

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

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


__A      		=     logging.getLogger(__name__)



@dataclass
class   lowercase_   :
      UpperCamelCase_      :		str										=    field(
          metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}					)
      UpperCamelCase_      :		Optional[str]										=    field(
          default=__lowercase       ,							metadata={"help": "Pretrained config name or path if not the same as model_name"}					)
      UpperCamelCase_      :		Optional[str]										=    field(
          default=__lowercase       ,							metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}					)
      UpperCamelCase_      :		Optional[str]										=    field(
          default=__lowercase       ,							metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}       ,							)
      UpperCamelCase_      :		bool										=    field(default=__lowercase       ,							metadata={"help": "Whether tp freeze the encoder."}					)
      UpperCamelCase_      :		bool										=    field(default=__lowercase       ,							metadata={"help": "Whether  to freeze the embeddings."}					)



@dataclass
class   lowercase_   :
      UpperCamelCase_      :		str										=    field(
          metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}					)
      UpperCamelCase_      :		Optional[str]										=    field(
          default="summarization"       ,							metadata={"help": "Task name, summarization (or summarization_{dataset} for pegasus) or translation"}       ,							)
      UpperCamelCase_      :		Optional[int]										=    field(
          default=1_0_2_4       ,							metadata={
              "help": (
                  "The maximum total input sequence length after tokenization. Sequences longer "
                  "than this will be truncated, sequences shorter will be padded."
              )
          }       ,							)
      UpperCamelCase_      :		Optional[int]										=    field(
          default=1_2_8       ,							metadata={
              "help": (
                  "The maximum total sequence length for target text after tokenization. Sequences longer "
                  "than this will be truncated, sequences shorter will be padded."
              )
          }       ,							)
      UpperCamelCase_      :		Optional[int]										=    field(
          default=1_4_2       ,							metadata={
              "help": (
                  "The maximum total sequence length for validation target text after tokenization. Sequences longer "
                  "than this will be truncated, sequences shorter will be padded. "
                  "This argument is also used to override the ``max_length`` param of ``model.generate``, which is used "
                  "during ``evaluate`` and ``predict``."
              )
          }       ,							)
      UpperCamelCase_      :		Optional[int]										=    field(
          default=1_4_2       ,							metadata={
              "help": (
                  "The maximum total sequence length for test target text after tokenization. Sequences longer "
                  "than this will be truncated, sequences shorter will be padded."
              )
          }       ,							)
      UpperCamelCase_      :		Optional[int]										=    field(default=-1       ,							metadata={"help": "# training examples. -1 means use all."}					)
      UpperCamelCase_      :		Optional[int]										=    field(default=-1       ,							metadata={"help": "# validation examples. -1 means use all."}					)
      UpperCamelCase_      :		Optional[int]										=    field(default=-1       ,							metadata={"help": "# test examples. -1 means use all."}					)
      UpperCamelCase_      :		Optional[str]										=    field(default=__lowercase       ,							metadata={"help": "Source language id for translation."}					)
      UpperCamelCase_      :		Optional[str]										=    field(default=__lowercase       ,							metadata={"help": "Target language id for translation."}					)
      UpperCamelCase_      :		Optional[int]										=    field(default=__lowercase       ,							metadata={"help": "# num_beams to use for evaluation."}					)
      UpperCamelCase_      :		bool										=    field(
          default=__lowercase       ,							metadata={"help": "If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."}       ,							)



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)							->     List[Any]:



  """simple docstring"""






  logger.info(F"""***** {split} metrics *****"""					)
  for key in sorted(metrics.keys()					):
    logger.info(F"""  {key} = {metrics[key]}"""					)
  save_json(_UpperCamelCase			,	os.path.join(_UpperCamelCase			,	F"""{split}_results.json"""					)					)



def 				snake_case_()							->     List[Any]:



  """simple docstring"""






  _snake_case						=							HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)					)

  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()

  check_output_dir(_UpperCamelCase					)

