'''simple docstring'''




import itertools
import os
import subprocess
from os.path import dirname

from parameterized import parameterized

from tests.trainer.test_trainer import TrainerIntegrationCommon  # noqa
from transformers import is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_tests_dir,
    require_deepspeed,
    require_torch_gpu,
    slow,
)
from transformers.trainer_utils import set_seed


if is_torch_available():
  from tests.trainer.test_trainer import (  # noqa
      RegressionModelConfig,
      RegressionPreTrainedModel,
      get_regression_trainer,
  )


set_seed(42)

__SCREAMING_SNAKE_CASE :  Optional[int]  =						get_tests_dir("""fixtures""")
__SCREAMING_SNAKE_CASE :  Optional[int]  =						os.path.join(dirname(get_tests_dir()))
__SCREAMING_SNAKE_CASE :  int  =						dirname(os.path.abspath(__file__))

# default torch.distributed port
__SCREAMING_SNAKE_CASE :  Optional[Any]  =						"""10999"""

__SCREAMING_SNAKE_CASE :  Optional[Any]  =						"""t5-small"""

# *** Working Models ***
__SCREAMING_SNAKE_CASE :  str  =						"""hf-internal-testing/tiny-albert"""
__SCREAMING_SNAKE_CASE :  Any  =						"""sshleifer/bart-tiny-random"""
__SCREAMING_SNAKE_CASE :  str  =						"""hf-internal-testing/tiny-bert"""
__SCREAMING_SNAKE_CASE :  Dict  =						"""hf-internal-testing/tiny-random-bigbird_pegasus"""
__SCREAMING_SNAKE_CASE :  List[str]  =						"""hf-internal-testing/tiny-random-big_bird"""
__SCREAMING_SNAKE_CASE :  str  =						"""hf-internal-testing/tiny-random-blenderbot"""
__SCREAMING_SNAKE_CASE :  Optional[int]  =						"""bigscience/bigscience-small-testing"""
__SCREAMING_SNAKE_CASE :  int  =						"""hf-internal-testing/tiny-random-deberta"""
__SCREAMING_SNAKE_CASE :  int  =						"""hf-internal-testing/tiny-random-deberta-v2"""
__SCREAMING_SNAKE_CASE :  Dict  =						"""sshleifer/tiny-distilbert-base-cased"""
__SCREAMING_SNAKE_CASE :  str  =						"""hf-internal-testing/tiny-electra"""
__SCREAMING_SNAKE_CASE :  List[str]  =						"""hf-internal-testing/tiny-random-flaubert"""
__SCREAMING_SNAKE_CASE :  int  =						"""stas/tiny-wmt19-en-de"""
__SCREAMING_SNAKE_CASE :  Tuple  =						"""hf-internal-testing/tiny-random-funnel"""
__SCREAMING_SNAKE_CASE :  Any  =						"""sshleifer/tiny-gpt2"""
__SCREAMING_SNAKE_CASE :  Optional[Any]  =						"""hf-internal-testing/tiny-random-gptj"""
__SCREAMING_SNAKE_CASE :  Tuple  =						"""hf-internal-testing/tiny-random-gpt_neo"""
__SCREAMING_SNAKE_CASE :  List[Any]  =						"""hf-internal-testing/tiny-layoutlm"""
__SCREAMING_SNAKE_CASE :  int  =						"""hf-internal-testing/tiny-random-led"""
__SCREAMING_SNAKE_CASE :  Optional[Any]  =						"""hf-internal-testing/tiny-random-longformer"""
__SCREAMING_SNAKE_CASE :  str  =						"""stas/tiny-m2m_100"""  # hf tiny model is unsuitable
__SCREAMING_SNAKE_CASE :  int  =						"""sshleifer/tiny-marian-en-de"""
__SCREAMING_SNAKE_CASE :  Optional[int]  =						"""sshleifer/tiny-mbart"""
__SCREAMING_SNAKE_CASE :  Dict  =						"""hf-internal-testing/tiny-random-mobilebert"""
__SCREAMING_SNAKE_CASE :  Any  =						"""hf-internal-testing/tiny-random-mpnet"""
__SCREAMING_SNAKE_CASE :  Tuple  =						"""stas/pegasus-cnn_dailymail-tiny-random"""
__SCREAMING_SNAKE_CASE :  int  =						"""hf-internal-testing/tiny-random-prophetnet"""
__SCREAMING_SNAKE_CASE :  int  =						"""sshleifer/tiny-distilroberta-base"""
__SCREAMING_SNAKE_CASE :  List[str]  =						"""hf-internal-testing/tiny-random-squeezebert"""
__SCREAMING_SNAKE_CASE :  Dict  =						"""patrickvonplaten/t5-tiny-random"""
__SCREAMING_SNAKE_CASE :  Optional[Any]  =						"""hf-internal-testing/tiny-random-t5-v1.1"""
__SCREAMING_SNAKE_CASE :  Optional[Any]  =						"""hf-internal-testing/tiny-random-vit"""
__SCREAMING_SNAKE_CASE :  List[Any]  =						"""hf-internal-testing/tiny-xlm-roberta"""
__SCREAMING_SNAKE_CASE :  Dict  =						"""sshleifer/tiny-xlnet-base-cased"""


