


'''simple docstring'''


import unittest

from transformers import XLMConfig, 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 (
          XLMForMultipleChoice,
          XLMForQuestionAnswering,
          XLMForQuestionAnsweringSimple,
          XLMForSequenceClassification,
          XLMForTokenClassification,
          XLMModel,
          XLMWithLMHeadModel,
      )
      from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST







class 							__magic_name__	:

     """simple docstring"""






     def __init__(   self			,     lowerCamelCase			,     lowerCamelCase=13			,     lowerCamelCase=7			,     lowerCamelCase=True			,     lowerCamelCase=True			,     lowerCamelCase=True			,     lowerCamelCase=True			,     lowerCamelCase=True			,     lowerCamelCase=False			,     lowerCamelCase=False			,     lowerCamelCase=False			,     lowerCamelCase=2			,     lowerCamelCase=99			,     lowerCamelCase=0			,     lowerCamelCase=32			,     lowerCamelCase=5			,     lowerCamelCase=4			,     lowerCamelCase=0.1			,     lowerCamelCase=0.1			,     lowerCamelCase=512			,     lowerCamelCase=2			,     lowerCamelCase=0.02			,     lowerCamelCase=2			,     lowerCamelCase=4			,     lowerCamelCase="last"			,     lowerCamelCase=True			,     lowerCamelCase=None			,     lowerCamelCase=0			,     ):


          '''simple docstring'''





          __A	:       Tuple      =							parent
          __A	:       Optional[Any]      =							batch_size
          __A	:       int      =							seq_length
          __A	:       int      =							is_training
          __A	:       List[str]      =							use_input_lengths
          __A	:       Optional[Any]      =							use_token_type_ids
          __A	:       Any      =							use_labels
          __A	:       Dict      =							gelu_activation
          __A	:       List[str]      =							sinusoidal_embeddings
          __A	:       List[Any]      =							causal
          __A	:       Optional[int]      =							asm
          __A	:       List[Any]      =							n_langs
          __A	:       List[str]      =							vocab_size
          __A	:       Any      =							n_special
          __A	:       Optional[int]      =							hidden_size
          __A	:       Optional[Any]      =							num_hidden_layers
          __A	:       Tuple      =							num_attention_heads
          __A	:       List[str]      =							hidden_dropout_prob
          __A	:       Any      =							attention_probs_dropout_prob
          __A	:       Dict      =							max_position_embeddings
          __A	:       Optional[int]      =							type_sequence_label_size
          __A	:       Tuple      =							initializer_range
          __A	:       List[Any]      =							num_labels
          __A	:       List[Any]      =							num_choices
          __A	:       Any      =							summary_type
          __A	:       List[str]      =							use_proj
          __A	:       List[Any]      =							scope
          __A	:       str      =							bos_token_id


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       int      =							ids_tensor([self.batch_size, self.seq_length]			,     self.vocab_size		)
          __A	:       int      =							random_attention_mask([self.batch_size, self.seq_length]		)

          __A	:       Union[str, Any]      =							None
          if self.use_input_lengths:
               __A	:       int      =							(
                   ids_tensor([self.batch_size]			,     vocab_size=2		) + self.seq_length - 2
               )  # small variation of seq_length

          __A	:       Any      =							None
          if self.use_token_type_ids:
               __A	:       Optional[int]      =							ids_tensor([self.batch_size, self.seq_length]			,     self.n_langs		)

          __A	:       int      =							None
          __A	:       Any      =							None
          __A	:       Tuple      =							None
          if self.use_labels:
               __A	:       List[Any]      =							ids_tensor([self.batch_size]			,     self.type_sequence_label_size		)
               __A	:       Optional[Any]      =							ids_tensor([self.batch_size, self.seq_length]			,     self.num_labels		)
               __A	:       int      =							ids_tensor([self.batch_size]			,     2		).float()
               __A	:       List[str]      =							ids_tensor([self.batch_size]			,     self.num_choices		)

          __A	:       List[Any]      =							self.get_config()

          return (
              config,
              input_ids,
              token_type_ids,
              input_lengths,
              sequence_labels,
              token_labels,
              is_impossible_labels,
              choice_labels,
              input_mask,
          )


