


import argparse
import logging
import os
import sys

import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers

import transformers
from transformers import BartForConditionalGeneration, BartTokenizer


logging.basicConfig(
    format="%(asctime)s | %(levelname)s | %(name)s |  [%(filename)s:%(lineno)d] %(message)s",
    datefmt="%Y-%m-%d %H:%M:%S",
    level=os.environ.get("LOGLEVEL", "INFO").upper(),
    stream=sys.stdout,
)

SCREAMING_SNAKE_CASE :   int							      =				logging.getLogger(__name__)

SCREAMING_SNAKE_CASE :   Tuple							      =				{"facebook/bart-base": BartForConditionalGeneration}
SCREAMING_SNAKE_CASE :   Optional[int]							      =				{"facebook/bart-base": BartTokenizer}



def        UpperCamelCase_(   )					->		List[str]:
				_lowercase :							Optional[Any]   	=	argparse.ArgumentParser(description='Export Bart model + Beam Search to ONNX graph.'				)
				parser.add_argument(
				    '--validation_file'							,				type=lowerCamelCase_							,				default=lowerCamelCase_							,				help='A csv or a json file containing the validation data.'				)
				parser.add_argument(
				    '--max_length'							,				type=lowerCamelCase_							,				default=5							,				help='The maximum total input sequence length after tokenization.'							,				)
				parser.add_argument(
				    '--num_beams'							,				type=lowerCamelCase_							,				default=lowerCamelCase_							,				help=(
				        'Number of beams to use for evaluation. This argument will be '
				        'passed to ``model.generate``, which is used during ``evaluate`` and ``predict``.'
				    )							,				)
				parser.add_argument(
				    '--model_name_or_path'							,				type=lowerCamelCase_							,				help='Path to pretrained model or model identifier from huggingface.co/models.'							,				required=lowerCamelCase_							,				)
				parser.add_argument(
				    '--config_name'							,				type=lowerCamelCase_							,				default=lowerCamelCase_							,				help='Pretrained config name or path if not the same as model_name'							,				)
				parser.add_argument(
				    '--device'							,				type=lowerCamelCase_							,				default='cpu'							,				help='Device where the model will be run'							,				)
				parser.add_argument('--output_file_path'							,				type=lowerCamelCase_							,				default=lowerCamelCase_							,				help='Where to store the final ONNX file.'				)

				_lowercase :							int   	=	parser.parse_args()

				return args



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_="cpu"				)					->		Optional[int]:
				_lowercase :							str   	=	model_dict[model_name].from_pretrained(lowerCamelCase_				).to(lowerCamelCase_				)
				_lowercase :							Union[str, Any]   	=	tokenizer_dict[model_name].from_pretrained(lowerCamelCase_				)

				if model_name in ["facebook/bart-base"]:
								_lowercase :							Optional[Any]   	=	0
								_lowercase :							Tuple   	=	None
								_lowercase :							Dict   	=	0

				return huggingface_model, tokenizer



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_				)					->		List[Any]:
				model.eval()

				_lowercase :							str   	=	None
				_lowercase :							Union[str, Any]   	=	torch.jit.script(BARTBeamSearchGenerator(lowerCamelCase_				)				)

				with torch.no_grad():
								_lowercase :							List[Any]   	=	'My friends are cool but they eat too many carbs.'
								_lowercase :							List[str]   	=	tokenizer([ARTICLE_TO_SUMMARIZE]							,				max_length=1024							,				return_tensors='pt'				).to(model.device				)

