

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

import datasets
import evaluate
import numpy as np
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')

require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/text-classification/requirements.txt')

lowerCAmelCase  : str      =	logging.getLogger(__name__)
@dataclass
class 			_A		:
  SCREAMING_SNAKE_CASE     :   Dict							=	field(
      default=128 ,  metadata={
          '''help''': (
              '''The maximum total input sequence length after tokenization. Sequences longer '''
              '''than this will be truncated, sequences shorter will be padded.'''
          )
      } ,  )
  SCREAMING_SNAKE_CASE     :   Tuple							=	field(
      default=__magic_name__ ,  metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''})
  SCREAMING_SNAKE_CASE     :   Union[str, Any]							=	field(
      default=__magic_name__ ,  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.'''
          )
      } ,  )
  SCREAMING_SNAKE_CASE     :   List[Any]							=	field(
      default=__magic_name__ ,  metadata={
          '''help''': (
              '''For debugging purposes or quicker training, truncate the number of training examples to this '''
              '''value if set.'''
          )
      } ,  )
  SCREAMING_SNAKE_CASE     :   Union[str, Any]							=	field(
      default=__magic_name__ ,  metadata={
          '''help''': (
              '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
              '''value if set.'''
          )
      } ,  )
  SCREAMING_SNAKE_CASE     :   Optional[int]							=	field(
      default=__magic_name__ ,  metadata={
          '''help''': (
              '''For debugging purposes or quicker training, truncate the number of prediction examples to this '''
              '''value if set.'''
          )
      } ,  )

@dataclass
class 			_A		:
  SCREAMING_SNAKE_CASE     :   Any							=	field(
      default=__magic_name__ ,  metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''})
  SCREAMING_SNAKE_CASE     :   Union[str, Any]							=	field(
      default=__magic_name__ ,  metadata={'''help''': '''Evaluation language. Also train language if `train_language` is set to None.'''})
  SCREAMING_SNAKE_CASE     :   Union[str, Any]							=	field(
      default=__magic_name__ ,  metadata={'''help''': '''Train language if it is different from the evaluation language.'''})
  SCREAMING_SNAKE_CASE     :   Optional[Any]							=	field(
      default=__magic_name__ ,  metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''})
  SCREAMING_SNAKE_CASE     :   Any							=	field(
      default=__magic_name__ ,  metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''})
  SCREAMING_SNAKE_CASE     :   str							=	field(
      default=__magic_name__ ,  metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} ,  )
  SCREAMING_SNAKE_CASE     :   List[str]							=	field(
      default=__magic_name__ ,  metadata={'''help''': '''arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()'''} ,  )
  SCREAMING_SNAKE_CASE     :   List[str]							=	field(
      default=__magic_name__ ,  metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''} ,  )
  SCREAMING_SNAKE_CASE     :   Optional[Any]							=	field(
      default='''main''' ,  metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} ,  )
  SCREAMING_SNAKE_CASE     :   Optional[Any]							=	field(
      default=__magic_name__ ,  metadata={
          '''help''': (
              '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
              '''with private models).'''
          )
      } ,  )
  SCREAMING_SNAKE_CASE     :   List[str]							=	field(
      default=__magic_name__ ,  metadata={'''help''': '''Will enable to load a pretrained model whose head dimensions are different.'''} ,  )

def  A_	(				):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_					:	Optional[int]     							=  HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)	)
      SCREAMING_SNAKE_CASE_					:	Any     							=  parser.parse_args_into_dataclasses()

      # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
      # information sent is the one passed as arguments along with your Python/PyTorch versions.
      send_example_telemetry('run_xnli'       , a	)

      # 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	)]       , )

      if training_args.should_log:
            # The default of training_args.log_level is passive, so we set log level at info here to have that default.
            transformers.utils.logging.set_verbosity_info()

      SCREAMING_SNAKE_CASE_					:	int     							=  training_args.get_process_log_level()
      logger.setLevel(a	)
      datasets.utils.logging.set_verbosity(a	)
      transformers.utils.logging.set_verbosity(a	)
      transformers.utils.logging.enable_default_handler()
      transformers.utils.logging.enable_explicit_format()

      # 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}"	)
      logger.info(f"Training/evaluation parameters {training_args}"	)

      # Detecting last checkpoint.
      SCREAMING_SNAKE_CASE_					:	Union[str, Any]     							=  None
      if os.path.isdir(training_args.output_dir	) and training_args.do_train and not training_args.overwrite_output_dir:
            SCREAMING_SNAKE_CASE_					:	Dict     							=  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.'	)