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          return XLMConfig(
              vocab_size=self.vocab_size			,     n_special=self.n_special			,     emb_dim=self.hidden_size			,     n_layers=self.num_hidden_layers			,     n_heads=self.num_attention_heads			,     dropout=self.hidden_dropout_prob			,     attention_dropout=self.attention_probs_dropout_prob			,     gelu_activation=self.gelu_activation			,     sinusoidal_embeddings=self.sinusoidal_embeddings			,     asm=self.asm			,     causal=self.causal			,     n_langs=self.n_langs			,     max_position_embeddings=self.max_position_embeddings			,     initializer_range=self.initializer_range			,     summary_type=self.summary_type			,     use_proj=self.use_proj			,     num_labels=self.num_labels			,     bos_token_id=self.bos_token_id			,     )


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     ):


          '''simple docstring'''





          __A	:       Tuple      =							XLMModel(config=lowerCamelCase		)
          model.to(lowerCamelCase		)
          model.eval()
          __A	:       Tuple      =							model(lowerCamelCase			,     lengths=lowerCamelCase			,     langs=lowerCamelCase		)
          __A	:       Dict      =							model(lowerCamelCase			,     langs=lowerCamelCase		)
          __A	:       List[Any]      =							model(lowerCamelCase		)
          self.parent.assertEqual(result.last_hidden_state.shape			,     (self.batch_size, self.seq_length, self.hidden_size)		)


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     ):


          '''simple docstring'''





          __A	:       str      =							XLMWithLMHeadModel(lowerCamelCase		)
          model.to(lowerCamelCase		)
          model.eval()

          __A	:       List[str]      =							model(lowerCamelCase			,     token_type_ids=lowerCamelCase			,     labels=lowerCamelCase		)
          self.parent.assertEqual(result.loss.shape			,     ()		)
          self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.seq_length, self.vocab_size)		)


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     ):


          '''simple docstring'''





          __A	:       str      =							XLMForQuestionAnsweringSimple(lowerCamelCase		)
          model.to(lowerCamelCase		)
          model.eval()

          __A	:       int      =							model(lowerCamelCase		)

          __A	:       List[str]      =							model(lowerCamelCase			,     start_positions=lowerCamelCase			,     end_positions=lowerCamelCase		)
          __A	:       List[Any]      =							outputs
          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 lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     ):


          '''simple docstring'''





          __A	:       Optional[Any]      =							XLMForQuestionAnswering(lowerCamelCase		)
          model.to(lowerCamelCase		)
          model.eval()

          __A	:       str      =							model(lowerCamelCase		)

          __A	:       Optional[int]      =							model(
              lowerCamelCase			,     start_positions=lowerCamelCase			,     end_positions=lowerCamelCase			,     cls_index=lowerCamelCase			,     is_impossible=lowerCamelCase			,     p_mask=lowerCamelCase			,     )

          __A	:       Any      =							model(
              lowerCamelCase			,     start_positions=lowerCamelCase			,     end_positions=lowerCamelCase			,     cls_index=lowerCamelCase			,     is_impossible=lowerCamelCase			,     )

          ((__A)  ,)	:       List[str]      =							result_with_labels.to_tuple()

          __A	:       int      =							model(lowerCamelCase			,     start_positions=lowerCamelCase			,     end_positions=lowerCamelCase		)

          ((__A)  ,)	:       List[Any]      =							result_with_labels.to_tuple()

          self.parent.assertEqual(result_with_labels.loss.shape			,     ()		)
          self.parent.assertEqual(result.start_top_log_probs.shape			,     (self.batch_size, model.config.start_n_top)		)
          self.parent.assertEqual(result.start_top_index.shape			,     (self.batch_size, model.config.start_n_top)		)
          self.parent.assertEqual(
              result.end_top_log_probs.shape			,     (self.batch_size, model.config.start_n_top * model.config.end_n_top)		)
          self.parent.assertEqual(
              result.end_top_index.shape			,     (self.batch_size, model.config.start_n_top * model.config.end_n_top)		)
          self.parent.assertEqual(result.cls_logits.shape			,     (self.batch_size,)		)


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     ):


          '''simple docstring'''





          __A	:       Optional[int]      =							XLMForSequenceClassification(lowerCamelCase		)
          model.to(lowerCamelCase		)
          model.eval()

          __A	:       str      =							model(lowerCamelCase		)
          __A	:       Optional[int]      =							model(lowerCamelCase			,     labels=lowerCamelCase		)
          self.parent.assertEqual(result.loss.shape			,     ()		)
          self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.type_sequence_label_size)		)


