





import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from datasets import Dataset, load_dataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoModelForMaskedLM,
    AutoTokenizer,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


snake_case_				=			logging.getLogger(__name__)
snake_case_				=			list(MODEL_FOR_MASKED_LM_MAPPING.keys())
snake_case_				=			tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)

@dataclass
class 		SCREAMING_SNAKE_CASE__				:
      A_						:				Optional[str]							=    field(
          default=_UpperCAmelCase						,      metadata={
              'help': (
                  'The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'
              )
          }						,      )
      A_						:				Optional[str]							=    field(
          default=_UpperCAmelCase						,      metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(_UpperCAmelCase  )}						,      )
      A_						:				Optional[str]							=    field(
          default=_UpperCAmelCase						,      metadata={
              'help': (
                  'Override some existing default config settings when a model is trained from scratch. Example: '
                  'n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'
              )
          }						,      )
      A_						:				Optional[str]							=    field(
          default=_UpperCAmelCase						,      metadata={'help': 'Pretrained config name or path if not the same as model_name'}  )
      A_						:				Optional[str]							=    field(
          default=_UpperCAmelCase						,      metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}  )
      A_						:				Optional[str]							=    field(
          default=_UpperCAmelCase						,      metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}						,      )
      A_						:				bool							=    field(
          default=_UpperCAmelCase						,      metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'}						,      )
      A_						:				str							=    field(
          default='main'						,      metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}						,      )
      A_						:				bool							=    field(
          default=_UpperCAmelCase						,      metadata={
              'help': (
                  'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
                  'with private models).'
              )
          }						,      )


      def    a (self       :      Tuple      ):





         """simple docstring"""



         if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
            raise ValueError(
                '''--config_overrides can\'t be used in combination with --config_name or --model_name_or_path'''      )






@dataclass
class 		SCREAMING_SNAKE_CASE__				:
      A_						:				Optional[str]							=    field(
          default=_UpperCAmelCase						,      metadata={'help': 'The name of the dataset to use (via the datasets library).'}  )
      A_						:				Optional[str]							=    field(
          default=_UpperCAmelCase						,      metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}  )
      A_						:				Optional[str]							=    field(default=_UpperCAmelCase						,      metadata={'help': 'The input training data file (a text file).'}  )
      A_						:				Optional[str]							=    field(
          default=_UpperCAmelCase						,      metadata={'help': 'An optional input evaluation data file to evaluate the perplexity on (a text file).'}						,      )
      A_						:				Optional[str]							=    field(
          default=_UpperCAmelCase						,      metadata={'help': 'An optional input train ref data file for whole word masking in Chinese.'}						,      )
      A_						:				Optional[str]							=    field(
          default=_UpperCAmelCase						,      metadata={'help': 'An optional input validation ref data file for whole word masking in Chinese.'}						,      )
      A_						:				bool							=    field(
          default=_UpperCAmelCase						,      metadata={'help': 'Overwrite the cached training and evaluation sets'}  )
      A_						:				Optional[int]							=    field(
          default=5						,      metadata={
              'help': 'The percentage of the train set used as validation set in case there\'s no validation split'
          }						,      )
      A_						:				Optional[int]							=    field(
          default=_UpperCAmelCase						,      metadata={
              'help': (
                  'The maximum total input sequence length after tokenization. Sequences longer '
                  'than this will be truncated. Default to the max input length of the model.'
              )
          }						,      )
      A_						:				Optional[int]							=    field(
          default=_UpperCAmelCase						,      metadata={'help': 'The number of processes to use for the preprocessing.'}						,      )
      A_						:				float							=    field(
          default=0.15						,      metadata={'help': 'Ratio of tokens to mask for masked language modeling loss'}  )
      A_						:				bool							=    field(
          default=_UpperCAmelCase						,      metadata={
              'help': (
                  'Whether to pad all samples to `max_seq_length`. '
                  'If False, will pad the samples dynamically when batching to the maximum length in the batch.'
              )
          }						,      )


      def    a (self       :      int      ):