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
__SCREAMING_SNAKE_CASE :  Optional[int]  =						"""hf-internal-testing/tiny-random-mt5"""
__SCREAMING_SNAKE_CASE :  Optional[int]  =						"""hf-internal-testing/tiny-random-camembert"""
__SCREAMING_SNAKE_CASE :  Optional[int]  =						"""hf-internal-testing/tiny-random-openai-gpt"""

# missing tokenizer files
__SCREAMING_SNAKE_CASE :  Dict  =						"""hf-internal-testing/tiny-random-convbert"""
__SCREAMING_SNAKE_CASE :  Optional[Any]  =						"""hf-internal-testing/tiny-random-layoutlmv2"""
__SCREAMING_SNAKE_CASE :  List[str]  =						"""hf-internal-testing/tiny-random-hubert"""

# issues with tokenizer
__SCREAMING_SNAKE_CASE :  Optional[Any]  =						"""hf-internal-testing/tiny-random-ctrl"""
__SCREAMING_SNAKE_CASE :  Optional[int]  =						"""hf-internal-testing/tiny-random-transfo-xl"""  # same as ctrl

# other issues with tiny models
__SCREAMING_SNAKE_CASE :  Any  =						"""hf-internal-testing/tiny-random-ibert"""  # multiple issues with either mlm/qa/clas
__SCREAMING_SNAKE_CASE :  Any  =						"""hf-internal-testing/tiny-random-reformer"""  # multiple issues with either mlm/qa/clas

# *** Lacking official examples to test with ***
# or not working with examples
__SCREAMING_SNAKE_CASE :  Any  =						"""hf-internal-testing/tiny-random-dpr"""
# - "dpr"  examples/research_projects/rag-end2end-retriever/
__SCREAMING_SNAKE_CASE :  str  =						"""hf-internal-testing/tiny-random-rag"""
# - "rag" research_projects
__SCREAMING_SNAKE_CASE :  List[str]  =						""""""
# - "luke" Entities classes - no plan to make such example
__SCREAMING_SNAKE_CASE :  Union[str, Any]  =						"""hf-internal-testing/tiny-random-lxmert"""
# - "lxmert" doesn't work with run_qa.py
__SCREAMING_SNAKE_CASE :  int  =						"""hf-internal-testing/tiny-random-clip"""
# - "clip" nothing under pytorch examples - XXX: Suraj is working on adding some - check by end of Sep
__SCREAMING_SNAKE_CASE :  Dict  =						"""hf-internal-testing/tiny-random-speech_to_text"""
# - "speech_to_text", nothing under pytorch examples


# *** Reactive mode ***
# models with low usage, unstable API, things about to change - do nothing about the following until someone runs into a problem
__SCREAMING_SNAKE_CASE :  Any  =						"""hf-internal-testing/tiny-random-tapas"""

def 			UpperCamelCase_							(      _UpperCAmelCase						:   List[Any]=False					)					->					List[Any]:


       """simple docstring"""







       _UpperCAmelCase  :						Optional[Any]							   =					min(2    ,			get_gpu_count()					) if distributed else 1
       _UpperCAmelCase  :						List[str]							   =					os.environ.get("DS_TEST_PORT"    ,			_UpperCAmelCase					)
       return F"""deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}""".split()

def 			UpperCamelCase_							(      )					->					Any:


