
import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_SCREAMING_SNAKE_CASE    :      Any          =			get_tests_dir('fixtures/test_sentencepiece_no_bos.model')







@require_sentencepiece
@require_tokenizers
class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_,			unittest.TestCase   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				PegasusTokenizer
	_lowerCAmelCase							       =				PegasusTokenizerFast
	_lowerCAmelCase							       =				True
	_lowerCAmelCase							       =				True





	def   __snake_case				(			self		):
					super().setUp()

					# We have a SentencePiece fixture for testing
					A__  :	List[str]         =							PegasusTokenizer(UpperCamelCase__		)
					tokenizer.save_pretrained(self.tmpdirname		)





	@cached_property
	def   __snake_case				(			self		):
					return PegasusTokenizer.from_pretrained('''google/pegasus-large'''		)





	def   __snake_case				(			self				,       **UpperCamelCase__		):
					return PegasusTokenizer.from_pretrained(self.tmpdirname				,       **UpperCamelCase__		)





	def   __snake_case				(			self				,       UpperCamelCase__		):
					return ("This is a test", "This is a test")





	def   __snake_case				(			self		):
					A__  :	List[Any]         =							'''</s>'''
					A__  :	List[Any]         =							1

					self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCamelCase__		)				,       UpperCamelCase__		)
					self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCamelCase__		)				,       UpperCamelCase__		)





	def   __snake_case				(			self		):
					A__  :	Optional[int]         =							list(self.get_tokenizer().get_vocab().keys()		)

					self.assertEqual(vocab_keys[0]				,       '''<pad>'''		)
					self.assertEqual(vocab_keys[1]				,       '''</s>'''		)
					self.assertEqual(vocab_keys[-1]				,       '''v'''		)
					self.assertEqual(len(UpperCamelCase__		)				,       1103		)





	def   __snake_case				(			self		):
					self.assertEqual(self.get_tokenizer().vocab_size				,       1103		)





	def   __snake_case				(			self		):
					A__  :	Tuple         =							self.rust_tokenizer_class.from_pretrained(self.tmpdirname		)
					A__  :	List[str]         =							self.tokenizer_class.from_pretrained(self.tmpdirname		)
					A__  :	List[Any]         =							(
					    '''Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'''
					    ''' </s> <pad> <pad> <pad>'''
					)
					A__  :	str         =							rust_tokenizer([raw_input_str]				,       return_tensors=UpperCamelCase__				,       add_special_tokens=UpperCamelCase__		).input_ids[0]
					A__  :	List[str]         =							py_tokenizer([raw_input_str]				,       return_tensors=UpperCamelCase__				,       add_special_tokens=UpperCamelCase__		).input_ids[0]
					self.assertListEqual(UpperCamelCase__				,       UpperCamelCase__		)





	def   __snake_case				(			self		):
					A__  :	List[str]         =							self._large_tokenizer
					# <mask_1> masks whole sentence while <mask_2> masks single word
					A__  :	List[str]         =							'''<mask_1> To ensure a <mask_2> flow of bank resolutions.'''
					A__  :	List[Any]         =							[2, 413, 615, 114, 3, 1971, 113, 1679, 1_0710, 107, 1]
					A__  :	Union[str, Any]         =							tokenizer([raw_input_str]				,       return_tensors=UpperCamelCase__		).input_ids[0]
					self.assertListEqual(UpperCamelCase__				,       UpperCamelCase__		)





	def   __snake_case				(			self		):
					A__  :	Any         =							self._large_tokenizer
					# The tracebacks for the following asserts are **better** without messages or self.assertEqual
					assert tokenizer.vocab_size == 9_6103
					assert tokenizer.pad_token_id == 0
					assert tokenizer.eos_token_id == 1
					assert tokenizer.offset == 103
					assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
					assert tokenizer.unk_token == "<unk>"
					assert tokenizer.model_max_length == 1024
					A__  :	List[Any]         =							'''To ensure a smooth flow of bank resolutions.'''
					A__  :	Union[str, Any]         =							[413, 615, 114, 2291, 1971, 113, 1679, 1_0710, 107, 1]
					A__  :	List[str]         =							tokenizer([raw_input_str]				,       return_tensors=UpperCamelCase__		).input_ids[0]
					self.assertListEqual(UpperCamelCase__				,       UpperCamelCase__		)
					assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]		) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]





	@require_torch
	def   __snake_case				(			self		):
					A__  :	Union[str, Any]         =							['''This is going to be way too long.''' * 150, '''short example''']
					A__  :	Tuple         =							['''not super long but more than 5 tokens''', '''tiny''']
					A__  :	Tuple         =							self._large_tokenizer(UpperCamelCase__				,       padding=UpperCamelCase__				,       truncation=UpperCamelCase__				,       return_tensors='''pt'''		)
					A__  :	Any         =							self._large_tokenizer(
					    text_target=UpperCamelCase__				,       max_length=5				,       padding=UpperCamelCase__				,       truncation=UpperCamelCase__				,       return_tensors='''pt'''		)

					assert batch.input_ids.shape == (2, 1024)
					assert batch.attention_mask.shape == (2, 1024)
					assert targets["input_ids"].shape == (2, 5)
					assert len(UpperCamelCase__		) == 2  # input_ids, attention_mask.