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

      # In distributed training, the load_dataset function guarantees that only one local process can concurrently
      # download the dataset.
      # Downloading and loading xnli dataset from the hub.
      if training_args.do_train:
            if model_args.train_language is None:
                  SCREAMING_SNAKE_CASE_					:	int     							=  load_dataset(
                      'xnli'       , model_args.language       , split='train'       , cache_dir=model_args.cache_dir       , use_auth_token=True if model_args.use_auth_token else None       , )
            else:
                  SCREAMING_SNAKE_CASE_					:	Optional[int]     							=  load_dataset(
                      'xnli'       , model_args.train_language       , split='train'       , cache_dir=model_args.cache_dir       , use_auth_token=True if model_args.use_auth_token else None       , )
            SCREAMING_SNAKE_CASE_					:	int     							=  train_dataset.features["label"].names

      if training_args.do_eval:
            SCREAMING_SNAKE_CASE_					:	Tuple     							=  load_dataset(
                'xnli'       , model_args.language       , split='validation'       , cache_dir=model_args.cache_dir       , use_auth_token=True if model_args.use_auth_token else None       , )
            SCREAMING_SNAKE_CASE_					:	Dict     							=  eval_dataset.features["label"].names

      if training_args.do_predict:
            SCREAMING_SNAKE_CASE_					:	Union[str, Any]     							=  load_dataset(
                'xnli'       , model_args.language       , split='test'       , cache_dir=model_args.cache_dir       , use_auth_token=True if model_args.use_auth_token else None       , )
            SCREAMING_SNAKE_CASE_					:	Any     							=  predict_dataset.features["label"].names

      # Labels
      SCREAMING_SNAKE_CASE_					:	Tuple     							=  len(a	)

      # Load pretrained model and tokenizer
      # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
      # download model & vocab.
      SCREAMING_SNAKE_CASE_					:	Union[str, Any]     							=  AutoConfig.from_pretrained(
          model_args.config_name if model_args.config_name else model_args.model_name_or_path       , num_labels=a       , idalabel={str(a	): label for i, label in enumerate(a	)}       , labelaid={label: i for i, label in enumerate(a	)}       , finetuning_task='xnli'       , cache_dir=model_args.cache_dir       , revision=model_args.model_revision       , use_auth_token=True if model_args.use_auth_token else None       , )
      SCREAMING_SNAKE_CASE_					:	Dict     							=  AutoTokenizer.from_pretrained(
          model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path       , do_lower_case=model_args.do_lower_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       , )
      SCREAMING_SNAKE_CASE_					:	Optional[Any]     							=  AutoModelForSequenceClassification.from_pretrained(
          model_args.model_name_or_path       , from_tf=bool('.ckpt' in model_args.model_name_or_path	)       , config=a       , cache_dir=model_args.cache_dir       , revision=model_args.model_revision       , use_auth_token=True if model_args.use_auth_token else None       , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes       , )

      # Preprocessing the datasets
      # Padding strategy
      if data_args.pad_to_max_length:
            SCREAMING_SNAKE_CASE_					:	Optional[int]     							=  "max_length"
      else:
            # We will pad later, dynamically at batch creation, to the max sequence length in each batch
            SCREAMING_SNAKE_CASE_					:	Optional[Any]     							=  False

      def preprocess_function(a	):
            # Tokenize the texts
            return tokenizer(
                examples['premise']       , examples['hypothesis']       , padding=a       , max_length=data_args.max_seq_length       , truncation=a       , )

      if training_args.do_train:
            if data_args.max_train_samples is not None:
                  SCREAMING_SNAKE_CASE_					:	int     							=  min(len(a	)       , data_args.max_train_samples	)
                  SCREAMING_SNAKE_CASE_					:	Optional[Any]     							=  train_dataset.select(range(a	)	)
            with training_args.main_process_first(desc='train dataset map pre-processing'	):
                  SCREAMING_SNAKE_CASE_					:	List[str]     							=  train_dataset.map(
                      a       , batched=a       , load_from_cache_file=not data_args.overwrite_cache       , desc='Running tokenizer on train dataset'       , )
            # Log a few random samples from the training set:
            for index in random.sample(range(len(a	)	)       , 3	):
                  logger.info(f"Sample {index} of the training set: {train_dataset[index]}."	)

