


'''simple docstring'''
import gc
import unittest

from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
		    CTRLForSequenceClassification,
		    CTRLLMHeadModel,
		    CTRLModel,
		)
class   lowercase      :

		"""simple docstring"""

		def __init__(						self       ,  UpperCamelCase_       ,  UpperCamelCase_=14       ,  UpperCamelCase_=7       ,  UpperCamelCase_=True       ,  UpperCamelCase_=True       ,  UpperCamelCase_=True       ,  UpperCamelCase_=True       ,  UpperCamelCase_=True       ,  UpperCamelCase_=99       ,  UpperCamelCase_=32       ,  UpperCamelCase_=5       ,  UpperCamelCase_=4       ,  UpperCamelCase_=37       ,  UpperCamelCase_="gelu"       ,  UpperCamelCase_=0.1       ,  UpperCamelCase_=0.1       ,  UpperCamelCase_=512       ,  UpperCamelCase_=16       ,  UpperCamelCase_=2       ,  UpperCamelCase_=0.02       ,  UpperCamelCase_=3       ,  UpperCamelCase_=4       ,  UpperCamelCase_=None       ,  ):

							'''simple docstring'''


							UpperCamelCase__  :Optional[Any]				   =					parent
							UpperCamelCase__  :Union[str, Any]				   =					batch_size
							UpperCamelCase__  :Tuple				   =					seq_length
							UpperCamelCase__  :Dict				   =					is_training
							UpperCamelCase__  :Union[str, Any]				   =					use_token_type_ids
							UpperCamelCase__  :Optional[int]				   =					use_input_mask
							UpperCamelCase__  :Tuple				   =					use_labels
							UpperCamelCase__  :Any				   =					use_mc_token_ids
							UpperCamelCase__  :Any				   =					vocab_size
							UpperCamelCase__  :Tuple				   =					hidden_size
							UpperCamelCase__  :Dict				   =					num_hidden_layers
							UpperCamelCase__  :Dict				   =					num_attention_heads
							UpperCamelCase__  :Dict				   =					intermediate_size
							UpperCamelCase__  :List[str]				   =					hidden_act
							UpperCamelCase__  :List[Any]				   =					hidden_dropout_prob
							UpperCamelCase__  :Optional[int]				   =					attention_probs_dropout_prob
							UpperCamelCase__  :Optional[int]				   =					max_position_embeddings
							UpperCamelCase__  :Dict				   =					type_vocab_size
							UpperCamelCase__  :Union[str, Any]				   =					type_sequence_label_size
							UpperCamelCase__  :Tuple				   =					initializer_range
							UpperCamelCase__  :str				   =					num_labels
							UpperCamelCase__  :int				   =					num_choices
							UpperCamelCase__  :int				   =					scope
							UpperCamelCase__  :Tuple				   =					self.vocab_size - 1




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :List[str]				   =					ids_tensor([self.batch_size, self.seq_length]       ,  self.vocab_size				)

							UpperCamelCase__  :Optional[int]				   =					None
							if self.use_input_mask:
												UpperCamelCase__  :Optional[Any]				   =					random_attention_mask([self.batch_size, self.seq_length]				)

							UpperCamelCase__  :Dict				   =					None
							if self.use_token_type_ids:
												UpperCamelCase__  :int				   =					ids_tensor([self.batch_size, self.seq_length]       ,  self.type_vocab_size				)

							UpperCamelCase__  :Union[str, Any]				   =					None
							if self.use_mc_token_ids:
												UpperCamelCase__  :Union[str, Any]				   =					ids_tensor([self.batch_size, self.num_choices]       ,  self.seq_length				)

							UpperCamelCase__  :Any				   =					None
							UpperCamelCase__  :Union[str, Any]				   =					None
							UpperCamelCase__  :List[str]				   =					None
							if self.use_labels:
												UpperCamelCase__  :List[str]				   =					ids_tensor([self.batch_size]       ,  self.type_sequence_label_size				)
												UpperCamelCase__  :List[str]				   =					ids_tensor([self.batch_size, self.seq_length]       ,  self.num_labels				)
												UpperCamelCase__  :List[Any]				   =					ids_tensor([self.batch_size]       ,  self.num_choices				)

							UpperCamelCase__  :Any				   =					self.get_config()