       """simple docstring"""







       _UpperCAmelCase  :						int							   =					F"""{FIXTURE_DIRECTORY}/tests_samples"""
       _UpperCAmelCase  :						Any							   =					F"""{data_dir_samples}/wmt_en_ro"""
       _UpperCAmelCase  :						Tuple							   =					F"""{data_dir_samples}/xsum"""
       _UpperCAmelCase  :						Tuple							   =					"\n        --do_train\n        --max_train_samples 4\n        --per_device_train_batch_size 2\n        --num_train_epochs 1\n        --fp16\n        --report_to none\n        --overwrite_output_dir\n        ".split()

       # try to cover as many models as possible once (it's enough to run on one task per model)
       # but need a tiny model for each
       #
       # should have "{model_type.upper()}_TINY" corresponding vars defined, e.g., T5_TINY, etc.
       _UpperCAmelCase  :						Any							   =					{
           "trans": [
               "bart",
               "fsmt",
               "m2m_100",
               "marian",
               "mbart",
               "t5",
               "t5_v1",
               # "mt5", missing model files
           ],
           "sum": [
               "pegasus",
           ],
           "clm": [
               "big_bird",
               "bigbird_pegasus",
               "blenderbot",
               "bloom",
               "gpt2",
               "gpt_neo",
               "gptj",
               "xlm-roberta",
               "prophetnet",
               # "camembert", missing model files
           ],
           "mlm": [
               "albert",
               "deberta",
               "deberta-v2",
               "distilbert",
               "electra",
               "flaubert",
               "funnel",
               "layoutlm",
               # "reformer", # multiple issues with either mlm/qa/clas
           ],
           "qa": [
               "led",
               "longformer",
               "mobilebert",
               "mpnet",
               "roberta",
               "squeezebert",
               # "convbert", # missing tokenizer files
               # "layoutlmv2", missing model files
           ],
           "clas": [
               "bert",
               "xlnet",
               # "hubert", # missing tokenizer files
               # "ibert", # multiple issues with either mlm/qa/clas
               # "transfo-xl", # tokenizer issues as ctrl
               # "ctrl", # tokenizer issues
               # "openai-gpt", missing model files
               # "tapas", multiple issues
           ],
           "img_clas": [
               "vit",
           ],
       }

       _UpperCAmelCase  :						List[Any]							   =					F"""{ROOT_DIRECTORY}/examples/pytorch"""

       _UpperCAmelCase  :						Union[str, Any]							   =					{
           "trans": F"""
        {scripts_dir}/translation/run_translation.py
        --train_file {data_dir_wmt}/train.json
        --source_lang en
        --target_lang ro
        """,
           "sum": F"""
        {scripts_dir}/summarization/run_summarization.py
        --train_file {data_dir_xsum}/sample.json
        --max_source_length 12
        --max_target_length 12
        --lang en
        """,
           "clm": F"""
        {scripts_dir}/language-modeling/run_clm.py
        --train_file {FIXTURE_DIRECTORY}/sample_text.txt
        --block_size 8
        """,
           "mlm": F"""
        {scripts_dir}/language-modeling/run_mlm.py
        --train_file {FIXTURE_DIRECTORY}/sample_text.txt
        """,
           "qa": F"""
        {scripts_dir}/question-answering/run_qa.py
        --train_file {data_dir_samples}/SQUAD/sample.json
        """,
           "clas": F"""
        {scripts_dir}/text-classification/run_glue.py
        --train_file {data_dir_samples}/MRPC/train.csv
        --max_seq_length 12
        --task_name MRPC
        """,
           "img_clas": F"""
        {scripts_dir}/image-classification/run_image_classification.py
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --remove_unused_columns False
            --max_steps 10
            --image_processor_name {DS_TESTS_DIRECTORY}/vit_feature_extractor.json
        """,
       }

       _UpperCAmelCase  :						Union[str, Any]							   =					get_launcher(distributed=_UpperCAmelCase					)

       _UpperCAmelCase  :						str							   =					{}
       for task, args in tasks.items():
              _UpperCAmelCase  :						int							   =					args.split()
              for model in tasksamodels[task]:
                     _UpperCAmelCase  :						Any							   =					globals()[F"""{model.upper().replace('-'    ,			'_'					)}_TINY"""]
                     _UpperCAmelCase  :						List[str]							   =					F"""--model_name_or_path {model_name}""".split()
                     _UpperCAmelCase  :						Dict							   =					launcher + args + args_model + args_main

