



from __future__ import annotations

import unittest

from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import 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, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel






@require_tf
class        SCREAMING_SNAKE_CASE__    :

							"""simple docstring"""






							_snake_case     		=  BlenderbotSmallConfig
							_snake_case     		=  {}
							_snake_case     		=  'gelu'



							def __init__(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=13     ,	SCREAMING_SNAKE_CASE_=7     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_=99     ,	SCREAMING_SNAKE_CASE_=32     ,	SCREAMING_SNAKE_CASE_=2     ,	SCREAMING_SNAKE_CASE_=4     ,	SCREAMING_SNAKE_CASE_=37     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=20     ,	SCREAMING_SNAKE_CASE_=2     ,	SCREAMING_SNAKE_CASE_=1     ,	SCREAMING_SNAKE_CASE_=0     ,	)->   List[Any]:





													'''simple docstring'''






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

													__UpperCamelCase       =			hidden_dropout_prob
													__UpperCamelCase       =			attention_probs_dropout_prob
													__UpperCamelCase       =			max_position_embeddings
													__UpperCamelCase       =			eos_token_id
													__UpperCamelCase       =			pad_token_id
													__UpperCamelCase       =			bos_token_id



							def     A__		(  self			)->   Any:





													'''simple docstring'''






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

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

													__UpperCamelCase       =			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     ,	)
													__UpperCamelCase       =			prepare_blenderbot_small_inputs_dict(_a     ,	_a     ,	_a			)
													return config, inputs_dict



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)->   Any:





													'''simple docstring'''






													__UpperCamelCase       =			TFBlenderbotSmallModel(config=_a			).get_decoder()
													__UpperCamelCase       =			inputs_dict['''input_ids''']

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

													# first forward pass
													__UpperCamelCase       =			model(_a     ,	attention_mask=_a     ,	head_mask=_a     ,	use_cache=_a			)

													__UpperCamelCase  ,							__UpperCamelCase       =			outputs.to_tuple()

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

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

													__UpperCamelCase       =			model(_a     ,	attention_mask=_a			)[0]
													__UpperCamelCase       =			model(_a     ,	attention_mask=_a     ,	past_key_values=_a			)[0]

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

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

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





def 	A_ (							snake_case							:   Optional[Any] ,      snake_case							:   Optional[int] ,      snake_case							:   Dict ,      snake_case							:   Any=None ,      snake_case							:   str=None ,      snake_case							:   Dict=None ,      snake_case							:   Optional[int]=None ,      snake_case							:   str=None ,      )      ->    Dict:



						'''simple docstring'''

						if attention_mask is None:
												__UpperCamelCase       =			tf.cast(tf.math.not_equal(lowerCAmelCase__ ,      config.pad_token_id ) ,      tf.inta )
						if decoder_attention_mask is None:
												__UpperCamelCase       =			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:
												__UpperCamelCase       =			tf.ones((config.encoder_layers, config.encoder_attention_heads) )
						if decoder_head_mask is None:
												__UpperCamelCase       =			tf.ones((config.decoder_layers, config.decoder_attention_heads) )
						if cross_attn_head_mask is None:
												__UpperCamelCase       =			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        SCREAMING_SNAKE_CASE__    (							__SCREAMING_SNAKE_CASE   ,		__SCREAMING_SNAKE_CASE   ,		unittest.TestCase    ):

							"""simple docstring"""






							_snake_case     		=  (
							    (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
							)
							_snake_case     		=  (TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
							_snake_case     		=  (
							    {
							        'conversational': TFBlenderbotSmallForConditionalGeneration,
							        'feature-extraction': TFBlenderbotSmallModel,
							        'summarization': TFBlenderbotSmallForConditionalGeneration,
							        'text2text-generation': TFBlenderbotSmallForConditionalGeneration,
							        'translation': TFBlenderbotSmallForConditionalGeneration,
							    }
							    if is_tf_available()
							    else {}
							)
							_snake_case     		=  True
							_snake_case     		=  False
							_snake_case     		=  False



							def     A__		(  self			)->   Tuple:





													'''simple docstring'''






													__UpperCamelCase       =			TFBlenderbotSmallModelTester(self			)
													__UpperCamelCase       =			ConfigTester(self     ,	config_class=_a			)



							def     A__		(  self			)->   Optional[Any]:





													'''simple docstring'''






													self.config_tester.run_common_tests()



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





													'''simple docstring'''






													__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs_for_common()
													self.model_tester.check_decoder_model_past_large_inputs(*_a			)






@require_tokenizers
@require_tf
class        SCREAMING_SNAKE_CASE__    (							unittest.TestCase    ):

							"""simple docstring"""






							_snake_case     		=  [
							    'Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like '
							    '  i\'m going to throw up.\nand why is that?'
							]
							_snake_case     		=  'facebook/blenderbot_small-90M'



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





													'''simple docstring'''






													return BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''			)



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





													'''simple docstring'''






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



							@slow
							def     A__		(  self			)->   int:





													'''simple docstring'''






													__UpperCamelCase       =			self.tokenizer(self.src_text     ,	return_tensors='''tf'''			)
													__UpperCamelCase       =			self.model.generate(
													    model_inputs.input_ids     ,	attention_mask=model_inputs.attention_mask     ,	num_beams=2     ,	use_cache=_a     ,	)
													__UpperCamelCase       =			self.tokenizer.batch_decode(generated_ids.numpy()     ,	skip_special_tokens=_a			)[0]
													assert generated_words in (
													    "i don't know. i just feel like i'm going to throw up. it's not fun.",
													    "i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
													    "i'm not sure. i just feel like i've been in a bad situation.",
													)






