



"""simple docstring"""




from collections import OrderedDict
from typing import Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


_lowerCAmelCase							:   Any				=				logging.get_logger(__name__)

_lowerCAmelCase							:   List[str]				=				{
    "deepmind/language-perceiver": "https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json",
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}






class       UpperCAmelCase_    (							_UpperCamelCase		):
      __SCREAMING_SNAKE_CASE		:  str						=					'perceiver'



      def __init__(	self	:      List[Any]	,      A	:      Any=2_5_6	,      A	:      int=1_2_8_0	,      A	:      List[str]=7_6_8	,      A	:      Tuple=1	,      A	:      int=2_6	,      A	:      Optional[int]=8	,      A	:      List[str]=8	,      A	:      Optional[int]=None	,      A	:      Any=None	,      A	:      int="kv"	,      A	:      Dict=1	,      A	:      int=1	,      A	:      Optional[Any]="gelu"	,      A	:      Tuple=0.1	,      A	:      Union[str, Any]=0.02	,      A	:      Any=1e-12	,      A	:      Union[str, Any]=True	,      A	:      List[Any]=2_6_2	,      A	:      List[str]=2_0_4_8	,      A	:      Tuple=5_6	,      A	:      str=[3_6_8, 4_9_6]	,      A	:      str=1_6	,      A	:      int=1_9_2_0	,      A	:      Tuple=1_6	,      A	:      Optional[Any]=[1, 1_6, 2_2_4, 2_2_4]	,      **A	:      Any	,      ):
           super().__init__(**A		)

           _UpperCAmelCase     :      str														=       num_latents
           _UpperCAmelCase     :      List[str]														=       d_latents
           _UpperCAmelCase     :      int														=       d_model
           _UpperCAmelCase     :      Optional[Any]														=       num_blocks
           _UpperCAmelCase     :      Any														=       num_self_attends_per_block
           _UpperCAmelCase     :      List[Any]														=       num_self_attention_heads
           _UpperCAmelCase     :      Any														=       num_cross_attention_heads
           _UpperCAmelCase     :      str														=       qk_channels
           _UpperCAmelCase     :      Optional[int]														=       v_channels
           _UpperCAmelCase     :      int														=       cross_attention_shape_for_attention
           _UpperCAmelCase     :      Union[str, Any]														=       self_attention_widening_factor
           _UpperCAmelCase     :      str														=       cross_attention_widening_factor
           _UpperCAmelCase     :      Dict														=       hidden_act
           _UpperCAmelCase     :      Optional[int]														=       attention_probs_dropout_prob
           _UpperCAmelCase     :      Union[str, Any]														=       initializer_range
           _UpperCAmelCase     :      Dict														=       layer_norm_eps
           _UpperCAmelCase     :      List[str]														=       use_query_residual
           # masked language modeling attributes
           _UpperCAmelCase     :      str														=       vocab_size
           _UpperCAmelCase     :      List[Any]														=       max_position_embeddings
           # image classification attributes
           _UpperCAmelCase     :      Optional[Any]														=       image_size
           # flow attributes
           _UpperCAmelCase     :      Any														=       train_size
           # multimodal autoencoding attributes
           _UpperCAmelCase     :      int														=       num_frames
           _UpperCAmelCase     :      str														=       audio_samples_per_frame
           _UpperCAmelCase     :      Any														=       samples_per_patch
           _UpperCAmelCase     :      str														=       output_shape







class       UpperCAmelCase_    (							_UpperCamelCase		):



      @property
      def 		snake_case_   (	self	:      Union[str, Any]		):
           if self.task == "multiple-choice":
                _UpperCAmelCase     :      str														=       {0: "batch", 1: "choice", 2: "sequence"}
           else:
                _UpperCAmelCase     :      Tuple														=       {0: "batch", 1: "sequence"}
           return OrderedDict(
               [
                   ("inputs", dynamic_axis),
                   ("attention_mask", dynamic_axis),
               ]		)



