





'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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 (
       TFRoFormerForCausalLM,
       TFRoFormerForMaskedLM,
       TFRoFormerForMultipleChoice,
       TFRoFormerForQuestionAnswering,
       TFRoFormerForSequenceClassification,
       TFRoFormerForTokenClassification,
       TFRoFormerModel,
   )
   from transformers.models.roformer.modeling_tf_roformer import (
       TFRoFormerSelfAttention,
       TFRoFormerSinusoidalPositionalEmbedding,
   )
class     __lowerCAmelCase					:






       """simple docstring"""





       def __init__(							self       :		int	,			lowerCAmelCase__       :		List[Any]	,			lowerCAmelCase__       :		Optional[int]=13	,			lowerCAmelCase__       :		int=7	,			lowerCAmelCase__       :		Optional[Any]=True	,			lowerCAmelCase__       :		Optional[Any]=True	,			lowerCAmelCase__       :		Optional[int]=True	,			lowerCAmelCase__       :		str=True	,			lowerCAmelCase__       :		Tuple=99	,			lowerCAmelCase__       :		Optional[int]=32	,			lowerCAmelCase__       :		str=2	,			lowerCAmelCase__       :		Optional[Any]=4	,			lowerCAmelCase__       :		Optional[int]=37	,			lowerCAmelCase__       :		str="gelu"	,			lowerCAmelCase__       :		Dict=0.1	,			lowerCAmelCase__       :		Any=0.1	,			lowerCAmelCase__       :		Dict=512	,			lowerCAmelCase__       :		int=16	,			lowerCAmelCase__       :		List[Any]=2	,			lowerCAmelCase__       :		Optional[Any]=0.02	,			lowerCAmelCase__       :		List[str]=3	,			lowerCAmelCase__       :		int=4	,			lowerCAmelCase__       :		Optional[Any]=None	,			)   ->					List[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	parent
            _UpperCamelCase       =	13
            _UpperCamelCase       =	7
            _UpperCamelCase       =	True
            _UpperCamelCase       =	True
            _UpperCamelCase       =	True
            _UpperCamelCase       =	True
            _UpperCamelCase       =	99
            _UpperCamelCase       =	32
            _UpperCamelCase       =	2
            _UpperCamelCase       =	4
            _UpperCamelCase       =	37
            _UpperCamelCase       =	'''gelu'''
            _UpperCamelCase       =	0.1
            _UpperCamelCase       =	0.1
            _UpperCamelCase       =	512
            _UpperCamelCase       =	16
            _UpperCamelCase       =	2
            _UpperCamelCase       =	0.02
            _UpperCamelCase       =	3
            _UpperCamelCase       =	4
            _UpperCamelCase       =	None





       def snake_case__						(							self       :		Dict					)   ->					Optional[int]:

            '''simple docstring'''




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

            _UpperCamelCase       =	None
            if self.use_input_mask:
                 _UpperCamelCase       =	random_attention_mask([self.batch_size, self.seq_length]					)

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

            _UpperCamelCase       =	None
            _UpperCamelCase       =	None
            _UpperCamelCase       =	None
            if self.use_labels:
                 _UpperCamelCase       =	ids_tensor([self.batch_size]	,			self.type_sequence_label_size					)
                 _UpperCamelCase       =	ids_tensor([self.batch_size, self.seq_length]	,			self.num_labels					)
                 _UpperCamelCase       =	ids_tensor([self.batch_size]	,			self.num_choices					)

            _UpperCamelCase       =	RoFormerConfig(
                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	,			hidden_dropout_prob=self.hidden_dropout_prob	,			attention_probs_dropout_prob=self.attention_probs_dropout_prob	,			max_position_embeddings=self.max_position_embeddings	,			type_vocab_size=self.type_vocab_size	,			initializer_range=self.initializer_range	,			return_dict=lowerCAmelCase__	,			)

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





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

            '''simple docstring'''




            _UpperCamelCase       =	TFRoFormerModel(config=lowerCAmelCase__					)
            _UpperCamelCase       =	{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

            _UpperCamelCase       =	[input_ids, input_mask]
            _UpperCamelCase       =	model(lowerCAmelCase__					)