      if training_args.do_eval:
            if data_args.max_eval_samples is not None:
                  SCREAMING_SNAKE_CASE_					:	List[str]     							=  min(len(a	)       , data_args.max_eval_samples	)
                  SCREAMING_SNAKE_CASE_					:	Optional[Any]     							=  eval_dataset.select(range(a	)	)
            with training_args.main_process_first(desc='validation dataset map pre-processing'	):
                  SCREAMING_SNAKE_CASE_					:	Any     							=  eval_dataset.map(
                      a       , batched=a       , load_from_cache_file=not data_args.overwrite_cache       , desc='Running tokenizer on validation dataset'       , )

      if training_args.do_predict:
            if data_args.max_predict_samples is not None:
                  SCREAMING_SNAKE_CASE_					:	Any     							=  min(len(a	)       , data_args.max_predict_samples	)
                  SCREAMING_SNAKE_CASE_					:	int     							=  predict_dataset.select(range(a	)	)
            with training_args.main_process_first(desc='prediction dataset map pre-processing'	):
                  SCREAMING_SNAKE_CASE_					:	str     							=  predict_dataset.map(
                      a       , batched=a       , load_from_cache_file=not data_args.overwrite_cache       , desc='Running tokenizer on prediction dataset'       , )

    # Get the metric function
      SCREAMING_SNAKE_CASE_					:	Any     							=  evaluate.load('xnli'	)

      # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
      # predictions and label_ids field) and has to return a dictionary string to float.
      def compute_metrics(a	):
            SCREAMING_SNAKE_CASE_					:	Optional[int]     							=  p.predictions[0] if isinstance(p.predictions       , a	) else p.predictions
            SCREAMING_SNAKE_CASE_					:	int     							=  np.argmax(a       , axis=1	)
            return metric.compute(predictions=a       , references=p.label_ids	)

      # Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
      if data_args.pad_to_max_length:
            SCREAMING_SNAKE_CASE_					:	Optional[int]     							=  default_data_collator
      elif training_args.fpaa:
            SCREAMING_SNAKE_CASE_					:	Any     							=  DataCollatorWithPadding(a       , pad_to_multiple_of=8	)
      else:
            SCREAMING_SNAKE_CASE_					:	List[str]     							=  None

      # Initialize our Trainer
      SCREAMING_SNAKE_CASE_					:	List[str]     							=  Trainer(
          model=a       , args=a       , train_dataset=train_dataset if training_args.do_train else None       , eval_dataset=eval_dataset if training_args.do_eval else None       , compute_metrics=a       , tokenizer=a       , data_collator=a       , )

      # Training
      if training_args.do_train:
            SCREAMING_SNAKE_CASE_					:	Tuple     							=  None
            if training_args.resume_from_checkpoint is not None:
                  SCREAMING_SNAKE_CASE_					:	List[str]     							=  training_args.resume_from_checkpoint
            elif last_checkpoint is not None:
                  SCREAMING_SNAKE_CASE_					:	Dict     							=  last_checkpoint
            SCREAMING_SNAKE_CASE_					:	Any     							=  trainer.train(resume_from_checkpoint=a	)
            SCREAMING_SNAKE_CASE_					:	List[str]     							=  train_result.metrics
            SCREAMING_SNAKE_CASE_					:	Any     							=  (
                data_args.max_train_samples if data_args.max_train_samples is not None else len(a	)
            )
            SCREAMING_SNAKE_CASE_					:	List[str]     							=  min(a       , len(a	)	)

            trainer.save_model()  # Saves the tokenizer too for easy upload

            trainer.log_metrics('train'       , a	)
            trainer.save_metrics('train'       , a	)
            trainer.save_state()

      # Evaluation
      if training_args.do_eval:
            logger.info('*** Evaluate ***'	)
            SCREAMING_SNAKE_CASE_					:	str     							=  trainer.evaluate(eval_dataset=a	)

            SCREAMING_SNAKE_CASE_					:	Any     							=  data_args.max_eval_samples if data_args.max_eval_samples is not None else len(a	)
            SCREAMING_SNAKE_CASE_					:	List[Any]     							=  min(a       , len(a	)	)

            trainer.log_metrics('eval'       , a	)
            trainer.save_metrics('eval'       , a	)

      # Prediction
      if training_args.do_predict:
            logger.info('*** Predict ***'	)
            SCREAMING_SNAKE_CASE_					:	Tuple     							=  trainer.predict(a       , metric_key_prefix='predict'	)

            SCREAMING_SNAKE_CASE_					:	Union[str, Any]     							=  (
                data_args.max_predict_samples if data_args.max_predict_samples is not None else len(a	)
            )
            SCREAMING_SNAKE_CASE_					:	List[str]     							=  min(a       , len(a	)	)

            trainer.log_metrics('predict'       , a	)
            trainer.save_metrics('predict'       , a	)

