


'''simple docstring'''



import unittest

from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

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 (
	    SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
	    SqueezeBertForMaskedLM,
	    SqueezeBertForMultipleChoice,
	    SqueezeBertForQuestionAnswering,
	    SqueezeBertForSequenceClassification,
	    SqueezeBertForTokenClassification,
	    SqueezeBertModel,
	)





class 							__UpperCamelCase				(		lowerCAmelCase_					):




	def __init__(   self						,				__a						,				__a=13						,				__a=7						,				__a=True						,				__a=True						,				__a=False						,				__a=True						,				__a=99						,				__a=32						,				__a=5						,				__a=4						,				__a=64						,				__a="gelu"						,				__a=0.1						,				__a=0.1						,				__a=512						,				__a=16						,				__a=2						,				__a=0.02						,				__a=3						,				__a=4						,				__a=None						,				__a=2						,				__a=2						,				__a=2						,				__a=2						,				__a=4						,				__a=1						,				):

						'''simple docstring'''





						__a			:       Union[str, Any]					    =  parent
						__a			:       Union[str, Any]					    =  batch_size
						__a			:       str					    =  seq_length
						__a			:       str					    =  is_training
						__a			:       str					    =  use_input_mask
						__a			:       Dict					    =  use_token_type_ids
						__a			:       List[str]					    =  use_labels
						__a			:       Union[str, Any]					    =  vocab_size
						__a			:       int					    =  hidden_size
						__a			:       Any					    =  num_hidden_layers
						__a			:       Dict					    =  num_attention_heads
						__a			:       str					    =  intermediate_size
						__a			:       Optional[int]					    =  hidden_act
						__a			:       int					    =  hidden_dropout_prob
						__a			:       str					    =  attention_probs_dropout_prob
						__a			:       List[str]					    =  max_position_embeddings
						__a			:       List[str]					    =  type_vocab_size
						__a			:       Tuple					    =  type_sequence_label_size
						__a			:       Optional[Any]					    =  initializer_range
						__a			:       Tuple					    =  num_labels
						__a			:       Tuple					    =  num_choices
						__a			:       Dict					    =  scope
						__a			:       int					    =  q_groups
						__a			:       Optional[Any]					    =  k_groups
						__a			:       List[str]					    =  v_groups
						__a			:       Union[str, Any]					    =  post_attention_groups
						__a			:       List[Any]					    =  intermediate_groups
						__a			:       Dict					    =  output_groups




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Dict					    =  ids_tensor([self.batch_size, self.seq_length]						,				self.vocab_size							)

						__a			:       str					    =  None
						if self.use_input_mask:
											__a			:       int					    =  random_attention_mask([self.batch_size, self.seq_length]							)

						__a			:       Tuple					    =  None
						__a			:       List[str]					    =  None
						__a			:       Tuple					    =  None
						if self.use_labels:
											__a			:       Tuple					    =  ids_tensor([self.batch_size]						,				self.type_sequence_label_size							)
											__a			:       List[Any]					    =  ids_tensor([self.batch_size, self.seq_length]						,				self.num_labels							)
											__a			:       Optional[int]					    =  ids_tensor([self.batch_size]						,				self.num_choices							)

						__a			:       Optional[int]					    =  self.get_config()

						return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return SqueezeBertConfig(
						    embedding_size=self.hidden_size						,				vocab_size=self.vocab_size						,				hidden_size=self.hidden_size						,				num_hidden_layers=self.num_hidden_layers						,				num_attention_heads=self.num_attention_heads						,				intermediate_size=self.intermediate_size						,				hidden_act=self.hidden_act						,				attention_probs_dropout_prob=self.hidden_dropout_prob						,				attention_dropout=self.attention_probs_dropout_prob						,				max_position_embeddings=self.max_position_embeddings						,				initializer_range=self.initializer_range						,				q_groups=self.q_groups						,				k_groups=self.k_groups						,				v_groups=self.v_groups						,				post_attention_groups=self.post_attention_groups						,				intermediate_groups=self.intermediate_groups						,				output_groups=self.output_groups						,				)




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a							):

						'''simple docstring'''





						__a			:       List[str]					    =  SqueezeBertModel(config=__a							)
						model.to(__a							)
						model.eval()
						__a			:       List[Any]					    =  model(__a						,				__a							)
						__a			:       List[str]					    =  model(__a							)
						self.parent.assertEqual(result.last_hidden_state.shape						,				(self.batch_size, self.seq_length, self.hidden_size)							)




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a							):

						'''simple docstring'''





