





'''simple docstring'''







from __future__ import annotations

import unittest

from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers import (
					    TFFunnelBaseModel,
					    TFFunnelForMaskedLM,
					    TFFunnelForMultipleChoice,
					    TFFunnelForPreTraining,
					    TFFunnelForQuestionAnswering,
					    TFFunnelForSequenceClassification,
					    TFFunnelForTokenClassification,
					    TFFunnelModel,
					)


class 					__a      :

							def __init__(							self							:     Union[str, Any]       ,     __magic_name__							:     int       ,     __magic_name__							:     List[Any]=13       ,     __magic_name__							:     str=7       ,     __magic_name__							:     Union[str, Any]=True       ,     __magic_name__							:     Tuple=True       ,     __magic_name__							:     Tuple=True       ,     __magic_name__							:     Optional[Any]=True       ,     __magic_name__							:     List[Any]=99       ,     __magic_name__							:     List[str]=[1, 1, 2]       ,     __magic_name__							:     Optional[Any]=1       ,     __magic_name__							:     int=32       ,     __magic_name__							:     Optional[Any]=4       ,     __magic_name__							:     Optional[Any]=8       ,     __magic_name__							:     List[str]=37       ,     __magic_name__							:     Optional[int]="gelu_new"       ,     __magic_name__							:     str=0.1       ,     __magic_name__							:     Union[str, Any]=0.1       ,     __magic_name__							:     Union[str, Any]=0.0       ,     __magic_name__							:     List[Any]=5_12       ,     __magic_name__							:     List[Any]=3       ,     __magic_name__							:     Optional[Any]=0.0_2       ,     __magic_name__							:     Optional[int]=3       ,     __magic_name__							:     List[str]=4       ,     __magic_name__							:     int=None       ,     __magic_name__							:     Tuple=False       ,     )					->       Dict:







													"""simple docstring"""
													UpperCAmelCase_			:      List[Any]     						=  parent
													UpperCAmelCase_			:      Optional[int]     						=  batch_size
													UpperCAmelCase_			:      Dict     						=  seq_length
													UpperCAmelCase_			:      Tuple     						=  is_training
													UpperCAmelCase_			:      Dict     						=  use_input_mask
													UpperCAmelCase_			:      Union[str, Any]     						=  use_token_type_ids
													UpperCAmelCase_			:      Any     						=  use_labels
													UpperCAmelCase_			:      Union[str, Any]     						=  vocab_size
													UpperCAmelCase_			:      Any     						=  block_sizes
													UpperCAmelCase_			:      Optional[Any]     						=  num_decoder_layers
													UpperCAmelCase_			:      Dict     						=  d_model
													UpperCAmelCase_			:      Dict     						=  n_head
													UpperCAmelCase_			:      Union[str, Any]     						=  d_head
													UpperCAmelCase_			:      Dict     						=  d_inner
													UpperCAmelCase_			:      List[str]     						=  hidden_act
													UpperCAmelCase_			:      int     						=  hidden_dropout
													UpperCAmelCase_			:      Any     						=  attention_dropout
													UpperCAmelCase_			:      List[Any]     						=  activation_dropout
													UpperCAmelCase_			:      int     						=  max_position_embeddings
													UpperCAmelCase_			:      Any     						=  type_vocab_size
													UpperCAmelCase_			:      List[str]     						=  2
													UpperCAmelCase_			:      Union[str, Any]     						=  num_labels
													UpperCAmelCase_			:      Dict     						=  num_choices
													UpperCAmelCase_			:      Optional[int]     						=  scope
													UpperCAmelCase_			:      int     						=  initializer_std

													# Used in the tests to check the size of the first attention layer
													UpperCAmelCase_			:      Optional[Any]     						=  n_head
													# Used in the tests to check the size of the first hidden state
													UpperCAmelCase_			:      Union[str, Any]     						=  self.d_model
													# Used in the tests to check the number of output hidden states/attentions
													UpperCAmelCase_			:      Optional[Any]     						=  sum(self.block_sizes   ) + (0 if base else self.num_decoder_layers)
													# FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
													# the last hidden state of the first block (which is the first hidden state of the decoder).
													if not base:
																			UpperCAmelCase_			:      int     						=  self.num_hidden_layers + 2

							def     UpperCAmelCase__	(							self							:     List[str]   )					->       List[Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      int     						=  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_			:      List[str]     						=  None
													if self.use_token_type_ids:
																			UpperCAmelCase_			:      Optional[Any]     						=  ids_tensor([self.batch_size, self.seq_length]       ,     self.type_vocab_size   )

