


'''simple docstring'''


import argparse
import csv
import logging
import os
import random

import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange

from transformers import (
    CONFIG_NAME,
    WEIGHTS_NAME,
    AdamW,
    OpenAIGPTDoubleHeadsModel,
    OpenAIGPTTokenizer,
    get_linear_schedule_with_warmup,
)


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
_UpperCamelCase 							=  logging.getLogger(__name__)


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       Optional[Any]      =							np.argmax(SCREAMING_SNAKE_CASE       ,					axis=1							)
     return np.sum(outputs == labels							)


def 		_lowercase (SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     with open(SCREAMING_SNAKE_CASE       ,					encoding="utf_8"							) as f:
          __A	:       List[str]      =							csv.reader(SCREAMING_SNAKE_CASE							)
          __A	:       int      =							[]
          next(SCREAMING_SNAKE_CASE							)  # skip the first line
          for line in tqdm(SCREAMING_SNAKE_CASE							):
               output.append((" ".join(line[1:5]							), line[5], line[6], int(line[-1]							) - 1)							)
     return output


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       List[Any]      =							[]
     for dataset in encoded_datasets:
          __A	:       str      =							len(SCREAMING_SNAKE_CASE							)
          __A	:       Dict      =							np.zeros((n_batch, 2, input_len)       ,					dtype=np.intaa							)
          __A	:       List[str]      =							np.zeros((n_batch, 2)       ,					dtype=np.intaa							)
          __A	:       Union[str, Any]      =							np.full((n_batch, 2, input_len)       ,					fill_value=-100       ,					dtype=np.intaa							)
          __A	:       Optional[int]      =							np.zeros((n_batch,)       ,					dtype=np.intaa							)
          for (
              i,
              (story, conta, conta, mc_label),
          ) in enumerate(SCREAMING_SNAKE_CASE							):
               __A	:       Union[str, Any]      =							[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
               __A	:       Any      =							[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
               __A	:       Optional[Any]      =							with_conta
               __A	:       List[Any]      =							with_conta
               __A	:       List[Any]      =							len(SCREAMING_SNAKE_CASE							) - 1
               __A	:       Dict      =							len(SCREAMING_SNAKE_CASE							) - 1
               __A	:       Union[str, Any]      =							with_conta
               __A	:       int      =							with_conta
               __A	:       Any      =							mc_label
          __A	:       Any      =							(input_ids, mc_token_ids, lm_labels, mc_labels)
          tensor_datasets.append(tuple(torch.tensor(SCREAMING_SNAKE_CASE							) for t in all_inputs							)							)
     return tensor_datasets


def 		_lowercase ():




     '''simple docstring'''