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 the experiment tracking capability,
# 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
#
########################################################################

lowercase__							:							Union[str, Any]									=			1_6
lowercase__							:							Tuple									=			3_2




def 	A_ (							snake_case							:   Accelerator ,      snake_case							:   int = 16 )      ->    List[Any]:



						'''simple docstring'''

						__UpperCamelCase       =			AutoTokenizer.from_pretrained('''bert-base-cased''' )
						__UpperCamelCase       =			load_dataset('''glue''' ,      '''mrpc''' )

						def tokenize_function(snake_case							:   Tuple ):
												# max_length=None => use the model max length (it's actually the default)
												__UpperCamelCase       =			tokenizer(examples['''sentence1'''] ,      examples['''sentence2'''] ,      truncation=snake_case ,      max_length=snake_case )
												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       =			datasets.map(
												    snake_case ,      batched=snake_case ,      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       =			tokenized_datasets.rename_column('''label''' ,      '''labels''' )

						def collate_fn(snake_case							:   Union[str, Any] ):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												__UpperCamelCase       =			128 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       =			16
												elif accelerator.mixed_precision != "no":
																		__UpperCamelCase       =			8
												else:
																		__UpperCamelCase       =			None

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

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

						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

				lowercase__							:							List[str]									=			mocked_dataloaders  # noqa: F811




def 	A_ (							snake_case							:   str ,      snake_case							:   Optional[Any] )      ->    Optional[int]:



						'''simple docstring'''

						if os.environ.get('''TESTING_MOCKED_DATALOADERS''' ,      snake_case ) == "1":
												__UpperCamelCase       =			2
						# Initialize Accelerator

						# New Code #
						# We pass in "all" to `log_with` to grab all available trackers in the environment
						# Note: If using a custom `Tracker` class, should be passed in here such as:
						# >>> log_with = ["all", MyCustomTrackerClassInstance()]
						if args.with_tracking:
												__UpperCamelCase       =			Accelerator(
												    cpu=args.cpu ,      mixed_precision=args.mixed_precision ,      log_with='''all''' ,      project_dir=args.project_dir )
						else:
												__UpperCamelCase       =			Accelerator(cpu=args.cpu ,      mixed_precision=args.mixed_precision )
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						__UpperCamelCase       =			config['''lr''']
						__UpperCamelCase       =			int(config['''num_epochs'''] )
						__UpperCamelCase       =			int(config['''seed'''] )
						__UpperCamelCase       =			int(config['''batch_size'''] )
						set_seed(snake_case )

						__UpperCamelCase  ,							__UpperCamelCase       =			get_dataloaders(snake_case ,      snake_case )
						__UpperCamelCase       =			evaluate.load('''glue''' ,      '''mrpc''' )

						# If the batch size is too big we use gradient accumulation
						__UpperCamelCase       =			1
						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
												__UpperCamelCase       =			batch_size // MAX_GPU_BATCH_SIZE
												__UpperCamelCase       =			MAX_GPU_BATCH_SIZE

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						__UpperCamelCase       =			AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' ,      return_dict=snake_case )

						# 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       =			model.to(accelerator.device )

						# Instantiate optimizer
						__UpperCamelCase       =			AdamW(params=model.parameters() ,      lr=snake_case )

						# Instantiate scheduler
						__UpperCamelCase       =			get_linear_schedule_with_warmup(
						    optimizer=snake_case ,      num_warmup_steps=100 ,      num_training_steps=(len(snake_case ) * 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       =			accelerator.prepare(
						    snake_case ,      snake_case ,      snake_case ,      snake_case ,      snake_case )

						# New Code #
						# We need to initialize the trackers we use. Overall configurations can also be stored
						if args.with_tracking:
												__UpperCamelCase       =			os.path.split(snake_case )[-1].split('''.''' )[0]
												accelerator.init_trackers(snake_case ,      snake_case )

						# Now we train the model
						for epoch in range(snake_case ):
												model.train()
												# New Code #
												# For our tracking example, we will log the total loss of each epoch
												if args.with_tracking:
																		__UpperCamelCase       =			0
												for step, batch in enumerate(snake_case ):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device )
																		__UpperCamelCase       =			model(**snake_case )
																		__UpperCamelCase       =			outputs.loss
																		# New Code #
																		if args.with_tracking:
																								total_loss += loss.detach().float()
																		__UpperCamelCase       =			loss / gradient_accumulation_steps
																		accelerator.backward(snake_case )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

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

												__UpperCamelCase       =			metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(f"epoch {epoch}:" ,      snake_case )

												# New Code #
												# To actually log, we call `Accelerator.log`
												# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
												if args.with_tracking:
																		accelerator.log(
																		    {
																		        '''accuracy''': eval_metric['''accuracy'''],
																		        '''f1''': eval_metric['''f1'''],
																		        '''train_loss''': total_loss.item() / len(snake_case ),
																		        '''epoch''': epoch,
																		    } ,      step=snake_case ,      )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
						if args.with_tracking:
												accelerator.end_training()




def 	A_ (							)      ->    int:



						'''simple docstring'''

						__UpperCamelCase       =			argparse.ArgumentParser(description='''Simple example of training script.''' )
						parser.add_argument(
						    '''--mixed_precision''' ,      type=snake_case ,      default=snake_case ,      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.''' )
						parser.add_argument(
						    '''--with_tracking''' ,      action='''store_true''' ,      help='''Whether to load in all available experiment trackers from the environment and use them for logging.''' ,      )
						parser.add_argument(
						    '''--project_dir''' ,      type=snake_case ,      default='''logs''' ,      help='''Location on where to store experiment tracking logs` and relevent project information''' ,      )
						__UpperCamelCase       =			parser.parse_args()
						__UpperCamelCase       =			{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
						training_function(snake_case ,      snake_case )


if __name__ == "__main__":
				main()




