choice questions. It utilizes the TensorFlow backend.

**Key Details:**

* Initializes instance using `__init__()` method
* Requires the TensorFlow backend (`requires_backends()`) to be set

**Notable Dependencies:**

* `DummyObject`: metaclass used to define the class
* `tf` (TensorFlow): required backend for this model


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**Class Summary: `TFRobertaForQuestionAnswering`**

*   **Purpose and Functionality:** `TFRobertaForQuestionAnswering` is a class implementing the Hugging Face's RoBERTa model for question-answering tasks using TensorFlow.

*   **Key Implementation Details:**
    *   The model uses the TensorFlow backend.

    ```
    def __init__(self, *args, **kwargs):
        requires_backends(self, ["tf"])
```

    This constructor ensures that the `TFRobertaForQuestionAnswering` class is instantiated with the TensorFlow backend.

*   **Notable Dependencies and Requirements:**

    *   The model's backend is specified as `"tf"`.
    *   Related classes/functions:
        +   `DummyObject` (metaclass)
        +   `requires_backends` (a function or method)

There are no external dependencies listed in this code snippet.


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**Summary:**

The `TFRobertaForSequenceClassification` class is a PyTorch implementation of the RoBERTa model optimized for sequence classification tasks. It inherits from `DummyObject` and requires TensorFlow as its backend.

**Key Details:**

* Primary purpose: Sequence classification with pre-trained RoBERTa model.
* Implementation details:
	+ Uses the `"tf"` backend.
	+ Requires TensorFlow (checked via `requires_backends` method).

**Notable Dependencies:**

* Related classes/functions: `DummyObject` (inherited), `Provides` is not standard Python, likely a typo.
* External dependencies:
	+ TensorFlow (as specified by `_backends`).


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**Class Summary**

`TFRobertaForTokenClassification`: A token classification model with the `Roberta` checkpoint, optimized for TensorFlow.

**Key Features:**

- Supports the `tf` backend
- Utilizes the `Roberta` checkpoint architecture
- Token classification model for sequence marking tasks

**Implementation Details:**

* The `__init__` method requires the `tf` backend to be available.
* The class uses a custom metaclass (`DummyObject`) with a predefined set of supported backends (`"tf"`).

**Dependencies and Requirements:**

- External dependency on TensorFlow (`tf`)
- Inherited from `Roberta` checkpoint architecture


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**Summary**

This is the `TFRobertaMainLayer` class, which inherits from a `DummyObject` metaclass. It serves as a main layer for TFRoberta models in TensorFlow. The class initializes with `tf` backend.

**Key Details**

* Has one dependency: `requires_backends(self, ["tf"])`, enforcing the use of the `tf` backend.
* Has one property: `_backends = ["tf"]`, which specifies the supported backends.

Note that the implementation choices and key algorithms aren't explicitly mentioned in this snippet. The class appears to be a basic container for model parameters and configurations, without significant additional logic.


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**Summary**

The `TFRobertaModel` class represents a TensorFlow implementation of the RoBERTa model. Its primary purpose is to initialize and configure the model.

*   **Purpose**: Initializes and configures the RoBERTa model in TensorFlow.
*   **Key Implementation Details**:
    *   The model requires TensorFlow as a backend (implemented through the `requires_backends` method with `"tf"` as an argument).
*   **Notable Dependencies/Requirements**:
    *   TensorFlow (`"tf"`) as a required backend.
    *   The `DummyObject` metaclass is used to define the class.

Note: There are no external dependencies listed.


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**Summary:**

`TFRobertaPreTrainedModel` Class:
---------------------------

*   Purpose: A pre-trained model for the BERT-Roberta architecture, built on top of TensorFlow (`tf` backend).
*   Implementation details:

    *   Initializes with `args` and `kwargs`, requiring the `tf` backend for execution.
*   Dependencies/Requirements:

    *   TFRobertaPreTrainedModel uses the `tkinter` library under-the-hood via the `tfevenln` library, but does not explicitly mention it.

Note: The class's metaclass is `DummyObject`, which typically indicates a placeholder or utility class rather than an active interface.


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**Summary:**

The `TFRobertaPreLayerNormForCausalLM` class is a custom layer implementation for the TensorFlow backend. It is specifically designed for causal LMs.

**Key Components:**

* Uses the `DummyObject` metaclass.
* Requires the `tf` backend.
* Initializes with variable arguments and keyword arguments.

**Notable Details:**

* The `_backends` attribute specifies that this class supports only the `tf` backend.
* The `requires_backends` function is used to validate the required backend, ensuring it's always set to `["tf"]`.


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**TFRobertaPreLayerNormForMaskedLM Class Summary**

**Main Purpose and Functionality**

* Implementation of Roberta Pre-Layer Normalization for Masked Language Models using TensorFlow backend (`tf`).

**Key Implementation Details**

* Uses `require_backends` function to check for required backend (`tf`) compatibility.
* Initializes the class with input arguments (not specified as they are not provided in this snippet).
 
**Notable Dependencies and Requirements**

* Requires TensorFlow backend (`tf`)
* Inherently meets requirements of compatible backends.


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**Summary**

`TFRobertaPreLayerNormForMultipleChoice`: A pre-layer normalization layer for the RoBERTa model architecture, specifically designed for multiple-choice tasks. The class is built on top of TensorFlow (`_backends=["tf"]`) and requires access to the `tf` backend.

**Implementation Details**

* Initializes the layer with a list of supported backends (only `"tf"`)
* Calls `requires_backends` function to verify that the required `tf` backend is available

**Notable Dependencies/Requirements**

* TensorFlow (`_backends=["tf"]`)


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**Summary:**

The `TFRobertaPreLayerNormForQuestionAnswering` class is a pre-layer normalization layer for question answering tasks using the Hugging Face RoBERTa model. It utilizes TensorFlow (* `_backends = ["tf"]` ) as its backend.

**Key Functions and Details:**

* Initializes the layer with support for the `tf` backend.
* Requires TensorFlow (`requires_backends(self, ["tf"])`) to be installed.

**Notable Dependencies:**

* Hugging Face's RoBERTa model
* TensorFlow (TF) library


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**Summary:**

`TFRobertaPreLayerNormForSequenceClassification` is a PyTorch-based model class that implements the Roberta pre layer norm for sequence classification tasks. It utilizes the TensorFlow (TF) backend.

**Key Details:**

- Implements `RobertaPreLayerNorm`
- Uses TensorFlow (TF) backend
- Requires TF as a dependency

**Notable Dependencies:**

- [tf](https://www.tensorflow.org/)


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**Summary:**

*Class Name:* `TFRobertaPreLayerNormForTokenClassification`
*Purpose:* An implementation of a layer normalization pre-layer for token classification using the TFLib backend.

**Key Implementation Details:**
- Uses TensorFlow (`tf`) backend
- Initializes with support for TF-specific arguments (`requires_backends`, TF backend requirement)

**Note:** Inheritor of implicit interfaces and does not implement any external dependencies.


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**Summary:**

`TFRobertaPreLayerNormMainLayer` is a class implementing Pre Layer Normalization for the Robberta model using the TensorFlow backend. It initializes and checks prerequisites for its operation.

**Key Implementation Details:**

*   **Initialization Method (`__init__`)**:
    *   Takes variable `args` and `kwargs`.
*   **Prerequisites Check**:
    *   Requires the TensorFlow backend to be supported by calling `requires_backends(self, ["tf"])`.

**Notable Dependencies and Requirements:**

*   Uses the TensorFlow backend.
*   Inherited from `DummyObject` metaclass.


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**Class Summary: `TFRobertaPreLayerNormModel`**

* **Main Purpose**: `TFRobertaPreLayerNormModel` is a model class that extends the pre-layer normalization functionality for Roberta models on top of TensorFlow backend.

* **Key Implementation Details**:
	+ Initializes the model with `tf` backend using the `requires_backends` function.
	+ Uses default arguments and keyword-only arguments (`*args` and `**kwargs`) in the constructor.

* **Notable Dependencies**:
	+ `DummyObject` (metaclass)
	+ `tf`
	+ `requires_backends` function


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**Summary:**

`TFRobertaPreLayerNormPreTrainedModel`: A transformer-based language model pre-trained on RoBerta, implementing Layer Normalization and designed to work with TensorFlow backend.

**Implementation Details:**

* Uses Layer Normalization (LN) for layer normalization
* Requires TensorFlow as a backend

**Notable Dependencies/Requirements:**

* `DummyObject` metaclass
* `tf` backend (implied by `_backends`)
* RoBerta pre-training dataset and model


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Here's a concise and focused summary of the class code chunk:

**Class Summary:**

`TFRoFormerForCausalLM`

**Purpose:** An instance of this class is used for causal language modeling (CLM) predictions using TensorFlow Roformer backend.

**Main Purpose and Functionality:**
The `__init__` method initializes an instance with required dependencies, specifically specifying the use of the TensorFlow (`tf`) backend.

**Implementation Details:**

* Uses `requires_backends` function to enforce the use of the `"tf"` backend.
* Utilizes the TensorFlow Roformer algorithm for causal language modeling.

**Notable Dependencies and Requirements:**

* Dependent on:
	+ `DummyObject` metaclass
	+ `tf` (TensorFlow) backend and library
* Requires:
	+ TensorFlow library version compatibility


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**Summary:**

The `TFRoFormerForMaskedLM` class inherits from a metaclass and defines an initialization method that requires the TensorFlow backend (`"tf"`).

**Key Details:**

* **Purpose:** A library-specific implementation of a language model Roformer for masked language modeling.
* **Functionality:** Initialization method that ensures compatibility with TensorFlow backend.

**Notable Dependencies:**

* `DummyObject` metaclass
* `requires_backends` function ( reference required)
* TensorFlow (`"tf"` backend)


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**Summary**

`TFRoFormerForMultipleChoice` is an implementation of a Roformer model using TensorFlow. It inherits from `DummyObject` and requires the TF backend.

*   **Class purpose**: This class serves as a specific variant of the Roformer for multiple-choice tasks, leveraging the TFX library with the TensorFlow backend.
*   **Key implementation details**:
    *   `_backends=["tf"]`: specifies that this model uses TensorFlow as its backend.
    *   `__init__`: initializes the object and verifies that the required TF backend is available using `requires_backends`.
*   **Notable dependencies**:
    +   External dependency: TFX library
    +   Backend dependency: TensorFlow


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**Concise Summary**

`TFRoFormerForQuestionAnswering` class:

*   Primary purpose: A transformer-based model for question answering, backed by TensorFlow (`"tf"`) backend.

**Key Implementation Details**

*   Requires `tf` backend; throws an error if not present.
*   Takes variable arguments (`*args` and `**kwargs`) in the constructor.

**Notable Dependencies/Requirements**

*   Inherited from: `DummyObject` metaclass
*   External dependency: TensorFlow (`"tf"` backend)


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**Class Summary:**

`TFRoFormerForSequenceClassification`

* Purpose: A transformer-based neural network model for sequence classification tasks, utilizing the TensorFlow backend.
* Primary Functionality: Performs sequential data input processing and classification.

**Key Implementation Details:**

* Utilizes the `DummyObject` metaclass.
* Requires the TensorFlow backend (`"tf"`).

**Notable Dependencies:**

* `DummyObject`: Metaclass used to define the class.
* `requires_backends`: A function requiring a specific backend (TensorFlow) for model instantiation.
* External Dependency: TensorFlow (`"tf"` library).


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**Summary:**

The `TFRoFormerForTokenClassification` class is a token classification model utilizing the TensorFlow backend. It ensures that at least the 'tf' backend is required.

**Key Details:**

* **Class Purpose**: Token classification model for sequence labeling tasks
* **Backend:** 'tf'
* **Implementation:** Requires 'tf' backend via `requires_backends` function

**Notable Dependencies:**

* `DummyObject`: Metaclass used to define the class
* Related Functionals: `requires_backends`


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**Code Summary**

The `TFRoFormerModel` class is a wrapper for RoFormer models using the TensorFlow backend. It:

*   Initializes with `tf` as the required backend and performs backend validation
*   Does not provide further implementation details

**Key Implementations:**

*   `__init__` method requires the TensorFlow backend (`"tf"`)
*   The `requires_backends` function is used to validate the required backend (not shown in this snippet)

**Notable Dependencies and Requirements:**

*   `DummyObject`: An unspecified metaclass
*   `require_backends`: A function that is not defined in this snippet but is used for backend validation

No external dependencies are listed.


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**Class Summary: `TFRoFormerPreTrainedModel`**

* **Purpose:** A pre-trained RoFormer model for TensorFlow (TF) backend.
* **Functionality:** Initializes the model with support for TF backend.

Key Implementation Details:

* Initializes the model using `TFRoFormerArchitecture` class, requiring the TF backend (`tf`).
* Enforces backend requirements using `requires_backends` function.

Notable Dependencies and Requirements:

* Uses `DummyObject` metaclass.
* ReQUIRES TF backend (imported as `tf` is not present in this snippet).


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**Class Summary:**

`TFSamModel`

A simplified data model class utilizing `TensorFlow` backend (`"tf"`).

**Key Details:**

* Initializes the object with support for TensorFlow backend (using `requires_backends` method).
* Does not define any instance variables or methods.

**Notable Dependencies and Requirements:**

* Requires TensorFlow backend ("tf").


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**Class Summary**

The `TFSamPreTrainedModel` class is a meta-class for a transformer-based language model pre-trained using TensorFlow. It has a primary purpose of providing a framework for building and utilizing pre-trained models in TensorFlow.

**Main Functionality:**

* Enforces use of the TF backend
* Initializes the model with input arguments

**Key Implementation Details:**

* Requires the `tf` backend to be available (not an actual instance)
* Employs the use of a dummy object (`DummyObject`) for metaclass operations

**Dependencies/Requirements:**

* None explicitly listed, but references the TensorFlow backend (`tf`) is implicit.


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**TFSegformerDecodeHead Class Summary**

The `TFSegformerDecodeHead` class is a metaclass-based implementation of a decoder head for theSegformer architecture, specifically designed to work with the TensorFlow backend.

**Primary Purpose:**
To provide a decoder blockhead component for the Segformer model in TensorFlow.

**Key Implementation Details:**

* Initializes the segmental attention (T-FOCS) layer.
* Requires the TensorFlow backend, "tf", to be enabled.

**Notable Dependencies/Requirements:**

* `DummyObject` metaclass
* `requires_backends` function
* TensorFlow backend ("tf")


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**Class Summary**

`TFSegformerForImageClassification`

#### Primary Purpose
A PyTorch implementation of the `SegFormer` model for image classification.

#### Implementation Details

* Utilizes the TensorFlow backend (`self._backends = ["tf"]`)
* Checks if the required TensorFlow backend is available before initialization in the `__init__` method

#### Notable Dependencies/Requirements

* TensorFlow (`self._backends = ["tf"]`)

Note: The metaclass used for this class implementation is `DummyObject`, which does not provide any additional functionality.


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**Class Summary**

`TFSegformerForSemanticSegmentation`

**Primary Functionality**: Implements SegFormer model for semantic segmentation using the TensorFlow backend.

**Key Implementation Details**:

* Initializes with TF backend support
* Validates required backends in constructor (`requires_backends` method)

**Notable Dependencies and Requirements**:

* **Related Classes/Functions**: `DummyObject`, `requires_backends`
* **External Dependencies**: TensorFlow (TF)


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**TFSegformerModel Class Summary**

**Main Purpose and Functionality:**
The `TFSegformerModel` class is an implementation of the TFSegformer model using TensorFlow backend (`self._backends = ["tf"]`). It initializes the model with optional arguments `*args` and keyword arguments `**kwargs`.

**Key Implementation Details:**

* Enforces the use of the specified TensorFlow backend (`requires_backends(self, ["tf"])`) at model initialization.

**Notable Dependencies or Requirements:**

* TensorFlow backend (`self._backends = ["tf"]`)
* Requires a dummy object metaclass (`metaclass=DummyObject`)

This code snippet initializes an instance of the TFSegformerModel class while ensuring that it uses the TensorFlow backend. The `requires_backends` function checks if the required backend is available before proceeding with the model initialization.


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**Summary:**

`TFSegFormerPreTrainedModel`

This class is an implementation of a pre-trained model using the TensorFlow backend.

**Key Features:**

* **Backend Support**: The model supports the `tf` backend.
* **Initialization Requirements**: Requires the `tf` backend to be installed (`requires_backends(self, ["tf"])`).

**Notable Dependencies:**
- `DummyObject`: metaclass
- External dependency:
  - `tf` (TensorFlow)


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**TFSpeech2TextForConditionalGeneration Class Summary**

* **Purpose:** A class for conditional generation of speech-to-text using TensorFlow (`tf`) backend.
* **Implementation Details:**
	+ Initializes the instance with arbitrary arguments and assigns them to `self`.
	+ Verifies that the required TensorFlow backend is available before proceeding.
* **Notable Dependencies/Requirements:**
	+ TensorFlow (assigned to class attribute `_backends` as `"tf"`).
	+ The `requires_backends` function, which is not defined in this snippet.


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**Class Summary**

`TFSpeech2TextModel`: A speech-to-text model class utilizing the `tf` backend.

*   **Primary Purpose:** Transposes speech data into text using the specified backends.
    *   The primary purpose of this class is to enable speech-to-text functionality, leveraging machine learning (ML) models.
*   **Key Implementation Details:**

    *   This class uses the `tf` framework as its backend (`_backends = ["tf"]`).
*   **Notable Dependencies and Requirements:**

    *   The `requires_backends` function is used to check for the presence of specific backends, ensuring compatibility.
        *   In this case, only the "tf" backend is required.


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**Summary:**

`TFSpeech2TextPreTrainedModel`

A pre-trained model for speech-to-text functionality, leveraging the TensorFlow backend (`"tf"`).

**Details:**

*   **Implementation:** A class-based implementation with a `DummyObject` metaclass.
*   **Primary purpose:** To provide a pre-trained model for speech-to-text processing using TensorFlow.

**Key components:**

*   `requires_backends` method to ensure the required backend is specified (`"tf"`).
*   No additional notable dependencies or interfaces are listed.


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**Class Summary: TFSwiftFormerForImageClassification**

**Purpose and Functionality:**
The `TFSwiftFormerForImageClassification` class is an image classification model based on the SwiftFormer algorithm. It uses the TensorFlow backend.

**Key Implementation Details:**

* Utilizes the `requires_backends` function to enforce the use of the "tf" backend.
* Initializes with a variable number of arguments (`args`) and keyword arguments (`kwargs`).

**Notable Dependencies and Requirements:**
- Requires the "tf" backend implementation.
- Inherited from `DummyObject`, indicating it serves as a metaclass for this class. 

Note: Further analysis on other parts of the codebase would be required to provide additional context, but this summary focuses on the provided method and metaclass.


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**Summary:**

The `TFSwiftFormerModel` class is a machine learning model implementation utilizing the TF (TensorFlow) backend. It checks for compatible backends using the `requires_backends` function and initializes with an optional set of arguments (`args`, `kwargs`). 

**Key Implementation Details:**

* Uses the `DummyObject` metaclass.
* Specifically targets TensorFlow backend (`_backends = ["tf"]`).
* Requires TF backend for initialization.

(Note: The `Provide` statement is not a standard Python keyword and its meaning may vary depending on the context. However, it can be inferred as an auxiliary function.)


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**TFSwiftFormer Pre-Trained Model Class Summary**

**Class Description:** `TFSwiftFormerPreTrainedModel` is a pre-trained model based on the Swiftformer algorithm.

**Main Purpose and Functionality:**
The primary purpose of this class is to provide a pre-trained instance of the Swiftformer model, which is a type of transformer architecture. The model is built using the TensorFlow backend.

**Key Implementation Details:**

* The `__init__` method initializes the model by checking if the required backend (TensorFlow) is available.
* The method takes variable number of arguments (`*args`) and keyword arguments (`**kwargs`).

**Notable Dependencies and Requirements:**

* Built using: TensorFlow (`tf`)
* Requires backend check to ensure availability of `tf` backend.


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**TFSwinForImageClassification Class Summary**

The `TFSwinForImageClassification` class is an implementation of the Swin transformer model for image classification tasks. 

### Primary Purpose
This class serves as a wrapper for the Swin transformer model using TensorFlow backend.

### Key Implementation Details
- Uses a dummy object metaclass.
- Requires TensorFlow backend installation (`requires_backends(self, ["tf"])`).

### Notable Dependencies or Requirements

*   TensorFlow (_backend: "tf" )
*   DummyObject metaclass

Note: The `DummyObject` metaclass is assumed to be part of the class's implementation but not explicitly described in this summary. 

Please let me know if you want any modification on the summary.


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**Class Summary**

`TFSwinForMaskedImageModeling`: A PyTorch-based class implementing the SWIN model for masked image modeling. The primary purpose is to utilize a PyTorch implementation of the SWIN transformer.

**Key Implementation Details**

* Utilizes the `"tf"` backend
* Enforces `"tf"` as the required backend during instantiation

**Dependencies and Requirements**

* Inhernts from `DummyObject` metaclass
* Requiring `"tf"` as a backend for initialization
* No external dependencies listed


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**Summary**

`TFSwinModel`: A class implementing the `TFBackend` interface with metadata indicating support for TensorFlow (TF) backend.

**Key Details**

- Implemented using `DummyObject` metaclass.
- Requires the "tf" backend to be initialized.

**Notes**

- This class does not explicitly implement any algorithms or patterns; it primarily serves as a setup class.
- No explicit method signatures are provided.


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Here's a concise and focused summary of the provided class code chunk:

**TFSwinPreTrainedModel Class Summary**

* `__init__` method initializes the model with TensorFlow backend support (`_backends=["tf"]`). 

Note: I've highlighted the key details, removed unnecessary information, and preserved the exact class names.


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**TFT5EncoderModel Class Summary**

### Purpose and Functionality
The `TFT5EncoderModel` class is an implementation of a data encoding model using the TensorFlow 5 (TF5) framework. It serves as a backend agnostic wrapper for TF5-backed encoders.

### Key Implementation Details

* The class initializes with a list of supported backends (`self._backends = ["tf"]`).
* The `__init__` method checks for required backends using the `requires_backends` function, ensuring TF is present.
* The class is designed to be backed by TensorFlow 5.

### Notable Dependencies and Requirements

* The class depends on the `DummyObject` metaclass.
* TensorFlow 5 (TF) is a required backend.


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**TFT5ForConditionalGeneration Class Summary**

*Main Purpose and Functionality*: Implementator of the TFT5 algorithm for conditional generation using TensorFlow backend.

**Implementation Details**:
- Initializes with `args` and `kwargs`, requiring the "tf" backend via the `requires_backends` method.
- Utilizes `DummyObject` metaclass.

**Notable Dependencies/Requirements**:

*   Backend: `tf`
*   Interface: `dummy_object`
*   External Dependency: N/A


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**TFT5Model Class Summary**

### Main Purpose and Functionality
The `TFT5Model` class is a dataclass that defines a machine learning model using the TensorFlow backend.

### Key Implementation Details

* Initializes with TF backend, enforcing it automatically through the `requires_backends` function when constructing an instance.
* Has no additional attributes or methods beyond back-end configuration; this seems to be a base class for other models built on top of it.

(Note: The `DummyObject` metaclass is used in the original code but does not add any functionality at the given level and serves more as a placeholder. Its purpose might be overridden in actual model classes, which isn't immediately clear from the snippet provided.)


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**Concise Summary:**
```markdown
# TFT5 Pre-Trained Model Class

**Class Overview:** A pre-trained model class for TFT5, utilizing the TensorFlow backend.

**Primary Functionality:** Initializes and sets up the model with TF backend requirements.

**Key Implementation Details:**

* **Backend Requirements:** Verifies that the 'tf' backend is required.
```

If you would like me to expand on this let me know.


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**Class Summary: `TFTapasForMaskedLM`**

*   **Purpose**: Instantiates a masked language model with TensorFlow backend.

### Key Implementation Details:

*   Requires TensorFlow (`[["require Tf", "Tf"])`) as the sole backend for this class.

### Notable Dependencies/Requirements:

*   Inherited from `DummyObject`, `requires_backends` function, and interface reference to `Tf`.


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**Summary**

The `TFTapasForQuestionAnswering` class is a meta-class designed to integrate the TFApas library for question answering tasks. It provides an initialization method that ensures the presence of at least one backend ('tf'), indicating compatibility with TensorFlow.

**Key Implementation Details**

* The class utilizes a meta-class (`DummyObject`) as its own metaclass.
* It requires at least one backend (`tf`), ensuring compatibility with TensorFlow.
* The `requires_backends` function is called to validate the specified backend.

**Notable Dependencies and Requirements**

* `tf`: TensorFlow backend
* `DummyObject`: Parent meta-class


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**TFApasForSequenceClassification Class Summary**

`TFTapasForSequenceClassification` is a class for sequence classification using TFApas library. Its primary purpose is to define a backend-dependent implementation.

**Key Implementation Details:**

* Initializes the backend with "tf" (TensorFlow) in the `__init__` method.
* Imports `requires_backends` function from an unspecified module, which ensures the backend is set correctly.

**Notable Dependencies and Requirements:**

* `_backends` list specifies the required backend ("tf") for this class to function.
* `DummyObject` metaclass indicates that no instance creation is expected for this class (its intended use is likely as a blueprint or mixin).


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Here is a concise and focused summary of the provided code chunk:

**ClassSummary**

* Class: `TFTapasModel`
	+ Purpose: Data model for a TensorFlow backend
	+ Primary Functionality: Initialize with TF backend requirement

Key Implementation Details:

* Requires backends in the constructor (`__init__`) to be specifically "tf"
* Uses custom `DummyObject` metaclass (no implementation details provided)

Notable Dependencies:

* TensorFlow backend (hardcoded as ["tf"])
* Custom metaclass `DummyObject`


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**Summary:** 

TFTapasPreTrainedModel: A pre-trained model class for TensorFlow backend, requiring TF as a supporting backend.

**Details:**

* Main purpose: Utilizes TensorFlow backend (TF) for its operation.
* Key implementation details:
	+ Requires TF backend support (_backends = ["tf"]).
	+ Initializes with arguments and keyword arguments (*args, **kwargs).
* Notable dependencies or requirements:
	+ `DummyObject`: A metaclass used to define the class hierarchy.
	+ `requires_backends()` function: Verifies the required backends are available.
	+ `TensorFlow` (TF): As a supporting backend.


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**Summary:**

TFVisionEncoderDecoderModel is an abstract model class for TF backend. The main purpose is to facilitate the usage of TFVision models in encoder-decoder applications.

**Key Features and Implementation:**

*   **Abstract Base Class:** TFVisionEncoderDecoderModel serves as an abstract base class, inheriting from a DummyObject metaclass.
*   **Backend Support:** It supports only one backend (TF) using the `requires_backends` function to validate user inputs.
*   **Initialization Method:** The `__init__` method initializes the model with variable arguments \(*args\), keyword arguments \**kwargs, ensuring compatibility for future extensions.

While no implementation details are provided in this sample code snippet, TFVisionEncoderDecoderModel likely abstracts over complexities inherent to specific backend configurations and enables users to utilize a unified interface across various backends, including TensorFlow. Future implementations will expand upon the features provided by this class, such as training and prediction functionalities with varied data types.


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**Concise Summary**

The `TFVisionTextDualEncoderModel` class is a text processing model implemented using the TensorFlow backend. It has an initializer that requires the "tf" backend to be specified.

**Key Details:**

* **Backend**: Utilizes the "tf" backing implementation
* **Purpose**: Text processing model (with dual encoding capabilities)
* **Implementation Notes**:
	+ `requires_backends` is a method used to verify required backends
* **External Dependencies**:
	+ TensorFlow backend

Let me know if you'd like me to analyze further or refine this summary!


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**TFViTForImageClassification Class Summary**

The `TFViTForImageClassification` class is a deep learning model implemented using the TensorFlow backend.

*   **Primary Purpose:** This class creates an instance of a Vision Transformers (ViT) model for image classification tasks, utilizing the TensorFlow backend.
*   **Key Implementation Details:**
    *   The model requires the `tf` backend to function.
    *   The `__init__` method initializes the model and checks if the required `tf` backend is supported.
*   **Notable Dependencies or Requirements:**
    *   The `DummyObject` metaclass used for class definition.
    *   The ` requires_backends` function, which ensures the usage of a specified backend (`tf` in this case).
    *   TensorFlow as the primary backend for model implementation.


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**Class Summary**

`TFViTModel`
===============

*   **Purpose**: A class representing a model backed by TensorFlow (TFViTModel).
*   **Key Features**:
    *   Uses the `tf` backend
    *   Initializes with configuration arguments (`args`) and keyword arguments (`kwargs`)
*   **Methods**: Initialize (`__init__`) method that sets up backend requirements using `requires_backends`.

**Implementation Details**

*   Instantiates backbone class using `metaclass=DummyObject`.
*   Calls `requires_backends` to ensure the correct backend (TF) is specified.

**Requirements and Inherited Interfaces**

*   Relies on the TensorFlow (`tf`) backend.
*   Uses the `DummyObject` metaclass, which could be a placeholder or an actual class.


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**Summary:**

`TFViTPreTrainedModel` class is a pre-trained model using the TF backend. The `__init__` method initializes the model with args and kwargs, ensuring that the "tf" backend is required.

**Detailed Summary:**

*   **Class Purpose:** This class represents a pre-trained model using the TensorFlow (TF) backend.
*   **Primary Purpose:** To initialize and set up a pre-trained TF model.
*   **Key Implementation Details:**
    *   The `__init__` method requires the "tf" backend for the model to function correctly.
*   **Notable Dependencies or Requirements:**
    *   Required backends: ["tf"]
        +       External dependencies:
            *   TensorFlow (`tf`)
        +       Implemented or inherited interfaces: Not specified


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**Class Summary:**
```markdown
**TFViTMAEForPreTraining Class**

A class implementing TFViTMAE (Transformers for Vision and Task Learning of Multimodal Attention Embeddings) architecture for pre-training.

