



from __future__ import annotations

import copy
import inspect
import unittest

import numpy as np

from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property

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


if is_tf_available():
						import tensorflow as tf

						from transformers import (
						    TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
						    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
						    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
						    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
						    LayoutLMvaConfig,
						    TFLayoutLMvaForQuestionAnswering,
						    TFLayoutLMvaForSequenceClassification,
						    TFLayoutLMvaForTokenClassification,
						    TFLayoutLMvaModel,
						)

if is_vision_available():
						from PIL import Image

						from transformers import LayoutLMvaImageProcessor






class    SCREAMING_SNAKE_CASE			:





							def __init__(   self		:		List[Any]      ,				__lowercase		:		Dict      ,				__lowercase		:		Union[str, Any]=2      ,				__lowercase		:		Optional[int]=3      ,				__lowercase		:		List[str]=4      ,				__lowercase		:		Union[str, Any]=2      ,				__lowercase		:		Optional[int]=7      ,				__lowercase		:		Tuple=True      ,				__lowercase		:		str=True      ,				__lowercase		:		Union[str, Any]=True      ,				__lowercase		:		Any=True      ,				__lowercase		:		int=99      ,				__lowercase		:		Any=36      ,				__lowercase		:		List[Any]=2      ,				__lowercase		:		Tuple=4      ,				__lowercase		:		Any=37      ,				__lowercase		:		Union[str, Any]="gelu"      ,				__lowercase		:		str=0.1      ,				__lowercase		:		Union[str, Any]=0.1      ,				__lowercase		:		List[Any]=512      ,				__lowercase		:		Union[str, Any]=16      ,				__lowercase		:		int=2      ,				__lowercase		:		Optional[Any]=0.02      ,				__lowercase		:		Optional[int]=6      ,				__lowercase		:		str=6      ,				__lowercase		:		int=3      ,				__lowercase		:		List[str]=4      ,				__lowercase		:		str=None      ,				__lowercase		:		str=1000      ,				):

											'''simple docstring'''
											__a       =		parent
											__a       =		batch_size
											__a       =		num_channels
											__a       =		image_size
											__a       =		patch_size
											__a       =		is_training
											__a       =		use_input_mask
											__a       =		use_token_type_ids
											__a       =		use_labels
											__a       =		vocab_size
											__a       =		hidden_size
											__a       =		num_hidden_layers
											__a       =		num_attention_heads
											__a       =		intermediate_size
											__a       =		hidden_act
											__a       =		hidden_dropout_prob
											__a       =		attention_probs_dropout_prob
											__a       =		max_position_embeddings
											__a       =		type_vocab_size
											__a       =		type_sequence_label_size
											__a       =		initializer_range
											__a       =		coordinate_size
											__a       =		shape_size
											__a       =		num_labels
											__a       =		num_choices
											__a       =		scope
											__a       =		range_bbox

											# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
											__a       =		text_seq_length
											__a       =		(image_size // patch_size) ** 2 + 1
											__a       =		self.text_seq_length + self.image_seq_length





							def 						UpperCamelCase_					(   self		:		Any	):

											'''simple docstring'''
											__a       =		ids_tensor([self.batch_size, self.text_seq_length]      ,				self.vocab_size	)

											__a       =		ids_tensor([self.batch_size, self.text_seq_length, 4]      ,				self.range_bbox	)
											__a       =		bbox.numpy()
											# Ensure that bbox is legal
											for i in range(bbox.shape[0]	):
															for j in range(bbox.shape[1]	):
																			if bbox[i, j, 3] < bbox[i, j, 1]:
																							__a       =		bbox[i, j, 3]
																							__a       =		bbox[i, j, 1]
																							__a       =		tmp_coordinate
																			if bbox[i, j, 2] < bbox[i, j, 0]:
																							__a       =		bbox[i, j, 2]
																							__a       =		bbox[i, j, 0]
																							__a       =		tmp_coordinate
											__a       =		tf.constant(__lowercase	)