								_lowercase :							List[Any]   	=	model.generate(
								    inputs['input_ids']							,				attention_mask=inputs['attention_mask']							,				num_beams=lowerCamelCase_							,				max_length=lowerCamelCase_							,				early_stopping=lowerCamelCase_							,				decoder_start_token_id=model.config.decoder_start_token_id							,				)

								torch.onnx.export(
								    lowerCamelCase_							,				(
								        inputs['input_ids'],
								        inputs['attention_mask'],
								        num_beams,
								        max_length,
								        model.config.decoder_start_token_id,
								    )							,				lowerCamelCase_							,				opset_version=14							,				input_names=['input_ids', 'attention_mask', 'num_beams', 'max_length', 'decoder_start_token_id']							,				output_names=['output_ids']							,				dynamic_axes={
								        'input_ids': {0: 'batch', 1: 'seq'},
								        'output_ids': {0: 'batch', 1: 'seq_out'},
								    }							,				example_outputs=lowerCamelCase_							,				)

								logger.info('Model exported to {}'.format(lowerCamelCase_				)				)

								_lowercase :							Optional[int]   	=	remove_dup_initializers(os.path.abspath(lowerCamelCase_				)				)

								logger.info('Deduplicated and optimized model written to {}'.format(lowerCamelCase_				)				)

								_lowercase :							List[str]   	=	onnxruntime.InferenceSession(lowerCamelCase_				)
								_lowercase :							Any   	=	ort_sess.run(
								    lowerCamelCase_							,				{
								        'input_ids': inputs['input_ids'].cpu().numpy(),
								        'attention_mask': inputs['attention_mask'].cpu().numpy(),
								        'num_beams': np.array(lowerCamelCase_				),
								        'max_length': np.array(lowerCamelCase_				),
								        'decoder_start_token_id': np.array(model.config.decoder_start_token_id				),
								    }							,				)

								np.testing.assert_allclose(summary_ids.cpu().numpy()							,				ort_out[0]							,				rtol=1e-3							,				atol=1e-3				)

								logger.info('Model outputs from torch and ONNX Runtime are similar.'				)
								logger.info('Success.'				)



def        UpperCamelCase_(   )					->		Optional[int]:
				_lowercase :							List[str]   	=	parse_args()
				_lowercase :							Tuple   	=	5
				_lowercase :							Optional[int]   	=	4

				# Make one log on every process with the configuration for debugging.
				logging.basicConfig(
				    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'							,				datefmt='%m/%d/%Y %H:%M:%S'							,				level=logging.INFO							,				)

				logger.setLevel(logging.INFO				)
				transformers.utils.logging.set_verbosity_error()

				_lowercase :							List[Any]   	=	torch.device(args.device				)

				_lowercase :							Union[str, Any]   	=	load_model_tokenizer(args.model_name_or_path							,				lowerCamelCase_				)

				if model.config.decoder_start_token_id is None:
								raise ValueError('Make sure that `config.decoder_start_token_id` is correctly defined'				)

				model.to(lowerCamelCase_				)

				if args.max_length:
								_lowercase :							Tuple   	=	args.max_length

				if args.num_beams:
								_lowercase :							str   	=	args.num_beams

				if args.output_file_path:
								_lowercase :							List[Any]   	=	args.output_file_path
				else:
								_lowercase :							int   	=	'BART.onnx'

				logger.info('Exporting model to ONNX'				)
				export_and_validate_model(lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_				)


if __name__ == "__main__":
	main()






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 _lowerCamelCase:
       def __init__(      self,   lowerCamelCase,   lowerCamelCase=2,   lowerCamelCase=8,   lowerCamelCase=True,   lowerCamelCase=True,   lowerCamelCase=True,   lowerCamelCase=True,   lowerCamelCase=99,   lowerCamelCase=16,   lowerCamelCase=5,   lowerCamelCase=2,   lowerCamelCase=36,   lowerCamelCase="gelu",   lowerCamelCase=0.0,   lowerCamelCase=0.0,   lowerCamelCase=5_12,   lowerCamelCase=16,   lowerCamelCase=2,   lowerCamelCase=0.0_2,   lowerCamelCase=3,   lowerCamelCase=4,   lowerCamelCase=None,   ) ->     Dict:




           """simple docstring"""