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

													UpperCAmelCase_			:      Optional[int]     						=  FunnelConfig(
													    vocab_size=self.vocab_size       ,     block_sizes=self.block_sizes       ,     num_decoder_layers=self.num_decoder_layers       ,     d_model=self.d_model       ,     n_head=self.n_head       ,     d_head=self.d_head       ,     d_inner=self.d_inner       ,     hidden_act=self.hidden_act       ,     hidden_dropout=self.hidden_dropout       ,     attention_dropout=self.attention_dropout       ,     activation_dropout=self.activation_dropout       ,     max_position_embeddings=self.max_position_embeddings       ,     type_vocab_size=self.type_vocab_size       ,     initializer_std=self.initializer_std       ,     )

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

							def     UpperCAmelCase__	(							self							:     Optional[Any]       ,     __magic_name__							:     Optional[int]       ,     __magic_name__							:     Any       ,     __magic_name__							:     Tuple       ,     __magic_name__							:     int       ,     __magic_name__							:     Dict       ,     __magic_name__							:     Union[str, Any]       ,     __magic_name__							:     int       ,     )					->       str:







													"""simple docstring"""
													UpperCAmelCase_			:      int     						=  TFFunnelModel(config=__A   )
													UpperCAmelCase_			:      List[Any]     						=  {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
													UpperCAmelCase_			:      Tuple     						=  model(__A   )

													UpperCAmelCase_			:      List[str]     						=  [input_ids, input_mask]
													UpperCAmelCase_			:      str     						=  model(__A   )

													UpperCAmelCase_			:      Optional[int]     						=  model(__A   )
													self.parent.assertEqual(result.last_hidden_state.shape       ,     (self.batch_size, self.seq_length, self.d_model)   )

													UpperCAmelCase_			:      Optional[int]     						=  False
													UpperCAmelCase_			:      Optional[int]     						=  TFFunnelModel(config=__A   )
													UpperCAmelCase_			:      Dict     						=  model(__A   )
													self.parent.assertEqual(result.last_hidden_state.shape       ,     (self.batch_size, self.seq_length, self.d_model)   )

													UpperCAmelCase_			:      Any     						=  False
													UpperCAmelCase_			:      Any     						=  TFFunnelModel(config=__A   )
													UpperCAmelCase_			:      int     						=  model(__A   )
													self.parent.assertEqual(result.last_hidden_state.shape       ,     (self.batch_size, self.seq_length, self.d_model)   )

							def     UpperCAmelCase__	(							self							:     Optional[int]       ,     __magic_name__							:     Dict       ,     __magic_name__							:     List[str]       ,     __magic_name__							:     List[Any]       ,     __magic_name__							:     Optional[int]       ,     __magic_name__							:     Union[str, Any]       ,     __magic_name__							:     int       ,     __magic_name__							:     List[Any]       ,     )					->       Union[str, Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      Any     						=  TFFunnelBaseModel(config=__A   )
													UpperCAmelCase_			:      List[Any]     						=  {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
													UpperCAmelCase_			:      Optional[int]     						=  model(__A   )

													UpperCAmelCase_			:      Optional[Any]     						=  [input_ids, input_mask]
													UpperCAmelCase_			:      List[str]     						=  model(__A   )

													UpperCAmelCase_			:      Tuple     						=  model(__A   )
													self.parent.assertEqual(result.last_hidden_state.shape       ,     (self.batch_size, 2, self.d_model)   )

													UpperCAmelCase_			:      List[Any]     						=  False
													UpperCAmelCase_			:      Dict     						=  TFFunnelBaseModel(config=__A   )
													UpperCAmelCase_			:      int     						=  model(__A   )
													self.parent.assertEqual(result.last_hidden_state.shape       ,     (self.batch_size, 3, self.d_model)   )