         """simple docstring"""



         if self.train_file is not None:
            __snake_case							       =   self.train_file.split('''.'''      )[-1]
            assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
         if self.validation_file is not None:
            __snake_case							       =   self.validation_file.split('''.'''      )[-1]
            assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."






def lowerCamelCase__    (							snake_case_	:  Optional[int]       ,      snake_case_	:  int       )      ->				Optional[Any]:
   with open(snake_case_       ,      '''r'''       ,      encoding='''utf-8'''       ) as f:
      __snake_case							       =   [json.loads(snake_case_       ) for line in f.read().splitlines() if (len(snake_case_       ) > 0 and not line.isspace())]
   assert len(snake_case_       ) == len(snake_case_       )

   __snake_case							       =   {c: dataset[c] for c in dataset.column_names}
   __snake_case							       =   refs
   return Dataset.from_dict(snake_case_       )






def lowerCamelCase__    (							)      ->				List[str]:
   # See all possible arguments in src/transformers/training_args.py
   # or by passing the --help flag to this script.
   # We now keep distinct sets of args, for a cleaner separation of concerns.

   __snake_case							       =   HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)       )
   if len(sys.argv       ) == 2 and sys.argv[1].endswith('''.json'''       ):
      # If we pass only one argument to the script and it's the path to a json file,
      # let's parse it to get our arguments.
      __snake_case       ,	__snake_case       ,	__snake_case							       =   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
   else:
      __snake_case       ,	__snake_case       ,	__snake_case							       =   parser.parse_args_into_dataclasses()

   # Detecting last checkpoint.
   __snake_case							       =   None
   if os.path.isdir(training_args.output_dir       ) and training_args.do_train and not training_args.overwrite_output_dir:
      __snake_case							       =   get_last_checkpoint(training_args.output_dir       )
      if last_checkpoint is None and len(os.listdir(training_args.output_dir       )       ) > 0:
         raise ValueError(
             f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
             '''Use --overwrite_output_dir to overcome.'''       )
      elif last_checkpoint is not None:
         logger.info(
             f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
             '''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'''       )

    # Setup logging
   logging.basicConfig(
       format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''       ,      datefmt='''%m/%d/%Y %H:%M:%S'''       ,      handlers=[logging.StreamHandler(sys.stdout       )]       ,      )
   logger.setLevel(logging.INFO if is_main_process(training_args.local_rank       ) else logging.WARN       )

   # Log on each process the small summary:
   logger.warning(
       f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
       + f"""distributed training: {bool(training_args.local_rank != -1       )}, 16-bits training: {training_args.fpaa}"""       )
   # Set the verbosity to info of the Transformers logger (on main process only):
   if is_main_process(training_args.local_rank       ):
      transformers.utils.logging.set_verbosity_info()
      transformers.utils.logging.enable_default_handler()
      transformers.utils.logging.enable_explicit_format()
   logger.info('''Training/evaluation parameters %s'''       ,      snake_case_       )

   # Set seed before initializing model.
   set_seed(training_args.seed       )

   # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
   # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
   # (the dataset will be downloaded automatically from the datasets Hub).
   #
   # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
   # 'text' is found. You can easily tweak this behavior (see below).
   #
   # In distributed training, the load_dataset function guarantee that only one local process can concurrently
   # download the dataset.
   if data_args.dataset_name is not None:
      # Downloading and loading a dataset from the hub.
      __snake_case							       =   load_dataset(data_args.dataset_name       ,      data_args.dataset_config_name       )
      if "validation" not in datasets.keys():
         __snake_case							       =   load_dataset(
             data_args.dataset_name       ,      data_args.dataset_config_name       ,      split=f"""train[:{data_args.validation_split_percentage}%]"""       ,      )
         __snake_case							       =   load_dataset(
             data_args.dataset_name       ,      data_args.dataset_config_name       ,      split=f"""train[{data_args.validation_split_percentage}%:]"""       ,      )
   else:
      __snake_case							       =   {}
      if data_args.train_file is not None:
         __snake_case							       =   data_args.train_file
      if data_args.validation_file is not None:
         __snake_case							       =   data_args.validation_file
      __snake_case							       =   data_args.train_file.split('''.'''       )[-1]
      if extension == "txt":
         __snake_case							       =   '''text'''
      __snake_case							       =   load_dataset(snake_case_       ,      data_files=snake_case_       )
   # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
   # https://huggingface.co/docs/datasets/loading_datasets.html.