                     # # generation special case
                     # if task == "gen":
                     #     launcher = f"deepspeed --num_nodes 1 --num_gpus 1".split()
                     #     args_model += f"--model_type {model}".split()
                     #     cmds[f"{task}_{model}"] = launcher + args + args_model
                     # else:

       return cmds


__SCREAMING_SNAKE_CASE :  Union[str, Any]  =						make_task_cmds()

__SCREAMING_SNAKE_CASE :  Any  =						"""zero2"""
__SCREAMING_SNAKE_CASE :  Optional[Any]  =						"""zero3"""

__SCREAMING_SNAKE_CASE :  List[Any]  =						[ZEROa, ZEROa]

def 			UpperCamelCase_							(      _UpperCAmelCase						:   Optional[Any]    ,			_UpperCAmelCase						:   Union[str, Any]    ,			_UpperCAmelCase						:   List[str]					)					->					int:


       """simple docstring"""







       _UpperCAmelCase  :						Optional[int]							   =					parameterized.to_safe_name("_".join(str(_UpperCAmelCase					) for x in param.args					)					)
       return F"""{func.__name__}_{param_based_name}"""


# Cartesian-product of zero stages with models to test
__SCREAMING_SNAKE_CASE :  Dict  =						list(itertools.product(stages, task_cmds.keys()))


@slow
@require_deepspeed
@require_torch_gpu
class 			lowerCamelCase_			(snake_case__					):
      '''simple docstring'''

      def   _A			( self		:       List[str]		,      A		:       int		,      A		:       Union[str, Any] ):
             # return a ready to run train cmd
             if task not in task_cmds:
                    raise ValueError(F"""don't know of task {task}, have {task_cmds.keys()}""" )

             _UpperCAmelCase  :						List[Any]							   =					task_cmds[task]
             _UpperCAmelCase  :						Any							   =					F"""--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json""".split()

             _UpperCAmelCase  :						Tuple							   =					self.get_auto_remove_tmp_dir()
             _UpperCAmelCase  :						int							   =					F"""--output_dir {output_dir}""".split()

             cmd += args_ds + args_out

             return cmd, output_dir





      @parameterized.expand(A		,      name_func=A )
      def   _A			( self		:       int		,      A		:       Optional[int]		,      A		:       Optional[Any] ):
             # testing the ability to do a run followed by recovery of full fp32 weights

             _UpperCAmelCase   ,						_UpperCAmelCase  :						Union[str, Any]							   =					self.get_task_cmd(A		,      A )

             # 1. generate the checkpoint
             cmd += "--save_steps 1".split()
             # keep for quick debug
             # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] + cmd)); die
             execute_subprocess_async(A		,      env=self.get_env() )

             # 2. test that the fp32 weights get reconsolidated
             _UpperCAmelCase  :						Tuple							   =					F"""{output_dir}/checkpoint-1"""
             _UpperCAmelCase  :						List[Any]							   =					F"""{chkpt_dir}/out.bin"""
             _UpperCAmelCase  :						Optional[Any]							   =					F"""{chkpt_dir}/zero_to_fp32.py {chkpt_dir} {recovered_model_path}"""
             # keep for quick debug
             # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
             subprocess.check_call(A		,      shell=A )
             assert os.path.exists(A ), F"""{recovered_model_path} was not found"""

             # possibly could also test that the resulting saved model is usable but given that we use
             # random models we won't know if it's any good


'''simple docstring'''




from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFImageClassifierOutputWithNoAttention,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_resnet import ResNetConfig


__SCREAMING_SNAKE_CASE :  Tuple  =						logging.get_logger(__name__)

# General docstring
__SCREAMING_SNAKE_CASE :  List[str]  =						"""ResNetConfig"""

# Base docstring
__SCREAMING_SNAKE_CASE :  List[str]  =						"""microsoft/resnet-50"""
__SCREAMING_SNAKE_CASE :  Tuple  =						[1, 2_048, 7, 7]