													UpperCAmelCase_			:      str     						=  False
													UpperCAmelCase_			:      Union[str, Any]     						=  TFFunnelBaseModel(config=__A   )
													UpperCAmelCase_			:      int     						=  model(__A   )
													self.parent.assertEqual(result.last_hidden_state.shape       ,     (self.batch_size, 2, self.d_model)   )

							def     UpperCAmelCase__	(							self							:     List[Any]       ,     __magic_name__							:     List[str]       ,     __magic_name__							:     int       ,     __magic_name__							:     Optional[int]       ,     __magic_name__							:     Dict       ,     __magic_name__							:     Any       ,     __magic_name__							:     Optional[Any]       ,     __magic_name__							:     Dict       ,     )					->       List[str]:







													"""simple docstring"""
													UpperCAmelCase_			:      List[Any]     						=  TFFunnelForPreTraining(config=__A   )
													UpperCAmelCase_			:      Tuple     						=  {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
													UpperCAmelCase_			:      str     						=  model(__A   )
													self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.seq_length)   )

							def     UpperCAmelCase__	(							self							:     str       ,     __magic_name__							:     Optional[Any]       ,     __magic_name__							:     Optional[int]       ,     __magic_name__							:     Optional[Any]       ,     __magic_name__							:     Optional[int]       ,     __magic_name__							:     Optional[int]       ,     __magic_name__							:     Dict       ,     __magic_name__							:     Optional[Any]       ,     )					->       Dict:







													"""simple docstring"""
													UpperCAmelCase_			:      Union[str, Any]     						=  TFFunnelForMaskedLM(config=__A   )
													UpperCAmelCase_			:      str     						=  {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
													UpperCAmelCase_			:      Any     						=  model(__A   )
													self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.seq_length, self.vocab_size)   )

							def     UpperCAmelCase__	(							self							:     List[str]       ,     __magic_name__							:     List[str]       ,     __magic_name__							:     Union[str, Any]       ,     __magic_name__							:     Optional[Any]       ,     __magic_name__							:     List[Any]       ,     __magic_name__							:     Union[str, Any]       ,     __magic_name__							:     str       ,     __magic_name__							:     Tuple       ,     )					->       List[Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[Any]     						=  self.num_labels
													UpperCAmelCase_			:      str     						=  TFFunnelForSequenceClassification(config=__A   )
													UpperCAmelCase_			:      Tuple     						=  {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
													UpperCAmelCase_			:      Optional[Any]     						=  model(__A   )
													self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.num_labels)   )

							def     UpperCAmelCase__	(							self							:     Any       ,     __magic_name__							:     Optional[Any]       ,     __magic_name__							:     List[Any]       ,     __magic_name__							:     Any       ,     __magic_name__							:     Any       ,     __magic_name__							:     Any       ,     __magic_name__							:     Dict       ,     __magic_name__							:     Optional[Any]       ,     )					->       List[str]:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[int]     						=  self.num_choices
													UpperCAmelCase_			:      Union[str, Any]     						=  TFFunnelForMultipleChoice(config=__A   )
													UpperCAmelCase_			:      List[str]     						=  tf.tile(tf.expand_dims(__A       ,     1   )       ,     (1, self.num_choices, 1)   )
													UpperCAmelCase_			:      str     						=  tf.tile(tf.expand_dims(__A       ,     1   )       ,     (1, self.num_choices, 1)   )
													UpperCAmelCase_			:      int     						=  tf.tile(tf.expand_dims(__A       ,     1   )       ,     (1, self.num_choices, 1)   )
													UpperCAmelCase_			:      Optional[int]     						=  {
													    "input_ids": multiple_choice_inputs_ids,
													    "attention_mask": multiple_choice_input_mask,
													    "token_type_ids": multiple_choice_token_type_ids,
													}
													UpperCAmelCase_			:      Optional[int]     						=  model(__A   )
													self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.num_choices)   )

							def     UpperCAmelCase__	(							self							:     Optional[int]       ,     __magic_name__							:     int       ,     __magic_name__							:     Optional[int]       ,     __magic_name__							:     Dict       ,     __magic_name__							:     str       ,     __magic_name__							:     List[Any]       ,     __magic_name__							:     Optional[int]       ,     __magic_name__							:     List[str]       ,     )					->       Tuple:







													"""simple docstring"""
													UpperCAmelCase_			:      List[str]     						=  self.num_labels
													UpperCAmelCase_			:      str     						=  TFFunnelForTokenClassification(config=__A   )
													UpperCAmelCase_			:      int     						=  {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
													UpperCAmelCase_			:      Any     						=  model(__A   )
													self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.seq_length, self.num_labels)   )

							def     UpperCAmelCase__	(							self							:     Optional[int]       ,     __magic_name__							:     Tuple       ,     __magic_name__							:     List[Any]       ,     __magic_name__							:     Union[str, Any]       ,     __magic_name__							:     Dict       ,     __magic_name__							:     Dict       ,     __magic_name__							:     Dict       ,     __magic_name__							:     Dict       ,     )					->       Dict:







													"""simple docstring"""
													UpperCAmelCase_			:      int     						=  TFFunnelForQuestionAnswering(config=__A   )
													UpperCAmelCase_			:      List[Any]     						=  {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
													UpperCAmelCase_			:      int     						=  model(__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							:     int   )					->       Dict:







													"""simple docstring"""
													UpperCAmelCase_			:      Tuple     						=  self.prepare_config_and_inputs()
													(
													    UpperCAmelCase_		
													)			:      Tuple     						=  config_and_inputs
													UpperCAmelCase_			:      Any     						=  {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
													return config, inputs_dict





@require_tf
class 					__a      (UpperCamelCase_       ,		UpperCamelCase_       ,		unittest.TestCase     ):
							__a					:   Optional[Any]									=       (
							    (
							        TFFunnelModel,
							        TFFunnelForMaskedLM,
							        TFFunnelForPreTraining,
							        TFFunnelForQuestionAnswering,
							        TFFunnelForTokenClassification,
							    )
							    if is_tf_available()
							    else ()
							)
							__a					:   Union[str, Any]									=       (
							    {
							        "feature-extraction": (TFFunnelBaseModel, TFFunnelModel),
							        "fill-mask": TFFunnelForMaskedLM,
							        "question-answering": TFFunnelForQuestionAnswering,
							        "text-classification": TFFunnelForSequenceClassification,
							        "token-classification": TFFunnelForTokenClassification,
							        "zero-shot": TFFunnelForSequenceClassification,
							    }
							    if is_tf_available()
							    else {}
							)
							__a					:   Optional[Any]									=       False
							__a					:   Optional[int]									=       False

							def     UpperCAmelCase__	(							self							:     Dict   )					->       str:







													"""simple docstring"""
													UpperCAmelCase_			:      Dict     						=  TFFunnelModelTester(self   )
													UpperCAmelCase_			:      List[Any]     						=  ConfigTester(self       ,     config_class=__A   )

							def     UpperCAmelCase__	(							self							:     Union[str, Any]   )					->       Optional[Any]:







													"""simple docstring"""
													self.config_tester.run_common_tests()

							def     UpperCAmelCase__	(							self							:     List[str]   )					->       int:







													"""simple docstring"""
													UpperCAmelCase_			:      List[Any]     						=  self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*__A   )

							def     UpperCAmelCase__	(							self							:     Optional[Any]   )					->       Dict:







													"""simple docstring"""
													UpperCAmelCase_			:      Dict     						=  self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_pretraining(*__A   )

							def     UpperCAmelCase__	(							self							:     int   )					->       Optional[int]:







													"""simple docstring"""
													UpperCAmelCase_			:      int     						=  self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_masked_lm(*__A   )

							def     UpperCAmelCase__	(							self							:     Tuple   )					->       str:







													"""simple docstring"""
													UpperCAmelCase_			:      Dict     						=  self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_token_classification(*__A   )



							def     UpperCAmelCase__	(							self							:     Optional[Any]   )					->       Union[str, Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[int]     						=  self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_question_answering(*__A   )





@require_tf
class 					__a      (UpperCamelCase_       ,		unittest.TestCase     ):
							__a					:   List[str]									=       (
							    (TFFunnelBaseModel, TFFunnelForMultipleChoice, TFFunnelForSequenceClassification) if is_tf_available() else ()
							)
							__a					:   int									=       False
							__a					:   List[str]									=       False

							def     UpperCAmelCase__	(							self							:     Any   )					->       Optional[int]:







													"""simple docstring"""
													UpperCAmelCase_			:      Any     						=  TFFunnelModelTester(self       ,     base=__A   )
													UpperCAmelCase_			:      Dict     						=  ConfigTester(self       ,     config_class=__A   )

							def     UpperCAmelCase__	(							self							:     List[str]   )					->       int:







													"""simple docstring"""
													self.config_tester.run_common_tests()

							def     UpperCAmelCase__	(							self							:     Optional[Any]   )					->       Dict:







													"""simple docstring"""
													UpperCAmelCase_			:      str     						=  self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_base_model(*__A   )

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







													"""simple docstring"""
													UpperCAmelCase_			:      Union[str, Any]     						=  self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_sequence_classification(*__A   )



							def     UpperCAmelCase__	(							self							:     Any   )					->       List[str]:







													"""simple docstring"""
													UpperCAmelCase_			:      List[str]     						=  self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_multiple_choice(*__A   )












'''simple docstring'''







import argparse
import json
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.utils.deepspeed import DummyOptim, DummyScheduler


snake_case_     :       Optional[int]						=  16
snake_case_     :       Tuple						=  32





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Accelerator,			SCREAMING_SNAKE_CASE__      :			int = 16,			SCREAMING_SNAKE_CASE__      :			str = "bert-base-cased"   ) ->	Dict:
						UpperCAmelCase_			:      Dict     						=  AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__   )
						UpperCAmelCase_			:      int     						=  load_dataset('''glue''',			'''mrpc'''   )

						def tokenize_function(SCREAMING_SNAKE_CASE__      :			Optional[int]   ):
												# max_length=None => use the model max length (it's actually the default)
												UpperCAmelCase_			:      Union[str, 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
						UpperCAmelCase_			:      Tuple     						=  datasets.map(
						    SCREAMING_SNAKE_CASE__,			batched=SCREAMING_SNAKE_CASE__,			remove_columns=['''idx''', '''sentence1''', '''sentence2'''],			load_from_cache_file=SCREAMING_SNAKE_CASE__   )

						# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
						# transformers library
						UpperCAmelCase_			:      Optional[Any]     						=  tokenized_datasets.rename_column('''label''',			'''labels'''   )

						def collate_fn(SCREAMING_SNAKE_CASE__      :			str   ):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												if accelerator.distributed_type == DistributedType.TPU:
																		return tokenizer.pad(SCREAMING_SNAKE_CASE__,			padding='''max_length''',			max_length=128,			return_tensors='''pt'''   )
												return tokenizer.pad(SCREAMING_SNAKE_CASE__,			padding='''longest''',			return_tensors='''pt'''   )

						# Instantiate dataloaders.
						UpperCAmelCase_			:      str     						=  DataLoader(
						    tokenized_datasets['''train'''],			shuffle=SCREAMING_SNAKE_CASE__,			collate_fn=SCREAMING_SNAKE_CASE__,			batch_size=SCREAMING_SNAKE_CASE__   )
						UpperCAmelCase_			:      int     						=  DataLoader(
						    tokenized_datasets['''validation'''],			shuffle=SCREAMING_SNAKE_CASE__,			collate_fn=SCREAMING_SNAKE_CASE__,			batch_size=SCREAMING_SNAKE_CASE__   )

						return train_dataloader, eval_dataloader





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Optional[Any],			SCREAMING_SNAKE_CASE__      :			Optional[int],			SCREAMING_SNAKE_CASE__      :			Tuple,			SCREAMING_SNAKE_CASE__      :			Any   ) ->	Any:
						model.eval()
						UpperCAmelCase_			:      List[str]     						=  0
						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():
																		UpperCAmelCase_			:      Dict     						=  model(**SCREAMING_SNAKE_CASE__   )
												UpperCAmelCase_			:      str     						=  outputs.logits.argmax(dim=-1   )
												# It is slightly faster to call this once, than multiple times
												UpperCAmelCase_						,       UpperCAmelCase_			:      List[str]     						=  accelerator.gather(
												    (predictions, batch['''labels'''])   )  # If we are in a multiprocess environment, the last batch has duplicates
												if accelerator.use_distributed:
																		if step == len(SCREAMING_SNAKE_CASE__   ) - 1:
																								UpperCAmelCase_			:      Tuple     						=  predictions[: len(eval_dataloader.dataset   ) - samples_seen]
																								UpperCAmelCase_			:      int     						=  references[: len(eval_dataloader.dataset   ) - samples_seen]
																		else:
																								samples_seen += references.shape[0]
												metric.add_batch(
												    predictions=SCREAMING_SNAKE_CASE__,			references=SCREAMING_SNAKE_CASE__,			)

