


"""simple docstring"""






import argparse
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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the checkpointing capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

lowercase  		=			16
lowercase  		=			32



def 			A__		(					_UpperCAmelCase    :      Accelerator				,    _UpperCAmelCase    :      int = 16	)							->	str:

	'''simple docstring'''


	snake_case__  :						Optional[int]		=      AutoTokenizer.from_pretrained("bert-base-cased"	)
	snake_case__  :						int		=      load_dataset("glue"				,    "mrpc"	)

	def tokenize_function(_UpperCAmelCase    :      Tuple	):
		# max_length=None => use the model max length (it's actually the default)
		snake_case__  :						str		=      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
	# starting with the main process first:
	with accelerator.main_process_first():
		snake_case__  :						List[Any]		=      datasets.map(
		    _UpperCAmelCase				,    batched=_UpperCAmelCase				,    remove_columns=["idx", "sentence1", "sentence2"]				,    )

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

	def collate_fn(_UpperCAmelCase    :      List[Any]	):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		snake_case__  :						str		=      1_28 if accelerator.distributed_type == DistributedType.TPU else None
		# When using mixed precision we want round multiples of 8/16
		if accelerator.mixed_precision == "fp8":
			snake_case__  :						List[Any]		=      16
		elif accelerator.mixed_precision != "no":
			snake_case__  :						Tuple		=      8
		else:
			snake_case__  :						int		=      None

		return tokenizer.pad(
		    _UpperCAmelCase				,    padding="longest"				,    max_length=_UpperCAmelCase				,    pad_to_multiple_of=_UpperCAmelCase				,    return_tensors="pt"				,    )

	# Instantiate dataloaders.
	snake_case__  :						Tuple		=      DataLoader(
	    tokenized_datasets["train"]				,    shuffle=_UpperCAmelCase				,    collate_fn=_UpperCAmelCase				,    batch_size=_UpperCAmelCase	)
	snake_case__  :						Optional[int]		=      DataLoader(
	    tokenized_datasets["validation"]				,    shuffle=_UpperCAmelCase				,    collate_fn=_UpperCAmelCase				,    batch_size=_UpperCAmelCase	)

	return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
					from accelerate.test_utils.training import mocked_dataloaders

					lowercase  		=			mocked_dataloaders  # noqa: F811



def 			A__		(					_UpperCAmelCase    :      Any				,    _UpperCAmelCase    :      Optional[Any]	)							->	List[str]:

	'''simple docstring'''


	if os.environ.get("TESTING_MOCKED_DATALOADERS"				,    _UpperCAmelCase	) == "1":
		snake_case__  :						int		=      2
	# Initialize accelerator
	snake_case__  :						List[Any]		=      Accelerator(cpu=args.cpu				,    mixed_precision=args.mixed_precision	)
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	snake_case__  :						Optional[int]		=      config["lr"]
	snake_case__  :						Optional[int]		=      int(config["num_epochs"]	)
	snake_case__  :						List[str]		=      int(config["seed"]	)
	snake_case__  :						List[Any]		=      int(config["batch_size"]	)

	# New Code #
	# Parse out whether we are saving every epoch or after a certain number of batches
	if hasattr(args.checkpointing_steps				,    "isdigit"	):
		if args.checkpointing_steps == "epoch":
			snake_case__  :						Optional[Any]		=      args.checkpointing_steps
		elif args.checkpointing_steps.isdigit():
			snake_case__  :						List[Any]		=      int(args.checkpointing_steps	)
		else:
			raise ValueError(
			    F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""	)
	else:
		snake_case__  :						Optional[Any]		=      None

	set_seed(_UpperCAmelCase	)

	snake_case__,					snake_case__  :						str		=      get_dataloaders(_UpperCAmelCase				,    _UpperCAmelCase	)
	snake_case__  :						Optional[int]		=      evaluate.load("glue"				,    "mrpc"	)

	# If the batch size is too big we use gradient accumulation
	snake_case__  :						List[str]		=      1
	if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
		snake_case__  :						Dict		=      batch_size // MAX_GPU_BATCH_SIZE
		snake_case__  :						List[Any]		=      MAX_GPU_BATCH_SIZE

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	snake_case__  :						Any		=      AutoModelForSequenceClassification.from_pretrained("bert-base-cased"				,    return_dict=_UpperCAmelCase	)

	# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
	# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
	# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
	snake_case__  :						Tuple		=      model.to(accelerator.device	)

	# Instantiate optimizer
	snake_case__  :						Tuple		=      AdamW(params=model.parameters()				,    lr=_UpperCAmelCase	)

	# Instantiate scheduler
	snake_case__  :						List[Any]		=      get_linear_schedule_with_warmup(
	    optimizer=_UpperCAmelCase				,    num_warmup_steps=1_00				,    num_training_steps=(len(_UpperCAmelCase	) * num_epochs) // gradient_accumulation_steps				,    )

	# 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.
	snake_case__,					snake_case__,					snake_case__,					snake_case__,					snake_case__  :						Optional[int]		=      accelerator.prepare(
	    _UpperCAmelCase				,    _UpperCAmelCase				,    _UpperCAmelCase				,    _UpperCAmelCase				,    _UpperCAmelCase	)

	# New Code #
	# We need to keep track of how many total steps we have iterated over
	snake_case__  :						List[Any]		=      0
	# We also need to keep track of the stating epoch so files are named properly
	snake_case__  :						int		=      0

	# We need to load the checkpoint back in before training here with `load_state`
	# The total number of epochs is adjusted based on where the state is being loaded from,
	# as we assume continuation of the same training script
	if args.resume_from_checkpoint:
		if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
			accelerator.print(F"""Resumed from checkpoint: {args.resume_from_checkpoint}"""	)
			accelerator.load_state(args.resume_from_checkpoint	)
			snake_case__  :						Any		=      os.path.basename(args.resume_from_checkpoint	)
		else:
			# Get the most recent checkpoint
			snake_case__  :						Optional[int]		=      [f.name for f in os.scandir(os.getcwd()	) if f.is_dir()]
			dirs.sort(key=os.path.getctime	)
			snake_case__  :						Tuple		=      dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
		# Extract `epoch_{i}` or `step_{i}`
		snake_case__  :						Any		=      os.path.splitext(_UpperCAmelCase	)[0]

		if "epoch" in training_difference:
			snake_case__  :						Dict		=      int(training_difference.replace("epoch_"				,    ""	)	) + 1
			snake_case__  :						Union[str, Any]		=      None
		else:
			snake_case__  :						Union[str, Any]		=      int(training_difference.replace("step_"				,    ""	)	)
			snake_case__  :						List[str]		=      resume_step // len(_UpperCAmelCase	)
			resume_step -= starting_epoch * len(_UpperCAmelCase	)

    # Now we train the model
	for epoch in range(_UpperCAmelCase				,    _UpperCAmelCase	):
		model.train()
		# New Code #
		if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
			# We need to skip steps until we reach the resumed step
			snake_case__  :						Dict		=      accelerator.skip_first_batches(_UpperCAmelCase				,    _UpperCAmelCase	)
			overall_step += resume_step
		else:
			# After the first iteration though, we need to go back to the original dataloader
			snake_case__  :						List[str]		=      train_dataloader
		for step, batch in enumerate(_UpperCAmelCase	):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device	)
			snake_case__  :						Optional[Any]		=      model(**_UpperCAmelCase	)
			snake_case__  :						int		=      outputs.loss
			snake_case__  :						Any		=      loss / gradient_accumulation_steps
			accelerator.backward(_UpperCAmelCase	)
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()
			# New Code #
			overall_step += 1

			# New Code #
			# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
			# These are saved to folders named `step_{overall_step}`
			# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
			# If mixed precision was used, will also save a "scalar.bin" file
			if isinstance(_UpperCAmelCase				,    _UpperCAmelCase	):
				snake_case__  :						List[Any]		=      F"""step_{overall_step}"""
				if overall_step % checkpointing_steps == 0:
					if args.output_dir is not None:
						snake_case__  :						int		=      os.path.join(args.output_dir				,    _UpperCAmelCase	)
					accelerator.save_state(_UpperCAmelCase	)

		model.eval()
		for step, batch in enumerate(_UpperCAmelCase	):
			# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
			batch.to(accelerator.device	)
			with torch.no_grad():
				snake_case__  :						Any		=      model(**_UpperCAmelCase	)
			snake_case__  :						Any		=      outputs.logits.argmax(dim=-1	)
			snake_case__,					snake_case__  :						Union[str, Any]		=      accelerator.gather_for_metrics((predictions, batch["labels"])	)
			metric.add_batch(
			    predictions=_UpperCAmelCase				,    references=_UpperCAmelCase				,    )

		snake_case__  :						Union[str, Any]		=      metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(F"""epoch {epoch}:"""				,    _UpperCAmelCase	)

