





'''simple docstring'''






from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig


lowerCAmelCase       		=      logging.get_logger(__name__)

# General docstring
lowerCAmelCase       		=      """RegNetConfig"""

# Base docstring
lowerCAmelCase       		=      """facebook/regnet-y-040"""
lowerCAmelCase       		=      [1, 10_88, 7, 7]

# Image classification docstring
lowerCAmelCase       		=      """facebook/regnet-y-040"""
lowerCAmelCase       		=      """tabby, tabby cat"""

lowerCAmelCase       		=      [
    """facebook/regnet-y-040""",
    # See all regnet models at https://huggingface.co/models?filter=regnet
]
class 		lowerCamelCase  (      tf.keras.layers.Layer       ):



	def __init__(   self		,      a_		,      a_ = 3		,      a_ = 1		,      a_ = 1		,      a_ = "relu"		,      **a_		,      ):
			super().__init__(**a_	)
			# The padding and conv has been verified in
			# https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
			lowerCAmelCase     :    List[Any]						=   tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2	)
			lowerCAmelCase     :    Any						=   tf.keras.layers.ConvaD(
			    filters=a_		,      kernel_size=a_		,      strides=a_		,      padding="VALID"		,      groups=a_		,      use_bias=a_		,      name="convolution"		,      )
			lowerCAmelCase     :    Dict						=   tf.keras.layers.BatchNormalization(epsilon=1e-5		,      momentum=0.9		,      name="normalization"	)
			lowerCAmelCase     :    Any						=   ACTaFN[activation] if activation is not None else tf.identity





	def   _lowerCamelCase    (   self		,      a_	):
			lowerCAmelCase     :    str						=   self.convolution(self.padding(a_	)	)
			lowerCAmelCase     :    List[str]						=   self.normalization(a_	)
			lowerCAmelCase     :    Tuple						=   self.activation(a_	)
			return hidden_state






class 		lowerCamelCase  (      tf.keras.layers.Layer       ):



	def __init__(   self		,      a_		,      **a_	):
			super().__init__(**a_	)
			lowerCAmelCase     :    Optional[int]						=   config.num_channels
			lowerCAmelCase     :    Tuple						=   TFRegNetConvLayer(
			    out_channels=config.embedding_size		,      kernel_size=3		,      stride=2		,      activation=config.hidden_act		,      name="embedder"		,      )





	def   _lowerCamelCase    (   self		,      a_	):
			lowerCAmelCase     :    str						=   shape_list(a_	)[1]
			if tf.executing_eagerly() and num_channels != self.num_channels:
					raise ValueError(
					    "Make sure that the channel dimension of the pixel values match with the one set in the configuration."	)

			# When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
			# So change the input format from `NCHW` to `NHWC`.
			# shape = (batch_size, in_height, in_width, in_channels=num_channels)
			lowerCAmelCase     :    str						=   tf.transpose(a_		,      perm=(0, 2, 3, 1)	)
			lowerCAmelCase     :    List[str]						=   self.embedder(a_	)
			return hidden_state






class 		lowerCamelCase  (      tf.keras.layers.Layer       ):



	def __init__(   self		,      a_		,      a_ = 2		,      **a_	):
			super().__init__(**a_	)
			lowerCAmelCase     :    str						=   tf.keras.layers.ConvaD(
			    filters=a_		,      kernel_size=1		,      strides=a_		,      use_bias=a_		,      name="convolution"	)
			lowerCAmelCase     :    Tuple						=   tf.keras.layers.BatchNormalization(epsilon=1e-5		,      momentum=0.9		,      name="normalization"	)





	def   _lowerCamelCase    (   self		,      a_		,      a_ = False	):
			return self.normalization(self.convolution(a_	)		,      training=a_	)






class 		lowerCamelCase  (      tf.keras.layers.Layer       ):



	def __init__(   self		,      a_		,      a_		,      **a_	):
			super().__init__(**a_	)
			lowerCAmelCase     :    List[str]						=   tf.keras.layers.GlobalAveragePoolingaD(keepdims=a_		,      name="pooler"	)
			lowerCAmelCase     :    str						=   [
			    tf.keras.layers.ConvaD(filters=a_		,      kernel_size=1		,      activation="relu"		,      name="attention.0"	),
			    tf.keras.layers.ConvaD(filters=a_		,      kernel_size=1		,      activation="sigmoid"		,      name="attention.2"	),
			]





	def   _lowerCamelCase    (   self		,      a_	):
			# [batch_size, h, w, num_channels] -> [batch_size, 1, 1, num_channels]
			lowerCAmelCase     :    Tuple						=   self.pooler(a_	)
			for layer_module in self.attention:
					lowerCAmelCase     :    Dict						=   layer_module(a_	)
			lowerCAmelCase     :    Dict						=   hidden_state * pooled
			return hidden_state