      @property
      def 		snake_case_   (	self	:      Union[str, Any]		):
           return 1e-4



      def 		snake_case_   (	self	:      Optional[int]	,      A	:      Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]	,      A	:      int = -1	,      A	:      int = -1	,      A	:      int = -1	,      A	:      bool = False	,      A	:      Optional[TensorType] = None	,      A	:      int = 3	,      A	:      int = 4_0	,      A	:      int = 4_0	,      ):
           # copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

           if isinstance(A	,      A		):
                # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
                _UpperCAmelCase     :      Dict														=       compute_effective_axis_dimension(
                    A	,      fixed_dimension=OnnxConfig.default_fixed_batch	,      num_token_to_add=0		)
                # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
                _UpperCAmelCase     :      str														=       preprocessor.num_special_tokens_to_add(A		)
                _UpperCAmelCase     :      List[str]														=       compute_effective_axis_dimension(
                    A	,      fixed_dimension=OnnxConfig.default_fixed_sequence	,      num_token_to_add=A		)
                # Generate dummy inputs according to compute batch and sequence
                _UpperCAmelCase     :      Union[str, Any]														=       [" ".join(["a"]		) * seq_length] * batch_size
                _UpperCAmelCase     :      Tuple														=       dict(preprocessor(A	,      return_tensors=A		)		)
                _UpperCAmelCase     :      Tuple														=       inputs.pop("input_ids"		)
                return inputs
           elif isinstance(A	,      A		) and preprocessor.model_input_names[0] == "pixel_values":
                # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
                _UpperCAmelCase     :      int														=       compute_effective_axis_dimension(A	,      fixed_dimension=OnnxConfig.default_fixed_batch		)
                _UpperCAmelCase     :      List[Any]														=       self._generate_dummy_images(A	,      A	,      A	,      A		)
                _UpperCAmelCase     :      Tuple														=       dict(preprocessor(images=A	,      return_tensors=A		)		)
                _UpperCAmelCase     :      Optional[int]														=       inputs.pop("pixel_values"		)
                return inputs
           else:
                raise ValueError(
                    "Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor."		)





"""simple docstring"""




import importlib
import os
import sys


# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append(".")

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Optional[int]							)    ->  List[str]:





     '''simple docstring'''

     _UpperCAmelCase     :      Any														=       test_file.split(os.path.sep							)
     if components[0:2] != ["tests", "models"]:
          raise ValueError(
              "`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got "
              f'{test_file} instead.'							)
     _UpperCAmelCase     :      int														=       components[-1]
     if not test_fn.endswith("py"							):
          raise ValueError(f'`test_file` should be a python file. Got {test_fn} instead.'							)
     if not test_fn.startswith("test_modeling_"							):
          raise ValueError(
              f'`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead.'							)

     _UpperCAmelCase     :      Any														=       components[:-1] + [test_fn.replace(".py"		,    ""							)]
     _UpperCAmelCase     :      List[str]														=       ".".join(SCREAMING_SNAKE_CASE__							)

     return test_module_path

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		List[Any]							)    ->  Dict:





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[int]														=       get_module_path(SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      List[Any]														=       importlib.import_module(SCREAMING_SNAKE_CASE__							)

     return test_module

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Union[str, Any]							)    ->  List[str]:





     '''simple docstring'''

     _UpperCAmelCase     :      Union[str, Any]														=       []
     _UpperCAmelCase     :      str														=       get_test_module(SCREAMING_SNAKE_CASE__							)
     for attr in dir(SCREAMING_SNAKE_CASE__							):
          if attr.endswith("ModelTester"							):
               tester_classes.append(getattr(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)							)

    # sort with class names
     return sorted(SCREAMING_SNAKE_CASE__		,    key=lambda SCREAMING_SNAKE_CASE__							: x.__name__							)

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		int							)    ->  Optional[int]:





     '''simple docstring'''

