


'''simple docstring'''

import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
	from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
	_UpperCAmelCase					:			Dict				     =					False

if is_vision_available():
	from PIL import Image

	from transformers import (
	    AutoProcessor,
	    PixaStructImageProcessor,
	    PixaStructProcessor,
	    PreTrainedTokenizerFast,
	    TaTokenizer,
	)


@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11				,    reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'				,    )
@require_vision
@require_torch
class    a__					(			unittest.TestCase   ):







				"""simple docstring"""



				def        _snake_case	(self		):
										__lowerCAmelCase											=					tempfile.mkdtemp()

										__lowerCAmelCase											=					PixaStructImageProcessor()
										__lowerCAmelCase											=					TaTokenizer.from_pretrained('''t5-small'''		)

										__lowerCAmelCase											=					PixaStructProcessor(__lowercase							,      __lowercase		)

										processor.save_pretrained(self.tmpdirname		)




				def        _snake_case	(self							,      **__lowercase		):
										return AutoProcessor.from_pretrained(self.tmpdirname							,      **__lowercase		).tokenizer




				def        _snake_case	(self							,      **__lowercase		):
										return AutoProcessor.from_pretrained(self.tmpdirname							,      **__lowercase		).image_processor




				def        _snake_case	(self		):
										shutil.rmtree(self.tmpdirname		)




				def        _snake_case	(self		):
										__lowerCAmelCase											=					[np.random.randint(2_55							,      size=(3, 30, 4_00)							,      dtype=np.uinta		)]

										__lowerCAmelCase											=					[Image.fromarray(np.moveaxis(__lowercase							,      0							,      -1		)		) for x in image_inputs]

										return image_inputs




				def        _snake_case	(self		):
										__lowerCAmelCase											=					PixaStructProcessor(tokenizer=self.get_tokenizer()							,      image_processor=self.get_image_processor()		)
										processor.save_pretrained(self.tmpdirname		)

										__lowerCAmelCase											=					self.get_tokenizer(bos_token='''(BOS)'''							,      eos_token='''(EOS)'''		)
										__lowerCAmelCase											=					self.get_image_processor(do_normalize=__lowercase							,      padding_value=1.0		)

										__lowerCAmelCase											=					PixaStructProcessor.from_pretrained(
										    self.tmpdirname							,      bos_token='''(BOS)'''							,      eos_token='''(EOS)'''							,      do_normalize=__lowercase							,      padding_value=1.0		)

										self.assertEqual(processor.tokenizer.get_vocab()							,      tokenizer_add_kwargs.get_vocab()		)
										self.assertIsInstance(processor.tokenizer							,      __lowercase		)

										self.assertEqual(processor.image_processor.to_json_string()							,      image_processor_add_kwargs.to_json_string()		)
										self.assertIsInstance(processor.image_processor							,      __lowercase		)




				def        _snake_case	(self		):
										__lowerCAmelCase											=					self.get_image_processor()
										__lowerCAmelCase											=					self.get_tokenizer()

										__lowerCAmelCase											=					PixaStructProcessor(tokenizer=__lowercase							,      image_processor=__lowercase		)

										__lowerCAmelCase											=					self.prepare_image_inputs()

										__lowerCAmelCase											=					image_processor(__lowercase							,      return_tensors='''np'''		)
										__lowerCAmelCase											=					processor(images=__lowercase							,      return_tensors='''np'''		)

										for key in input_feat_extract.keys():
																self.assertAlmostEqual(input_feat_extract[key].sum()							,      input_processor[key].sum()							,      delta=1e-2		)




				def        _snake_case	(self		):
										__lowerCAmelCase											=					self.get_image_processor()
										__lowerCAmelCase											=					self.get_tokenizer()

										__lowerCAmelCase											=					PixaStructProcessor(tokenizer=__lowercase							,      image_processor=__lowercase		)

										__lowerCAmelCase											=					'''lower newer'''

										__lowerCAmelCase											=					processor(text=__lowercase		)

										__lowerCAmelCase											=					tokenizer(__lowercase							,      return_token_type_ids=__lowercase							,      add_special_tokens=__lowercase		)

										for key in encoded_tok.keys():
																self.assertListEqual(encoded_tok[key]							,      encoded_processor[key]		)




				def        _snake_case	(self		):
										__lowerCAmelCase											=					self.get_image_processor()
										__lowerCAmelCase											=					self.get_tokenizer()