# Image classification docstring
__SCREAMING_SNAKE_CASE :  Optional[Any]  =						"""microsoft/resnet-50"""
__SCREAMING_SNAKE_CASE :  Union[str, Any]  =						"""tiger cat"""

__SCREAMING_SNAKE_CASE :  Dict  =						[
    """microsoft/resnet-50""",
    # See all resnet models at https://huggingface.co/models?filter=resnet
]

class 			lowerCamelCase_			(tf.keras.layers.Layer					):
      '''simple docstring'''

      def __init__( self		:       List[Any]		,      A		:       int		,      A		:       int = 3		,      A		:       int = 1		,      A		:       str = "relu"		,      **A		:       List[Any] ):
             super().__init__(**A )
             _UpperCAmelCase  :						Dict							   =					kernel_size // 2
             _UpperCAmelCase  :						Dict							   =					tf.keras.layers.ConvaD(
                 A		,      kernel_size=A		,      strides=A		,      padding="valid"		,      use_bias=A		,      name="convolution" )
             # Use same default momentum and epsilon as PyTorch equivalent
             _UpperCAmelCase  :						Optional[Any]							   =					tf.keras.layers.BatchNormalization(epsilon=1E-5		,      momentum=0.9		,      name="normalization" )
             _UpperCAmelCase  :						Tuple							   =					ACTaFN[activation] if activation is not None else tf.keras.layers.Activation("linear" )




      def   _A			( self		:       Optional[Any]		,      A		:       tf.Tensor ):
             # Pad to match that done in the PyTorch Conv2D model
             _UpperCAmelCase  :						Optional[Any]							   =					(self.pad_value, self.pad_value)
             _UpperCAmelCase  :						Any							   =					tf.pad(A		,      [(0, 0), height_pad, width_pad, (0, 0)] )
             _UpperCAmelCase  :						Dict							   =					self.conv(A )
             return hidden_state





      def   _A			( self		:       List[str]		,      A		:       tf.Tensor		,      A		:       bool = False ):
             _UpperCAmelCase  :						int							   =					self.convolution(A )
             _UpperCAmelCase  :						Optional[int]							   =					self.normalization(A		,      training=A )
             _UpperCAmelCase  :						List[str]							   =					self.activation(A )
             return hidden_state

class 			lowerCamelCase_			(tf.keras.layers.Layer					):
      '''simple docstring'''

      def __init__( self		:       List[str]		,      A		:       ResNetConfig		,      **A		:       Tuple ):
             super().__init__(**A )
             _UpperCAmelCase  :						str							   =					TFResNetConvLayer(
                 config.embedding_size		,      kernel_size=7		,      stride=2		,      activation=config.hidden_act		,      name="embedder"		,      )
             _UpperCAmelCase  :						Optional[int]							   =					tf.keras.layers.MaxPoolaD(pool_size=3		,      strides=2		,      padding="valid"		,      name="pooler" )
             _UpperCAmelCase  :						List[str]							   =					config.num_channels





      def   _A			( self		:       List[str]		,      A		:       tf.Tensor		,      A		:       bool = False ):
             _UpperCAmelCase   ,						_UpperCAmelCase   ,						_UpperCAmelCase   ,						_UpperCAmelCase  :						Any							   =					shape_list(A )
             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." )
             _UpperCAmelCase  :						Any							   =					pixel_values
             _UpperCAmelCase  :						Union[str, Any]							   =					self.embedder(A )
             _UpperCAmelCase  :						int							   =					tf.pad(A		,      [[0, 0], [1, 1], [1, 1], [0, 0]] )
             _UpperCAmelCase  :						Tuple							   =					self.pooler(A )
             return hidden_state

class 			lowerCamelCase_			(tf.keras.layers.Layer					):
      '''simple docstring'''

      def __init__( self		:       str		,      A		:       int		,      A		:       int = 2		,      **A		:       Union[str, Any] ):
             super().__init__(**A )
             _UpperCAmelCase  :						Dict							   =					tf.keras.layers.ConvaD(
                 A		,      kernel_size=1		,      strides=A		,      use_bias=A		,      name="convolution" )
             # Use same default momentum and epsilon as PyTorch equivalent
             _UpperCAmelCase  :						Tuple							   =					tf.keras.layers.BatchNormalization(epsilon=1E-5		,      momentum=0.9		,      name="normalization" )