            _UpperCamelCase       =	model(lowerCAmelCase__					)

            self.parent.assertEqual(result.last_hidden_state.shape	,			(self.batch_size, self.seq_length, self.hidden_size)					)





       def snake_case__						(							self       :		Union[str, Any]	,			lowerCAmelCase__       :		Union[str, Any]	,			lowerCAmelCase__       :		str	,			lowerCAmelCase__       :		Any	,			lowerCAmelCase__       :		Dict	,			lowerCAmelCase__       :		List[Any]	,			lowerCAmelCase__       :		Tuple	,			lowerCAmelCase__       :		List[str]					)   ->					Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	True
            _UpperCamelCase       =	TFRoFormerForCausalLM(config=lowerCAmelCase__					)
            _UpperCamelCase       =	{
                '''input_ids''': input_ids,
                '''attention_mask''': input_mask,
                '''token_type_ids''': token_type_ids,
            }
            _UpperCamelCase       =	model(lowerCAmelCase__					)['''logits''']
            self.parent.assertListEqual(
                list(prediction_scores.numpy().shape					)	,			[self.batch_size, self.seq_length, self.vocab_size]					)





       def snake_case__						(							self       :		str	,			lowerCAmelCase__       :		Optional[Any]	,			lowerCAmelCase__       :		List[Any]	,			lowerCAmelCase__       :		Any	,			lowerCAmelCase__       :		List[Any]	,			lowerCAmelCase__       :		List[str]	,			lowerCAmelCase__       :		Optional[Any]	,			lowerCAmelCase__       :		int					)   ->					Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	TFRoFormerForMaskedLM(config=lowerCAmelCase__					)
            _UpperCamelCase       =	{
                '''input_ids''': input_ids,
                '''attention_mask''': input_mask,
                '''token_type_ids''': token_type_ids,
            }
            _UpperCamelCase       =	model(lowerCAmelCase__					)
            self.parent.assertEqual(result.logits.shape	,			(self.batch_size, self.seq_length, self.vocab_size)					)





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

            '''simple docstring'''




            _UpperCamelCase       =	self.num_labels
            _UpperCamelCase       =	TFRoFormerForSequenceClassification(config=lowerCAmelCase__					)
            _UpperCamelCase       =	{
                '''input_ids''': input_ids,
                '''attention_mask''': input_mask,
                '''token_type_ids''': token_type_ids,
            }

            _UpperCamelCase       =	model(lowerCAmelCase__					)
            self.parent.assertEqual(result.logits.shape	,			(self.batch_size, self.num_labels)					)





       def snake_case__						(							self       :		int	,			lowerCAmelCase__       :		Union[str, Any]	,			lowerCAmelCase__       :		Tuple	,			lowerCAmelCase__       :		List[Any]	,			lowerCAmelCase__       :		int	,			lowerCAmelCase__       :		str	,			lowerCAmelCase__       :		Any	,			lowerCAmelCase__       :		Union[str, Any]					)   ->					List[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	self.num_choices
            _UpperCamelCase       =	TFRoFormerForMultipleChoice(config=lowerCAmelCase__					)
            _UpperCamelCase       =	tf.tile(tf.expand_dims(lowerCAmelCase__	,			1					)	,			(1, self.num_choices, 1)					)
            _UpperCamelCase       =	tf.tile(tf.expand_dims(lowerCAmelCase__	,			1					)	,			(1, self.num_choices, 1)					)
            _UpperCamelCase       =	tf.tile(tf.expand_dims(lowerCAmelCase__	,			1					)	,			(1, self.num_choices, 1)					)
            _UpperCamelCase       =	{
                '''input_ids''': multiple_choice_inputs_ids,
                '''attention_mask''': multiple_choice_input_mask,
                '''token_type_ids''': multiple_choice_token_type_ids,
            }
            _UpperCamelCase       =	model(lowerCAmelCase__					)
            self.parent.assertEqual(result.logits.shape	,			(self.batch_size, self.num_choices)					)





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

            '''simple docstring'''




