import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


_lowerCamelCase					:   Any							=		16
_lowerCamelCase					:   Union[str, Any]							=		32





def 	__lowerCamelCase							(UpperCAmelCase__     :							Accelerator      , UpperCAmelCase__     :							int = 1_6      , UpperCAmelCase__     :							str = "bert-base-cased"  ):
					SCREAMING_SNAKE_CASE    			=       AutoTokenizer.from_pretrained(UpperCAmelCase__  )
					SCREAMING_SNAKE_CASE    			=       load_dataset("glue"      , "mrpc"  )

					def tokenize_function(UpperCAmelCase__     :							Optional[int]  ):
										# max_length=None => use the model max length (it's actually the default)
										SCREAMING_SNAKE_CASE    			=       tokenizer(examples["sentence1"]      , examples["sentence2"]      , truncation=UpperCAmelCase__      , max_length=UpperCAmelCase__  )
										return outputs

					# Apply the method we just defined to all the examples in all the splits of the dataset
					SCREAMING_SNAKE_CASE    			=       datasets.map(
					    UpperCAmelCase__      , batched=UpperCAmelCase__      , remove_columns=["idx", "sentence1", "sentence2"]      , load_from_cache_file=UpperCAmelCase__  )

					# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
					# transformers library
					SCREAMING_SNAKE_CASE    			=       tokenized_datasets.rename_column("label"      , "labels"  )

					def collate_fn(UpperCAmelCase__     :							List[str]  ):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										if accelerator.distributed_type == DistributedType.TPU:
															return tokenizer.pad(UpperCAmelCase__      , padding="max_length"      , max_length=1_2_8      , return_tensors="pt"  )
										return tokenizer.pad(UpperCAmelCase__      , padding="longest"      , return_tensors="pt"  )

					# Instantiate dataloaders.
					SCREAMING_SNAKE_CASE    			=       DataLoader(
					    tokenized_datasets["train"]      , shuffle=UpperCAmelCase__      , collate_fn=UpperCAmelCase__      , batch_size=UpperCAmelCase__  )
					SCREAMING_SNAKE_CASE    			=       DataLoader(
					    tokenized_datasets["validation"]      , shuffle=UpperCAmelCase__      , collate_fn=UpperCAmelCase__      , batch_size=UpperCAmelCase__  )

					return train_dataloader, eval_dataloader





def 	__lowerCamelCase							(UpperCAmelCase__     :							Optional[int]      , UpperCAmelCase__     :							Any      , UpperCAmelCase__     :							int      , UpperCAmelCase__     :							Union[str, Any]  ):
					model.eval()
					SCREAMING_SNAKE_CASE    			=       0
					for step, batch in enumerate(UpperCAmelCase__  ):
										# We could avoid this line since we set the accelerator with `device_placement=True`.
										batch.to(accelerator.device  )
										with torch.no_grad():
															SCREAMING_SNAKE_CASE    			=       model(**UpperCAmelCase__  )
										SCREAMING_SNAKE_CASE    			=       outputs.logits.argmax(dim=-1  )
										# It is slightly faster to call this once, than multiple times
										SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       accelerator.gather(
										    (predictions, batch["labels"])  )  # If we are in a multiprocess environment, the last batch has duplicates
										if accelerator.use_distributed:
															if step == len(UpperCAmelCase__  ) - 1:
																				SCREAMING_SNAKE_CASE    			=       predictions[: len(eval_dataloader.dataset  ) - samples_seen]
																				SCREAMING_SNAKE_CASE    			=       references[: len(eval_dataloader.dataset  ) - samples_seen]
															else:
																				samples_seen += references.shape[0]
										metric.add_batch(
										    predictions=UpperCAmelCase__      , references=UpperCAmelCase__      , )

					SCREAMING_SNAKE_CASE    			=       metric.compute()
					return eval_metric["accuracy"]





def 	__lowerCamelCase							(UpperCAmelCase__     :							str      , UpperCAmelCase__     :							Tuple  ):
					# Initialize accelerator
					SCREAMING_SNAKE_CASE    			=       Accelerator()

