

from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers import (
         TFDebertaVaForMaskedLM,
         TFDebertaVaForQuestionAnswering,
         TFDebertaVaForSequenceClassification,
         TFDebertaVaForTokenClassification,
         TFDebertaVaModel,
     )



class 						_A     :





  '''simple docstring'''



  def __init__(  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_=13   ,SCREAMING_SNAKE_CASE_=7   ,SCREAMING_SNAKE_CASE_=True   ,SCREAMING_SNAKE_CASE_=True   ,SCREAMING_SNAKE_CASE_=True   ,SCREAMING_SNAKE_CASE_=True   ,SCREAMING_SNAKE_CASE_=99   ,SCREAMING_SNAKE_CASE_=32   ,SCREAMING_SNAKE_CASE_=2   ,SCREAMING_SNAKE_CASE_=4   ,SCREAMING_SNAKE_CASE_=37   ,SCREAMING_SNAKE_CASE_="gelu"   ,SCREAMING_SNAKE_CASE_=0.1   ,SCREAMING_SNAKE_CASE_=0.1   ,SCREAMING_SNAKE_CASE_=512   ,SCREAMING_SNAKE_CASE_=16   ,SCREAMING_SNAKE_CASE_=2   ,SCREAMING_SNAKE_CASE_=0.02   ,SCREAMING_SNAKE_CASE_=False   ,SCREAMING_SNAKE_CASE_=True   ,SCREAMING_SNAKE_CASE_="None"   ,SCREAMING_SNAKE_CASE_=3   ,SCREAMING_SNAKE_CASE_=4   ,SCREAMING_SNAKE_CASE_=None   ,):

      '''simple docstring'''
      snake_case							:		List[Any]        =		parent
      snake_case							:		Dict        =		batch_size
      snake_case							:		Dict        =		seq_length
      snake_case							:		Optional[Any]        =		is_training
      snake_case							:		Optional[Any]        =		use_input_mask
      snake_case							:		Any        =		use_token_type_ids
      snake_case							:		str        =		use_labels
      snake_case							:		int        =		vocab_size
      snake_case							:		Optional[int]        =		hidden_size
      snake_case							:		Optional[int]        =		num_hidden_layers
      snake_case							:		str        =		num_attention_heads
      snake_case							:		List[Any]        =		intermediate_size
      snake_case							:		str        =		hidden_act
      snake_case							:		Optional[Any]        =		hidden_dropout_prob
      snake_case							:		Any        =		attention_probs_dropout_prob
      snake_case							:		Union[str, Any]        =		max_position_embeddings
      snake_case							:		Tuple        =		type_vocab_size
      snake_case							:		Optional[Any]        =		type_sequence_label_size
      snake_case							:		List[Any]        =		initializer_range
      snake_case							:		Tuple        =		num_labels
      snake_case							:		int        =		num_choices
      snake_case							:		List[Any]        =		relative_attention
      snake_case							:		int        =		position_biased_input
      snake_case							:		Any        =		pos_att_type
      snake_case							:		Optional[int]        =		scope





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Optional[Any]        =		ids_tensor([self.batch_size, self.seq_length]   ,self.vocab_size    )

      snake_case							:		List[Any]        =		None
      if self.use_input_mask:
          snake_case							:		Any        =		random_attention_mask([self.batch_size, self.seq_length]    )

      snake_case							:		List[str]        =		None
      if self.use_token_type_ids:
          snake_case							:		Tuple        =		ids_tensor([self.batch_size, self.seq_length]   ,self.type_vocab_size    )

      snake_case							:		Any        =		None
      snake_case							:		Optional[Any]        =		None
      snake_case							:		str        =		None
      if self.use_labels:
          snake_case							:		Optional[Any]        =		ids_tensor([self.batch_size]   ,self.type_sequence_label_size    )
          snake_case							:		Union[str, Any]        =		ids_tensor([self.batch_size, self.seq_length]   ,self.num_labels    )