           _lowercase :							Union[str, Any]   	=	parent
           _lowercase :							List[str]   	=	batch_size
           _lowercase :							Dict   	=	seq_length
           _lowercase :							List[str]   	=	is_training
           _lowercase :							Any   	=	use_input_mask
           _lowercase :							Any   	=	use_token_type_ids
           _lowercase :							Optional[int]   	=	use_labels
           _lowercase :							str   	=	vocab_size
           _lowercase :							str   	=	hidden_size
           _lowercase :							str   	=	num_hidden_layers
           _lowercase :							List[Any]   	=	num_attention_heads
           _lowercase :							Union[str, Any]   	=	intermediate_size
           _lowercase :							Tuple   	=	hidden_act
           _lowercase :							Optional[int]   	=	hidden_dropout_prob
           _lowercase :							Optional[Any]   	=	attention_probs_dropout_prob
           _lowercase :							int   	=	max_position_embeddings
           _lowercase :							str   	=	type_vocab_size
           _lowercase :							Tuple   	=	type_sequence_label_size
           _lowercase :							Optional[Any]   	=	initializer_range
           _lowercase :							Dict   	=	num_labels
           _lowercase :							Dict   	=	num_choices
           _lowercase :							int   	=	scope
       def     UpperCamelCase  (      self) ->     Tuple:




           """simple docstring"""

           _lowercase :							str   	=	ids_tensor([self.batch_size, self.seq_length],   self.vocab_size)

           _lowercase :							List[Any]   	=	None
           if self.use_input_mask:
               _lowercase :							List[str]   	=	random_attention_mask([self.batch_size, self.seq_length])

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

           _lowercase :							Optional[Any]   	=	None
           _lowercase :							Optional[int]   	=	None
           _lowercase :							Dict   	=	None
           if self.use_labels:
               _lowercase :							Tuple   	=	ids_tensor([self.batch_size],   self.type_sequence_label_size)
               _lowercase :							Tuple   	=	ids_tensor([self.batch_size, self.seq_length],   self.num_labels)
               _lowercase :							Tuple   	=	ids_tensor([self.batch_size],   self.num_choices)

           _lowercase :							Optional[int]   	=	self.get_config()

           return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
       def     UpperCamelCase  (      self) ->     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=lowerCamelCase,   initializer_range=self.initializer_range,   )
       def     UpperCamelCase  (      self) ->     Union[str, Any]:




           """simple docstring"""

           _lowercase :							Any   	=	self.get_config()
           _lowercase :							List[str]   	=	3_00
           return config
       def     UpperCamelCase  (      self) ->     int:




           """simple docstring"""

           (
               (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       
           ) :							List[str]   	=	self.prepare_config_and_inputs()

           _lowercase :							Tuple   	=	True
           _lowercase :							Any   	=	floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
           _lowercase :							Union[str, Any]   	=	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     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     Optional[int]:




           """simple docstring"""

           _lowercase :							Dict   	=	MraModel(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase :							Dict   	=	model(lowerCamelCase,   attention_mask=lowerCamelCase,   token_type_ids=lowerCamelCase)
           _lowercase :							Optional[Any]   	=	model(lowerCamelCase,   token_type_ids=lowerCamelCase)
           _lowercase :							Tuple   	=	model(lowerCamelCase)
           self.parent.assertEqual(result.last_hidden_state.shape,   (self.batch_size, self.seq_length, self.hidden_size))
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   ) ->     Optional[Any]:




           """simple docstring"""

           _lowercase :							int   	=	True
           _lowercase :							List[Any]   	=	MraModel(lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase :							Tuple   	=	model(
               lowerCamelCase,   attention_mask=lowerCamelCase,   token_type_ids=lowerCamelCase,   encoder_hidden_states=lowerCamelCase,   encoder_attention_mask=lowerCamelCase,   )
           _lowercase :							Union[str, Any]   	=	model(
               lowerCamelCase,   attention_mask=lowerCamelCase,   token_type_ids=lowerCamelCase,   encoder_hidden_states=lowerCamelCase,   )
           _lowercase :							Any   	=	model(lowerCamelCase,   attention_mask=lowerCamelCase,   token_type_ids=lowerCamelCase)
           self.parent.assertEqual(result.last_hidden_state.shape,   (self.batch_size, self.seq_length, self.hidden_size))
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     List[str]:




           """simple docstring"""

           _lowercase :							Optional[Any]   	=	MraForMaskedLM(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase :							int   	=	model(lowerCamelCase,   attention_mask=lowerCamelCase,   token_type_ids=lowerCamelCase,   labels=lowerCamelCase)
           self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.seq_length, self.vocab_size))
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     Any:




           """simple docstring"""

           _lowercase :							Optional[Any]   	=	MraForQuestionAnswering(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase :							Union[str, Any]   	=	model(
               lowerCamelCase,   attention_mask=lowerCamelCase,   token_type_ids=lowerCamelCase,   start_positions=lowerCamelCase,   end_positions=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     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     str:




           """simple docstring"""

           _lowercase :							str   	=	self.num_labels
           _lowercase :							List[Any]   	=	MraForSequenceClassification(lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase :							Optional[int]   	=	model(lowerCamelCase,   attention_mask=lowerCamelCase,   token_type_ids=lowerCamelCase,   labels=lowerCamelCase)
           self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.num_labels))
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     str:




           """simple docstring"""

           _lowercase :							Optional[Any]   	=	self.num_labels
           _lowercase :							str   	=	MraForTokenClassification(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase :							Optional[int]   	=	model(lowerCamelCase,   attention_mask=lowerCamelCase,   token_type_ids=lowerCamelCase,   labels=lowerCamelCase)
           self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.seq_length, self.num_labels))
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     int:




           """simple docstring"""

           _lowercase :							Any   	=	self.num_choices
           _lowercase :							Optional[Any]   	=	MraForMultipleChoice(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase :							List[Any]   	=	input_ids.unsqueeze(1).expand(-1,   self.num_choices,   -1).contiguous()
           _lowercase :							str   	=	token_type_ids.unsqueeze(1).expand(-1,   self.num_choices,   -1).contiguous()
           _lowercase :							Dict   	=	input_mask.unsqueeze(1).expand(-1,   self.num_choices,   -1).contiguous()
           _lowercase :							Dict   	=	model(
               lowerCamelCase,   attention_mask=lowerCamelCase,   token_type_ids=lowerCamelCase,   labels=lowerCamelCase,   )
           self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.num_choices))




       def     UpperCamelCase  (      self) ->     Dict:




           """simple docstring"""

           _lowercase :							List[Any]   	=	self.prepare_config_and_inputs()
           (
               (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       
           ) :							Tuple   	=	config_and_inputs
           _lowercase :							Optional[Any]   	=	{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
           return config, inputs_dict






@require_torch
class _lowerCamelCase(		_a,				unittest.TestCase     ):
       lowercase_				:			Optional[int]									=    (
           (
               MraModel,
               MraForMaskedLM,
               MraForMultipleChoice,
               MraForQuestionAnswering,
               MraForSequenceClassification,
               MraForTokenClassification,
           )
           if is_torch_available()
           else ()
       )
       lowercase_				:			Optional[int]									=    False
       lowercase_				:			Optional[int]									=    False
       lowercase_				:			int									=    False
       lowercase_				:			Optional[Any]									=    False

       lowercase_				:			Any									=    ()
       def     UpperCamelCase  (      self) ->     Optional[Any]:




           """simple docstring"""

           _lowercase :							List[str]   	=	MraModelTester(self)
           _lowercase :							Any   	=	ConfigTester(self,   config_class=lowerCamelCase,   hidden_size=37)
       def     UpperCamelCase  (      self) ->     int:




           """simple docstring"""

           self.config_tester.run_common_tests()
       def     UpperCamelCase  (      self) ->     int:




           """simple docstring"""

           _lowercase :							Optional[Any]   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowerCamelCase)
       def     UpperCamelCase  (      self) ->     List[Any]:




           """simple docstring"""

           _lowercase :							Tuple   	=	self.model_tester.prepare_config_and_inputs()
           for type in ["absolute", "relative_key", "relative_key_query"]:
               _lowercase :							Optional[int]   	=	type
               self.model_tester.create_and_check_model(*lowerCamelCase)
       def     UpperCamelCase  (      self) ->     List[Any]:




           """simple docstring"""

           _lowercase :							Optional[Any]   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase)
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           _lowercase :							int   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_multiple_choice(*lowerCamelCase)
       def     UpperCamelCase  (      self) ->     Any:




           """simple docstring"""

           _lowercase :							Tuple   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(*lowerCamelCase)
       def     UpperCamelCase  (      self) ->     Tuple:




           """simple docstring"""

           _lowercase :							List[str]   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase)
       def     UpperCamelCase  (      self) ->     Tuple:




           """simple docstring"""

           _lowercase :							Optional[int]   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*lowerCamelCase)
       @slow
       def     UpperCamelCase  (      self) ->     str:




           """simple docstring"""

           for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _lowercase :							List[str]   	=	MraModel.from_pretrained(lowerCamelCase)
               self.assertIsNotNone(lowerCamelCase)




       @unittest.skip(reason='MRA does not output attentions')
       def     UpperCamelCase  (      self) ->     Tuple:




           """simple docstring"""

           return






@require_torch
class _lowerCamelCase(		unittest.TestCase     ):
       @slow
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           _lowercase :							Dict   	=	MraModel.from_pretrained('uw-madison/mra-base-512-4')
           _lowercase :							Any   	=	torch.arange(2_56).unsqueeze(0)

           with torch.no_grad():
               _lowercase :							str   	=	model(lowerCamelCase)[0]

           _lowercase :							Union[str, Any]   	=	torch.Size((1, 2_56, 7_68))
           self.assertEqual(output.shape,   lowerCamelCase)

           _lowercase :							Dict   	=	torch.tensor(
               [[[-0.0_1_4_0, 0.0_8_3_0, -0.0_3_8_1], [0.1_5_4_6, 0.1_4_0_2, 0.0_2_2_0], [0.1_1_6_2, 0.0_8_5_1, 0.0_1_6_5]]])

           self.assertTrue(torch.allclose(output[:, :3, :3],   lowerCamelCase,   atol=1E-4))
       @slow
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           _lowercase :							List[str]   	=	MraForMaskedLM.from_pretrained('uw-madison/mra-base-512-4')
           _lowercase :							Optional[int]   	=	torch.arange(2_56).unsqueeze(0)

           with torch.no_grad():
               _lowercase :							str   	=	model(lowerCamelCase)[0]

           _lowercase :							Union[str, Any]   	=	5_02_65

           _lowercase :							int   	=	torch.Size((1, 2_56, vocab_size))
           self.assertEqual(output.shape,   lowerCamelCase)

           _lowercase :							Optional[Any]   	=	torch.tensor(
               [[[9.2_5_9_5, -3.6_0_3_8, 1_1.8_8_1_9], [9.3_8_6_9, -3.2_6_9_3, 1_1.0_9_5_6], [1_1.8_5_2_4, -3.4_9_3_8, 1_3.1_2_1_0]]])

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




       @slow
       def     UpperCamelCase  (      self) ->     str:




           """simple docstring"""

           _lowercase :							Any   	=	MraForMaskedLM.from_pretrained('uw-madison/mra-base-4096-8-d3')
           _lowercase :							Optional[int]   	=	torch.arange(40_96).unsqueeze(0)

           with torch.no_grad():
               _lowercase :							str   	=	model(lowerCamelCase)[0]

           _lowercase :							Optional[Any]   	=	5_02_65

           _lowercase :							Union[str, Any]   	=	torch.Size((1, 40_96, vocab_size))
           self.assertEqual(output.shape,   lowerCamelCase)

           _lowercase :							Any   	=	torch.tensor(
               [[[5.4_7_8_9, -2.3_5_6_4, 7.5_0_6_4], [7.9_0_6_7, -1.3_3_6_9, 9.9_6_6_8], [9.0_7_1_2, -1.8_1_0_6, 7.0_3_8_0]]])

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