											__a       =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

											__a       =		None
											if self.use_input_mask:
															__a       =		random_attention_mask([self.batch_size, self.text_seq_length]	)

											__a       =		None
											if self.use_token_type_ids:
															__a       =		ids_tensor([self.batch_size, self.text_seq_length]      ,				self.type_vocab_size	)

											__a       =		None
											__a       =		None
											if self.use_labels:
															__a       =		ids_tensor([self.batch_size]      ,				self.type_sequence_label_size	)
															__a       =		ids_tensor([self.batch_size, self.text_seq_length]      ,				self.num_labels	)

											__a       =		LayoutLMvaConfig(
											    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      ,				coordinate_size=self.coordinate_size      ,				shape_size=self.shape_size      ,				input_size=self.image_size      ,				patch_size=self.patch_size      ,				)

											return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels





							def 						UpperCamelCase_					(   self		:		Dict      ,				__lowercase		:		Tuple      ,				__lowercase		:		Optional[Any]      ,				__lowercase		:		Optional[Any]      ,				__lowercase		:		Union[str, Any]      ,				__lowercase		:		Any      ,				__lowercase		:		List[str]	):

											'''simple docstring'''
											__a       =		TFLayoutLMvaModel(config=__lowercase	)

											# text + image
											__a       =		model(__lowercase      ,				pixel_values=__lowercase      ,				training=__lowercase	)
											__a       =		model(
											    __lowercase      ,				bbox=__lowercase      ,				pixel_values=__lowercase      ,				attention_mask=__lowercase      ,				token_type_ids=__lowercase      ,				training=__lowercase      ,				)
											__a       =		model(__lowercase      ,				bbox=__lowercase      ,				pixel_values=__lowercase      ,				training=__lowercase	)

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

											# text only
											__a       =		model(__lowercase      ,				training=__lowercase	)
											self.parent.assertEqual(
											    result.last_hidden_state.shape      ,				(self.batch_size, self.text_seq_length, self.hidden_size)	)

											# image only
											__a       =		model({"""pixel_values""": pixel_values}      ,				training=__lowercase	)
											self.parent.assertEqual(
											    result.last_hidden_state.shape      ,				(self.batch_size, self.image_seq_length, self.hidden_size)	)





							def 						UpperCamelCase_					(   self		:		Union[str, Any]      ,				__lowercase		:		int      ,				__lowercase		:		List[Any]      ,				__lowercase		:		Dict      ,				__lowercase		:		List[str]      ,				__lowercase		:		Optional[Any]      ,				__lowercase		:		Dict      ,				__lowercase		:		Any	):

											'''simple docstring'''
											__a       =		self.num_labels
											__a       =		TFLayoutLMvaForSequenceClassification(config=__lowercase	)
											__a       =		model(
											    __lowercase      ,				bbox=__lowercase      ,				pixel_values=__lowercase      ,				attention_mask=__lowercase      ,				token_type_ids=__lowercase      ,				labels=__lowercase      ,				training=__lowercase      ,				)
											self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.num_labels)	)





							def 						UpperCamelCase_					(   self		:		int      ,				__lowercase		:		List[Any]      ,				__lowercase		:		Any      ,				__lowercase		:		List[str]      ,				__lowercase		:		List[str]      ,				__lowercase		:		List[str]      ,				__lowercase		:		Dict      ,				__lowercase		:		Dict	):

											'''simple docstring'''
											__a       =		self.num_labels
											__a       =		TFLayoutLMvaForTokenClassification(config=__lowercase	)
											__a       =		model(
											    __lowercase      ,				bbox=__lowercase      ,				pixel_values=__lowercase      ,				attention_mask=__lowercase      ,				token_type_ids=__lowercase      ,				labels=__lowercase      ,				training=__lowercase      ,				)
											self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.text_seq_length, self.num_labels)	)





							def 						UpperCamelCase_					(   self		:		List[Any]      ,				__lowercase		:		Union[str, Any]      ,				__lowercase		:		List[str]      ,				__lowercase		:		str      ,				__lowercase		:		Optional[Any]      ,				__lowercase		:		List[str]      ,				__lowercase		:		Optional[int]      ,				__lowercase		:		Tuple	):