      snake_case							:		int        =		DebertaVaConfig(
          vocab_size=self.vocab_size   ,hidden_size=self.hidden_size   ,num_hidden_layers=self.num_hidden_layers   ,num_attention_heads=self.num_attention_heads   ,intermediate_size=self.intermediate_size   ,hidden_act=self.hidden_act   ,hidden_dropout_prob=self.hidden_dropout_prob   ,attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,max_position_embeddings=self.max_position_embeddings   ,type_vocab_size=self.type_vocab_size   ,relative_attention=self.relative_attention   ,position_biased_input=self.position_biased_input   ,initializer_range=self.initializer_range   ,return_dict=SCREAMING_SNAKE_CASE_   ,)

      return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		Optional[Any]        =		TFDebertaVaModel(config=SCREAMING_SNAKE_CASE_    )
      snake_case							:		Optional[int]        =		{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

      snake_case							:		Dict        =		[input_ids, input_mask]
      snake_case							:		List[Any]        =		model(SCREAMING_SNAKE_CASE_    )

      snake_case							:		Optional[Any]        =		model(SCREAMING_SNAKE_CASE_    )

      self.parent.assertEqual(result.last_hidden_state.shape   ,(self.batch_size, self.seq_length, self.hidden_size)    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		Any        =		TFDebertaVaForMaskedLM(config=SCREAMING_SNAKE_CASE_    )
      snake_case							:		int        =		{
          """input_ids""": input_ids,
          """attention_mask""": input_mask,
          """token_type_ids""": token_type_ids,
      }
      snake_case							:		Any        =		model(SCREAMING_SNAKE_CASE_    )
      self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.seq_length, self.vocab_size)    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		Any        =		self.num_labels
      snake_case							:		Any        =		TFDebertaVaForSequenceClassification(config=SCREAMING_SNAKE_CASE_    )
      snake_case							:		List[str]        =		{
          """input_ids""": input_ids,
          """attention_mask""": input_mask,
          """token_type_ids""": token_type_ids,
      }

      snake_case							:		List[str]        =		model(SCREAMING_SNAKE_CASE_    )
      self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.num_labels)    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		Tuple        =		self.num_labels
      snake_case							:		Union[str, Any]        =		TFDebertaVaForTokenClassification(config=SCREAMING_SNAKE_CASE_    )
      snake_case							:		Optional[int]        =		{
          """input_ids""": input_ids,
          """attention_mask""": input_mask,
          """token_type_ids""": token_type_ids,
      }
      snake_case							:		Union[str, Any]        =		model(SCREAMING_SNAKE_CASE_    )
      self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.seq_length, self.num_labels)    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		Dict        =		TFDebertaVaForQuestionAnswering(config=SCREAMING_SNAKE_CASE_    )
      snake_case							:		List[Any]        =		{
          """input_ids""": input_ids,
          """attention_mask""": input_mask,
          """token_type_ids""": token_type_ids,
      }

      snake_case							:		Tuple        =		model(SCREAMING_SNAKE_CASE_    )
      self.parent.assertEqual(result.start_logits.shape   ,(self.batch_size, self.seq_length)    )
      self.parent.assertEqual(result.end_logits.shape   ,(self.batch_size, self.seq_length)    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Tuple        =		self.prepare_config_and_inputs()
      (
          (
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		(
          snake_case
      )      ,		
      )							:		Optional[Any]        =		config_and_inputs
      snake_case							:		List[Any]        =		{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
      return config, inputs_dict







@require_tf
class 						_A     (				snake_case						,    snake_case						,    unittest.TestCase				):





  '''simple docstring'''



  __lowerCamelCase			:      Optional[int]					=   (
      (
          TFDebertaVaModel,
          TFDebertaVaForMaskedLM,
          TFDebertaVaForQuestionAnswering,
          TFDebertaVaForSequenceClassification,
          TFDebertaVaForTokenClassification,
      )
      if is_tf_available()
      else ()
  )
  __lowerCamelCase			:      Any					=   (
      {
          '''feature-extraction''': TFDebertaVaModel,
          '''fill-mask''': TFDebertaVaForMaskedLM,
          '''question-answering''': TFDebertaVaForQuestionAnswering,
          '''text-classification''': TFDebertaVaForSequenceClassification,
          '''token-classification''': TFDebertaVaForTokenClassification,
          '''zero-shot''': TFDebertaVaForSequenceClassification,
      }
      if is_tf_available()
      else {}
  )