class 		lowerCamelCase  (      tf.keras.layers.Layer       ):



	def __init__(   self		,      a_		,      a_		,      a_		,      a_ = 1		,      **a_	):
			super().__init__(**a_	)
			lowerCAmelCase     :    int						=   in_channels != out_channels or stride != 1
			lowerCAmelCase     :    Any						=   max(1		,      out_channels // config.groups_width	)
			lowerCAmelCase     :    Any						=   (
			    TFRegNetShortCut(a_		,      stride=a_		,      name="shortcut"	)
			    if should_apply_shortcut
			    else tf.keras.layers.Activation("linear"		,      name="shortcut"	)
			)
			# `self.layers` instead of `self.layer` because that is a reserved argument.
			lowerCAmelCase     :    Optional[Any]						=   [
			    TFRegNetConvLayer(a_		,      kernel_size=1		,      activation=config.hidden_act		,      name="layer.0"	),
			    TFRegNetConvLayer(
			        a_		,      stride=a_		,      groups=a_		,      activation=config.hidden_act		,      name="layer.1"	),
			    TFRegNetConvLayer(a_		,      kernel_size=1		,      activation=a_		,      name="layer.2"	),
			]
			lowerCAmelCase     :    List[str]						=   ACTaFN[config.hidden_act]





	def   _lowerCamelCase    (   self		,      a_	):
			lowerCAmelCase     :    Any						=   hidden_state
			for layer_module in self.layers:
					lowerCAmelCase     :    Optional[Any]						=   layer_module(a_	)
			lowerCAmelCase     :    Dict						=   self.shortcut(a_	)
			hidden_state += residual
			lowerCAmelCase     :    Any						=   self.activation(a_	)
			return hidden_state






class 		lowerCamelCase  (      tf.keras.layers.Layer       ):



	def __init__(   self		,      a_		,      a_		,      a_		,      a_ = 1		,      **a_	):
			super().__init__(**a_	)
			lowerCAmelCase     :    Any						=   in_channels != out_channels or stride != 1
			lowerCAmelCase     :    str						=   max(1		,      out_channels // config.groups_width	)
			lowerCAmelCase     :    int						=   (
			    TFRegNetShortCut(a_		,      stride=a_		,      name="shortcut"	)
			    if should_apply_shortcut
			    else tf.keras.layers.Activation("linear"		,      name="shortcut"	)
			)
			lowerCAmelCase     :    int						=   [
			    TFRegNetConvLayer(a_		,      kernel_size=1		,      activation=config.hidden_act		,      name="layer.0"	),
			    TFRegNetConvLayer(
			        a_		,      stride=a_		,      groups=a_		,      activation=config.hidden_act		,      name="layer.1"	),
			    TFRegNetSELayer(a_		,      reduced_channels=int(round(in_channels / 4	)	)		,      name="layer.2"	),
			    TFRegNetConvLayer(a_		,      kernel_size=1		,      activation=a_		,      name="layer.3"	),
			]
			lowerCAmelCase     :    Optional[Any]						=   ACTaFN[config.hidden_act]





	def   _lowerCamelCase    (   self		,      a_	):
			lowerCAmelCase     :    Dict						=   hidden_state
			for layer_module in self.layers:
					lowerCAmelCase     :    int						=   layer_module(a_	)
			lowerCAmelCase     :    str						=   self.shortcut(a_	)
			hidden_state += residual
			lowerCAmelCase     :    List[Any]						=   self.activation(a_	)
			return hidden_state






class 		lowerCamelCase  (      tf.keras.layers.Layer       ):



	def __init__(   self		,      a_		,      a_		,      a_		,      a_ = 2		,      a_ = 2		,      **a_	):
			super().__init__(**a_	)

			lowerCAmelCase     :    Union[str, Any]						=   TFRegNetXLayer if config.layer_type == "x" else TFRegNetYLayer
			lowerCAmelCase     :    Union[str, Any]						=   [
			    # downsampling is done in the first layer with stride of 2
			    layer(a_		,      a_		,      a_		,      stride=a_		,      name="layers.0"	),
			    *[layer(a_		,      a_		,      a_		,      name=F'''layers.{i+1}'''	) for i in range(depth - 1	)],
			]





	def   _lowerCamelCase    (   self		,      a_	):
			for layer_module in self.layers:
					lowerCAmelCase     :    Tuple						=   layer_module(a_	)
			return hidden_state






class 		lowerCamelCase  (      tf.keras.layers.Layer       ):