					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					SCREAMING_SNAKE_CASE    			=       config["lr"]
					SCREAMING_SNAKE_CASE    			=       int(config["num_epochs"]  )
					SCREAMING_SNAKE_CASE    			=       int(config["seed"]  )
					SCREAMING_SNAKE_CASE    			=       int(config["batch_size"]  )
					SCREAMING_SNAKE_CASE    			=       args.model_name_or_path

					set_seed(UpperCAmelCase__  )
					SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       get_dataloaders(UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__  )

					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					SCREAMING_SNAKE_CASE    			=       AutoModelForSequenceClassification.from_pretrained(UpperCAmelCase__      , return_dict=UpperCAmelCase__  )

					# Instantiate optimizer
					SCREAMING_SNAKE_CASE    			=       (
					    AdamW
					    if accelerator.state.deepspeed_plugin is None
					    or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
					    else DummyOptim
					)
					SCREAMING_SNAKE_CASE    			=       optimizer_cls(params=model.parameters()      , lr=UpperCAmelCase__  )

					if accelerator.state.deepspeed_plugin is not None:
										SCREAMING_SNAKE_CASE    			=       accelerator.state.deepspeed_plugin.deepspeed_config[
										    "gradient_accumulation_steps"
										]
					else:
										SCREAMING_SNAKE_CASE    			=       1
					SCREAMING_SNAKE_CASE    			=       (len(UpperCAmelCase__  ) * num_epochs) // gradient_accumulation_steps

					# Instantiate scheduler
					if (
					    accelerator.state.deepspeed_plugin is None
					    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
					):
										SCREAMING_SNAKE_CASE    			=       get_linear_schedule_with_warmup(
										    optimizer=UpperCAmelCase__      , num_warmup_steps=0      , num_training_steps=UpperCAmelCase__      , )
					else:
										SCREAMING_SNAKE_CASE    			=       DummyScheduler(UpperCAmelCase__      , total_num_steps=UpperCAmelCase__      , warmup_num_steps=0  )

					# Prepare everything
					# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
					# prepare method.
					SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       accelerator.prepare(
					    UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__  )

					# We need to keep track of how many total steps we have iterated over
					SCREAMING_SNAKE_CASE    			=       0
					# We also need to keep track of the stating epoch so files are named properly
					SCREAMING_SNAKE_CASE    			=       0
					SCREAMING_SNAKE_CASE    			=       evaluate.load("glue"      , "mrpc"  )
					SCREAMING_SNAKE_CASE    			=       num_epochs

					if args.partial_train_epoch is not None:
										SCREAMING_SNAKE_CASE    			=       args.partial_train_epoch

					if args.resume_from_checkpoint:
										accelerator.load_state(args.resume_from_checkpoint  )
										SCREAMING_SNAKE_CASE    			=       args.resume_from_checkpoint.split("epoch_"  )[1]
										SCREAMING_SNAKE_CASE    			=       ""
										for char in epoch_string:
															if char.isdigit():
																				state_epoch_num += char
															else:
																				break
										SCREAMING_SNAKE_CASE    			=       int(UpperCAmelCase__  ) + 1
										SCREAMING_SNAKE_CASE    			=       evaluation_loop(UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__  )
										accelerator.print("resumed checkpoint performance:"      , UpperCAmelCase__  )
										accelerator.print("resumed checkpoint's scheduler's lr:"      , lr_scheduler.get_lr()[0]  )
										accelerator.print("resumed optimizers's lr:"      , optimizer.param_groups[0]["lr"]  )
										with open(os.path.join(args.output_dir      , F"state_{starting_epoch-1}.json"  )      , "r"  ) as f:
															SCREAMING_SNAKE_CASE    			=       json.load(UpperCAmelCase__  )
															assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
															assert (
															    resumed_state["lr"] == lr_scheduler.get_lr()[0]
															), "Scheduler learning rate mismatch, loading from checkpoint failed"
															assert (
															    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
															), "Optimizer learning rate mismatch, loading from checkpoint failed"
															assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
															return