     __A	:       Optional[int]      =							argparse.ArgumentParser()
     parser.add_argument("--model_name"       ,					type=SCREAMING_SNAKE_CASE       ,					default="openai-gpt"       ,					help="pretrained model name"							)
     parser.add_argument("--do_train"       ,					action="store_true"       ,					help="Whether to run training."							)
     parser.add_argument("--do_eval"       ,					action="store_true"       ,					help="Whether to run eval on the dev set."							)
     parser.add_argument(
         "--output_dir"       ,					default=SCREAMING_SNAKE_CASE       ,					type=SCREAMING_SNAKE_CASE       ,					required=SCREAMING_SNAKE_CASE       ,					help="The output directory where the model predictions and checkpoints will be written."       ,					)
     parser.add_argument("--train_dataset"       ,					type=SCREAMING_SNAKE_CASE       ,					default=""							)
     parser.add_argument("--eval_dataset"       ,					type=SCREAMING_SNAKE_CASE       ,					default=""							)
     parser.add_argument("--seed"       ,					type=SCREAMING_SNAKE_CASE       ,					default=42							)
     parser.add_argument("--num_train_epochs"       ,					type=SCREAMING_SNAKE_CASE       ,					default=3							)
     parser.add_argument("--train_batch_size"       ,					type=SCREAMING_SNAKE_CASE       ,					default=8							)
     parser.add_argument("--eval_batch_size"       ,					type=SCREAMING_SNAKE_CASE       ,					default=16							)
     parser.add_argument("--adam_epsilon"       ,					default=1E-8       ,					type=SCREAMING_SNAKE_CASE       ,					help="Epsilon for Adam optimizer."							)
     parser.add_argument("--max_grad_norm"       ,					type=SCREAMING_SNAKE_CASE       ,					default=1							)
     parser.add_argument(
         "--max_steps"       ,					default=-1       ,					type=SCREAMING_SNAKE_CASE       ,					help=(
             "If > 0: set total number of training                         steps to perform. Override num_train_epochs."
         )       ,					)
     parser.add_argument(
         "--gradient_accumulation_steps"       ,					type=SCREAMING_SNAKE_CASE       ,					default=1       ,					help="Number of updates steps to accumulate before                        performing a backward/update pass."       ,					)
     parser.add_argument("--learning_rate"       ,					type=SCREAMING_SNAKE_CASE       ,					default=6.25E-5							)
     parser.add_argument("--warmup_steps"       ,					default=0       ,					type=SCREAMING_SNAKE_CASE       ,					help="Linear warmup over warmup_steps."							)
     parser.add_argument("--lr_schedule"       ,					type=SCREAMING_SNAKE_CASE       ,					default="warmup_linear"							)
     parser.add_argument("--weight_decay"       ,					type=SCREAMING_SNAKE_CASE       ,					default=0.01							)
     parser.add_argument("--lm_coef"       ,					type=SCREAMING_SNAKE_CASE       ,					default=0.9							)
     parser.add_argument("--n_valid"       ,					type=SCREAMING_SNAKE_CASE       ,					default=374							)

     parser.add_argument("--server_ip"       ,					type=SCREAMING_SNAKE_CASE       ,					default=""       ,					help="Can be used for distant debugging."							)
     parser.add_argument("--server_port"       ,					type=SCREAMING_SNAKE_CASE       ,					default=""       ,					help="Can be used for distant debugging."							)
     __A	:       List[str]      =							parser.parse_args()
     print(SCREAMING_SNAKE_CASE							)

     if args.server_ip and args.server_port:
          # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
          import ptvsd

          print("Waiting for debugger attach"							)
          ptvsd.enable_attach(address=(args.server_ip, args.server_port)       ,					redirect_output=SCREAMING_SNAKE_CASE							)
          ptvsd.wait_for_attach()

     random.seed(args.seed							)
     np.random.seed(args.seed							)
     torch.manual_seed(args.seed							)
     torch.cuda.manual_seed_all(args.seed							)

     __A	:       str      =							torch.device("cuda" if torch.cuda.is_available() else "cpu"							)
     __A	:       Optional[Any]      =							torch.cuda.device_count()
     logger.info("device: {}, n_gpu {}".format(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)							)

     if not args.do_train and not args.do_eval:
          raise ValueError("At least one of `do_train` or `do_eval` must be True."							)

     if not os.path.exists(args.output_dir							):
          os.makedirs(args.output_dir							)

     # Load tokenizer and model
     # This loading functions also add new tokens and embeddings called `special tokens`
     # These new embeddings will be fine-tuned on the RocStories dataset
     __A	:       List[Any]      =							["_start_", "_delimiter_", "_classify_"]
     __A	:       Dict      =							OpenAIGPTTokenizer.from_pretrained(args.model_name							)
     tokenizer.add_tokens(SCREAMING_SNAKE_CASE							)
     __A	:       Tuple      =							tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE							)
     __A	:       Optional[int]      =							OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name							)
     model.resize_token_embeddings(len(SCREAMING_SNAKE_CASE							)							)
     model.to(SCREAMING_SNAKE_CASE							)