            _UpperCamelCase       =	self.num_labels
            _UpperCamelCase       =	TFRoFormerForTokenClassification(config=lowerCAmelCase__					)
            _UpperCamelCase       =	{
                '''input_ids''': input_ids,
                '''attention_mask''': input_mask,
                '''token_type_ids''': token_type_ids,
            }
            _UpperCamelCase       =	model(lowerCAmelCase__					)
            self.parent.assertEqual(result.logits.shape	,			(self.batch_size, self.seq_length, self.num_labels)					)





       def snake_case__						(							self       :		List[str]	,			lowerCAmelCase__       :		str	,			lowerCAmelCase__       :		Optional[int]	,			lowerCAmelCase__       :		Optional[Any]	,			lowerCAmelCase__       :		Optional[Any]	,			lowerCAmelCase__       :		Tuple	,			lowerCAmelCase__       :		List[str]	,			lowerCAmelCase__       :		List[str]					)   ->					List[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	TFRoFormerForQuestionAnswering(config=lowerCAmelCase__					)
            _UpperCamelCase       =	{
                '''input_ids''': input_ids,
                '''attention_mask''': input_mask,
                '''token_type_ids''': token_type_ids,
            }

            _UpperCamelCase       =	model(lowerCAmelCase__					)
            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 snake_case__						(							self       :		Optional[int]					)   ->					List[str]:

            '''simple docstring'''




            _UpperCamelCase       =	self.prepare_config_and_inputs()
            (
                (
                _UpperCamelCase
            )   ,    (
                _UpperCamelCase
            )   ,    (
                _UpperCamelCase
            )   ,    (
                _UpperCamelCase
            )   ,    (
                _UpperCamelCase
            )   ,    (
                _UpperCamelCase
            )   ,    (
                _UpperCamelCase
            )   ,    
            )       =	config_and_inputs
            _UpperCamelCase       =	{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
            return config, inputs_dict

@require_tf
class     __lowerCAmelCase					(   __magic_name__						,						__magic_name__						,						unittest.TestCase			):






       """simple docstring"""
       _snake_case     :					List[str]						    =   (
           (
               TFRoFormerModel,
               TFRoFormerForCausalLM,
               TFRoFormerForMaskedLM,
               TFRoFormerForQuestionAnswering,
               TFRoFormerForSequenceClassification,
               TFRoFormerForTokenClassification,
               TFRoFormerForMultipleChoice,
           )
           if is_tf_available()
           else ()
       )
       _snake_case     :					Any						    =   (
           {
               'feature-extraction': TFRoFormerModel,
               'fill-mask': TFRoFormerForMaskedLM,
               'question-answering': TFRoFormerForQuestionAnswering,
               'text-classification': TFRoFormerForSequenceClassification,
               'text-generation': TFRoFormerForCausalLM,
               'token-classification': TFRoFormerForTokenClassification,
               'zero-shot': TFRoFormerForSequenceClassification,
           }
           if is_tf_available()
           else {}
       )

       _snake_case     :					Optional[int]						    =   False
       _snake_case     :					Optional[Any]						    =   False





       def snake_case__						(							self       :		Optional[int]	,			lowerCAmelCase__       :		Tuple	,			lowerCAmelCase__       :		Any	,			lowerCAmelCase__       :		List[Any]	,			lowerCAmelCase__       :		Union[str, Any]	,			lowerCAmelCase__       :		List[str]					)   ->					str:

            '''simple docstring'''




            if pipeline_test_casse_name == "TextGenerationPipelineTests":
                 return True

            return False





       def snake_case__						(							self       :		List[str]					)   ->					List[str]:

            '''simple docstring'''




            _UpperCamelCase       =	TFRoFormerModelTester(self					)
            _UpperCamelCase       =	ConfigTester(self	,			config_class=lowerCAmelCase__	,			hidden_size=37					)





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

            '''simple docstring'''




            self.config_tester.run_common_tests()





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

            '''simple docstring'''




            _UpperCamelCase       =	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*lowerCAmelCase__					)





       def snake_case__						(							self       :		Optional[int]					)   ->					Tuple:

            '''simple docstring'''




            _UpperCamelCase       =	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase__					)





       def snake_case__						(							self       :		Tuple					)   ->					List[str]:

            '''simple docstring'''




            _UpperCamelCase       =	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_lm_head(*lowerCAmelCase__					)





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

            '''simple docstring'''




            _UpperCamelCase       =	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_multiple_choice(*lowerCAmelCase__					)





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

            '''simple docstring'''




            _UpperCamelCase       =	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase__					)





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

            '''simple docstring'''




            _UpperCamelCase       =	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase__					)





       def snake_case__						(							self       :		List[str]					)   ->					List[str]:

            '''simple docstring'''




            _UpperCamelCase       =	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase__					)