		# New Code #
		# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
		# These are saved to folders named `epoch_{epoch}`
		# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
		# If mixed precision was used, will also save a "scalar.bin" file
		if checkpointing_steps == "epoch":
			snake_case__  :						Dict		=      F"""epoch_{epoch}"""
			if args.output_dir is not None:
				snake_case__  :						Union[str, Any]		=      os.path.join(args.output_dir				,    _UpperCAmelCase	)
			accelerator.save_state(_UpperCAmelCase	)



def 			A__		(					)							->	Tuple:

	'''simple docstring'''


	snake_case__  :						str		=      argparse.ArgumentParser(description="Simple example of training script."	)
	parser.add_argument(
	    "--mixed_precision"				,    type=_UpperCAmelCase				,    default=_UpperCAmelCase				,    choices=["no", "fp16", "bf16", "fp8"]				,    help="Whether to use mixed precision. Choose"
	    "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
	    "and an Nvidia Ampere GPU."				,    )
	parser.add_argument("--cpu"				,    action="store_true"				,    help="If passed, will train on the CPU."	)
	parser.add_argument(
	    "--checkpointing_steps"				,    type=_UpperCAmelCase				,    default=_UpperCAmelCase				,    help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."				,    )
	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."				,    )
	snake_case__  :						str		=      parser.parse_args()
	snake_case__  :						Optional[int]		=      {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
	training_function(_UpperCAmelCase				,    _UpperCAmelCase	)


if __name__ == "__main__":
					main()












"""simple docstring"""
import inspect
import unittest

from transformers import ResNetConfig
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_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch
		from torch import nn

		from transformers import ResNetBackbone, ResNetForImageClassification, ResNetModel
		from transformers.models.resnet.modeling_resnet import RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor






class    _UpperCAmelCase  :


					def __init__(   self     :Optional[Any]	,			__UpperCamelCase     :Dict	,			__UpperCamelCase     :Optional[Any]=3	,			__UpperCamelCase     :Optional[int]=32	,			__UpperCamelCase     :str=3	,			__UpperCamelCase     :Union[str, Any]=10	,			__UpperCamelCase     :Union[str, Any]=[10, 20, 30, 40]	,			__UpperCamelCase     :int=[1, 1, 2, 1]	,			__UpperCamelCase     :Dict=True	,			__UpperCamelCase     :Optional[Any]=True	,			__UpperCamelCase     :Any="relu"	,			__UpperCamelCase     :Optional[Any]=3	,			__UpperCamelCase     :int=None	,			__UpperCamelCase     :str=["stage2", "stage3", "stage4"]	,			__UpperCamelCase     :str=[2, 3, 4]	,			):
												A						=    parent
												A						=    batch_size
												A						=    image_size
												A						=    num_channels
												A						=    embeddings_size
												A						=    hidden_sizes
												A						=    depths
												A						=    is_training
												A						=    use_labels
												A						=    hidden_act
												A						=    num_labels
												A						=    scope
												A						=    len(__UpperCamelCase						)
												A						=    out_features
												A						=    out_indices


					def       lowerCamelCase			(   self     :List[Any]						):
												A						=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

												A						=    None
												if self.use_labels:
																			A						=    ids_tensor([self.batch_size]	,			self.num_labels						)

												A						=    self.get_config()

												return config, pixel_values, labels


					def       lowerCamelCase			(   self     :Optional[Any]						):
												return ResNetConfig(
												    num_channels=self.num_channels	,			embeddings_size=self.embeddings_size	,			hidden_sizes=self.hidden_sizes	,			depths=self.depths	,			hidden_act=self.hidden_act	,			num_labels=self.num_labels	,			out_features=self.out_features	,			out_indices=self.out_indices	,			)