											'''simple docstring'''
											__a       =		2
											__a       =		TFLayoutLMvaForQuestionAnswering(config=__lowercase	)
											__a       =		model(
											    __lowercase      ,				bbox=__lowercase      ,				pixel_values=__lowercase      ,				attention_mask=__lowercase      ,				token_type_ids=__lowercase      ,				start_positions=__lowercase      ,				end_positions=__lowercase      ,				training=__lowercase      ,				)
											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		:		List[Any]	):

											'''simple docstring'''
											__a       =		self.prepare_config_and_inputs()
											((__a)     ,					(__a)     ,					(__a)     ,					(__a)     ,					(__a)     ,					(__a)     ,					(__a)     ,					(__a))       =		config_and_inputs
											__a       =		{
											    """input_ids""": input_ids,
											    """bbox""": bbox,
											    """pixel_values""": pixel_values,
											    """token_type_ids""": token_type_ids,
											    """attention_mask""": input_mask,
											}
											return config, inputs_dict






@require_tf
class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__			,      lowerCamelCase__			,      unittest.TestCase					):
							__lowerCamelCase	:	str    =(
							    (
							        TFLayoutLMvaModel,
							        TFLayoutLMvaForQuestionAnswering,
							        TFLayoutLMvaForSequenceClassification,
							        TFLayoutLMvaForTokenClassification,
							    )
							    if is_tf_available()
							    else ()
							)
							__lowerCamelCase	:	Dict    =(
							    {'document-question-answering': TFLayoutLMvaForQuestionAnswering, 'feature-extraction': TFLayoutLMvaModel}
							    if is_tf_available()
							    else {}
							)

							__lowerCamelCase	:	int    =False
							__lowerCamelCase	:	List[Any]    =False
							__lowerCamelCase	:	str    =False





							def 						UpperCamelCase_					(   self		:		str      ,				__lowercase		:		List[Any]      ,				__lowercase		:		Dict      ,				__lowercase		:		Union[str, Any]      ,				__lowercase		:		Union[str, Any]      ,				__lowercase		:		Any	):

											'''simple docstring'''
											return True





							def 						UpperCamelCase_					(   self		:		Optional[int]      ,				__lowercase		:		List[Any]      ,				__lowercase		:		List[Any]      ,				__lowercase		:		Union[str, Any]=False	):

											'''simple docstring'''
											__a       =		copy.deepcopy(__lowercase	)

											if model_class in get_values(__lowercase	):
															__a       =		{
															    k: tf.tile(tf.expand_dims(__lowercase      ,				1	)      ,				(1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)	)
															    if isinstance(__lowercase      ,				tf.Tensor	) and v.ndim > 0
															    else v
															    for k, v in inputs_dict.items()
															}

											if return_labels:
															if model_class in get_values(__lowercase	):
																			__a       =		tf.ones(self.model_tester.batch_size      ,				dtype=tf.intaa	)
															elif model_class in get_values(__lowercase	):
																			__a       =		tf.zeros(self.model_tester.batch_size      ,				dtype=tf.intaa	)
																			__a       =		tf.zeros(self.model_tester.batch_size      ,				dtype=tf.intaa	)
															elif model_class in get_values(__lowercase	):
																			__a       =		tf.zeros(self.model_tester.batch_size      ,				dtype=tf.intaa	)
															elif model_class in get_values(__lowercase	):
																			__a       =		tf.zeros(
																			    (self.model_tester.batch_size, self.model_tester.text_seq_length)      ,				dtype=tf.intaa	)

											return inputs_dict





							def 						UpperCamelCase_					(   self		:		Any	):

											'''simple docstring'''
											__a       =		TFLayoutLMvaModelTester(self	)
											__a       =		ConfigTester(self      ,				config_class=__lowercase      ,				hidden_size=37	)





							def 						UpperCamelCase_					(   self		:		Tuple	):

