

import unittest

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
					import jax

					from transformers.models.big_bird.modeling_flax_big_bird import (
					    FlaxBigBirdForCausalLM,
					    FlaxBigBirdForMaskedLM,
					    FlaxBigBirdForMultipleChoice,
					    FlaxBigBirdForPreTraining,
					    FlaxBigBirdForQuestionAnswering,
					    FlaxBigBirdForSequenceClassification,
					    FlaxBigBirdForTokenClassification,
					    FlaxBigBirdModel,
					)


class 	A_		(	unittest.TestCase    ):
							def __init__(		self		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=2       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=5_6       ,SCREAMING_SNAKE_CASE__		:      Dict=True       ,SCREAMING_SNAKE_CASE__		:      int=True       ,SCREAMING_SNAKE_CASE__		:      int=True       ,SCREAMING_SNAKE_CASE__		:      List[str]=True       ,SCREAMING_SNAKE_CASE__		:      Tuple=9_9       ,SCREAMING_SNAKE_CASE__		:      List[str]=3_2       ,SCREAMING_SNAKE_CASE__		:      Optional[int]=2       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=2       ,SCREAMING_SNAKE_CASE__		:      List[Any]=7       ,SCREAMING_SNAKE_CASE__		:      List[Any]="gelu_new"       ,SCREAMING_SNAKE_CASE__		:      List[Any]=0.1       ,SCREAMING_SNAKE_CASE__		:      Any=0.1       ,SCREAMING_SNAKE_CASE__		:      List[str]=5_1_2       ,SCREAMING_SNAKE_CASE__		:      Optional[int]=1_6       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=2       ,SCREAMING_SNAKE_CASE__		:      Any=0.02       ,SCREAMING_SNAKE_CASE__		:      List[Any]=4       ,SCREAMING_SNAKE_CASE__		:      List[Any]="block_sparse"       ,SCREAMING_SNAKE_CASE__		:      List[str]=True       ,SCREAMING_SNAKE_CASE__		:      Dict=False       ,SCREAMING_SNAKE_CASE__		:      Tuple=2       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=3       ,):
								__lowerCamelCase    :    str 	=			parent
								__lowerCamelCase    :    List[str] 	=			batch_size
								__lowerCamelCase    :    int 	=			seq_length
								__lowerCamelCase    :    Tuple 	=			is_training
								__lowerCamelCase    :    int 	=			use_attention_mask
								__lowerCamelCase    :    Dict 	=			use_token_type_ids
								__lowerCamelCase    :    Any 	=			use_labels
								__lowerCamelCase    :    int 	=			vocab_size
								__lowerCamelCase    :    List[Any] 	=			hidden_size
								__lowerCamelCase    :    Dict 	=			num_hidden_layers
								__lowerCamelCase    :    Dict 	=			num_attention_heads
								__lowerCamelCase    :    str 	=			intermediate_size
								__lowerCamelCase    :    List[Any] 	=			hidden_act
								__lowerCamelCase    :    Tuple 	=			hidden_dropout_prob
								__lowerCamelCase    :    Optional[Any] 	=			attention_probs_dropout_prob
								__lowerCamelCase    :    int 	=			max_position_embeddings
								__lowerCamelCase    :    Dict 	=			type_vocab_size
								__lowerCamelCase    :    Tuple 	=			type_sequence_label_size
								__lowerCamelCase    :    Any 	=			initializer_range
								__lowerCamelCase    :    int 	=			num_choices

								__lowerCamelCase    :    Dict 	=			rescale_embeddings
								__lowerCamelCase    :    Dict 	=			attention_type
								__lowerCamelCase    :    Tuple 	=			use_bias
								__lowerCamelCase    :    int 	=			block_size
								__lowerCamelCase    :    Union[str, Any] 	=			num_random_blocks
							def    lowerCAmelCase					(		self		:      Any):
								__lowerCamelCase    :    List[Any] 	=			ids_tensor([self.batch_size, self.seq_length]       ,self.vocab_size)

								__lowerCamelCase    :    int 	=			None
								if self.use_attention_mask:
									__lowerCamelCase    :    Optional[int] 	=			random_attention_mask([self.batch_size, self.seq_length])

								__lowerCamelCase    :    int 	=			None
								if self.use_token_type_ids:
									__lowerCamelCase    :    List[Any] 	=			ids_tensor([self.batch_size, self.seq_length]       ,self.type_vocab_size)

								__lowerCamelCase    :    Optional[Any] 	=			BigBirdConfig(
								    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=_lowerCamelCase       ,initializer_range=self.initializer_range       ,attention_type=self.attention_type       ,block_size=self.block_size       ,num_random_blocks=self.num_random_blocks       ,use_bias=self.use_bias       ,rescale_embeddings=self.rescale_embeddings       ,)

								return config, input_ids, token_type_ids, attention_mask


							def    lowerCAmelCase					(		self		:      List[Any]):
								__lowerCamelCase    :    List[Any] 	=			self.prepare_config_and_inputs()
								__lowerCamelCase    :    List[str] 	=			config_and_inputs
								__lowerCamelCase    :    Any 	=			{
								    '''input_ids''': input_ids,
								    '''token_type_ids''': token_type_ids,
								    '''attention_mask''': attention_mask,
								}
								return config, inputs_dict