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     ):


          '''simple docstring'''





          __A	:       str      =							self.num_labels
          __A	:       int      =							XLMForTokenClassification(lowerCamelCase		)
          model.to(lowerCamelCase		)
          model.eval()

          __A	:       str      =							model(lowerCamelCase			,     attention_mask=lowerCamelCase			,     labels=lowerCamelCase		)
          self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.seq_length, self.num_labels)		)


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     ):


          '''simple docstring'''





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


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       List[Any]      =							self.prepare_config_and_inputs()
          (
              (
              __A
          )  ,(
              __A
          )  ,(
              __A
          )  ,(
              __A
          )  ,(
              __A
          )  ,(
              __A
          )  ,(
              __A
          )  ,(
              __A
          )  ,(
              __A
          )  ,
          )	:       str      =							config_and_inputs
          __A	:       int      =							{"input_ids": input_ids, "token_type_ids": token_type_ids, "lengths": input_lengths}
          return config, inputs_dict







@require_torch
class 							__magic_name__	(       lowerCAmelCase    ,		lowerCAmelCase    ,		lowerCAmelCase    ,		unittest.TestCase ):

     """simple docstring"""






     lowerCamelCase__       						=				(
         (
             XLMModel,
             XLMWithLMHeadModel,
             XLMForQuestionAnswering,
             XLMForSequenceClassification,
             XLMForQuestionAnsweringSimple,
             XLMForTokenClassification,
             XLMForMultipleChoice,
         )
         if is_torch_available()
         else ()
     )
     lowerCamelCase__       						=				(
         (XLMWithLMHeadModel,) if is_torch_available() else ()
     )  # TODO (PVP): Check other models whether language generation is also applicable
     lowerCamelCase__       						=				(
         {
             'feature-extraction': XLMModel,
             'fill-mask': XLMWithLMHeadModel,
             'question-answering': XLMForQuestionAnsweringSimple,
             'text-classification': XLMForSequenceClassification,
             'text-generation': XLMWithLMHeadModel,
             'token-classification': XLMForTokenClassification,
             'zero-shot': XLMForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase		):


          '''simple docstring'''





          if (
              pipeline_test_casse_name == "QAPipelineTests"
              and tokenizer_name is not None
              and not tokenizer_name.endswith("Fast"		)
          ):
               # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
               # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
               # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
               return True

          return False


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase=False		):


          '''simple docstring'''





          __A	:       Optional[Any]      =							super()._prepare_for_class(lowerCamelCase			,     lowerCamelCase			,     return_labels=lowerCamelCase		)

          if return_labels:
               if model_class.__name__ == "XLMForQuestionAnswering":
                    __A	:       List[Any]      =							torch.zeros(
                        self.model_tester.batch_size			,     dtype=torch.long			,     device=lowerCamelCase		)
                    __A	:       Tuple      =							torch.zeros(
                        self.model_tester.batch_size			,     dtype=torch.long			,     device=lowerCamelCase		)

          return inputs_dict


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Optional[int]      =							XLMModelTester(self		)
          __A	:       Any      =							ConfigTester(self			,     config_class=lowerCamelCase			,     emb_dim=37		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          self.config_tester.run_common_tests()


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Optional[int]      =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_model(*lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Dict      =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_lm_head(*lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Any      =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_simple_qa(*lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       int      =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_qa(*lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Tuple      =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_sequence_classif(*lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Optional[int]      =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_token_classif(*lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       List[Any]      =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_for_multiple_choice(*lowerCamelCase		)


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase=False			,     lowerCamelCase=1		):


          '''simple docstring'''





          self.assertIsInstance(lowerCamelCase			,     lowerCamelCase		)
          self.assertListEqual(
              [isinstance(lowerCamelCase			,     lowerCamelCase		) for iter_attentions in attentions]			,     [True] * len(lowerCamelCase		)		)
          self.assertEqual(len(lowerCamelCase		)			,     (max_length - min_length) * num_beam_groups		)

          for idx, iter_attentions in enumerate(lowerCamelCase		):
               # adds PAD dummy token
               __A	:       Optional[Any]      =							min_length + idx + 1
               __A	:       Optional[int]      =							min_length + idx + 1

