


"""simple docstring"""
from __future__ import annotations

import unittest

from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

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


if is_tf_available():
				import tensorflow as tf

				from transformers import TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel





@require_tf
class        __A       :





							'''simple docstring'''

							__lowercase:						int							 =      PegasusConfig
							__lowercase:						Tuple							 =      {}
							__lowercase:						Tuple							 =      """gelu"""



							def __init__(			self							: str						,    UpperCAmelCase_							: Union[str, Any]						,    UpperCAmelCase_							: Union[str, Any]=13						,    UpperCAmelCase_							: Any=7						,    UpperCAmelCase_							: int=True						,    UpperCAmelCase_							: Tuple=False						,    UpperCAmelCase_							: Union[str, Any]=99						,    UpperCAmelCase_							: Tuple=32						,    UpperCAmelCase_							: List[Any]=2						,    UpperCAmelCase_							: Dict=4						,    UpperCAmelCase_							: List[Any]=37						,    UpperCAmelCase_							: int=0.1						,    UpperCAmelCase_							: Tuple=0.1						,    UpperCAmelCase_							: List[Any]=40						,    UpperCAmelCase_							: Dict=2						,    UpperCAmelCase_							: int=1						,    UpperCAmelCase_							: str=0						,    )   ->List[str]:



														"""simple docstring"""



														snake_case_          =						parent
														snake_case_          =						batch_size
														snake_case_          =						seq_length
														snake_case_          =						is_training
														snake_case_          =						use_labels
														snake_case_          =						vocab_size
														snake_case_          =						hidden_size
														snake_case_          =						num_hidden_layers
														snake_case_          =						num_attention_heads
														snake_case_          =						intermediate_size

														snake_case_          =						hidden_dropout_prob
														snake_case_          =						attention_probs_dropout_prob
														snake_case_          =						max_position_embeddings
														snake_case_          =						eos_token_id
														snake_case_          =						pad_token_id
														snake_case_          =						bos_token_id



							def 						lowerCAmelCase    (			self							: Tuple    )   ->str:



														"""simple docstring"""



														snake_case_          =						ids_tensor([self.batch_size, self.seq_length - 1]						,    self.vocab_size    )
														snake_case_          =						tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size    )						,    1    )
														snake_case_          =						tf.concat([input_ids, eos_tensor]						,    axis=1    )

														snake_case_          =						ids_tensor([self.batch_size, self.seq_length]						,    self.vocab_size    )

														snake_case_          =						self.config_cls(
														    vocab_size=self.vocab_size						,    d_model=self.hidden_size						,    encoder_layers=self.num_hidden_layers						,    decoder_layers=self.num_hidden_layers						,    encoder_attention_heads=self.num_attention_heads						,    decoder_attention_heads=self.num_attention_heads						,    encoder_ffn_dim=self.intermediate_size						,    decoder_ffn_dim=self.intermediate_size						,    dropout=self.hidden_dropout_prob						,    attention_dropout=self.attention_probs_dropout_prob						,    max_position_embeddings=self.max_position_embeddings						,    eos_token_ids=[2]						,    bos_token_id=self.bos_token_id						,    pad_token_id=self.pad_token_id						,    decoder_start_token_id=self.pad_token_id						,    **self.config_updates						,    )
														snake_case_          =						prepare_pegasus_inputs_dict(UpperCAmelCase_						,    UpperCAmelCase_						,    UpperCAmelCase_    )
														return config, inputs_dict



							def 						lowerCAmelCase    (			self							: Optional[int]						,    UpperCAmelCase_							: List[str]						,    UpperCAmelCase_							: Tuple    )   ->Optional[Any]:



														"""simple docstring"""



														snake_case_          =						TFPegasusModel(config=UpperCAmelCase_    ).get_decoder()
														snake_case_          =						inputs_dict["""input_ids"""]

														snake_case_          =						input_ids[:1, :]
														snake_case_          =						inputs_dict["""attention_mask"""][:1, :]
														snake_case_          =						inputs_dict["""head_mask"""]
														snake_case_          =						1