            SCREAMING_SNAKE_CASE_					:	Optional[Any]     							=  np.argmax(a       , axis=1	)
            SCREAMING_SNAKE_CASE_					:	Optional[Any]     							=  os.path.join(training_args.output_dir       , 'predictions.txt'	)
            if trainer.is_world_process_zero():
                  with open(a       , 'w'	) as writer:
                        writer.write('index\tprediction\n'	)
                        for index, item in enumerate(a	):
                              SCREAMING_SNAKE_CASE_					:	Tuple     							=  label_list[item]
                              writer.write(f"{index}\t{item}\n"	)


if __name__ == "__main__":
    main()










'''simple docstring'''


import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch
      from torch import nn

      from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
      from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image







class 							__magic_name__	:

     """simple docstring"""






     def __init__(   self			,     lowerCamelCase			,     lowerCamelCase=13			,     lowerCamelCase=64			,     lowerCamelCase=2			,     lowerCamelCase=3			,     lowerCamelCase=True			,     lowerCamelCase=True			,     lowerCamelCase=32			,     lowerCamelCase=5			,     lowerCamelCase=4			,     lowerCamelCase=37			,     lowerCamelCase="gelu"			,     lowerCamelCase=0.1			,     lowerCamelCase=0.1			,     lowerCamelCase=10			,     lowerCamelCase=0.02			,     lowerCamelCase=[1, 16, 4, 4]			,     lowerCamelCase=None			,     ):


          '''simple docstring'''





          __A	:       Any      =							parent
          __A	:       Union[str, Any]      =							batch_size
          __A	:       Any      =							image_size
          __A	:       Dict      =							patch_size
          __A	:       Dict      =							num_channels
          __A	:       Dict      =							is_training
          __A	:       Optional[Any]      =							use_labels
          __A	:       Any      =							hidden_size
          __A	:       Optional[Any]      =							num_hidden_layers
          __A	:       Optional[int]      =							num_attention_heads
          __A	:       str      =							intermediate_size
          __A	:       int      =							hidden_act
          __A	:       Any      =							hidden_dropout_prob
          __A	:       int      =							attention_probs_dropout_prob
          __A	:       Any      =							type_sequence_label_size
          __A	:       Union[str, Any]      =							initializer_range
          __A	:       Dict      =							scope
          __A	:       Union[str, Any]      =							backbone_featmap_shape

          # in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
          # the number of patches is based on the feature map of the backbone, which by default uses an output stride
          # of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
          __A	:       int      =							(self.image_size // 32) ** 2
          __A	:       Union[str, Any]      =							num_patches + 1


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       str      =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

          __A	:       Any      =							None
          if self.use_labels:
               __A	:       Optional[Any]      =							ids_tensor([self.batch_size]			,     self.type_sequence_label_size		)

          __A	:       Any      =							self.get_config()

          return config, pixel_values, labels


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Union[str, Any]      =							{
              "global_padding": "same",
              "layer_type": "bottleneck",
              "depths": [3, 4, 9],
              "out_features": ["stage1", "stage2", "stage3"],
              "embedding_dynamic_padding": True,
              "hidden_sizes": [4, 8, 16, 32],
              "num_groups": 2,
          }

          return ViTHybridConfig(
              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=lowerCamelCase			,     initializer_range=self.initializer_range			,     backbone_featmap_shape=self.backbone_featmap_shape			,     backbone_config=lowerCamelCase			,     )


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase		):


          '''simple docstring'''





          __A	:       int      =							ViTHybridModel(config=lowerCamelCase		)
          model.to(lowerCamelCase		)
          model.eval()
          __A	:       Optional[Any]      =							model(lowerCamelCase		)
          self.parent.assertEqual(result.last_hidden_state.shape			,     (self.batch_size, self.seq_length, self.hidden_size)		)


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase		):


          '''simple docstring'''





          __A	:       Any      =							self.type_sequence_label_size
          __A	:       int      =							ViTHybridForImageClassification(lowerCamelCase		)
          model.to(lowerCamelCase		)
          model.eval()
          __A	:       Any      =							model(lowerCamelCase			,     labels=lowerCamelCase		)
          self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.type_sequence_label_size)		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Dict      =							self.prepare_config_and_inputs()
          __A  ,__A  ,__A	:       Optional[int]      =							config_and_inputs
          __A	:       Dict      =							{"pixel_values": pixel_values}
          return config, inputs_dict