**Key Highlights:**

*   Metaclass used: `DummyObject`
*   Backend required: `tf` (TensorFlow)
```

This summary captures the essential details about the class, its metaclass, and required backend. However, please note that there is no actual code or method implementation present in this snippet.

If you provide the remaining code chunk, I can give a more comprehensive analysis.


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**Summary:**

The `TFViTMAEModel` class models a ViT-MAE (Visual Transformed MAE) neural network using TensorFlow backend. It features:

*   Initialization with TensorFlow backend support (`requires_backends(self, ["tf"])`)
*   Implementation of TFViTMAE model architecture

**(Key details: TFViTMAE Model)**


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**TFViTMAEPreTrainedModel Class Summary**

*   **Primary Functionality:** `TFViTMAEPreTrainedModel` is a pre-trained model for the Vision Transformers (ViT) based architecture TFViT-MAE. It utilizes TensorFlow as its backend.
*   **Implementation Details:**
    *   Initializes with `requires_backends(self, ["tf"])`, indicating that the model requires the TensorFlow backend, which is specified by default due to `_backends = ["tf"]`.
    *   Enforces using both positional (`*args`) and keyword arguments (`**kwargs`) in its constructor.

**Notable Dependencies/Requirements:**

*   TensorFlow (`tf`)
*   `DummyObject` metaclass.


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**Summary:**

`TFWav2Vec2ForCTC` is a class for text-to-speech synthesis using Wav2Vec 2 architecture with TensorFlow backend. The primary function is to process input sequences and generate audio.

**Key Implementation Details:**

- Utilizes the `tf` backend.
- Implements `requires_backends` method to enforce usage of specified backends.

**Notable Dependencies and Requirements:**

- References the `DummyObject` metaclass.
- Requires TensorFlow (TF) as a dependency.


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**TFWav2Vec2ForSequenceClassification Class Summary**

### Purpose and Functionality
The `TFWav2Vec2ForSequenceClassification` class is a custom neural network model using TensorFlow (`tf`) backend for sequence classification tasks.

### Key Implementation Details
* Initializes with the `tf` backend.
* Verifies that the required `tf` backend is available before instantiation.

### Notable Dependencies or Requirements
* `DummyObject`: A metaclass used to define the class.
* `requires_backends`: A function verifying the availability of required backends.
* `tf`: The TensorFlow library, acting as the primary backend.


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**Summary:**

TFWav2Vec2Model class:

*   A PyTorch-based audio model (`TFWav2Vec2Model`) utilizing TensorFlow backend (`tf`).
*   Initializes with TensorFlow backend checks and `requires_backends` decorator.

**Key Implementation Details:**

*   **Requires Backends:** Uses the `requires_backends` decorator to check for required backends.
*   **Backend Usage:** Implemented using the TensorFlow backend (`"tf"`).

**Notable Dependencies/Requirements:**

*   TensorFlow backend (`"tf"`)
*   DummyObject metaclass


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**Summary:**

`TFWav2Vec2PreTrainedModel`

A pre-trained model class for TFWav2Vec2, leveraging the TensorFlow backend. The model is initialized with the `__init__` method, which ensures compatibility with the "tf" backend.

**Key details:**

* `metaclass=DummyObject`: Specifies the metaclass for this class.
* `requires_backends(self, ["tf"])`: Verifies that the "tf" backend is required.

(Note: The actual implementation and key details beyond this method signature are not included in the provided code chunk.)


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**Summary:**

`TFWhisperForConditionalGeneration` is a class utilizing the TF backend (`self._backends["tf"]`) for conditional generation tasks.

**Key Details:**

* Requires at least one of the following backends to be provided to instances of this class: `tf`.
* The `__init__` method initializes the instance with any number of positional arguments and keyword arguments.

**Notable Dependencies/Requirements:**

* `DummyObject` (metaclass)
* `requires_backends` function
* TF backend (backend "tf") 
 * Related classes/functions:
    + `self._backends`: a class attribute specifying supported backends


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**Summary:**

TFWhisper Model Class

*   **Class Purpose:** The TFWhisperModel class is a Python implementation of the Whisper model using TensorFlow as its backend.
*   **Key Implementation Details:**
    *   Requires the "tf" backend to be specified during instantiation (`requires_backends(self, ["tf"])`).
    *   Uses `metaclass=DummyObject`, indicating it does not have a direct superclass or base implementation.
*   **Notable Dependencies/Requirements:**

    *   TensorFlow as its backend (referenced by `_backends = ["tf"]`)
    *   DummyObject metaclass (not directly referenced, but implies inheritance)
*   **Primary Method:** `__init__`: Initializes the model with optional arguments (`*args` and `**kwargs`).


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**TFWhisperPreTrainedModel Class Summary**

* **Main Purpose and Functionality:** A pre-trained model for TFWhisper, supporting the backend "tf".
* **Key Implementation Details:**
	+ Initializes the model with the required `tf` backend using the `requires_backends` function.
	+ Uses the `DummyObject` metaclass.
* **Notable Dependencies or Requirements:**
	+ Requires the `tf` backend for functionality.


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**Class Summary:**

* `TFXGLMForCausalLM` is an implementation of a Language Model (LM) using the TensorFlow backend (`tf`).
* Its primary purpose is to process causal LMs.

**Key Implementation Details:**

* Requires the `tf` backend.
* Initializes with variable arguments and keyword arguments.

**Notable Dependencies and Requirements:**

* Relies on the `requires_backends` function from TFX, requiring the `"tf"` backend.


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**Summary:**
TFX GLM Model Class
--------------------

### Purpose and Functionality

`TFXGLMModel` is a lightweight model class for TFX (TensorFlow Extended) using TensorFlow backend. It provides a basic structure for working with models in a scalable data processing pipeline.

### Key Implementation Details

*   Requires the TensorFlow backend (`requires_backends(self, ["tf"])`) to be used.
*   Initializes with any additional arguments and keyword arguments supported by the `metaclass` (not explicitly listed).

**Notable Dependencies/Requirements**

*   TensorFlow (backend: "tf")
           


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Here is a concise summary of the class code chunk:

**TFXGLMPreTrainedModel**

*   A pre-trained model that supports TensorFlow backend (`_backends=["tf"]`).
*  
*   Initializes with `requires_backends(self, ["tf"])`, implying compatibility only with TensorFlow.
*   The class is designed to be instantiated (e.g., `TFXGLMPreTrainedModel()`).
*


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**Class Summary: TFXLMForMultipleChoice**

**Primary Purpose:** Implementation of a multiple choice inference model using TensorFlow (`tf`) backend.

**Key Implementation Details:**

* Utilizes the `DummyObject` metaclass.
* Initializes with arbitrary arguments and keyword arguments using `*args` and `**kwargs`.
* Requires TensorFlow as a backend for execution.

**Notable Dependencies and Requirements:**

* External dependency: `TensorFlow (tf)`
* Implemented interface or class: None
* Inherited interfaces or classes: Not specified


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**Summary:**

The `TFXLMForQuestionAnsweringSimple` class is a custom implementation of the BERT model for Question Answering (QA) using TensorFlow. It meets the requirements to be used with the TF XLFML library.


**Key Details:**

*   **Primary Purpose:** Implement the BERT model for QA
*   **Implementation Details:**
    *   Uses the `tf` backend.
    *   Requires the `requires_backends` function which checks if the required backends are supported.
*   **Notable Dependencies/Rerequisites:**
    *   Relies on TF XLFML library (`tf` backend)


**Note:** The provided code snippet does not define any method or class, it only checks if the required backend is available and stores the backend in the class scope.


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**Summary**

TFFL-MultiHead (TFXLMForSequenceClassification) is a sequence classification model for multi-head attention mechanism. Its main purpose is to perform tasks like sentiment analysis or text classification.

**Key Implementation Details**

*   Uses the TensorFlow backend (`tf`).
*   Applies multi-head attention mechanism.
*   Requires `requires_backends` method that sets up required backends at initialization.

**Notable Dependencies/Requirements**

*   `DummyObject`: a metaclass used for defining the class.
*   `requires_backends` function (not shown in this code snippet): responsible for setting up required backends during model initialization.


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**Concise Summary**

The `TFXMLOnlyTokenClassification` class is a wrapper for the `tf` backend using the Hugging Face models library. The class has an initializer that enforces the use of only the `"tf"` backend, and does not perform any additional tasks.

Here is a more detailed summary:

*   **Purpose**: Wraps the `TfModelForTokenClassification` in the `TFXMLOnlyTokenClassification` context.
*   **Key Implementation Details**:
    *   Enforces the use of only the `"tf"` backend via the `requires_backends` method.
        *   Method: `__init__`
        *   Parameters: `self, *args, **kwargs`
*   **Notable Dependencies or Requirements**:
    *   Hugging Face models library
    *   TensorFlow (`tf`) backend.


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**Summary:**

 TFXMFLayer class provides a main layer implementation for TFXLM (TensorFlow XLA-based) model. This class is designed to use the TensorFlow backend.

**Key Details:**

* Main purpose: Initialize and validate the TFEngineLayer object (`requires_backends` function).
* Key Implementation:
	+ Uses `tf` backend.
	+ Verifies that `tf` backend is available using the `requires_backends` function.

**Notable Dependencies:**

* TensorFlow (TF) backend.
* `DummyObject` metaclass.

Note: This summary focuses on capturing the essential functionality and implementation details of the provided code chunk, while omitting unnecessary information.


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**Summary:** 
TF-XL Model Class
================

### Main Purpose and Functionality

The `TFXLMModel` class inherits from `DummyObject` with the primary purpose of creating an optimized language model for TensorFlow.

### Key Implementation Details

*   **Backend Specification**: Reaches out to the `requires_backends` function to verify that the `tf` backend is selected.
*   `_backends = ["tf"]`: Specifies that only TensorFlow (`tf`) can be used as a backend.

### Notable Dependencies or Requirements

*   `DummyObject`: The metaclass for this class.
*   `requires_backends` function: Verifies and sets the backend for the model.


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**TFXLMPreTrainedModel Class Summary**

*   **Primary Purpose**: Custom model with `Tf` backend support for the XLM pre-training approach.
*   **Key Implementation Details**:
    *   Initializes the model using `requires_backends` ensuring compatibility with TF backend.
        -   Supported backend: `tf`
*   **Dependencies and Requirements**:
    *   Uses `DummyObject` metaclass
    *   Requires TF backend for operations (Tf is specified in `_backends`)

**Detailed Breakdown**:

-  Class is a custom implementation of TFXLMPreTrainedModel with Tf backend support.
-  Initialize checks ensure compatibility using require_backends method, indicating the required Tf backend.


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**TFXLMWithLMHead Model Class Summary**

**Class Purpose:**
The `TFXLMWithLMHeadModel` class implements the TFXLM (Transformative Factor-based Language Model) architecture with an LMHead (Language Modeling Head) model.

**Key Features:**

* Uses TensorFlow backend (`_backends = ["tf"]`)
* Initializes with TF-compatible arguments (`__init__(self, *args, **kwargs)`)

**Notable Implementation Details:**

* Calls `requires_backends` method with `"tf"` as a required backend
* Does not specify any additional implementation details or class variables beyond the metadata defined by `_backends`

**Dependencies and Requirements:**

* TensorFlow (backend)


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**Summary**

`TFXLMRobertaForCausalLM` is a model class that utilizes the TF backend for causal language modeling tasks. It initializes with an input queue (not specified) and enforces the use of the TF backend (`requires_backends`) at construction time.

### Key Details

*   Primary purpose: Causal Language Modeling
*   Model Class: `TFXLMRobertaForCausalLM`
*   Backend requirement: TF (specified by `_backends` attribute)

### Implementation Notes

*   No explicit method signatures provided.
*   Interfaces not explicitly mentioned, but relies on the parent class's method (`requires_backends`). 

### Dependencies

*   No external dependencies listed in this snippet. 
    However, it is implied that **this model requires [Parent Class]** to function.
        The Parent Class name isn’t specified here.


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**TFXLMRobertaForMaskedLM Class Summary**

*   **Purpose**: A masked language modeling transformer model (`TFXLMRoberta`) using the TensorFlow backend.
*   **Key Implementation Details**: Initializes the model with TF for masked language modeling tasks.

**Notable Dependencies and Requirements**: 
- `DummyObject` metaclass
- Requires `tf` backend support

Note: Class name, method signatures, and other attributes were preserved exactly as they appeared in the original code.


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**Summary:**

TFXLMRobertaForMultipleChoice class: A Hugging Face implementation of the Roberta model for multiple-choice text classification.

* Main Purpose: This class provides pre-trained Roberta model for multiple-choice tasks using TensorFlow backend (tf).

**Key Implementation Details:**

* Requires TensorFlow backend (tf) via the `requires_backends` method.

**Notable Dependencies/Requirements:**

* External dependencies:
	+ TensorFlow
* Inherited Interfaces:
	+ Hugging Face's TFXLMRobertaModel interface


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**Concise Summary:**
```markdown
# TFXLMRobertaForQuestionAnswering Class

A model class for QA applications utilizing TF-IDF and Roberta architecture.

## Key Implementation Details:

- Instantiates with *args, **kwargs*.
- Requires `tf` backend support.

## Dependencies/Requirements:
- TF-IDF (text processing) integration
- [Robertta] interface
```
Note: DummyObject metaclass is not analyzed in this summary as it's used for demonstration purposes only and might be implementation-specific.


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**TFXLMRobertart For Sequence Classification Class Summary**

*   **Purpose**: The `TFXLMRobertaForSequenceClassification` class is an implementation of the RoBERTa model for sequence classification tasks using the TensorFlow backend.

*   **Primary Functionality**: The class is designed to handle input data and provide output predictions based on the class's trained parameters.

**Key Implementation Details:**

*   Initializes with the `tf` backend.
*   Ensures the required backend (`tf`) is available before proceeding with initialization.

**Notable Dependencies/Requirements:**

*   TensorFlow (`tf`)
*   The implementation assumes the presence of a related `DummyObject` class.


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**Summary**

TFXLM Roberta Token Classification Model

*   Main purpose: Provides a pre-trained language model for token classification tasks using the TFXLM Roberta architecture specifically designed for token classification.
*   Implementation details:
    *   Uses the `tf` backend library.
    *   Requires the TFXLM Roberta model to be loaded in the constructor using `requires_backends`.
    *   Takes variable arguments and keyword arguments (`args` and `kwargs`) in the constructor.

**Notable Dependency:**

*   Related classes/functions:
    *   `DummyObject`: The metaclass used to create this class.
        + Reference: `DummyObject`
*   External dependencies:
    + TensorFlow (TF) library
        + Reference: `tf`


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**Class Summary:**

`TFXLMRobertaModel`

A PyTorch-based implementation of the Hugging Face's TFXLMRoberta model using the TensorFlow backend.

**Key Functions and Details:**

- Initializes the model with support for the "tf" backend (required).
- No additional initialization methods are outlined in this snippet; however, it uses `requires_backends` which is not shown in this piece of code but would likely be a method within the class or module to enforce required backends.

**Notable Dependencies and Requirements:**

- TensorFlow (`"tf"` backend)
  - No additional dependencies listed in this snippet.


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**TFXLMRobertaPreTrainedModel**

This is a pre-trained model class for the TFXLM (Transformer-XL) Roberta architecture. It uses the TensorFlow backend and extends `DummyObject` as its metaclass.

Key implementation details:

* Uses Transformer-XL Roberta model
* Requires TensorFlow backend (`requires_backends(["tf"])`)
* Has an initializer method that accepts arbitrary arguments (`__init__`)

No notable dependencies or requirements are listed, but it is clear the model relies on TensorFlow for functionality.


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**Summary:**

The `TFXLNetForMultipleChoice` class is a custom model for multiple-choice questions, utilizing the TensorFlow (`tf`) backend.

**Key Details:**

* The model requires the `tf` backend to be set upfront in its initialization process.
* The class uses a meta-class named `DummyObject`.

**Implementation Notes:**

The `requires_backends` method ensures that the `tf` backend is installed and configured before creating an instance of this class.


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**TFXLNetForQuestionAnsweringSimple Class Summary**

**Purpose and Functionality:**
The `TFXLNetForQuestionAnsweringSimple` class is a model designed for simple question answering tasks.

**Key Implementation Details:**

* Initializes with `tf` backend support.
* Requires the `tf` backend for instantiation (ensured by `requires_backends` method).

**Notable Dependencies and Requirements:**
- `_backends = ["tf"]`: Uses the `tf` backend.
- `DummyObject` metaclass: Used for this class definition.


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**TFXL Net for Sequence Classification Class Summary**

### Main Purpose and Functionality
This class is an implementation of TFXL (TensorFlow XLNet) for sequence classification tasks. It inherits from a metaclass `DummyObject` and includes the requirement of TensorFlow backend.

### Key Implementation Details
* Requires TensorFlow (tf) as a backend.
* Initializes the object with variable arguments and keyword arguments.

### Notable Dependencies or Requirements
- tensorflow (tf)
- DummyObject (metaclass)

### Class Signature
```python
class TFXLNetForSequenceClassification(TFCoreMixin, TFModel):
...
```
Note: The full class definition is not provided in the given code snippet.


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**Concise Summary:**

The `TFXLNetForTokenClassification` class is a container for a token classification model using the TF-XL library and TensorFlow backend. 

*   The primary purpose of this model is token classification.
*   Implementation Details:
    *   It uses TF-XL as the backbone model.
        *   Key dependency: TensorFlow backend (`TFXLNetForTokenClassification._backends = ["tf"]`).
*   Dependencies/Requirements:
    *   Requires the `tf` backend.


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**Summary**

TFXLNetLMHeadModel is a PyTorch-based language model head model that utilizes the TensorFlow (TF) backend. 

**Key Details**

- Primary Purpose: Implementing a transformer-inspired language model with TF as the backend.
- Key Algorithm: None stated, but likely leveraging transformer architecture.
- Method Signatures:
  - `__init__`: Initializes the model with compatible backends, specifically checking for TF.

**Notable Dependencies/Requirements**

- Backend: TensorFlow (TF) (`tf`)
- Interfaces/Inherited:
  - `DummyObject` (metaclass)

Note: The code assumes knowledge of PyTorch and transformer architecture. Without additional information about implementation details or relationships with other classes, this summary remains concise.


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**Summary:**

TFXLNetMainLayer Class:

*   Purpose: Main layer of the TensorFlow XLA (TFXL) library.
*   Primary Functionality: Configures and initializes the main layer for TFXL operations.

**Implementation Details:**

*   **Initialization**: The `__init__` method initializes the main layer with a list of required backends ("tf").
*   **Dependency Checking**: The `requires_backends` function is used to verify that "tf" is one of the required backends for this class.

**Notable Dependencies and Requirements:**

*   Backend: TensorFlow (TF)
*   Required Functions: `require_backend`, `DummyObject` (used as metaclass)


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**Summary:**

The `TFXL Net Model` class is a PyTorch-based model implemented using the TFXL library. It inherits from PyTorch's dummy object and uses the `DummyObject` metaclass.

**Key Implementation Details:**

* The model requires TensorFlow (`tf`) as its backend.
* The `__init__` method initializes the model with arbitrary arguments and checks that the required backend is installed (`requires_backends`).

**Notable Dependencies/Requirements:**

* PyTorch
* TFXL library
* TensorFlow (`tf`)
 
Note: `DummyObject` is not a standard Python class, and its implementation is specific to the TFXL library. If provided, its description should be included in the summary.


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**Summary:**

*   `TFXLNetPreTrainedModel`: A class representing a pre-trained model using the TF_XL backend (only "tf" is currently supported).
*   Primary purpose: Pre-trained language model with TF_XL backend integration.

**Key Implementation Details:**

*   Initializes the object with a specific backend requirement.
*   Requires the `tf` backend to be present at runtime (`requires_backends(self, ["tf"])`).

**Notable Dependencies and Requirements:**

*   External dependency on the TF_XL library for pre-trained language model integration.
*   Backend specification: `"tf"` (currently only supported), potentially requiring manual configuration or setup.


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**Summary**

The `AdamWeightDecay` class is an implementation of the weight decay regularization technique for Adam optimization algorithm. It inherits from PyTorch's DummyObject metaclass.

**Key Details**

*   Initializes with `requires_backends` to specify the supported backend (`tf`).
*   No additional configuration details are provided, implying that a standard Adam optimizer with weight decay is implemented.

**Notable Dependencies and Requirements**

*   **Backend:** `tf`
*   **Interfaces:** implements the PyTorch optimizer interface (inherited from DummyObject).


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Here is a concise summary of the provided class code chunk:

**Class Summary: GradientAccumulator**

* Metaclass: `DummyObject`
* Backends: Only supports "tf"
* Primary purpose: Accumulates gradient information for TensorFlow backends
* Implementation detail: Enforces "tf" as a required backend using `requires_backends` method.

 Key elements:
- `DummyObject` metaclass (no specific implementation)
- `backends` attribute set to `["tf"]`
- `__init__` takes variable arguments and keyword arguments, enforcing TensorFlow backend requirement


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**Class Summary: `WarmUp`**

**Purpose and Functionality**
The `WarmUp` class serves as a backend selector for machine learning models. It ensures that the required TensorFlow backend is installed using the `requires_backends` function.

**Key Implementation Details**

* The class uses a metaclass (`DummyObject`) to define a static list of supported backends (`_backends = ["tf"]`).
* The `__init__` method initializes the instance with optional arguments and verifies that the TensorFlow backend is installed via the `requires_backends` function.

**Notable Dependencies or Requirements**

* TensorFlow backend (required)
* `DummyObject` metaclass
* `requires_backends` function


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**Summary**

This is the `create_optimizer` function, which appears to be part of a backend-agnostic optimization framework.

* **Purpose**: The function likely creates an optimizer instance with customizable settings (via `args` and `kwargs`) for various backends.
* **Key implementation details**: The function uses the `requires_backends` decorator to specify required backends (`tf` in this case).
* **Important method signature**: Not explicitly stated, but it is expected to take some form of backend-specific arguments or configurations.

**Notable dependencies/requirements**

* Backends: [tf] (TensorFlow)
* Dependencies not specified, but likely involves additional library imports or setup.


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**Summary**

`_get_default_logging_level()` function:
Returns the default log level from an environment variable `TRANSFORMERS_VERBOSity`. If it's not set, falls back to `_default_log_level`. It checks against a predefined list of valid log levels (`log_levels`) and logs a warning if an invalid option is passed.

**Key details:**

* Retrieves environment variable `TRANSFORMERS_VERBOSITY` and returns its corresponding value from `log_levels`.
* Logs a warning if the provided level is not in the defined list.
* Uses `_default_log_level` as fallback.

**Notable dependencies/requirements:**
	+ `os` module (for getting environment variables)
	+ `logging` module
	+ predefined dictionary `log_levels`
	+ `_default_log_level` function (not shown)


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**Summary:**

`_get_library_name()` returns the name of the current library by splitting the `__name__` attribute at the '.' separator and taking the first segment.

  
**Key Details:**

* **Function Purpose:** Extracts and returns the library name from the `__name__` attribute.
* **Method Signature:** `_get_library_name() -> str`
* **Implementation:** Uses `__name__.split(".")[0]` to extract the first part of the library path.

  
**Notable Dependencies/Requirements:**

* None (self-referential and uses built-in Python attributes).


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**Summary:**

This function returns a root logger object for the library, initialized with name retrieved from `_get_library_name()`.

**Key Details:**

* Returns `logging.Logger` instance
* Uses `_get_library_name()` to determine logger name
* Establishes a root logger for the library

**Example Signature:** `_get_library_root_logger() -> logging.getLogger[_get_library_name()]`

**Notable Dependency:**
- `logging` module (specifically, `getLogger` function)


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**Function Summary: `_configure_library_root_logger`**

This function configures the root logger for a library. It ensures the logger has a handler set to capture output, sets logging levels and formatting, and disables propagation of logs.

**Implementation Details:**

* Configures the library root logger with a `logging.StreamHandler` to write to `sys.stderr`.
* Sets default configuration based on PyInstaller issue 7334.
* Applies configuration settings on logger level and formatting using `_get_library_root_logger()` and `_get_default_logging_level()`.
* Disables log propagation.

**Notable Dependencies:**

* `logging`: for logging configuration
* `sys`: for accessing standard error stream (`sys.stderr`) and flushing output to the stream
* `_lock` (global variable): not explicitly used in this snippet, but likely a lock mechanism for thread-safe access to logger settings.
* `_get_library_root_logger()` and `_get_default_logging_level()`: functions that retrieve configuration for library root logger.


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**Summary**

### Function: `_reset_library_root_logger()`

**:Purpose and Functionality:** Resets the configuration of the root logger for the library to its default state. Reverses the removal of the default handler, sets the logger level to `NOTSET`, and updates the default handler.

#### Key Implementation Details:

* Removes the default handler from the library root logger.
* Sets the logger level to `logging.NOTSET`.
* Updates the default handler to `None`.

#### Dependencies/Requirements:

* `_lock`: a locking mechanism for thread safety.
* `_default_handler`: an instance of a logging handler.
* `_get_library_root_logger()`: a function returning the root logger instance.

#### Notable Interfaces:
`logging.NOTSET`, `logging.Handler`

### Additional Information

The function is designed to restore the default configuration of the library's root logger, ensuring that it functions as intended.


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**Summary:**

The `get_log_levels_dict` function returns the existing `log_levels` dictionary without modification. This suggests that the `log_levels` dictionary is intended to be a configuration or settings value in the application.

**Key Insights:**

* Function purpose: Returns the log levels dictionary
* Implementation detail: Does not modify the original `log_levels` dictionary

This function seems to be a simple getter, providing direct access to the current log levels configuration without any modifications.


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**Summary:**

`captureWarnings` function enables the management of warnings emitted by the `warnings` library. It configures the logging interface and calls the underlying `_captureWarnings` method.

**Key Details:**

* Calls `logging.captureWarnings` to manage library warnings
* Configures logging interface for "py.warnings" logger
* Updates logging level and adds handler if not already present

**Notable Dependencies/Requirements:**

* Logging library (`logging`)
* `get_logger` function (defined elsewhere in the codebase)
* `_default_handler` object (defined elsewhere in the codebase)
* `PyWarnings` logger (_library_root_logger)


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**Function Summary: `get_logger`**

 Creates and returns a logger with the specified `name`. If no `name` is provided, it defaults to the current library name.

**Key Implementation Details:**

* Uses `_configure_library_root_logger()` to initialize logging configuration for the library root logger.
* Returns a logger object with the specified `name`.

**Notable Dependencies or Requirements:**

* `logging.Logger`
* `_get_library_name()` (returns the current library name if no specific name is provided)
* `_configure_library_root_logger()` (configures logging for the library root logger)


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**Summary:**

`get_verbosity()` function returns the current logging level of the 🤗 Transformers's root logger as an integer.

**Key Details:**

* Calls `_configure_library_root_logger()` to configure the logger
* Returns the effective logging level using `_get_library_root_logger().getEffectiveLevel()`

**Notable Dependencies:**

* `transformers.logging.CRITICAL`
* `transformers.logging.ERROR`
* `transformers.logging.WARNING`
* `transformers.logging.INFO`
* `transformers/logging.WARN`

**Implementation Details:**

* Uses `_config` library's utility functions to configure the logger
* Implements a simple log level mapping (50-10) using specific logging levels from the `transformers` package.


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**Summary:**

Sets the verbosity level for Transformers' root logger, configuring the logging level using `_configure_library_root_logger()` and setting it to the specified value.

**Key Details:**

* `verbosity`: Integer logging level (e.g., `transformers.logging.DEBUG`, `transformers=logging.WARNING`)
* Calls `_configure_library_root_logger()` and then sets the log level using `_get_library_root_logger().setLevel(verbosity)`

**Notable Dependencies:**

* `_configure_library_root_logger()`
* `_get_library_root_logger()`


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**Function Summary:**

`set_verbosity_info()` sets the verbosity level to INFO, delegating the action to `set_verbosity(INFO)`.


**Key Details:**

* Function name: `set_verbosity_info`
* Primary purpose: Sets verbosity level to INFO
* Important Method Signature: `set_verbosity(ERROR | INFO)`

**Notable Dependencies/Requirements:**

* `INFO` class/interfaces (not explicitly imported in this code snippet, but required for function to work)
* `ERROR` interface/class (referenced as an alternative verbosity level)


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**Function Summary**

Defines a function `set_verbosity_warning()` that sets the verbosity to warning level, utilizing the `set_verbosity()` method with the predefined constant `WARNING`. Primary purpose: Adjusts verbosity level.

**Implementation Details**

* Uses the `set_verbosity()` method
* Specifically targets the `WARNING` level

**Notable Dependencies and Requirements**

* None identified in this code snippet.
* Inherited or implemented interface: Not applicable.


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**Summary:**

- **Main Purpose:** Sets the verbosity level of an application to DEBUG, enabling debug mode.
- **Code Details:** Calls the `set_verbosity` function with the `DEBUG` constant.
  - Key implementation detail: The `set_verbosity` function is not shown in this snippet, but it is assumed to be defined elsewhere in the codebase.
- **Notable Dependencies/Requirements:** None explicitly listed, but assumes access to the ` set_verbosity` function and the `DEBUG` constant.

**RAG System Entry:**

* Name: set_verbosity_debug
* Type: Function
* Purpose: Sets verbosity level to DEBUG
* Code Snippet: [Insert code snippet]
* Dependencies:
  - set_verbosity (function)
  - DEBUG (constant)


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**Summary:**

The `set_verbosity_error` function sets the verbosity of the application to an error level by calling the `set_verbosity` function with `ERROR` as its parameter.

**Key Implementation Details:**

- Sets verbosity to `ERROR` level
- Calls `set_verbosity(ERROR)` internally
- Returns without altering the verbosity outcome

**Notable Dependencies/Requirements:**

- `ERROR`: a built-in or defined constant representing an error verbosity level
- `set_verbosity ERROR_interface`: assumes an interface named `set_verbosity` that operates on verbosity levels


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**Summary:**

This function, `disable_default_handler`, disables the default handler of HuggingFace's root logger. It configures the library's root logger and then removes the existing default handler from the loggers output.

**Key Details:**

- Enables `_configure_library_root_logger()`.
- Verifies existence of `_default_handler` (assertion).
- Removes `_default_handler` from the loggers configuration using `_get_library_root_logger().removeHandler(_default_handler)`. 

**Notable Dependencies/Requirements:**

- `HuggingFace` library for Transformers.
- Logger object with `rootLogger` attribute.


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**Summary:**

`enable_default_handler()` enables the default handler for the HuggingFace Transformers's root logger. It configures the library root logger, asserts its existence, and adds it to itself.

* Primary purpose: Configure default logging setup for HuggingFace Transformers.
* Key implementation details:
	+ Calls `_configure_library_root_logger()`
	+ Verifies existence of `_default_handler` before adding it
	+ Adds `_default_handler` to the root logger's handlers

**Notable dependencies and requirements:**
- `_default_handler`: default logging handler for HuggingFace Transformers
- `_get_library_root_logger()` function: returns the library root logger instance


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**Summary:**

`add_handler` function adds a logging handler to the root logger of the HuggingFace Transformers library.

**Key Details:**

- Sets up the library's root logger using `_configure_library_root_logger()`.
- Verifies that the provided `handler` is not None.
- Adds the `handler` to the transformed.root_logger instance using `_get_library_root_logger().addHandler(handler)`.

**Notable Dependencies/Requirements:**

* HuggingFace Transformers' logging module
* Library-root logger configuration and setup (not explicit in this code snippet)


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** Summary **

Removes a logging `Handler` instance from the HuggingFace Transformers's root logger.

**Key Implementation Details**

* Configures library root logger using `_configure_library_root_logger()`
* Verifies that the handler is valid and not already removed from the logger
* Removes the specified handler from the logger using `_get_library_root_logger().removeHandler(handler)`

**Notable Dependencies/Requirements**

* HuggingFace Transformers's `logging` module (`_configures library root logger`, `_get_library_root_logger()`)
* External dependency: None (built-in Python modules), unless overridden by custom configurations.


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**Function Summary: `disable_propagation`**

*   **Purpose:** Disable propagation of the library log outputs to prevent excessive logging.

**Implementation Details:**

*   Configures the root logger using `_configure_library_root_logger()`.
*   Sets the `_propagate` attribute of the root logger to `False`, effectively disabling propagation.


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**Summary**

* **Purpose**: Enables propagation of library log outputs while preventing double logging by disabling HuggingFace Transformers' default handler.
* **Key Implementation Details**:
	+ Calls `_configure_library_root_logger()` to set up the root logger.
	+ Sets `propagate` attribute of the root logger to `True` using `_get_library_root_logger()`.
* **Notable Dependencies**: None
* **External Requirements**: HuggingFace Transformers library (specifically, its default handler)


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**Function Summary: `enable_explicit_format`**

Enables explicit formatting for the HuggingFace Transformers logger. Sets the formatter to a custom format that includes log level, file name, line number, timestamp, and message.

**Implementation Details:**

* Replaces existing log handlers with a new formatter.
* New formatter format: `[LEVELNAME|FILENAME|LINE NUMBER] TIME >> MESSAGE`
	+ Uses `logging.Formatter` with specific attributes:
		- `%levelname`: Log level name
		- `%filename`: File name (not fully qualified)
		- `%lineno`: Line number
		- `%asctime`: Timestamp
		- `%message`: Actual log message

**Notable Dependencies/Requirements:**

* `_get_library_root_logger()` returns the root logger for HuggingFace Transformers.


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**Summary**

`reset_format()` Function:

* Resets formatting for HuggingFace Transformers's loggers, impacting all bound handlers to the root logger.

**Key Details**

* Iterates over and clears formatatters of all handlers in the root logger using `setFormatter(None)`.
* Affects all currently bound handlers when called on the root logger.

**Note**: This summary neglects notational conventions but is concise and accurate.


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**Summary:**

`warning_advice` method: Logs warning message with adjustable verbosity. If `TRANSFORMERS_NO_ADVISORY_WARNINGS` environment variable is set to 1, the warning is not printed.

**Implementation Details:**

* Utilizes `logger.warning()` internally
* Environment variable override based on ` TRANSFORMERS_NO_ADVISORY_WARNINGS`
* Primary purpose: Determine whether to output warning message

**Key Implementation Highlights:**

* Method call to self's `warning` method with unpacked `args` and `kwargs`
* Conditional logic using environment variable retrieval with default value of 0 (False)


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**Summary**

The `EmptyTqdm` class is a dummy implementation of the `tqdm` progress bar class. It provides an empty interface for iterating over data and handling special methods (`__enter__`, `__exit__`) without any actual functionality.

**Key Details:**

* **Purpose:** To mimic the behavior of `tqdm` while disabling its features.
* **Implementation:**
	+ The constructor takes an optional iterator that defaults to being set if provided.
	+ The `__iter__` method returns the underlying iterator.
	+ The `__getattr__` method returns a mock function `empty_fn` (except for `_` attribute) which does nothing, effectively allowing all attributes to be accessed on this instance without exception.
	+ The `__enter__` and `__exit__` methods are overridden with empty implementations, preventing any progress updates or exceptions from being handled.

**Notable Dependencies:**
None

This class seems to be a test or mock implementation of the `tqdm` class, likely used for testing purposes.


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**Summary**

The `_tqdm_cls` class provides a custom TQDM wrapper with conditional behavior based on the `_tqdm_active` flag. The main purpose and functionality are:

*   **Progress bar rendering**: Renders a progress bar with arguments passed to the `__call__` method, returning either an active or inactive (`EmptyTqdm`) progressive bar depending on the value of `_tqdm_active`.

**Key Implementation Details**

*   Uses `_tqdm_active` flag to conditionally return either the active TQDM wrapper (`tqdm_lib.tqdm(*args, **kwargs)`) or an inactive (`EmptyTqdm(*args, **kwargs`)).
*   Implements `set_lock` method, which sets the lock and calls the corresponding `set_lock` method from the `tqdm_lib` module if `_tqdm_active`.
*   Defines `get_lock` method, which returns the result from `tqdm.get_lock()` only when `_tqdm_active`.

**Dependances/Requirements**

*   Uses the `tqdm` library and its related classes (`EmptyTqdm`, `tqdm_lib`) explicitly.
*   Inherits or uses interfaces from the `tqdm` library, particularly `get_lock` method.


key: chroma:document
string_value:
**Function Summary**

```python
def is_progress_bar_enabled() -> bool:
    """
    Returns True if tqdm progress bars are enabled, False otherwise.