@require_flax
class 	A_		(	a__      ,							unittest.TestCase    ):
							_UpperCAmelCase   :  Tuple										=				(
							    (
							        FlaxBigBirdForCausalLM,
							        FlaxBigBirdModel,
							        FlaxBigBirdForPreTraining,
							        FlaxBigBirdForMaskedLM,
							        FlaxBigBirdForMultipleChoice,
							        FlaxBigBirdForQuestionAnswering,
							        FlaxBigBirdForSequenceClassification,
							        FlaxBigBirdForTokenClassification,
							    )
							    if is_flax_available()
							    else ()
							)

							_UpperCAmelCase   :  str										=				False
							_UpperCAmelCase   :  str										=				False
							def    lowerCAmelCase					(		self		:      Any):
								__lowerCamelCase    :    Dict 	=			FlaxBigBirdModelTester(self)
							@slow
							# copied from `test_modeling_flax_common` because it takes much longer than other models
							def    lowerCAmelCase					(		self		:      Dict):
								super().test_from_pretrained_save_pretrained()
							@slow
							# copied from `test_modeling_flax_common` because it takes much longer than other models
							def    lowerCAmelCase					(		self		:      Union[str, Any]):
								super().test_from_pretrained_with_no_automatic_init()
							@slow
							# copied from `test_modeling_flax_common` because it takes much longer than other models
							def    lowerCAmelCase					(		self		:      str):
								super().test_no_automatic_init()
							@slow
							# copied from `test_modeling_flax_common` because it takes much longer than other models
							def    lowerCAmelCase					(		self		:      Dict):
								super().test_hidden_states_output()
							@slow
							def    lowerCAmelCase					(		self		:      List[str]):
								for model_class_name in self.all_model_classes:
									__lowerCamelCase    :    Optional[Any] 	=			model_class_name.from_pretrained('google/bigbird-roberta-base')
									self.assertIsNotNone(_lowerCamelCase)
							def    lowerCAmelCase					(		self		:      Optional[int]):
								if self.test_attn_probs:
									super().test_attention_outputs()
							@slow
							# copied from `test_modeling_flax_common` because it takes much longer than other models
							def    lowerCAmelCase					(		self		:      Tuple):
								__lowerCamelCase    :    Tuple 	=			self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__):
										__lowerCamelCase    :    Tuple 	=			self._prepare_for_class(_lowerCamelCase       ,_lowerCamelCase)
										__lowerCamelCase    :    List[Any] 	=			model_class(_lowerCamelCase)

										@jax.jit
										def model_jitted(SCREAMING_SNAKE_CASE__		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      str=None       ,**SCREAMING_SNAKE_CASE__		:      List[Any]):
											return model(input_ids=_lowerCamelCase       ,attention_mask=_lowerCamelCase       ,**_lowerCamelCase)

										with self.subTest('JIT Enabled'):
											__lowerCamelCase    :    str 	=			model_jitted(**_lowerCamelCase).to_tuple()

										with self.subTest('JIT Disabled'):
											with jax.disable_jit():
												__lowerCamelCase    :    str 	=			model_jitted(**_lowerCamelCase).to_tuple()

										self.assertEqual(len(_lowerCamelCase)       ,len(_lowerCamelCase))
										for jitted_output, output in zip(_lowerCamelCase       ,_lowerCamelCase):
											self.assertEqual(jitted_output.shape       ,output.shape)


							def    lowerCAmelCase					(		self		:      Any       ,SCREAMING_SNAKE_CASE__		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      str       ,SCREAMING_SNAKE_CASE__		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=1E-5       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]="outputs"       ,SCREAMING_SNAKE_CASE__		:      Optional[int]=None):
								# `bigbird_block_sparse_attention` in `FlaxBigBird` returns `attention_probs = None`, while in PyTorch version,
								# an effort was done to return `attention_probs` (yet to be verified).
								if name.startswith('outputs.attentions'):
									return
								else:
									super().check_pt_flax_outputs(_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase)







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        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__=8_2     ,			lowerCamelCase__=1_6      )  ->					List[Any]:
	set_seed(4_2      )
	__lowerCamelCase    :    Tuple 	=			RegressionModel()
	__lowerCamelCase    :    str 	=			deepcopy(lowerCamelCase__      )
	__lowerCamelCase    :    Optional[int] 	=			RegressionDataset(length=lowerCamelCase__      )
	__lowerCamelCase    :    Union[str, Any] 	=			DataLoader(lowerCamelCase__     ,			batch_size=lowerCamelCase__      )
	model.to(accelerator.device      )
	__lowerCamelCase	,			__lowerCamelCase    :    Tuple 	=			accelerator.prepare(lowerCamelCase__     ,			lowerCamelCase__      )
	return model, ddp_model, dataloader