	@slow
	def   __snake_case				(			self		):
					# fmt: off
					A__  :	Optional[Any]         =							{'''input_ids''': [[3_8979, 143, 1_8485, 606, 130, 2_6669, 8_7686, 121, 5_4189, 1129, 111, 2_6669, 8_7686, 121, 9114, 1_4787, 121, 1_3249, 158, 592, 956, 121, 1_4621, 3_1576, 143, 6_2613, 108, 9688, 930, 4_3430, 1_1562, 6_2613, 304, 108, 1_1443, 897, 108, 9314, 1_7415, 6_3399, 108, 1_1443, 7614, 1_8316, 118, 4284, 7148, 1_2430, 143, 1400, 2_5703, 158, 111, 4284, 7148, 1_1772, 143, 2_1297, 1064, 158, 122, 204, 3506, 1754, 1133, 1_4787, 1581, 115, 3_3224, 4482, 111, 1355, 110, 2_9173, 317, 5_0833, 108, 2_0147, 9_4665, 111, 7_7198, 107, 1], [110, 6_2613, 117, 638, 112, 1133, 121, 2_0098, 1355, 7_9050, 1_3872, 135, 1596, 5_3541, 1352, 141, 1_3039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [139, 1235, 2799, 1_8289, 1_7780, 204, 109, 9474, 1296, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
					# fmt: on

					self.tokenizer_integration_test_util(
					    expected_encoding=UpperCamelCase__				,       model_name='''google/bigbird-pegasus-large-arxiv'''				,       revision='''ba85d0851d708441f91440d509690f1ab6353415'''				,       )







@require_sentencepiece
@require_tokenizers
class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_,			unittest.TestCase   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				PegasusTokenizer
	_lowerCAmelCase							       =				PegasusTokenizerFast
	_lowerCAmelCase							       =				True
	_lowerCAmelCase							       =				True





	def   __snake_case				(			self		):
					super().setUp()

					# We have a SentencePiece fixture for testing
					A__  :	Optional[int]         =							PegasusTokenizer(UpperCamelCase__				,       offset=0				,       mask_token_sent=UpperCamelCase__				,       mask_token='''[MASK]'''		)
					tokenizer.save_pretrained(self.tmpdirname		)





	@cached_property
	def   __snake_case				(			self		):
					return PegasusTokenizer.from_pretrained('''google/bigbird-pegasus-large-arxiv'''		)





	def   __snake_case				(			self				,       **UpperCamelCase__		):
					return PegasusTokenizer.from_pretrained(self.tmpdirname				,       **UpperCamelCase__		)





	def   __snake_case				(			self				,       UpperCamelCase__		):
					return ("This is a test", "This is a test")





	def   __snake_case				(			self		):
					A__  :	Any         =							self.rust_tokenizer_class.from_pretrained(self.tmpdirname		)
					A__  :	Tuple         =							self.tokenizer_class.from_pretrained(self.tmpdirname		)
					A__  :	Tuple         =							(
					    '''Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'''
					    ''' <pad> <pad> <pad>'''
					)
					A__  :	List[str]         =							rust_tokenizer([raw_input_str]				,       return_tensors=UpperCamelCase__				,       add_special_tokens=UpperCamelCase__		).input_ids[0]
					A__  :	str         =							py_tokenizer([raw_input_str]				,       return_tensors=UpperCamelCase__				,       add_special_tokens=UpperCamelCase__		).input_ids[0]
					self.assertListEqual(UpperCamelCase__				,       UpperCamelCase__		)





	@require_torch
	def   __snake_case				(			self		):
					A__  :	Dict         =							['''This is going to be way too long.''' * 1000, '''short example''']
					A__  :	str         =							['''not super long but more than 5 tokens''', '''tiny''']
					A__  :	List[Any]         =							self._large_tokenizer(UpperCamelCase__				,       padding=UpperCamelCase__				,       truncation=UpperCamelCase__				,       return_tensors='''pt'''		)
					A__  :	Dict         =							self._large_tokenizer(
					    text_target=UpperCamelCase__				,       max_length=5				,       padding=UpperCamelCase__				,       truncation=UpperCamelCase__				,       return_tensors='''pt'''		)

					assert batch.input_ids.shape == (2, 4096)
					assert batch.attention_mask.shape == (2, 4096)
					assert targets["input_ids"].shape == (2, 5)
					assert len(UpperCamelCase__		) == 2  # input_ids, attention_mask.