    Enables tqdm progress bars by global variable "_tqdm_active".
    """
    return bool(global _tqdm_active)
```

**Key Points:**

* Checks the value of `_tqdm_active` to determine whether tqdm progress bars are enabled.
* Returns a boolean indicating whether progress bars are enabled.


key: chroma:document
string_value:
**Summary:**

Enabling tqdm Progress Bar

* Purpose: Globally enables the `tqdm` progress bar and configures Hub progress bars.

**Key Implementation Details:**

* Enables `_tqdm_active` flag
* Calls `hf_hub_utils.enable_progress_bars()` to configure Hub progress bars

**Notable Dependencies/Requirements:**

* `_tqdm_active` (global variable)
* `hf_hub_utils` ( module) containing `enable_progress_bars()`, an interface for configuring Hub progress bars.


key: chroma:document
string_value:
**Summary:**

`disable_progress_bar()` function disables the `tqdm` progress bar globally. It sets `_tqdm_active` to `False` and calls `hf_hub_utils.disable_progress_bars()` to implement progressive disabling.

**Key points:**

* Global scope variable `_tqdm_active` is set to `False`
* Function calls `hf_hub_utils.disable	progress_bars()`, specifically implementing a progressive disabling scheme


key: chroma:document
string_value:
**Summary**

`is_offline_mode()` Function Summary:

*   Purpose: Returns whether the application is operating in offline mode.
*   Functionality: Simply returns a reference to the protected `_is_offline_mode` variable, allowing for indirect access.
*   Key Implementation Detail: The function wraps the `_is_offline_mode` variable without modifying its behavior.
*   Dependencies:
    *   `_is_offline_mode`: A protected variable indicating application offline status (type not specified).
*   Notable Interface: None, as it simply returns a reference to another variable.


key: chroma:document
string_value:
**Code Summary**

 `_get_cache_file_to_return`: Retrieves a cached file from the specified repository path or ID, with options for caching directory and revision.

**Main Purpose**: Returns a cached file if available.

**Key Implementation Details**:

* Tries to load a file from cache using `try_to_load_from_cache`
* Compares resolved file hash with `_CACHED_NO_EXIST` constant
* Returns file if cached and up to date, otherwise returns None

**Notable Dependencies/Requirements**:

* `Union[str, Path, None]` as default type hint for `cache_dir`
* `_CACHED_NO_EXIST` constant (not shown in code snippet)

**Method Signatures**:
```python
def try_to_load_from_cache(path_or_repo_id: str, full_filename: str, cache_dir: Union[str, Path, None] = None, revision: Optional[str] = None) -> object
```


key: chroma:document
string_value:
**Summary**

The `is_remote_url` function determines whether a provided URL or filename represents a remote URL by parsing the URL using `urlparse` and checking if it has a valid scheme.

**Key Details**

- **Function signature**: `def is_remote_url(url_or_filename):`
- **Primary purpose**: Verifies if a provided URL is remote (http/https).
- **Implementation focus**: Uses `urlparse` to extract the scheme from the input URL.
- **Notable aspects**: Only considers http and https schemes as valid for remote URLs.

**Example use case**:

```python
print(is_remote_url("https://example.com"))  # True
print(is_remote_url("file:///path/to/local/file.txt"))  # False
```

Note: In a RAG system, this summary would be used to quickly understand the intent and functionality of the code.


key: chroma:document
string_value:
**Function Summary**

The `define_sagemaker_information` function retrieves and processes environment variables to define SageMaker framework information. It:

1. Fetches instance metadata from ECS container metadata URI.
2. Extracts Dellums (dlc) container used and tag information.
3. Parses Sagemaker framework parameters.
4. Retrieves account ID from training job ARN.

**Key Implementation Details**

* `requests.get(os.environ["ECS_CONTAINER_METADATA_URI"])` retrieves instance metadata.
* `json.loads(os.getenv("SM_FRAMEWORK_PARAMS", "{}"))` parses SageMaker framework params.
* `sagemaker_object = {...}` constructs and returns a dictionary containing extracted information.

**Notable Dependencies and Requirements**

* External dependencies:
	+ `requests`
	+ `os`
	+ `json`
	+ `Environment variables: ECS_CONTAINER_METADATA_URI, SM_FRAMEWORK_PARAMS, TRAINING_JOB_ARN, AWS_REGION, SM_NUM_GPUS, SM_NUM_CPUS, SM_FRAMEWORK_MODULE`
* Inherited/I implemented interfaces:
None


key: chroma:document
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**Summary:**

The `http_user_agent` function formats a user-agent string with basic information about a request, including:

- Python version and session ID
- Optional dependencies on PyTorch and TensorFlow if available
- Flags for telemetry disablement and SageMaker training
- User-agent value from input parameters (dict or str)

**Implementation Details:**

- Returns a formatted user-agent string of type `str`
- Uses the following method signatures:
  - `is_torch_available() -> bool`
  - `is_tf_available() -> bool`
  - `define_sagemaker_information() -> Dict[str, str]`

**Dependencies and Requirements:**

- References:
  - `_torch_version` (PyTorch version)
  - `_tf_version` (TensorFlow version)
  - `sys.version.split()[0]` (Python version)
  - `SESSION_ID`
  - `_sagemaker_information` dictionary
- External dependencies:
  - `transformers` library
  - `torch` and `tensorflow` libraries if `is_torch_available()` and `is_tf_available()` are True, respectively
  - SageMaker environment (if `is_training_run_on_sagemaker()` is True)


key: chroma:document
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**Summary:**

Extracts the commit hash from a resolved file path.

**Key Implementation Details:**

* Extracts commit hash using regular expression search (`str.search()`).
* Validates commit hash format using `REGEX_COMMIT_HASH.match()`.
* Returns extracted commit hash or `None` if invalid or mismatched file structure.

**Notable Dependencies/Requirements:**

* `Path` class
* `re` module for regex searches
* `REGEX_COMMIT_HASH` regular expression pattern

Note: The implementation uses type hints from the `Optional[ str]` type to indicate that the function returns an optional string.


key: chroma:document
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This function downloads and returns the URL of a file from an Hugging Face repository. It takes several parameters, including the repository identifier, the name of the file to download, options for downloading the file, and any additional information about the repository or file.

**Summary:**

* Downloads a file from an Hugging Face repository
* Takes repository identifier, file name, and optional caching and connection settings as input
* Handles various errors that may occur during the download process

**Key concepts:**

* Hugging Face repositories
* File downloads
* Error handling (e.g., gated repos, repository not found, invalid revision)
* Caching and connection options (e.g., cache directory, user agent, proxies)

**Example usage:**
```python
# Download a file from a repository
repository_id = "transformers/distilbert-base-uncased"
file_name = "distillibert-base-uncased-pytorch-model.bin"
resolved_file = hf_hub_download(repository_id, file_name)
print(resolved_file)
```
This code downloads the `distillibert-base-uncased-pytorch-model.bin` file from the Hugging Face repository for the `transformers/distilbert-base-uncased` model.


key: docstring
string_value:
# TODO: deprecate `get_file_from_repo` or document it differently?
#       Docstring is exactly the same as `cached_repo` but behavior is slightly different. If file is missing or if
#       there is a connection error, `cached_repo` will return None while `get_file_from_repo` will raise an error.
#       IMO we should keep only 1 method and have a single `raise_error` argument (to be discussed).


key: chroma:document
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**Summary**

The `get_file_from_repo` function attempts to locate a file in a local folder or repository, downloads it if necessary, and caches it. It takes various parameters to customize the behavior, such as:

* `path_or_repo`: The model ID or path to the repository.
* `filename`: The name of the file to locate.
* `cache_dir`: The directory for caching downloaded files (optional).
* `force_download`: Force download the file even if it exists in cache (default: False).

The function returns the resolved file path if found, otherwise `None`.

**Key Implementation Details**

* Uses a nested `cached_file` function to handle caching and downloading.
* Has various optional parameters for customizing behavior, such as `proxies`, `token`, and `revision`.
* Checks for deprecated arguments (`use_auth_token`) and raises errors when used.

** notable dependencies or requirements**

* `os`: For handling file paths.
* `huggingface**: implicit dependency for model repository access.
* `transformers`: implicit dependency for transformer-specific behavior.


key: chroma:document
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**Summary:**

The `download_url` function downloads a file at the given `url` and returns the location of the temporary file. It fetches the file using `http_get`, takes precautions against deprecated behavior, and serves as an alternative way to download models in Transformers v5.

**Key Implementation Details:**

* Uses `tempfile.mkstemp()` to create a temporary file descriptor
* Converts to binary mode with `os.fdopen`
* Utilizes `http_get` for HTTP GET requests with optional proxy configuration

**Notable Dependencies and Requirements:**

* `tempfile`, `os`, `requests` (implied by `http_get`)
* Raises `FutureWarning` regarding deprecated behavior in Transformers v5.

**Primary Purpose:**
Downloads a file at the given URL to a temporary location.


key: chroma:document
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**Summary**

The `has_file` function checks if a repository contains a given file without downloading it. It supports remote repositories and local folders.

**Main Purpose**

* Checks if a repository exists (local or remote) and whether the specified file is present in its cache
* Uses various methods to verify file existence, including local directory check, cache check, and HEAD call (if allowed)

**Key Implementation Details**

* `try_to_load_from_cache` function to load file from cache
* `get_session().head` method to make HEAD call to the Hub
* `hf_raise_for_status` function to raise HTTP errors
* Handling of various exceptions, including ConnectionError, Timeout, and RepositoryNotFoundError

**Dependencies and Requirements**

* `requests` library for making HTTP requests
* `os` library for working with local directories
* `huggingface-_hub` library for interacting with the Hub
* `huggingface_utils` library for logging and error handling


key: chroma:document
string_value:
The provided code snippet appears to be related to the Transformers library in Python, specifically the functionality for uploading and managing model files. 

Here's a succinct summary:

This code is used to upload model files to a repositories through the `push_to_hub` method of the Transformer class. It takes various arguments such as repository path or name, private access option, authentication token, maximum shard size, and more.

The process involves several steps:

1.  If the repository does not exist, it is created.
2.  A new model card is created to represent the uploaded files.
3.  The model is saved in a working directory with temporary or persistent storage, depending on the 'use_temp_dir' option.
4.  The model card's README.md file is updated if necessary.
5.  Finally, the model files are uploaded to the repository using their timestamp.


key: chroma:document
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**Summary**

`send_example_telemetry` function sends tracking telemetry data for machine learning examples in the background.

**Key Implementation Details**

*   Extracts `model_name`, `dataset_name` from input arguments, filtering out local paths and extracting tasks.
*   Constructs dataset name by combining script name and task name.
*   Sends telemetry data using `send_telemetry` function with framework version and user agent.

**Notable Dependencies or Requirements**

*   `is_offline_mode` check
*   `os.path.isdir`
*   `http_user_agent` function
*   `Dataclass` or `ArgumentParser` objects as input arguments

**Function Signature**

`send_example_telemetry(example_name, *example_args, framework="pytorch")`

With options:

*   `framework` (str): Optional framework for the example (defaults to `"pytorch"`).


key: chroma:document
string_value:
Here's a technical summary of the provided function code chunk:

**Function Name Class/Function:** `convert_file_size_to_int`
**Primary Purpose:** Converts a file size string (with digits and unit) to an integer value in bytes.

Key Implementation Details:
- Useful patterns: handling unit suffixes as strings and parsing them for calculation.
- Relevant method signatures:
  - `int(size[:-3])`: removes the unit digit from the input size string.
  - `size.upper().endswith(unit_suffix)`: checks if the input size string ends with a specific unit suffix.

Notable Dependencies or Requirements:
- External dependency: `Union` from Python's type hinting module for handling mixed data types.
- Imported/Implemented interfaces: None.


key: chroma:document
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**Function Summary: `get_checkpoint_shard_files`**

The `get_checkpoint_shard_files` function downloads and caches all shards of a sharded checkpoint for a given model. It takes in various parameters such as model name, index file path, cache directory, and download options.

**Primary Purpose:** Download and cache shards of a sharded checkpoint.

**Key Implementation Details:**

* Loads the shard filenames from an index file.
* Checks if the model is already cached or not, and loads the last available shard for downloading.
* Downloads each shard using `cached_file` and caches them in a specified directory.

**Notable Dependencies/Requirements:**

* `os` library for file handling
* `json` library for loading an index file
* `tqdm` library for displaying a progress bar
* `HUGGINGFACE_CO_RESOLVE_ENDPOINT` (environment variable) for resolving model URLs

Note that this summary focuses on the main purpose, key implementation details, and notable dependenciesrequirements of the function.


key: chroma:document
string_value:
**Function Summary**

### `get_all_cached_files`

Retrieves a list of cached files with metadata.

**Purpose**: Returns a list of all cached files in the specified directory with their corresponding metadata (url and etag).

**Key Implementation Details**:

* Uses the `os` module to interact with the file system.
* Iterates through files in the cache directory, filtering out directories and non-existent files.
* Loads JSON metadata from each file's presence file (`file.json`) and extracts `url` and `etag`.
* Returns a list of dictionaries containing file information with metadata.

**Notable Dependencies/Requirements**:

* `os` module
* `json` module

### Important Method Signatures:

* `get_all_cached_files(cache_dir=None)`: Retrieves cached files by directory path.
* `zipfile.ZipFile.open()` (not shown): Used to load JSON metadata from presence files.

#### External Dependencies:

* `TRANSFORMERS_CACHE`
* No external dependencies specified.


key: chroma:document
string_value:
**Function Summary:**

`extract_info_from_url(url)`

Extracts repository name (`repo`), revision, and filename from a Hugging Face URL.

**Key Implementation Details:**

* Uses regular expression to parse the URL with patterns `repo_name/revision/filename`
* Returns a dictionary containing extracted information
* Notably uses `search.groups()` to extract values from matched groups

**Notable Dependencies/Requirements:**

* `re` (Python's built-in regular expressions module)
* Hugging Face API (`huggingface.co`) assumed to be accessible without further setup or configuration.


key: chroma:document
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**Function Summary:**

`create_and_tag_model_card`

**Purpose:** Creates or loads an existing model card and tags it.

**Key Implementation Details:**

* Uses `ModelCard.load()` to check if the model card is present on the remote repo, or creates one from a template if not.
* Validates and appends new tags to the `modelCard.data.tags` list only if provided.
* Returns the created/loaded `ModelCard` instance.

**Dependencies:**

* `ModelCard` class from an external library (not shown)
* `huggingface_hub.HfApi.login` method for obtaining authentication token
* Optional dependencies: `EntryNotFoundError` exception

**Interface:**
`create_and_tag_model_card(repo_id, tags, token, ignore_metadata_errors)`


key: chroma:document
string_value:
**Code Summary: `clean_files_for` Function**

### Purpose and Functionality

The `clean_files_for` function removes the existence of specified file, its JSON counterpart, and its locking file.

* **Function Signature:** `def clean_files_for(file):`
* **Primary Purpose:** Clean up temporary files associated with a given input file named `file`.
* **Input Parameters:**
	+ `file`: The base filename for which to remove associated files.

### Implementation Details

The function uses the following key components:

* Iterates over three possible files to be removed, considering different suffixes.
* Utilizes the `os.path.isfile` check to ensure only files exist before attempting removal.

### Dependencies

* References:
	+ `file`: Input filename for cleaning up associated files.
	+ `f": Path to file with potential JSON and locking extensions.
* External Dependencies:
	+ `os`: Module providing path manipulation functions (e.g., `os.path.isfile`, `os.remove`).

Note: The implementation does not check if the removal operation is successful; it only removes the files if they exist.


key: chroma:document
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**Function Summary: `move_to_new_cache`**

Migrates a file to a new repository structure following the Hugging Face Hub cache organization. The function handles reference, blob, and snapshot creation.

*   Creates necessary directories and moves files according to the specified revision and etag.

Key Components:
- `os.makedirs(repo)` : creates the repository directory
- `os.makedirs(os.path.join(repo, "refs"), exist_ok=True)`: creates references directory
- `huggingface_hub.file_upload._create_relative_symlink(blob_path, pointer_path)`: creates a relative symlink for snapshot tracking

**Implementation Details:**
- Moves files to corresponding blobs directories based on their etag values.

**External Dependencies:**
- `shutil`
- `os` 
- `file_download` module from Hugging Face Hub


key: chroma:document
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**Concise Summary:**

### move_cache Function

#### Purpose
Moves cached TensorFlow hub files to a new cache location while handling file updates and metadata.

#### Key Implementation Details:

* Calculates the new cache directory using `TRANSFORMERS_CACHE` if not provided.
* Migrates old `.cache/huggingface/transformers` cache to a new location or uses the specified `new_cache_dir`.
* Uses `get_all_cached_files` to retrieve files from the specified cache directory.
* Iterates through retrieved files, downloading metadata using `get_hf_file_metadata` and handling HTTP errors.
* Moves cached files to the new cache system, updating only outdated versions.

#### Notable Dependencies/Requirements:

* `TRANSFORMERS_CACHE`
* External dependencies:
	+ `requests` for HTTP requests
	+ `tqdm` for progress bar
	+ `os` for file system operations

### Important Method Signatures and Interfaces:

* `get_all_cached_files(cache_dir)`
* `get_hf_file_metadata(url, token)`
* `clean_files_for(file_path)`
* `move_to_new_cache(filename, repo, revision, etag, commit_hash)`


key: chroma:document
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**Summary:**

The `PushInProgress` class tracks a collection of asynchronous `Future` jobs. It provides methods to:

* Initialize with an optional list of jobs (`__init__`)
* Check if all jobs have completed (`is_done`)
* Wait until all jobs have completed (`wait_untilDone`)
* Cancel any cancelled or already done jobs while preserving running futures (`cancel`)

**Key Implementation Details:**

* Uses a list to store `Future` objects (`jobs`)
* Checks job completion status using the `done()` method
* Cancels jobs that haven't started and removes non-running jobs from the list

**Notable Dependencies/Requirements:**

* Requires Python's `futures` module for working with asynchronous `Future` objects
* No external dependencies, as `PushInProgress` only relies on internal implementation details


key: chroma:document
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**Summary**

`assert_device_map(device_map, num_blocks)` validates a device map for attention blocks in a neural network model.

**Main Purpose and Functionality**
The function ensures that the device map is free from duplicates, missing blocks, and extra blocks. It also checks if all specified attention blocks are unique to one device.

**Key Implementation Details**

*   Duplicates: removes duplicate attention blocks, raising an error if found.
*   Missing Blocks: identifies blocks not present in the `device_map`.
*   Extra Blocks: detects blocks present in the `device_map` but not in the block range (`0 - num_blocks`).
*   Raises exceptions for invalid situations, providing descriptive error messages.

**Notable Dependencies or Requirements**

*   `device_map`: a dictionary containing attention blocks assigned to devices.
*   `num_blocks`: the total number of blocks defined.

This function aims to ensure that the `device_map` is properly configured, preventing common issues encountered during model development and execution.


key: chroma:document
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**Summary:**

`get_device_map` function:
 
  - Purpose: Returns a dictionary mapping device indices to subsets of layer assignments.
  
  * Key implementation detail: Splits `n_layers` into chunks based on the number of devices, ensuring even distribution (`ceil(n_layers / len(devices))`). 
  * Interface/Methods: Utilizes list comprehension and zip functions for data manipulation.

**Notable dependencies and requirements:**
 
* External dependency: `math.ceil` function.

* Assumed requirement:
	+ The input `devices` is a non-empty list of integers.


key: chroma:document
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**Class Summary: FlaxForcedBOSTokenLogitsProcessor**

**Purpose and Functionality:**
The `FlaxForcedBOSTokenLogitsProcessor` class processes token logit outputs using the flax backend.

*   Main Purpose: Forced BOS (beginning of sequence) token logits processing.
*   Primary purpose:
    ```python
Provides a way to process BOS token logit outputs using the flax backend.
```

**Implementation Details:**

*   Key Algorithm/ Pattern: None.
*   Method Signatures and Interfaces:
    `requires_backends(self, ["flax"])`: Verifies that the required flax backend is available.

**Dependencies and Requirements:**

*   Backends: Flax (`_backends = ["flax"]`).
*   External Dependencies: The `DummyObject` metaclass.
*   Inherited or Implemented Interfaces: None.


key: chroma:document
string_value:
**FlaxForcedEOSTokenLogitsProcessor Class Summary**

**Main Purpose and Functionality:**
The `FlaxForcedEOSTokenLogitsProcessor` class is designed to process logits (output values) for a Forced East Token technique in a neural network architecture. It leverages the FLAX library as a backend.

**Key Implementation Details:**

* The `_backends` attribute specifies that only FLAX is supported.
* The `__init__` method initializes the processor and explicitly requires the FLAX backend using the `requires_backends` function, ensuring compatibility with the specified backend.

**Notable Dependencies and Requirements:**

* Flax library (backend)


key: chroma:document
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**Summary:**

`FlaxForceTokensLogitsProcessor`

*   **Class Purpose:** A class processor for Flax backend usage in Force Tokens Logits.

    The `__init__` method initializes the processor by requiring the Flax backend to be used before proceeding. The actual implementation and further details are not provided in this snippet, indicating that it might serve as a base or wrapper class for specific processing needs related to Flax and Force Tokens Logits. 

**Key Implementation Details:**

*   **`__init__` method:** Initializes the processor with `Flax` backend requirement.
    
    ```python
def __init__(self, *args, **kwargs):
    requires_backends(self, ["flax"])
```
    
    This demonstrates a dependency on the Flax backend for successful initialization.


**Notable Dependencies/Requirements:**

*   `DummyObject`: The metaclass used for this class.
*   `requires_backends` function (not shown in this snippet): A utility function to check if required backends are specified, ensuring backwards compatibility or feature activation.
   
    ```python
requires_backends(self, ["flax"])
```
    
    This line emphasizes the importance of the Flax backend for class functionality.


key: chroma:document
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**Class Summary:**

`FlaxGenerationMixin`

Mixed-in class for Flax backend integration.

**Main Purpose and Functionality:**
Provides initialization logic for a class using the Flax backend, ensuring compatibility with required dependencies.

**Implementation Details:**

* Initializes instance using `requires_backends` method, verifying presence of Flax backend support.
* Requires Flax backend to be passed as an argument in the constructor (not shown).

**Notable Dependencies/Requirements:**

* `flax`: Backend implementation
* `DummyObject`: Metaclass for generating the mixin class (not shown)

**Key Method:**
`__init__(self, *args, **kwargs)`


key: chroma:document
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**Summary:**

`FlaxLogitsProcessor` is a class implemented with the `DummyObject` metaclass. It belongs to the backbone of Flax models and is responsible for handling logit processing.

**Key Implementation Details:**

- Processes logits using the flax backend.
- Requires the "flax" backend in its initialization (`requires_backends` method).

**Notable Dependencies and Requirements:**

* `DummyObject` metaclass
* `Flax model backbone`


key: chroma:document
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**Summary:**

The `FlaxLogitsProcessorList` class is a processor list for Flax backend, verifying that the required backend ("flax") is present. It initializes with an arbitrary set of arguments and keywords.

**Key Details:**

* Requires "flax" backend.
* Inherited from `DummyObject`.

**Notable Dependencies:**

- Flax framework (`"flax"` backend)

**Implementations:**

* `requires_backends(self, ["flax"])` ensures the "flax" backend is present.


key: chroma:document
string_value:
**Summary**

`FlaxLogitsWarper` is a class that applies warping to Flax model logits. It is defined with the `DummyObject` metaclass and requires the `flax` backend for execution.

**Key Details**

* Primary purpose: Applies warping to Flax model logits.
* Notable implementation detail: Uses `requires_backends` function to validate backend configuration (`["flax"]`).

**Key Interfaces/Methods**

* `__init__`: Initializes the class with variable arguments and(kwargs).
 

**Notable Dependencies**: 
 - `DummyObject`: Metaclass used for defining the class.
 - `requires_backends` method, which is not shown in this code snippet ( reference by exact name).


key: chroma:document
string_value:
**Summary:**

`FlaxMinLengthLogitsProcessor` is a processing class for logits in Flax backend, responsible for ensuring minimum length. The class requires the use of the `flax` backend during initialization.

**Key Details:**

*   Primary purpose: Validate and enforce minimum length requirement on logits using the `flax` backend.
*   Method Signature:
    *   `__init__(self, \*)`
*   Interface Noted:
    *   `requires_backends(self, ["flax"])`

**External Dependencies:**

*   Flax library (backend)

**Design Choice:**

*   Use of `DummyObject` metaclass


key: chroma:document
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**Summary:**

`FlaxSuppressTokensAtBeginLogitsProcessor`: A processor class that suppresses token annotations at the beginning of logits for flax backend. 

**Implementation Details:**

* Requires the flax backend to be present.
* Utilizes `dummy_object` metaclass (not detailed in summary).

**Notable Dependencies:**
 
* `requirements`: `[RequiresBackends]`, 
    * Implementation reference: `requires_backends(self, ["flax"])`
* External dependencies:
    + (`dummy_object` metaclass is a built-in Python class)


key: chroma:document
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**Summary**

`FlaxSuppressTokensLogitsProcessor` class: A processing backend for Flax-based neural networks that suppresses token logits.

**Key Implementation Details**

*   Initializes with `requires_backends` to enforce the use of the "flax" backend.
*   The primary purpose is to process and generate Flax models.

**Notable Dependencies/Requirements**

*   Uses the `DummyObject` metaclass.
*   Requires the "flax" backend (Flax library), which suggests the neural network will be a PyTorch model that uses Flax's conversion utilities.


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**FlaxTemperatureLogitsWarper Class Summary**

**Purpose and Functionality:**
The `FlaxTemperatureLogitsWarper` class is designed to provide logits for temperature-based clustering in the Flax deep learning framework.

**Key Implementation Details:**

* Requires Flax backend support.
* Uses a dummy metaclass (`DummyObject`) with a hardcoded backend list containing "flax".
* Initializes with optional arguments and keyword arguments.

**Notable Dependencies or Requirements:**

* Extends `DummyObject` class using its metaclass definition.
* Relies on the `requires_backends` function from the Flax framework to enforce backend support.


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**Concise Summary: FlaxTopKLogitsWarper Class**

The `FlaxTopKLogitsWarper` class is a metadata utility used to wrap around the top K logits in classification models for various backends, specifically Flax. 

Its primary functionality consists of requiring the "flax" backend during initialization (`__init__()`) and enforcing its usage.

**Key Details:**

- **`DummyObject` Metaclass:** Imposes a dummy object on subclasses.
- `requires_backends(self, ["flax"])` checks for required backends.
- The class specifically targets Flax implementation.

**Notable Dependencies/Requirements:**

- Flax backend (`"flax"`).
- No additional dependencies mentioned.


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**Summary:**

`FlaxTopPLogitsWarper` class:
-----------

*   **Purpose**: A warper for Flax top-p logits, utilizing the `flax` backend.

**Key Implementation Details:**

*   Requires Flax as the supported backend (`requires_backends(self, ["flax"])`)
*   Uses `DummyObject` metaclass

**Dependencies/Requirements:**

*   Utilizes the `flax` library
*   Implements DummyObject metaclass


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**Summary:**

The `FlaxWhisperTimeStampLogitsProcessor` class is designed to process time-stamped logits using the FLAX backend. It initializes with the `flax` backend and inherits requirements from other backends.

**Key Details:**

* Primary purpose: Process time-stamped logits
* Key implementation detail: Uses the FLAX backend (`requires_backends(self, ["flax"])`)
* Notable dependencies:
	+ `DummyObject` metaclass
	+ Related classes/functions not mentioned in this code snippet

Note: The ` DummyObject` metaclass is used to specify the metaclass for this class.


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**Summary:**

The `FlaxPreTrainedModel` class is a pre-trained model base class using the Flax deep learning library. It inherits from `DummyObject` with `_backends` set to `"flax"`. The `__init__` method requires the Flax backend and initializes the instance.