       @slow
       def snake_case__						(							self       :		Optional[int]					)   ->					Any:

            '''simple docstring'''




            _UpperCamelCase       =	TFRoFormerModel.from_pretrained('''junnyu/roformer_chinese_base'''					)
            self.assertIsNotNone(lowerCAmelCase__					)

@require_tf
class     __lowerCAmelCase					(   unittest.TestCase			):






       """simple docstring"""





       @slow
       def snake_case__						(							self       :		List[str]					)   ->					int:

            '''simple docstring'''




            _UpperCamelCase       =	TFRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''					)
            _UpperCamelCase       =	tf.constant([[0, 1, 2, 3, 4, 5]]					)
            _UpperCamelCase       =	model(lowerCAmelCase__					)[0]

            # TODO Replace vocab size
            _UpperCamelCase       =	50000

            _UpperCamelCase       =	[1, 6, vocab_size]
            self.assertEqual(output.shape	,			lowerCAmelCase__					)

            print(output[:, :3, :3]					)

            # TODO Replace values below with what was printed above.
            _UpperCamelCase       =	tf.constant(
                [
                    [
                        [-0.12053341, -1.0264901, 0.29221946],
                        [-1.5133783, 0.197433, 0.15190607],
                        [-5.0135403, -3.900256, -0.84038764],
                    ]
                ]					)
            tf.debugging.assert_near(output[:, :3, :3]	,			lowerCAmelCase__	,			atol=1e-4					)

@require_tf
class     __lowerCAmelCase					(   unittest.TestCase			):






       """simple docstring"""
       _snake_case     :					Union[str, Any]						    =   1e-4





       def snake_case__						(							self       :		str					)   ->					List[str]:

            '''simple docstring'''




            _UpperCamelCase       =	tf.constant([[4, 10]]					)
            _UpperCamelCase       =	TFRoFormerSinusoidalPositionalEmbedding(num_positions=6	,			embedding_dim=6					)

            _UpperCamelCase       =	emba(input_ids.shape					)
            _UpperCamelCase       =	tf.constant(
                [[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]]					)

            tf.debugging.assert_near(lowerCAmelCase__	,			lowerCAmelCase__	,			atol=self.tolerance					)







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

            '''simple docstring'''




            _UpperCamelCase       =	tf.constant(
                [
                    [0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
                    [0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
                    [0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
                ]					)
            _UpperCamelCase       =	TFRoFormerSinusoidalPositionalEmbedding(num_positions=512	,			embedding_dim=512					)
            emba([2, 16, 512]					)
            _UpperCamelCase       =	emba.weight[:3, :5]

            tf.debugging.assert_near(lowerCAmelCase__	,			lowerCAmelCase__	,			atol=self.tolerance					)

@require_tf
class     __lowerCAmelCase					(   unittest.TestCase			):






       """simple docstring"""
       _snake_case     :					Any						    =   1e-4





       def snake_case__						(							self       :		List[str]					)   ->					str:

            '''simple docstring'''




            _UpperCamelCase       =	tf.reshape(tf.range(2 * 12 * 16 * 64	,			dtype=tf.floataa					)	,			shape=(2, 12, 16, 64)					) / 100

            _UpperCamelCase       =	-tf.reshape(tf.range(2 * 12 * 16 * 64	,			dtype=tf.floataa					)	,			shape=(2, 12, 16, 64)					) / 100

            _UpperCamelCase       =	TFRoFormerSinusoidalPositionalEmbedding(num_positions=32	,			embedding_dim=64					)
            _UpperCamelCase       =	embed_positions([2, 16, 768]					)[None, None, :, :]

            _UpperCamelCase   ,    _UpperCamelCase       =	TFRoFormerSelfAttention.apply_rotary_position_embeddings(
                lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__					)

            _UpperCamelCase       =	tf.constant(
                [
                    [0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
                    [-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
                    [-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
                    [-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
                    [0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
                    [3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
                ]					)
            _UpperCamelCase       =	tf.constant(
                [
                    [0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
                    [0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
                    [1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
                    [2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
                    [-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
                    [-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
                ]					)

            tf.debugging.assert_near(query_layer[0, 0, :6, :8]	,			lowerCAmelCase__	,			atol=self.tolerance					)
            tf.debugging.assert_near(key_layer[0, 0, :6, :8]	,			lowerCAmelCase__	,			atol=self.tolerance					)