  # Setup logging
  logging.basicConfig(
      format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''			,	datefmt='''%m/%d/%Y %H:%M:%S'''			,	level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN			,	)
  logger.warning(
      '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''			,	training_args.local_rank			,	training_args.device			,	training_args.n_gpu			,	bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED					)			,	training_args.fpaa			,	)
  transformers.utils.logging.enable_default_handler()
  transformers.utils.logging.enable_explicit_format()
  # 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()
  logger.info('''Training/evaluation parameters %s'''			,	_UpperCamelCase					)

  # Set seed
  set_seed(training_args.seed					)

  # Load pretrained model and tokenizer
  #
  # Distributed training:
  # The .from_pretrained methods guarantee that only one local process can concurrently
  # download model & vocab.

  _snake_case						=							AutoConfig.from_pretrained(
      model_args.config_name if model_args.config_name else model_args.model_name_or_path			,	cache_dir=model_args.cache_dir			,	)

  _snake_case						=							('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
  for p in extra_model_params:
    if getattr(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					):
      assert hasattr(_UpperCamelCase			,	_UpperCamelCase					), F"""({config.__class__.__name__}) doesn't have a `{p}` attribute"""
      setattr(_UpperCamelCase			,	_UpperCamelCase			,	getattr(_UpperCamelCase			,	_UpperCamelCase					)					)

  _snake_case						=							AutoTokenizer.from_pretrained(
      model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path			,	cache_dir=model_args.cache_dir			,	)
  _snake_case						=							AutoModelForSeqaSeqLM.from_pretrained(
      model_args.model_name_or_path			,	from_tf='''.ckpt''' in model_args.model_name_or_path			,	config=_UpperCamelCase			,	cache_dir=model_args.cache_dir			,	)

  # use task specific params
  use_task_specific_params(_UpperCamelCase			,	data_args.task					)

  # set num_beams for evaluation
  if data_args.eval_beams is None:
    _snake_case						=							model.config.num_beams

  # set decoder_start_token_id for MBart
  if model.config.decoder_start_token_id is None and isinstance(_UpperCamelCase			,	(MBartTokenizer, MBartTokenizerFast)					):
    assert (
        data_args.tgt_lang is not None and data_args.src_lang is not None
    ), "mBart requires --tgt_lang and --src_lang"
    if isinstance(_UpperCamelCase			,	_UpperCamelCase					):
      _snake_case						=							tokenizer.lang_code_to_id[data_args.tgt_lang]
    else:
      _snake_case						=							tokenizer.convert_tokens_to_ids(data_args.tgt_lang					)

  if model_args.freeze_embeds:
    freeze_embeds(_UpperCamelCase					)
  if model_args.freeze_encoder:
    freeze_params(model.get_encoder()					)
    assert_all_frozen(model.get_encoder()					)

  _snake_case						=							SeqaSeqDataset

  # Get datasets
  _snake_case						=							(
      dataset_class(
          _UpperCamelCase			,	type_path='''train'''			,	data_dir=data_args.data_dir			,	n_obs=data_args.n_train			,	max_target_length=data_args.max_target_length			,	max_source_length=data_args.max_source_length			,	prefix=model.config.prefix or ''''''			,	)
      if training_args.do_train
      else None
  )
  _snake_case						=							(
      dataset_class(
          _UpperCamelCase			,	type_path='''val'''			,	data_dir=data_args.data_dir			,	n_obs=data_args.n_val			,	max_target_length=data_args.val_max_target_length			,	max_source_length=data_args.max_source_length			,	prefix=model.config.prefix or ''''''			,	)
      if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
      else None
  )
  _snake_case						=							(
      dataset_class(
          _UpperCamelCase			,	type_path='''test'''			,	data_dir=data_args.data_dir			,	n_obs=data_args.n_test			,	max_target_length=data_args.test_max_target_length			,	max_source_length=data_args.max_source_length			,	prefix=model.config.prefix or ''''''			,	)
      if training_args.do_predict
      else None
  )