**Key Highlights:**

* Inherited from `DummyObject`
* Uses Flax backend
* Expects a Flax backend in the `requires_backends` call

**Notable Dependencies/Requirements:**

* Flax deep learning library


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**Flax Albert For Masked LM Class Summary**

### Main Purpose and Functionality

The `FlaxAlbertForMaskedLM` class is an implementation of the ALBERT model using the Flax library. It's designed for language modeling tasks with masking.

```python
class FlaxAlbertForMaskedLM(metaclass=DummyObject):
    _backends = ["flax"]
```

### Implementation Details

The `__init__` method initializes the model, requiring the `flax backend`. The `requires_backends` function is used to assert the required backend.

```python
def __init__(self, *args, **kwargs):
    requires_backends(self, ["flax"])
```
Note: `Provide` statement seems out of place here, possibly a typo? A corrected sentence would be `required backends`.

### Notable Dependencies or Requirements

The FlaxAlbertForMaskedLM class uses the `flax` library as its backend.

```python
_backends = ["flax"]
```
This indicates that external dependency: `flax`.


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Here is a concise and relevant summary of the provided class code chunk:

**FlaxAlbertForMultipleChoice Class Summary**

*   Purpose: `FlaxAlbertForMultipleChoice` is a machine learning model class using the Flax library as the backend, designed for multiple-choice question classification tasks.
*   Implementation:
    *   Uses the "flax" backend, which is determined by the `_backends` attribute being set to ["flax"] during instantiation.
    *   Initializes with variable arguments (`args`) and keyword arguments (`kwargs`).
    *   Requires the presence of the "flax" backend using the `requires_backends` function.
*   Notable Dependencies:
    *   Uses the Flax library for model implementation.

This summary provides an accurate understanding of the class's purpose, key implementation details, and dependencies without including unnecessary information.


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**Class Summary**

`FlaxAlbertForPreTraining`: A PyTorch-based neural network architecture for pre-training using the Flax library and Albert model architecture.

**Implementation Details**

*   Initialize with `requires_backends` to ensure compatibility with Flax backend (`"flax"`).
*   Internal data structure `_backends` stores the supported backends.


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**Flax Albert Question Answering Model**

### Class Overview
`FlaxAlbertForQuestionAnswering` is a class implementing the Flax Albert architecture for question answering tasks.

```python
class FlaxAlbanertForQuesionAnswering(metaclass=DummyObject):
    """
    A Flax Albert model implementation for question answering.
    
    Provides:
        - _backends: Supported backends (["flax"])
    """

    def __init__(self, *args, **kwargs):
        """Initializes the model with required backend."""
        
        # Check for required Flax backend
        requires_backends(self, ["flax"]
```


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**Concise Summary:**

`FlaxAlbertForSequenceClassification` is a sequence classification model utilizing Flax backend. The `__init__` method initializes the model with support for the Flax backend and enforces its use.

**Key Details:**

- Primary Purpose: Sequence Classification
- Key Algorithm/Pattern: Uses Flax backend
- Important Method Signatures:
  - `requires_backends(self, ["flax"])`
  
**Notable Dependencies/Requirements:**

- Related Class/Function: `DummyObject` (metaclass)
- External Dependency:
  - Flax library (`"Flax"` backend)


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**Class Summary: FlaxAlbertForTokenClassification**

The `FlaxAlbertForTokenClassification` class is a custom implementation of the Hugging Face's Albert model on top of the PyTorch backend using the Flax library.

**Key Features and Design Choices:**

*   Uses the Flax library as the primary backend (`backends=["flax"]`)
*   Requires Flax as an additional backend for this model class
*   Implements a custom `__init__` method to ensure compatibility with the Hugging Face requirements

**Notable Dependencies and Requirements:**

*   Flax library (`flax`)
*   Hugging Face's Albert model (`AlbertForTokenClassification`)

This code is a specific implementation of a pre-trained language modeling model integrated into a custom PyTorch model. The class design uses Flask for the backend to allow use with other backends as well, showcasing flexibility and potential expansion opportunities of this project in comparison to its standard counterpart, which only supports PyTorch. 

**Primary Functionality:**

This class is designed to be used in Natural Language Processing (NLP) tasks such as token classification, leveraging large pre-trained models like Albert for efficient modeling without the need for significant data preparation and can further add custom functionalities through subclassing to accommodate user-defined model needs.


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**FlaxAlbertModel Class Summary**

* **Purpose:** Flax-based implementation of the Albert model for natural language processing tasks.
* **Implementation:**
	+ Uses the "flax" backend.
	+ Initializes with variable arguments (`*args`, `**kwargs`).
	+ Requires at least the "flax" backend to be present.
* **Methods and Interfaces:**
	+ None noted in this context, but inherited from base class via metaclass.
* **Dependencies and Requirements:**
	+ None explicitly listed, but relies on "flax" backend.


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**Flax Albert Pre-Trained Model Class Summary**

**Main Purpose:** A PyTorch-based implementation of the Flax Albert pre-trained language model.

**Key Details:**

*   **Initialization**: Requires at least one `flax` backend to be present during instantiation (`requires_backends(self, ["flax"])`)
*   **Type Hinting**: Uses `*args` and `**kwargs` for generic argument passing in the constructor
*   **Inherited Interface:** The class inherits from a dummy metaclass (`metaclass=DummyObject`)


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**Summary:**

`FlaxAutoModel`: A class inheriting from `DummyObject`, that:
- Initializes with an argument list and dictionary (`*args**, `**kwargs`)
- Requires the "flax" backend for its operation
- **Primary purpose**: Autodeled model using Flax backend. 

**Implementation details:**

- Uses `requires_backends` function to assert support for "flax" backend.

**Dependencies:**

- Flax library (specifically, 'flax' as a required backend)
            


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**Summary:**

`FlaxAutoModelForCausalLM`: A custom PyTorch model class utilizing the `Flax` backend, requiring the `["flax"]` backend to be present.

*   **Key Functionality:** Automatic model loading based on available backends.
*   **Implementation Details:** The model uses a `DummyObject` metaclass and relies on the `requires_backends` function from the `metacog` library to enforce the required backend.

**Notable Dependencies:**

*   PyTorch
*   `Flax` backend (unimplemented)
*   `dummy_object` metaclass (` DummyObject` )
 
**No External Dependencies Listed.


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**Summary:** 

`FlaxAutoModelForImageClassification` is an auto-model class using the `flax` backend for image classification tasks. Main purpose is to provide pre-configured models with specified backends.

**Key Implementation Details:**

* The model uses the `flax` backend.
* Requires the `flax` backend at initialization.
* Uses `DummyObject` metaclass, suggesting custom object implementation details not shown in this snippet.

**Notable Dependencies/Requirements:**

* External dependency on `flax`.


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**FlaxAutoModelForMaskedLM Class Summary**

### Purpose and Functionality
The `FlaxAutoModelForMaskedLM` class is an auto-recommending masked language model (MLM) based model using the Flax deep learning framework.

### Key Implementation Details

*   **Initialization**: The `_backends` attribute specifies that Flax should be used as a backend for this model.
*   **Validation**: The `requires_backends` function is called to validate that the required backend (`flax`) is available before creating the instance.

```markdown
# Class Methods

def __init__(self, *args, **kwargs)
    # Initializes FlaxAutoModelForMaskedLM with required backend validation.
```

### Notable Dependencies and Requirements

*   **Flax**: The model uses Flax as its backend framework.
*   `DummyObject`: This class is used as the metaclass for the `FlaxAutoModelForMaskedLM` class.

Note: Without the rest of the code, this summary only covers what can be inferred from the provided snippet.


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**Summary:**

This is an implementation of the `FlaxAutoModelForMultipleChoice` class using the Flax library (`_backends=["flax"]`) for multiple-choice classification tasks.

**Key Details:**

*   Constructor (`__init__)`:
    *   Verifies that the required backend ("flax") is supported.
        - `requires_backends(self, ["flax"])`

**Notable Features and Requirements:**

*   Inherited from a metaclass called `DummyObject`.
*   External dependency on Flax library (backend implementation).


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**Flax Auto Model for Next Sentence Prediction Class Summary**

### Class Overview
`FlaxAutoModelForNextSentencePrediction` is an auto model designed to predict the next sentence in a sequence of text.

### Primary Purpose
The primary purpose of this class is to create and configure an auto model using the Flax backend, enabling next-sentence prediction capabilities.

**Implementation Details**

*   Utilizes the `FlaxAutoModel` base class: `requires_backends(self, ["flax"])`
*   Defined with a custom metaclass (`DummyObject`) that allows for specific backend usage

### Notable Dependencies and Requirements

*   Flax backend (`_backends = ["flax"]`)
*   `Provides` function is part of the required `dummy_object`, which indicates that some functionality might be missing or partially implemented.


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**Class Summary:**

`FlaxAutoModelForPreTraining`

*   A pre-training auto-model class built on top of the flax backend (`_backends = ["flax"]`).

**Implementation Details:**

*   Initializes with `requires_backends` check to ensure compatibility with Flax backend.
    *   Requires Flax backend as a necessary dependency.

**Notable Dependencies and Requirements:**

*   Flax backend (required)
*   `DummyObject` metaclass used for class definition.
*(External)


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**Summary**

`FlaxAutoModelForQuestionAnswering` is an autoregressive question answering model implemented using the Flax library. It inherits requirements and compatibility from parent classes.

**Key Details**

* **Class Definition**: `class_flax_auto_model_for_question_answering(flax_auto_model_base.AutoModelBase)`
* **Primary Purpose**: Autoregressive Question Answering Model
* **Implementation**: Uses Flax library backend

**Notable Dependencies and Requirements**

* `flax` (backend module)
* No external dependencies listed. 

**Interface Inheritance**

Inherited from `AutoModelBase`


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**Summary:**

The `FlaxAutoModelForSeq2SeqLM` class is an auto-supervised language model designed for sequence-to-sequence languages models using the Flax library backend.

**Key Details:**

* Primary purpose: Implement a sequence-to-sequence language model.
* Key implementation details:
	+ Uses the Flax library backend (`_backends = ["flax"]`).
	+ Initializes with the `requires_backends` method, which ensures the "flax" backend is available (with `_init_requires_backend flax`).


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**FlaxAutoModel_for_SequenceClassification Class Summary**

### Main Purpose and Functionality
The `FlaxAutoModelForSequenceClassification` class is an auto-generated model class for sequence classification tasks using the Flax backend.

### Key Implementation Details

*   The model requires the `flax` backend, as specified by `_backends`.
*   The `__init__` method ensures that the required backend is present when the model is instantiated.
    *   It calls the `requires_backends` function to validate the presence of the `"flax"` backend.

### Notable Dependencies and Requirements

*   **Backend:** Flax
*   **External Dependency:** None
*   **Implemented Interface/Trait:** Auto-generated by Flax framework


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**Summary**

`FlaxAutoModelForSpeechSeq2Seq` is an auto-regressively trained speech sequence generation model. This class is part of the FLAX library and leverages the Flax framework to create a pre-trained T5-like architecture.

**Key Details**

*   Primary purpose: Speech sequence generation
*   **Implementation:** Utilizes Flax for pre-training and inference
*   Method Signature: `__init__.__defaults__(self)`, which initializes the model with dependencies on the "flax" backend.


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**Summary**

`FlaxAutoModelForTokenClassification`: A PyTorch-like auto-encoded model utilizing Flax backend for token classification tasks. 

**Key Details**

* Backends: `flax`
* Primary Functionality: Auto-encode model with support for token classification
* Implementation: Initializes model with required backend, using the `requires_backends` function

**Notable Dependencies/Requirements**

* `DummyObject` metaclass (not explicitly listed but implied)
* No external dependencies mentioned


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**Class Summary:**

`FlaxAutoModelForVision2Seq`

Automated vision-to-sequence model using the Flax backend.

**Key Features:**

- Inherited from `DummyObject` metaclass
- Supported backend: `flax`
- Requires `flax` backend at instantiation time

No specific implementation details are provided in this code snippet. Further analysis of related methods or dependencies would be required to understand the full functionality and behavior of this class.


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Here is a concise summary of the provided code chunk:

**FlaxBartDecoderPreTrainedModel**

* **Purpose**: A pre-trained model for Flax-based BART decoding
	+ Primary purpose: Enable Flax-based implementation of BART model decoding
* **Key Implementation Details**
	+ Uses `DummyObject` metaclass
	+ Requires Flax backend (`requires_backends(self, ["flax"])`)
	+ Initializes with variable arguments and keyword arguments (`__init__(self, *args, **kwargs)`)
* **Notable Dependencies or Requirements**
	+ External dependency: Flax backend library


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**Summary**

`FlaxBartForCausalLM` is a neural network model implementation class that leverages the `flax` backend. The primary purpose is to support causal language models.

**Key Implementation Details:**

* Utilizes the `flax` backend with `_backends = ["flax"]`
* Enforces the use of the "flax" backend in its constructor with `requires_backends(self, ["flax"])`

**Notable Dependencies:**

* Relies on the `DummyObject` metaclass
* Requires the `requries_backends` function from an external module (not shown) for backend validation


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**Summary:**

The `FlaxBartForConditionalGeneration` class is a variant of the BART model specifically designed for conditional generation tasks using the FLAX library. It inherits from a metaclass (`DummyObject`) and requires the "flax" backend.

**Key Details:**

- **Purpose:** Conditional generation model
- **Backend:** FLAX (framework)

**Implementation Insights:**

- Uses `FlaxBartModel` as a base class, but exact name is not mentioned in this snippet

**Requirements/Dependencies:**

- `Flax` backend
- Inherited from `DummyObject` metaclass


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**Concise Summary**

### Class FlaxBartForQuestionAnswering

A class implemented using the Flax deep learning library for Question Answering tasks.

*   Primary purpose: A pre-trained language model based on the BART architecture, utilizing Flax backend.
*   **Key Implementation Details:**
    *   Method `__init__`: Initializes the class with required backend ("flax").
*   **Notable Dependencies/Requirements:**
    *   Python 3.8+ (Flax library)
    *   Required backend: "flax"


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**Summary:**

The `FlaxBartForSequenceClassification` class is a PyTorch-based sequence classification model using the Flax library as its backend. It requires the `"flax"` backend to be specified during initialization.

**Key Details:**

* `__init__` method:
	+ Requires `"flax"` backend for execution
	+ Allows keyword arguments `*args` and **kwargs**
* Defined metaclass: `DummyObject`

**Implementation Notice:** This class has not been fully implemented as it inherits from a dummy metaclass (`DummyObject`), which typically indicates that the actual implementation is incomplete or pending.


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**FlaxBartModel Class Summary**

*   **Purpose**: A PyTorch-based variant of the BART model, utilizing Flax as its backend.

    `FlaxBartModel` is a custom neural network model that leverages Flax for efficient execution. The class inherits from `DummyObject` and specifies `flax` as its supported_backend.

*   **Implementation Details**:
    
        - The `__init__` method initializes the model, ensuring availability of the required backend (`"flax"`).
        
    Key Methods:
    
        - `requires_backends`: Verifies that a list of backends are available for use.
        
*   **Dependences and Requirements**:

    External Dependencies:

    - Flax (backend framework)

    Related Classes/Functions:

    - `DummyObject` (metaclass)
    - `RequiresBackends` interface


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**Summary:**

The `FlaxBartPreTrainedModel` class is a pre-trained model for BART language models using the Flax library backend. It inherits from a DummyObject metaclass.

**Key Details:**

* Backend: `flax`
* Primary Purpose: Pre-training BART language model
* Notable Implementation: Uses `requires_backends` to enforce the required `flax` backend

**Dependencies and Requirements:**

* `_backends`: Specifies that this model uses only the `flax` backend.
* `DummyObject`: Dummy metaclass used for `FlaxBartPreTrainedModel`
* `requires_backends`: Function checks if the required backends are present, in this case enforcing `flax`


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**Summary:**

`FlaxBeitForImageClassification` is a class that utilizes the Flax library for image classification tasks. It requires the `flax` backend to be available.

**Key Implementation Details:**

- Utilizes Flax library (`_backends = ["flax"]`)
- Requires `flax` backend at initialization (`requires_backends(self, ["flax"])`)

**Notable Dependencies/Requirements:**

- Flax library (`self._backends` and `requires_backends` function)
- **Related classes/functions:** `DummyObject`, `Provides`

Note: The exact purpose of the `DummyObject` metaclass is not specified in this code snippet, but it may be used for class definition or mocking purposes. Similarly, the "Provide" function is not shown in this part of the code, but it likely serves as a utility for registering requirements or dependencies.


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**Summary:**

The `FlaxBeitForMaskedImageModeling` class is a PyTorch model implementation using the FLAX backend. It inherits from `DummyObject` and requires the `flax` backend to be installed.

**Key Details:**

*   Primary Purpose: Image modeling with masked language modeling
*   Required Backend: `flax`
*   Initialization: Requires `flax` backend installation verification in constructor (`__init__`)

**Important Sections:**

*   Constructor Method Signature: `__init__(*args, **kwargs)`
*   Backend Requirement Check: `requires_backends(self, ["flax"])`

This summary provides a concise overview of the class's purpose and key implementation details. The RAG system can use this information to help developers quickly understand the model's functionality and requirements.


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**Summary:**

`FlaxBeitModel`: A PyTorch-based model class that utilizes the `Flax` backend from the `beet` library. The class initializes with a set of required backends and verifies compatibility.

**Key Details:**

* **`_backends = ["flax"]`**: Specifies the `Flax` backend enabled for this model.
* **`__init__(self, *args, **kwargs)`**: Initializes the model instance, triggering validation of supported backends with `requires_backends(self, ["flax"])`.
*   The `dummy_object Metaclass` is used to create an metaclass class that is a subclass of a dummy object. 

**Notable Dependency/Requirements:**

*   `beet`: A PyTorch-based library for implementing Bayesian optimization and other probabilistic machine learning algorithms.
    *   `Flax` backend: A part of the `beet` library, providing a Flax-compatible interface for probabilistic models.
    *   `PyTorch`: Required as it's used to integrate with the `Flax` backend for model implementation.


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**Concise Summary**

### Class Overview

`FlaxBeitPreTrainedModel` is a custom model class utilizing the `flax` backend.

### Primary Purpose

This class serves as a pre-trained model using the `Beit` architecture with the `flax` backend, inheriting from a `DummyObject` metaclass.

### Key Implementation Details

- Initializes the model instance with input arguments (`\*args` and `**kwargs`) and checks if the required `FlaxBackend` is present.
- Requires the `flax` backend to be used.

### Notable Dependencies

- Inheriting from `DummyObject` metaclass.
- Utilizes the `flax` backend as the primary dependency.


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**Concise Summary:**

*Class FlaxBertForCausalLM*

`FlaxBertForCausalLM` is a PyTorch model based on the BERT architecture, utilizing the Flax deep learning library. It requires the `flax` backend and initializes it.

**Key Details:**

* Initializes with `requires_backends`: specifies required backends as ["flax"]
* Uses `DummyObject` metaclass
* Utilizes `Flax` deep learning library for model implementation


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**Flax BERT Model Class Summary**

* **Purpose**: `FlaxBertForMaskedLM` is a PyTorch-Flax implementation of the BERT model for masked language modeling tasks using the Flax library.

* **Key Implementation Details**:
	+ Utilizes the `DummyObject` metaclass with `flax` as the backend.
	+ Requires the `flax` backend, which initializes the necessary dependencies.

* **Notable Dependencies/Requirements**:
	+ External dependency: `flax`
	+ Implemented interface or class reference: `DummyObject`


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**Summary:**

The `FlaxBertForMultipleChoice` class is a PyTorch-based model utilizing the FLAX library for multiple choice language modeling. It requires FLAX backend.

**Key Details:**

* **Method Signature:** `__init__(*args, **kwargs)`
* **Backend Requirements:** FLAX (specifically, `"flax"`)
* **Interface Inheritance:** `DummyObject` metaclass
* **External Dependency:** PyTorch library

**Primary Purpose:**
The class is designed for multiple choice language modeling using FLAX and PyTorch.


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**Summary**

`FlaxBertForNextSentencePrediction`

*   A PyTorch-based next sentence prediction model using Flax backend (`"flax"`).
*   Initializes the model with required Flax backend.

**Key Details**

*   Method Signature: `__init__(self, *args, **kwargs)`
*   Backend Sourcing Class: `DummyObject` ( metaclass ) -> Does not directly source any backend functions. Instead it indicates backends can be sourced from.
*   Required Backend: "flax"

**Dependencies and Requirements**

*   External Dependencies: `flax`, PyTorch
*   Implemented Interfaces: Not specified


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**Class Summary:**

`FlaxBertForPreTraining`

* **Purpose:**
	+ A pre-training variant of the Flax BERT model using the `flax` backend.
* **Key Functionality:**
	+ Initializes the model with required `flax` backend dependency.
* **Implementation Details:**
	+ Uses `requires_backends` function to ensure compliance with the `flax` backend (`"flax"`).
* **Notable Dependencies:**
	+ Exclusive backend dependency on `flax`.
	+
**Note:** The code uses a metaclass `DummyObject`, but its purpose is not specified in the provided context.


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**Summary**

`FlaxBertForQuestionAnswering` is a class that uses the Flax library (`_backends=["flax"]`) to implement a question-answering model. 

* Primary Purpose: Implement BERT-based QA model.
* Key Implementation Details:
	+ Uses Flax as backend (`self.requires_backends(["flax"])`)
	+ Requires `requires_backends` method for initialization
* Notable Dependencies or Requirements:
	+ Extends from `DummyObject` metaclass.
	+ Relies on internal Flax functionality.

**Technical Details**

- Class Implementation: Extends `DummyObject`
- Library/Framework Used: Flax
- Key Methods:
    - Initializes the model with required backends (`__init__`)
    - Requires specified backend support during initialization (`self.requires_backends(["flax"])`)


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**Summary: Contextualized FlaxBERT Model Class**

The `FlaxBertForSequenceClassification` class inherits from a `DummyObject` metaclass. It is designed to be an implementation of a BERT-based sequence classification model using the Flax library backend.

**Primary Purpose and Functionality**
Create a custom sequence classification model utilizing FLAX's backend for FlaxBert architecture.

**Key Implementation Details**

*   **Backend:** The class utilizes the "flax" backend, indicating compatibility with FLAX's optimized tensor operations.
*   **Initialization:** Initializes the model using any additional arguments. Notably, it calls `requires_backends` to ensure the required "flax" backend is available.

**Notable Dependencies or Requirements**

*   **Backend Dependency:** Relies on the Flax library (`flax`) for its functionality.
*   **Interface Compliance:** Compliant with FLAX's tensor and model framework interfaces.


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**FlaxBertForTokenClassification Class Summary**

### Main Purpose and Functionality
The `FlaxBertForTokenClassification` class implements a PyTorch-Bert-based model using the Flax library for token classification tasks.

### Key Implementation Details

*   The model is built with the Flax backend (`"flax"`).
*   The `__init__` method initializes the model instance and requires the Flax backend.

### Notable Dependencies or Requirements

*   Uses **Flax** as a backend.
*   References the **Provides** decorator from the meta-class. 

This summary captures the essential features of the code while omitting unnecessary details, focusing on providing a clear understanding of the class's purpose and key implementation aspects.


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**Flax BERT Model Class Summary**

*Primary Purpose:* Implements the Flax BERT model using a dynamic backend system.

**Key Implementation Details:**

* Initializes the model with a flax backend using the `requires_backends` function.
* Enforces the flax backend at runtime.

**Notable Dependencies and Requirements:**

* ` DummyObject` metaclass
* `requires_backends` function (not shown in this code snippet)
* Flax library dependencies
* BERT-specific implementation details not shown in this summary.


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**Flax BERT Pre-Trained Model Summary**

### Class Definition
```python
class FlaxBertPreTrainedModel(metaclass=DummyObject):
```

*   **Purpose**: A pre-trained model for BERT-based tasks using the Flax library.

### Key Features

#### Initialization
*   Requires Flax backend (`"flax"`).

### Important Implementation Details

*   The `FlaxBertPreTrainedModel` class is initialized with no specific parameters, relying on the `requires_backends` function for backend setup.


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**Summary:**

The `FlaxBigBirdForCausalLM` class is a wrapper for BigBird neural network model implemented using the Flax library. It initializes with various backend requirements.

* **Main Purpose:** Implement BigBird model for causal language modeling tasks using Flax.
* **Key Implementation Details:**
	+ Initializes model with Flax backend requirement.
* **Notable Dependencies:**

• `RequiresBackends`: Verifies backend compatibility.
• `flax` (backend library)
• `BigBird` (related class or module)


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**Summary**

`FlaxBigBirdForMaskedLM`: A masked language modeling model based on Flax backend.

**Key Details**

* Primary purpose: Implementations of BigBird architecture using the Flax framework for masked language modeling tasks.
* Notable Implementation:
  - `dummy requires_backends("flax")`: Verifies the availability of the required Flax backend.

**Dependencies/Requirements**

* Flax library (`backends`: ["flax"])
* DummyObject metaclass
* inherits from: Not specified


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**Summary**

`FlaxBigBirdForMultipleChoice` is a class that implements the BigBird model from the Flax library for multiple-choice classification tasks. The primary purpose is to provide a neural network architecture.

**Key Details**

* **Metaclass**: `DummyObject`
* **Backend Requirements**: Only supports "flax" backend
* **Initialization**: Verifies required backends are present before instantiation

(Note: The provided code snippet only serves as the basic structure of the class. Further analysis would require reviewing its implementation to fully understand its functionality.)


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**Summary:**

`FlaxBigBirdForPreTraining`: A PyTorch-based pre-training model class using Flax backend. 

*   Primary purpose: Implements the BigBird model architecture for pre-training tasks.
*   Key implementation details:
    *   Requires the "flax" backend. 
        (Method `requires_backends` taking `self` and a list of required backends)
*   Notable dependencies or requirements:
    *   Uses Flax as its backend.

Note: The exact PyTorch implementation is not provided in this summary. This analysis focuses solely on the class definition and required parameters.


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**Summary:**

The `FlaxBigBirdForQuestionAnswering` class is a BERT-based question-answering model using the FLAX library. It initializes with the "flax" backend.

**Key Details:**

*   **Primary Purpose:** BERT-based question-answering model.
*   **Initialization Requirements:**
    *   `requires_backends(self, ["flax"])` ensures the use of the "flax" backend.
*   **Implementation Choices:**
    *   Uses `metaclass=DummyObject`, a metaclass that creates an instance with provided arguments (`args` and `kwargs`).


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**Concise Summary:**

* Class: `FlaxBigBirdForSequenceClassification` using the Flax backend (`[_backends = ["flax"]]`)
* Initializes object with argument validation using `requires_backends()`, ensuring the "flax" backend is present. 

**Key Implementation Details:**
- Uses the Flax backend.
 
**Notable Dependencies/Requirements:**
- Requires the "flax" backend to be implemented or available.


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**Summary:**

The `FlaxBigBirdForTokenClassification` class builds upon the BigBird tokenizer using the Flax backend. The constructor initializes the model with specific requirements for the "flax" backend. 

**Key Details:**

- `FlaxBigBirdForTokenClassification`: Model class name.
- `metaclass=DummyObject`: Metaclass used to define the class.
- `_backends = ["flax"]`: Backend dependencies required for the model.
- `__init__`: Method initialization with arguments and keyword arguments.

**Dependencies and Requirements:**

- Related classes/functions: `requires_backends` (unknown function), `DummyObject` (metaclass).
- External Dependencies:
  - "flax" backend.

Note: The specifics of the BigBird tokenizer and Flax backend are not provided, but this summary aims to capture the essential information.


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**Summary:**

* `FlaxBigBirdModel` is a class implementing the BigBird model architecture using flax as its backend.
* Primary purpose: instantiates and configures the FLAX-based implementation of the BigBird language modeling model.

**Key Implementation Details:**

* Utilizes the `flax` library for model construction.
* Specifies that `flax` should be installed as a required dependency.

**Notable Dependencies or Requirements:**

* Related class/function: `requires_backends(self, ["flax"])`
* External dependencies:
  + `flax` (library)


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Here is the analysis of the provided class code chunk:

**Summary:**
`FlaxBigBirdPreTrainedModel` is a custom pre-trained model implementation built on top of the Flax deep learning library. It inherits from `DummyObject`, specifying that it primarily uses the "flax" backend.

**Key Components:**

1. **Initialization**: The `__init__` method requires at least one Flax backend, which is explicitly set to ["flax"] using the `requires_backends` function.
2. **Primary Purpose**: This model suggests extending pre-trained big bird language models with custom capabilities using the Flax library.

**Implementation Details:**

1. **Backend Specification**: The model uses the Flax Flax deep learning framework (`_backends=["flax"]`) for its backend requirements.

No other implementation details are presented in this snippet, implying that additional specifics may be handled elsewhere within the larger codebase.


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**FlaxBlenderbotForConditionalGeneration Class Summary**

*   **Main Purpose and Functionality**: A custom Flax-based Conditional Generation model implemented using the BlenderBot architecture.
*   **Key Implementation Details**:
    *   The `__init__` method initializes the model by checking the required backend, which is set to "flax".
*   **Notable Dependencies or Requirements**:
    *   Requires a Backend with "flax" implementation (hence `_backends = ["flax"]`)
    
Note: `DummyObject` is not explained as its purpose is unclear and might be related to metaclasses; therefore, it's left unexplained in the summary.


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**Summary:**

The `FlaxBlenderbotModel` class is a Blender model using the Flax backend. It initializes with Flax as the required backend and expects additional arguments to be passed in.

**Key Details:**

- **Backend**: Flax
- **Primary Functionality**: Initializes Blender model with specified backend requirements.
- **Implementation**: Requires "flax" as a backend implementation during class instantiation.

Note: `DummyObject` is being used as the metaclass, which means it doesn't add any behavior to this class as its primary intent is likely testing or placeholder.


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**Summary**

`FlaxBlenderbotPreTrainedModel`: A custom pre-trained model class that utilizes the `flax` backend. It initializes with a flax backend requirement and uses the `requires_backends` function to enforce compatibility.

**Key Implementation Details**

* Uses `flax` as its primary backend (`_backends = ["flax"]`)
* Requires `flax` backend for initialization (`__init__` method calls `requires_backends` with ["flax"])


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**Summary**

`FlaxBlenderbotSmallForConditionalGeneration`

A specialized `FlaxBlenderbot` class for conditional generation tasks using the Flax backend. It initializes with the required Flax backend setup.

* **Implementation Highlights:**
	+ Initializes with `flax` backend requirements.
* **Class Attributes:**
	+ `_backends`: Set to ["flax"] defining the backend usage.
* **Parent Classes/Interfaces:** None

**Additional Notes:**

- This class uses the `DummyObject` metaclass.

Note that `requires_backends(self, ["flax"])` is not a standard Python function and seems to be a custom or internal implementation detail.


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**Flax BlenderBot Small Model Class Summary**

*   **Main Purpose and Functionality**: Defines the FlaxBlenderBotSmallModel class using PyTorch (via Flax) as its backend.

    -   Primary purpose: Provides a smaller, optimized alternative for leveraging the Flask-PCLN library.
*   **Key Implementation Details**:
    *   `requires_backends` method to validate necessary backends (specifically "flax").
        *   Method Signature: None specified in this snippet, but assuming it's defined elsewhere in the codebase. The method requires a backend list ("flax") as an argument.
    *   No additional key implementations are identified within this fragment of the class definition.
*   **Notable Dependencies or Requirements**:
    -   `DummyObject` metaclass (imported from elsewhere in the codebase).
    -   `FlaskPCLN` library dependency implicit through its usage, utilizing "flax" backend.


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**Summary:**

The `FlaxBlenderbotSmallPreTrainedModel` is a pre-trained model class utilizing the Flax backend ( `_backends = ["flax"]`) that inherits from `DummyObject`. The constructor (`__init__` method) takes variable arguments but requires all models to pass `requires_backends` with `["flax"]`.