						__a			:       Tuple					    =  SqueezeBertForMaskedLM(config=__a							)
						model.to(__a							)
						model.eval()
						__a			:       Dict					    =  model(__a						,				attention_mask=__a						,				labels=__a							)
						self.parent.assertEqual(result.logits.shape						,				(self.batch_size, self.seq_length, self.vocab_size)							)




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a							):

						'''simple docstring'''





						__a			:       Optional[int]					    =  SqueezeBertForQuestionAnswering(config=__a							)
						model.to(__a							)
						model.eval()
						__a			:       int					    =  model(
						    __a						,				attention_mask=__a						,				start_positions=__a						,				end_positions=__a							)
						self.parent.assertEqual(result.start_logits.shape						,				(self.batch_size, self.seq_length)							)
						self.parent.assertEqual(result.end_logits.shape						,				(self.batch_size, self.seq_length)							)




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a							):

						'''simple docstring'''





						__a			:       List[Any]					    =  self.num_labels
						__a			:       int					    =  SqueezeBertForSequenceClassification(__a							)
						model.to(__a							)
						model.eval()
						__a			:       List[Any]					    =  model(__a						,				attention_mask=__a						,				labels=__a							)
						self.parent.assertEqual(result.logits.shape						,				(self.batch_size, self.num_labels)							)




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a							):

						'''simple docstring'''





						__a			:       Optional[Any]					    =  self.num_labels
						__a			:       Dict					    =  SqueezeBertForTokenClassification(config=__a							)
						model.to(__a							)
						model.eval()

						__a			:       List[Any]					    =  model(__a						,				attention_mask=__a						,				labels=__a							)
						self.parent.assertEqual(result.logits.shape						,				(self.batch_size, self.seq_length, self.num_labels)							)




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a							):

						'''simple docstring'''





						__a			:       str					    =  self.num_choices
						__a			:       int					    =  SqueezeBertForMultipleChoice(config=__a							)
						model.to(__a							)
						model.eval()
						__a			:       Union[str, Any]					    =  input_ids.unsqueeze(1							).expand(-1						,				self.num_choices						,				-1							).contiguous()
						__a			:       Tuple					    =  input_mask.unsqueeze(1							).expand(-1						,				self.num_choices						,				-1							).contiguous()
						__a			:       Dict					    =  model(
						    __a						,				attention_mask=__a						,				labels=__a						,				)
						self.parent.assertEqual(result.logits.shape						,				(self.batch_size, self.num_choices)							)





	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Optional[Any]					    =  self.prepare_config_and_inputs()
						((__a)    ,				(__a)    ,				(__a)    ,				(__a)    ,				(__a)    ,				(__a))			:       Any					    =  config_and_inputs
						__a			:       Optional[int]					    =  {'input_ids': input_ids, 'attention_mask': input_mask}
						return config, inputs_dict





@require_torch
class 							__UpperCamelCase				(		lowerCAmelCase_		,      lowerCAmelCase_		,      unittest.TestCase					):
	A_				=    (
	    (
	        SqueezeBertModel,
	        SqueezeBertForMaskedLM,
	        SqueezeBertForMultipleChoice,
	        SqueezeBertForQuestionAnswering,
	        SqueezeBertForSequenceClassification,
	        SqueezeBertForTokenClassification,
	    )
	    if is_torch_available()
	    else None
	)
	A_				=    (
	    {
	        "feature-extraction": SqueezeBertModel,
	        "fill-mask": SqueezeBertForMaskedLM,
	        "question-answering": SqueezeBertForQuestionAnswering,
	        "text-classification": SqueezeBertForSequenceClassification,
	        "token-classification": SqueezeBertForTokenClassification,
	        "zero-shot": SqueezeBertForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	A_				=    False
	A_				=    True
	A_				=    False




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Any					    =  SqueezeBertModelTester(self							)
						__a			:       Optional[int]					    =  ConfigTester(self						,				config_class=__a						,				dim=37							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						self.config_tester.run_common_tests()




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Optional[int]					    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_squeezebert_model(*__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Any					    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_squeezebert_for_masked_lm(*__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Any					    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_squeezebert_for_question_answering(*__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Union[str, Any]					    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_squeezebert_for_sequence_classification(*__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Optional[int]					    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_squeezebert_for_token_classification(*__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       int					    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_squeezebert_for_multiple_choice(*__a							)





	@slow
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__a			:       int					    =  SqueezeBertModel.from_pretrained(__a							)
											self.assertIsNotNone(__a							)





@require_sentencepiece
@require_tokenizers
@require_torch
class 							__UpperCamelCase				(		unittest.TestCase					):




	@slow
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Dict					    =  SqueezeBertForSequenceClassification.from_pretrained('squeezebert/squeezebert-mnli'							)