					def       lowerCamelCase			(   self     :int	,			__UpperCamelCase     :Optional[Any]	,			__UpperCamelCase     :str	,			__UpperCamelCase     :List[Any]						):
												A						=    ResNetModel(config=__UpperCamelCase						)
												model.to(__UpperCamelCase						)
												model.eval()
												A						=    model(__UpperCamelCase						)
												# expected last hidden states: B, C, H // 32, W // 32
												self.parent.assertEqual(
												    result.last_hidden_state.shape	,			(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)	,			)


					def       lowerCamelCase			(   self     :int	,			__UpperCamelCase     :Optional[Any]	,			__UpperCamelCase     :str	,			__UpperCamelCase     :str						):
												A						=    self.num_labels
												A						=    ResNetForImageClassification(__UpperCamelCase						)
												model.to(__UpperCamelCase						)
												model.eval()
												A						=    model(__UpperCamelCase	,			labels=__UpperCamelCase						)
												self.parent.assertEqual(result.logits.shape	,			(self.batch_size, self.num_labels)						)


					def       lowerCamelCase			(   self     :Dict	,			__UpperCamelCase     :Optional[Any]	,			__UpperCamelCase     :Optional[int]	,			__UpperCamelCase     :str						):
												A						=    ResNetBackbone(config=__UpperCamelCase						)
												model.to(__UpperCamelCase						)
												model.eval()
												A						=    model(__UpperCamelCase						)

												# verify feature maps
												self.parent.assertEqual(len(result.feature_maps						)	,			len(config.out_features						)						)
												self.parent.assertListEqual(list(result.feature_maps[0].shape						)	,			[self.batch_size, self.hidden_sizes[1], 4, 4]						)

												# verify channels
												self.parent.assertEqual(len(model.channels						)	,			len(config.out_features						)						)
												self.parent.assertListEqual(model.channels	,			config.hidden_sizes[1:]						)

												# verify backbone works with out_features=None
												A						=    None
												A						=    ResNetBackbone(config=__UpperCamelCase						)
												model.to(__UpperCamelCase						)
												model.eval()
												A						=    model(__UpperCamelCase						)

												# verify feature maps
												self.parent.assertEqual(len(result.feature_maps						)	,			1						)
												self.parent.assertListEqual(list(result.feature_maps[0].shape						)	,			[self.batch_size, self.hidden_sizes[-1], 1, 1]						)

												# verify channels
												self.parent.assertEqual(len(model.channels						)	,			1						)
												self.parent.assertListEqual(model.channels	,			[config.hidden_sizes[-1]]						)


					def       lowerCamelCase			(   self     :List[Any]						):
												A						=    self.prepare_config_and_inputs()
												A,      A,      A						=    config_and_inputs
												A						=    {"pixel_values": pixel_values}
												return config, inputs_dict







@require_torch
class    _UpperCAmelCase  (      lowercase_	,				lowercase_	,				unittest.TestCase					):
					UpperCamelCase						=				(
					    (
					        ResNetModel,
					        ResNetForImageClassification,
					        ResNetBackbone,
					    )
					    if is_torch_available()
					    else ()
					)
					UpperCamelCase						=				(
					    {'''feature-extraction''': ResNetModel, '''image-classification''': ResNetForImageClassification}
					    if is_torch_available()
					    else {}
					)

					UpperCamelCase						=				True
					UpperCamelCase						=				False
					UpperCamelCase						=				False
					UpperCamelCase						=				False
					UpperCamelCase						=				False


					def       lowerCamelCase			(   self     :List[Any]						):
												A						=    ResNetModelTester(self						)
												A						=    ConfigTester(self	,			config_class=__UpperCamelCase	,			has_text_modality=__UpperCamelCase						)


					def       lowerCamelCase			(   self     :List[Any]						):
												self.create_and_test_config_common_properties()
												self.config_tester.create_and_test_config_to_json_string()
												self.config_tester.create_and_test_config_to_json_file()
												self.config_tester.create_and_test_config_from_and_save_pretrained()
												self.config_tester.create_and_test_config_with_num_labels()
												self.config_tester.check_config_can_be_init_without_params()
												self.config_tester.check_config_arguments_init()


					def       lowerCamelCase			(   self     :Union[str, Any]						):
												return


					@unittest.skip(reason="ResNet does not use inputs_embeds"						)
					def       lowerCamelCase			(   self     :Optional[int]						):
												pass