	def __init__(   self		,      a_		,      **a_	):
			super().__init__(**a_	)
			lowerCAmelCase     :    Dict						=   []
			# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
			self.stages.append(
			    TFRegNetStage(
			        a_		,      config.embedding_size		,      config.hidden_sizes[0]		,      stride=2 if config.downsample_in_first_stage else 1		,      depth=config.depths[0]		,      name="stages.0"		,      )	)
			lowerCAmelCase     :    Optional[int]						=   zip(config.hidden_sizes		,      config.hidden_sizes[1:]	)
			for i, ((in_channels, out_channels), depth) in enumerate(zip(a_		,      config.depths[1:]	)	):
					self.stages.append(TFRegNetStage(a_		,      a_		,      a_		,      depth=a_		,      name=F'''stages.{i+1}'''	)	)





	def   _lowerCamelCase    (   self		,      a_		,      a_ = False		,      a_ = True	):
			lowerCAmelCase     :    Union[str, Any]						=   () if output_hidden_states else None

			for stage_module in self.stages:
					if output_hidden_states:
							lowerCAmelCase     :    Tuple						=   hidden_states + (hidden_state,)

					lowerCAmelCase     :    int						=   stage_module(a_	)

			if output_hidden_states:
					lowerCAmelCase     :    Union[str, Any]						=   hidden_states + (hidden_state,)

			if not return_dict:
					return tuple(v for v in [hidden_state, hidden_states] if v is not None	)

			return TFBaseModelOutputWithNoAttention(last_hidden_state=a_		,      hidden_states=a_	)






@keras_serializable
class 		lowerCamelCase  (      tf.keras.layers.Layer       ):
	snake_case_      =   RegNetConfig



	def __init__(   self		,      a_		,      **a_	):
			super().__init__(**a_	)
			lowerCAmelCase     :    str						=   config
			lowerCAmelCase     :    Any						=   TFRegNetEmbeddings(a_		,      name="embedder"	)
			lowerCAmelCase     :    Union[str, Any]						=   TFRegNetEncoder(a_		,      name="encoder"	)
			lowerCAmelCase     :    Optional[int]						=   tf.keras.layers.GlobalAveragePoolingaD(keepdims=a_		,      name="pooler"	)





	@unpack_inputs
	def   _lowerCamelCase    (   self		,      a_		,      a_ = None		,      a_ = None		,      a_ = False		,      ):
			lowerCAmelCase     :    List[str]						=   (
			    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
			)
			lowerCAmelCase     :    int						=   return_dict if return_dict is not None else self.config.use_return_dict

			lowerCAmelCase     :    List[Any]						=   self.embedder(a_		,      training=a_	)

			lowerCAmelCase     :    Optional[int]						=   self.encoder(
			    a_		,      output_hidden_states=a_		,      return_dict=a_		,      training=a_	)

			lowerCAmelCase     :    List[Any]						=   encoder_outputs[0]
			lowerCAmelCase     :    Dict						=   self.pooler(a_	)

			# Change to NCHW output format have uniformity in the modules
			lowerCAmelCase     :    Any						=   tf.transpose(a_		,      perm=(0, 3, 1, 2)	)
			lowerCAmelCase     :    int						=   tf.transpose(a_		,      perm=(0, 3, 1, 2)	)

			# Change the other hidden state outputs to NCHW as well
			if output_hidden_states:
					lowerCAmelCase     :    Optional[Any]						=   tuple([tf.transpose(a_		,      perm=(0, 3, 1, 2)	) for h in encoder_outputs[1]]	)

			if not return_dict:
					return (last_hidden_state, pooled_output) + encoder_outputs[1:]

			return TFBaseModelOutputWithPoolingAndNoAttention(
			    last_hidden_state=a_		,      pooler_output=a_		,      hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states		,      )






class 		lowerCamelCase  (      lowercase__       ):
	snake_case_      =   RegNetConfig
	snake_case_      =   'regnet'
	snake_case_      =   'pixel_values'



	@property
	def   _lowerCamelCase    (   self	):
			return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224)		,      dtype=tf.floataa	)}






lowerCAmelCase       		=      R"""\n    Parameters:\n    This model is a Tensorflow\n    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a\n    regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and\n    behavior.\n        config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n"""

lowerCAmelCase       		=      R"""\n    Args:\n        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConveNextImageProcessor.__call__`] for details.\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n"""
@add_start_docstrings(
    "The bare RegNet model outputting raw features without any specific head on top."      ,   lowercase__      ,   )
class 		lowerCamelCase  (      lowercase__       ):



	def __init__(   self		,      a_		,      *a_		,      **a_	):
			super().__init__(a_		,      *a_		,      **a_	)
			lowerCAmelCase     :    Optional[Any]						=   TFRegNetMainLayer(a_		,      name="regnet"	)