							UpperCamelCase__  :str				   =					ids_tensor([self.num_hidden_layers, self.num_attention_heads]       ,  2				)

							return (
							    config,
							    input_ids,
							    input_mask,
							    head_mask,
							    token_type_ids,
							    mc_token_ids,
							    sequence_labels,
							    token_labels,
							    choice_labels,
							)




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							return CTRLConfig(
							    vocab_size=self.vocab_size       ,  n_embd=self.hidden_size       ,  n_layer=self.num_hidden_layers       ,  n_head=self.num_attention_heads       ,  n_positions=self.max_position_embeddings       ,  pad_token_id=self.pad_token_id       ,  )




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  *UpperCamelCase_				):

							'''simple docstring'''


							UpperCamelCase__  :int				   =					CTRLModel(config=UpperCamelCase_				)
							model.to(UpperCamelCase_				)
							model.eval()

							model(UpperCamelCase_       ,  token_type_ids=UpperCamelCase_       ,  head_mask=UpperCamelCase_				)
							model(UpperCamelCase_       ,  token_type_ids=UpperCamelCase_				)
							UpperCamelCase__  :Optional[int]				   =					model(UpperCamelCase_				)
							self.parent.assertEqual(result.last_hidden_state.shape       ,  (self.batch_size, self.seq_length, self.hidden_size)				)
							self.parent.assertEqual(len(result.past_key_values				)       ,  config.n_layer				)




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  *UpperCamelCase_				):

							'''simple docstring'''


							UpperCamelCase__  :List[str]				   =					CTRLLMHeadModel(UpperCamelCase_				)
							model.to(UpperCamelCase_				)
							model.eval()

							UpperCamelCase__  :Dict				   =					model(UpperCamelCase_       ,  token_type_ids=UpperCamelCase_       ,  labels=UpperCamelCase_				)
							self.parent.assertEqual(result.loss.shape       ,  ()				)
							self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.seq_length, self.vocab_size)				)




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Tuple				   =					self.prepare_config_and_inputs()

							(
							    (
							    UpperCamelCase__
							)   ,							(
							    UpperCamelCase__
							)   ,							(
							    UpperCamelCase__
							)   ,							(
							    UpperCamelCase__
							)   ,							(
							    UpperCamelCase__
							)   ,							(
							    UpperCamelCase__
							)   ,							(
							    UpperCamelCase__
							)   ,							(
							    UpperCamelCase__
							)   ,							(
							    UpperCamelCase__
							)   ,							
							)  :Dict				   =					config_and_inputs

							UpperCamelCase__  :Optional[int]				   =					{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''head_mask''': head_mask}

							return config, inputs_dict




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  *UpperCamelCase_				):

							'''simple docstring'''


							UpperCamelCase__  :Optional[int]				   =					self.num_labels
							UpperCamelCase__  :Optional[int]				   =					CTRLForSequenceClassification(UpperCamelCase_				)
							model.to(UpperCamelCase_				)
							model.eval()
							UpperCamelCase__  :int				   =					ids_tensor([self.batch_size]       ,  self.type_sequence_label_size				)
							UpperCamelCase__  :Optional[int]				   =					model(UpperCamelCase_       ,  token_type_ids=UpperCamelCase_       ,  labels=UpperCamelCase_				)
							self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.num_labels)				)



@require_torch
class   lowercase      (				A__      ,						A__      ,						A__      ,						unittest.TestCase   ):

		"""simple docstring"""

		_a       					=							(CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
		_a       					=							(CTRLLMHeadModel,) if is_torch_available() else ()
		_a       					=							(
		    {
		        'feature-extraction': CTRLModel,
		        'text-classification': CTRLForSequenceClassification,
		        'text-generation': CTRLLMHeadModel,
		        'zero-shot': CTRLForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)
		_a       					=							True
		_a       					=							False
		_a       					=							False




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_				):

							'''simple docstring'''


							if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
												# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
												# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
												# config could not be created.
												return True

							return False




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :List[str]				   =					CTRLModelTester(self				)
							UpperCamelCase__  :Optional[int]				   =					ConfigTester(self       ,  config_class=UpperCamelCase_       ,  n_embd=37				)




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							super().tearDown()
							# clean-up as much as possible GPU memory occupied by PyTorch
							gc.collect()
							torch.cuda.empty_cache()




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							self.config_tester.run_common_tests()




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Optional[int]				   =					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_ctrl_model(*UpperCamelCase_				)