'''simple docstring'''

import argparse
import os

# New Code #
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 import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


lowercase__						:      str	=    16
lowercase__						:      int	=    32







def  a__    (    lowercase :					Accelerator, lowercase :					int = 16				)		->		List[str]:





     """simple docstring"""

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

     def tokenize_function(lowercase :					Union[str, Any]				):
          # max_length=None => use the model max length (it's actually the default)
          _UpperCamelCase       =	tokenizer(examples['''sentence1'''], examples['''sentence2'''], truncation=lowercase, max_length=lowercase				)
          return outputs

     # Apply the method we just defined to all the examples in all the splits of the dataset
     # starting with the main process first:
     with accelerator.main_process_first():
          _UpperCamelCase       =	datasets.map(
              lowercase, batched=lowercase, remove_columns=['''idx''', '''sentence1''', '''sentence2'''], )

     # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
     # transformers library
     _UpperCamelCase       =	tokenized_datasets.rename_column('''label''', '''labels'''				)

     def collate_fn(lowercase :					Any				):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          _UpperCamelCase       =	128 if accelerator.distributed_type == DistributedType.TPU else None
          # When using mixed precision we want round multiples of 8/16
          if accelerator.mixed_precision == "fp8":
               _UpperCamelCase       =	16
          elif accelerator.mixed_precision != "no":
               _UpperCamelCase       =	8
          else:
               _UpperCamelCase       =	None

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

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

     return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
   from accelerate.test_utils.training import mocked_dataloaders

   lowercase__						:      str	=    mocked_dataloaders  # noqa: F811







def  a__    (    lowercase :					List[Any], lowercase :					List[str]				)		->		Any:





     """simple docstring"""

     if os.environ.get('''TESTING_MOCKED_DATALOADERS''', lowercase				) == "1":
          _UpperCamelCase       =	2
     # Initialize accelerator
     _UpperCamelCase       =	Accelerator(cpu=args.cpu, mixed_precision=args.mixed_precision				)
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     _UpperCamelCase       =	config['''lr''']
     _UpperCamelCase       =	int(config['''num_epochs''']				)
     _UpperCamelCase       =	int(config['''seed''']				)
     _UpperCamelCase       =	int(config['''batch_size''']				)

     _UpperCamelCase       =	evaluate.load('''glue''', '''mrpc'''				)

     # New Code #
     # We now can define an inner training loop function. It should take a batch size as the only parameter,
     # and build the dataloaders in there.
     # It also gets our decorator
     @find_executable_batch_size(starting_batch_size=lowercase				)
     def inner_training_loop(lowercase :					List[Any]				):
          # And now just move everything below under this function
          # We need to bring in the Accelerator object from earlier
          nonlocal accelerator
          # And reset all of its attributes that could hold onto any memory:
          accelerator.free_memory()

          # Then we can declare the model, optimizer, and everything else:
          set_seed(lowercase				)

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

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

          # Instantiate optimizer
          _UpperCamelCase       =	AdamW(params=model.parameters(), lr=lowercase				)
          _UpperCamelCase   ,    _UpperCamelCase       =	get_dataloaders(lowercase, lowercase				)

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

          # Now we train the model
          for epoch in range(lowercase				):
               model.train()
               for step, batch in enumerate(lowercase				):
                    # We could avoid this line since we set the accelerator with `device_placement=True`.
                    batch.to(accelerator.device				)
                    _UpperCamelCase       =	model(**lowercase				)
                    _UpperCamelCase       =	outputs.loss
                    accelerator.backward(lowercase				)
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

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

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

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
     inner_training_loop()







def  a__    (    )		->		str:





     """simple docstring"""

     _UpperCamelCase       =	argparse.ArgumentParser(description='''Simple example of training script.'''				)
     parser.add_argument(
         '''--mixed_precision''', type=lowercase, default=lowercase, choices=['''no''', '''fp16''', '''bf16''', '''fp8'''], help='''Whether to use mixed precision. Choose'''
         '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
         '''and an Nvidia Ampere GPU.''', )
     parser.add_argument('''--cpu''', action='''store_true''', help='''If passed, will train on the CPU.'''				)
     _UpperCamelCase       =	parser.parse_args()
     _UpperCamelCase       =	{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
     training_function(lowercase, lowercase				)


if __name__ == "__main__":
   main()