@require_torch
class 							__magic_name__	(       lowerCAmelCase    ,		lowerCAmelCase    ,		unittest.TestCase ):

     """simple docstring"""






     lowerCamelCase__       						=				(ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
     lowerCamelCase__       						=				(
         {'feature-extraction': ViTHybridModel, 'image-classification': ViTHybridForImageClassification}
         if is_torch_available()
         else {}
     )
     lowerCamelCase__       						=				False
     lowerCamelCase__       						=				False
     lowerCamelCase__       						=				False


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       str      =							ViTHybridModelTester(self		)
          __A	:       Optional[Any]      =							ConfigTester(self			,     config_class=lowerCamelCase			,     has_text_modality=lowerCamelCase			,     hidden_size=37		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          self.config_tester.run_common_tests()


     @unittest.skip(reason="ViT does not use inputs_embeds"		)
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          pass


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A  ,__A	:       Dict      =							self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               __A	:       List[str]      =							model_class(lowerCamelCase		)
               self.assertIsInstance(model.get_input_embeddings()			,     (nn.Module)		)
               __A	:       int      =							model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(lowerCamelCase			,     nn.Linear		)		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A  ,__A	:       Any      =							self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               __A	:       Union[str, Any]      =							model_class(lowerCamelCase		)
               __A	:       Union[str, Any]      =							inspect.signature(model.forward		)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               __A	:       Optional[Any]      =							[*signature.parameters.keys()]

               __A	:       Dict      =							["pixel_values"]
               self.assertListEqual(arg_names[:1]			,     lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       int      =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Optional[int]      =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A  ,__A	:       Any      =							self.model_tester.prepare_config_and_inputs_for_common()

          __A	:       Dict      =							_config_zero_init(lowerCamelCase		)
          for model_class in self.all_model_classes:
               __A	:       List[str]      =							model_class(config=lowerCamelCase		)
               # Skip the check for the backbone
               for name, module in model.named_modules():
                    if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
                         __A	:       int      =							[f"{name}.{key}" for key in module.state_dict().keys()]
                         break

               for name, param in model.named_parameters():
                    if param.requires_grad:
                         if name in backbone_params:
                              continue
                         self.assertIn(
                             ((param.data.mean() * 1E9).round() / 1E9).item()			,     [0.0, 1.0]			,     msg=f"Parameter {name} of model {model_class} seems not properly initialized"			,     )


     @slow
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __A	:       Tuple      =							ViTHybridModel.from_pretrained(lowerCamelCase		)
               self.assertIsNotNone(lowerCamelCase		)


def 		_lowercase ():




     '''simple docstring'''





     __A	:       Optional[Any]      =							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
     return image







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

     """simple docstring"""






     @cached_property
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          return (
              ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)
              if is_vision_available()
              else None
          )


     @slow
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       str      =							ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(
              lowerCamelCase		)

          __A	:       str      =							self.default_image_processor
          __A	:       Union[str, Any]      =							prepare_img()
          __A	:       List[str]      =							image_processor(images=lowerCamelCase			,     return_tensors="pt"		).to(lowerCamelCase		)

          # forward pass
          with torch.no_grad():
               __A	:       Any      =							model(**lowerCamelCase		)

          # verify the logits
          __A	:       List[str]      =							torch.Size((1, 1000)		)
          self.assertEqual(outputs.logits.shape			,     lowerCamelCase		)

          __A	:       Optional[int]      =							torch.tensor([-1.9090, -0.4993, -0.2389]		).to(lowerCamelCase		)

          self.assertTrue(torch.allclose(outputs.logits[0, :3]			,     lowerCamelCase			,     atol=1E-4		)		)


     @slow
     @require_accelerate
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       List[Any]      =							ViTHybridImageProcessor.from_pretrained("google/vit-hybrid-base-bit-384"		)
          __A	:       Optional[Any]      =							ViTHybridForImageClassification.from_pretrained("google/vit-hybrid-base-bit-384"			,     device_map="auto"		)

          __A	:       Union[str, Any]      =							prepare_img()

          __A	:       Tuple      =							image_processor(images=lowerCamelCase			,     return_tensors="pt"		)
          __A	:       Optional[Any]      =							model(**lowerCamelCase		)
          __A	:       List[Any]      =							outputs.logits
          # model predicts one of the 1000 ImageNet classes
          __A	:       Tuple      =							logits.argmax(-1		).item()

          self.assertTrue(model.config.idalabel[predicted_class_idx]			,     "tabby, tabby cat"		)