										__lowerCAmelCase											=					PixaStructProcessor(tokenizer=__lowercase							,      image_processor=__lowercase		)

										__lowerCAmelCase											=					'''lower newer'''
										__lowerCAmelCase											=					self.prepare_image_inputs()

										__lowerCAmelCase											=					processor(text=__lowercase							,      images=__lowercase		)

										self.assertListEqual(
										    list(inputs.keys()		)							,      ['''flattened_patches''', '''attention_mask''', '''decoder_attention_mask''', '''decoder_input_ids''']		)

										# test if it raises when no input is passed
										with pytest.raises(__lowercase		):
																processor()




				def        _snake_case	(self		):
										__lowerCAmelCase											=					self.get_image_processor()
										__lowerCAmelCase											=					self.get_tokenizer()

										__lowerCAmelCase											=					PixaStructProcessor(tokenizer=__lowercase							,      image_processor=__lowercase		)

										__lowerCAmelCase											=					'''lower newer'''
										__lowerCAmelCase											=					self.prepare_image_inputs()

										__lowerCAmelCase											=					processor(text=__lowercase							,      images=__lowercase		)

										__lowerCAmelCase											=					[5_12, 10_24, 20_48, 40_96]
										__lowerCAmelCase											=					[7_70, 7_70, 7_70, 7_70]
										# with text
										for i, max_patch in enumerate(__lowercase		):
																__lowerCAmelCase											=					processor(text=__lowercase							,      images=__lowercase							,      max_patches=__lowercase		)
																self.assertEqual(inputs['''flattened_patches'''][0].shape[0]							,      __lowercase		)
																self.assertEqual(inputs['''flattened_patches'''][0].shape[1]							,      expected_hidden_size[i]		)

										# without text input
										for i, max_patch in enumerate(__lowercase		):
																__lowerCAmelCase											=					processor(images=__lowercase							,      max_patches=__lowercase		)
																self.assertEqual(inputs['''flattened_patches'''][0].shape[0]							,      __lowercase		)
																self.assertEqual(inputs['''flattened_patches'''][0].shape[1]							,      expected_hidden_size[i]		)




				def        _snake_case	(self		):
										__lowerCAmelCase											=					self.get_image_processor()
										__lowerCAmelCase											=					self.get_tokenizer()

										__lowerCAmelCase											=					PixaStructProcessor(tokenizer=__lowercase							,      image_processor=__lowercase		)

										__lowerCAmelCase											=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

										__lowerCAmelCase											=					processor.batch_decode(__lowercase		)
										__lowerCAmelCase											=					tokenizer.batch_decode(__lowercase		)

										self.assertListEqual(__lowercase							,      __lowercase		)




				def        _snake_case	(self		):
										__lowerCAmelCase											=					self.get_image_processor()
										__lowerCAmelCase											=					self.get_tokenizer()

										__lowerCAmelCase											=					PixaStructProcessor(tokenizer=__lowercase							,      image_processor=__lowercase		)

										__lowerCAmelCase											=					'''lower newer'''
										__lowerCAmelCase											=					self.prepare_image_inputs()

										__lowerCAmelCase											=					processor(text=__lowercase							,      images=__lowercase		)

										# For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
										self.assertListEqual(
										    list(inputs.keys()		)							,      ['''flattened_patches''', '''attention_mask''', '''decoder_attention_mask''', '''decoder_input_ids''']		)

										__lowerCAmelCase											=					processor(text=__lowercase		)

										# For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
										self.assertListEqual(list(inputs.keys()		)							,      ['''input_ids''', '''attention_mask''']		)





'''simple docstring'''

import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic gradient accumulation
#
# 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)
#
# 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
#
########################################################################

_UpperCAmelCase					:			List[Any]				     =					3_2

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase = 1_6):
						__lowerCAmelCase											=					AutoTokenizer.from_pretrained('''bert-base-cased''')
						__lowerCAmelCase											=					load_dataset('''glue''',					'''mrpc''')

						def tokenize_function(lowerCamelCase):
												# max_length=None => use the model max length (it's actually the default)
												__lowerCAmelCase											=					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():
												__lowerCAmelCase											=					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
						__lowerCAmelCase											=					tokenized_datasets.rename_column('''label''',					'''labels''')

						def collate_fn(lowerCamelCase):
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		__lowerCAmelCase											=					1_6
												elif accelerator.mixed_precision != "no":
																		__lowerCAmelCase											=					8
												else:
																		__lowerCAmelCase											=					None