      def   _A			( self		:       List[Any]		,      A		:       tf.Tensor		,      A		:       bool = False ):
             _UpperCAmelCase  :						List[Any]							   =					x
             _UpperCAmelCase  :						Tuple							   =					self.convolution(A )
             _UpperCAmelCase  :						List[str]							   =					self.normalization(A		,      training=A )
             return hidden_state

class 			lowerCamelCase_			(tf.keras.layers.Layer					):
      '''simple docstring'''

      def __init__( self		:       Optional[Any]		,      A		:       int		,      A		:       int		,      A		:       int = 1		,      A		:       str = "relu"		,      **A		:       Dict ):
             super().__init__(**A )
             _UpperCAmelCase  :						List[Any]							   =					in_channels != out_channels or stride != 1
             _UpperCAmelCase  :						Dict							   =					TFResNetConvLayer(A		,      stride=A		,      name="layer.0" )
             _UpperCAmelCase  :						int							   =					TFResNetConvLayer(A		,      activation=A		,      name="layer.1" )
             _UpperCAmelCase  :						List[Any]							   =					(
                 TFResNetShortCut(A		,      stride=A		,      name="shortcut" )
                 if should_apply_shortcut
                 else tf.keras.layers.Activation("linear"		,      name="shortcut" )
             )
             _UpperCAmelCase  :						Optional[int]							   =					ACTaFN[activation]





      def   _A			( self		:       Any		,      A		:       tf.Tensor		,      A		:       bool = False ):
             _UpperCAmelCase  :						Dict							   =					hidden_state
             _UpperCAmelCase  :						Optional[Any]							   =					self.conva(A		,      training=A )
             _UpperCAmelCase  :						List[str]							   =					self.conva(A		,      training=A )
             _UpperCAmelCase  :						Dict							   =					self.shortcut(A		,      training=A )
             hidden_state += residual
             _UpperCAmelCase  :						List[str]							   =					self.activation(A )
             return hidden_state

class 			lowerCamelCase_			(tf.keras.layers.Layer					):
      '''simple docstring'''

      def __init__( self		:       List[str]		,      A		:       int		,      A		:       int		,      A		:       int = 1		,      A		:       str = "relu"		,      A		:       int = 4		,      **A		:       str		,      ):
             super().__init__(**A )
             _UpperCAmelCase  :						List[Any]							   =					in_channels != out_channels or stride != 1
             _UpperCAmelCase  :						List[str]							   =					out_channels // reduction
             _UpperCAmelCase  :						List[Any]							   =					TFResNetConvLayer(A		,      kernel_size=1		,      name="layer.0" )
             _UpperCAmelCase  :						Dict							   =					TFResNetConvLayer(A		,      stride=A		,      name="layer.1" )
             _UpperCAmelCase  :						str							   =					TFResNetConvLayer(A		,      kernel_size=1		,      activation=A		,      name="layer.2" )
             _UpperCAmelCase  :						Optional[Any]							   =					(
                 TFResNetShortCut(A		,      stride=A		,      name="shortcut" )
                 if should_apply_shortcut
                 else tf.keras.layers.Activation("linear"		,      name="shortcut" )
             )
             _UpperCAmelCase  :						Union[str, Any]							   =					ACTaFN[activation]





      def   _A			( self		:       Optional[int]		,      A		:       tf.Tensor		,      A		:       bool = False ):
             _UpperCAmelCase  :						Optional[Any]							   =					hidden_state
             _UpperCAmelCase  :						int							   =					self.conva(A		,      training=A )
             _UpperCAmelCase  :						Any							   =					self.conva(A		,      training=A )
             _UpperCAmelCase  :						List[Any]							   =					self.conva(A		,      training=A )
             _UpperCAmelCase  :						List[str]							   =					self.shortcut(A		,      training=A )
             hidden_state += residual
             _UpperCAmelCase  :						Tuple							   =					self.activation(A )
             return hidden_state

class 			lowerCamelCase_			(tf.keras.layers.Layer					):
      '''simple docstring'''

      def __init__( self		:       Dict		,      A		:       ResNetConfig		,      A		:       int		,      A		:       int		,      A		:       int = 2		,      A		:       int = 2		,      **A		:       Dict ):
             super().__init__(**A )