  # Initialize our Trainer
  _snake_case						=							(
      build_compute_metrics_fn(data_args.task			,	_UpperCamelCase					) if training_args.predict_with_generate else None
  )
  _snake_case						=							SeqaSeqTrainer(
      model=_UpperCamelCase			,	args=_UpperCamelCase			,	data_args=_UpperCamelCase			,	train_dataset=_UpperCamelCase			,	eval_dataset=_UpperCamelCase			,	data_collator=SeqaSeqDataCollator(
          _UpperCamelCase			,	_UpperCamelCase			,	model.config.decoder_start_token_id			,	training_args.tpu_num_cores					)			,	compute_metrics=_UpperCamelCase			,	tokenizer=_UpperCamelCase			,	)

  _snake_case						=							{}
  # Training
  if training_args.do_train:
    logger.info('''*** Train ***'''					)

    _snake_case						=							trainer.train(
        model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path					) else None					)
    _snake_case						=							train_result.metrics
    _snake_case						=							data_args.n_train

    trainer.save_model()  # this also saves the tokenizer

    if trainer.is_world_process_zero():
      handle_metrics('''train'''			,	_UpperCamelCase			,	training_args.output_dir					)
      all_metrics.update(_UpperCamelCase					)

      # 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'''					)					)

      # For convenience, we also re-save the tokenizer to the same directory,
      # so that you can share your model easily on huggingface.co/models =)
      tokenizer.save_pretrained(training_args.output_dir					)

    # Evaluation
  if training_args.do_eval:
    logger.info('''*** Evaluate ***'''					)

    _snake_case						=							trainer.evaluate(metric_key_prefix='''val'''					)
    _snake_case						=							data_args.n_val
    _snake_case						=							round(metrics['''val_loss''']			,	4					)

    if trainer.is_world_process_zero():
      handle_metrics('''val'''			,	_UpperCamelCase			,	training_args.output_dir					)
      all_metrics.update(_UpperCamelCase					)

  if training_args.do_predict:
    logger.info('''*** Predict ***'''					)

    _snake_case						=							trainer.predict(test_dataset=_UpperCamelCase			,	metric_key_prefix='''test'''					)
    _snake_case						=							test_output.metrics
    _snake_case						=							data_args.n_test

    if trainer.is_world_process_zero():
      _snake_case						=							round(metrics['''test_loss''']			,	4					)
      handle_metrics('''test'''			,	_UpperCamelCase			,	training_args.output_dir					)
      all_metrics.update(_UpperCamelCase					)

      if training_args.predict_with_generate:
        _snake_case						=							tokenizer.batch_decode(
            test_output.predictions			,	skip_special_tokens=_UpperCamelCase			,	clean_up_tokenization_spaces=_UpperCamelCase					)
        _snake_case						=							lmap(str.strip			,	_UpperCamelCase					)
        write_txt_file(_UpperCamelCase			,	os.path.join(training_args.output_dir			,	'''test_generations.txt'''					)					)

  if trainer.is_world_process_zero():
    save_json(_UpperCamelCase			,	os.path.join(training_args.output_dir			,	'''all_results.json'''					)					)

  return all_metrics



def 				snake_case_(_UpperCamelCase					)							->     List[str]:



  """simple docstring"""






  main()


if __name__ == "__main__":
 main()




import inspect
import unittest

from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import YolosForObjectDetection, YolosModel
 from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor



class   lowercase_   :



