






import unittest

from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_torch_available():
							import torch

							from transformers import (
							    MraForMaskedLM,
							    MraForMultipleChoice,
							    MraForQuestionAnswering,
							    MraForSequenceClassification,
							    MraForTokenClassification,
							    MraModel,
							)
							from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST
class    _UpperCAmelCase      :

				"""simple docstring"""




				def __init__(  self		:					Union[str, Any],						lowerCamelCase		:					Optional[int],						lowerCamelCase		:					Union[str, Any]=2,						lowerCamelCase		:					str=8,						lowerCamelCase		:					int=True,						lowerCamelCase		:					List[str]=True,						lowerCamelCase		:					List[Any]=True,						lowerCamelCase		:					str=True,						lowerCamelCase		:					int=99,						lowerCamelCase		:					str=16,						lowerCamelCase		:					int=5,						lowerCamelCase		:					Dict=2,						lowerCamelCase		:					str=36,						lowerCamelCase		:					int="gelu",						lowerCamelCase		:					Any=0.0,						lowerCamelCase		:					Union[str, Any]=0.0,						lowerCamelCase		:					List[str]=512,						lowerCamelCase		:					Union[str, Any]=16,						lowerCamelCase		:					List[str]=2,						lowerCamelCase		:					Any=0.02,						lowerCamelCase		:					Tuple=3,						lowerCamelCase		:					List[str]=4,						lowerCamelCase		:					List[Any]=None,						):

								'''simple docstring'''
								lowercase__     				=    parent
								lowercase__     				=    batch_size
								lowercase__     				=    seq_length
								lowercase__     				=    is_training
								lowercase__     				=    use_input_mask
								lowercase__     				=    use_token_type_ids
								lowercase__     				=    use_labels
								lowercase__     				=    vocab_size
								lowercase__     				=    hidden_size
								lowercase__     				=    num_hidden_layers
								lowercase__     				=    num_attention_heads
								lowercase__     				=    intermediate_size
								lowercase__     				=    hidden_act
								lowercase__     				=    hidden_dropout_prob
								lowercase__     				=    attention_probs_dropout_prob
								lowercase__     				=    max_position_embeddings
								lowercase__     				=    type_vocab_size
								lowercase__     				=    type_sequence_label_size
								lowercase__     				=    initializer_range
								lowercase__     				=    num_labels
								lowercase__     				=    num_choices
								lowercase__     				=    scope




				def        lowercase__     (  self		:					Optional[Any]						):

								'''simple docstring'''
								lowercase__     				=    ids_tensor([self.batch_size, self.seq_length],						self.vocab_size						)

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

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

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

								lowercase__     				=    self.get_config()

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




				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								return MraConfig(
								    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,						)




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								lowercase__     				=    self.get_config()
								lowercase__     				=    300
								return config




				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								(
								    (
								    lowercase__       
								)				,		(
								    lowercase__       
								)				,		(
								    lowercase__       
								)				,		(
								    lowercase__       
								)				,		(
								    lowercase__       
								)				,		(
								    lowercase__       
								)				,		(
								    lowercase__       
								)				,		       
								)     				=    self.prepare_config_and_inputs()

								lowercase__     				=    True
								lowercase__     				=    floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
								lowercase__     				=    ids_tensor([self.batch_size, self.seq_length],						vocab_size=2						)

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




				def        lowercase__     (  self		:					str,						lowerCamelCase		:					int,						lowerCamelCase		:					Optional[Any],						lowerCamelCase		:					Dict,						lowerCamelCase		:					Optional[Any],						lowerCamelCase		:					Union[str, Any],						lowerCamelCase		:					Optional[int],						lowerCamelCase		:					Union[str, Any]						):

								'''simple docstring'''
								lowercase__     				=    MraModel(config=__UpperCamelCase						)
								model.to(__UpperCamelCase						)
								model.eval()
								lowercase__     				=    model(__UpperCamelCase,						attention_mask=__UpperCamelCase,						token_type_ids=__UpperCamelCase						)
								lowercase__     				=    model(__UpperCamelCase,						token_type_ids=__UpperCamelCase						)
								lowercase__     				=    model(__UpperCamelCase						)
								self.parent.assertEqual(result.last_hidden_state.shape,						(self.batch_size, self.seq_length, self.hidden_size)						)




				def        lowercase__     (  self		:					Optional[Any],						lowerCamelCase		:					Any,						lowerCamelCase		:					List[str],						lowerCamelCase		:					Tuple,						lowerCamelCase		:					List[Any],						lowerCamelCase		:					List[Any],						lowerCamelCase		:					str,						lowerCamelCase		:					List[str],						lowerCamelCase		:					List[Any],						lowerCamelCase		:					Any,						):