						UpperCAmelCase_			:      List[str]     						=  metric.compute()
						return eval_metric["accuracy"]





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Tuple,			SCREAMING_SNAKE_CASE__      :			int   ) ->	Tuple:
						# Initialize accelerator
						UpperCAmelCase_			:      Union[str, Any]     						=  Accelerator()

						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						UpperCAmelCase_			:      int     						=  config['''lr''']
						UpperCAmelCase_			:      Optional[int]     						=  int(config['''num_epochs''']   )
						UpperCAmelCase_			:      Optional[int]     						=  int(config['''seed''']   )
						UpperCAmelCase_			:      List[str]     						=  int(config['''batch_size''']   )
						UpperCAmelCase_			:      Optional[int]     						=  args.model_name_or_path

						set_seed(SCREAMING_SNAKE_CASE__   )
						UpperCAmelCase_						,       UpperCAmelCase_			:      Union[str, Any]     						=  get_dataloaders(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						UpperCAmelCase_			:      List[Any]     						=  AutoModelForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE__,			return_dict=SCREAMING_SNAKE_CASE__   )

						# Instantiate optimizer
						UpperCAmelCase_			:      str     						=  (
						    AdamW
						    if accelerator.state.deepspeed_plugin is None
						    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
						    else DummyOptim
						)
						UpperCAmelCase_			:      List[str]     						=  optimizer_cls(params=model.parameters(),			lr=SCREAMING_SNAKE_CASE__   )

						if accelerator.state.deepspeed_plugin is not None:
												UpperCAmelCase_			:      List[Any]     						=  accelerator.state.deepspeed_plugin.deepspeed_config[
												    '''gradient_accumulation_steps'''
												]
						else:
												UpperCAmelCase_			:      Tuple     						=  1
						UpperCAmelCase_			:      int     						=  (len(SCREAMING_SNAKE_CASE__   ) * num_epochs) // gradient_accumulation_steps

						# Instantiate scheduler
						if (
						    accelerator.state.deepspeed_plugin is None
						    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
						):
												UpperCAmelCase_			:      Tuple     						=  get_linear_schedule_with_warmup(
												    optimizer=SCREAMING_SNAKE_CASE__,			num_warmup_steps=0,			num_training_steps=SCREAMING_SNAKE_CASE__,			)
						else:
												UpperCAmelCase_			:      Any     						=  DummyScheduler(SCREAMING_SNAKE_CASE__,			total_num_steps=SCREAMING_SNAKE_CASE__,			warmup_num_steps=0   )

						# 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_			:      Union[str, Any]     						=  accelerator.prepare(
						    SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )

						# We need to keep track of how many total steps we have iterated over
						UpperCAmelCase_			:      Union[str, Any]     						=  0
						# We also need to keep track of the stating epoch so files are named properly
						UpperCAmelCase_			:      Dict     						=  0
						UpperCAmelCase_			:      int     						=  evaluate.load('''glue''',			'''mrpc'''   )
						UpperCAmelCase_			:      Optional[Any]     						=  num_epochs

						if args.partial_train_epoch is not None:
												UpperCAmelCase_			:      List[Any]     						=  args.partial_train_epoch