      def __init__(      self				:    Optional[Any]      , A__				:    str      , A__				:    Any=13      , A__				:    str=[30, 30]      , A__				:    int=2      , A__				:    Dict=3      , A__				:    str=True      , A__				:    Union[str, Any]=True      , A__				:    Any=32      , A__				:    int=5      , A__				:    str=4      , A__				:    List[Any]=37      , A__				:    Union[str, Any]="gelu"      , A__				:    Dict=0.1      , A__				:    Dict=0.1      , A__				:    Tuple=10      , A__				:    Dict=0.02      , A__				:    Any=3      , A__				:    Union[str, Any]=None      , A__				:    Optional[Any]=8      , A__				:    Dict=10      , )   ->					Optional[Any]:
        _snake_case						=							parent
        _snake_case						=							batch_size
        _snake_case						=							image_size
        _snake_case						=							patch_size
        _snake_case						=							num_channels
        _snake_case						=							is_training
        _snake_case						=							use_labels
        _snake_case						=							hidden_size
        _snake_case						=							num_hidden_layers
        _snake_case						=							num_attention_heads
        _snake_case						=							intermediate_size
        _snake_case						=							hidden_act
        _snake_case						=							hidden_dropout_prob
        _snake_case						=							attention_probs_dropout_prob
        _snake_case						=							type_sequence_label_size
        _snake_case						=							initializer_range
        _snake_case						=							num_labels
        _snake_case						=							scope
        _snake_case						=							n_targets
        _snake_case						=							num_detection_tokens
        # we set the expected sequence length (which is used in several tests)
        # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
        _snake_case						=							(image_size[1] // patch_size) * (image_size[0] // patch_size)
        _snake_case						=							num_patches + 1 + self.num_detection_tokens



      def 						UpperCamelCase_		(      self				:    List[str]		)   ->					str:
        _snake_case						=							floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]		)

        _snake_case						=							None
        if self.use_labels:
          # labels is a list of Dict (each Dict being the labels for a given example in the batch)
          _snake_case						=							[]
          for i in range(self.batch_size		):
            _snake_case						=							{}
            _snake_case						=							torch.randint(
                high=self.num_labels      , size=(self.n_targets,)      , device=A__		)
            _snake_case						=							torch.rand(self.n_targets      , 4      , device=A__		)
            labels.append(A__		)

        _snake_case						=							self.get_config()

        return config, pixel_values, labels



      def 						UpperCamelCase_		(      self				:    Dict		)   ->					List[Any]:
        return YolosConfig(
            image_size=self.image_size      , patch_size=self.patch_size      , num_channels=self.num_channels      , 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      , is_decoder=A__      , initializer_range=self.initializer_range      , num_detection_tokens=self.num_detection_tokens      , num_labels=self.num_labels      , )



      def 						UpperCamelCase_		(      self				:    Any      , A__				:    Any      , A__				:    str      , A__				:    Tuple		)   ->					Dict:
        _snake_case						=							YolosModel(config=A__		)
        model.to(A__		)
        model.eval()
        _snake_case						=							model(A__		)
        self.parent.assertEqual(
            result.last_hidden_state.shape      , (self.batch_size, self.expected_seq_len, self.hidden_size)		)



      def 						UpperCamelCase_		(      self				:    Dict      , A__				:    List[str]      , A__				:    Optional[Any]      , A__				:    str		)   ->					int:
        _snake_case						=							YolosForObjectDetection(A__		)
        model.to(A__		)
        model.eval()

        _snake_case						=							model(pixel_values=A__		)
        _snake_case						=							model(A__		)

        self.parent.assertEqual(result.logits.shape      , (self.batch_size, self.num_detection_tokens, self.num_labels + 1)		)
        self.parent.assertEqual(result.pred_boxes.shape      , (self.batch_size, self.num_detection_tokens, 4)		)

        _snake_case						=							model(pixel_values=A__      , labels=A__		)

        self.parent.assertEqual(result.loss.shape      , ()		)
        self.parent.assertEqual(result.logits.shape      , (self.batch_size, self.num_detection_tokens, self.num_labels + 1)		)
        self.parent.assertEqual(result.pred_boxes.shape      , (self.batch_size, self.num_detection_tokens, 4)		)







      def 						UpperCamelCase_		(      self				:    Optional[Any]		)   ->					Tuple:
        _snake_case						=							self.prepare_config_and_inputs()
        _snake_case,					_snake_case,					_snake_case						=							config_and_inputs
        _snake_case						=							{'''pixel_values''': pixel_values}
        return config, inputs_dict



@require_torch
class   lowercase_   (  __lowercase       ,							__lowercase       ,							unittest.TestCase					):
      UpperCamelCase_      :		Optional[int]										=    (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
      UpperCamelCase_      :		int										=    (
          {"feature-extraction": YolosModel, "object-detection": YolosForObjectDetection} if is_torch_available() else {}
      )