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





							def 						UpperCamelCase_					(   self		:		List[Any]	):

											'''simple docstring'''
											__a     ,					__a       =		self.model_tester.prepare_config_and_inputs_for_common()
											for model_class in self.all_model_classes:
															__a       =		model_class(__lowercase	)
															if getattr(__lowercase      ,				"""hf_compute_loss"""      ,				__lowercase	):
																			# The number of elements in the loss should be the same as the number of elements in the label
																			__a       =		self._prepare_for_class(inputs_dict.copy()      ,				__lowercase      ,				return_labels=__lowercase	)
																			__a       =		prepared_for_class[
																			    sorted(prepared_for_class.keys() - inputs_dict.keys()      ,				reverse=__lowercase	)[0]
																			]
																			__a       =		added_label.shape.as_list()[:1]

																			# Test that model correctly compute the loss with kwargs
																			__a       =		self._prepare_for_class(inputs_dict.copy()      ,				__lowercase      ,				return_labels=__lowercase	)
																			__a       =		prepared_for_class.pop("""input_ids"""	)

																			__a       =		model(__lowercase      ,				**__lowercase	)[0]
																			self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]	)

																			# Test that model correctly compute the loss when we mask some positions
																			__a       =		self._prepare_for_class(inputs_dict.copy()      ,				__lowercase      ,				return_labels=__lowercase	)
																			__a       =		prepared_for_class.pop("""input_ids"""	)
																			if "labels" in prepared_for_class:
																							__a       =		prepared_for_class["""labels"""].numpy()
																							if len(labels.shape	) > 1 and labels.shape[1] != 1:
																											__a       =		-100
																											__a       =		tf.convert_to_tensor(__lowercase	)
																											__a       =		model(__lowercase      ,				**__lowercase	)[0]
																											self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]	)
																											self.assertTrue(not np.any(np.isnan(loss.numpy()	)	)	)

                # Test that model correctly compute the loss with a dict
																			__a       =		self._prepare_for_class(inputs_dict.copy()      ,				__lowercase      ,				return_labels=__lowercase	)
																			__a       =		model(__lowercase	)[0]
																			self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]	)

																			# Test that model correctly compute the loss with a tuple
																			__a       =		self._prepare_for_class(inputs_dict.copy()      ,				__lowercase      ,				return_labels=__lowercase	)

																			# Get keys that were added with the _prepare_for_class function
																			__a       =		prepared_for_class.keys() - inputs_dict.keys()
																			__a       =		inspect.signature(model.call	).parameters
																			__a       =		list(signature.keys()	)

																			# Create a dictionary holding the location of the tensors in the tuple
																			__a       =		{0: """input_ids"""}
																			for label_key in label_keys:
																							__a       =		signature_names.index(__lowercase	)
																							__a       =		label_key
																			__a       =		sorted(tuple_index_mapping.items()	)
																			# Initialize a list with their default values, update the values and convert to a tuple
																			__a       =		[]

																			for name in signature_names:
																							if name != "kwargs":
																											list_input.append(signature[name].default	)

																			for index, value in sorted_tuple_index_mapping:
																							__a       =		prepared_for_class[value]

																			__a       =		tuple(__lowercase	)

																			# Send to model
																			__a       =		model(tuple_input[:-1]	)[0]

																			self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]	)





							def 						UpperCamelCase_					(   self		:		Union[str, Any]	):

											'''simple docstring'''
											(
											    (
											    __a
											)     ,					(
											    __a
											)     ,					(
											    __a
											)     ,					(
											    __a
											)     ,					(
											    __a
											)     ,					(
											    __a
											)     ,					(
											    __a
											)     ,					(
											    __a
											)     ,					
											)       =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase	)





							def 						UpperCamelCase_					(   self		:		int	):