     # Load and encode the datasets
     def tokenize_and_encode(SCREAMING_SNAKE_CASE							):
          if isinstance(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):
               return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(SCREAMING_SNAKE_CASE							)							)
          elif isinstance(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):
               return obj
          return [tokenize_and_encode(SCREAMING_SNAKE_CASE							) for o in obj]

     logger.info("Encoding dataset..."							)
     __A	:       Dict      =							load_rocstories_dataset(args.train_dataset							)
     __A	:       Optional[Any]      =							load_rocstories_dataset(args.eval_dataset							)
     __A	:       Union[str, Any]      =							(train_dataset, eval_dataset)
     __A	:       str      =							tokenize_and_encode(SCREAMING_SNAKE_CASE							)

     # Compute the max input length for the Transformer
     __A	:       Union[str, Any]      =							model.config.n_positions // 2 - 2
     __A	:       Optional[int]      =							max(
         len(story[:max_length]							) + max(len(conta[:max_length]							)       ,					len(conta[:max_length]							)							) + 3
         for dataset in encoded_datasets
         for story, conta, conta, _ in dataset							)
     __A	:       Dict      =							min(SCREAMING_SNAKE_CASE       ,					model.config.n_positions							)  # Max size of input for the pre-trained model

     # Prepare inputs tensors and dataloaders
     __A	:       Union[str, Any]      =							pre_process_datasets(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					*SCREAMING_SNAKE_CASE							)
     __A  ,__A	:       int      =							tensor_datasets[0], tensor_datasets[1]

     __A	:       Union[str, Any]      =							TensorDataset(*SCREAMING_SNAKE_CASE							)
     __A	:       Tuple      =							RandomSampler(SCREAMING_SNAKE_CASE							)
     __A	:       int      =							DataLoader(SCREAMING_SNAKE_CASE       ,					sampler=SCREAMING_SNAKE_CASE       ,					batch_size=args.train_batch_size							)

     __A	:       int      =							TensorDataset(*SCREAMING_SNAKE_CASE							)
     __A	:       str      =							SequentialSampler(SCREAMING_SNAKE_CASE							)
     __A	:       Tuple      =							DataLoader(SCREAMING_SNAKE_CASE       ,					sampler=SCREAMING_SNAKE_CASE       ,					batch_size=args.eval_batch_size							)

     # Prepare optimizer
     if args.do_train:
          if args.max_steps > 0:
               __A	:       Dict      =							args.max_steps
               __A	:       Optional[int]      =							args.max_steps // (len(SCREAMING_SNAKE_CASE							) // args.gradient_accumulation_steps) + 1
          else:
               __A	:       Optional[Any]      =							len(SCREAMING_SNAKE_CASE							) // args.gradient_accumulation_steps * args.num_train_epochs

          __A	:       Tuple      =							list(model.named_parameters()							)
          __A	:       Any      =							["bias", "LayerNorm.bias", "LayerNorm.weight"]
          __A	:       List[str]      =							[
              {
                  "params": [p for n, p in param_optimizer if not any(nd in n for nd in no_decay							)],
                  "weight_decay": args.weight_decay,
              },
              {"params": [p for n, p in param_optimizer if any(nd in n for nd in no_decay							)], "weight_decay": 0.0},
          ]
          __A	:       Tuple      =							AdamW(SCREAMING_SNAKE_CASE       ,					lr=args.learning_rate       ,					eps=args.adam_epsilon							)
          __A	:       Optional[int]      =							get_linear_schedule_with_warmup(
              SCREAMING_SNAKE_CASE       ,					num_warmup_steps=args.warmup_steps       ,					num_training_steps=SCREAMING_SNAKE_CASE							)