   # Load pretrained model and tokenizer
   #
   # Distributed training:
   # The .from_pretrained methods guarantee that only one local process can concurrently
   # download model & vocab.
   __snake_case							       =   {
       '''cache_dir''': model_args.cache_dir,
       '''revision''': model_args.model_revision,
       '''use_auth_token''': True if model_args.use_auth_token else None,
   }
   if model_args.config_name:
      __snake_case							       =   AutoConfig.from_pretrained(model_args.config_name       ,      **snake_case_       )
   elif model_args.model_name_or_path:
      __snake_case							       =   AutoConfig.from_pretrained(model_args.model_name_or_path       ,      **snake_case_       )
   else:
      __snake_case							       =   CONFIG_MAPPING[model_args.model_type]()
      logger.warning('''You are instantiating a new config instance from scratch.'''       )
      if model_args.config_overrides is not None:
         logger.info(f"""Overriding config: {model_args.config_overrides}"""       )
         config.update_from_string(model_args.config_overrides       )
         logger.info(f"""New config: {config}"""       )

   __snake_case							       =   {
       '''cache_dir''': model_args.cache_dir,
       '''use_fast''': model_args.use_fast_tokenizer,
       '''revision''': model_args.model_revision,
       '''use_auth_token''': True if model_args.use_auth_token else None,
   }
   if model_args.tokenizer_name:
      __snake_case							       =   AutoTokenizer.from_pretrained(model_args.tokenizer_name       ,      **snake_case_       )
   elif model_args.model_name_or_path:
      __snake_case							       =   AutoTokenizer.from_pretrained(model_args.model_name_or_path       ,      **snake_case_       )
   else:
      raise ValueError(
          '''You are instantiating a new tokenizer from scratch. This is not supported by this script.'''
          '''You can do it from another script, save it, and load it from here, using --tokenizer_name.'''       )

   if model_args.model_name_or_path:
      __snake_case							       =   AutoModelForMaskedLM.from_pretrained(
          model_args.model_name_or_path       ,      from_tf=bool('''.ckpt''' in model_args.model_name_or_path       )       ,      config=snake_case_       ,      cache_dir=model_args.cache_dir       ,      revision=model_args.model_revision       ,      use_auth_token=True if model_args.use_auth_token else None       ,      )
   else:
      logger.info('''Training new model from scratch'''       )
      __snake_case							       =   AutoModelForMaskedLM.from_config(snake_case_       )

   model.resize_token_embeddings(len(snake_case_       )       )

   # Preprocessing the datasets.
   # First we tokenize all the texts.
   if training_args.do_train:
      __snake_case							       =   datasets['''train'''].column_names
   else:
      __snake_case							       =   datasets['''validation'''].column_names
   __snake_case							       =   '''text''' if '''text''' in column_names else column_names[0]

   __snake_case							       =   '''max_length''' if data_args.pad_to_max_length else False

   def tokenize_function(snake_case_	:  Any       ):
      # Remove empty lines
      __snake_case							       =   [line for line in examples['''text'''] if len(snake_case_       ) > 0 and not line.isspace()]
      return tokenizer(examples['''text''']       ,      padding=snake_case_       ,      truncation=snake_case_       ,      max_length=data_args.max_seq_length       )

   __snake_case							       =   datasets.map(
       snake_case_       ,      batched=snake_case_       ,      num_proc=data_args.preprocessing_num_workers       ,      remove_columns=[text_column_name]       ,      load_from_cache_file=not data_args.overwrite_cache       ,      )