    # Now we train the model
					SCREAMING_SNAKE_CASE    			=       {}
					for epoch in range(UpperCAmelCase__      , UpperCAmelCase__  ):
										model.train()
										for step, batch in enumerate(UpperCAmelCase__  ):
															SCREAMING_SNAKE_CASE    			=       model(**UpperCAmelCase__  )
															SCREAMING_SNAKE_CASE    			=       outputs.loss
															SCREAMING_SNAKE_CASE    			=       loss / gradient_accumulation_steps
															accelerator.backward(UpperCAmelCase__  )
															if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

															overall_step += 1
										SCREAMING_SNAKE_CASE    			=       F"epoch_{epoch}"
										SCREAMING_SNAKE_CASE    			=       os.path.join(args.output_dir      , UpperCAmelCase__  )
										accelerator.save_state(UpperCAmelCase__  )
										SCREAMING_SNAKE_CASE    			=       evaluation_loop(UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__  )
										SCREAMING_SNAKE_CASE    			=       accuracy
										SCREAMING_SNAKE_CASE    			=       lr_scheduler.get_lr()[0]
										SCREAMING_SNAKE_CASE    			=       optimizer.param_groups[0]["lr"]
										SCREAMING_SNAKE_CASE    			=       epoch
										SCREAMING_SNAKE_CASE    			=       overall_step
										accelerator.print(F"epoch {epoch}:"      , UpperCAmelCase__  )

										accelerator.wait_for_everyone()
										if accelerator.is_main_process:
															with open(os.path.join(args.output_dir      , F"state_{epoch}.json"  )      , "w"  ) as f:
																				json.dump(UpperCAmelCase__      , UpperCAmelCase__  )





def 	__lowerCamelCase							():
					SCREAMING_SNAKE_CASE    			=       argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."  )
					parser.add_argument(
					    "--model_name_or_path"      , type=UpperCAmelCase__      , default="bert-base-cased"      , help="Path to pretrained model or model identifier from huggingface.co/models."      , required=UpperCAmelCase__      , )
					parser.add_argument(
					    "--output_dir"      , type=UpperCAmelCase__      , default="."      , help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."      , )
					parser.add_argument(
					    "--resume_from_checkpoint"      , type=UpperCAmelCase__      , default=UpperCAmelCase__      , help="If the training should continue from a checkpoint folder."      , )
					parser.add_argument(
					    "--partial_train_epoch"      , type=UpperCAmelCase__      , default=UpperCAmelCase__      , help="If passed, the training will stop after this number of epochs."      , )
					parser.add_argument(
					    "--num_epochs"      , type=UpperCAmelCase__      , default=2      , help="Number of train epochs."      , )
					SCREAMING_SNAKE_CASE    			=       parser.parse_args()
					SCREAMING_SNAKE_CASE    			=       {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 4_2, "batch_size": 1_6}

					training_function(UpperCAmelCase__      , UpperCAmelCase__  )


if __name__ == "__main__":
							main()





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 						lowercase    :