      UpperCamelCase_      :		List[str]										=    False
      UpperCamelCase_      :		List[Any]										=    False
      UpperCamelCase_      :		List[Any]										=    False
      UpperCamelCase_      :		Tuple										=    False



      def 						UpperCamelCase_		(      self				:    Dict      , A__				:    List[Any]      , A__				:    List[str]      , A__				:    Optional[int]=False		)   ->					Optional[int]:
        _snake_case						=							super()._prepare_for_class(A__      , A__      , return_labels=A__		)

        if return_labels:
          if model_class.__name__ == "YolosForObjectDetection":
            _snake_case						=							[]
            for i in range(self.model_tester.batch_size		):
              _snake_case						=							{}
              _snake_case						=							torch.ones(
                  size=(self.model_tester.n_targets,)      , device=A__      , dtype=torch.long		)
              _snake_case						=							torch.ones(
                  self.model_tester.n_targets      , 4      , device=A__      , dtype=torch.float		)
              labels.append(A__		)
            _snake_case						=							labels

        return inputs_dict



      def 						UpperCamelCase_		(      self				:    List[Any]		)   ->					List[str]:
        _snake_case						=							YolosModelTester(self		)
        _snake_case						=							ConfigTester(self      , config_class=A__      , has_text_modality=A__      , hidden_size=37		)



      def 						UpperCamelCase_		(      self				:    Optional[int]		)   ->					Dict:
        self.config_tester.run_common_tests()



      def 						UpperCamelCase_		(      self				:    List[Any]		)   ->					str:
        # YOLOS does not use inputs_embeds
        pass



      def 						UpperCamelCase_		(      self				:    Union[str, Any]		)   ->					List[str]:
        _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__		)
          self.assertIsInstance(model.get_input_embeddings()      , (nn.Module)		)
          _snake_case						=							model.get_output_embeddings()
          self.assertTrue(x is None or isinstance(A__      , nn.Linear		)		)



      def 						UpperCamelCase_		(      self				:    List[Any]		)   ->					Optional[Any]:
        _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 						UpperCamelCase_		(      self				:    List[str]		)   ->					List[Any]:
        _snake_case						=							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*A__		)



      def 						UpperCamelCase_		(      self				:    Union[str, Any]		)   ->					int:
        _snake_case,					_snake_case						=							self.model_tester.prepare_config_and_inputs_for_common()
        _snake_case						=							True

        # in YOLOS, the seq_len is different
        _snake_case						=							self.model_tester.expected_seq_len
        for model_class in self.all_model_classes:
          _snake_case						=							True
          _snake_case						=							False
          _snake_case						=							True
          _snake_case						=							model_class(A__		)
          model.to(A__		)
          model.eval()
          with torch.no_grad():
            _snake_case						=							model(**self._prepare_for_class(A__      , A__		)		)
          _snake_case						=							outputs.attentions
          self.assertEqual(len(A__		)      , self.model_tester.num_hidden_layers		)

          # check that output_attentions also work using config
          del inputs_dict["output_attentions"]
          _snake_case						=							True
          _snake_case						=							model_class(A__		)
          model.to(A__		)
          model.eval()
          with torch.no_grad():
            _snake_case						=							model(**self._prepare_for_class(A__      , A__		)		)
          _snake_case						=							outputs.attentions
          self.assertEqual(len(A__		)      , self.model_tester.num_hidden_layers		)

          self.assertListEqual(
              list(attentions[0].shape[-3:]		)      , [self.model_tester.num_attention_heads, seq_len, seq_len]      , )
          _snake_case						=							len(A__		)

          # Check attention is always last and order is fine
          _snake_case						=							True
          _snake_case						=							True
          _snake_case						=							model_class(A__		)
          model.to(A__		)
          model.eval()
          with torch.no_grad():
            _snake_case						=							model(**self._prepare_for_class(A__      , A__		)		)