   # Add the chinese references if provided
   if data_args.train_ref_file is not None:
      __snake_case							       =   add_chinese_references(tokenized_datasets['''train''']       ,      data_args.train_ref_file       )
   if data_args.validation_ref_file is not None:
      __snake_case							       =   add_chinese_references(
          tokenized_datasets['''validation''']       ,      data_args.validation_ref_file       )
   # If we have ref files, need to avoid it removed by trainer
   __snake_case							       =   data_args.train_ref_file or data_args.validation_ref_file
   if has_ref:
      __snake_case							       =   False

   # Data collator
   # This one will take care of randomly masking the tokens.
   __snake_case							       =   DataCollatorForWholeWordMask(tokenizer=snake_case_       ,      mlm_probability=data_args.mlm_probability       )

   # Initialize our Trainer
   __snake_case							       =   Trainer(
       model=snake_case_       ,      args=snake_case_       ,      train_dataset=tokenized_datasets['''train'''] if training_args.do_train else None       ,      eval_dataset=tokenized_datasets['''validation'''] if training_args.do_eval else None       ,      tokenizer=snake_case_       ,      data_collator=snake_case_       ,      )

   # Training
   if training_args.do_train:
      if last_checkpoint is not None:
         __snake_case							       =   last_checkpoint
      elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path       ):
         __snake_case							       =   model_args.model_name_or_path
      else:
         __snake_case							       =   None
      __snake_case							       =   trainer.train(resume_from_checkpoint=snake_case_       )
      trainer.save_model()  # Saves the tokenizer too for easy upload

      __snake_case							       =   os.path.join(training_args.output_dir       ,      '''train_results.txt'''       )
      if trainer.is_world_process_zero():
         with open(snake_case_       ,      '''w'''       ) as writer:
            logger.info('''***** Train results *****'''       )
            for key, value in sorted(train_result.metrics.items()       ):
               logger.info(f"""  {key} = {value}"""       )
               writer.write(f"""{key} = {value}\n"""       )

            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
         trainer.state.save_to_json(os.path.join(training_args.output_dir       ,      '''trainer_state.json'''       )       )

    # Evaluation
   __snake_case							       =   {}
   if training_args.do_eval:
      logger.info('''*** Evaluate ***'''       )

      __snake_case							       =   trainer.evaluate()

      __snake_case							       =   math.exp(eval_output['''eval_loss''']       )
      __snake_case							       =   perplexity

      __snake_case							       =   os.path.join(training_args.output_dir       ,      '''eval_results_mlm_wwm.txt'''       )
      if trainer.is_world_process_zero():
         with open(snake_case_       ,      '''w'''       ) as writer:
            logger.info('''***** Eval results *****'''       )
            for key, value in sorted(results.items()       ):
               logger.info(f"""  {key} = {value}"""       )
               writer.write(f"""{key} = {value}\n"""       )

   return results






def lowerCamelCase__    (							snake_case_	:  int       )      ->				Optional[int]:
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
    main()








import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
    import torch

    from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin

class 		SCREAMING_SNAKE_CASE__				:


      def __init__(self       :      str				,			a__       :      Dict				,			a__       :      Tuple=None				,			a__       :      List[Any]=None				,			a__       :      Dict=None				,			a__       :      Union[str, Any]="resnet50"				,			a__       :      Dict=3				,			a__       :      str=32				,			a__       :      int=3				,			a__       :      Dict=True				,			a__       :      Any=True				,			):





         """simple docstring"""



         __snake_case							       =   parent
         __snake_case							       =   out_indices if out_indices is not None else [4]
         __snake_case							       =   stage_names
         __snake_case							       =   out_features
         __snake_case							       =   backbone
         __snake_case							       =   batch_size
         __snake_case							       =   image_size
         __snake_case							       =   num_channels
         __snake_case							       =   use_pretrained_backbone
         __snake_case							       =   is_training


      def    a (self       :      Union[str, Any]      ):





         """simple docstring"""



         __snake_case							       =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )
         __snake_case							       =   self.get_config()

         return config, pixel_values


      def    a (self       :      Any      ):





         """simple docstring"""



         return TimmBackboneConfig(
             image_size=self.image_size				,			num_channels=self.num_channels				,			out_features=self.out_features				,			out_indices=self.out_indices				,			stage_names=self.stage_names				,			use_pretrained_backbone=self.use_pretrained_backbone				,			backbone=self.backbone				,			)


      def    a (self       :      List[Any]				,			a__       :      int				,			a__       :      int      ):