  __lowerCamelCase			:      Optional[int]					=   False
  __lowerCamelCase			:      Dict					=   False





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Optional[int]        =		TFDebertaVaModelTester(self    )
      snake_case							:		Dict        =		ConfigTester(self   ,config_class=SCREAMING_SNAKE_CASE_   ,hidden_size=37    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      self.config_tester.run_common_tests()





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		str        =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		List[Any]        =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		List[str]        =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		int        =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		int        =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE_    )





  @slow
  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Any        =		TFDebertaVaModel.from_pretrained("""kamalkraj/deberta-v2-xlarge"""    )
      self.assertIsNotNone(SCREAMING_SNAKE_CASE_    )







@require_tf
class 						_A     (				unittest.TestCase				):





  '''simple docstring'''



  @unittest.skip(reason="""Model not available yet"""    )
  def    snake_case_       (  self    ):

      '''simple docstring'''
      pass





  @slow
  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Dict        =		TFDebertaVaModel.from_pretrained("""kamalkraj/deberta-v2-xlarge"""    )
      snake_case							:		Dict        =		tf.constant([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]    )
      snake_case							:		Tuple        =		tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]    )
      snake_case							:		Optional[Any]        =		model(SCREAMING_SNAKE_CASE_   ,attention_mask=SCREAMING_SNAKE_CASE_    )[0]

      snake_case							:		Optional[Any]        =		tf.constant(
          [[[0.23_56, 0.19_48, 0.03_69], [-0.10_63, 0.35_86, -0.51_52], [-0.63_99, -0.02_59, -0.25_25]]]    )
      tf.debugging.assert_near(output[:, 1:4, 1:4]   ,SCREAMING_SNAKE_CASE_   ,atol=1E-4    )





'''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					:    Dict           = logging.get_logger(__name__)

# General docstring
lowerCAmelCase					:    str           = 'RegNetConfig'

# Base docstring
lowerCAmelCase					:    str           = 'facebook/regnet-y-040'
lowerCAmelCase					:    Dict           = [1, 10_88, 7, 7]

# Image classification docstring
lowerCAmelCase					:    Dict           = 'facebook/regnet-y-040'
lowerCAmelCase					:    int           = 'tabby, tabby cat'

lowerCAmelCase					:    int           = [
    'facebook/regnet-y-040',
    # See all regnet models at https://huggingface.co/models?filter=regnet
]


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







				def __init__(       self						,				A_						,				A_ = 3						,				A_ = 1						,				A_ = 1						,				A_ = "relu"						,				**A_						,				)->		str:







											'''simple docstring'''






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







				def 						UpperCAmelCase_    (       self						,				A_     )->		Any:







											'''simple docstring'''






											UpperCamelCase   			=     self.convolution(self.padding(A_     )     )
											UpperCamelCase   			=     self.normalization(A_     )
											UpperCamelCase   			=     self.activation(A_     )
											return hidden_state


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







				def __init__(       self						,				A_						,				**A_     )->		Optional[Any]:







											'''simple docstring'''






											super().__init__(**A_     )
											UpperCamelCase   			=     config.num_channels
											UpperCamelCase   			=     TFRegNetConvLayer(
											    out_channels=config.embedding_size						,				kernel_size=3						,				stride=2						,				activation=config.hidden_act						,				name='embedder'						,				)







				def 						UpperCAmelCase_    (       self						,				A_     )->		List[Any]:







											'''simple docstring'''






											UpperCamelCase   			=     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)
											UpperCamelCase   			=     tf.transpose(A_						,				perm=(0, 2, 3, 1)     )
											UpperCamelCase   			=     self.embedder(A_     )
											return hidden_state


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







				def __init__(       self						,				A_						,				A_ = 2						,				**A_     )->		List[Any]:







											'''simple docstring'''






											super().__init__(**A_     )
											UpperCamelCase   			=     tf.keras.layers.ConvaD(
											    filters=A_						,				kernel_size=1						,				strides=A_						,				use_bias=A_						,				name='convolution'     )
											UpperCamelCase   			=     tf.keras.layers.BatchNormalization(epsilon=1e-5						,				momentum=0.9						,				name='normalization'     )







				def 						UpperCAmelCase_    (       self						,				A_						,				A_ = False     )->		tf.Tensor:







											'''simple docstring'''






											return self.normalization(self.convolution(A_     )						,				training=A_     )


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







				def __init__(       self						,				A_						,				A_						,				**A_     )->		Optional[Any]:







											'''simple docstring'''






											super().__init__(**A_     )
											UpperCamelCase   			=     tf.keras.layers.GlobalAveragePoolingaD(keepdims=A_						,				name='pooler'     )
											UpperCamelCase   			=     [
											    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 						UpperCAmelCase_    (       self						,				A_     )->		Optional[int]:







											'''simple docstring'''






											UpperCamelCase   			=     self.pooler(A_     )
											for layer_module in self.attention:
																		UpperCamelCase   			=     layer_module(A_     )
											UpperCamelCase   			=     hidden_state * pooled
											return hidden_state


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







				def __init__(       self						,				A_						,				A_						,				A_						,				A_ = 1						,				**A_     )->		Dict:







											'''simple docstring'''






											super().__init__(**A_     )
											UpperCamelCase   			=     in_channels != out_channels or stride != 1
											UpperCamelCase   			=     max(1						,				out_channels // config.groups_width     )
											UpperCamelCase   			=     (
											    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.
											UpperCamelCase   			=     [
											    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'     ),
											]
											UpperCamelCase   			=     ACTaFN[config.hidden_act]







				def 						UpperCAmelCase_    (       self						,				A_     )->		Tuple:







											'''simple docstring'''






											UpperCamelCase   			=     hidden_state
											for layer_module in self.layers:
																		UpperCamelCase   			=     layer_module(A_     )
											UpperCamelCase   			=     self.shortcut(A_     )
											hidden_state += residual
											UpperCamelCase   			=     self.activation(A_     )
											return hidden_state


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







				def __init__(       self						,				A_						,				A_						,				A_						,				A_ = 1						,				**A_     )->		Any:







											'''simple docstring'''






											super().__init__(**A_     )
											UpperCamelCase   			=     in_channels != out_channels or stride != 1
											UpperCamelCase   			=     max(1						,				out_channels // config.groups_width     )
											UpperCamelCase   			=     (
											    TFRegNetShortCut(A_						,				stride=A_						,				name='shortcut'     )
											    if should_apply_shortcut
											    else tf.keras.layers.Activation('linear'						,				name='shortcut'     )
											)
											UpperCamelCase   			=     [
											    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'     ),
											]
											UpperCamelCase   			=     ACTaFN[config.hidden_act]







				def 						UpperCAmelCase_    (       self						,				A_     )->		List[Any]:







											'''simple docstring'''






											UpperCamelCase   			=     hidden_state
											for layer_module in self.layers:
																		UpperCamelCase   			=     layer_module(A_     )
											UpperCamelCase   			=     self.shortcut(A_     )
											hidden_state += residual
											UpperCamelCase   			=     self.activation(A_     )
											return hidden_state


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







				def __init__(       self						,				A_						,				A_						,				A_						,				A_ = 2						,				A_ = 2						,				**A_     )->		Dict:







											'''simple docstring'''






											super().__init__(**A_     )

											UpperCamelCase   			=     TFRegNetXLayer if config.layer_type == 'x' else TFRegNetYLayer
											UpperCamelCase   			=     [
											    # 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 						UpperCAmelCase_    (       self						,				A_     )->		List[Any]:







											'''simple docstring'''






											for layer_module in self.layers:
																		UpperCamelCase   			=     layer_module(A_     )
											return hidden_state


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







				def __init__(       self						,				A_						,				**A_     )->		str:







											'''simple docstring'''






											super().__init__(**A_     )
											UpperCamelCase   			=     []
											# 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'						,				)     )
											UpperCamelCase   			=     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 						UpperCAmelCase_    (       self						,				A_						,				A_ = False						,				A_ = True     )->		TFBaseModelOutputWithNoAttention:







											'''simple docstring'''






											UpperCamelCase   			=     () if output_hidden_states else None

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

																		UpperCamelCase   			=     stage_module(A_     )

											if output_hidden_states:
																		UpperCamelCase   			=     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 			SCREAMING_SNAKE_CASE__      (     tf.keras.layers.Layer):
				lowerCAmelCase_     			=							RegNetConfig







				def __init__(       self						,				A_						,				**A_     )->		Union[str, Any]:







											'''simple docstring'''






											super().__init__(**A_     )
											UpperCamelCase   			=     config
											UpperCamelCase   			=     TFRegNetEmbeddings(A_						,				name='embedder'     )
											UpperCamelCase   			=     TFRegNetEncoder(A_						,				name='encoder'     )
											UpperCamelCase   			=     tf.keras.layers.GlobalAveragePoolingaD(keepdims=A_						,				name='pooler'     )







				@unpack_inputs
				def 						UpperCAmelCase_    (       self						,				A_						,				A_ = None						,				A_ = None						,				A_ = False						,				)->		TFBaseModelOutputWithPoolingAndNoAttention:







											'''simple docstring'''






											UpperCamelCase   			=     (
											    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
											)
											UpperCamelCase   			=     return_dict if return_dict is not None else self.config.use_return_dict

											UpperCamelCase   			=     self.embedder(A_						,				training=A_     )

											UpperCamelCase   			=     self.encoder(
											    A_						,				output_hidden_states=A_						,				return_dict=A_						,				training=A_     )

											UpperCamelCase   			=     encoder_outputs[0]
											UpperCamelCase   			=     self.pooler(A_     )

											# Change to NCHW output format have uniformity in the modules
											UpperCamelCase   			=     tf.transpose(A_						,				perm=(0, 3, 1, 2)     )
											UpperCamelCase   			=     tf.transpose(A_						,				perm=(0, 3, 1, 2)     )

											# Change the other hidden state outputs to NCHW as well
											if output_hidden_states:
																		UpperCamelCase   			=     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 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							RegNetConfig
				lowerCAmelCase_     			=							"""regnet"""
				lowerCAmelCase_     			=							"""pixel_values"""







				@property
				def 						UpperCAmelCase_    (       self     )->		List[str]:







											'''simple docstring'''






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


lowerCAmelCase					:    str           = 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					:    List[str]           = 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."""							,       snake_case_							,       )
class 			SCREAMING_SNAKE_CASE__      (     snake_case_):







				def __init__(       self						,				A_						,				*A_						,				**A_     )->		List[Any]:







											'''simple docstring'''






											super().__init__(A_						,				*A_						,				**A_     )
											UpperCamelCase   			=     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 						UpperCAmelCase_    (       self						,				A_						,				A_ = None						,				A_ = None						,				A_=False						,				)->		Union[TFBaseModelOutputWithPoolingAndNoAttention, Tuple[tf.Tensor]]:







											'''simple docstring'''






											UpperCamelCase   			=     (
											    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
											)
											UpperCamelCase   			=     return_dict if return_dict is not None else self.config.use_return_dict

											UpperCamelCase   			=     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(
    """
    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    """							,       snake_case_							,       )
class 			SCREAMING_SNAKE_CASE__      (     snake_case_							,       snake_case_):







				def __init__(       self						,				A_						,				*A_						,				**A_     )->		str:







											'''simple docstring'''






											super().__init__(A_						,				*A_						,				**A_     )
											UpperCamelCase   			=     config.num_labels
											UpperCamelCase   			=     TFRegNetMainLayer(A_						,				name='regnet'     )
											# classification head
											UpperCamelCase   			=     [
											    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 						UpperCAmelCase_    (       self						,				A_ = None						,				A_ = None						,				A_ = None						,				A_ = None						,				A_=False						,				)->		Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:







											'''simple docstring'''






											UpperCamelCase   			=     (
											    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
											)
											UpperCamelCase   			=     return_dict if return_dict is not None else self.config.use_return_dict

											UpperCamelCase   			=     self.regnet(
											    A_						,				output_hidden_states=A_						,				return_dict=A_						,				training=A_     )

											UpperCamelCase   			=     outputs.pooler_output if return_dict else outputs[1]

											UpperCamelCase   			=     self.classifier[0](A_     )
											UpperCamelCase   			=     self.classifier[1](A_     )

											UpperCamelCase   			=     None if labels is None else self.hf_compute_loss(labels=A_						,				logits=A_     )

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

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