**Key Details:**

- **Backend**: `Flax`
  - Implemented by: `DummyObject`

**Notable Dependencies/Requirements:**

- `Flax`: Pre-trained model uses Flax backend.
- ` DummyObject`: Inherited class that provides metaclass functionality.


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**Class Summary:**

`FlaxBloomForCausalLM`

* A class implementing model for causal language models using the Flax library.

**Key Implementation Details:**

- Initialize model with `requires_backends` function ensuring use of the "flax" backend.

**Notable Dependencies/Requirements:**

- Implemented with `metaclass=DummyObject`
- Specifically requires "flax" backend.


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**Summary:**

The `FlaxBloomModel` class is a machine learning model built using the Flax framework, specifically designed for bloom-based models. Its primary purpose is to implement a Bloom filter algorithm. The class is defined with the metaclass `DummyObject`, indicating that it does not inherit any properties from its parent class.

**Key Details:**

* The class only supports the Flax backend, as specified in `_backends = ["flax"]`.
* The `__init__` method requires the "flax" backend to be present at instantiation time.
 
**Notable Dependencies/Requirements:**

* Flax library
* Dummy object metaclass


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**Summary:**

`FlaxBloomPreTrainedModel` is a pre-trained model class implementation using the Flax library, inheriting from `DummyObject`. The model requires the `"flax"` backend.

**Key Implementation Details:**

* Initializes with `args` and `kwargs`
* Requires `flax` backend
* Utilizes `DummyObject` metaclass

**Notable Dependencies and Requirements:**

* Related class: `DummyObject`
* External Dependency:
* + `flax` library


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**Summary:**

`FlaxCLIPModel` is a subclass of `DummyObject` that represents a model built with the Flax backend.

**Key Features:**

* Enforces Flax backend usage through `requires_backends`
* Initialization takes variable arguments and keyword arguments

**Notable Dependencies/Requirements:**

* Requires Flax backend library (`"flax"`)
 
This summary preserves class name, methods (notably `__init__`), and primary purpose, while focusing on key features and dependencies.


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**Summary:**

`FlaxCLIPPreTrainedModel` is a pre-trained model implemented in the Flax library. It inherits from `DummyObject` and has an implementation detail that requires the `FLAX_BACKEND` backend to be provided.

* Main purpose and functionality: Pre-trained model with Flax backend.
* Key implementation details:
	+ Requires `FLAX_BACKEND` for execution.
* Notable dependencies or requirements: 
    * `FLAX_BACKEND`
* Dependencies and interfaces: Inherited from `DummyObject`.


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**FlaxCLIPTextModel Class Summary**

The `FlaxCLIPTextModel` class is an instance of the `Flax` backend using the CLIP text model. The class initializes with the following parameters:

- Main purpose: Initialize a Flax text model instance for CLIP.
- Key implementation details: Validates that the required 'flax' backend is present.

**Class Details**

- **Metaclass:** DummyObject
- Primary function: `FlaxCLIPTextModel`
- Backend: `["flax"]`

No other notable dependencies or requirements are specified.


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**Class Summary:**

`FlaxCLIPTextModelWithProjection`

This class implements a text model using the Flax backend and is designed to work with [Clippers](https://huggingface.co/callbacks/clippers).

**Main Purpose/FUNCTIONALITY:** 

Defines a custom text model architecture, leveraging the `flax` backend.

**Key Implementation Details:**

* Requires the `flax` backend for initialization.
* Initializes with variable arguments (`*args`, `**kwargs`) using the `__init__` method.

**Notable Dependencies/Requirements:**

- External dependency: [Flax](https://www.tensorflow.org/modular/models/flax) backend,
 
 - Inherited/interface: Requires the `requires_backends` function implementation.


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**Summary:**

`FlaxCLIPTextPreTrainedModel`: A pre-trained model for text processing using the Flax library. 

**Main Purpose and Functionality:** 
This class represents a pre-trained model leveraging Flax backend, encapsulating CLIP's text processing capabilities.

**Key Implementation Details:**

- **Initialization**: The `__init__` method initializes the model using *args* and **kwargs**, ensuring that the Flax backend is compatible (`requires_backends([FlaxBackend], ...)`). 

(Note: Further implementation details would require more of the class definition.)

**Notable Dependencies or Requirements:** 
- **Backends**: The Flax library.
- **Interfaces (Inherited)** : FlaxBackend (possibly implicit through `requires_backends` requirement).


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**Class Summary: FlaxCLIPVisionModel**

*   **Purpose**: The `FlaxCLIPVisionModel` class is a type of machine learning model that utilizes the Flax library and is based on the CLIP (Contrastive Language-Image Pre-training) dataset.

*   **Implementation Details**: This model inherits from a custom metaclass (`DummyObject`) with a backend dependency on Flax. The `__init__` method initializes the model by enforcing this backend requirement and checking if it's fully implemented.

**Key Takeaways:**

*   `metaclass`: `DummyObject`
*   Backend dependency: `flax`
*   Method signature: `__init__(*args, **kwargs)`

Note that a more thorough analysis would require examining the surrounding code to understand the full functionality of this class. However, based on this initial snippet, we can establish the fundamental properties and relationships.


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**Class Summary**

`FlaxCLIPVisionPreTrainedModel`

A pre-trained vision model built on top of the FLAX library, utilizing the CLIP model.

**Key Implementation Details**

* Initializes with `self` argument and checks if the required backend "flax" is supported.
* Requires "flax" backend to be present at instantiation.

**Notable Dependencies/Requirements**

* `[cls_token]`: Not explicitly required but implied through the parent class (not shown in this snippet).
* Backend: `Flax`, specifically `dummy_object` metaclass and `requires_backends` function are used to ensure consistent and correct implementation.
 
Note: The provided code does not contain any data processing or model logic, suggesting that some crucial implementation details are likely defined in parent classes.


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**Class Summary: FlaxDinov2ForImageClassification**

**Purpose and Functionality:** `FlaxDinov2ForImageClassification` is an image classification model using the flax backend.

* **Primary Purpose:** This class is designed for image classification tasks, leveraging the capabilities of the Flax library.
* **Implementation Details:**
	+ **Backend:** Utilizes the "flax" runtime engine, specified in the `__init__` method and inherited through the `requires_backends` function.
	+ **Initialization:** Verifies that the required flax backend is available during instantiation.

**Key Dependencies/Requirements:**

* **Related Classes/Functions:** `DummyObject`, `requirements_backends` (not shown in this snippet)
* **External Dependencies:**
	+ Flax library
	+ DummyObject metaclass


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**Summary:**

The `FlaxDinov2Model` class is a PyTorch-like model implemented using the Flax library. It inherits from a dummy object class with a backend specification of "flax". The `__init__` method requires the "flax" backend, which suggests a dependency on the Flax library for model initialization.

**Key points:**

* **Purpose:** Model implementation with Flax backend
* **Primary purpose:** Initializes the model with the required "flax" backend
* **Implementation details:**
	+_backend specification set to "flax"
	+ Requires "flax" backend in `__init__` method
* **Notable dependencies:**
	+ Flax library (backend dependency)
	+ No external dependencies listed
* **Interfaces:** Implicitly inherits from a dummy object class with no explicitly defined interfaces


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**Summary:**
The `FlaxDinov2PreTrainedModel` class is a pre-trained model built on top of the Flax library. Its primary purpose is to leverage the capabilities of TensorFlow and its flax backend for efficient distributed training.

*   **Main Purpose and Functionality:** This class enables developers to utilize a pre-trained Dinov2 model with Flax, facilitating fast and scalable modeling.
*   **Key Implementation Details:**
    *   The model follows the `DummyObject` metaclass to specify that it's built on top of the Flax library (`_backends = ["flax"]`).
    *   In the constructor (`__init__`), `requires_backends(self, ["flax"])` is used to ensure the presence of the required flax backend.

**Notable Dependencies or Requirements:**

*   Related classes/functions:
    *   `DummyObject`: The metaclass used for constructing the FlaxDinov2PreTrainedModel.
*   External dependencies:
    *   TensorFlow (with its associated libraries)
    *   Flax library

This summary captures the essential characteristics of the provided class code.


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**Summary**

`FlaxDistilBertForMaskedLM` Model Class

*   Main purpose: A Flax-compiled implementation of the DistilBERT model for masked language modeling tasks.
*   Primary function: Initializes and configures the model using the `flax` backend.

**Implementation Details**

*   Uses the `DummyObject` metaclass.
*   Requires the `flax` backend and performs validation at initialization.

**Note**: The exact implementation of the DistilBERT model is not included in this code snippet.


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**Flax Distil BERT Model Class Summary**

*   **Class Type**: (`FlaxDistilBertForMultipleChoice`) - A Flax-based implementation of DistillBERT for Multiple Choice tasks.

### Primary Purpose
This class creates a machine learning model, specifically a DistillBERT variant, designed to handle multiple-choice questions, utilizing the flax deep learning framework.

#### Key Implementation Details

*   Initialize the model using the `__init__` method, requiring the `"flax"` backend.
*   The `requires_backends` function ensures that the required "flax" backend is available when creating an instance of this class.
*   No specific implementation details are provided within this snippet, as this summary focuses on the overall structure and purpose.

#### Notable Dependencies

*   (`DummyObject`) - A metaclass used to define the `FlaxDistilBertForMultipleChoice` class.
*   (`flax`) - The required backend for creating instances of this model.
*   (`DistilBert`, `MultipleChoice`) - Suggested related classes and their tasks, not explicitly implemented in this provided code snippet.

### Method Signatures
The following method signatures are relevant to the functionality of this class:

*   (`__init__`): Initializes the Flax Distil BERT model instance, requiring the "flax" backend.
*   (`requires_backends`): Validates that the required backends ("flax") are available before creating an instance of `FlaxDistilBertForMultipleChoice`.


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**Concise Summary:**

The `FlaxDistilBertForQuestionAnswering` class extends the Flaky models using the Flax library (`metaclass=DummyObject`). It is designed to require the Flax backend for execution, with no additional details on initialization parameters beyond regular keyword arguments (`*args` and `**kwargs`).


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**Class Summary: `FlaxDistilBertForSequenceClassification`**

**Main Purpose and Functionality:**
This class implements a Flax-based variant of the DistilBERT model for sequence classification tasks.

**Key Implementation Details:**

* The `__init__` method initializes the model with the "flax" backend.
* It requires the presence of the "flax" backend using `requires_backends`.

**Notable Dependencies or Requirements:**

* Flax library (`flax`) as the underlying backend.
* Uses `RequiresBackends` to ensure compatibility.


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**Summary:**

The `FlaxDistilBertForTokenClassification` class is a PyTorch XLA extension that utilizes the Flax backend. It inherits from `DummyObject` and requires the Flax backend for initialization.

**Key Details:**

*   Class: `FlaxDistilBertForTokenClassification`
*   Backend: Flax
*   Required Backends: ["flax"]
*   Initializes with `args` and `kwargs`

**Dependencies/Requirements:**

*   Python 3.8+ for PyTorch XLA support
*   External dependency on the "flax" library


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**Flax Distil BERT Model**

*Main Purpose:* Implements the DistilBert model using the Flax backend.

**Key Implementation Details:**
- Uses the `DummyObject` metaclass as specified in its definition (`metaclass=DummyObject`).

**Dependencies and Requirements:**
- Requires the 'flax' backend to function (`_backends = ["flax"]`).


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**FlaxDistilBertPreTrainedModel Class Summary**

The `FlaxDistilBertPreTrainedModel` class is a pre-trained model based on Flax, utilizing the DistilBERT architecture. Its primary purpose is to provide pre-trained language models with efficient inference capabilities.

**Key Implementation Details:**

*   The model uses the flax backend.
*   It extends the `DummyObject` metaclass for its definition.

**External Dependencies/Requirements:**

*   Requires the "flax" backend, which provides key algorithms and patterns for Efficient neural network models.


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**Class Summary: FlaxElectraForCausalLM**

*   **Main Purpose:** A class implementing the Flax Electra model architecture for causal language models using the Flax backend.

### Key Implementation Details

*   Initializes the `FlaxElectraForCausalLM` instance with custom arguments.
*   Verifies if the required backend (`flax`) is specified at instantiation.

### Notable Dependencies and Requirements

*   **Backend:** Flax
*   **Implemented Interfaces:** `requires_backends` (verifies backend specification)


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**Summary:**

`FlaxElectraForMaskedLM` is an implementation of the Electra model, leveraging the `flax` library as its backend.

**Key Implementation Details:**

* Uses the `dummy` metaclass (`DummyObject`) to define the class.
* Requires the "flax" backend for instantiation.
* Utilizes the `requires_backends` function to enforce compliance with the required backend.

**Notable Dependencies and Requirements:**

* Redepends on `metaclass=DummyObject`
* Inherit from or implement interfaces related to:
	+ `backend="flax"`
	+ `Electra` model (not shown in this snippet)
* External dependency:
	+ `flax`: a library for automatic differentiation and optimization.


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**Summary:**

`FlaxElectraForMultipleChoice` is a PyTorch model that leverages the `Flax` backend for Multi-Choice Questions classification using the Electra architecture.

**Key Implementation Details:**

* Utilizes `requires_backends` to verify requirement of `["flax"]` backend.
* Uses Electra architecture, likely serving as a base case for further customization or specialization.

**Notable Dependencies/Requirements:**

* `DummyObject` metaclass
* `Flax`
* PyTorch (unspecified version)

Note: Without more code context, the specific details of the Electra architecture and how it's tailored to Multi-Choice Questions might remain unknown.


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Here is a concise and focused summary of the code chunk:

**Class:** `FlaxElectraForPreTraining`

**Purpose:** An implementation of Electra model using Flax library for pre-training.

**Implementation:**

* **Metaclass**: Uses `DummyObject` to create an instance.
* **Backend Requirements**: Requires Flax backend (`_backends = ["flax"]`) for initialization.

**No notable dependencies or requirements mentioned.**


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**Class Summary:**

`FlaxElectraForQuestionAnswering`

A pre-trained question answering model built using the Flax library (`flax`) backend.

**Primary Purpose:**
Implement a custom implementation of the Electra model for question answering tasks.

**Key Implementation Details:**

* Uses the `flax` backend.
* Requires initialization with the `requires_backends` function, which checks if the specified backends are available.

**Notable Dependencies/Requirements:**

* `flax`: The specific backend library used to implement this class.
* `DummyObject`: A metaclass that defines the class structure.


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**Summary:**

The `FlaxElectraForSequenceClassification` class is a sequence classification model utilizing the Flax library as its backend. It initializes with the specified backend requirements and doesn't provide additional functionality.

**Key Points:**

* **Class Name:** `FlaxElectraForSequenceClassification`
* **Backend:** Flax
* **Purpose:** Sequence Classification Model

**Implementation Details:**

No specific implementation details are provided, but it relies on the `flax` library for its backend. The `__init__` method checks if the correct backend is installed using the `requires_backends` function.

**Notable Dependencies:**

* Flax library (`"flax"`)

This summary is in line with RAG's guidelines by providing a clear and concise overview of the class, focusing on its main purpose and functionality.


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**Summary**

The `FlaxElectraForTokenClassification` class is a subclass of the `DummyObject` metaclass, inheriting from an unshown parent. It defines a transformer model with a single backend:
* **Primary purpose**: A token classification model using the Flax library.
* **Key implementation details**:
	+ `_backends = ["flax"]`: specifies the required backend for this model.
	+ `requires_backends(self, ["flax"])`: asserts that only the "flax" backend is available.
* **Notable dependencies or requirements**: None explicitly listed, but relies on the Flax library.

Note: The parent class/desinger/related classes are not shown in this class chunk, so its actual hierarchy and inheritance relationships cannot be determined.


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**Summary:**

The `FlaxElectraModel` class is an implementation of the Electra model using the Flax backend. Its primary purpose is to enable model training with the "flax" backend.

**Key Implementation Details:**

* Initializes the model using the `requirements_backends` function, which checks for compatibility with the "flax" backend.
* Sets up the `_backends` attribute to indicate that this model supports the Flax backend.

**Notable Dependencies/Requirements:**

* Flax library is required for this model implementation.


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**Summary:**

* `FlaxElectraPreTrainedModel` class extends `DummyObject` with Flax backend usage.
	+ Initializes the model with Flax backend.
	+ Requires Flax backend for execution.

**Key Details:**
- `metaclass=DummyObject`
- `_backends = ["flax"]`

**Notable Dependencies/R requirements:**
- External dependencies:
  - None listed
- Inherited/Implemented interfaces:
  - `DummyObject` metaclass


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**Concise Summary**

* `FlaxEncoderDecoderModel`: A PyTorch model using the Flax backend.
* Primary purpose: Model implementation for encoding and decoding data.
* Implementation details:
	+ Uses PyTorch with the Flax backend (`_backends = ["flax"]`).
	+ Requires a valid Flax backend configuration (`requires_backends(self, ["flax"])`).

**Key Takeaways**

* `DummyObject`: Metaclass used to define the model.
* Flax backend configuration required.


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**Summary:**

**Class Name:** `FlaxGemmaForCausalLM`

**Primary Purpose:** A Flair-based causal language model using the flax backend.

**Key Implementation Details:**
- Uses `Drynet's requires_backends` method to enforce the required `flax` backend.
 
**Dependencies:**
 
- Uses `Flax` library as a backend.


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**Class Summary:**

`FlaxGemmaModel`

A PyTorch implementation of the Gemma library using Flax backend. The main purpose is to provide an optimized variant of the deep learning model for efficient computations.

**Key Features and Details:**

* Utilizes the `flax` backend
* Requires Flax backend installation (`requires_backends` function)
* Implements a custom initialization method (`__init__`) that takes variable arguments and keyword arguments

**Notable Inheritance/Interfaces:**
Inherited from `DummyObject`, which is not shown in the provided code snippet.

Note: Further analysis may include examining the implementation of the `requires_backends` function, the underlying Flax library, or other related dependencies. However, based on the given code chunk, this summary provides a concise overview of the class's primary purpose and key features.


key: chroma:document
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Here is a focused summary of the provided class code chunk:

**Class Summary**

`FlaxGemmaPreTrainedModel`: A pre-trained model class utilizing the `flax` backend.

**Main Purpose and Functionality**
The primary purpose of this class is to provide a pre-trained model implemented using the `flax` backend.

**Key Implementation Details**

* The `__init__` method requires the `flax` backend.
	+ `requires_backends(self, ["flax"])`: Ensures the correct backend is used for initialization.

**Notable Dependencies and Requirements**

* `flax`: The primary backend library used by this model class.


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**Summary:**

The `FlaxGPT2LMHeadModel` class is a PyTorch-based neural network model implemented with the Flax library. 

**Key Details:**
- Primary purpose: This model serves as a language model head for the GPT-2 architecture.
- Implementation: Initializes with a variable number of positional arguments (`*args`) and keyword arguments (`**kwargs`).
 
**Notable Dependencies/Requirements:**
- Utilizes Flax library backend.
- Requires the `flax` backend to be configured before instance creation.


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**Summary:**

The `FlaxGPT2Model` class is a PyTorch-based text encoding model utilizing the FLAX framework. It inherits from `DummyObject` as its metaclass.

**Key Implementation Details:**

- **Initialization**: The class requires the 'flax' backend and initializes with positional and keyword arguments (`args` and `kwargs`, respectively).

**Notable Dependencies or Requirements:**

- Related classes/functions:
  - `requires_backends(self, ["flax"])`: Verifies that the FLAX backend is installed.
  
- External dependencies:
  - The FLAX framework library.

This summary captures the essence of the provided class code chunk while excluding redundant details.


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**Summary:**

`FlaxGPT2PreTrainedModel` is a pre-trained model based on the Flax library, utilizing the `flax` backend. It initializes with various input arguments, requiring the `flax` backend.

**Key Features:**

- **Backend:** `flax`
- **Initialization:** Requires one or more `flax` backends
- **Purpose:** Pre-trained model for GPT2

**Method Signature:**
- `__init__(self, *args, **kwargs)`: Initializes the model with variables `*args` and `**kwargs`.

**Related Classes/Functions:**

- `DummyObject`: Meta-class used for this class.
- `requires_backends(self, ["flax"])`: Function called during initialization to check backend requirements.


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**Class Summary: FlaxGPTNeoForCausalLM**

The `FlaxGPTNeoForCausalLM` class is a PyTorch implementation of the GPT-Nano variant, optimized for causal language modeling using the Flax library.

**Main Purpose and Functionality**: It's a sequence-to-sequence model designed for causal language modeling tasks.

**Key Implementation Details:**

*   Initializes with Flax as the backend.
*   Enforces usage of the "flax" backend through the `requires_backends` method.

**Notable Dependencies and Requirements:**

*   Uses Flax (`_backends = ["flax"]`) for computation.
*   Dependent on PyTorch library.


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**Summary:**

The `FlaxGPTNeoModel` class is a custom neural network model implemented using the Flax library. It features a single backend (`flax`) and requires initialization with Flax as an enabled backend.

**Key Points:**

* **Main Purpose:** Custom neural network model implementation
* **Primary Functionality:** Initializes with Flax backend, allowing for efficient computation.
* **Implementation Details:**
	+ Uses `DummyObject` metaclass.
	+ Enables Flax backend (`self._backends == ["flax"]`) upon class construction.
* **Notable Dependencies:**
	+ Flax library (backend)

This summary provides a clear and concise overview of the `FlaxGPTNeoModel` class, highlighting its primary functionality and key implementation details.


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**Summary:**

The `FlaxGPTNeoPreTrainedModel` class is a pre-trained language model utilizing the Flax library. It initializes with the "flax" backend and enforces this requirement during instantiation.

**Key Details:**

- Primary purpose: Pre-trained language model implementation
- Implementation details:
  - Uses the Flax library for backend support (`self._backends = ["flax"]`)
  - Requires the "flax" backend during initialization (`requires_backends(self, ["flax"])`)

**Notable Dependencies and Requirements:**

- Backend: `Flax`
- Required libraries:
  - `Flax` (native Python module)
  - `dummy_object` class or context (used in metaclass)


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**Summary:**

`FlaxGPTJForCausalLM` is a class implementing the Hugging Face model FlaxGPT-J for causal language modeling. It uses the `flax` backend.

**Key Details:**

- Primary purpose: Implement FlaxGPT-J for causal LM.
- Notable dependencies:
	+ `flax`: The underlying machine learning library used by the class.
- Implementation details:
	+ Initializes with any number of positional arguments (`*args`) and keyword arguments (`**kwargs`).
	+ Verifies that the "flax" backend is required using the `_requires_backends` method.


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**Class Summary: FlaxGPTJModel**

*   **Main Purpose and Functionality:** A PyTorch-based implementation of the GPT-J model using the FLAX backend.
*   
*   ```python
    def __init__(self, *args, **kwargs):
        """
        Initializes a new instance of the FlaxGPTJModel class.
        
        The following backend is required:
            - `"flax"`
        """
```

**Key Implementation Details:**

*   The `FlaxGPTJModel` requires the FLAX backend to be installed and configured (`"flax"` backend).

**Notable Dependencies or Requirements:**

*   FLAX backend (`self._backends = ["flax"]`)
*   Required backend setup using `requires_backends` function with `"flax"` as argument.


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**Summary:**

This class `FlaxGPTJPreTrainedModel` is a pre-trained sequence model implementation utilizing the Flax library. 
Its primary function is to initialize and verify compatibility with the "flax" backend. 

**Key Features:**

* Initialize with args and kwargs
* Verifies backend compatibility using the `requires_backends` method

**Notable Dependencies/Requirements:**

* `DummyObject` metaclass
* Flax library for backend support


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**Summary**: 


The `FlaxLlamaForCausalLM` class utilizes the Flax backend (as determined by `_backends=["flax"]`) to establish a causal language model. Its initialization (`__init__`) method ensures that the "flax" backend is required among the provided backends, as checked via the `requires_backends()` function.


**Key Takeaways**


- Class name: `FlaxLlamaForCausalLM`
- Backend used: Flax
- Primary purpose: Establishing a causal language model


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**FlaxLlamaModel Class Summary**

*   Primary Purpose: Implement Flax-specific LLaMA model using the Flax library.

*   **Implementation Details:**
    *   Requires the `flax` backend for computation.
    *   Initializes the model with generic arguments (`*args`, `**kwargs`) and enforces the use of the `flax` backend.

*   **Notable Dependencies/Requirements:**
    *   Uses the `flax` library as its primary backend.
    *   Inherently requires the ` DummyObject` metaclass.


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**Summary:**

The `FlaxLlamaPreTrainedModel` class is a pre-trained model based on the Flax library, implementing a variant of the LLaMA model. It inherits from `DummyObject` and requires the Flax backend by default.

**Key Details:**

*   **Class Signature:** `FlaxLlamaPreTrainedModel(metaclass= DummyObject)`
*   **Backend Requirement:** Requires Flax backend by default (`backends = ["flax"]`)
*   **Initialization:** Initializes with arbitrary arguments (`\*\*kwargs`) and checks for required backends using the `requires_backends` function

**External Dependencies:**

*   `Flax library`
*   `Requires backends` mechanism (not part of this summary, but crucial to implementation)

Note that I excluded unnecessary details, focusing on the class purpose, key implementation details, and referenced classes/functions.


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**Class Summary: `FlaxLongT5ForConditionalGeneration`**

*Main Purpose and Functionality*
The `FlaxLongT5ForConditionalGeneration` class is a custom transformer model implementation using the Flax library. It defines a model architecture for conditional generation tasks.

**Key Implementation Details**
- Utilizes the Flax backend: `_backends = ["flax"]`
  - Specifics: employs flax's automatic differentiation capabilities for efficient computations.
- Requires the 'flax' backend during initialization
  - Key detail: utilizes the `requires_backends` function to enforce backend compatibility.

**Notable Dependencies and Requirements**
- Flax library (`"flax"`): required for model implementation and computation.
- Inherited/Implemented interfaces not specified in this snippet.


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**Class Summary:**

`FlaxLongT5Model`

The `FlaxLongT5Model` class is a machine learning model implementation using the Flax library (`flax`) for natural language processing tasks. Its primary purpose is to process long sequences of text data.

**Key Implementation Details:**

* **Initialization**: The constructor (`__init__`) initializes the model by checking if the required `flax` backend is supported.
* **Backend Check**: The `requires_backends` function ensures that the Flax library is installed and available for use (`requires_backends(self, ["flax"])`).

**Notable Requirements:**

*   Requires Flax library (`flax`) as a dependency.


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**Summary:**

The `FlaxLongT5PreTrainedModel` class is a pre-trained language model using the Flax framework. 

**Key Points:**

* Primary purpose: Pre-trained LongT5 model with Flax backend support.
* Implementation details:
	+ Requires Flax backend for functionality.
* Notable dependencies:
	+ `Flax` (backend framework).
	+ `DummyObject` (metaclass).


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**Summary:**

The `FlaxMarianModel` class is a neural network model utilizing the Flax library as its backend. It inherits from `DummyObject` and defines an initialization method that ensures the presence of the Flax backend.

**Key Points:**

* **Primary Purpose:** To create a neural network model using the Flax library.
* **Key Implementation Details:**
	+ Uses Flax (`_backends = ["flax"]`) as its backend.
	+ Ensures Flax installation with `requires_backends` method.
* **Notable Dependencies/Requirements:**
	+ Flax (library)


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**Summary:**
`FlaxMarianMTModel` is an instance of the Flax model backend, utilizing the `DummyObject` metaclass. Its primary purpose is to implement Marian machine translation models using the Flax library. 

* **Primary Purpose:** Marian MT Model implementation with Flax library.
* **Key Implementation Details:**
  - Utilizes the `Flax` backend
  - Requires `flax` backend for instantiation
* **Notable Dependencies/Requirements:**
  - Flax library (`__init__` requires `flax`)
  - `DummyObject` metaclass 
  (no external dependencies listed)


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**Flax Marian Pre-Trained Model Class Summary**

### Main Purpose and Functionality

The `FlaxMarianPreTrainedModel` class is a Flax-based implementation of the Marian pre-trained language model. Its primary purpose is to provide a trainable neural network architecture optimized for natural language processing tasks.

### Key Implementation Details

*   The model is designed using the Flax deep learning framework and utilizes the TPU/TPU2 as a backend.
*   The `__init__` method initializes the model's parameters, enforcing the requirement of having a compatible backend (`"flax"`).

### Notable Dependencies or Requirements

*   The `DummyObject` metaclass is used to create the class instance.
*   No external dependencies are explicitly listed; however, the Flax deep learning framework and TPU/TPU2 might be implied as prerequisites.

### Further Investigation Suggested

To fully understand this model's functionality and behavior, additional analysis of its components, such as the `forward` method, optimizer, and loss functions, would provide valuable insights.


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**Summary:**

`FlaxMBartForConditionalGeneration` is a class implementing the Hugging Face Model variant using Flax backend. It initializes with `flax` backend and enforces its requirement.

**Key Details:**

* **Class Metaclass:** `_backends = ["flax"]`
* **Method Signatures:**
	+ `__init__(*args, **kwargs)`: Initializer that requires Flask execution (`requires_backends(self, ["flax"])`)
* **Notable Dependencies/Requirements:** Flax backend is mandatory (`_backends = ["flax"]`)

**Additional Information (if needed):**

None


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**Summary:**

The `FlaxMBartForQuestionAnswering` class is a PyTorch model that utilizes the Flax backend for Question Answering tasks. It inherits from a metaclass with no explicit functionality.

* Primary purpose: Implement a Flax-based Q&A model
* Key implementation detail: Requires the Flax backend (`requires_backends(self, ["flax"])`)
* Notable dependencies:
	+ `flax` backend
	+ PyTorch (`requires_backends` function)

Note: The `DummyObject` metaclass is used but its purpose and functionality are not clear from this snippet.


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**Summary:**

```python
FlaxMBartForSequenceClassification is a sequence classification model built with the Flax library. Its primary purpose is to implement the BART (Bidirectional Transformer) architecture for text classification tasks using the FLAX backend.

Key implementation details:
- Implements `Flax` backend for acceleration.
- Initializes with `flax` requirements.

Notable dependencies:
- Requires FLAX library as a backend.
```

**RAG System Representation:**

* **Class Name:** FlaxMBartForSequenceClassification
* **Purpose:** Sequence Classification using BART Architecture
* **Backend:** FLAX
* **Requirements:** [FLAX]


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**FlaxMBartModel Class Summary**

*   **Type:** `FlaxMBartModel`
*   **Primary Purpose:**
    Establishes a Flax backend model for the Hugging Face library, specifically designed for the MBART task.
*   **Key Implementation Details:**
    *   Initializes with *args and **kwargs*
    *   Requires Flax backend (`requires_backends` method)
        +   Supported backend: `flax`
*   **Notable Dependencies or Requirements:**
    *   Related class: `DummyObject` (metaclass)
    *   External dependencies:
        \- Flax library


(Note: The provided code does not include the definition of `requires_backends`, which is likely a separate function.


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**Summary**

`FlaxMBartPreTrainedModel` is a PyTorch model class using the Flax backend, implemented as a subclass of `DummyObject`. The primary purpose is to provide a pre-trained model for BART. 

Key Implementation Details:
- Utilizes Flax backend (`_backends = ["flax"]`)
- `requires_backends` method checks if Flax backend is present at creation time

Notable Dependencies/Requirements:
- None explicitly mentioned, but inherited from `DummyObject`
- Requires PyTorch and related libraries


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**Summary:**

`FlaxMistralForCausalLM` is a class using the Flax library (`"flax"` backend) as its framework. It initializes with `*args` and `**kwargs`, enforcing the presence of the `"flax"` backend.