         """simple docstring"""



         __snake_case							       =   TimmBackbone(config=a__      )
         model.to(a__      )
         model.eval()
         with torch.no_grad():
            __snake_case							       =   model(a__      )
         self.parent.assertEqual(
             result.feature_map[-1].shape				,			(self.batch_size, model.channels[-1], 14, 14)				,			)


      def    a (self       :      str      ):





         """simple docstring"""



         __snake_case							       =   self.prepare_config_and_inputs()
         __snake_case       ,	__snake_case							       =   config_and_inputs
         __snake_case							       =   {'''pixel_values''': pixel_values}
         return config, inputs_dict






@require_torch
@require_timm
class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase						,      unittest.TestCase  ):
      A_						:				Union[str, Any]							=    (TimmBackbone,) if is_torch_available() else ()
      A_						:				Optional[Any]							=    {'feature-extraction': TimmBackbone} if is_torch_available() else {}
      A_						:				List[Any]							=    False
      A_						:				Dict							=    False
      A_						:				Any							=    False
      A_						:				List[Any]							=    False


      def    a (self       :      Tuple      ):





         """simple docstring"""



         __snake_case							       =   TimmBackboneModelTester(self      )
         __snake_case							       =   ConfigTester(self				,			config_class=a__				,			has_text_modality=a__      )


      def    a (self       :      Any      ):





         """simple docstring"""



         self.config_tester.create_and_test_config_to_json_string()
         self.config_tester.create_and_test_config_to_json_file()
         self.config_tester.create_and_test_config_from_and_save_pretrained()
         self.config_tester.create_and_test_config_with_num_labels()
         self.config_tester.check_config_can_be_init_without_params()
         self.config_tester.check_config_arguments_init()


      def    a (self       :      int      ):





         """simple docstring"""



         __snake_case							       =   '''resnet18'''
         __snake_case							       =   '''microsoft/resnet-18'''

         __snake_case							       =   AutoBackbone.from_pretrained(a__				,			use_timm_backbone=a__      )
         __snake_case							       =   AutoBackbone.from_pretrained(a__      )

         self.assertEqual(len(timm_model.out_features      )				,			len(transformers_model.out_features      )      )
         self.assertEqual(len(timm_model.stage_names      )				,			len(transformers_model.stage_names      )      )
         self.assertEqual(timm_model.channels				,			transformers_model.channels      )
         # Out indices are set to the last layer by default. For timm models, we don't know
         # the number of layers in advance, so we set it to (-1,), whereas for transformers
         # models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
         self.assertEqual(timm_model.out_indices				,			(-1,)      )
         self.assertEqual(transformers_model.out_indices				,			[len(timm_model.stage_names      ) - 1]      )

         __snake_case							       =   AutoBackbone.from_pretrained(a__				,			use_timm_backbone=a__				,			out_indices=[1, 2, 3]      )
         __snake_case							       =   AutoBackbone.from_pretrained(a__				,			out_indices=[1, 2, 3]      )

         self.assertEqual(timm_model.out_indices				,			transformers_model.out_indices      )
         self.assertEqual(len(timm_model.out_features      )				,			len(transformers_model.out_features      )      )
         self.assertEqual(timm_model.channels				,			transformers_model.channels      )


      @unittest.skip('''TimmBackbone doesn\'t support feed forward chunking'''      )
      def    a (self       :      str      ):





         """simple docstring"""



         pass


      @unittest.skip('''TimmBackbone doesn\'t have num_hidden_layers attribute'''      )
      def    a (self       :      int      ):





         """simple docstring"""



         pass


      @unittest.skip('''TimmBackbone initialization is managed on the timm side'''      )
      def    a (self       :      Union[str, Any]      ):





         """simple docstring"""



         pass


      @unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds'''      )
      def    a (self       :      Optional[int]      ):





         """simple docstring"""



         pass


      @unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds'''      )
      def    a (self       :      int      ):