								'''simple docstring'''
								lowercase__     				=    True
								lowercase__     				=    MraModel(__UpperCamelCase						)
								model.to(__UpperCamelCase						)
								model.eval()
								lowercase__     				=    model(
								    __UpperCamelCase,						attention_mask=__UpperCamelCase,						token_type_ids=__UpperCamelCase,						encoder_hidden_states=__UpperCamelCase,						encoder_attention_mask=__UpperCamelCase,						)
								lowercase__     				=    model(
								    __UpperCamelCase,						attention_mask=__UpperCamelCase,						token_type_ids=__UpperCamelCase,						encoder_hidden_states=__UpperCamelCase,						)
								lowercase__     				=    model(__UpperCamelCase,						attention_mask=__UpperCamelCase,						token_type_ids=__UpperCamelCase						)
								self.parent.assertEqual(result.last_hidden_state.shape,						(self.batch_size, self.seq_length, self.hidden_size)						)




				def        lowercase__     (  self		:					str,						lowerCamelCase		:					Tuple,						lowerCamelCase		:					Any,						lowerCamelCase		:					List[str],						lowerCamelCase		:					Union[str, Any],						lowerCamelCase		:					Optional[int],						lowerCamelCase		:					Union[str, Any],						lowerCamelCase		:					str						):

								'''simple docstring'''
								lowercase__     				=    MraForMaskedLM(config=__UpperCamelCase						)
								model.to(__UpperCamelCase						)
								model.eval()
								lowercase__     				=    model(__UpperCamelCase,						attention_mask=__UpperCamelCase,						token_type_ids=__UpperCamelCase,						labels=__UpperCamelCase						)
								self.parent.assertEqual(result.logits.shape,						(self.batch_size, self.seq_length, self.vocab_size)						)




				def        lowercase__     (  self		:					int,						lowerCamelCase		:					List[str],						lowerCamelCase		:					int,						lowerCamelCase		:					str,						lowerCamelCase		:					List[str],						lowerCamelCase		:					List[str],						lowerCamelCase		:					str,						lowerCamelCase		:					str						):

								'''simple docstring'''
								lowercase__     				=    MraForQuestionAnswering(config=__UpperCamelCase						)
								model.to(__UpperCamelCase						)
								model.eval()
								lowercase__     				=    model(
								    __UpperCamelCase,						attention_mask=__UpperCamelCase,						token_type_ids=__UpperCamelCase,						start_positions=__UpperCamelCase,						end_positions=__UpperCamelCase,						)
								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        lowercase__     (  self		:					List[Any],						lowerCamelCase		:					Tuple,						lowerCamelCase		:					Any,						lowerCamelCase		:					Union[str, Any],						lowerCamelCase		:					Any,						lowerCamelCase		:					Optional[int],						lowerCamelCase		:					Union[str, Any],						lowerCamelCase		:					Optional[int]						):

								'''simple docstring'''
								lowercase__     				=    self.num_labels
								lowercase__     				=    MraForSequenceClassification(__UpperCamelCase						)
								model.to(__UpperCamelCase						)
								model.eval()
								lowercase__     				=    model(__UpperCamelCase,						attention_mask=__UpperCamelCase,						token_type_ids=__UpperCamelCase,						labels=__UpperCamelCase						)
								self.parent.assertEqual(result.logits.shape,						(self.batch_size, self.num_labels)						)




				def        lowercase__     (  self		:					List[Any],						lowerCamelCase		:					List[Any],						lowerCamelCase		:					int,						lowerCamelCase		:					Tuple,						lowerCamelCase		:					Optional[Any],						lowerCamelCase		:					List[str],						lowerCamelCase		:					Tuple,						lowerCamelCase		:					List[str]						):

								'''simple docstring'''
								lowercase__     				=    self.num_labels
								lowercase__     				=    MraForTokenClassification(config=__UpperCamelCase						)
								model.to(__UpperCamelCase						)
								model.eval()
								lowercase__     				=    model(__UpperCamelCase,						attention_mask=__UpperCamelCase,						token_type_ids=__UpperCamelCase,						labels=__UpperCamelCase						)
								self.parent.assertEqual(result.logits.shape,						(self.batch_size, self.seq_length, self.num_labels)						)




				def        lowercase__     (  self		:					Optional[Any],						lowerCamelCase		:					Union[str, Any],						lowerCamelCase		:					Union[str, Any],						lowerCamelCase		:					Any,						lowerCamelCase		:					str,						lowerCamelCase		:					str,						lowerCamelCase		:					Optional[int],						lowerCamelCase		:					Tuple						):