**Key Details:**

*   Framework: `Flax`
*   Backend: `"flax"`
*   Implementation Method: `requires_backends("flax")`

**Notable Dependencies/Requirements:** No external dependencies listed. The class relies solely on Flax and its built-in functionality.


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**Summary:**

The `FlaxMistralModel` class is a model implementation using the Flax library (`"_flax"` backend). It initializes with the required `"flax"` backend and enforces its use at runtime.


**Detailed Analysis:**

*   **Primary Purpose:** Builds a Flax-based model (`FlaxMistralModel`).
*   **Key Implementation Details:**
    *   Requires `["flax"]` as a backend.
*   **Notable Dependencies or Requirements:**
    *   Uses the `"flax"` library.
    *   Implemented with the `DummyObject` metaclass.

This concise summary highlights the class's purpose, key implementation details, and dependencies.


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### Flax Mistral Pre-Trained Model Class Summary

#### Main Purpose and Functionality
The `FlaxMistralPreTrainedModel` class is a pre-trained model implementation using the Flax library. It inherits from the `DummyObject` metaclass and provides an instance for a specific backend using "flax".

#### Key Implementation Details
- `_backends` attribute specifies the supported backend as "flax".
- The `__init__` method initializes the instance, which requires the "flax" backend to be present. 

#### Notable Dependencies or Requirements
- The class uses the `DummyObject` metaclass and relies on the `requires_backends` function from an unspecified module.
- The implementation depends on the Flax library for the "flax" backend.

### Method Signatures and Interfaces

| Interface/Method | Description |
| --- | --- |
| `__init__` | Initializes the model instance, requiring a "flax" backend. |

#### External Dependencies
The Flax library is required for this class to function.


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**Summary:**

The `FlaxMT5EncoderModel` class is a machine learning model implemented using the flax library. It primarily functions as an encoder component for the MT5 (Multi-Task 5) architecture.

**Key Details:**

*   Uses the flax backend, requiring flax dependencies.
*   Initializes with various arguments (`*args` and `**kwargs`, but details of these are not specified in this snippet).

**Notable Dependencies/Requirements:**

*   Flax library
*   `requires_backends` function (implied but not explicitly stated, suggesting the model relies on external support for backend implementation)

**(Class Structure Not Entirely Fulfilled as per your Instructions)**

Upon closer inspection of the code structure specified in instructions I noticed that I had missed some details. The FlaxMT5EncoderModel class adheres to certain design elements and structural conventions mentioned in the detailed specification however not all points fully captured by my summary here.


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**Class Summary**

`FlaxMT5ForConditionalGeneration`: A machine translation model for conditional generation using the Flax deep learning library.

**Key Implementation Details**

* Requires backends: `self requirements.backends == ["flax"]`
 
**Notable Dependencies or Requirements**

* Deep learning library: Flax
* Backend interfaces:
	+ backend.flax.backend (inherited)
	
**Primary Purpose**
Instantiates a machine translation model for conditional generation with Flax as the backend.


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**Summary**

`FlaxMT5Model` is a PyTorch-based neural network model implemented using the Flax library. 

*Primary Purpose*: This class represents a variant of the MT5 transformer model, utilizing the `flax` backend for efficient computing.

**Key Implementation Details**:

* The class initializes with `requires_backends` function which checks if flax is installed and compatible.
* No specific algorithm or design choice is noted; however, this class inherits from PyTorch architecture.

**Notable Dependencies or Requirements**:

* `dummy.py`: Not used in the code snippet provided
* `jittor_flax_backend`: Not used in the code snippet provided 
* `Flax` Library (imported but not used within class)
* `PyTorch` 
* `DummyObject` metaclass


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**Class Summary:**

`FlaxOPTForCausalLM`

**Main Purpose and Functionality:**
```python
class FlaxOPTForCausalLM(metaclass=DummyObject):
    """
    A variant of the OPT model for causal language models using Flax backend.
    """

    # Requires Flax backend
    _backends = ["flax"]
```
    
**Key Implementation Details:**

* `dummy metaclass` ensures proper constructor invocation.

   
 
   `requires_backends(self, ["flax"])`: Verifies dependency on "flax" library for backend.


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**Summary:**

`FlaxOPTModel` class: A PyTorch Flax model implementation for optimization tasks. 

*   Purpose: Supports FLAX backend ("flax") with optional framework and device support.
*   Key Implementation:
    -   `__init__`: Initializes the model, requiring FLAX backend and enforcing it using `requires_backends`.
        +   Note: Calls `Provide` method from the `flax` package (not shown in the code snippet), suggesting that the class depends on Flax's API for device and framework support.
*   Notable Dependencies:
    -   External Dependency: Flax library ("flax")
        +   Interfaces/Classes: Requires access to Flax's provided models (`Provide` method) and backend management (`requires_backends` function).


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**Summary:**

This is the base class `FlaxOPTPreTrainedModel` for a pre-trained model implemented on top of Flax, utilizing the Opt architecture. The primary purpose is to provide a pre-trained model with optimal performance.

**Details:**

* **Primary Purpose:** Pre-trained models using Flax and Opt architectures.
* **Key Implementation:** Initializes a pre-trained model using `Opt` algorithm with Flax backend.
* **Notable Dependencies:**
	+ Flax library (`flax`)
	+ `DummyObject` metaclass
	+ `requires_backends` function
* **Interfaces/Methods:** 
	+ `FlaxOPTPreTrainedModel.__init__`: Initializes the model.


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**Flax Pegasus For Conditional Generation Class Summary**

### Main Purpose and Functionality
The `FlaxPegasusForConditionalGeneration` class is designed as a conditional generation model using the Flax library backend. It initializes the model with Flax.

### Key Implementation Details

*   Uses the `DummyObject` metaclass.
*   Specifies the required backend as "flax" during initialization.

### Notable Dependencies and Requirements
The class depends on the following:
-   The `requires_backends` function, which is not shown in this snippet.
-   The `Flax` library backend.


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**Summary:**

The `FlaxPegasusModel` class is a machine learning model factory using the Flax backend. It requires the "flax" backend for initialization.

**Key Details:**

*   Backend: Flax
*   Required Backend Version (not shown in code snippet): Requires 'flax backend version' to be installed.
*   Dependencies:
    *   `requires_backends` function

Note that other dependencies or backends used by the model might not be immediately apparent from this code snippet.


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**Summary:**

`FlaxPegasusPreTrainedModel`

**Class Overview:** 
A pre-trained model implemented using the "flax" backend.

**Key Implementation Details:** 

* Initializes with `args` and `kwargs`, enforcing `flax` backend requirement.
* Requires backends support (`requires_backends`) with the specified backend ("flax").

**Dependencies/Requirements:**

* Backend: `flax`
* Interface: `DummyObject `(implied by `metaclass=DummyObject`)

**Context:** 
This class is likely part of a larger framework or library for natural language processing or computer vision tasks, utilizing pre-trained models for efficient inference.


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**Summary:**

The `FlaxRegNetForImageClassification` class is a metaclass-based implementation of the RegNet model for image classification using the Flax deep learning framework. It uses the `flax` backend and ensures that it runs on that backend.

**Key Implementation Details:**

- Uses Flax as its backend (`_backends` attribute)
- Initializes with required backend configuration (`requires_backends` method)

**Notable Dependencies or Requirements:**

- Flax deep learning framework (`flax` backend)


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**Summary:**

The `FlaxRegNetModel` class represents a regression neural network model using the Flax backend. It initializes with a type check ensuring only the Flax backend is supported.

*   **Purpose and Functionality:** Regression neural network model with Flax backend support.
*   **Key Implementation Details:**
    *   Uses `FlaxRegNetModel` (not shown in this snippet).
*   **Notable Dependencies or Requirements:**
    *   Flax library for deep learning infrastructure.


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**Concise Summary**

`FlaxRegNetPreTrainedModel`: A pre-trained model class using the Flax backend. This model is a subclass of `DummyObject` and has been pre-processed with dependencies for the Flax backend.

* **Class Purpose**: Pre-trained RegNet model
* **Main Functionality**: Initializes with required Flax backend compatibility

**Key Implementation Details**

* Uses Flax backend (`"flax"`)

**Notable Dependencies and Requirements**

* Requires `requests` and `urllib3` for backend dependencies (assumed from context)


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**Summary**

The `FlaxResNetForImageClassification` class is a neural network architecture implemented using the Flax library. It inherits from a metaclass (`DummyObject`) and uses the "flax" backend.

**Main Purpose and Functionality**

* Class: Neural Network Architecture
* Backend: Flax
* Functionality: Image classification

**Key Implementation Details**

* Initialization method `__init__` requires the "flax" backend.
* Method signature: `requires_backends(self, ["flax"])`

**Notable Dependencies or Requirements**

* Library: Flax
* Interface/Class: `DummyObject` (metaclass)


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**Flax ResNet Model Class Summary**

*   **Purpose:** The FlaxResNetModel is a neural network model implementation using the Flax library, leveraging the `DummyObject` metaclass for dynamic backend configuration.

**Key Implementation Details:**

*   Supports `flax` as its primary backend.
*   Uses the `requires_backends` function to enforce backend compatibility at initialization time.

**Notable Dependencies and Requirements:**

*   **External Dependency:** The Flax library (`flax`)
*   **Implemented Interfaces:** Not mentioned, but assumes compatibility with other backends (likely enforced by the `DummyObject` metaclass).


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**Flax ResNet Pre-Trained Model Class Summary**

### Purpose and Functionality

This class represents a pre-trained Flax implementation of the ResNet model. It utilizes the `DummyObject` metaclass to define a backend-specific version of the model.

### Implementation Details

* The model requires the Flax backend (`"flax"`).
* The `__init__` method initializes the model, requiring the "flax" backend.
* No additional implementation details are provided in this snippet.

### Dependencies and Requirements

* Reqs the following backend: "flax"
* Uses a DummyObject metaclass for backend-specific configuration.


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**Class Summary:**

`FlaxRobertaForCausalLM`

A Flax-based implementation of the Roberta model for causal language modeling.

**Key Features:**

* Primary purpose: Causal language modeling using Flax backend.
* Backend requirement: `flax`.
* Initializes with required frameworks.

**Implementation Details:**

* The `_backends` attribute specifies the supported backends, with `flax` being the only one enabled.

**Notable Dependencies:**

* `requires_backends`: a function that ensures a compatible frontend is present (`"flax"` in this case).


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**Summary**

The `FlaxRobertaForMaskedLM` class is a custom implementation of the Roberta Model using Flax backend (`backend="flax"`). The class initializes with the `requires_backends` function to ensure Flax is available.

**Key Points**

* **Main Purpose**: Custom Flax-based implementation of the Roberta Masked Language Modeling (MLM) model.
* **Implementation Details**:
	+ Utilizes the `Flax` backend (`backend="flax"`).
	+ Verifies the availability of the `Flax` backend in the initialization process.
* **Dependencies/Requirements**:
	+ **External Dependencies**: Flax library.
	+ **Implemented Interfaces**: RobertaModel (inherited).


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**Summary**

The `FlaxRobertaForMultipleChoice` class is an instance of the `Flax` backend (specifically `flax`) for a multiple choice question classification model, built using the Hugging Face Transformers library. 

**Key Points:**

* **Primary Purpose:** implements Flax's version of Roberta For Multiple Choice Question Classification
* **Backends:** Uses 'flax' backend
* **Notable Dependencies:**
  - Requires the Hugging Face Transformers library (`Provide`)
  - Uses `DummyObject` metaclass for definition


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**Concise Summary**

`FlaxRobertaForQuestionAnswering` is a PyTorch implementation of the RoBERTa tokenizer and model for question-answering tasks using the flax library (`"flax"` backend).

### Key Implementation Details

*   Requires `flax` backend.
*   Uses RoBERTa tokenizer and model architecture.

### Notable Dependencies or Requirements

*   Relies on `flax` backend and Python 3.8+ compatibility.
*   Inherited from the PyTorch implementation of RoBERTa (`requires_backends(self, ["flax"])`).


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**Class Summary:(FlaxRobertaForSequenceClassification)**

*   **Main Purpose and Functionality**: Implementations of the FlaxRoberta model for sequence classification tasks using the Flax backend.
*   **Key Implementation Details**: 
    *   Uses `Flax` backend with a dummy object class metaclass.
        *   Implemented in `__init__` method with required backends: flax
*   **Notable Dependencies and Requirements**:
    *   Requires `Flax` as the backend for sequence classification tasks.


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**Summary:**

The `FlaxRobertaForTokenClassification` class is a PyTorch-like model implementation using the Flax library, based on the RoBERTa architecture. It has a single backend requirement for Flax and initializes with the necessary backends configuration.

**Key Details:**

* Primary purpose: Token classification model
* Backend: Flax
* Implementation details:
	+ Method `requires_backends`: Ensures the required backend (Flax) is provided
* Notable dependencies:
	+ `DummyObject` metaclass
	+ `Provide`: A decorator not shown in this code snippet, likely used for backend configuration


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**Summary:**

The `FlaxRobertaModel` class is an implementation of the Roberta model using the Flax backend. It initializes a BERT-like language model with a required Flax backend.

**Key Details:**

*   Primary purpose: To create a BERT-like language model.
*   Implementation details:
    *   Uses the `DummyObject` metaclass.
    *   Requires the Flax backend (`requires_backends(self, ["flax"])`).
*   Notable dependencies or requirements:
    *   The `roberta_model` configuration is not shown in this chunk. 
    *   Required by the class: The Flax library.

**No external dependencies listed**, but likely `transformers`, `pytorch_lightning`, and/or other frameworks might be used elsewhere in the codebase.


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**Summary:**

`FlaxRobertaPreTrainedModel`: A pre-trained language model using the Flax backend.

* **Purpose**: Implements the RoBERTa architecture optimized for performance on the Flax deep learning framework.
* **Key Implementation Details**:
	+ Uses the `flax` backend, which allows for efficient and dynamic computation graph optimization.
	+ Requires the Flax framework (`requires_backends(self, ["flax"])`) to be initialized.
* **Notable Dependencies or Requirements**:
	+ `DummyObject`: Metaclass used to define the class.
	+ `flax`: Deep learning backend required for model initialization.

Note: No external dependencies are explicitly listed in this summary. The context assumes the use of the Flax framework and its associated dependencies.


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**FlaxRobertaPreLayerNormForCausalLM Class Summary**

*   **Purpose**: A pre-layer normalization (PFN) layer for the Flax library, designed to work with causal language models (CMLMs).
*   **Main Functionality**: Applies layer normalization only to the last dimension of input data before passing it through a linear transformation.
*   **Key Implementation Details**:
    *   Utilizes PFN algorithm to compute scale and shifted mean values in parallel.
    *   Only accepts Flax backend (`"flax"`).
*   **Notable Dependencies/Requirements**:
    *   `requires_backends` decorator, which ensures the correct backend is used.


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Here is a concise summary of the class code chunk:

**Class Summary:**
`FlaxRobertaPreLayerNormForMaskedLM`

**Main Purpose and Functionality:**
This class is a pre-layer normalization layer for masked language models, implemented using the Flax library.

**Key Implementation Details:**

* Uses `Flax` backend
* Initializes with optional `args` and `kwargs`
* Requires Flax backend installation

**Notable Dependencies/Requirements:**

* Related class: `dummy_object` (not shown in code snippet)
* External dependency: `flax` library


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**Summary: FlaxRobertamaxPreLayerNormForMultipleChoice Class**

```markdown
# Primary Purpose:
The `FlaxRobertaPreLayerNormForMultipleChoice` class is a pre-layer normalization layer for multiple-choice tasks using the Flax library.

# Key Implementation Details:

*   Implemented backend: `flax`
*   Requires `requires_backends(self, ["flax"])` in `__init__` method
    -   to enforce the use of the Flax backend

# Notable Dependencies:
- Uses the `Flax` backend (`_backends = ["flax"]`)
```

**Additional Information:**
This class is a subclass of an unknown parent class (not shown in the provided code snippet), utilizing pre-layer normalization for multiple-choice tasks. The exact behavior and interface details are not explicitly stated in this summary, but it can be inferred from the module or class name.


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**Summary:**
`FlaxRobertaPreLayerNormForQuestionAnswering`

*   A PyTorch module for question-answering tasks utilizing the Flax library's `Roberta` model architecture with layer normalization.

**Key Details:**

*   `_backends = ["flax"]`: Utilizes the Flax library.
*   `requires_backends(self, ["flax"])`: Ensures the use of the Flax backend at initialization.
*   **Note:** Inherited from a parent class likely in PyTorch's Hub or registry.

No notable dependencies or requirements are listed outside of the Flax library.


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**Class Summary: `FlaxRobertaPreLayerNormForSequenceClassification`**

*   **Purpose:** A pre-layer normalization model for sequence classification using the Flax deep learning library.

### Key Implementation Details:

*   **Backend**: Utilizes the Flax backend (`flax`) for implementation.
*   **Initialization**: Verifies if required backends are installed (`requires_backends(self, ["flax"])`).

### Notable Dependencies or Requirements:

*   The `DummyObject` metaclass is used to define the class.
*   The Flax deep learning library (specifically, the "flax" backend) is required for instantiation.


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**Code Summary**

This class `FlaxRobertaPreLayerNormForTokenClassification` is part of the Hugging Face model family. It extends a pre-trained Roberta architecture and applies layer normalization for token classification tasks.

**Primary Purpose**: Implements a pre-trained language model with layer normalization for token classification tasks using the Flax library.

**Key Implementation Details**:

* Uses the `flax` backend
* Initializes with any additional arguments (`args`) and keyword-only arguments (`kwargs`)
* Requires the `flax` backend to be supported

**Notable Dependencies and Requirements**:
 
* Inherited from: Not specified, but likely other models or base classes in the model family.
* External dependency: None mentioned, but likely dependent on other models or libraries in the Hugging Face ecosystem.

 This summary should provide a clear understanding of the class's main purpose, key implementation details, and any notable dependencies or requirements.


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**FlaxRobertaPreLayerNormModel Class Summary**

* **Class Purpose:** Implementations of the Roberta pre-trained language model using Flax backend.
* **Key Features:**
	+ Uses Flax backend
	+ Supports initialization with various arguments and keyword parameters
* **Notable Implementation Details:**
	+ Requires a valid `flax` backend for instantiation (`requires_backends`)
	+ No additional implementation details provided in this snippet

**Technical Terms and Key Elements:**

- `FlaxRobertaPreLayerNormModel`: Custom class implementing Roberta pre-trained language model
- `_backends = ["flax"]`: Specifies supported backends (in this case, Flax)
- `__init__`: Constructor method for instantiation of the class
- `requires_backends`: Library function to validate backend requirements


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**Summary**

FlaxRobertaPreLayerNormPreTrainedModel:

* Main Purpose: Pre-trained RoBERTa model using Flax backend
* Primary Interface:
	+ `requires backends`: Verifies required Flax backend is present
* Implementation Details:
	+ Uses Flax backend for tensor operations (`__init__` method)
	+ Inheritage:
		- From `DummyObject`

**Key Points:**

* Backend support: Flax (Flax _backends = ["flax"])
* Dependencies: None explicitly listed, but requires backends to be present


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**Class Summary:**

`FlaxRoformerForMaskedLM`

A custom transformer architecture implemented for masked language modeling tasks using the `flax` backend.

**Key Implementation Details:**

* Uses `flax` as its frontend backend
* Requires `flax` at initialization with `self.requires_backends(["flax"])`

**Notable Dependencies and Requirements:**

* Inherently uses `FlaxRoformer` and other related classes (not explicitly listed in this snippet)
* Relies on the `DummyObject` metaclass for instantiation
* External dependency on the `flax` library

Note: The `DummyObject` metaclass is not a standard Python class, and it's unclear what its actual behavior is without further context.


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**Summary:**

`FlaxRoFormerForMultipleChoice` class, a neural network architecture specific to multiple choice problems.

**Key Details:**

* `requires_backends`: Ensures the backend is set to "flax" through the `_backends` list.

**Implementation Notable:**

* Uses `DummyObject` as its metaclass.

**Requirements and Dependencies:**

* Backend dependency on `flax`.


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**Summary:**

The `FlaxRoFormerForQuestionAnswering` class is an implementation of the RoFormer model for question answering tasks using the Flax library backend. It initializes with `flax` as the required backend.

**Key Details:**

- **Initialization**: The class requires a `flax` backend to be initialized.
- **Backend Specification**: `_backends = ["flax"]`

No notable dependencies or requirements are listed.


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Here is a concise summary of the class code chunk:

**FlaxRoFormerForSequenceClassification Class**

* A PyTorch model class (`metaclass=DummyObject`) specializing in sequence classification tasks using the `flax` backend.
* Initializes the model with `requirements=['flax']` and checks for compliance.

Key Implementation Details:
- Uses the `requires_backends` utility to enforce dependency on the `flax` backend.

Notable Dependencies:
- `flax`
- Requires a backend implementation that can run on `FlaxBackend`.


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**Summary:**

`FlaxRoFormerForTokenClassification`: A custom model class utilizing the Flax library backend for token classification tasks. 

**Key Details:**

*   Primary purpose: Implement RoFormer architecture for token classification using Flax.
*   Notable implementation trait: Uses `RequireBackends` decorator to enforce the Flax backend requirement.

**Dependencies and Implementations:**

*   Backend: Flax (`_backends = ["flax"]`)
*   Inherited Interface: `DummyObject` (metaclass)
*   External Dependencies:
    *   None explicitly mentioned
    However, it's assumed Flax library is required as backend.

**Context and Purpose:** This class is designed to implement the RoFormer architecture specifically for token classification tasks using a custom front-end framework.


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**Summary**

`FlaxRoFormerModel` class: A neural network model implementation using the FLAX library (backends=["flax"]), requiring Flax as a dependency. 

**Key Details**

- **Model Architecture**: RoFormer-based model
- **Dependencies**
  - `requirements`: [Flax (`"flax"`)]
  - `inherited.implemented_interfaces`: None explicitly stated but likely conforming to a standard library interface 
  - `external.dependencies`: External backend dependency on `Flax`


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**Summary:**

`FlaxRoFormerPreTrainedModel`: A pre-trained model using FLAX backend. This class is initialized with various arguments and ensures the 'flax' backend is supported.

**Key Details:**

* **Backend**: Uses the FLAX (Transformers Library Accelerated) backend.
* **Initialization**: Initializes with `*args` and `**kwargs`, requiring the 'flax' backend to be available (`requires_backends()` check).

**Notable Methods/Variables:** 

* `__init__`: Initializes the model.
* `_backends`: Stores supported backends (in this case, ['flax']) as a class attribute.


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**Class Summary:**

`FlaxSpeechEncoderDecoderModel`

A speech encoder-decoder model implementation using the Flax backend.

**Key Implementation Details:**

* Utilizes the `requires_backends` function to ensure the existence of the Flax backend.
* Accepts variable arguments and keyword arguments in its constructor (`__init__` method).

**Notable Dependencies/Requirements:**

* Flax library for model implementation
* `DummyObject` metaclass
* `requires_backends` function (not shown in this snippet)


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**Summary:**

* `FlaxT5EncoderModel` class definition using Flax backend (`_backends = ["flax"]`)
* Initializes the model with input arguments and checks required backends (`requires_backends`) 
* Primary purpose: A PyTorch model (inheriting from `DummyObject`) optimized for use with the Flax backend.

**Further Details:**

* **Backend:** Flax
* **Implementing Class:** `FlaxT5EncoderModel` (inherits from `DummyObject`)
* **Implementation:** Uses `requires_backends` to verify presence of required backends

No external dependencies or notable requirements are introduced in this code chunk.


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**Summary:**

The `FlaxT5ForConditionalGeneration` class is a PyTorch-based model using the Flax library for conditional generation tasks. It is designed to work exclusively with the Flax backend.

**Key Implementation Details:**
- The model uses the `Provides` decorator, indicating that it needs additional functionality from the `requires_backends` method.
- The `__init__` method initializes the model instance and requires the Flax backend using the `requires_backends` decorator.

**Notable Dependencies and Requirements:**

- Flax library (`flax`)
- PyTorch (inherited through meta-class)
- `dummy_object` metaclass (not used)

Note that this summary focuses on the essential aspects of the code chunk, omitting generic information to provide a concise and context-rich overview.


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**Summary:**

The `FlaxT5Model` class is a T5 model implementation using the Flax deep learning framework. It inherits from a metaclass `DummyObject`.

**Key Details:**

* Primary purpose: Implementation of the T5 model using Flax.
* Notable dependencies:
	+ `flax`: Backend library used by the model.
	+ `Provide`: A required decorator to validate backend usage (not shown in this snippet).

**Implementation Observations:**

* The `FlaxT5Model` class uses the `requires_backends` function to ensure compatibility with the Flax backend.

This summary provides a concise overview of the code, focusing on its main purpose and key implementation details.


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**Summary:**

The `FlaxT5PreTrainedModel` class is a pre-trained model based on the Flax library (`FlaxT5PreTrainedModel`) that relies on the `flax` backend. It initializes the model, enforcing the `flax` backend requirement in its constructor (`__init__` method). 

**Key Details:**

- Relies on `FlaxT5PreTrainedModel` class
- Supported backend: `flax`
- Constructor ensures `flax` backend is required

**Dependencies/Requirements:**

- Flax library (`FlaxT5PreTrainedModel`)
- flax framework (backend is explicitly supported)


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**Flax Vision Encoder Decoder Model Class Summary**

### Class Purpose
`FlaxVisionEncoderDecoderModel` is a PyTorch model based on the Flax library, defined using metaclass `DummyObject`.

### Primary Functionality
It is an encoder-decoder model designed for vision tasks, with a specific backend support for Flax.

**Key Implementation Details:**

* Uses the `_backends = ["flax"]` attribute to specify its supported backends.
* Requires the "flax" backend to be enabled using `requires_backends(self, ["flax"])` in the constructor (`__init__`).

### Notable Dependencies/Requirements:

* [Flax Library](https://github.com/google/flax)
 

(Note: The above summary is based on the provided code snippet. For a more detailed analysis and code overview, additional context would be necessary.)


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**Summary:**

The `FlaxVisionTextDualEncoderModel` class is a PyTorch model utilizing the Flax backend (specifically "flax" backend). It meets initialization requirements for this backend.

**Key Details:**

- Primary Functionality: Dual encoder model using Flax backend.
- Notable Algorithm/Design Decision: Model requires specific Flax backend ("flax").
  
- Important Method Signature:
  - `requires_backends(self, ["flax"])`

- External Dependencies:
  - `DummyObject` metaclass
  - PyTorch (unspecified backend, but "flax" is specified)

**Note:** The code is missing the implementation of the model's `__init__` method. Without more context or the rest of the class definition, further analysis cannot be performed.


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**Flax ViT For Image Classification Model**

```summary
FlaxViTForImageClassification is a PyTorch-based image classification model using flax backend. 
It initializes with dependencies and enforces the use of flax.

Key Features:
- `metaclass=DummyObject`: Inheriting from a dummy object for class definition.
  
Notable Dependencies:
- `flax`: Backend framework used.
```

**Implementation Details**

- The `requires_backends` function is called to enforce flax backend usage in the model's initialization.
  

No notable methods or variables are defined explicitly, indicating this code snippet serves as a base class template rather than a fully functional module.


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**Summary:**
```markdown
Flax ViT Model
================

Class `FlaxViTModel` implements a Vision Transformer (ViT) model using the Flax library. It requires the flax backend to function.

Primary Purpose:
---------------

* Implements a Flax-based Vision Transformer (ViT) model

Key Implementation Details:
-------------------------

* Utilizes the `flax` backend
* Requires validation of required backends during initialization (`requires_backends` method)

Notable Dependencies and Requirements:
--------------------------------------

* `flax`: Central library for building and training machine learning models
```
**RAG Code Summary:**
```plain
require_flax: [FlaxViTModel]
```
This summary concisely highlights the class's primary purpose, key implementation details, and dependencies. It is optimized for quick reference within a RAG system.


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**Flax ViT Pre-Trained Model Class**

### Main Purpose and Functionality
The `FlaxViTPreTrainedModel` class is a pre-trained model based on Flax backend for Vision Transformers (ViT). It inherits from the `DummyObject` metaclass. The primary purpose is to provide a pre-trained model for vision transformer tasks.

**Key Implementation Details**

* Requires Flax backend implementation using the `requires_backends` function.
* Uses Flax backend with `_backends = ["flax"]`.

### Notable Dependencies or Requirements

* Flax library for backend implementation
* Requires backends implementation for "flax"


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**Summary: Flax Wav2Vec2ForCTC Class**

**Purpose and Functionality:** `FlaxWav2Vec2ForCTC` is a PyTorch model that implements Wav2Vec 2.0 with CTC loss using the FLAX deep learning framework.

* Primary purpose: Natural Language Processing (NLP) tasks, such as speech recognition and text-to-speech synthesis.
* Expected uses: BERT-style pretraining for sequence generation and sentiment analysis tasks.

**Implementation Details:**

* Uses FLAX backend with `__init__` method requiring either the "flax" backend.
* Utilizes Wav2Vec 2.0 architecture to process sequential data.

**Dependencies:**

* Requires PyTorch library (not explicitly listed)
* Inherited from PyTorch's TorchModule class through `DummyObject` metaclass
* Implicitly depends on Flax framework and its tensor operations

**Notable Interfaces and Methods:**

* Notably uses the CTC loss function, but no method signatures are provided in this code snippet.


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**Class Summary: FlaxWav2Vec2ForPreTraining**

*   **Purpose**: A pre-training model based on Wav2Vec2 using the Flax library.
*   **Metadata**:
    -   `metaclass=DummyObject`: Indicates that this class uses a dummy object metaclass.
    -   `_backends=["flax"]`: States that the supported backend is Flax.

**Init Method:**

The class's initialization (`__init__`) method:

*   Requires the `flax` backend to be present (`require_backends(self, ["flax"])`).


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**Summary**

`FlaxWav2Vec2Model`: A Flax-based implementation of the Wav2Vec 2 model architecture. The primary purpose is to process sequential data.

**Key Implementation Details**

- `__init__` method: Initializes the model with the required backend, "flax".
- Requires Flax backend implementation (`requires_backends(self, ["flax"])`.)

**Notable Dependencies or Requirements**

- Flax library
- `DummyObject` metaclass
- Wav2Vec 2 model architecture (not explicitly implemented, but referenced)


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**Summary**

`FlaxWav2Vec2PreTrainedModel` is a PyTorch-based model class that utilizes the Flax library. The primary purpose of this class is to pre-train a Wav2Vec 2.x model.

**Key Implementation Details:**

* Initializes with `requires_backends` verification, specifying support for Flax backend.
* Employs Flax-specific architecture in `_backends`.
 
**Notable Dependencies and Requirements:**
 
* Uses Python's `metaclass=DummyObject`.
* Reqs Flax library as backend.


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**Summary:**

`FlaxWhisperForAudioClassification` is an audio classification model using the `flax` backend. It initializes with the required `flax` backend.

**Key Details:**

*   **Class Signature:** `FlaxWhipherForAudioClassification(metaclass=DummyObject)`
*   **Primary Purpose:** Audio classification model using the `flax` backend.
*   **Implementation:**
    *   Initializes with variable arguments (`self, args`) and keyword arguments (`self, kwargs`)
    *   Verifies that the `requires_backends` method is called with the argument `"flax"`


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**Class Summary:**

`FlaxWhisperForConditionalGeneration`

*   **Purpose:** A conditional generation model using the Flax library for inference.
*   **Functionality:** Initializes an instance of `FlaxWhisperForConditionalGeneration`, ensuring it requires only Flax backend (`"flax"`).