					@unittest.skip(reason="ResNet does not support input and output embeddings"						)
					def       lowerCamelCase			(   self     :Optional[Any]						):
												pass


					def       lowerCamelCase			(   self     :List[Any]						):
												A,      A						=    self.model_tester.prepare_config_and_inputs_for_common()

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

																			A						=    ["pixel_values"]
																			self.assertListEqual(arg_names[:1]	,			__UpperCamelCase						)


					def       lowerCamelCase			(   self     :Tuple						):
												A						=    self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__UpperCamelCase						)


					def       lowerCamelCase			(   self     :Union[str, Any]						):
												A						=    self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_backbone(*__UpperCamelCase						)


					def       lowerCamelCase			(   self     :Optional[Any]						):
												A,      A						=    self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			A						=    model_class(config=__UpperCamelCase						)
																			for name, module in model.named_modules():
																										if isinstance(__UpperCamelCase	,			(nn.BatchNormad, nn.GroupNorm)						):
																																	self.assertTrue(
																																	    torch.all(module.weight == 1						)	,			msg=f"Parameter {name} of model {model_class} seems not properly initialized"	,			)
																																	self.assertTrue(
																																	    torch.all(module.bias == 0						)	,			msg=f"Parameter {name} of model {model_class} seems not properly initialized"	,			)


					def       lowerCamelCase			(   self     :Optional[Any]						):
												def check_hidden_states_output(__UpperCamelCase     :int	,			__UpperCamelCase     :Any	,			__UpperCamelCase     :Union[str, Any]						):
																			A						=    model_class(__UpperCamelCase						)
																			model.to(__UpperCamelCase						)
																			model.eval()

																			with torch.no_grad():
																										A						=    model(**self._prepare_for_class(__UpperCamelCase	,			__UpperCamelCase						)						)

																			A						=    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																			A						=    self.model_tester.num_stages
																			self.assertEqual(len(__UpperCamelCase						)	,			expected_num_stages + 1						)

																			# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
																			self.assertListEqual(
																			    list(hidden_states[0].shape[-2:]						)	,			[self.model_tester.image_size // 4, self.model_tester.image_size // 4]	,			)

												A,      A						=    self.model_tester.prepare_config_and_inputs_for_common()
												A						=    ["basic", "bottleneck"]
												for model_class in self.all_model_classes:
																			for layer_type in layers_type:
																										A						=    layer_type
																										A						=    True
																										check_hidden_states_output(__UpperCamelCase	,			__UpperCamelCase	,			__UpperCamelCase						)

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

																										check_hidden_states_output(__UpperCamelCase	,			__UpperCamelCase	,			__UpperCamelCase						)


					@unittest.skip(reason="ResNet does not use feedforward chunking"						)
					def       lowerCamelCase			(   self     :Tuple						):
												pass


					def       lowerCamelCase			(   self     :Optional[int]						):
												A						=    self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase						)


					@slow
					def       lowerCamelCase			(   self     :Union[str, Any]						):
												for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			A						=    ResNetModel.from_pretrained(__UpperCamelCase						)
																			self.assertIsNotNone(__UpperCamelCase						)







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






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


					@cached_property
					def       lowerCamelCase			(   self     :List[Any]						):
												return (
												    AutoImageProcessor.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)
												    if is_vision_available()
												    else None
												)


					@slow
					def       lowerCamelCase			(   self     :Dict						):
												A						=    ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(__UpperCamelCase						)

												A						=    self.default_image_processor
												A						=    prepare_img()
												A						=    image_processor(images=__UpperCamelCase	,			return_tensors="pt"						).to(__UpperCamelCase						)

												# forward pass
												with torch.no_grad():
																			A						=    model(**__UpperCamelCase						)

												# verify the logits
												A						=    torch.Size((1, 10_00)						)
												self.assertEqual(outputs.logits.shape	,			__UpperCamelCase						)

												A						=    torch.tensor([-11.1_069, -9.7_877, -8.3_777]						).to(__UpperCamelCase						)

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







@require_torch
class    _UpperCAmelCase  (      lowercase_	,				unittest.TestCase					):
					UpperCamelCase						=				(ResNetBackbone,) if is_torch_available() else ()
					UpperCamelCase						=				False
					UpperCamelCase						=				ResNetConfig


					def       lowerCamelCase			(   self     :Dict						):
												A						=    ResNetModelTester(self						)