             _UpperCAmelCase  :						Optional[int]							   =					TFResNetBottleNeckLayer if config.layer_type == "bottleneck" else TFResNetBasicLayer

             _UpperCAmelCase  :						Dict							   =					[layer(A		,      A		,      stride=A		,      activation=config.hidden_act		,      name="layers.0" )]
             layers += [
                 layer(A		,      A		,      activation=config.hidden_act		,      name=F"""layers.{i + 1}""" )
                 for i in range(depth - 1 )
             ]
             _UpperCAmelCase  :						int							   =					layers





      def   _A			( self		:       List[Any]		,      A		:       tf.Tensor		,      A		:       bool = False ):
             for layer in self.stage_layers:
                    _UpperCAmelCase  :						int							   =					layer(A		,      training=A )
             return hidden_state

class 			lowerCamelCase_			(tf.keras.layers.Layer					):
      '''simple docstring'''

      def __init__( self		:       Optional[int]		,      A		:       ResNetConfig		,      **A		:       Optional[Any] ):
             super().__init__(**A )
             # based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
             _UpperCAmelCase  :						Optional[Any]							   =					[
                 TFResNetStage(
                     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"		,      )
             ]
             for i, (in_channels, out_channels, depth) in enumerate(
                 zip(config.hidden_sizes		,      config.hidden_sizes[1:]		,      config.depths[1:] ) ):
                    self.stages.append(TFResNetStage(A		,      A		,      A		,      depth=A		,      name=F"""stages.{i + 1}""" ) )





      def   _A			( self		:       Tuple		,      A		:       tf.Tensor		,      A		:       bool = False		,      A		:       bool = True		,      A		:       bool = False		,      ):
             _UpperCAmelCase  :						List[str]							   =					() if output_hidden_states else None

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

                    _UpperCAmelCase  :						str							   =					stage_module(A		,      training=A )

             if output_hidden_states:
                    _UpperCAmelCase  :						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 )

class 			lowerCamelCase_			(snake_case__					):
      '''simple docstring'''

      __UpperCamelCase:						str      			=		ResNetConfig
      __UpperCamelCase:						List[str]      			=		"resnet"
      __UpperCamelCase:						int      			=		"pixel_values"




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

__SCREAMING_SNAKE_CASE :  Dict  =						R"""
    This model is a TensorFlow
    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a
    regular TensorFlow Module and refer to the TensorFlow documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
"""


__SCREAMING_SNAKE_CASE :  int  =						R"""
    Args:
        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConvNextImageProcessor.__call__`] for details.

        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""

@keras_serializable
class 			lowerCamelCase_			(tf.keras.layers.Layer					):
      '''simple docstring'''

      __UpperCamelCase:						Any      			=		ResNetConfig




      def __init__( self		:       int		,      A		:       ResNetConfig		,      **A		:       str ):
             super().__init__(**A )
             _UpperCAmelCase  :						int							   =					config
             _UpperCAmelCase  :						str							   =					TFResNetEmbeddings(A		,      name="embedder" )
             _UpperCAmelCase  :						List[str]							   =					TFResNetEncoder(A		,      name="encoder" )
             _UpperCAmelCase  :						List[Any]							   =					tf.keras.layers.GlobalAveragePoolingaD(keepdims=A )





      @unpack_inputs
      def   _A			( self		:       str		,      A		:       tf.Tensor		,      A		:       Optional[bool] = None		,      A		:       Optional[bool] = None		,      A		:       bool = False		,      ):
             _UpperCAmelCase  :						Union[str, Any]							   =					(
                 output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
             )
             _UpperCAmelCase  :						int							   =					return_dict if return_dict is not None else self.config.use_return_dict

             # TF 2.0 image layers can't use NCHW format when running on CPU.
             # We transpose to NHWC format and then transpose back after the full forward pass.
             # (batch_size, num_channels, height, width) -> (batch_size, height, width, num_channels)
             _UpperCAmelCase  :						Optional[Any]							   =					tf.transpose(A		,      perm=[0, 2, 3, 1] )
             _UpperCAmelCase  :						int							   =					self.embedder(A		,      training=A )

             _UpperCAmelCase  :						List[Any]							   =					self.encoder(
                 A		,      output_hidden_states=A		,      return_dict=A		,      training=A )

             _UpperCAmelCase  :						Any							   =					encoder_outputs[0]