          _snake_case						=							1
          self.assertEqual(out_len + added_hidden_states      , len(A__		)		)

          _snake_case						=							outputs.attentions

          self.assertEqual(len(A__		)      , self.model_tester.num_hidden_layers		)
          self.assertListEqual(
              list(self_attentions[0].shape[-3:]		)      , [self.model_tester.num_attention_heads, seq_len, seq_len]      , )



      def 						UpperCamelCase_		(      self				:    int		)   ->					Dict:
        def check_hidden_states_output(A__				:    Optional[int]      , A__				:    Union[str, Any]      , A__				:    int		):
          _snake_case						=							model_class(A__		)
          model.to(A__		)
          model.eval()

          with torch.no_grad():
            _snake_case						=							model(**self._prepare_for_class(A__      , A__		)		)

          _snake_case						=							outputs.hidden_states

          _snake_case						=							getattr(
              self.model_tester      , '''expected_num_hidden_layers'''      , self.model_tester.num_hidden_layers + 1		)
          self.assertEqual(len(A__		)      , A__		)

          # YOLOS has a different seq_length
          _snake_case						=							self.model_tester.expected_seq_len

          self.assertListEqual(
              list(hidden_states[0].shape[-2:]		)      , [seq_length, self.model_tester.hidden_size]      , )

        _snake_case,					_snake_case						=							self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          _snake_case						=							True
          check_hidden_states_output(A__      , A__      , A__		)

          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          _snake_case						=							True

          check_hidden_states_output(A__      , A__      , A__		)



      def 						UpperCamelCase_		(      self				:    Optional[Any]		)   ->					str:
        _snake_case						=							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_object_detection(*A__		)







      @slow
      def 						UpperCamelCase_		(      self				:    List[str]		)   ->					Dict:
        for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          _snake_case						=							YolosModel.from_pretrained(A__		)
          self.assertIsNotNone(A__		)



def 				snake_case_()							->     str:



  """simple docstring"""






  _snake_case						=							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)
  return image



@require_torch
@require_vision
class   lowercase_   (  unittest.TestCase					):



      @cached_property
      def 						UpperCamelCase_		(      self				:    Any		)   ->					str:
        return AutoImageProcessor.from_pretrained('''hustvl/yolos-small'''		) if is_vision_available() else None







      @slow
      def 						UpperCamelCase_		(      self				:    Tuple		)   ->					str:
        _snake_case						=							YolosForObjectDetection.from_pretrained('''hustvl/yolos-small'''		).to(A__		)

        _snake_case						=							self.default_image_processor
        _snake_case						=							prepare_img()
        _snake_case						=							image_processor(images=A__      , return_tensors='''pt'''		).to(A__		)

        # forward pass
        with torch.no_grad():
          _snake_case						=							model(inputs.pixel_values		)

        # verify outputs
        _snake_case						=							torch.Size((1, 100, 92)		)
        self.assertEqual(outputs.logits.shape      , A__		)

        _snake_case						=							torch.tensor(
            [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]      , device=A__      , )
        _snake_case						=							torch.tensor(
            [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]]      , device=A__		)
        self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]      , A__      , atol=1e-4		)		)
        self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]      , A__      , atol=1e-4		)		)

        # verify postprocessing
        _snake_case						=							image_processor.post_process_object_detection(
            A__      , threshold=0.3      , target_sizes=[image.size[::-1]]		)[0]
        _snake_case						=							torch.tensor([0.9994, 0.9790, 0.9964, 0.9972, 0.9861]		).to(A__		)
        _snake_case						=							[75, 75, 17, 63, 17]
        _snake_case						=							torch.tensor([335.0609, 79.3848, 375.4216, 187.2495]		).to(A__		)

        self.assertEqual(len(results['''scores''']		)      , 5		)
        self.assertTrue(torch.allclose(results['''scores''']      , A__      , atol=1e-4		)		)
        self.assertSequenceEqual(results['''labels'''].tolist()      , A__		)
        self.assertTrue(torch.allclose(results['''boxes'''][0, :]      , A__		)		)