							def __init__(						self   :					Any	,							_UpperCamelCase   :					Any	,							_UpperCamelCase   :					Dict=13	,							_UpperCamelCase   :					List[Any]=64	,							_UpperCamelCase   :					Union[str, Any]=2	,							_UpperCamelCase   :					int=3	,							_UpperCamelCase   :					Union[str, Any]=True	,							_UpperCamelCase   :					Optional[int]=True	,							_UpperCamelCase   :					Tuple=32	,							_UpperCamelCase   :					str=5	,							_UpperCamelCase   :					Tuple=4	,							_UpperCamelCase   :					Any=37	,							_UpperCamelCase   :					List[str]="gelu"	,							_UpperCamelCase   :					int=0.1	,							_UpperCamelCase   :					int=0.1	,							_UpperCamelCase   :					Optional[int]=10	,							_UpperCamelCase   :					Tuple=0.0_2	,							_UpperCamelCase   :					Union[str, Any]=[1, 16, 4, 4]	,							_UpperCamelCase   :					Optional[Any]=None	,							)					->				Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       parent
												SCREAMING_SNAKE_CASE    			=       batch_size
												SCREAMING_SNAKE_CASE    			=       image_size
												SCREAMING_SNAKE_CASE    			=       patch_size
												SCREAMING_SNAKE_CASE    			=       num_channels
												SCREAMING_SNAKE_CASE    			=       is_training
												SCREAMING_SNAKE_CASE    			=       use_labels
												SCREAMING_SNAKE_CASE    			=       hidden_size
												SCREAMING_SNAKE_CASE    			=       num_hidden_layers
												SCREAMING_SNAKE_CASE    			=       num_attention_heads
												SCREAMING_SNAKE_CASE    			=       intermediate_size
												SCREAMING_SNAKE_CASE    			=       hidden_act
												SCREAMING_SNAKE_CASE    			=       hidden_dropout_prob
												SCREAMING_SNAKE_CASE    			=       attention_probs_dropout_prob
												SCREAMING_SNAKE_CASE    			=       type_sequence_label_size
												SCREAMING_SNAKE_CASE    			=       initializer_range
												SCREAMING_SNAKE_CASE    			=       scope
												SCREAMING_SNAKE_CASE    			=       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
												SCREAMING_SNAKE_CASE    			=       (self.image_size // 32) ** 2
												SCREAMING_SNAKE_CASE    			=       num_patches + 1




							def 	__snake_case(						self   :					Union[str, Any]      )					->				Optional[Any]:



												'''simple docstring'''

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

												SCREAMING_SNAKE_CASE    			=       None
												if self.use_labels:
																	SCREAMING_SNAKE_CASE    			=       ids_tensor([self.batch_size]	,							self.type_sequence_label_size      )

												SCREAMING_SNAKE_CASE    			=       self.get_config()

												return config, pixel_values, labels




							def 	__snake_case(						self   :					Dict      )					->				List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       {
												    "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=_UpperCamelCase	,							initializer_range=self.initializer_range	,							backbone_featmap_shape=self.backbone_featmap_shape	,							backbone_config=_UpperCamelCase	,							)




							def 	__snake_case(						self   :					Dict	,							_UpperCamelCase   :					int	,							_UpperCamelCase   :					Union[str, Any]	,							_UpperCamelCase   :					Optional[int]      )					->				int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       ViTHybridModel(config=_UpperCamelCase      )
												model.to(_UpperCamelCase      )
												model.eval()
												SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase      )
												self.parent.assertEqual(result.last_hidden_state.shape	,							(self.batch_size, self.seq_length, self.hidden_size)      )




							def 	__snake_case(						self   :					Any	,							_UpperCamelCase   :					List[Any]	,							_UpperCamelCase   :					List[Any]	,							_UpperCamelCase   :					Tuple      )					->				Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.type_sequence_label_size
												SCREAMING_SNAKE_CASE    			=       ViTHybridForImageClassification(_UpperCamelCase      )
												model.to(_UpperCamelCase      )
												model.eval()
												SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase	,							labels=_UpperCamelCase      )
												self.parent.assertEqual(result.logits.shape	,							(self.batch_size, self.type_sequence_label_size)      )





							def 	__snake_case(						self   :					str      )					->				List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.prepare_config_and_inputs()
												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       config_and_inputs
												SCREAMING_SNAKE_CASE    			=       {"pixel_values": pixel_values}
												return config, inputs_dict



@require_torch
class 						lowercase    (     a				,       a				,       unittest.TestCase     ):
							lowercase__						:       Optional[int]								=							(ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
							lowercase__						:       List[Any]								=							(
							    {"""feature-extraction""": ViTHybridModel, """image-classification""": ViTHybridForImageClassification}
							    if is_torch_available()
							    else {}
							)
							lowercase__						:       int								=							False
							lowercase__						:       Any								=							False
							lowercase__						:       Optional[int]								=							False




							def 	__snake_case(						self   :					Union[str, Any]      )					->				Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       ViTHybridModelTester(self      )
												SCREAMING_SNAKE_CASE    			=       ConfigTester(self	,							config_class=_UpperCamelCase	,							has_text_modality=_UpperCamelCase	,							hidden_size=37      )




							def 	__snake_case(						self   :					Optional[Any]      )					->				Any:



												'''simple docstring'''

												self.config_tester.run_common_tests()




							@unittest.skip(reason="ViT does not use inputs_embeds"      )
							def 	__snake_case(						self   :					Optional[int]      )					->				Optional[Any]:



												'''simple docstring'''

												pass




							def 	__snake_case(						self   :					List[Any]      )					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	SCREAMING_SNAKE_CASE    			=       model_class(_UpperCamelCase      )
																	self.assertIsInstance(model.get_input_embeddings()	,							(nn.Module)      )
																	SCREAMING_SNAKE_CASE    			=       model.get_output_embeddings()
																	self.assertTrue(x is None or isinstance(_UpperCamelCase	,							nn.Linear      )      )




							def 	__snake_case(						self   :					Optional[int]      )					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs_for_common()

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

																	SCREAMING_SNAKE_CASE    			=       ["pixel_values"]
																	self.assertListEqual(arg_names[:1]	,							_UpperCamelCase      )




							def 	__snake_case(						self   :					List[str]      )					->				Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*_UpperCamelCase      )




							def 	__snake_case(						self   :					Dict      )					->				List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*_UpperCamelCase      )




							def 	__snake_case(						self   :					Optional[int]      )					->				List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.model_tester.prepare_config_and_inputs_for_common()

												SCREAMING_SNAKE_CASE    			=       _config_zero_init(_UpperCamelCase      )
												for model_class in self.all_model_classes:
																	SCREAMING_SNAKE_CASE    			=       model_class(config=_UpperCamelCase      )
																	# Skip the check for the backbone
																	for name, module in model.named_modules():
																						if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
																											SCREAMING_SNAKE_CASE    			=       [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 	__snake_case(						self   :					Any      )					->				List[Any]:



												'''simple docstring'''

												for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	SCREAMING_SNAKE_CASE    			=       ViTHybridModel.from_pretrained(_UpperCamelCase      )
																	self.assertIsNotNone(_UpperCamelCase      )





def 	__lowerCamelCase							():
					SCREAMING_SNAKE_CASE    			=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
					return image


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




							@cached_property
							def 	__snake_case(						self   :					List[Any]      )					->				Any:



												'''simple docstring'''

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




							@slow
							def 	__snake_case(						self   :					Tuple      )					->				str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]      ).to(
												    _UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       self.default_image_processor
												SCREAMING_SNAKE_CASE    			=       prepare_img()
												SCREAMING_SNAKE_CASE    			=       image_processor(images=_UpperCamelCase	,							return_tensors="pt"      ).to(_UpperCamelCase      )

												# forward pass
												with torch.no_grad():
																	SCREAMING_SNAKE_CASE    			=       model(**_UpperCamelCase      )

												# verify the logits
												SCREAMING_SNAKE_CASE    			=       torch.Size((1, 1_000)      )
												self.assertEqual(outputs.logits.shape	,							_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       torch.tensor([-1.9_0_9_0, -0.4_9_9_3, -0.2_3_8_9]      ).to(_UpperCamelCase      )

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





							@slow
							@require_accelerate
							def 	__snake_case(						self   :					Optional[Any]      )					->				Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       ViTHybridImageProcessor.from_pretrained("google/vit-hybrid-base-bit-384"      )
												SCREAMING_SNAKE_CASE    			=       ViTHybridForImageClassification.from_pretrained("google/vit-hybrid-base-bit-384"	,							device_map="auto"      )

												SCREAMING_SNAKE_CASE    			=       prepare_img()

												SCREAMING_SNAKE_CASE    			=       image_processor(images=_UpperCamelCase	,							return_tensors="pt"      )
												SCREAMING_SNAKE_CASE    			=       model(**_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       outputs.logits
												# model predicts one of the 1000 ImageNet classes
												SCREAMING_SNAKE_CASE    			=       logits.argmax(-1      ).item()

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




