






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 		lowercase_					:






      '''simple docstring'''




      __snake_case       =  PegasusConfig
      __snake_case       =  {}
      __snake_case       =  '''gelu'''







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




       """simple docstring"""


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

       a		  =      hidden_dropout_prob
       a		  =      attention_probs_dropout_prob
       a		  =      max_position_embeddings
       a		  =      eos_token_id
       a		  =      pad_token_id
       a		  =      bos_token_id







      def       __lowerCAmelCase					(      self      :						Tuple  )    ->Optional[Any]:




       """simple docstring"""


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

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

       a		  =      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 ,					)
       a		  =      prepare_pegasus_inputs_dict(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase  )
       return config, inputs_dict







      def       __lowerCAmelCase					(      self      :						Optional[int] ,					__UpperCAmelCase      :						List[Any] ,					__UpperCAmelCase      :						List[Any]  )    ->List[str]:




       """simple docstring"""


       a		  =      TFPegasusModel(config=__UpperCAmelCase  ).get_decoder()
       a		  =      inputs_dict['''input_ids''']

       a		  =      input_ids[:1, :]
       a		  =      inputs_dict['''attention_mask'''][:1, :]
       a		  =      inputs_dict['''head_mask''']
       a		  =      1

       # first forward pass
       a		  =      model(__UpperCAmelCase ,					attention_mask=__UpperCAmelCase ,					head_mask=__UpperCAmelCase ,					use_cache=__UpperCAmelCase  )

       a  ,     a		  =      outputs.to_tuple()

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

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

       a		  =      model(__UpperCAmelCase ,					attention_mask=__UpperCAmelCase  )[0]
       a		  =      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
       a		  =      int(ids_tensor((1,) ,					output_from_past.shape[-1]  )  )
       a		  =      output_from_no_past[:, -3:, random_slice_idx]
       a		  =      output_from_past[:, :, random_slice_idx]

       # test that outputs are equal for slice
       tf.debugging.assert_near(__UpperCAmelCase ,					__UpperCAmelCase ,					rtol=1e-3  )
def 						_a    (      a :Optional[Any]	,							a :List[Any]	,							a :Union[str, Any]	,							a :Optional[int]=None	,							a :str=None	,							a :str=None	,							a :str=None	,							a :Any=None	,							)							->			Union[str, Any]:
 if attention_mask is None:
  a		  =      tf.cast(tf.math.not_equal(a	,							config.pad_token_id )	,							tf.inta )
 if decoder_attention_mask is None:
  a		  =      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:
  a		  =      tf.ones((config.encoder_layers, config.encoder_attention_heads) )
 if decoder_head_mask is None:
  a		  =      tf.ones((config.decoder_layers, config.decoder_attention_heads) )
 if cross_attn_head_mask is None:
  a		  =      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 		lowercase_					(	lowercase ,  lowercase ,  unittest.TestCase   ):






      '''simple docstring'''




      __snake_case       =  (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
      __snake_case       =  (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
      __snake_case       =  (
          {
              '''conversational''': TFPegasusForConditionalGeneration,
              '''feature-extraction''': TFPegasusModel,
              '''summarization''': TFPegasusForConditionalGeneration,
              '''text2text-generation''': TFPegasusForConditionalGeneration,
              '''translation''': TFPegasusForConditionalGeneration,
          }
          if is_tf_available()
          else {}
      )
      __snake_case       =  True
      __snake_case       =  False
      __snake_case       =  False







      def       __lowerCAmelCase					(      self      :						Union[str, Any]  )    ->Dict:




       """simple docstring"""


       a		  =      TFPegasusModelTester(self  )
       a		  =      ConfigTester(self ,					config_class=__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						Dict  )    ->Tuple:




       """simple docstring"""


       self.config_tester.run_common_tests()







      def       __lowerCAmelCase					(      self      :						List[Any]  )    ->Any:




       """simple docstring"""


       a		  =      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 		lowercase_					(	unittest.TestCase   ):






      '''simple docstring'''




      __snake_case       =  [
          ''' 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!" ''',
      ]
      __snake_case       =  [
          '''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
      __snake_case       =  '''google/pegasus-xsum'''