             _UpperCAmelCase  :						List[Any]							   =					self.pooler(A )

             # Transpose all the outputs to the NCHW format
             # (batch_size, height, width, num_channels) -> (batch_size, num_channels, height, width)
             _UpperCAmelCase  :						str							   =					tf.transpose(A		,      (0, 3, 1, 2) )
             _UpperCAmelCase  :						Optional[int]							   =					tf.transpose(A		,      (0, 3, 1, 2) )
             _UpperCAmelCase  :						Dict							   =					()
             for hidden_state in encoder_outputs[1:]:
                    _UpperCAmelCase  :						Optional[Any]							   =					hidden_states + tuple(tf.transpose(A		,      (0, 3, 1, 2) ) for h in hidden_state )

             if not return_dict:
                    return (last_hidden_state, pooled_output) + hidden_states

             _UpperCAmelCase  :						List[Any]							   =					hidden_states if output_hidden_states else None

             return TFBaseModelOutputWithPoolingAndNoAttention(
                 last_hidden_state=A		,      pooler_output=A		,      hidden_states=A		,      )

@add_start_docstrings(
    "The bare ResNet model outputting raw features without any specific head on top."							,				snake_case__							,				)
class 			lowerCamelCase_			(snake_case__					):
      '''simple docstring'''

      def __init__( self		:       str		,      A		:       ResNetConfig		,      **A		:       Optional[int] ):
             super().__init__(A		,      **A )
             _UpperCAmelCase  :						List[str]							   =					TFResNetMainLayer(config=A		,      name="resnet" )





      @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		,      )
      @unpack_inputs
      def   _A			( self		:       Optional[int]		,      A		:       tf.Tensor		,      A		:       Optional[bool] = None		,      A		:       Optional[bool] = None		,      A		:       bool = False		,      ):
             _UpperCAmelCase  :						List[str]							   =					(
                 output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
             )
             _UpperCAmelCase  :						Tuple							   =					return_dict if return_dict is not None else self.config.use_return_dict

             _UpperCAmelCase  :						str							   =					self.resnet(
                 pixel_values=A		,      output_hidden_states=A		,      return_dict=A		,      training=A		,      )
             return resnet_outputs

@add_start_docstrings(
    "\n    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    "							,				snake_case__							,				)
class 			lowerCamelCase_			(snake_case__							,				snake_case__					):
      '''simple docstring'''

      def __init__( self		:       Tuple		,      A		:       ResNetConfig		,      **A		:       Any ):
             super().__init__(A		,      **A )
             _UpperCAmelCase  :						List[Any]							   =					config.num_labels
             _UpperCAmelCase  :						int							   =					TFResNetMainLayer(A		,      name="resnet" )
             # classification head
             _UpperCAmelCase  :						Optional[int]							   =					(
                 tf.keras.layers.Dense(config.num_labels		,      name="classifier.1" )
                 if config.num_labels > 0
                 else tf.keras.layers.Activation("linear"		,      name="classifier.1" )
             )




      def   _A			( self		:       Optional[int]		,      A		:       tf.Tensor ):
             _UpperCAmelCase  :						List[Any]							   =					tf.keras.layers.Flatten()(A )
             _UpperCAmelCase  :						List[str]							   =					self.classifier_layer(A )
             return logits





      @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		,      )
      @unpack_inputs
      def   _A			( self		:       List[Any]		,      A		:       tf.Tensor = None		,      A		:       tf.Tensor = None		,      A		:       bool = None		,      A		:       bool = None		,      A		:       bool = False		,      ):
             _UpperCAmelCase  :						Any							   =					return_dict if return_dict is not None else self.config.use_return_dict

             _UpperCAmelCase  :						Optional[Any]							   =					self.resnet(
                 A		,      output_hidden_states=A		,      return_dict=A		,      training=A )

             _UpperCAmelCase  :						List[Any]							   =					outputs.pooler_output if return_dict else outputs[1]

             _UpperCAmelCase  :						Optional[int]							   =					self.classifier(A )

             _UpperCAmelCase  :						Dict							   =					None if labels is None else self.hf_compute_loss(A		,      A )

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

             return TFImageClassifierOutputWithNoAttention(loss=A		,      logits=A		,      hidden_states=outputs.hidden_states )

