

import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


a_      =     'true'

def 			lowerCamelCase__			(       _a				,      _a=82				,      _a=16):
	set_seed(42)
	SCREAMING_SNAKE_CASE :   List[Any]					  =							RegressionModel()
	SCREAMING_SNAKE_CASE :   Optional[Any]					  =							deepcopy(_a)
	SCREAMING_SNAKE_CASE :   Any					  =							RegressionDataset(length=_a)
	SCREAMING_SNAKE_CASE :   List[Any]					  =							DataLoader(_a				,      batch_size=_a)
	model.to(accelerator.device)
	SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Optional[int]					  =							accelerator.prepare(_a				,      _a)
	return model, ddp_model, dataloader

def 			lowerCamelCase__			(       _a				,      _a=False):
	SCREAMING_SNAKE_CASE :   Optional[Any]					  =							AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased")
	SCREAMING_SNAKE_CASE :   Optional[int]					  =							load_dataset("glue"				,      "mrpc"				,      split="validation")

	def tokenize_function(_a):
		SCREAMING_SNAKE_CASE :   str					  =							tokenizer(examples["sentence1"]				,      examples["sentence2"]				,      truncation=_a				,      max_length=_a)
		return outputs

	with accelerator.main_process_first():
		SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							dataset.map(
		    _a				,      batched=_a				,      remove_columns=["idx", "sentence1", "sentence2"]				,      )

	SCREAMING_SNAKE_CASE :   Any					  =							tokenized_datasets.rename_column("label"				,      "labels")

	def collate_fn(_a):
		if use_longest:
			return tokenizer.pad(_a				,      padding="longest"				,      return_tensors="pt")
		return tokenizer.pad(_a				,      padding="max_length"				,      max_length=128				,      return_tensors="pt")

	return DataLoader(_a				,      shuffle=_a				,      collate_fn=_a				,      batch_size=16)

def 			lowerCamelCase__			(       _a				,      _a):
	SCREAMING_SNAKE_CASE :   Any					  =							Accelerator(dispatch_batches=_a				,      split_batches=_a)
	SCREAMING_SNAKE_CASE :   str					  =							get_dataloader(_a				,      not dispatch_batches)
	SCREAMING_SNAKE_CASE :   Any					  =							AutoModelForSequenceClassification.from_pretrained(
	    "hf-internal-testing/mrpc-bert-base-cased"				,      return_dict=_a)
	SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Dict					  =							accelerator.prepare(_a				,      _a)
	return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator

def 			lowerCamelCase__			(       _a				,      _a				,      _a):
	SCREAMING_SNAKE_CASE :   int					  =							[]
	for batch in dataloader:
		SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   List[Any]					  =							batch.values()
		with torch.no_grad():
			SCREAMING_SNAKE_CASE :   str					  =							model(_a)
			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Any					  =							accelerator.gather_for_metrics((logit, target))
			logits_and_targets.append((logit, target))
	SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   List[Any]					  =							[], []
	for logit, targ in logits_and_targets:
		logits.append(_a)
		targs.append(_a)
	SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   List[str]					  =							torch.cat(_a), torch.cat(_a)
	return logits, targs

def 			lowerCamelCase__			(       _a				,      _a=82				,      _a=False				,      _a=False				,      _a=16):
	SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   int					  =							get_basic_setup(_a				,      _a				,      _a)
	SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Optional[int]					  =							generate_predictions(_a				,      _a				,      _a)
	assert (
	    len(_a) == num_samples
	), f"Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(_a)}"

def 			lowerCamelCase__			(       _a = False				,      _a = False):
	SCREAMING_SNAKE_CASE :   List[str]					  =							evaluate.load("glue"				,      "mrpc")
	SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   int					  =							get_mrpc_setup(_a				,      _a)
	# First do baseline
	SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   int					  =							setup["no"]
	model.to(_a)
	model.eval()
	for batch in dataloader:
		batch.to(_a)
		with torch.inference_mode():
			SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							model(**_a)
		SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							outputs.logits.argmax(dim=-1)
		metric.add_batch(predictions=_a				,      references=batch["labels"])
	SCREAMING_SNAKE_CASE :   Dict					  =							metric.compute()