      @cached_property
      def       __lowerCAmelCase					(      self      :						List[str]  )    ->Optional[int]:




       """simple docstring"""


       return AutoTokenizer.from_pretrained(self.model_name  )







      @cached_property
      def       __lowerCAmelCase					(      self      :						List[Any]  )    ->str:




       """simple docstring"""


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







      def       __lowerCAmelCase					(      self      :						Union[str, Any] ,					**__UpperCAmelCase      :						str  )    ->int:




       """simple docstring"""


       a		  =      self.translate_src_text(**__UpperCAmelCase  )
       assert self.expected_text == generated_words







      def       __lowerCAmelCase					(      self      :						Optional[Any] ,					**__UpperCAmelCase      :						List[Any]  )    ->List[Any]:




       """simple docstring"""


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







      @slow
      def       __lowerCAmelCase					(      self      :						Tuple  )    ->List[Any]:




       """simple docstring"""


       self._assert_generated_batch_equal_expected()







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
#
########################################################################


__A   			=1_6
__A   			=3_2

def        a						(      _UpperCAmelCase							:					Accelerator ,   _UpperCAmelCase							:					int = 16						):







		'''simple docstring'''





		__UpperCAmelCase    :     Union[str, Any]													=	AutoTokenizer.from_pretrained('''bert-base-cased'''						)
		__UpperCAmelCase    :     Any													=	load_dataset('''glue''' ,   '''mrpc'''						)

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

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

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

		# Instantiate dataloaders.
		__UpperCAmelCase    :     Optional[Any]													=	DataLoader(
		    tokenized_datasets['''train'''] ,   shuffle=_UpperCAmelCase ,   collate_fn=_UpperCAmelCase ,   batch_size=_UpperCAmelCase						)
		__UpperCAmelCase    :     str													=	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

				__A   			=mocked_dataloaders  # noqa: F811

def        a						(      _UpperCAmelCase							:					List[str] ,   _UpperCAmelCase							:					Any						):







		'''simple docstring'''





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

		__UpperCAmelCase    :     Optional[Any]													=	evaluate.load('''glue''' ,   '''mrpc'''						)

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

		set_seed(_UpperCAmelCase						)
		__UpperCAmelCase							,					__UpperCAmelCase    :     Union[str, Any]													=	get_dataloaders(_UpperCAmelCase ,   _UpperCAmelCase						)
		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		__UpperCAmelCase    :     List[str]													=	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).
		__UpperCAmelCase    :     Tuple													=	model.to(accelerator.device						)

		# Instantiate optimizer
		__UpperCAmelCase    :     Any													=	AdamW(params=model.parameters() ,   lr=_UpperCAmelCase						)

		# Instantiate scheduler
		__UpperCAmelCase    :     str													=	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.
		__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase    :     List[str]													=	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						)
						__UpperCAmelCase    :     int													=	model(**_UpperCAmelCase						)
						__UpperCAmelCase    :     str													=	outputs.loss
						__UpperCAmelCase    :     str													=	loss / gradient_accumulation_steps
						accelerator.backward(_UpperCAmelCase						)
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

				model.eval()
				__UpperCAmelCase    :     Tuple													=	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():
								__UpperCAmelCase    :     Dict													=	model(**_UpperCAmelCase						)
						__UpperCAmelCase    :     str													=	outputs.logits.argmax(dim=-1						)
						__UpperCAmelCase							,					__UpperCAmelCase    :     List[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
										__UpperCAmelCase    :     Dict													=	predictions[: len(eval_dataloader.dataset						) - samples_seen]
										__UpperCAmelCase    :     List[str]													=	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 ,   )

				__UpperCAmelCase    :     Tuple													=	metric.compute()
				# Use accelerator.print to print only on the main process.
				accelerator.print(f'epoch {epoch}:' ,   _UpperCAmelCase						)

def        a						(      ):







		'''simple docstring'''





		__UpperCAmelCase    :     int													=	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.'''						)
		__UpperCAmelCase    :     int													=	parser.parse_args()
		__UpperCAmelCase    :     Union[str, Any]													=	{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
		training_function(_UpperCAmelCase ,   _UpperCAmelCase						)


if __name__ == "__main__":
				main()