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Tuple				   =					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_lm_head_model(*UpperCamelCase_				)




		@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''				)
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							pass




		@slow
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												UpperCamelCase__  :Tuple				   =					CTRLModel.from_pretrained(UpperCamelCase_				)
												self.assertIsNotNone(UpperCamelCase_				)




		@unittest.skip('''The model doesn\'t support left padding'''				)  # and it's not used enough to be worth fixing :)
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							pass



@require_torch
class   lowercase      (				unittest.TestCase   ):

		"""simple docstring"""

		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							super().tearDown()
							# clean-up as much as possible GPU memory occupied by PyTorch
							gc.collect()
							torch.cuda.empty_cache()




		@slow
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Optional[int]				   =					CTRLLMHeadModel.from_pretrained('''ctrl'''				)
							model.to(UpperCamelCase_				)
							UpperCamelCase__  :int				   =					torch.tensor(
							    [[11859, 0, 1611, 8]]       ,  dtype=torch.long       ,  device=UpperCamelCase_				)  # Legal the president is
							UpperCamelCase__  :Optional[int]				   =					[
							    11859,
							    0,
							    1611,
							    8,
							    5,
							    150,
							    26449,
							    2,
							    19,
							    348,
							    469,
							    3,
							    2595,
							    48,
							    20740,
							    246533,
							    246533,
							    19,
							    30,
							    5,
							]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

							UpperCamelCase__  :Union[str, Any]				   =					model.generate(UpperCamelCase_       ,  do_sample=UpperCamelCase_				)
							self.assertListEqual(output_ids[0].tolist()       ,  UpperCamelCase_				)



'''simple docstring'''
import argparse

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
#
# 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 run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


__snake_case				 =	16
__snake_case				 =	32


def      a (     __a					,   __a = 16  )    ->      Dict:



					'''simple docstring'''






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

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

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

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

					# Instantiate dataloaders.
					UpperCamelCase__  :Any				   =					DataLoader(
					    tokenized_datasets['''train''']					,   shuffle=__a					,   collate_fn=__a					,   batch_size=__a					,   drop_last=__a  )
					UpperCamelCase__  :Any				   =					DataLoader(
					    tokenized_datasets['''validation''']					,   shuffle=__a					,   collate_fn=__a					,   batch_size=__a					,   drop_last=(accelerator.mixed_precision == '''fp8''')					,   )

					return train_dataloader, eval_dataloader


def      a (     __a					,   __a  )    ->      Dict:



					'''simple docstring'''






					UpperCamelCase__  :List[Any]				   =					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__  :List[str]				   =					int(config['''num_epochs''']  )
					UpperCamelCase__  :int				   =					int(config['''seed''']  )
					UpperCamelCase__  :str				   =					int(config['''batch_size''']  )

					UpperCamelCase__  :List[str]				   =					evaluate.load('''glue'''					,   '''mrpc'''  )

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

					set_seed(__a  )
					UpperCamelCase__   ,							UpperCamelCase__  :Tuple				   =					get_dataloaders(__a					,   __a  )
					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					UpperCamelCase__  :List[Any]				   =					AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''					,   return_dict=__a  )

					# 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__  :Optional[int]				   =					model.to(accelerator.device  )
					# Instantiate optimizer
					UpperCamelCase__  :Union[str, Any]				   =					AdamW(params=model.parameters()					,   lr=__a  )

					# Instantiate scheduler
					UpperCamelCase__  :Optional[Any]				   =					get_linear_schedule_with_warmup(
					    optimizer=__a					,   num_warmup_steps=100					,   num_training_steps=(len(__a  ) * 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__  :str				   =					accelerator.prepare(
					    __a					,   __a					,   __a					,   __a					,   __a  )

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

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

										UpperCamelCase__  :List[str]				   =					metric.compute()
										# Use accelerator.print to print only on the main process.
										accelerator.print(f'''epoch {epoch}:'''					,   __a  )


def      a (     )    ->      List[Any]:



					'''simple docstring'''






					UpperCamelCase__  :Union[str, Any]				   =					argparse.ArgumentParser(description='''Simple example of training script.'''  )
					parser.add_argument(
					    '''--mixed_precision'''					,   type=__a					,   default=__a					,   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__  :int				   =					{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
					training_function(__a					,   __a  )


if __name__ == "__main__":
		main()