								'''simple docstring'''
								lowercase__     				=    self.num_choices
								lowercase__     				=    MraForMultipleChoice(config=__UpperCamelCase						)
								model.to(__UpperCamelCase						)
								model.eval()
								lowercase__     				=    input_ids.unsqueeze(1						).expand(-1,						self.num_choices,						-1						).contiguous()
								lowercase__     				=    token_type_ids.unsqueeze(1						).expand(-1,						self.num_choices,						-1						).contiguous()
								lowercase__     				=    input_mask.unsqueeze(1						).expand(-1,						self.num_choices,						-1						).contiguous()
								lowercase__     				=    model(
								    __UpperCamelCase,						attention_mask=__UpperCamelCase,						token_type_ids=__UpperCamelCase,						labels=__UpperCamelCase,						)
								self.parent.assertEqual(result.logits.shape,						(self.batch_size, self.num_choices)						)




				def        lowercase__     (  self		:					str						):

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



@require_torch
class    _UpperCAmelCase      ( __snake_case	,unittest.TestCase			):

				"""simple docstring"""




				lowercase__       							= (
				    (
				        MraModel,
				        MraForMaskedLM,
				        MraForMultipleChoice,
				        MraForQuestionAnswering,
				        MraForSequenceClassification,
				        MraForTokenClassification,
				    )
				    if is_torch_available()
				    else ()
				)
				lowercase__       							= False
				lowercase__       							= False
				lowercase__       							= False
				lowercase__       							= False

				lowercase__       							= ()




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								lowercase__     				=    MraModelTester(self						)
								lowercase__     				=    ConfigTester(self,						config_class=__UpperCamelCase,						hidden_size=37						)




				def        lowercase__     (  self		:					Any						):

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




				def        lowercase__     (  self		:					Dict						):

								'''simple docstring'''
								lowercase__     				=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__UpperCamelCase						)




				def        lowercase__     (  self		:					List[Any]						):

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




				def        lowercase__     (  self		:					Tuple						):

								'''simple docstring'''
								lowercase__     				=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_lm(*__UpperCamelCase						)




				def        lowercase__     (  self		:					List[Any]						):

								'''simple docstring'''
								lowercase__     				=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_multiple_choice(*__UpperCamelCase						)




				def        lowercase__     (  self		:					Optional[Any]						):

								'''simple docstring'''
								lowercase__     				=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*__UpperCamelCase						)




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								lowercase__     				=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*__UpperCamelCase						)




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								lowercase__     				=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*__UpperCamelCase						)




				@slow
				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''


								for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												lowercase__     				=    MraModel.from_pretrained(__UpperCamelCase						)
												self.assertIsNotNone(__UpperCamelCase						)




				@unittest.skip(reason='''MRA does not output attentions'''						)
				def        lowercase__     (  self		:					Union[str, Any]						):

								'''simple docstring'''
								return



@require_torch
class    _UpperCAmelCase      ( unittest.TestCase			):

				"""simple docstring"""




				@slow
				def        lowercase__     (  self		:					int						):

								'''simple docstring'''
								lowercase__     				=    MraModel.from_pretrained('''uw-madison/mra-base-512-4'''						)
								lowercase__     				=    torch.arange(256						).unsqueeze(0						)

								with torch.no_grad():
												lowercase__     				=    model(__UpperCamelCase						)[0]

								lowercase__     				=    torch.Size((1, 256, 768)						)
								self.assertEqual(output.shape,						__UpperCamelCase						)

								lowercase__     				=    torch.tensor(
								    [[[-0.0140, 0.0830, -0.0381], [0.1546, 0.1402, 0.0220], [0.1162, 0.0851, 0.0165]]]						)

								self.assertTrue(torch.allclose(output[:, :3, :3],						__UpperCamelCase,						atol=1E-4						)						)