               __A	:       List[Any]      =							(
                   batch_size * num_beam_groups,
                   config.num_attention_heads,
                   tgt_len,
                   src_len,
               )
               # check attn size
               self.assertListEqual(
                   [layer_attention.shape for layer_attention in iter_attentions]			,     [expected_shape] * len(lowerCamelCase		)		)


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase=False			,     lowerCamelCase=1		):


          '''simple docstring'''





          self.assertIsInstance(lowerCamelCase			,     lowerCamelCase		)
          self.assertListEqual(
              [isinstance(lowerCamelCase			,     lowerCamelCase		) for iter_hidden_states in hidden_states]			,     [True] * len(lowerCamelCase		)			,     )
          self.assertEqual(len(lowerCamelCase		)			,     (max_length - min_length) * num_beam_groups		)

          for idx, iter_hidden_states in enumerate(lowerCamelCase		):
               # adds PAD dummy token
               __A	:       Dict      =							min_length + idx + 1
               __A	:       Union[str, Any]      =							(batch_size * num_beam_groups, seq_len, config.hidden_size)
               # check hidden size
               self.assertListEqual(
                   [layer_hidden_states.shape for layer_hidden_states in iter_hidden_states]			,     [expected_shape] * len(lowerCamelCase		)			,     )
          pass


     @slow
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __A	:       Any      =							XLMModel.from_pretrained(lowerCamelCase		)
               self.assertIsNotNone(lowerCamelCase		)







@require_torch
class 							__magic_name__	(       unittest.TestCase ):

     """simple docstring"""






     @slow
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Tuple      =							XLMWithLMHeadModel.from_pretrained("xlm-mlm-en-2048"		)
          model.to(lowerCamelCase		)
          __A	:       Optional[int]      =							torch.tensor([[14, 447]]			,     dtype=torch.long			,     device=lowerCamelCase		)  # the president
          __A	:       Optional[Any]      =							[
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
          ]  # the president the president the president the president the president the president the president the president the president the president
          # TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
          __A	:       Union[str, Any]      =							model.generate(lowerCamelCase			,     do_sample=lowerCamelCase		)
          self.assertListEqual(output_ids[0].cpu().numpy().tolist()			,     lowerCamelCase		)





'''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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# 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
#
########################################################################


_UpperCamelCase 							=  16
_UpperCamelCase 							=  32


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE = 16							):




     '''simple docstring'''





     __A	:       Tuple      =							AutoTokenizer.from_pretrained("bert-base-cased"							)
     __A	:       List[str]      =							load_dataset("glue"       ,					"mrpc"							)

     def tokenize_function(SCREAMING_SNAKE_CASE							):
          # max_length=None => use the model max length (it's actually the default)
          __A	:       Any      =							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
     # starting with the main process first:
     with accelerator.main_process_first():
          __A	:       Optional[Any]      =							datasets.map(
              SCREAMING_SNAKE_CASE       ,					batched=SCREAMING_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
     __A	:       List[Any]      =							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.
          __A	:       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":
               __A	:       List[str]      =							16
          elif accelerator.mixed_precision != "no":
               __A	:       Tuple      =							8
          else:
               __A	:       Optional[Any]      =							None

          return tokenizer.pad(
              SCREAMING_SNAKE_CASE       ,					padding="longest"       ,					max_length=SCREAMING_SNAKE_CASE       ,					pad_to_multiple_of=SCREAMING_SNAKE_CASE       ,					return_tensors="pt"       ,					)

     # Instantiate dataloaders.
     __A	:       Optional[int]      =							DataLoader(
         tokenized_datasets["train"]       ,					shuffle=SCREAMING_SNAKE_CASE       ,					collate_fn=SCREAMING_SNAKE_CASE       ,					batch_size=SCREAMING_SNAKE_CASE							)
     __A	:       Tuple      =							DataLoader(
         tokenized_datasets["validation"]       ,					shuffle=SCREAMING_SNAKE_CASE       ,					collate_fn=SCREAMING_SNAKE_CASE       ,					batch_size=SCREAMING_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

