
import math
import os
import unittest

from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import (
						    MODEL_FOR_PRETRAINING_MAPPING,
						    MegatronBertForCausalLM,
						    MegatronBertForMaskedLM,
						    MegatronBertForMultipleChoice,
						    MegatronBertForNextSentencePrediction,
						    MegatronBertForPreTraining,
						    MegatronBertForQuestionAnswering,
						    MegatronBertForSequenceClassification,
						    MegatronBertForTokenClassification,
						    MegatronBertModel,
						)
class 					lowercase__:



	"""simple docstring"""




	def __init__(			self    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=1_3			,				SCREAMING_SNAKE_CASE_    :     int=7			,				SCREAMING_SNAKE_CASE_    :     List[Any]=True			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=True			,				SCREAMING_SNAKE_CASE_    :     Tuple=True			,				SCREAMING_SNAKE_CASE_    :     str=True			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=9_9			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=6_4			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=3_2			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=5			,				SCREAMING_SNAKE_CASE_    :     Any=4			,				SCREAMING_SNAKE_CASE_    :     Dict=3_7			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]="gelu"			,				SCREAMING_SNAKE_CASE_    :     Dict=0.1			,				SCREAMING_SNAKE_CASE_    :     Any=0.1			,				SCREAMING_SNAKE_CASE_    :     Tuple=5_1_2			,				SCREAMING_SNAKE_CASE_    :     Any=1_6			,				SCREAMING_SNAKE_CASE_    :     str=2			,				SCREAMING_SNAKE_CASE_    :     int=0.02			,				SCREAMING_SNAKE_CASE_    :     Dict=3			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=4			,				SCREAMING_SNAKE_CASE_    :     str=None			,				)			->	List[Any]:
					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_	    =   embedding_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[int]			)			->	Optional[int]:
					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    :     Tuple			)			->	List[Any]:
					return MegatronBertConfig(
					    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			,				embedding_size=self.embedding_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=SCREAMING_SNAKE_CASE_			,				initializer_range=self.initializer_range			,				)
	def 		_lowercase    (			self    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     Any			,				SCREAMING_SNAKE_CASE_    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     str			)			->	str:
					lowercase_	    =   MegatronBertModel(config=SCREAMING_SNAKE_CASE_			)
					model.to(SCREAMING_SNAKE_CASE_			)
					model.eval()
					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			,				attention_mask=SCREAMING_SNAKE_CASE_			,				token_type_ids=SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			,				token_type_ids=SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			)