											'''simple docstring'''
											(
											    (
											    __a
											)     ,					(
											    __a
											)     ,					(
											    __a
											)     ,					(
											    __a
											)     ,					(
											    __a
											)     ,					(
											    __a
											)     ,					(
											    __a
											)     ,					(
											    __a
											)     ,					
											)       =		self.model_tester.prepare_config_and_inputs()
											for type in ["absolute", "relative_key", "relative_key_query"]:
															__a       =		type
															self.model_tester.create_and_check_model(__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase	)





							def 						UpperCamelCase_					(   self		:		Optional[Any]	):

											'''simple docstring'''
											(
											    (
											    __a
											)     ,					(
											    __a
											)     ,					(
											    __a
											)     ,					(
											    __a
											)     ,					(
											    __a
											)     ,					(
											    __a
											)     ,					(
											    __a
											)     ,					(
											    __a
											)     ,					
											)       =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_sequence_classification(
											    __lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase	)





							def 						UpperCamelCase_					(   self		:		str	):

											'''simple docstring'''
											(
											    (
											    __a
											)     ,					(
											    __a
											)     ,					(
											    __a
											)     ,					(
											    __a
											)     ,					(
											    __a
											)     ,					(
											    __a
											)     ,					(
											    __a
											)     ,					(
											    __a
											)     ,					
											)       =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_token_classification(
											    __lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase	)





							def 						UpperCamelCase_					(   self		:		str	):

											'''simple docstring'''
											(
											    (
											    __a
											)     ,					(
											    __a
											)     ,					(
											    __a
											)     ,					(
											    __a
											)     ,					(
											    __a
											)     ,					(
											    __a
											)     ,					(
											    __a
											)     ,					(
											    __a
											)     ,					
											)       =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_question_answering(
											    __lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase	)







							@slow
							def 						UpperCamelCase_					(   self		:		Dict	):

											'''simple docstring'''
											for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															__a       =		TFLayoutLMvaModel.from_pretrained(__lowercase	)
															self.assertIsNotNone(__lowercase	)



def   lowerCAmelCase__						(							):
				"""simple docstring"""







				__a       =		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
				return image






@require_tf
class    SCREAMING_SNAKE_CASE			(							unittest.TestCase					):





							@cached_property
							def 						UpperCamelCase_					(   self		:		str	):

											'''simple docstring'''
											return LayoutLMvaImageProcessor(apply_ocr=__lowercase	) if is_vision_available() else None







							@slow
							def 						UpperCamelCase_					(   self		:		str	):

											'''simple docstring'''
											__a       =		TFLayoutLMvaModel.from_pretrained("""microsoft/layoutlmv3-base"""	)

											__a       =		self.default_image_processor
											__a       =		prepare_img()
											__a       =		image_processor(images=__lowercase      ,				return_tensors="""tf"""	).pixel_values

											__a       =		tf.constant([[1, 2]]	)
											__a       =		tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]]	)      ,				axis=0	)

											# forward pass
											__a       =		model(input_ids=__lowercase      ,				bbox=__lowercase      ,				pixel_values=__lowercase      ,				training=__lowercase	)

											# verify the logits
											__a       =		(1, 199, 768)
											self.assertEqual(outputs.last_hidden_state.shape      ,				__lowercase	)

											__a       =		tf.constant(
											    [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]	)

											self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]      ,				__lowercase      ,				atol=1E-4	)	)





import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

lowerCamelCase__			= 16
lowerCamelCase__			= 32

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Accelerator				,     _SCREAMING_SNAKE_CASE       :			int = 16    ):
				"""simple docstring"""