														# first forward pass
														snake_case_          =						model(UpperCAmelCase_						,    attention_mask=UpperCAmelCase_						,    head_mask=UpperCAmelCase_						,    use_cache=UpperCAmelCase_    )

														snake_case_    ,  snake_case_          =						outputs.to_tuple()

														# create hypothetical next token and extent to next_input_ids
														snake_case_          =						ids_tensor((self.batch_size, 3)						,    config.vocab_size    )
														snake_case_          =						tf.cast(ids_tensor((self.batch_size, 3)						,    2    )						,    tf.inta    )

														# append to next input_ids and
														snake_case_          =						tf.concat([input_ids, next_tokens]						,    axis=-1    )
														snake_case_          =						tf.concat([attention_mask, next_attn_mask]						,    axis=-1    )

														snake_case_          =						model(UpperCAmelCase_						,    attention_mask=UpperCAmelCase_    )[0]
														snake_case_          =						model(UpperCAmelCase_						,    attention_mask=UpperCAmelCase_						,    past_key_values=UpperCAmelCase_    )[0]

														self.parent.assertEqual(next_tokens.shape[1]						,    output_from_past.shape[1]    )

														# select random slice
														snake_case_          =						int(ids_tensor((1,)						,    output_from_past.shape[-1]    )    )
														snake_case_          =						output_from_no_past[:, -3:, random_slice_idx]
														snake_case_          =						output_from_past[:, :, random_slice_idx]

														# test that outputs are equal for slice
														tf.debugging.assert_near(UpperCAmelCase_						,    UpperCAmelCase_						,    rtol=1E-3    )







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE=None			,	_SCREAMING_SNAKE_CASE=None			,	_SCREAMING_SNAKE_CASE=None			,	_SCREAMING_SNAKE_CASE=None			,	_SCREAMING_SNAKE_CASE=None			,	)      ->			List[Any]:
							if attention_mask is None:
														snake_case_          =						tf.cast(tf.math.not_equal(_SCREAMING_SNAKE_CASE			,	config.pad_token_id			)			,	tf.inta			)
							if decoder_attention_mask is None:
														snake_case_          =						tf.concat(
														    [
														        tf.ones(decoder_input_ids[:, :1].shape			,	dtype=tf.inta			),
														        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]			,	config.pad_token_id			)			,	tf.inta			),
														    ]			,	axis=-1			,	)
							if head_mask is None:
														snake_case_          =						tf.ones((config.encoder_layers, config.encoder_attention_heads)			)
							if decoder_head_mask is None:
														snake_case_          =						tf.ones((config.decoder_layers, config.decoder_attention_heads)			)
							if cross_attn_head_mask is None:
														snake_case_          =						tf.ones((config.decoder_layers, config.decoder_attention_heads)			)
							return {
							    "input_ids": input_ids,
							    "decoder_input_ids": decoder_input_ids,
							    "attention_mask": attention_mask,
							    "decoder_attention_mask": decoder_attention_mask,
							    "head_mask": head_mask,
							    "decoder_head_mask": decoder_head_mask,
							    "cross_attn_head_mask": cross_attn_head_mask,
							}





@require_tf
class        __A       (snake_case__  ,						snake_case__  ,						unittest.TestCase):





							'''simple docstring'''

							__lowercase:						Any							 =      (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
							__lowercase:						int							 =      (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
							__lowercase:						Dict							 =      (
							    {
							        """conversational""": TFPegasusForConditionalGeneration,
							        """feature-extraction""": TFPegasusModel,
							        """summarization""": TFPegasusForConditionalGeneration,
							        """text2text-generation""": TFPegasusForConditionalGeneration,
							        """translation""": TFPegasusForConditionalGeneration,
							    }
							    if is_tf_available()
							    else {}
							)
							__lowercase:						int							 =      True
							__lowercase:						str							 =      False
							__lowercase:						List[Any]							 =      False



							def 						lowerCAmelCase    (			self							: List[str]    )   ->Union[str, Any]:



														"""simple docstring"""



														snake_case_          =						TFPegasusModelTester(self    )
														snake_case_          =						ConfigTester(self						,    config_class=UpperCAmelCase_    )



							def 						lowerCAmelCase    (			self							: Tuple    )   ->Dict:



														"""simple docstring"""



														self.config_tester.run_common_tests()



							def 						lowerCAmelCase    (			self							: Tuple    )   ->List[Any]:



														"""simple docstring"""



														snake_case_          =						self.model_tester.prepare_config_and_inputs_for_common()
														self.model_tester.check_decoder_model_past_large_inputs(*UpperCAmelCase_    )





@require_sentencepiece
@require_tokenizers
@require_tf
class        __A       (unittest.TestCase):





							'''simple docstring'''

							__lowercase:						Any							 =      [
							    """ PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.""",
							    """ The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" """,
							]
							__lowercase:						Dict							 =      [
							    """California's largest electricity provider has cut power to hundreds of thousands of customers in an effort to"""
							    """ reduce the risk of wildfires.""",
							    """N-Dubz have revealed they\'re \"grateful\" to have been nominated for four Mobo Awards.""",
							]  # differs slightly from pytorch, likely due to numerical differences in linear layers
							__lowercase:						Optional[Any]							 =      """google/pegasus-xsum"""



							@cached_property
							def 						lowerCAmelCase    (			self							: Any    )   ->Union[str, Any]:



														"""simple docstring"""



														return AutoTokenizer.from_pretrained(self.model_name    )



							@cached_property
							def 						lowerCAmelCase    (			self							: int    )   ->Optional[Any]:



														"""simple docstring"""



														snake_case_          =						TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name    )
														return model



							def 						lowerCAmelCase    (			self							: Optional[Any]						,    **UpperCAmelCase_							: List[str]    )   ->Union[str, Any]:



														"""simple docstring"""



														snake_case_          =						self.translate_src_text(**UpperCAmelCase_    )
														assert self.expected_text == generated_words



							def 						lowerCAmelCase    (			self							: Tuple						,    **UpperCAmelCase_							: int    )   ->List[str]:



														"""simple docstring"""



														snake_case_          =						self.tokenizer(self.src_text						,    **UpperCAmelCase_						,    padding=UpperCAmelCase_						,    return_tensors="""tf"""    )
														snake_case_          =						self.model.generate(
														    model_inputs.input_ids						,    attention_mask=model_inputs.attention_mask						,    num_beams=2						,    use_cache=UpperCAmelCase_						,    )
														snake_case_          =						self.tokenizer.batch_decode(generated_ids.numpy()						,    skip_special_tokens=UpperCAmelCase_    )
														return generated_words



							@slow
							def 						lowerCAmelCase    (			self							: Union[str, Any]    )   ->int:



														"""simple docstring"""



														self._assert_generated_batch_equal_expected()







"""simple docstring"""
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


__SCREAMING_SNAKE_CASE							:      Tuple						=      16
__SCREAMING_SNAKE_CASE							:      int						=      32







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE = 16			,	_SCREAMING_SNAKE_CASE = "bert-base-cased"			)      ->			Optional[Any]:
							snake_case_          =						AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE			)
							snake_case_          =						load_dataset("""glue"""			,	"""mrpc"""			)

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

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

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

							def collate_fn(_SCREAMING_SNAKE_CASE			):
														# 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(_SCREAMING_SNAKE_CASE			,	padding="""max_length"""			,	max_length=128			,	return_tensors="""pt"""			)
														return tokenizer.pad(_SCREAMING_SNAKE_CASE			,	padding="""longest"""			,	return_tensors="""pt"""			)