      _UpperCamelCase 							=  mocked_dataloaders  # noqa: F811


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     if os.environ.get("TESTING_MOCKED_DATALOADERS"       ,					SCREAMING_SNAKE_CASE							) == "1":
          __A	:       int      =							2
     # New Code #
     __A	:       List[str]      =							int(args.gradient_accumulation_steps							)
     __A	:       int      =							int(args.local_sgd_steps							)
     # Initialize accelerator
     __A	:       int      =							Accelerator(
         cpu=args.cpu       ,					mixed_precision=args.mixed_precision       ,					gradient_accumulation_steps=SCREAMING_SNAKE_CASE							)
     if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
          raise NotImplementedError("LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)"							)
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     __A	:       Tuple      =							config["lr"]
     __A	:       Union[str, Any]      =							int(config["num_epochs"]							)
     __A	:       Optional[int]      =							int(config["seed"]							)
     __A	:       Optional[Any]      =							int(config["batch_size"]							)

     __A	:       Optional[int]      =							evaluate.load("glue"       ,					"mrpc"							)

     set_seed(SCREAMING_SNAKE_CASE							)
     __A  ,__A	:       Tuple      =							get_dataloaders(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)
     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     __A	:       List[str]      =							AutoModelForSequenceClassification.from_pretrained("bert-base-cased"       ,					return_dict=SCREAMING_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).
     __A	:       Union[str, Any]      =							model.to(accelerator.device							)

     # Instantiate optimizer
     __A	:       str      =							AdamW(params=model.parameters()       ,					lr=SCREAMING_SNAKE_CASE							)

     # Instantiate scheduler
     __A	:       Optional[int]      =							get_linear_schedule_with_warmup(
         optimizer=SCREAMING_SNAKE_CASE       ,					num_warmup_steps=100       ,					num_training_steps=(len(SCREAMING_SNAKE_CASE							) * num_epochs)       ,					)

     # 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.
     __A  ,__A  ,__A  ,__A  ,__A	:       List[str]      =							accelerator.prepare(
         SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)

     # Now we train the model
     for epoch in range(SCREAMING_SNAKE_CASE							):
          model.train()
          with LocalSGD(
              accelerator=SCREAMING_SNAKE_CASE       ,					model=SCREAMING_SNAKE_CASE       ,					local_sgd_steps=SCREAMING_SNAKE_CASE       ,					enabled=local_sgd_steps is not None							) as local_sgd:
               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							)
                    # New code #
                    # We use the new `accumulate` context manager to perform gradient accumulation
                    # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
                    with accelerator.accumulate(SCREAMING_SNAKE_CASE							):
                         __A	:       List[Any]      =							model(**SCREAMING_SNAKE_CASE							)
                         __A	:       List[str]      =							output.loss
                         accelerator.backward(SCREAMING_SNAKE_CASE							)
                         optimizer.step()
                         lr_scheduler.step()
                         optimizer.zero_grad()
                         # LocalSGD-specific line
                         local_sgd.step()

          model.eval()
          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():
                    __A	:       Tuple      =							model(**SCREAMING_SNAKE_CASE							)
               __A	:       Union[str, Any]      =							outputs.logits.argmax(dim=-1							)
               __A  ,__A	:       Tuple      =							accelerator.gather_for_metrics((predictions, batch["labels"])							)
               metric.add_batch(
                   predictions=SCREAMING_SNAKE_CASE       ,					references=SCREAMING_SNAKE_CASE       ,					)

          __A	:       List[Any]      =							metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(f"epoch {epoch}:"       ,					SCREAMING_SNAKE_CASE							)


def 		_lowercase ():




     '''simple docstring'''





     __A	:       Dict      =							argparse.ArgumentParser(description="Simple example of training script."							)
     parser.add_argument(
         "--mixed_precision"       ,					type=SCREAMING_SNAKE_CASE       ,					default=SCREAMING_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."       ,					)
     # New Code #
     parser.add_argument(
         "--gradient_accumulation_steps"       ,					type=SCREAMING_SNAKE_CASE       ,					default=1       ,					help="The number of minibatches to be ran before gradients are accumulated."       ,					)
     parser.add_argument(
         "--local_sgd_steps"       ,					type=SCREAMING_SNAKE_CASE       ,					default=8       ,					help="Number of local SGD steps or None to disable local SGD"							)
     parser.add_argument("--cpu"       ,					action="store_true"       ,					help="If passed, will train on the CPU."							)
     __A	:       List[str]      =							parser.parse_args()
     __A	:       List[str]      =							{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
     training_function(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
      main()