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__=False      )  ->					List[Any]:
	__lowerCamelCase    :    Tuple 	=			AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased'      )
	__lowerCamelCase    :    Any 	=			load_dataset('glue'     ,			'mrpc'     ,			split='validation'      )

	def tokenize_function(lowerCamelCase__      ):
		__lowerCamelCase    :    Union[str, Any] 	=			tokenizer(examples['sentence1']     ,			examples['sentence2']     ,			truncation=lowerCamelCase__     ,			max_length=lowerCamelCase__      )
		return outputs

	with accelerator.main_process_first():
		__lowerCamelCase    :    Union[str, Any] 	=			dataset.map(
		    lowerCamelCase__     ,			batched=lowerCamelCase__     ,			remove_columns=['idx', 'sentence1', 'sentence2']     ,			)

	__lowerCamelCase    :    Tuple 	=			tokenized_datasets.rename_column('label'     ,			'labels'      )

	def collate_fn(lowerCamelCase__      ):
		if use_longest:
			return tokenizer.pad(lowerCamelCase__     ,			padding='longest'     ,			return_tensors='pt'      )
		return tokenizer.pad(lowerCamelCase__     ,			padding='max_length'     ,			max_length=1_2_8     ,			return_tensors='pt'      )

	return DataLoader(lowerCamelCase__     ,			shuffle=lowerCamelCase__     ,			collate_fn=lowerCamelCase__     ,			batch_size=1_6      )






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






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					List[Any]:
	__lowerCamelCase    :    str 	=			[]
	for batch in dataloader:
		__lowerCamelCase	,			__lowerCamelCase    :    Union[str, Any] 	=			batch.values()
		with torch.no_grad():
			__lowerCamelCase    :    Tuple 	=			model(lowerCamelCase__      )
			__lowerCamelCase	,			__lowerCamelCase    :    Optional[Any] 	=			accelerator.gather_for_metrics((logit, target)      )
			logits_and_targets.append((logit, target)      )
	__lowerCamelCase	,			__lowerCamelCase    :    Dict 	=			[], []
	for logit, targ in logits_and_targets:
		logits.append(lowerCamelCase__      )
		targs.append(lowerCamelCase__      )
	__lowerCamelCase	,			__lowerCamelCase    :    Union[str, Any] 	=			torch.cat(lowerCamelCase__      ), torch.cat(lowerCamelCase__      )
	return logits, targs






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__=8_2     ,			lowerCamelCase__=False     ,			lowerCamelCase__=False     ,			lowerCamelCase__=1_6      )  ->					Dict:
	__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase    :    Optional[Any] 	=			get_basic_setup(lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )
	__lowerCamelCase	,			__lowerCamelCase    :    Dict 	=			generate_predictions(lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )
	assert (
	    len(lowerCamelCase__      ) == num_samples
	), F"Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(lowerCamelCase__      )}"






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

	# Then do distributed
	__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase    :    Union[str, Any] 	=			setup['ddp']
	model.eval()
	for batch in dataloader:
		with torch.inference_mode():
			__lowerCamelCase    :    List[str] 	=			model(**lowerCamelCase__      )
		__lowerCamelCase    :    List[Any] 	=			outputs.logits.argmax(dim=-1      )
		__lowerCamelCase    :    List[str] 	=			batch['labels']
		__lowerCamelCase	,			__lowerCamelCase    :    Union[str, Any] 	=			accelerator.gather_for_metrics((preds, references)      )
		metric.add_batch(predictions=lowerCamelCase__     ,			references=lowerCamelCase__      )
	__lowerCamelCase    :    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        SCREAMING_SNAKE_CASE__	(  )  ->					List[str]:
	__lowerCamelCase    :    int 	=			Accelerator(split_batches=lowerCamelCase__     ,			dispatch_batches=lowerCamelCase__      )
	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(lowerCamelCase__     ,			lowerCamelCase__      )
				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]:
			__lowerCamelCase    :    Optional[Any] 	=			Accelerator(split_batches=lowerCamelCase__     ,			dispatch_batches=lowerCamelCase__      )
			if accelerator.is_local_main_process:
				print(F"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"      )
			test_torch_metrics(lowerCamelCase__     ,			9_9      )
			accelerator.state._reset_state()
	if accelerator.is_local_main_process:
		print('**Test last batch is not dropped when perfectly divisible**'      )
	__lowerCamelCase    :    Dict 	=			Accelerator()
	test_torch_metrics(lowerCamelCase__     ,			5_1_2      )
	accelerator.state._reset_state()






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Optional[Any]:
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
					main()




