






import argparse

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
#
# 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 run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


SCREAMING_SNAKE_CASE__				     =			16
SCREAMING_SNAKE_CASE__				     =			32

def        UpperCAmelCase__		(						lowerCamelCase_       :       Accelerator   ,  lowerCamelCase_       :       int = 1_6				):
			__a						:  Tuple									=     AutoTokenizer.from_pretrained('bert-base-cased'				)
			__a						:  int									=     load_dataset('glue'   ,  'mrpc'				)

			def tokenize_function(lowerCamelCase_       :       Union[str, Any]				):
						# max_length=None => use the model max length (it's actually the default)
						__a						:  int									=     tokenizer(examples['sentence1']   ,  examples['sentence2']   ,  truncation=lowerCamelCase_   ,  max_length=lowerCamelCase_				)
						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():
						__a						:  Optional[Any]									=     datasets.map(
						    lowerCamelCase_   ,  batched=lowerCamelCase_   ,  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
			__a						:  Optional[int]									=     tokenized_datasets.rename_column('label'   ,  'labels'				)

			def collate_fn(lowerCamelCase_       :       Dict				):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						__a						:  Optional[int]									=     1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
						# When using mixed precision we want round multiples of 8/16
						if accelerator.mixed_precision == "fp8":
									__a						:  List[Any]									=     1_6
						elif accelerator.mixed_precision != "no":
									__a						:  Union[str, Any]									=     8
						else:
									__a						:  List[str]									=     None

						return tokenizer.pad(
						    lowerCamelCase_   ,  padding='longest'   ,  max_length=lowerCamelCase_   ,  pad_to_multiple_of=lowerCamelCase_   ,  return_tensors='pt'   ,  )

			# Instantiate dataloaders.
			__a						:  List[str]									=     DataLoader(
			    tokenized_datasets['train']   ,  shuffle=lowerCamelCase_   ,  collate_fn=lowerCamelCase_   ,  batch_size=lowerCamelCase_   ,  drop_last=lowerCamelCase_				)
			__a						:  List[str]									=     DataLoader(
			    tokenized_datasets['validation']   ,  shuffle=lowerCamelCase_   ,  collate_fn=lowerCamelCase_   ,  batch_size=lowerCamelCase_   ,  drop_last=(accelerator.mixed_precision == 'fp8')   ,  )

			return train_dataloader, eval_dataloader

def        UpperCAmelCase__		(						lowerCamelCase_       :       List[str]   ,  lowerCamelCase_       :       List[str]				):
			# Initialize accelerator
			__a						:  Tuple									=     Accelerator(cpu=args.cpu   ,  mixed_precision=args.mixed_precision				)
			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			__a						:  List[str]									=     config['lr']
			__a						:  Tuple									=     int(config['num_epochs']				)
			__a						:  Tuple									=     int(config['seed']				)
			__a						:  Union[str, Any]									=     int(config['batch_size']				)

			__a						:  str									=     evaluate.load('glue'   ,  'mrpc'				)

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

			set_seed(lowerCamelCase_				)
			__a						,					__a						:  List[str]									=     get_dataloaders(lowerCamelCase_   ,  lowerCamelCase_				)
			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			__a						:  Dict									=     AutoModelForSequenceClassification.from_pretrained('bert-base-cased'   ,  return_dict=lowerCamelCase_				)

			# 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).
			__a						:  int									=     model.to(accelerator.device				)
			# Instantiate optimizer
			__a						:  Any									=     AdamW(params=model.parameters()   ,  lr=lowerCamelCase_				)

			# Instantiate scheduler
			__a						:  Optional[int]									=     get_linear_schedule_with_warmup(
			    optimizer=lowerCamelCase_   ,  num_warmup_steps=1_0_0   ,  num_training_steps=(len(lowerCamelCase_				) * 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.

			__a						,					__a						,					__a						,					__a						,					__a						:  Dict									=     accelerator.prepare(
			    lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_				)

			# Now we train the model
			for epoch in range(lowerCamelCase_				):
						model.train()
						for step, batch in enumerate(lowerCamelCase_				):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device				)
									__a						:  int									=     model(**lowerCamelCase_				)
									__a						:  Optional[Any]									=     outputs.loss
									__a						:  Optional[int]									=     loss / gradient_accumulation_steps
									accelerator.backward(lowerCamelCase_				)
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

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

						__a						:  Optional[Any]									=     metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(f'''epoch {epoch}:'''   ,  lowerCamelCase_				)

def        UpperCAmelCase__		(						):
			__a						:  Optional[int]									=     argparse.ArgumentParser(description='Simple example of training script.'				)
			parser.add_argument(
			    '--mixed_precision'   ,  type=lowerCamelCase_   ,  default=lowerCamelCase_   ,  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.'				)
			__a						:  List[Any]									=     parser.parse_args()
			__a						:  Optional[int]									=     {'lr': 2e-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
			training_function(lowerCamelCase_   ,  lowerCamelCase_				)


if __name__ == "__main__":
							main()











from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers import TFResNetForImageClassification, TFResNetModel
						from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor



class    lowercase_		:



						def __init__(			self	:   Optional[Any]					,							snake_case__	:   List[Any]					,							snake_case__	:   Tuple=3					,							snake_case__	:   Union[str, Any]=32					,							snake_case__	:   Union[str, Any]=3					,							snake_case__	:   str=10					,							snake_case__	:   List[Any]=[10, 20, 30, 40]					,							snake_case__	:   Optional[Any]=[1, 1, 2, 1]					,							snake_case__	:   str=True					,							snake_case__	:   str=True					,							snake_case__	:   List[str]="relu"					,							snake_case__	:   Tuple=3					,							snake_case__	:   str=None					,							):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							parent
								SCREAMING_SNAKE_CASE_					      =							batch_size
								SCREAMING_SNAKE_CASE_					      =							image_size
								SCREAMING_SNAKE_CASE_					      =							num_channels
								SCREAMING_SNAKE_CASE_					      =							embeddings_size
								SCREAMING_SNAKE_CASE_					      =							hidden_sizes
								SCREAMING_SNAKE_CASE_					      =							depths
								SCREAMING_SNAKE_CASE_					      =							is_training
								SCREAMING_SNAKE_CASE_					      =							use_labels
								SCREAMING_SNAKE_CASE_					      =							hidden_act
								SCREAMING_SNAKE_CASE_					      =							num_labels
								SCREAMING_SNAKE_CASE_					      =							scope
								SCREAMING_SNAKE_CASE_					      =							len(snake_case__  )



						def 	__a					(			self	:   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.num_labels  )

								SCREAMING_SNAKE_CASE_					      =							self.get_config()

								return config, pixel_values, labels



						def 	__a					(			self	:   int  ):

								"""simple docstring"""

								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					,							image_size=self.image_size					,							)



						def 	__a					(			self	:   str					,							snake_case__	:   str					,							snake_case__	:   Tuple					,							snake_case__	:   Optional[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							TFResNetModel(config=snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							model(snake_case__  )
								# 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 	__a					(			self	:   int					,							snake_case__	:   Optional[Any]					,							snake_case__	:   int					,							snake_case__	:   Any  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.num_labels
								SCREAMING_SNAKE_CASE_					      =							TFResNetForImageClassification(snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							model(snake_case__					,							labels=snake_case__  )
								self.parent.assertEqual(result.logits.shape					,							(self.batch_size, self.num_labels)  )






						def 	__a					(			self	:   Dict  ):

								"""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_tf
class    lowercase_		(SCREAMING_SNAKE_CASE__							,	SCREAMING_SNAKE_CASE__							,	unittest.TestCase ):
						lowerCAmelCase__			      =(TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
						lowerCAmelCase__			      =(
						    {"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
						    if is_tf_available()
						    else {}
						)

						lowerCAmelCase__			      =False
						lowerCAmelCase__			      =False
						lowerCAmelCase__			      =False
						lowerCAmelCase__			      =False
						lowerCAmelCase__			      =False



						def 	__a					(			self	:   str  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							TFResNetModelTester(self  )
								SCREAMING_SNAKE_CASE_					      =							ConfigTester(self					,							config_class=snake_case__					,							has_text_modality=snake_case__  )



						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								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 	__a					(			self	:   List[Any]  ):

								"""simple docstring"""

								return



						@unittest.skip(reason='ResNet does not use inputs_embeds'  )
						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								pass



						@unittest.skip(reason='ResNet does not support input and output embeddings'  )
						def 	__a					(			self	:   Tuple  ):

								"""simple docstring"""

								pass



						def 	__a					(			self	:   List[Any]  ):

								"""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(snake_case__  )
										SCREAMING_SNAKE_CASE_					      =							inspect.signature(model.call  )
										# 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]					,							snake_case__  )



						def 	__a					(			self	:   Union[str, Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*snake_case__  )



						def 	__a					(			self	:   int  ):

								"""simple docstring"""

								def check_hidden_states_output(snake_case__	:   Optional[int]					,							snake_case__	:   int					,							snake_case__	:   Dict  ):
										SCREAMING_SNAKE_CASE_					      =							model_class(snake_case__  )
										SCREAMING_SNAKE_CASE_					      =							model(**self._prepare_for_class(snake_case__					,							snake_case__  )  )

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

										SCREAMING_SNAKE_CASE_					      =							self.model_tester.num_stages
										self.assertEqual(len(snake_case__  )					,							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]					,							)

								SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							self.model_tester.prepare_config_and_inputs_for_common()
								SCREAMING_SNAKE_CASE_					      =							['basic', 'bottleneck']
								for model_class in self.all_model_classes:
										for layer_type in layers_type:
												SCREAMING_SNAKE_CASE_					      =							layer_type
												SCREAMING_SNAKE_CASE_					      =							True
												check_hidden_states_output(snake_case__					,							snake_case__					,							snake_case__  )

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

												check_hidden_states_output(snake_case__					,							snake_case__					,							snake_case__  )



						def 	__a					(			self	:   List[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*snake_case__  )






						@slow
						def 	__a					(			self	:   Tuple  ):

								"""simple docstring"""

								for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										SCREAMING_SNAKE_CASE_					      =							TFResNetModel.from_pretrained(snake_case__  )
										self.assertIsNotNone(snake_case__  )


def 					_a							(					)->	Tuple:
		SCREAMING_SNAKE_CASE_					      =							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
		return image




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



						@cached_property
						def 	__a					(			self	:   List[Any]  ):

								"""simple docstring"""

								return (
								    AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]  )
								    if is_vision_available()
								    else None
								)






						@slow
						def 	__a					(			self	:   Any  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]  )

								SCREAMING_SNAKE_CASE_					      =							self.default_image_processor
								SCREAMING_SNAKE_CASE_					      =							prepare_img()
								SCREAMING_SNAKE_CASE_					      =							image_processor(images=snake_case__					,							return_tensors='tf'  )

								# forward pass
								SCREAMING_SNAKE_CASE_					      =							model(**snake_case__  )

								# verify the logits
								SCREAMING_SNAKE_CASE_					      =							tf.TensorShape((1, 10_00)  )
								self.assertEqual(outputs.logits.shape					,							snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							tf.constant([-11.10_69, -9.78_77, -8.37_77]  )

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