						if args.resume_from_checkpoint:
												accelerator.load_state(args.resume_from_checkpoint   )
												UpperCAmelCase_			:      Tuple     						=  args.resume_from_checkpoint.split('''epoch_'''   )[1]
												UpperCAmelCase_			:      int     						=  ''''''
												for char in epoch_string:
																		if char.isdigit():
																								state_epoch_num += char
																		else:
																								break
												UpperCAmelCase_			:      Union[str, Any]     						=  int(SCREAMING_SNAKE_CASE__   ) + 1
												UpperCAmelCase_			:      Dict     						=  evaluation_loop(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )
												accelerator.print('''resumed checkpoint performance:''',			SCREAMING_SNAKE_CASE__   )
												accelerator.print('''resumed checkpoint\'s scheduler\'s lr:''',			lr_scheduler.get_lr()[0]   )
												accelerator.print('''resumed optimizers\'s lr:''',			optimizer.param_groups[0]['''lr''']   )
												with open(os.path.join(args.output_dir,			F"""state_{starting_epoch-1}.json"""   ),			'''r'''   ) as f:
																		UpperCAmelCase_			:      Optional[int]     						=  json.load(SCREAMING_SNAKE_CASE__   )
																		assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
																		assert (
																		    resumed_state["lr"] == lr_scheduler.get_lr()[0]
																		), "Scheduler learning rate mismatch, loading from checkpoint failed"
																		assert (
																		    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
																		), "Optimizer learning rate mismatch, loading from checkpoint failed"
																		assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
																		return

    # Now we train the model
						UpperCAmelCase_			:      int     						=  {}
						for epoch in range(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   ):
												model.train()
												for step, batch in enumerate(SCREAMING_SNAKE_CASE__   ):
																		UpperCAmelCase_			:      Optional[int]     						=  model(**SCREAMING_SNAKE_CASE__   )
																		UpperCAmelCase_			:      Any     						=  outputs.loss
																		UpperCAmelCase_			:      Tuple     						=  loss / gradient_accumulation_steps
																		accelerator.backward(SCREAMING_SNAKE_CASE__   )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

																		overall_step += 1
												UpperCAmelCase_			:      Tuple     						=  F"""epoch_{epoch}"""
												UpperCAmelCase_			:      Optional[int]     						=  os.path.join(args.output_dir,			SCREAMING_SNAKE_CASE__   )
												accelerator.save_state(SCREAMING_SNAKE_CASE__   )
												UpperCAmelCase_			:      int     						=  evaluation_loop(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )
												UpperCAmelCase_			:      Optional[Any]     						=  accuracy
												UpperCAmelCase_			:      Any     						=  lr_scheduler.get_lr()[0]
												UpperCAmelCase_			:      List[str]     						=  optimizer.param_groups[0]['''lr''']
												UpperCAmelCase_			:      Tuple     						=  epoch
												UpperCAmelCase_			:      Dict     						=  overall_step
												accelerator.print(F"""epoch {epoch}:""",			SCREAMING_SNAKE_CASE__   )

												accelerator.wait_for_everyone()
												if accelerator.is_main_process:
																		with open(os.path.join(args.output_dir,			F"""state_{epoch}.json"""   ),			'''w'''   ) as f:
																								json.dump(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )





def        lowerCamelCase_      (  ) ->	List[str]:
						UpperCAmelCase_			:      Optional[int]     						=  argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''   )
						parser.add_argument(
						    '''--model_name_or_path''',			type=SCREAMING_SNAKE_CASE__,			default='''bert-base-cased''',			help='''Path to pretrained model or model identifier from huggingface.co/models.''',			required=SCREAMING_SNAKE_CASE__,			)
						parser.add_argument(
						    '''--output_dir''',			type=SCREAMING_SNAKE_CASE__,			default='''.''',			help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.''',			)
						parser.add_argument(
						    '''--resume_from_checkpoint''',			type=SCREAMING_SNAKE_CASE__,			default=SCREAMING_SNAKE_CASE__,			help='''If the training should continue from a checkpoint folder.''',			)
						parser.add_argument(
						    '''--partial_train_epoch''',			type=SCREAMING_SNAKE_CASE__,			default=SCREAMING_SNAKE_CASE__,			help='''If passed, the training will stop after this number of epochs.''',			)
						parser.add_argument(
						    '''--num_epochs''',			type=SCREAMING_SNAKE_CASE__,			default=2,			help='''Number of train epochs.''',			)
						UpperCAmelCase_			:      Optional[int]     						=  parser.parse_args()
						UpperCAmelCase_			:      List[Any]     						=  {'''lr''': 2E-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}

						training_function(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )


if __name__ == "__main__":
					main()