     if args.do_train:
          __A  ,__A  ,__A	:       Any      =							0, 0, None
          model.train()
          for _ in trange(int(args.num_train_epochs							)       ,					desc="Epoch"							):
               __A	:       Tuple      =							0
               __A	:       List[Any]      =							0
               __A	:       List[Any]      =							tqdm(SCREAMING_SNAKE_CASE       ,					desc="Training"							)
               for step, batch in enumerate(SCREAMING_SNAKE_CASE							):
                    __A	:       int      =							tuple(t.to(SCREAMING_SNAKE_CASE							) for t in batch							)
                    __A  ,__A  ,__A  ,__A	:       Any      =							batch
                    __A	:       Optional[int]      =							model(SCREAMING_SNAKE_CASE       ,					mc_token_ids=SCREAMING_SNAKE_CASE       ,					lm_labels=SCREAMING_SNAKE_CASE       ,					mc_labels=SCREAMING_SNAKE_CASE							)
                    __A	:       List[str]      =							args.lm_coef * losses[0] + losses[1]
                    loss.backward()
                    optimizer.step()
                    scheduler.step()
                    optimizer.zero_grad()
                    tr_loss += loss.item()
                    __A	:       List[str]      =							(
                        loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
                    )
                    nb_tr_steps += 1
                    __A	:       int      =							"Training loss: {:.2e} lr: {:.2e}".format(SCREAMING_SNAKE_CASE       ,					scheduler.get_lr()[0]							)

    # Save a trained model
     if args.do_train:
          # Save a trained model, configuration and tokenizer
          __A	:       Optional[int]      =							model.module if hasattr(SCREAMING_SNAKE_CASE       ,					"module"							) else model  # Only save the model itself

          # If we save using the predefined names, we can load using `from_pretrained`
          __A	:       List[str]      =							os.path.join(args.output_dir       ,					SCREAMING_SNAKE_CASE							)
          __A	:       Tuple      =							os.path.join(args.output_dir       ,					SCREAMING_SNAKE_CASE							)

          torch.save(model_to_save.state_dict()       ,					SCREAMING_SNAKE_CASE							)
          model_to_save.config.to_json_file(SCREAMING_SNAKE_CASE							)
          tokenizer.save_vocabulary(args.output_dir							)

          # Load a trained model and vocabulary that you have fine-tuned
          __A	:       str      =							OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir							)
          __A	:       List[str]      =							OpenAIGPTTokenizer.from_pretrained(args.output_dir							)
          model.to(SCREAMING_SNAKE_CASE							)

     if args.do_eval:
          model.eval()
          __A  ,__A	:       Any      =							0, 0
          __A  ,__A	:       List[str]      =							0, 0
          for batch in tqdm(SCREAMING_SNAKE_CASE       ,					desc="Evaluating"							):
               __A	:       Union[str, Any]      =							tuple(t.to(SCREAMING_SNAKE_CASE							) for t in batch							)
               __A  ,__A  ,__A  ,__A	:       str      =							batch
               with torch.no_grad():
                    __A  ,__A  ,__A  ,__A	:       Optional[int]      =							model(
                        SCREAMING_SNAKE_CASE       ,					mc_token_ids=SCREAMING_SNAKE_CASE       ,					lm_labels=SCREAMING_SNAKE_CASE       ,					mc_labels=SCREAMING_SNAKE_CASE							)

               __A	:       Dict      =							mc_logits.detach().cpu().numpy()
               __A	:       List[Any]      =							mc_labels.to("cpu"							).numpy()
               __A	:       Any      =							accuracy(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)

               eval_loss += mc_loss.mean().item()
               eval_accuracy += tmp_eval_accuracy

               nb_eval_examples += input_ids.size(0							)
               nb_eval_steps += 1

          __A	:       int      =							eval_loss / nb_eval_steps
          __A	:       Dict      =							eval_accuracy / nb_eval_examples
          __A	:       List[Any]      =							tr_loss / nb_tr_steps if args.do_train else None
          __A	:       Optional[int]      =							{"eval_loss": eval_loss, "eval_accuracy": eval_accuracy, "train_loss": train_loss}

          __A	:       Optional[int]      =							os.path.join(args.output_dir       ,					"eval_results.txt"							)
          with open(SCREAMING_SNAKE_CASE       ,					"w"							) as writer:
               logger.info("***** Eval results *****"							)
               for key in sorted(result.keys()							):
                    logger.info("  %s = %s"       ,					SCREAMING_SNAKE_CASE       ,					str(result[key]							)							)
                    writer.write("%s = %s\n" % (key, str(result[key]							))							)


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"		)