         """simple docstring"""



         pass


      @unittest.skip('''TimmBackbone model cannot be created without specifying a backbone checkpoint'''      )
      def    a (self       :      Tuple      ):





         """simple docstring"""



         pass


      @unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''      )
      def    a (self       :      int      ):





         """simple docstring"""



         pass


      @unittest.skip('''model weights aren\'t tied in TimmBackbone.'''      )
      def    a (self       :      Optional[Any]      ):





         """simple docstring"""



         pass


      @unittest.skip('''model weights aren\'t tied in TimmBackbone.'''      )
      def    a (self       :      Tuple      ):





         """simple docstring"""



         pass


      @unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''      )
      def    a (self       :      Dict      ):





         """simple docstring"""



         pass


      @unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''      )
      def    a (self       :      List[Any]      ):





         """simple docstring"""



         pass


      @unittest.skip('''TimmBackbone doesn\'t have hidden size info in its configuration.'''      )
      def    a (self       :      Optional[Any]      ):





         """simple docstring"""



         pass


      @unittest.skip('''TimmBackbone doesn\'t support output_attentions.'''      )
      def    a (self       :      List[Any]      ):





         """simple docstring"""



         pass


      @unittest.skip('''Safetensors is not supported by timm.'''      )
      def    a (self       :      Tuple      ):





         """simple docstring"""



         pass


      @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''      )
      def    a (self       :      Tuple      ):





         """simple docstring"""



         pass


      def    a (self       :      Tuple      ):





         """simple docstring"""



         __snake_case       ,	__snake_case							       =   self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            __snake_case							       =   model_class(a__      )
            __snake_case							       =   inspect.signature(model.forward      )
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            __snake_case							       =   [*signature.parameters.keys()]

            __snake_case							       =   ['''pixel_values''']
            self.assertListEqual(arg_names[:1]				,			a__      )


      def    a (self       :      Dict      ):





         """simple docstring"""



         __snake_case       ,	__snake_case							       =   self.model_tester.prepare_config_and_inputs_for_common()
         __snake_case							       =   True
         __snake_case							       =   self.has_attentions

         # no need to test all models as different heads yield the same functionality
         __snake_case							       =   self.all_model_classes[0]
         __snake_case							       =   model_class(a__      )
         model.to(a__      )

         __snake_case							       =   self._prepare_for_class(a__				,			a__      )
         __snake_case							       =   model(**a__      )
         __snake_case							       =   outputs[0][-1]

         # Encoder-/Decoder-only models
         __snake_case							       =   outputs.hidden_states[0]
         hidden_states.retain_grad()

         if self.has_attentions:
            __snake_case							       =   outputs.attentions[0]
            attentions.retain_grad()

         output.flatten()[0].backward(retain_graph=a__      )

         self.assertIsNotNone(hidden_states.grad      )

         if self.has_attentions:
            self.assertIsNotNone(attentions.grad      )


      def    a (self       :      Optional[int]      ):





         """simple docstring"""



         __snake_case       ,	__snake_case							       =   self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            __snake_case							       =   model_class(a__      )
            model.to(a__      )
            model.eval()
            __snake_case							       =   model(**a__      )

            self.assertEqual(len(result.feature_maps      )				,			len(config.out_indices      )      )
            self.assertEqual(len(model.channels      )				,			len(config.out_indices      )      )

            # Check output of last stage is taken if out_features=None, out_indices=None
            __snake_case							       =   copy.deepcopy(a__      )
            __snake_case							       =   None
            __snake_case							       =   model_class(a__      )
            model.to(a__      )
            model.eval()
            __snake_case							       =   model(**a__      )

            self.assertEqual(len(result.feature_maps      )				,			1      )
            self.assertEqual(len(model.channels      )				,			1      )

            # Check backbone can be initialized with fresh weights
            __snake_case							       =   copy.deepcopy(a__      )
            __snake_case							       =   False
            __snake_case							       =   model_class(a__      )
            model.to(a__      )
            model.eval()
            __snake_case							       =   model(**a__      )