						__a			:       Optional[int]					    =  torch.tensor([[1, 2_9414, 232, 328, 740, 1140, 1_2695, 69, 13, 1588, 2]]							)
						__a			:       List[Any]					    =  model(__a							)[0]
						__a			:       int					    =  torch.Size((1, 3)							)
						self.assertEqual(output.shape						,				__a							)
						__a			:       str					    =  torch.tensor([[0.6401, -0.0349, -0.6041]]							)
						self.assertTrue(torch.allclose(__a						,				__a						,				atol=1E-4							)							)




'''simple docstring'''

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

__a: Optional[Any]          =   16
__a: Any          =   32
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase = 16     ):
 lowercase__				:    Dict  =					AutoTokenizer.from_pretrained('''bert-base-cased'''     )
 lowercase__				:    Optional[int]  =					load_dataset('''glue'''		,     '''mrpc'''     )

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

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

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

 # Instantiate dataloaders.
 lowercase__				:    str  =					DataLoader(
     tokenized_datasets['''train''']		,     shuffle=UpperCAmelCase		,     collate_fn=UpperCAmelCase		,     batch_size=UpperCAmelCase     )
 lowercase__				:    Optional[int]  =					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: Tuple          =   mocked_dataloaders  # noqa: F811
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase     ):
 # For testing only
 if os.environ.get('''TESTING_MOCKED_DATALOADERS'''		,     UpperCAmelCase     ) == "1":
  lowercase__				:    Optional[int]  =					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:
  lowercase__				:    Union[str, Any]  =					Accelerator(
      cpu=args.cpu		,     mixed_precision=args.mixed_precision		,     log_with='''all'''		,     project_dir=args.project_dir     )
 else:
  lowercase__				:    Any  =					Accelerator(cpu=args.cpu		,     mixed_precision=args.mixed_precision     )
 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 lowercase__				:    int  =					config['''lr''']
 lowercase__				:    Optional[int]  =					int(config['''num_epochs''']     )
 lowercase__				:    Optional[Any]  =					int(config['''seed''']     )
 lowercase__				:    int  =					int(config['''batch_size''']     )
 set_seed(UpperCAmelCase     )

 lowercase__ ,							lowercase__				:    str  =					get_dataloaders(UpperCAmelCase		,     UpperCAmelCase     )
 lowercase__				:    Union[str, Any]  =					evaluate.load('''glue'''		,     '''mrpc'''     )

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

 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 lowercase__				:    Optional[int]  =					AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''		,     return_dict=UpperCAmelCase     )

 # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
 # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
 # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
 lowercase__				:    List[str]  =					model.to(accelerator.device     )

 # Instantiate optimizer
 lowercase__				:    List[Any]  =					AdamW(params=model.parameters()		,     lr=UpperCAmelCase     )

 # Instantiate scheduler
 lowercase__				:    List[str]  =					get_linear_schedule_with_warmup(
     optimizer=UpperCAmelCase		,     num_warmup_steps=100		,     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.
 lowercase__ ,							lowercase__ ,							lowercase__ ,							lowercase__ ,							lowercase__				:    List[str]  =					accelerator.prepare(
     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     )

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

 # Now we train the model
 for epoch in range(UpperCAmelCase     ):
  model.train()
  # New Code #
  # For our tracking example, we will log the total loss of each epoch
  if args.with_tracking:
   lowercase__				:    str  =					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     )
   lowercase__				:    List[str]  =					model(**UpperCAmelCase     )
   lowercase__				:    List[str]  =					outputs.loss
   # New Code #
   if args.with_tracking:
    total_loss += loss.detach().float()
   lowercase__				:    List[str]  =					loss / gradient_accumulation_steps
   accelerator.backward(UpperCAmelCase     )
   if step % gradient_accumulation_steps == 0:
    optimizer.step()
    lr_scheduler.step()
    optimizer.zero_grad()

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

  lowercase__				:    int  =					metric.compute()
  # Use accelerator.print to print only on the main process.
  accelerator.print(F"""epoch {epoch}:"""		,     UpperCAmelCase     )

  # 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(UpperCAmelCase     ),
           '''epoch''': epoch,
       }		,     step=UpperCAmelCase		,     )

    # 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 			__UpperCamelCase       (   ):
 lowercase__				:    Any  =					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.'''     )
 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=UpperCAmelCase		,     default='''logs'''		,     help='''Location on where to store experiment tracking logs` and relevent project information'''		,     )
 lowercase__				:    str  =					parser.parse_args()
 lowercase__				:    Tuple  =					{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
 training_function(UpperCAmelCase		,     UpperCAmelCase     )


if __name__ == "__main__":
  main()