**Key Implementation Details:**

*   Requires Flax backend (`"flax"`).
*   Uses `DummyObject` as its metaclass.

**Notable Dependencies/Requirements:**

*   Related class: `DummyObject`
*   External dependencies: None
*   Implemented/interface: None


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**Summary:**

The `FlaxWhisperModel` class is a meta-class model, part of the Whisper family, specifically utilizing the Flax backend. It requires the 'flax' backend to be provided during initialization.

**Key Details:**

- **Method Signatures:** None
- **Algorithms/Patterns:** Utilizes Flax backend
- **Implementation Choices:** Requires 'flax' as a supported backend

**Notable Dependencies/Requirements:**

- `DummyObject` (metaclass): Base class for meta-classes, not directly related to model functionality.
- External dependency on the `flax` library (`requires_backends(self, ["flax"])`)


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**Summary:**

The `FlaxWhisperPreTrainedModel` class is a PyTorch-based pre-trained model that utilizes the Flax library as its backend. It requires the Flax backend to be available.

**Key Details:**

* **Backend:** Flax
* **Primary Purpose:** Pre-trained model with Flax backend support
* **Implementation:** Utilizes `Provide` decorator to ensure required backend is installed

**Dependencies:**

* Flax library (`flax`)
* `_requires_backends` function (`FlaxWhisperPreTrainedModel`)


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Here is a concise and focused summary of the class code chunk:

**Class FlaxXGLMForCausalLM**

* **Purpose:** Provides an implementation for causal language modeling using the XGluM architecture with the Flax backend.
* 
 *   Main functionality:
     - Initializes the model with a given backend (`flax`).
       - Verifies that the required backend is present before initialization.

**Key Implementation Details:**
- The underlying architecture (XGluM) is used for causal language modeling.
 
**Notable Dependencies/R Requirements:** 
- Requires Flax backend (`["flax"]`) to be initialized.


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**Summary:**

The `FlaxXGLMModel` class is a specialized machine learning model implementation using the Flax library. It inherits from a metaclass (`DummyObject`) and initializes with arguments using the `requires_backends` function.

**Key Implementation:**

* Initializes the model instance with the required Flax backend.
* Enforces use of the Flax XGLM (Extreme GLMatrix) architecture.

**Notable Dependencies:**

* [Flax library](https://github.com/google/flax)
* The `_backends` class initializes with a hardcoded ["flax"] dependency.


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**Summary**

`FlaxXGLMPreTrainedModel`: A pre-trained model class utilizing the Flax library backend (`"flax"`). The `__init__` method initializes the instance with required `["flax"]` backend, enforcing proper backend setup.

**Details**

* **Implementation:** None
* **Design Choice:** Uses `Flax` as the backend
* **Key Method Signature:** `__init__(self, *args, **kwargs)`
* **Dependencies:**
	+ `DummyObject` metaclass
	+ `flax` library (backend)

Note: The provided code snippet lacks key details about the actual model implementation. Further analysis would require examining the rest of the class definition and its usage in the project.


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**Summary:**

The `FlaxXLMRobertaForCausalLM` class is a language model implementation using the Flax backend. It inherits from a metaclass (`DummyObject`) and has an initializer that requires the Flax backend to be specified at construction time.

**Detailed Breakdown:**

*   **Main Purpose and Functionality:** This class is used for sequence-to-sequence models, specifically designed for causal language modeling tasks.
*   **Key Implementation Details:**
    *   **Backend Constraint:** The model relies on the `Flax` backend, which is a deep learning framework.
    *   **Initialization Logic:** The initializer requires the Flax backend to be specified via the `requires_backends` function.
*   **Notable Dependencies or Requirements:**
    *   **Inherited Interface:** The class inherits from the `_DummyObject` metaclass, which provides additional functionality for creating dummy objects.
    *   **External Dependency:**
        ```
- ["flax"]  # Required backend
```
The provided code does not specify any external dependencies other than the Flax backend.


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**Concise Summary**

Class `FlaxXLMRobertaForMaskedLM`:
 
- Primary purpose: A masked language model implementation using the Flax library and the Roberta architecture.
 
- Key implementation details:
  - Uses Flax as its backend (implementation details omitted).
  - Calls `requires_backends` method, which is not defined in this snippet ( likely defined elsewhere).

- Notable dependencies/requirements:
  - Flax library
  - Roberta architecture
  - DummyObject metaclass


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**Summary:**

The `FlaxXLMRobertaForMultipleChoice` class is a pre-trained BERT model variant using the Flax deep learning framework. It's designed for multiple-choice classification tasks.

**Key Details:**

* Primary purpose: Multi-choice classification
* Functionality: Uses Flax backend and inherits from a base model
* Notable implementation:
	+ `requires_backends` method ensures use of "flax" backend

**Dependencies/Requirements:**

* Flax deep learning framework as backend
* Inherited traits from a base model (not specified in this snippet)


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**Summary:**

The `FlaxXLMRobertaForQuestionAnswering` class is a pre-trained language model based on the Flax library, specifically designed for question-answering tasks. It inherits from classes using the "flax" backend.

**Key Details:**

*   Class name: `FlaxXLMRobertaForQuestionAnswering`
*   Backend: `"flax"`
*   Primary purpose: Pre-trained language model for question-answering

**Implementation Notes:**

*   Requires `"flax"` as a mandatory backend.
*   Constructor (`__init__`) takes variable number of arguments and keyword arguments.

No notable dependencies or requirements are listed.


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**Summary:**

The `FlaxXLMRobertaForSequenceClassification` class is a PyTorch-based sequence classification model using the Flax framework. It takes input arguments and requires the 'flax' backend for initialization.

**Key Details:**

* **Class Name:** `FlaxXLMRobertaForSequenceClassification`
* **Model Type:** Sequence Classification
* **Backend:** FLAX

**Implementation Notable:**

* Requires specific backend (`"flax"`) for model initialization.


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**Summary:**

`FlaxXLMRobertaForTokenClassification` is a custom transformer-based class using the `flax` backend for token classification tasks. It inherits from a dummy object metaclass and features:

* **Backend requirement**: Ensures `flax` is installed as a pre-requisite.
* **Initialization**: Accepts arguments (`args`, `kwargs`) without defining any specific initialization parameters.

**Focused Details:**

* `metaclass`: Uses the `DummyObject` class for its metaclass definition.
* `_backends`: Specifies `flax` as the backend to be used.


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**Summary:**

The `FlaxXLMRobertaModel` class is a pre-trained natural language processing (NLP) model that utilizes the Flax library. It initializes with optional arguments and dependencies on Flax backend.

**Key Details:**

* Primary purpose: Implements XLM-based Roberta model using Flax library.
* Notable Implementation:
	+ `requires_backends` method ensures compatibility with Flax backend.
	+ Class uses the "flax" backend, specified in `_backends = ["flax"]`.

**Dependencies:**

* External dependency: Flax library ( referenced by exact name).
* Related class/interface: `DummyObject` metaclass.

**Assumed Context:**
This code is likely part of a larger NLP framework or application that utilizes pre-trained models for text processing.


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**Class Summary**

`FlaxXLMRobertaPreTrainedModel`

**Purpose and Functionality**
---------------------------

The `FlaxXLMRobertaPreTrainedModel` class is a Flax-based implementation of the XLM-Roberta pre-trained model. It utilizes the Flax library and inherits from the `DummyObject` metaclass.

**Key Implementation Details**
------------------------------

* The model requires the Flax backend to be initialized.
* The `__init__` method ensures that the required Flax backend is installed using the `requires_backends` function.

**Notable Dependencies or Requirements**
-----------------------------------------

* Flax library

**Interfaces and Inherited Methods**
--------------------------------------

Inherited from `DummyObject`, this class only provides a custom metaclass implementation. No specific interfaces are implemented.

**Action Items**

To use this model, ensure that the Flax library is installed with the required backend (`"flax"`).


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**Summary**

A custom enum class `BackboneType` defines two enumeration values: `TIMM` and `TRANSFORMERS`, representing different backbone types. The primary purpose is to categorize models.

**Implementation Details**
- Uses `enum.Enum` for definition
- Enumerates two distinct values (`TIMM`, `TRANSFORMERS`) with corresponding string representations

(Note: No external dependencies are referenced in this snippet, implying no additional required libraries or modules.)


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**Summary:**

The `verify_out_features_out_indices` function verifies that `out_features` and `out_indices` are valid for a given set of `stage_names`. 

**Key Implementation Details:**

* Validates `out_features`: checks if it's a list, subset of `stage_names`, and in the same order as `stage_names`.
* Validates `out_indices`: converts to positive indices, checks if they're within bounds, removes duplicates, and sorts them.

**Notable Dependencies or Requirements:**

* `Optional[Iterable[str]]` for `out_features`
* `Optional[Iterable[int]]` for `out_indices`
* `stage_names`: a set of strings representing stage names.


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**Function Summary: `_align_output_features_output_indices`**

* **Purpose:** Aligns output features and indices with stage names for a backbone network.

**Key Implementation Details:**

* Resolves `out_indices` and `out_features` when either is missing by finding corresponding values in the other.
* Special case handling for when both are missing, defaulting to last stage.
* Uses list comprehensions to efficiently create new lists for resolving indices and features.

**Notable Dependencies/Requirements:**

* `Optional[List[str]]`: Input feature names.
* `Union[List[int], Tuple[int]]`: Input index values or a tuple of index values.
* `List[str]`: Stage names.

**Method Signatures:**

* `_align_output_features_output_indices(out_features, out_indices, stage_names)`
* Returns: `(out_features: List[str], out_indices: Union[List[int], Tuple[int]])`


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**Concise Summary**

The `get_aligned_output_features_output_indices` function retrieves and aligns the `out_features` and `out_indices` for a backbone. It operates under different scenarios:

1.  **No** `out_features`: Uses corresponding indices, if provided.
2.  **No** `out_indices`: Uses corresponding features, if provided.
3.  **Neither**: Fallbacks to using last stage's indices and features.
4.  **Both set**: Verifies alignment.

**Key Implementation Details**

*   Utilizes `_align_output_features_output_indices` function from `_align` module for actual computation.
*   Calls `verify_out_features_out_indices` function twice: before and after computation to validate input data.
*   Returns aligned `output_features` and `output_indices`.

**Notable Dependencies and Requirements**

*   The `_align` module (assumedly responsible for the actual alignment logic).
*   The `verify_out_features_out_indices` function, also assumed to be part of some validation framework.

Assumes the existence of a separate module (`_align`) containing the `_align_output_features_output_indices` function.


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**Class Overview**

The `BackboneMixin` class is part of a PyTorch-based model implementation, providing a mixin for backbone models. It allows for initialization and configuration of two types of backbones: TIMM (the Modeling InterFace) and Transformers.

**Main Purpose and Functionality**

* Initialize and configure the backbone model depending on the `backbone_type`.
Two initialization methods are implemented:
	+ `_init_timm_backbone`: Initializes the TIMM backbone, setting stage names and feature numbers based on the loaded model's information.
	+ `_init_transformers_backbone`: Initializes the Transformers backbone, extracting stage names and feature numbers from the configuration.
* Setters for `out_features` and `out_indices` update these attributes with the new values after validation.

**Key Implementation Details**

* Custom validation functions (`verify_out_features_out_indices`, `get_aligned_output_features_output_indices`) to ensure consistency between output features and indices.
* Use of predefined configuration (`config`) to determine backbone type and initialize the corresponding method.

**Notable Dependencies or Requirements**

* **BackboneType**: An enumeration class, allowing for polymorphism between TIMM and Transformers backbones (not shown in this code snippet).
* **transformers.models.backbone**: An external dependency for the Transformers backbone model (not shown in this code snippet).

The `forward` method is left unimplemented, requiring derivation classes to implement it. The `to_dict` method serializes the instance with extracted `out_features` and `out_indices`, including these attributes in the serialized output.


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**Summary**

The `BackboneConfigMixin` class is a Python mix-in that provides specialized functionality for handling `out_features` and `out_indices` attributes in backbone configurations. The class:

* Provides getters and setters for `out_features` and `out_indices` with behavior of updating both attributes based on input
* Expects two functions (`get_aligned_output_features_output_indices`) to perform alignment between features and indices
* Includes a custom implementation of the `to_dict()` method that includes `out_features` and `out_indices` in the serialized dictionary

**Key Implementation Details**

* `get_aligned_output_features_outputIndices` function is called from setters to update both `out_features` and `out_indices`

**Notable Dependencies or Requirements**

* `List[str]`, `Union[Tuple[int], List[int]]` used as type hints for setter inputs
* External `PretrainedConfig` class with custom implementation of `to_dict()`
* `stage_names` attribute accessed within the class, implying a dependency on external configuration data

Note: The code summary focuses on providing an overview of the class's purpose and key features, without including unnecessary details.


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**Summary:**

The `load_backbone` function loads a backbone model from a configuration object. The primary purpose is to determine the loading behavior based on configuration flags, which can be either:

* Load a randomly initialized backbone (if config is from the backbone model itself)
* Load pretrained backbone weights (if config is from parent model of backbone)

**Key Implementation Details:**

1. **Configurations**:
	* `backbone_config`, `use_timm_backbone`, and `backbone_checkpoint` are retrieved from the config object.
2. **Validation**:
	* Checks for conflicting configurations, such as specifying both `backbone_kwargs` and `backbone_config`.
3. **Loading**:
	* If `backbone_config` is not specified, it uses the `AutoConfig` from_pretrained method with `backbone_checkpoint` to load a configuration object.

**Notable Dependencies or Requirements:**

1. `Transformers` library (version not specified)
2. `AutoBackbone`, `AutoConfig`, and related classes/functions

**External Dependencies:** 

None explicitly mentioned in the code snippet.

**Interfaces Implemented:**

None explicitly mentioned in the code snippet.


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**Summary:**

The `verify_backbone_config_arguments` function validates the configuration arguments for loading a backbone model. It checks for conflicts between specifying a backbone config, a pretrained config, or additional keyword arguments.

### Key Details:

* **Primary Purpose**: Verify that config arguments are valid and conflict-free.
* **Important Implementation**:
	+ Checks for conflicting specifications of `backbone` and `backbone_config`.
	+ Raises an error when both `use_timm_backbone` and a `backbone_config` are specified.
	+ Raises an error when specifying both `backbone_kwargs` and `backbone_config`.

### Notable Dependencies:

* `use_timm_backbone`
* `pretrained_config`
* `backbone`

Note: The function takes in several optional arguments, but the above summary focuses on the conflict-free validation logic.


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**Function Summary: `add_start_docstrings`**

**Purpose:** A decorator function that adds starting docstring text to an existing function, preventing it from being executed.

**Implementation Details:**

* Uses a decorator pattern (`docstring_decorator`) to modify the behavior of an input function (`fn`).
* Adds docstring text to the function's `__doc__` attribute using string concatenation.
* If no existing docstring is present, an empty string is prepended instead.

**Notable Dependencies and Requirements:**

* None declared or imported. Uses built-in Python decorators.


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**Summary**

This function, `add_start_docstrings_to_model_forward`, is a decorator factory that adds a custom docstring to models. It takes variable arguments (`*docstr`) and returns the `docstring_decorator` function.

The decorator modifies the original function's docstring by appending its own introductory text, a cautionary note about calling the `Module` instance without this method, and the provided variable arguments (as a single string).

**Key Implementation Details**

* Modifies the original function's docstring by prepending an introductory text with a formatted class name.
* Includes a warning about ignoring pre- and post-processing steps when using the `Module` instance.

**Notable Dependencies/Requirements**

* None explicitly, but relies on Python's built-in `__doc__`, `__qualname__` attributes for classes and functions.


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**Function Summary:**

`add_end_docstrings`: A function that generates a decorator to append end documentation strings to existing function docstrings.

**Key Implementation Details:**

* `docstring_decorator`: A function that takes additional `docstr` strings, which are concatenated and appended to the original function's docstring.
* The original function returns the modified docstring.

**Notable Dependencies or Requirements:**

* `*` in `args` notation indicates that the function accepts a variable number of arguments without specifying their names.


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**Summary**

This function, `_get_indent`, extracts and returns the indentation from the first line of a given string `t`. 

* **Purpose**: Retrieves indentation from a formatted input string.
* **Implementation**: Utilizes regular expression to detect indentation in the form of leading whitespace (`\s*`) followed by a non-whitespace character.
* **Methods/Interfaces**:
	+ Takes a single argument `t` of type string.
	+ Uses `re.search` and `groups()` method from Python's built-in `re` module for regex pattern matching.
	+ Returns the extracted indentation as a string, or an empty string if no indentation is found.

**Notable Dependencies/Requirements**:
* Python's built-in `re` (regex) module.


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**Function Summary: `_convert_output_args_doc`**

**Purpose:** Converts output arguments documentation into a display-friendly format.

**Key Implementation Details:**

* Parses `output_args_doc` in blocks and separates description from argument name.
* Removes excess indentation, aligns text for proper rendering.

**Notable Dependencies/Requirements:**

* `_get_indent` function (not shown)
* Regular expression (`re`) module for formatting
* External dependency: The code assumes the presence of a specific `output_args_doc` parameter, but its actual content is unknown without more context.


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**Function Summary: `_prepare_output_docstrings`**

Prepares the return part of a docstring by:

1. **Extracting Documentation**: Retrieves the `__doc__` attribute from `output_type`.
2. **Processing Args**: Removes the "Args" section and converts `output_args Docstring` using `_convert_output_args_doc`.
3. **Adding Return Introduction**: Incorporates the return introduction using `config_class` and `output_type.__name__`.

**Key Details:**

* Method signature: `_prepare_output_docstrings(output_type, config_class, min_indent=None)`
* Parameters: output_type (class), config_class (class)
* Returns: formatted docstring
* Notable dependencies:
	+ `_convert_output_args_doc`
	+ `TF_RETURN_INTRODUCTION` and `PT_RETURN_INTRODUCTION` constants

**Implementation Notes:**

* Incorporates minimum indent (`min_indent`) for alignment.
* Raises an error if the "Args" section is not found in the docstring.


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**Summary:`

The `filter_outputs_from_example` function modifies the provided `docstring` to remove testing output lines with doctest syntax, filtering out those sections when `None`. Notable implementation details:

* Uses a regex substitution to replace certain lines containing `doctest`-style output.
* Ignores output lines when their corresponding key in **kwargs is `None`.
* Returns the modified `docstring`.

**Key Implementation Details:**

* Regex pattern `r"\n([^\n]+)\n\s+{key}"`: Matches and replaces testing output lines with `doctest` syntax.


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**Function Summary:**

`add_code_sample_docs` is a decorator function that generates documentation for a given model's code sample. It takes various arguments, such as the model's class, processor configuration, and output type, to customize the generated docstring.

**Key Implementation Details:**

* Uses pre-defined docstring templates based on the model's class (TF, Flax, or PyTorch).
* Customizes the template with input arguments.
* Applies output formatting using `format(**doc_kwargs)` for the prepared `code_sample`.
* Conditionally applies changes to the checkout commit history of the code sample.

**Notable Dependencies or Requirements:**

* `model_cls` (model's class)
* `processor_class`
* `output_type`
* `config_class`

**Additional Notes:**

The function returns a decorator that can be applied to an existing function to generate its documentation. The generated docstring incorporates the specified arguments and includes output formatting based on the provided settings.


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**Function Summary**

`replace_return_docstrings`

* Updates the docstring of a function with a "Returns:" placeholder and replaces it with custom values if provided.

**Key Features:**

* Replaces existing "Returns:" or "Returns?:\s*" placeholders with custom output types and configuration class values.
* Uses regular expressions to identify the placeholder line in the docstring.
* Applies changes to the full docstring string, including formatting and indentation.

**Notable Parameters and Returns:**

* `output_type`: Custom output type for the returns section.
* `config_class`: Configuration class value for the returns section.
* `fn`: The function whose docstring needs updating (returned by the decorator).

**External Dependencies:**

* `_get_indent`, `_prepare_output_docstrings` functions with unknown implementations.


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**Summary**

This function `copy_func` creates a copy of the original input function `f`. It achieves this by:

* Creating a new function object `g` with the same code, global variables, and default arguments as `f`.
* Updating the wrapper information (e.g., docstring, name) of `g` using `functools.update_wrapper(f, g)`.
* Copying the keyword default values from `f`.

**Key Implementation Details**

* Creates a new function object with `types.FunctionType` constructor.
* Updates the wrapper information using `functools.update_wrapper(g, f)`.

**Notable Dependencies and Requirements**

* `types`: Built-in Python module for working with types and objects.
* `functools`: Built-in Python module for functional programming utilities.
* Original function `f` is copied and used as input.


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**Class Summary:**

`BaseImageProcessorFast`

**Purpose and Functionality:** 
Fast implementation of base image processor using the `torchvision` backend.

**Key Implementation Details:**

* Initializes with optional arguments `args` and `kwargs`.
* Requires the `torchvision` backend to be available.

**Notable Dependencies and Requirements:**

* `DumpyObject`: used as metaclass for `BaseImageProcessorFast`.
* `torchvision`: required backend for image processing.
 

Note: There's also a missing method called `requires_backends` that is referenced in the class initialization, however its implementation isn't included in this code snippet.


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**Summary:**

The `DeformableDetrImageProcessorFast` class is a fast image processor utilizing the `torchvision` backend. It initializes with required dependencies ensuring compatibility with `torchvision`.

**Key Details:**

* Utilizes `torchvision` backend
* Requires `requires_backends(self, ["torchvision"])` in initialization

**Notable Dependencies and Requirements:**

* `torchvision`: a computer vision library for deep learning (interface) 
* `DummyObject` as metaclass


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**Concise Summary:**

`DetrImageProcessorFast` is a class that processes images using the `torchvision` backend. The class initializes with `torchvision` as a required backend and does not provide any additional functionality in its provided code chunk.

**Key Implementation Details:**

* Uses `torchvision` backend
* Initializes with `*args` and `**kwargs`, but no specific method is defined in this snippet

**Notable Dependencies/Requirements:**

* `metaclass=DummyObject`
* `_requires_backends(smart_dataclass, ["torchvision"])`

Note that the actual implementation of the class methods or any external dependencies are not provided in this code chunk.


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**Concise Summary**

*Class: `PixtralImageProcessorFast`*
 
This class is a fast image processing pipeline defined using the `Pixtral` framework. It utilizes the `torchvision` backend.

**Key Details:**

* Requires the `torchvision` backend implementation (`requires_backends`)
* Implemented with the `DummyObject` metaclass
* Primary purpose: Fast image processing pipeline

**Notable Dependencies/Requirements:**

* `torchvision`
* Inherited interface: `DummyObject`


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**Summary:**

The `RTDetrImageProcessorFast` class is an image processing class using the TorchVision backend.

**Implementation Details:**

This class requires the `torchvision` backend to be installed (`requires_backends` method). It uses this backend for image processing.

**Key Information:**

* Metaclass: `DummyObject`
* Backend dependency: `torchvision`
* Primary purpose: Image processing
* Initializer takes variable arguments and keyword arguments.


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**Class Summary: `ViTImageProcessorFast`**

*   **Purpose and Functionality:** Implements fast processing for ViT models using the `torchvision` backend.

**Key Implementation Details:**

*   `__init__`: Initializes the processor with required `torchvision` backend.
    *   `requires_backends(self, ["torchvision"])`: Ensures the presence of the specified backend in the framework's configuration.

**Dependencies and Requirements:**

*   [dummy_object_metaclass](https://example.com/dummy-object-metaclass): Used as the metaclass for this class definition.
*   `[torchvision]` (unlisted dependency): Required for processing ViT images.


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**Concise Summary**

`cached_property` class: A descriptor-based property that caches its output in a member variable. This class extends Python's built-in `property` type, introduced in Python 3.8 via `functools`.

**Key Details**

* Caches property value by creating a new attribute with the same name but prefixed with `__cached_`
* Checks for existing cached values before calling the original getter function
* Sets the cached value in memory to avoid repeated computations

**Important Methods**

* `__get__(self, obj=None, objtype=None)`: override method that handles getting a property's attribute.
* `self.fget`: reference to the original property getter function.

**External Dependencies**

None. Built-in Python functionality with no external dependencies.


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**Summary**

`strtobool(val)` function is designed to convert string representations of boolean values into Python's `True` (1) or `False` (0).

**Key Implementation Details**

* Converted input value (`val`) to lowercase using the `lower()` method.
* Checks for valid true values ('y', 'yes', etc.) and returns `1`.
* Checks for invalid false values, raising a `ValueError` if any other string is provided.

**Notable Dependencies/Requirements**

* No external dependencies.
* None; utilizes built-in Python methods.


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**Code Summary:**

* Name: `infer_framework_from_repr`
* Purpose: Guesses the framework of an object from its repr string, facilitating smarter ordering in the `is_tensor` function.

**Key Implementation Details:**

* Uses `str(type(x))` to extract the type representation of the object.
* Leverages string prefixes to identify frameworks: torch, tensorflow, jax, numpy, and mlx.

**Notable Dependencies or Requirements:**

* Imports no external libraries; relies solely on Python's built-in `type()` function.


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**Summary**

This function `_get_frameworks_and_test_func` returns an ordered dictionary of frameworks to test functions. It prioritizes the most likely framework based on the input `x`, then falls back to NumPy or other supported libraries.

**Key Details**

* Prioritizes frameworks based on representation (`infer_framework_from_repr(x)`)
* Returns an ordered dictionary with preferred framework, then NumPy, and finally other supported libraries

**Notable Dependencies/Requirements**

* `is_torch_tensor`, `is_tf_tensor`, `is_jax_tensor`, `is_numpy_array`, `is_mlx_array` functions
* External dependency: `infer_framework_from_repr(x)` function (not shown)

**Method Signature**
```python
def _get_frameworks_and_test_func(x)
```
This code is written in Python and appears to be part of a larger testing framework. The specific implementation details, such as the logic for determining the preferred framework, are not included in this summary due to their complexity and potential variation depending on the surrounding context.


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**Function Summary:**

`is_tensor`: Tests if an input `x` is a valid tensor across multiple deep learning frameworks (PyTorch, TensorFlow, JAX) and other arrays.

**Key Features:**

- Checks multiple frameworks in a prioritized order
- Employs various tests for different framework types
- Covers additional cases like Tracers

**Notable Dependencies:**

- `_get_frameworks_and_test_func`
- `is_torch_fx_proxy`
- `is_flax_available`

**Primary Purpose:** Validate the tensor-like input `x` across supported frameworks and other data structures.


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**Summary**

`_is_numpy` function checks if an input `x` is a NumPy array. It uses the `isinstance` function to verify if `x` is an instance of `np.ndarray`, returning a boolean value.

**Key Implementation Details:**

* Uses `np.ndarray` as the class type to check against.
* Returns a boolean value indicating membership in `np.ndarray`.

**Notable Dependencies/Requirements:**

* NumPy library for access to `np.ndarray`.


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**Summary:**

The `is_numpy_array` function checks if an input object `x` is a NumPy array. It calls the underlying `_is_numpy` helper function, which is not shown in this code snippet.

**Details:**

* Primary purpose: Verify if `x` is a NumPy array.
* Important implementation detail: Calls `_is_numpy` helper function, but its exact logic is unknown without additional context.

Note: The `_is_numpy` function and its logic are not included in this summary due to lack of information.


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**Summary:**

This is a private method `_is_torch` that takes a single argument `x` and checks if it is an instance of `torch.Tensor`. The function utilizes the `isinstance` function from Python's built-in library to make this determination.

**RAG System Fields:**

* **Main Purpose**: Checks if input `x` is a PyTorch tensor.
* **Key Implementation Details**: Uses `isinstance(x, torch.Tensor)` inside a defined private method `_is_torch`.
* **Notable Dependencies/Requirements**: Requires the `torch` library to be installed and imported (`import torch`).
        


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**Code Summary**

```python
torch_tensor_identifier:
  purpose: Test if `x` is a PyTorch tensor.

  implementation:
  - `is_torch_AVAILABLE()` checks for the presence of PyTorch.
  - If available, `_is_torch(x)` is called with input `x`.

  dependencies:
    - `_is_torch`
    - `torch.is_available()`
```

Note: I corrected the spelling of "torcha_available" to match the original code.


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**Summary:**

The `_is_torch_device` function checks if the input `x` is an instance of PyTorch's `Device` class.

**Key Details:**

* Uses `torch.device` as the reference point for detection
* Returns a boolean indicating whether `x` matches the criteria

**Implementation:**


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**Concise Summary:**
```markdown
# `is_torch_device` Function

Checks if the input `x` represents a PyTorch device. Returns False if PyTorch is not available.

### Signature:
`is_torch_device(x)`

### Details:

* Calls `_is_torch_device` under the condition of PyTorch being available.
```
This summary maintains the essential information about the function, including its purpose, signature, and key implementation detail (the conditional call to `_is_torch_device`).


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**Summary:**

`_is_torch_dtype` function checks whether an input `x` is a valid Torch data type.

**Functionality:**

- Verifies if `x` is a string representing a Torch data type.
- Dynamically imports Torch module and its namespaces as required by the string input.
- Returns `True` if `x` is an instance of `torch.dtype`, indicating it's a valid Torch data type; otherwise, returns `False`.

**Key Implementation Details:**

- Uses dynamic import to resolve module aliases for string inputs.
- Only checks for existence in the torch namespace using `hasattr`.

**Notable Dependencies or Requirements:**

- **`torch`**: Torch library.
- `torch.dtype`: Torch data type interface.

Note: The function's purpose and behavior can be summarized as a thin wrapper around existing Torch functionality, which suggests it may not require standalone documentation.


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**Summary**

`is_torch_dtype` function checks if input `x` is a PyTorch data type. It first checks for the availability of PyTorch using `_is_torch_available_()` and returns False as a fallback if not found, before returning result from underlying `_is_torch_dtype(x)` call.

**Key Details**

- Uses _is_torch_available_() to check PyTorch availability
- Falls back to False when PyTorch is not installed or available


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**Function Summary:**

`_is_tensorflow(x)` 

* Purpose: Checks if an object `x` is a TensorFlow tensor.
* Implementation Details:
	+ Uses the `tf` module from the TensorFlow library (imported at the function definition level).
	+ Verifies if `x` is an instance of `tf.Tensor` using the `isinstance()` function.
* Dependencies/Requires:
	+ The `tensorflow` library and its internal `Tensor` class.


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**Summary**

```markdown
### is_tf_tensor Function

#### Purpose

Tests if the input `x` is a TensorFlow tensor, safely returning `False` even without TensorFlow installation.

#### Key Implementation Details

* Uses `_is_tensorflow(x)` function, only called when `tensorflow` is available.
* Relies on `is_tf_available()` to determine TensorFlow availability.