	def   __snake_case				(			self		):
					A__  :	Tuple         =							(
					    '''This is an example string that is used to test the original TF implementation against the HF'''
					    ''' implementation'''
					)

					A__  :	List[str]         =							self._large_tokenizer(UpperCamelCase__		).input_ids

					self.assertListEqual(
					    UpperCamelCase__				,       [182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 2_5016, 3137, 464, 109, 2_6955, 3137, 1]				,       )

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 how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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
#
########################################################################


_SCREAMING_SNAKE_CASE    :      str          =			1_6
_SCREAMING_SNAKE_CASE    :      Tuple          =			3_2

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Accelerator							,						__UpperCamelCase :						int = 16       ) ->       Optional[int]:

				"""simple docstring"""







				A__  :	List[str]         =							AutoTokenizer.from_pretrained('''bert-base-cased'''       )
				A__  :	Optional[int]         =							load_dataset('''glue'''							,						'''mrpc'''       )

				def tokenize_function(__UpperCamelCase :						Union[str, Any]       ):
								# max_length=None => use the model max length (it's actually the default)
								A__  :	int         =							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():
								A__  :	Optional[int]         =							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
				A__  :	List[Any]         =							tokenized_datasets.rename_column('''label'''							,						'''labels'''       )

				def collate_fn(__UpperCamelCase :						Any       ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								A__  :	Optional[Any]         =							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":
												A__  :	int         =							16
								elif accelerator.mixed_precision != "no":
												A__  :	Any         =							8
								else:
												A__  :	Union[str, Any]         =							None

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

				# Instantiate dataloaders.
				A__  :	Optional[int]         =							DataLoader(
				    tokenized_datasets['''train''']							,						shuffle=__UpperCamelCase							,						collate_fn=__UpperCamelCase							,						batch_size=__UpperCamelCase       )
				A__  :	Tuple         =							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

					_SCREAMING_SNAKE_CASE    :      Dict          =			mocked_dataloaders  # noqa: F811

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						int							,						__UpperCamelCase :						List[Any]       ) ->       Optional[Any]:

				"""simple docstring"""







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

				A__  :	int         =							evaluate.load('''glue'''							,						'''mrpc'''       )

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

				set_seed(__UpperCamelCase       )
				A__       ,							A__  :	int         =							get_dataloaders(__UpperCamelCase							,						__UpperCamelCase       )
				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				A__  :	Optional[int]         =							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).
				A__  :	Tuple         =							model.to(accelerator.device       )

				# Instantiate optimizer
				A__  :	Optional[int]         =							AdamW(params=model.parameters()							,						lr=__UpperCamelCase       )

				# Instantiate scheduler
				A__  :	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.
				A__       ,							A__       ,							A__       ,							A__       ,							A__  :	Dict         =							accelerator.prepare(
				    __UpperCamelCase							,						__UpperCamelCase							,						__UpperCamelCase							,						__UpperCamelCase							,						__UpperCamelCase       )

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

								model.eval()
								A__  :	Optional[int]         =							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():
																A__  :	Union[str, Any]         =							model(**__UpperCamelCase       )
												A__  :	int         =							outputs.logits.argmax(dim=-1       )
												A__       ,							A__  :	Optional[Any]         =							accelerator.gather((predictions, batch['''labels'''])       )
												# New Code #
												# First we check if it's a distributed system
												if accelerator.use_distributed:
																# Then see if we're on the last batch of our eval dataloader
																if step == len(__UpperCamelCase       ) - 1:
																				# Last batch needs to be truncated on distributed systems as it contains additional samples
																				A__  :	Tuple         =							predictions[: len(eval_dataloader.dataset       ) - samples_seen]
																				A__  :	int         =							references[: len(eval_dataloader.dataset       ) - samples_seen]
																else:
																				# Otherwise we add the number of samples seen
																				samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
												metric.add_batch(
												    predictions=__UpperCamelCase							,						references=__UpperCamelCase							,						)

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

def 				SCREAMING_SNAKE_CASE     (      ) ->       Union[str, Any]:

				"""simple docstring"""







				A__  :	Tuple         =							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.'''       )
				A__  :	Dict         =							parser.parse_args()
				A__  :	Any         =							{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
				training_function(__UpperCamelCase							,						__UpperCamelCase       )


if __name__ == "__main__":
					main()