				__a       =		AutoTokenizer.from_pretrained("""bert-base-cased"""    )
				__a       =		load_dataset("""glue"""				,     """mrpc"""    )

				def tokenize_function(_SCREAMING_SNAKE_CASE       :			int    ):
								# max_length=None => use the model max length (it's actually the default)
								__a       =		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       =		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       =		tokenized_datasets.rename_column("""label"""				,     """labels"""    )

				def collate_fn(_SCREAMING_SNAKE_CASE       :			Any    ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								__a       =		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       =		16
								elif accelerator.mixed_precision != "no":
												__a       =		8
								else:
												__a       =		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       =		DataLoader(
				    tokenized_datasets["""train"""]				,     shuffle=_SCREAMING_SNAKE_CASE				,     collate_fn=_SCREAMING_SNAKE_CASE				,     batch_size=_SCREAMING_SNAKE_CASE    )
				__a       =		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

						lowerCamelCase__			= mocked_dataloaders  # noqa: F811

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Dict				,     _SCREAMING_SNAKE_CASE       :			Dict    ):
				"""simple docstring"""







				if os.environ.get("""TESTING_MOCKED_DATALOADERS"""				,     _SCREAMING_SNAKE_CASE    ) == "1":
								__a       =		2
				# Initialize Accelerator

				# New Code #
				# We pass in "all" to `log_with` to grab all available trackers in the environment
				# Note: If using a custom `Tracker` class, should be passed in here such as:
				# >>> log_with = ["all", MyCustomTrackerClassInstance()]
				if args.with_tracking:
								__a       =		Accelerator(
								    cpu=args.cpu				,     mixed_precision=args.mixed_precision				,     log_with="""all"""				,     project_dir=args.project_dir    )
				else:
								__a       =		Accelerator(cpu=args.cpu				,     mixed_precision=args.mixed_precision    )
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				__a       =		config["""lr"""]
				__a       =		int(config["""num_epochs"""]    )
				__a       =		int(config["""seed"""]    )
				__a       =		int(config["""batch_size"""]    )
				set_seed(_SCREAMING_SNAKE_CASE    )

				__a     ,					__a       =		get_dataloaders(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )
				__a       =		evaluate.load("""glue"""				,     """mrpc"""    )

				# If the batch size is too big we use gradient accumulation
				__a       =		1
				if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
								__a       =		batch_size // MAX_GPU_BATCH_SIZE
								__a       =		MAX_GPU_BATCH_SIZE

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				__a       =		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       =		model.to(accelerator.device    )

				# Instantiate optimizer
				__a       =		AdamW(params=model.parameters()				,     lr=_SCREAMING_SNAKE_CASE    )

				# Instantiate scheduler
				__a       =		get_linear_schedule_with_warmup(
				    optimizer=_SCREAMING_SNAKE_CASE				,     num_warmup_steps=100				,     num_training_steps=(len(_SCREAMING_SNAKE_CASE    ) * num_epochs) // gradient_accumulation_steps				,     )

				# Prepare everything
				# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
				# prepare method.
				__a     ,					__a     ,					__a     ,					__a     ,					__a       =		accelerator.prepare(
				    _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )

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

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

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

								__a       =		metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(f"epoch {epoch}:"				,     _SCREAMING_SNAKE_CASE    )

								# New Code #
								# To actually log, we call `Accelerator.log`
								# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
								if args.with_tracking:
												accelerator.log(
												    {
												        """accuracy""": eval_metric["""accuracy"""],
												        """f1""": eval_metric["""f1"""],
												        """train_loss""": total_loss.item() / len(_SCREAMING_SNAKE_CASE    ),
												        """epoch""": epoch,
												    }				,     step=_SCREAMING_SNAKE_CASE				,     )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
				if args.with_tracking:
								accelerator.end_training()

def   lowerCAmelCase__						(							):
				"""simple docstring"""







				__a       =		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."""				,     )
				parser.add_argument("""--cpu"""				,     action="""store_true"""				,     help="""If passed, will train on the CPU."""    )
				parser.add_argument(
				    """--with_tracking"""				,     action="""store_true"""				,     help="""Whether to load in all available experiment trackers from the environment and use them for logging."""				,     )
				parser.add_argument(
				    """--project_dir"""				,     type=_SCREAMING_SNAKE_CASE				,     default="""logs"""				,     help="""Location on where to store experiment tracking logs` and relevent project information"""				,     )
				__a       =		parser.parse_args()
				__a       =		{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
				training_function(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
						main()