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

						# Instantiate dataloaders.
						__lowerCAmelCase											=					DataLoader(
						    tokenized_datasets['''train'''],					shuffle=lowerCamelCase,					collate_fn=lowerCamelCase,					batch_size=lowerCamelCase)
						__lowerCAmelCase											=					DataLoader(
						    tokenized_datasets['''validation'''],					shuffle=lowerCamelCase,					collate_fn=lowerCamelCase,					batch_size=lowerCamelCase)

						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

	_UpperCAmelCase					:			Optional[int]				     =					mocked_dataloaders  # noqa: F811

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase):
						# For testing only
						if os.environ.get('''TESTING_MOCKED_DATALOADERS''',					lowerCamelCase) == "1":
												__lowerCAmelCase											=					2
						# Initialize accelerator
						__lowerCAmelCase											=					Accelerator(cpu=args.cpu,					mixed_precision=args.mixed_precision)
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						__lowerCAmelCase											=					config['''lr''']
						__lowerCAmelCase											=					int(config['''num_epochs'''])
						__lowerCAmelCase											=					int(config['''seed'''])
						__lowerCAmelCase											=					int(config['''batch_size'''])

						__lowerCAmelCase											=					evaluate.load('''glue''',					'''mrpc''')

						# New Code #
						# We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
						# to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
						# half each time. From this, we can calculate the number of gradient accumulation steps needed
						# and modify the Accelerator object as a result
						@find_executable_batch_size(starting_batch_size=int(lowerCamelCase))
						def inner_training_loop(lowerCamelCase):
												# Since we need to modify the outside accelerator object, we need to bring it
												# to the local scope
												nonlocal accelerator

												# We can calculate the number of gradient accumulation steps based on the current
												# batch size vs the starting batch size
												__lowerCAmelCase											=					observed_batch_size // batch_size

												# And then set it in the Accelerator directly:
												__lowerCAmelCase											=					num_gradient_accumulation_steps

												# Next we need to free all of the stored model references in the Accelerator each time
												accelerator.free_memory()

												# And set the seed so our results are reproducable each reset
												set_seed(lowerCamelCase)

												# Instantiate the model (we build the model here so that the seed also control new weights initialization)
												__lowerCAmelCase											=					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).
												__lowerCAmelCase											=					model.to(accelerator.device)

												# Instantiate optimizer
												__lowerCAmelCase											=					AdamW(params=model.parameters(),					lr=lowerCamelCase)
												__lowerCAmelCase							,       __lowerCAmelCase											=					get_dataloaders(lowerCamelCase,					lowerCamelCase)

												# Instantiate scheduler
												__lowerCAmelCase											=					get_linear_schedule_with_warmup(
												    optimizer=lowerCamelCase,					num_warmup_steps=1_0_0,					num_training_steps=(len(lowerCamelCase) * num_epochs),					)

												# 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.
												__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase											=					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):
																								# And perform gradient accumulation
																								with accelerator.accumulate(lowerCamelCase):
																														# We could avoid this line since we set the accelerator with `device_placement=True`.
																														batch.to(accelerator.device)
																														__lowerCAmelCase											=					model(**lowerCamelCase)
																														__lowerCAmelCase											=					outputs.loss
																														accelerator.backward(lowerCamelCase)
																														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():
																														__lowerCAmelCase											=					model(**lowerCamelCase)
																								__lowerCAmelCase											=					outputs.logits.argmax(dim=-1)
																								__lowerCAmelCase							,       __lowerCAmelCase											=					accelerator.gather_for_metrics((predictions, batch['''labels''']))
																								metric.add_batch(
																								    predictions=lowerCamelCase,					references=lowerCamelCase,					)

																		__lowerCAmelCase											=					metric.compute()
																		# Use accelerator.print to print only on the main process.
																		accelerator.print(F"""epoch {epoch}:""",					lowerCamelCase)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
						inner_training_loop()

def 				__magic_name__(    ):
						__lowerCAmelCase											=					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.''')
						__lowerCAmelCase											=					parser.parse_args()
						# New Code #
						# We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
						__lowerCAmelCase											=					{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 2_5_6}
						training_function(lowerCamelCase,					lowerCamelCase)


if __name__ == "__main__":
	main()