	# Then do distributed
	SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Optional[Any]					  =							setup["ddp"]
	model.eval()
	for batch in dataloader:
		with torch.inference_mode():
			SCREAMING_SNAKE_CASE :   Optional[int]					  =							model(**_a)
		SCREAMING_SNAKE_CASE :   Tuple					  =							outputs.logits.argmax(dim=-1)
		SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							batch["labels"]
		SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   List[Any]					  =							accelerator.gather_for_metrics((preds, references))
		metric.add_batch(predictions=_a				,      references=_a)
	SCREAMING_SNAKE_CASE :   Dict					  =							metric.compute()

	for key in "accuracy f1".split():
		assert math.isclose(
		    baseline[key]				,      distributed[key]), f"Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"

def 			lowerCamelCase__			(       ):
	SCREAMING_SNAKE_CASE :   Tuple					  =							Accelerator(split_batches=_a				,      dispatch_batches=_a)
	if accelerator.is_local_main_process:
		datasets.utils.logging.set_verbosity_warning()
		transformers.utils.logging.set_verbosity_warning()
	else:
		datasets.utils.logging.set_verbosity_error()
		transformers.utils.logging.set_verbosity_error()
	# These are a bit slower so they should only be ran on the GPU or TPU
	if torch.cuda.is_available() or is_tpu_available():
		if accelerator.is_local_main_process:
			print("**Testing gather_for_metrics**")
		for split_batches in [True, False]:
			for dispatch_batches in [True, False]:
				if accelerator.is_local_main_process:
					print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`")
				test_mrpc(_a				,      _a)
				accelerator.state._reset_state()
	if accelerator.is_local_main_process:
		print("**Test torch metrics**")
	for split_batches in [True, False]:
		for dispatch_batches in [True, False]:
			SCREAMING_SNAKE_CASE :   Tuple					  =							Accelerator(split_batches=_a				,      dispatch_batches=_a)
			if accelerator.is_local_main_process:
				print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99")
			test_torch_metrics(_a				,      99)
			accelerator.state._reset_state()
	if accelerator.is_local_main_process:
		print("**Test last batch is not dropped when perfectly divisible**")
	SCREAMING_SNAKE_CASE :   str					  =							Accelerator()
	test_torch_metrics(_a				,      512)
	accelerator.state._reset_state()

def 			lowerCamelCase__			(       _a):
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
			main()







import unittest

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
			from transformers.models.roberta.modeling_flax_roberta import (
			    FlaxRobertaForCausalLM,
			    FlaxRobertaForMaskedLM,
			    FlaxRobertaForMultipleChoice,
			    FlaxRobertaForQuestionAnswering,
			    FlaxRobertaForSequenceClassification,
			    FlaxRobertaForTokenClassification,
			    FlaxRobertaModel,
			)




class 							snake_case_       (    unittest.TestCase      ):

						"""simple docstring"""



						def __init__(self:					Optional[int] ,					__UpperCAmelCase:					str ,					__UpperCAmelCase:					List[str]=13 ,					__UpperCAmelCase:					Any=7 ,					__UpperCAmelCase:					List[str]=True ,					__UpperCAmelCase:					Optional[int]=True ,					__UpperCAmelCase:					Dict=True ,					__UpperCAmelCase:					Optional[Any]=True ,					__UpperCAmelCase:					Optional[int]=99 ,					__UpperCAmelCase:					Optional[Any]=32 ,					__UpperCAmelCase:					int=5 ,					__UpperCAmelCase:					Dict=4 ,					__UpperCAmelCase:					Optional[int]=37 ,					__UpperCAmelCase:					int="gelu" ,					__UpperCAmelCase:					Tuple=0.1 ,					__UpperCAmelCase:					Any=0.1 ,					__UpperCAmelCase:					Union[str, Any]=512 ,					__UpperCAmelCase:					Optional[Any]=16 ,					__UpperCAmelCase:					List[Any]=2 ,					__UpperCAmelCase:					str=0.02 ,					__UpperCAmelCase:					int=4 ,					)    ->     str:




											'''simple docstring'''



											__a					:						Tuple      =					parent
											__a					:						int      =					batch_size
											__a					:						Optional[int]      =					seq_length
											__a					:						List[Any]      =					is_training
											__a					:						Tuple      =					use_attention_mask
											__a					:						Optional[int]      =					use_token_type_ids
											__a					:						Tuple      =					use_labels
											__a					:						str      =					vocab_size
											__a					:						Union[str, Any]      =					hidden_size
											__a					:						List[str]      =					num_hidden_layers
											__a					:						Optional[int]      =					num_attention_heads
											__a					:						Any      =					intermediate_size
											__a					:						Any      =					hidden_act
											__a					:						List[str]      =					hidden_dropout_prob
											__a					:						Dict      =					attention_probs_dropout_prob
											__a					:						Tuple      =					max_position_embeddings
											__a					:						Optional[int]      =					type_vocab_size
											__a					:						Tuple      =					type_sequence_label_size
											__a					:						List[Any]      =					initializer_range
											__a					:						int      =					num_choices



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




											'''simple docstring'''



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

											__a					:						Dict      =					None
											if self.use_attention_mask:
																__a					:						List[Any]      =					random_attention_mask([self.batch_size, self.seq_length]   )

											__a					:						Optional[int]      =					None
											if self.use_token_type_ids:
																__a					:						Dict      =					ids_tensor([self.batch_size, self.seq_length] ,					self.type_vocab_size   )

											__a					:						Dict      =					RobertaConfig(
											    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 ,					is_decoder=__UpperCAmelCase ,					initializer_range=self.initializer_range ,					)

											return config, input_ids, token_type_ids, attention_mask



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




											'''simple docstring'''



											__a					:						Union[str, Any]      =					self.prepare_config_and_inputs()
											__a      ,			__a      ,			__a      ,			__a					:						Dict      =					config_and_inputs
											__a					:						str      =					{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
											return config, inputs_dict



						def        UpperCAmelCase__					(self:					Dict   )    ->     str:




											'''simple docstring'''



											__a					:						Optional[Any]      =					self.prepare_config_and_inputs()
											__a      ,			__a      ,			__a      ,			__a					:						Tuple      =					config_and_inputs

											__a					:						int      =					True
											__a					:						List[str]      =					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
											__a					:						List[Any]      =					ids_tensor([self.batch_size, self.seq_length] ,					vocab_size=2   )

											return (
											    config,
											    input_ids,
											    token_type_ids,
											    encoder_hidden_states,
											    encoder_attention_mask,
											)




@require_flax
class 							snake_case_       (    __UpperCamelCase		,							unittest.TestCase      ):

						"""simple docstring"""



						snake_case__        =      True

						snake_case__        =      (
						    (
						        FlaxRobertaModel,
						        FlaxRobertaForCausalLM,
						        FlaxRobertaForMaskedLM,
						        FlaxRobertaForSequenceClassification,
						        FlaxRobertaForTokenClassification,
						        FlaxRobertaForMultipleChoice,
						        FlaxRobertaForQuestionAnswering,
						    )
						    if is_flax_available()
						    else ()
						)



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




											'''simple docstring'''



											__a					:						Tuple      =					FlaxRobertaModelTester(self   )



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




											'''simple docstring'''



											for model_class_name in self.all_model_classes:
																__a					:						int      =					model_class_name.from_pretrained("roberta-base" ,					from_pt=__UpperCAmelCase   )
																__a					:						Tuple      =					model(np.ones((1, 1)   )   )
																self.assertIsNotNone(__UpperCAmelCase   )