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

							return train_dataloader, eval_dataloader







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			List[str]:
							# Initialize accelerator
							snake_case_          =						Accelerator()

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

							set_seed(_SCREAMING_SNAKE_CASE			)
							snake_case_    ,  snake_case_          =						get_dataloaders(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)

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

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

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

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

							# We need to keep track of how many total steps we have iterated over
							snake_case_          =						0
							# We also need to keep track of the stating epoch so files are named properly
							snake_case_          =						0

							# Now we train the model
							snake_case_          =						evaluate.load("""glue"""			,	"""mrpc"""			)
							snake_case_          =						0
							snake_case_          =						{}
							for epoch in range(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			):
														model.train()
														for step, batch in enumerate(_SCREAMING_SNAKE_CASE			):
																					snake_case_          =						model(**_SCREAMING_SNAKE_CASE			)
																					snake_case_          =						outputs.loss
																					snake_case_          =						loss / gradient_accumulation_steps
																					accelerator.backward(_SCREAMING_SNAKE_CASE			)
																					if step % gradient_accumulation_steps == 0:
																												optimizer.step()
																												lr_scheduler.step()
																												optimizer.zero_grad()

																					overall_step += 1

														model.eval()
														snake_case_          =						0
														for step, batch in enumerate(_SCREAMING_SNAKE_CASE			):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					batch.to(accelerator.device			)
																					with torch.no_grad():
																												snake_case_          =						model(**_SCREAMING_SNAKE_CASE			)
																					snake_case_          =						outputs.logits.argmax(dim=-1			)
																					# It is slightly faster to call this once, than multiple times
																					snake_case_    ,  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(_SCREAMING_SNAKE_CASE			) - 1:
																																			snake_case_          =						predictions[: len(eval_dataloader.dataset			) - samples_seen]
																																			snake_case_          =						references[: len(eval_dataloader.dataset			) - samples_seen]
																												else:
																																			samples_seen += references.shape[0]
																					metric.add_batch(
																					    predictions=_SCREAMING_SNAKE_CASE			,	references=_SCREAMING_SNAKE_CASE			,	)

														snake_case_          =						metric.compute()
														# Use accelerator.print to print only on the main process.
														accelerator.print(f"""epoch {epoch}:"""			,	_SCREAMING_SNAKE_CASE			)
														snake_case_          =						eval_metric["""accuracy"""]

														if best_performance < eval_metric["accuracy"]:
																					snake_case_          =						eval_metric["""accuracy"""]

							if args.performance_lower_bound is not None:
														assert (
														    args.performance_lower_bound <= best_performance
														), f"""Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"""

							accelerator.wait_for_everyone()
							if accelerator.is_main_process:
														with open(os.path.join(args.output_dir			,	"""all_results.json"""			)			,	"""w"""			) as f:
																					json.dump(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)







def   _a			( )      ->			int:
							snake_case_          =						argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""			)
							parser.add_argument(
							    """--model_name_or_path"""			,	type=_SCREAMING_SNAKE_CASE			,	default="""bert-base-cased"""			,	help="""Path to pretrained model or model identifier from huggingface.co/models."""			,	required=_SCREAMING_SNAKE_CASE			,	)
							parser.add_argument(
							    """--output_dir"""			,	type=_SCREAMING_SNAKE_CASE			,	default="""."""			,	help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""			,	)
							parser.add_argument(
							    """--performance_lower_bound"""			,	type=_SCREAMING_SNAKE_CASE			,	default=_SCREAMING_SNAKE_CASE			,	help="""Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."""			,	)
							parser.add_argument(
							    """--num_epochs"""			,	type=_SCREAMING_SNAKE_CASE			,	default=3			,	help="""Number of train epochs."""			,	)
							snake_case_          =						parser.parse_args()
							snake_case_          =						{"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
							training_function(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
				main()