#### Notable Dependencies or Requirements

* Accesses `_is_tensorflow(x)` and `is_tf_available()` functions, which are not shown in this code snippet.
```

**RAG System Representation**

`TF(tensor) | TF(Tensor) | False`

This summary provides the precise function name, its purpose, key implementation details, and notable dependencies or requirements. The RAG system representation further clarifies the function's context by specifying the required input (`Tensor`) or return type (`Bool`).


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**Summary**

The `_is_tf_symbolic_tensor` function checks if an input `x` is a symbolic tensor in TensorFlow. It uses the following strategies:

- **TensorFlow 2.14+**: Directly utilizes the `tf.is_symbolic_tensor(x)` method.
- **TF < 2.14**: Falls back to using the `isinstance(x, tf.Tensor)` check.

**Key Implementation Details**

* The function leverages TensorFlow's built-in `is_symbolic_tensor` predicate for TF >= 2.14 versions.
* For earlier versions of TensorFlow, it uses a more basic check (`isinstance(x, tf.Tensor)`) to achieve similar results.

**Notable Dependencies and Requirements**

* TensorFlow library (version-dependent)


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**Summary**

This function, `is_tf_symbolic_tensor`, checks whether the input tensor `x` is a TensorFlow symbolic tensor or not. 

* Returns: A boolean indicating whether `x` is a TensorFlow symbolic tensor.
* Notable Implementation Details:
	+ Utilizes the `_is_tf_symbolic_tensor` function (not shown in this snippet).
* External Dependency:
	+ Requires the `tensorflow` library when necessary, tested using `is_tf_available()` check.


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**Summary**

`_is_jax` Function Summary

#### Purpose and Functionality

Tests whether an input `x` is an instance of `jax.numpy.ndarray`.

#### Key Implementation Details

- Uses `jnp.isArray()` alternative, `isinstance(x, jnp.ndarray)`, which relies on JAX's type checking instead of relying on `__array_interface__`.
- No additional metadata processing.

#### Notable Dependencies or Requirements

- **JAX**: (`jax.numpy` is required)

### Code Structure and Layout
```markdown
def _is_jax(x):
    import jax.numpy as jnp
    return isinstance(x, jnp.ndarray)
```

**RAG (Relevant, Actionable Grammar) Formatted Summary:**
 
 `_is_jax` Function:
  - Purpose: Test JAX `numpy.ndarray`.
  - Implementation:
    - Verifies using JAX-specific `isinstance(x, jnp.ndarray)` instead of relying on Python's built-in trait.
  - Dependencies:
    - JAX (`jax.numpy`)


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**Summary:**

`is_jax_tensor(x)` checks if `x` is a JAX tensor. It first determines the availability of Flax and returns false if unavailable, or calls `_is_jax(x)` to check for JAX tensors.

**Key Details:**

* Function purpose: Determine if an input `x` is a JAX tensor
* Implementation: Calls `_is_jax(x)` if Flax is available or immediately returns false if not

**Notable Dependencies/Requirements:**

* `_is_jax(x)`: A function to check for JAX tensors (not shown in the given code snippet)
* `is_flax_available()`: A function to determine if Flax is installed


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**Summary:**

`_is_mlx` checks if an input `x` is of type `mlx.array`, returning `True` if it is and `False` otherwise.

**Key Implementation Details:**

* Uses the `mx.array` class from `mlx.core`
* Utilizes `isinstance` to perform type checking

**Notable Dependencies/Requirements:**

* `mlx.core`: a module containing the `array` class used for type checking


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**Summary:**

The `is_mlx_array` function checks if an input object `x` is a multi-dimensional array (mlx array) without requiring the `mlx` library. It returns False when `mlx` is not available; otherwise, it calls `_is_mlx(x)` to perform the actual check.

**Key Implementation Details:**

* Method signature: `is_mlx_array(x)`
* Returns a boolean indicating whether `x` is an mlx array or not
* Calls `_is_mlx(x)` when `mlx` is available

**Dependence on External Libraries:**

* `mlx`: required for `_is_mlx(x)` (safe call without installation check)

No notable dependencies or interfaces are implemented.


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**Summary**

This function `to_py_obj` converts various types of objects (TensorFlow tensor, PyTorch tensor, Numpy array, Python list) to a Python list.

**Main Purpose and Functionality**
The primary purpose is to provide a unified interface for converting different types of objects to a Python list. The function leverages a dictionary-based approach to map framework-specific classes/tensors to their corresponding Python lists.

**Key Implementation Details**

* Uses lambda functions to define conversion mappings for frameworks (PT, TF, JAX, NP).
* Supports recursive conversion using `isinstance` checks.
* Involves testing against specific test functions generated by `_get_frameworks_and_test_func` (not included in this code snippet).

**Notable Dependencies or Requirements**

* TensorFlow
* PyTorch
* Numpy
* UserDict (presumably a custom class or module)
* Functions and classes from `_get_frameworks_and_test_func` are not included.


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**Summary:**

The `to_numpy` function converts various types of data objects to NumPy arrays. It supports:

* Detached PyTorch tensors and Tensors from other frameworks
* Numpy arrays (after conversion or as is)
* Python lists and tuples

Key features:

* **Framework-agnostic**: converts between TensorFlow, PyTorch, JAX, and NumPy arrays
* **Recursive handling**: handles nested dictionaries and lists with recursive conversion
* **Smart testing framework integration**: uses `_get_frameworks_and_test_func` to identify supported frameworks based on object properties


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**ModelOutput Class Summary**

The `ModelOutput` class is a dataclass subclass of `OrderedDict`, providing a flexible and type-aware dictionary-like interface. It overrides various methods to ensure consistency with PyTorch's requirements.

### Purpose and Functionality

* A drop-in replacement for regular Python dictionaries, allowing indexing by integer or slice/tuple or string keys.
* Supports inheritance from other dataclass-based dictionaries.
* Provides a way to synchronize gradients when using `torch.nn.parallel.DistributedDataParallel`.
* Implements the `to_tuple()` method to convert itself into a tuple containing non-`None` attributes.

### Key Implementation Details

* Custom implementation of `__getitem__`, `__setitem__`, and `__delitem__` methods.
* Override of `super().__reduce__` to include instance attribute values during reduction.

### Notable Dependencies or Requirements

* Inheritance from `OrderedDict`.
* Usage of PyTorch's `_torch_pytree` module for PyTree node registration (in subclass implementations).
* Dataclass decorator usage in subclasses.


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**Class Summary**

`ExplicitEnum`
================ _

*   A custom enumeration class that extends `str` and `Enum`.
*   Designed to provide more explicit error messages for missing values.

**Implementation Details**
------------------------

*   `_missing_`: A class method that raises a `ValueError` when an invalid value is encountered. The error message uses the enumerate name and lists all available values for clarification.

**Notable Dependencies or Requirements**

*   Implicitly depends on the `Enum` metaclass from the `enum` module.
*   Uses the `_value2member_map_` attribute of the parent `Enum` class to retrieve a list of available values.


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**Summary:**

This `PaddingStrategy` class defines three possible values for padding in a tokenizer. It is an enumeration, enabling tab-completion support.

* **Purpose:** Provides a way to specify the padding strategy for tokenization operations.
* **Key Implementation Detail:** An explicit enumeration of padding options (`LONGEST`, `MAX_LENGTH`, and `DO_NOT_PAD`).
* **Notable Dependencies:**
	+ None explicitly referenced
	+ Assumed to be part of a larger class hierarchy (e.g., `PreTrainedTokenizerBase`) that uses this enum for the `padding` argument.


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**Summary**

This `TensorType` class provides an enumeration of possible tensor types that can be used in the `return_tensors` argument for a pre-trained tokenizer. The class has 6 distinct values:
- `PYTORCH`: 'pt'
- `TENSORFLOW`: 'tf'
- `NUMPY`: 'np'
- `JAX`: 'jax'
- `MLX`: 'mlx'

These values enable tab-completion in IDEs and allow for flexible tensor handling across various deep learning frameworks.


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**Summary:**

`ContextManagers` class is a wrapper around `contextlib.ExitStack`, allowing the execution of multiple context managers. It initializes a stack and iterates through provided `context_managers` during entry and exit phases.

**Key Details:**

* Initializes an `ExitStack` instance
* Iterates through `self.context_managers` on enter and exit using `enter_context()` method
* Directly calls `__exit__(*args, **kwargs)` from the underlying `_ExitStack` instance

**Notable Dependencies/Features:**

* Built-in Python `contextlib` and `_ ExitStack`
* Uses a list of `ContextManager` instances (`self.context_managers`) as input


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**Summary**

`can_return_loss` function checks if a given model returns loss by inspecting its signature.

**Key Details**

* Checks for presence of `return_loss` parameter with default value `True`.
* Evaluates models based on inferred framework:
	+ TensorFlow: `model_class.call`
	+ PyTorch: `model_class.forward`
	+ Flax: `model_class.__call__`

**Primary Purpose**
Determine if a model returns loss.

**Notable Dependencies/Requirements**

- `infer_framework` function (not shown in code snippet)
- `inspect.signature` module
- Model classes themselves (e.g. TensorFlow, PyTorch, Flax)


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**Summary**

The `find_labels` function retrieves labels used by a model. It:

1. **Determines framework**: Infers the TensorFlow, PyTorch, or Flax framework based on the input model class using `infer_framework`.
2. **Accesses model signature**: Retrieves the function signature of the model's `call`/`forward` method (depending on framework) to identify parameters.
3. **Filters labels parameter names**: Identifies parameters containing "label" in their name.

**Key Findings**

* Utilizes `inspect.signature` for framework-dependent model access.
* Supports filtering of label parameter names: `start_positions`, `end_positions`, and standalone "label".
* Framework-specific calls:
	+ TensorFlow (`tf`): `model_class.call`
	+ PyTorch (`pt`): `model_class.forward`
	+ Flax (default): `model_class.__call__`

**Notable Dependencies**

* `infer_framework`: Function to detect the model framework.
* `inspect.signature`: Module for accessing Python function signatures.

This summary focuses on the essential functionality, algorithmic details, and dependencies relevant for understanding this code snippet.


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**Concise Summary:**

The `flatten_dict` function recursively flattens a nested dictionary into a single-level dictionary. It utilizes the `_flatten_dict` helper function to perform iteration.

### Key Implementation Details:

* **Recursive Generation**: `_flatten_dict` uses list comprehension (`yield from`) to recursively traverse and flatten key-value pairs.
* **Key Derivation**: The parent key is concatenated with child keys using a delimiter (default: `. `) to build the flattened dictionary keys.

### Notable Dependencies/Requirements:

* The function relies on Python's built-in `MutableMapping` type and dictionary iteration (`items()`) methods.
* External dependencies: None.


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**Function Summary**

The `transpose` function is a framework-agnostic version of NumPy's `transpose`, supporting various tensor types (TensorFlow, Jax, etc.) and NumPy arrays. It re-routs to the respective backend's transpose implementation with optional axis specification.

**Key Implementation Details:**

* Transpose operation supports multiple frameworks using conditional routing.
* `is_*_tensor` checks are used to determine the underlying tensor type.

### Notable Dependencies/Requirements

* `numpy` for NumPy arrays
* `torch` for PyTorch tensors
* `tensorflow`

### Method Signatures/API Details

* `array.T` (for PyTorch) or `array.permute(*axes)` (with axis specification).
* `tf.transpose(array, perm=axes)` (TensorFlow).
* `jnp.transpose(array, axes=axes)` (Jax).


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**Code Summary**

`reshape(array, newshape)`: Framework-agnostic version of NumPy's `reshape`, supporting tensors from various libraries (torch, TensorFlow, Jax), and NumPy arrays.

**Key Features:**

*   Automatically detects and adapts to different tensor frameworks:
    *   torch (Tensor)
    *   TensorFlow (TF)
    *   Jax (JAX)
    *   NumPy arrays
*   Uses the respective framework's `reshape`/`reshape_` method from the detected library.
*   Raising a `ValueError` for unsupported type.

**Method Signature:** 

```python
def reshape(array, newshape):
```

**Notable Dependencies:**

*   NumPy (`np`)
*   Torch/TensorFlow/Jax libraries (depending on input array type)
*   JAX library (as an alias)

**Assumptions:**

1. The functions `is_numpy_array`, `is_torch_tensor`, `is_tf_tensor` and `is_jax_tensor` are defined elsewhere in the codebase to detect the underlying framework of the provided `array`. 

2. The function `np.reshape(array, newshape)` is used for NumPy arrays, and equivalent or compatible reshaping functions are used according to the framework's specific usage syntax (e.g., using `*` operator in torch, `tf.reshape` in TensorFlow, etc.).


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**Summary**

The `squeeze` function is a framework-agnostic implementation of the NumPy `numpy.squeeze` function. It condenses single-dimensional entries in an n-dimensional `array`. The function checks the type of input and calls the corresponding squeeze method:

- For NumPy arrays, it uses `np.squeeze`.
- For PyTorch tensors, when no axis is specified (`axis=None`), it returns the original tensor; otherwise, it returns the squeezed tensor with the specified dimension.
- For TensorFlow and JAX tensors, it directly utilizes their respective `tf.squeeze` and `jnp.squeeze` methods.

**Key Implementation Details**

* It checks for specific input types: NumPy array, PyTorch tensor, TensorFlow tensor, and JAX tensor.
* For non-supported types, it raises a ValueError with the unsupported type and its corresponding exact function call (`is_numpy_array`, `is_torch_tensor`, etc.) as arguments.

**Notable Dependencies or Requirements**

* NumPy
* PyTorch (for torch tensors)
* TensorFlow
* JAX (for jax tensors)


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**Summary**

`expand_dims`: Framework-agnostic function that expands the dimensions of input arrays (`array`) by `axis`. Supports NumPy, PyTorch, TensorFlow, and JAX tensors.

**Key Features**

* Framework-agnostic to work on various tensor libraries
* Expands array dimensions by a specified `axis`
* Uses underlying library functions:
	+ NumPy: `np.expand_dims`
	+ PyTorch: `unsqueeze`
	+ TensorFlow: `tf.expand_dims`
	+ JAX/JPype: `jnp.expand_dims`

**Notable Dependencies**

* None (all built-in Python support)


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**Function Summary: `tensor_size`**

Calculates the size of a tensors in various frameworks.

**Main Purpose and Functionality:**
The `tensor_size` function determines the number of elements in a given array, supporting NumPy, PyTorch, TensorFlow, and JAX tensors. It uses framework-specific methods to calculate the tensor's size.

**Key Implementation Details:**

* Uses `np.size` for NumPy arrays
* Uses `array.numel()` for PyTorch tensors
* Uses `tf.size` for TensorFlow tensors
* Uses `array.size` for JAX tensors

**Notable Dependencies and Requirements:**

* Import of `numpy` (used for NumPy arrays)
* Import of `tensorflow` (used for TensorFlow tensors)

**Interface Implementation:** Implements a framework-agnostic interface to calculate tensor sizes in various libraries.


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**Function Summary**

`add_model_info_to_auto_map(auto_map, repo_id)`:

*   Adds repository information to an `auto_map`, modifying key-value pairs as necessary.
*   Iterates through each value in the `auto_map`. If:
    *   The value is a tuple or list, it replaces `None` values with the prefix `"repo_id--"`.
    *   The value is not `None` and does not contain `"--"`, it prefixes `"repo_id--"` to the value.
*   Returns the modified `auto_map`.

**Key Implementation Details**

*   Utilizes dictionary iteration (`for key, value in auto_map.items():`) for value replacement.
*   Leverages list comprehension to process tupletolist transformation.

**Notable Dependencies and Requirements**

None referenced.


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**Summary**

*   **Purpose**: Modifies a custom pipeline to include repository information by prepending the `repo_id` to task implementations that lack an existing separator.

**Implementation Details**

*   Iterates through each task in the custom pipeline and checks if the "impl" key exists.
*   If "impl" exists, updates the value with `${repo_id}--${module}`, where `module` is extracted from the original implementation path.
*   The updated pipeline is returned.

**Key Functions/Variables:**

*   `add_model_info_to_custom_pipelines`
*   `custom_pipeline`: dictionary of tasks with implementation paths

**External Dependencies: None specified. No external dependencies found.**

This function seems to be part of a custom pipeline management system, where repository information is added using the `$repo_id` syntax.


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Here is a concise and focused summary of the function:

**`infer_framework(model_class)`**

Detects the framework used by a given model without relying on `isinstance()` checks.

**Key Details:**

* Iterates through the Multiple Inheritance Result (MRO) of the input model class.
* Checks modules and class names to determine framework affiliation:
	+ `tf`: TensorFlow or Keras.
	+ `pt`: PyTorch or PreTrainedModel.
	+ `flax`: Flax or JAX.
* Raises a TypeError if framework cannot be inferred.

**Notable Dependencies/Requirements:**

* `inspect` for module and class information.


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**Function Summary:**

`torch_int(x)` casts input `x` to a PyTorch `int64` tensor or Python integer. 

*   Checks tracing context; if available, converts to `int64` tensor using `torch.jit.is_tracing()` and `isinstance()`.
*   Fallbacks to Python int conversion when tracing is not available or input is not a tensor.

**Key Details:**

*   Import of PyTorch for tensor operations.
*   Use of `is_torch_available()` check to ensure PyTorch is installed before converting to `int64` tensor.
*   Conditional expression using logical AND (`and`) to apply tracing context constraint.

**Notable Dependencies/Interfaces:**

*   Requires PyTorch installation (`is_torch_available()`, `torch.jit.is_tracing()`).
*   Uses `torch` module for tensor operations.


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**Summary**

This function `torch_float` casts input `x` to a PyTorch float32 tensor if tracing is enabled, or Python float otherwise.

**Key Details:**

* Checks if PyTorch is available and returns `int` if not.
* Returns PyTorch float32 tensor if in tracing mode and input is a tensor.
* Otherwise, converts input to Python float using the `int()` function (note: this may lead to loss of precision).

**Implementations and Requirements:**

* Uses `torch.jit.is_tracing()` to check tracing context
* Tries to cast input using `to()`, falling back to conversion with `int()` on failure


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**Summary**

`filter_out_non_signature_kwargs` is a decorator that filters out named arguments not in the function's signature or specified in the `extra` list. It ensures only valid keyword arguments are passed to the function, issuing warnings for invalid ones.

**Detailed Breakdown**

*   **Functionality:** Decorator-based filtering of non-signature keyword arguments.
*   **Primary Purpose:** Protects functions from receiving extraneous keyword arguments not defined in their signature or specified in `extra`.
*   **Key Implementation Details:**
    *   Uses `inspect.signature` to retrieve the function's signature.
    *   Specifies valid keyword argument names based on the function's signature and the `extra` list.
    *   Issues warnings using `warnings.warn` when invalid arguments are passed.
    *   Wraps the original function with a new wrapper that filters out invalid keyword arguments.

**Notable Dependencies**

*   *Inspect Signature:* used to retrieve the function's signature.
*   *Warnings.* issued for invalid keyword arguments.

**External Requirements & Interfaces:**
 
*   Classes and functions from the `inspect` and `warnings` modules.


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**Summary**

The `LossKwargs` class defines keyword arguments for passing to a loss function. Specifically, it allows for an optional `num_items_in_batch` parameter to track the number of items in each batch.

**Key Details**

* Type annotations: The class is typed using TypedDict with 'total' as False.
* Attributes:
	+ `num_items_in_batch`: An integer with an optional import specification (i.e., it can be missing when not needed).

**Notable Dependencies/Requirements**

* None explicitly listed, but assuming knowledge of Python's Typing library and its TypedDict feature.


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**Summary**

This function checks if a provided configuration dictionary (`config_dict`) meets the requirements of a Timm model configuration dictionary. The primary purpose is to verify the presence of a `"pretrained_cfg"` key within the dictionary.

**Implementation Details**

- Checks for the presence of a specific key ("`pretrained_cfg`") in the `config_dict`.
- Does not perform any validation or sanitization on the input dictionary.

**Notable Requirements**

- The function is specifically designed to work with dictionaries containing string keys and values of arbitrary types (as indicated by `Dict[str, Any]`) and may be intended for use with Timm-based models.


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**Summary: `is_timm_local_checkpoint` Function**

This function checks if a given file path (`pretrained_model_path`) represents a local checkpoint of a timm model.

**Primary Purpose:** Verify if the checkpoint is a timm model configuration.

**Implementation Details:**

* Checks if the provided file path exists and ends with `.json` (to identify as a model configuration file).
* Opens the file and loads its contents into a dictionary using `json.load()`.
* Calls the `is_timm_config_dict()` function to check if the loaded dictionary represents a timm config.

**Key Point:**
The function returns `True` if the checkpoint is identified as a timm config, else `False`.

**Notable Dependencies:**

* `os.path.isfile()`, `os.path.isdir()`, and related functions for file system interactions.
* `json.load()` for parsing JSON files.
* The `is_timm_config_dict()` function (not shown in this snippet) to verify the timm configuration.


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**MusicgenMelodyFeatureExtractor Class Summary**

*   **Main Purpose and Functionality:**  
    A class that extracts melody features from audio data using the `torchaudio` backend.

**Key Implementation Details:**

*   Initializes the feature extractor with optional `args` and `kwargs`.
*   Validates if the required `torchaudio` backend is available.

**Dependencies and Requirements:**

*   Requires the `torchaudio` backend to be installed.


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**Concise Summary**

The `MusicgenMelodyProcessor` class is a Python class that processes melodies using the `torchaudio` backend. It initializes itself with the `torchaudio` backend and verifies if it requires this backend.

*   **Primary Purpose:** Processes melodies
*   **Key Implementation Details:**
    *   Verifies requirement of `torchaudio` backend ([`requires_backends` function](https://example.com/requires_backends/))
        *   Specifics: The function is called on the instance (`self`) with the required backend name (`"torchaudio"`).
*   **Notable Dependencies or Requirements:**
    *   `DummyObject` metaclass
    *   `torchaudio`
    *   [`requires_backends` function](https://example.com/requires_backends/) (not shown in this snippet)


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**LayoutLMv2Model Class Summary**

**Main Purpose and Functionality:**
The `LayoutLMv2Model` class is a machine learning model that implements the LayoutLMv2 architecture. It provides an initializer (`__init__`) for building the model instance and a static method (`from_pretrained`) for loading pre-trained models.

**Key Implementation Details:**

* The `requires_backends` decorator checks if the required backend (Detectron2) is installed before initializing.
* The `from_pretrained` method loads a pre-trained model with Detectron2 backend.

**Notable Dependencies and Requirements:**

* Requires Detectron2 backend for initialization and loading pre-trained models.
* External Dependency: Detectedron2 library.


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**Concise Summary:**

`ImageProcessingMixin`

*   A class enabling image processing functionality with the `"vision"` backend.
*   Inits `self` using `requires_backends`, requiring `"vision"` backend availability.

**Deconstructed Breakdown:**

1.  **Purpose**: Facilitates image processing operations.
2.  **Key Implementation Details**: Requires the `"vision"` backend, which is stored in `_backends`.
3.  **Notable Dependencies or Requirements**:
    *   `_backends` containing `"vision"`.
    *   `requires_backends()` interface taking the backend name `"vision"` as a parameter.

Note: The class itself does not provide any image processing functionality; it defines a mixin, which needs to be combined with another class to utilize its capabilities.


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**Summary**

`BaseImageProcessor` is a class that serves as the base implementation for image processing using the `vision` backend. It initializes with optional arguments and asserts that the `vision` backend is available.

### Key Details

* Initiates the object with variable number of arguments (`*args`, `**kwargs`)
* Verifies visibility of the `vision` backend at initialization
* **Method Signatures:**
	+ `requires_backends(self, ["vision"])`

### Dependencies and Requirements

* Requires the `vision` backend
* Utilizes a metaclass (`DummyObject`) for class definition


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**ImageFeatureExtractionMixin**
=====================================

*-purpose:* This mixin is designed for image feature extraction and attaches it to a class using metaclass `DummyObject`.
*primary purpose: To enable image feature extraction with the "vision" backend.

**Implementation Details:**

* The `__init__` method requires the "vision" backend, which is enforced via the `requires_backends` function.

**Dependencies and Requirements:**
-----------------------------------

* Backend dependency on "vision".
* Inherited or implemented interface on `DummyObject`.


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**Summary:**

The `AriaImageProcessor` class is an image processing factory that initializes with one or more backend options. It requires the "vision" backend to be specified using the `requires_backends` function.

**Key Features:**

- **Backend Selection**: The class uses a `requirements string` (_backends = ["vision"]) for backbone selection.
- **Initialization**: Class takes keyword arguments which gets validated by `requires_backends` method

**Implementation Notes:**

*   `DummyObject`: Metaclass used to define the class.
*   `requires_backends(self, ...)` : validates backend type before initialization.


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**Class Summary:**

`BeitFeatureExtractor`

A feature extractor class for Visual Domain Knowledge (VDK) data using the `vision` backend.

**Key Implementation Details:**

- Initializes with optional args/kwargs; requires `vision` backend at creation (`requires_backends(self, ["vision"])`).

**Notable Dependencies and Requirements:**

- `DummyObject` metaclass.
- `vision` backend (implemented or required).

Note: The exact purpose of the `BeitFeatureExtractor` class is not provided in this snippet. However, based on its name and use of `vision` backend, it appears to be designed for feature extraction tasks related to visual domain knowledge.


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**Summary:**

`BeitImageProcessor` is an image processing class that requires the `vision` backend for initialization. It initializes with arbitrary arguments and keyword arguments, enforcing the use of the `vision` backend to ensure compatibility.

**Key Details:**

* **Initialization**: The class initializer takes variable arguments (`args`) and keyword arguments (`kwargs`).
* **Backend Requirement**: It requires the `vision` backend using the `requires_backends` function.
* **Notable Dependencies**:
	+ `DummyObject`: Metaclass used for class definition.
	+ `requires_backends`: Function used to enforce blueprint requirements.

**Note:** The `Provides` message seems out of place in this context, as it does not appear to be a method or attribute defined within the class. It is likely an error or placeholder that should be removed.


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**Summary:**

`BitImageProcessor` is a class that processes image data using the `vision` backend. It ensures the presence of this backend via the `requires_backends` function.


**Detailed Analysis:**

*   **Purpose and Functionality:** The main purpose of this class is to process image data. 
    *Primary Purpose:* Processing image data using the `vision` backend.
*   **Key Implementation Details:**
    *   The use of `DummyObject` as the metaclass suggests that it may require or provide a specific implementation for object creation.
    *   The presence of `requires_backends` function indicates dependency on another class or module that provides backend capabilities.
        *Key Algorithm/Pattern:* Ensures the availability of required backends via `requires_backends`.
*   **Notable Dependencies:**
    *   Related Classes/Functions: `DummyObject`, `requires_backends`
    *   External Dependencies: None identified in this code fragment.

Note: Further analysis would be necessary to fully understand the context and functionality of this class.


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**Class Summary**

`BlipImageProcessor`

*Primary Purpose: Processes image data using the `vision` backend.*

This class initializes with the `vision` backend as required. The implementation remains unspecified in this summary.

**Implementation Details:**

None specified, but it is likely to involve image processing tasks supported by the `vision` backend.

**Notable Dependencies/Requirements:**

* `requires_backends`: A function requiring the `vision` backend.
* `DummyObject`: The metaclass for the `BlipImageProcessor` class.


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**Class Summary:**

The `BridgeTowerImageProcessor` class is an image processing pipeline that utilizes the `vision` backend.

**Key Implementation Details:**

* Requires the `vision` backend (`requires_backends` method)
	+ Method signature: `requires_backends(self, backend_names)`

**Notable Dependencies and Requirements:**

* `DummyObject` metaclass
* `vision` backend (not an external dependency)

**Primary Purpose:**

To process image inputs using the `vision` backend.


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**Class Summary: ChameleonImageProcessor**

### Purpose and Functionality
The `ChameleonImageProcessor` class is designed to process images using the "vision" backend. Its primary purpose is to handle image-related tasks, leveraging the specified backend for execution.

### Key Implementation Details

*   **Backend Validation**: The `__init__` method enforces the use of specific backends by calling the `requires_backends` function with a list of allowed backends ("vision").

### Notable Dependencies
The class depends on:
*   `requires_backends`: A function that validates the usage of specific backends.
    *   Called from: `__init__`
    *   With arguments: Backend names to be validated (`"vision"`)

Note: External dependencies not directly mentioned, assume a context where 'DummyObject' and 'Provide' are defined elsewhere in the codebase.


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**Summary:**

ChineseCLIPFeatureExtractor class:

*   Initializes the feature extractor with the "vision" backend.
*   Requires the "vision" backend to be present at instantiation.

**Key Details:**

*   `metaclass=DummyObject`: Uses a custom metaclass (`DummyObject`) which is not shown in this code snippet.
*   `_backends = ["vision"]`: Specifies the supported backend (in this case, "vision").
*   `requires_backends(self, ["vision"])`: Verifies that the required backend ("vision") exists at initialization.

**Implementation Dependencies:**

None explicitly listed but likely relies on `DummyObject` and `RequiresBackends` (assuming this is a function or class not shown in this snippet).

No external dependencies mentioned.


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**Class Summary:**

`ChineseCLIPImageProcessor`

A class implementing image processing using the `vision` backend.

**Key Functionality:**

* Initializes with input arguments and ensures the presence of required `vision` backend.

**Implementation Details:**

* Uses the `_backends` list to specify available backends.
* In the `__init__` method, it performs backend initialization validation (`requires_backends`) for the `"vision"` backend.

**Notable Dependencies:**

* Requires the `vision` backend.


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**CLIP Feature Extractor Class Summary**

### Purpose and Functionality

The `CLIPFeatureExtractor` class is designed to extract features for visual representations. It inherits from `DummyObject` and has a specific backend architecture.

Primary purpose: **Visual feature extraction from CLIP model outputs**.

### Key Implementation Details

* The constructor (`__init__`) initializes the extractor with required backends, specifically "vision".
* The `requires_backends` function is used to ensure compatible backends are passed during instance creation.

### Notable Dependencies and Requirements

* **Required backend:** `vision`
* Inherited from: `DummyObject`

### Method Signatures and Interfaces (No public method signatures provided)


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**Class Summary: CLIPImageProcessor**

*   **Purpose:** A class for processing images using the CLIP vision backend.

**Key Features and Implementation Details:**

*   Initializes with `args` and `kwargs`.
*   Uses the `requires_backends` decorator to enforce the use of the "vision" backend.
*   Utilizes the `DummyObject` metaclass, likely for demonstration or testing purposes.

**Dependencies and Requirements:**

*   Requires the CLIP vision backend.
*   Optionally accepts `args` and `kwargs` during initialization.


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**Summary:**

`ConditionalDetrFeatureExtractor` is a feature extraction class that uses the `vision` backend. It initializes with the specified backend requirement and checks if backends are provided.

**Key Details:**

- **Purpose:** Conditional detr attention feature extraction
- **Implementation:** Uses `vision` backend
- **Method Signatures:**
  - `__init__(self, *args, **kwargs)`: Initializes class with backend requirements

**Notable Dependencies/Requirements:**

* `-vision` backend (required)
* `-DummyObject` metaclass
* `-requires_backends` function


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**Summary:**

`ConditionalDetrImageProcessor` class with `DummyObject` metaclass.

**Key Details:**

* Requires vision backend to be used (backends = ["vision"])
* Initializes the object using `self.requires_backends` method with "vision" as a required backend.

**Notable Dependencies or Requirements:**

* `dummy_object`: imported (not shown in this snippet)
* `requires_backends` function
* `DummyObject` metaclass


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**Summary**

`ConvNextFeatureExtractor` is a class for feature extraction using the `vision` backend. It initializes with a dummy object metaclass and enforces the use of the `vision` backend.

**Key Details**

* `__init__`: Initializes the instance, requiring the `vision` backend.
* `_backends = ["vision"]`: Defines the supported backends.
* `requires_backends(self, ["vision"])`: Verifies that the specified backend is available.

**Notable Dependencies/Requirements**

* `dummy_object` class (metaclass)
* `vision` backend


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**Summary**

The `ConvNextImageProcessor` class is an image processing pipeline that utilizes the "vision" backend. Its initialization method (`__init__()`) relies on the `requires_backends()` function to ensure the presence of the "vision" backend, raising an error if not available.

**Key Implementation Details:**

- Utilizes the "vision" backend
- Initialization requires validation of "vision" backend availability

**Notable Dependencies/Requirements:**

*   `DummyObject` metaclass
*   External dependency on "vision" backend (verified via `requires_backends()` function)
*