	@unpack_inputs
	@add_start_docstrings_to_model_forward(a_	)
	@add_code_sample_docstrings(
	    checkpoint=_CHECKPOINT_FOR_DOC		,      output_type=a_		,      config_class=_CONFIG_FOR_DOC		,      modality="vision"		,      expected_output=_EXPECTED_OUTPUT_SHAPE		,      )
	def   _lowerCamelCase    (   self		,      a_		,      a_ = None		,      a_ = None		,      a_=False		,      ):
			lowerCAmelCase     :    Optional[Any]						=   (
			    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
			)
			lowerCAmelCase     :    Union[str, Any]						=   return_dict if return_dict is not None else self.config.use_return_dict

			lowerCAmelCase     :    Optional[Any]						=   self.regnet(
			    pixel_values=a_		,      output_hidden_states=a_		,      return_dict=a_		,      training=a_		,      )
			if not return_dict:
					return (outputs[0],) + outputs[1:]

			return TFBaseModelOutputWithPoolingAndNoAttention(
			    last_hidden_state=outputs.last_hidden_state		,      pooler_output=outputs.pooler_output		,      hidden_states=outputs.hidden_states		,      )






@add_start_docstrings(
    "\n    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    "      ,   lowercase__      ,   )
class 		lowerCamelCase  (      lowercase__      ,   lowercase__       ):



	def __init__(   self		,      a_		,      *a_		,      **a_	):
			super().__init__(a_		,      *a_		,      **a_	)
			lowerCAmelCase     :    Optional[Any]						=   config.num_labels
			lowerCAmelCase     :    Optional[int]						=   TFRegNetMainLayer(a_		,      name="regnet"	)
			# classification head
			lowerCAmelCase     :    Optional[Any]						=   [
			    tf.keras.layers.Flatten(),
			    tf.keras.layers.Dense(config.num_labels		,      name="classifier.1"	) if config.num_labels > 0 else tf.identity,
			]





	@unpack_inputs
	@add_start_docstrings_to_model_forward(a_	)
	@add_code_sample_docstrings(
	    checkpoint=_IMAGE_CLASS_CHECKPOINT		,      output_type=a_		,      config_class=_CONFIG_FOR_DOC		,      expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT		,      )
	def   _lowerCamelCase    (   self		,      a_ = None		,      a_ = None		,      a_ = None		,      a_ = None		,      a_=False		,      ):
			lowerCAmelCase     :    Union[str, Any]						=   (
			    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
			)
			lowerCAmelCase     :    Optional[Any]						=   return_dict if return_dict is not None else self.config.use_return_dict

			lowerCAmelCase     :    List[Any]						=   self.regnet(
			    a_		,      output_hidden_states=a_		,      return_dict=a_		,      training=a_	)

			lowerCAmelCase     :    Tuple						=   outputs.pooler_output if return_dict else outputs[1]

			lowerCAmelCase     :    Tuple						=   self.classifier[0](a_	)
			lowerCAmelCase     :    str						=   self.classifier[1](a_	)

			lowerCAmelCase     :    Dict						=   None if labels is None else self.hf_compute_loss(labels=a_		,      logits=a_	)

			if not return_dict:
					lowerCAmelCase     :    int						=   (logits,) + outputs[2:]
					return ((loss,) + output) if loss is not None else output

			return TFSequenceClassifierOutput(loss=a_		,      logits=a_		,      hidden_states=outputs.hidden_states	)









'''simple docstring'''






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool


if is_datasets_available():
					from datasets import load_dataset
class 		lowerCamelCase  (      _A       ):
	snake_case_      =   "microsoft/speecht5_tts"
	snake_case_      =   (
	    "This is a tool that reads an English text out loud. It takes an input named `text` which should contain the "
	    "text to read (in English) and returns a waveform object containing the sound."
	)
	snake_case_      =   "text_reader"
	snake_case_      =   SpeechTaProcessor
	snake_case_      =   SpeechTaForTextToSpeech
	snake_case_      =   SpeechTaHifiGan

	snake_case_      =   ["text"]
	snake_case_      =   ["audio"]



	def   _lowerCamelCase    (   self	):
			if self.post_processor is None:
					lowerCAmelCase     :    str						=   "microsoft/speecht5_hifigan"
			super().setup()



	def   _lowerCamelCase    (   self		,      a_		,      a_=None	):
			lowerCAmelCase     :    str						=   self.pre_processor(text=a_		,      return_tensors="pt"		,      truncation=a_	)

			if speaker_embeddings is None:
					if not is_datasets_available():
							raise ImportError("Datasets needs to be installed if not passing speaker embeddings."	)

					lowerCAmelCase     :    Tuple						=   load_dataset("Matthijs/cmu-arctic-xvectors"		,      split="validation"	)
					lowerCAmelCase     :    Union[str, Any]						=   torch.tensor(embeddings_dataset[7_305]["xvector"]	).unsqueeze(0	)

			return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}



	def   _lowerCamelCase    (   self		,      a_	):
			with torch.no_grad():
					return self.model.generate_speech(**a_	)





	def   _lowerCamelCase    (   self		,      a_	):
			with torch.no_grad():
					return self.post_processor(a_	).cpu().detach()