     _UpperCAmelCase     :      Union[str, Any]														=       []
     _UpperCAmelCase     :      Tuple														=       get_test_module(SCREAMING_SNAKE_CASE__							)
     for attr in dir(SCREAMING_SNAKE_CASE__							):
          _UpperCAmelCase     :      List[Any]														=       getattr(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)
          # (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
          # `all_model_classes` is not empty (which also excludes other special classes).
          _UpperCAmelCase     :      Optional[Any]														=       getattr(SCREAMING_SNAKE_CASE__		,    "all_model_classes"		,    []							)
          if len(SCREAMING_SNAKE_CASE__							) > 0:
               test_classes.append(SCREAMING_SNAKE_CASE__							)

    # sort with class names
     return sorted(SCREAMING_SNAKE_CASE__		,    key=lambda SCREAMING_SNAKE_CASE__							: x.__name__							)

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		int							)    ->  int:





     '''simple docstring'''

     _UpperCAmelCase     :      Union[str, Any]														=       get_test_classes(SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      Tuple														=       set()
     for test_class in test_classes:
          model_classes.update(test_class.all_model_classes							)

     # sort with class names
     return sorted(SCREAMING_SNAKE_CASE__		,    key=lambda SCREAMING_SNAKE_CASE__							: x.__name__							)

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Tuple							)    ->  Any:





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[Any]														=       test_class()
     if hasattr(SCREAMING_SNAKE_CASE__		,    "setUp"							):
          test.setUp()

     _UpperCAmelCase     :      int														=       None
     if hasattr(SCREAMING_SNAKE_CASE__		,    "model_tester"							):
          # `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
          if test.model_tester is not None:
               _UpperCAmelCase     :      List[str]														=       test.model_tester.__class__

     return model_tester

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Optional[int]		,    SCREAMING_SNAKE_CASE__      :		int							)    ->  Union[str, Any]:





     '''simple docstring'''

     _UpperCAmelCase     :      str														=       get_test_classes(SCREAMING_SNAKE_CASE__							)

     _UpperCAmelCase     :      Optional[int]														=       []
     for test_class in test_classes:
          if model_class in test_class.all_model_classes:
               target_test_classes.append(SCREAMING_SNAKE_CASE__							)

    # sort with class names
     return sorted(SCREAMING_SNAKE_CASE__		,    key=lambda SCREAMING_SNAKE_CASE__							: x.__name__							)

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Optional[int]		,    SCREAMING_SNAKE_CASE__      :		List[Any]							)    ->  Optional[Any]:





     '''simple docstring'''

     _UpperCAmelCase     :      str														=       get_test_classes_for_model(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)

     _UpperCAmelCase     :      int														=       []
     for test_class in test_classes:
          _UpperCAmelCase     :      Optional[Any]														=       get_model_tester_from_test_class(SCREAMING_SNAKE_CASE__							)
          if tester_class is not None:
               tester_classes.append(SCREAMING_SNAKE_CASE__							)

    # sort with class names
     return sorted(SCREAMING_SNAKE_CASE__		,    key=lambda SCREAMING_SNAKE_CASE__							: x.__name__							)

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Any							)    ->  int:





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[Any]														=       get_test_classes(SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      List[Any]														=       {test_class: get_model_tester_from_test_class(SCREAMING_SNAKE_CASE__							) for test_class in test_classes}
     return test_tester_mapping

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Optional[int]							)    ->  Dict:





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[Any]														=       get_model_classes(SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      int														=       {
         model_class: get_test_classes_for_model(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							) for model_class in model_classes
     }
     return model_test_mapping

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Optional[int]							)    ->  Tuple:





     '''simple docstring'''

     _UpperCAmelCase     :      Tuple														=       get_model_classes(SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      Union[str, Any]														=       {
         model_class: get_tester_classes_for_model(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							) for model_class in model_classes
     }
     return model_to_tester_mapping

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		str							)    ->  List[str]:





     '''simple docstring'''

     if isinstance(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							):
          return o
     elif isinstance(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							):
          return o.__name__
     elif isinstance(SCREAMING_SNAKE_CASE__		,    (list, tuple)							):
          return [to_json(SCREAMING_SNAKE_CASE__							) for x in o]
     elif isinstance(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							):
          return {to_json(SCREAMING_SNAKE_CASE__							): to_json(SCREAMING_SNAKE_CASE__							) for k, v in o.items()}
     else:
          return o