				@slow
				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								lowercase__     				=    MraForMaskedLM.from_pretrained('''uw-madison/mra-base-512-4'''						)
								lowercase__     				=    torch.arange(256						).unsqueeze(0						)

								with torch.no_grad():
												lowercase__     				=    model(__UpperCamelCase						)[0]

								lowercase__     				=    50_265

								lowercase__     				=    torch.Size((1, 256, vocab_size)						)
								self.assertEqual(output.shape,						__UpperCamelCase						)

								lowercase__     				=    torch.tensor(
								    [[[9.2595, -3.6038, 11.8819], [9.3869, -3.2693, 11.0956], [11.8524, -3.4938, 13.1210]]]						)

								self.assertTrue(torch.allclose(output[:, :3, :3],						__UpperCamelCase,						atol=1E-4						)						)




				@slow
				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								lowercase__     				=    MraForMaskedLM.from_pretrained('''uw-madison/mra-base-4096-8-d3'''						)
								lowercase__     				=    torch.arange(4_096						).unsqueeze(0						)

								with torch.no_grad():
												lowercase__     				=    model(__UpperCamelCase						)[0]

								lowercase__     				=    50_265

								lowercase__     				=    torch.Size((1, 4_096, vocab_size)						)
								self.assertEqual(output.shape,						__UpperCamelCase						)

								lowercase__     				=    torch.tensor(
								    [[[5.4789, -2.3564, 7.5064], [7.9067, -1.3369, 9.9668], [9.0712, -1.8106, 7.0380]]]						)

								self.assertTrue(torch.allclose(output[:, :3, :3],						__UpperCamelCase,						atol=1E-4						)						)












import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


A__			:    Dict         =				50_00_00

A__     ,     A__			:    str         =				os.path.split(__file__)
A__			:    Optional[Any]         =				os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))






@get_duration
def 						a    (		lowerCamelCase_	,     **lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    dataset.map(**lowerCamelCase_       )






@get_duration
def 						a    (		lowerCamelCase_	,     **lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    dataset.filter(**lowerCamelCase_       )






def 						a    (		):






				'''simple docstring'''
				lowercase__     				=    {'''num examples''': SPEED_TEST_N_EXAMPLES}
				with tempfile.TemporaryDirectory() as tmp_dir:
								lowercase__     				=    datasets.Features({'''text''': datasets.Value('''string'''       ), '''numbers''': datasets.Value('''float32'''       )}       )
								lowercase__     				=    generate_example_dataset(
								    os.path.join(lowerCamelCase_	,     '''dataset.arrow'''       )	,     lowerCamelCase_	,     num_examples=lowerCamelCase_       )

								lowercase__     				=    transformers.AutoTokenizer.from_pretrained('''bert-base-cased'''	,     use_fast=lowerCamelCase_       )

								def tokenize(lowerCamelCase_       ):
												return tokenizer(examples['''text''']       )

								lowercase__     				=    map(lowerCamelCase_       )

								lowercase__     				=    map(lowerCamelCase_	,     batched=lowerCamelCase_       )

								lowercase__     				=    map(lowerCamelCase_	,     function=lambda lowerCamelCase_       : None	,     batched=lowerCamelCase_       )

								with dataset.formatted_as(type='''numpy'''       ):
												lowercase__     				=    map(lowerCamelCase_	,     function=lambda lowerCamelCase_       : None	,     batched=lowerCamelCase_       )

								with dataset.formatted_as(type='''pandas'''       ):
												lowercase__     				=    map(lowerCamelCase_	,     function=lambda lowerCamelCase_       : None	,     batched=lowerCamelCase_       )

								with dataset.formatted_as(type='''torch'''	,     columns='''numbers'''       ):
												lowercase__     				=    map(lowerCamelCase_	,     function=lambda lowerCamelCase_       : None	,     batched=lowerCamelCase_       )

								with dataset.formatted_as(type='''tensorflow'''	,     columns='''numbers'''       ):
												lowercase__     				=    map(lowerCamelCase_	,     function=lambda lowerCamelCase_       : None	,     batched=lowerCamelCase_       )

								lowercase__     				=    map(lowerCamelCase_	,     function=lowerCamelCase_	,     batched=lowerCamelCase_       )

								lowercase__     				=    filter(lowerCamelCase_       )

								# Activate later when tokenizer support batched inputs
								# with dataset.formatted_as(type='numpy'):
								#     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

				with open(lowerCamelCase_	,     '''wb'''       ) as f:
								f.write(json.dumps(lowerCamelCase_       ).encode('''utf-8'''       )       )


if __name__ == "__main__":  # useful to run the profiler
							benchmark_map_filter()