					self.parent.assertEqual(result.last_hidden_state.shape			,				(self.batch_size, self.seq_length, self.hidden_size)			)
					self.parent.assertEqual(result.pooler_output.shape			,				(self.batch_size, self.hidden_size)			)
	def 		_lowercase    (			self    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     int			)			->	Tuple:
					lowercase_	    =   MegatronBertForMaskedLM(config=SCREAMING_SNAKE_CASE_			)
					model.to(SCREAMING_SNAKE_CASE_			)
					model.eval()
					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			,				attention_mask=SCREAMING_SNAKE_CASE_			,				token_type_ids=SCREAMING_SNAKE_CASE_			,				labels=SCREAMING_SNAKE_CASE_			)
					self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.seq_length, self.vocab_size)			)
	def 		_lowercase    (			self    :     int			,				SCREAMING_SNAKE_CASE_    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     Optional[int]			)			->	Optional[int]:
					lowercase_	    =   MegatronBertForCausalLM(config=SCREAMING_SNAKE_CASE_			)
					model.to(SCREAMING_SNAKE_CASE_			)
					model.eval()
					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			,				attention_mask=SCREAMING_SNAKE_CASE_			,				token_type_ids=SCREAMING_SNAKE_CASE_			,				labels=SCREAMING_SNAKE_CASE_			)
					self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.seq_length, self.vocab_size)			)
	def 		_lowercase    (			self    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     Optional[int]			)			->	List[str]:
					lowercase_	    =   MegatronBertForNextSentencePrediction(config=SCREAMING_SNAKE_CASE_			)
					model.to(SCREAMING_SNAKE_CASE_			)
					model.eval()
					lowercase_	    =   model(
					    SCREAMING_SNAKE_CASE_			,				attention_mask=SCREAMING_SNAKE_CASE_			,				token_type_ids=SCREAMING_SNAKE_CASE_			,				labels=SCREAMING_SNAKE_CASE_			,				)
					self.parent.assertEqual(result.logits.shape			,				(self.batch_size, 2)			)
	def 		_lowercase    (			self    :     int			,				SCREAMING_SNAKE_CASE_    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]			)			->	int:
					lowercase_	    =   MegatronBertForPreTraining(config=SCREAMING_SNAKE_CASE_			)
					model.to(SCREAMING_SNAKE_CASE_			)
					model.eval()
					lowercase_	    =   model(
					    SCREAMING_SNAKE_CASE_			,				attention_mask=SCREAMING_SNAKE_CASE_			,				token_type_ids=SCREAMING_SNAKE_CASE_			,				labels=SCREAMING_SNAKE_CASE_			,				next_sentence_label=SCREAMING_SNAKE_CASE_			,				)
					self.parent.assertEqual(result.prediction_logits.shape			,				(self.batch_size, self.seq_length, self.vocab_size)			)
					self.parent.assertEqual(result.seq_relationship_logits.shape			,				(self.batch_size, 2)			)
	def 		_lowercase    (			self    :     str			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     str			)			->	Tuple:
					lowercase_	    =   MegatronBertForQuestionAnswering(config=SCREAMING_SNAKE_CASE_			)
					model.to(SCREAMING_SNAKE_CASE_			)
					model.eval()
					lowercase_	    =   model(
					    SCREAMING_SNAKE_CASE_			,				attention_mask=SCREAMING_SNAKE_CASE_			,				token_type_ids=SCREAMING_SNAKE_CASE_			,				start_positions=SCREAMING_SNAKE_CASE_			,				end_positions=SCREAMING_SNAKE_CASE_			,				)
					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    :     Dict			,				SCREAMING_SNAKE_CASE_    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     Dict			,				SCREAMING_SNAKE_CASE_    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     int			)			->	List[str]:
					lowercase_	    =   self.num_labels
					lowercase_	    =   MegatronBertForSequenceClassification(SCREAMING_SNAKE_CASE_			)
					model.to(SCREAMING_SNAKE_CASE_			)
					model.eval()
					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			,				attention_mask=SCREAMING_SNAKE_CASE_			,				token_type_ids=SCREAMING_SNAKE_CASE_			,				labels=SCREAMING_SNAKE_CASE_			)
					self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.num_labels)			)
	def 		_lowercase    (			self    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     Any			,				SCREAMING_SNAKE_CASE_    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     Any			,				SCREAMING_SNAKE_CASE_    :     Any			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]			)			->	Optional[Any]:
					lowercase_	    =   self.num_labels
					lowercase_	    =   MegatronBertForTokenClassification(config=SCREAMING_SNAKE_CASE_			)
					model.to(SCREAMING_SNAKE_CASE_			)
					model.eval()
					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			,				attention_mask=SCREAMING_SNAKE_CASE_			,				token_type_ids=SCREAMING_SNAKE_CASE_			,				labels=SCREAMING_SNAKE_CASE_			)
					self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.seq_length, self.num_labels)			)
	def 		_lowercase    (			self    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     Tuple			)			->	int:
					lowercase_	    =   self.num_choices
					lowercase_	    =   MegatronBertForMultipleChoice(config=SCREAMING_SNAKE_CASE_			)
					model.to(SCREAMING_SNAKE_CASE_			)
					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(
					    SCREAMING_SNAKE_CASE_			,				attention_mask=SCREAMING_SNAKE_CASE_			,				token_type_ids=SCREAMING_SNAKE_CASE_			,				labels=SCREAMING_SNAKE_CASE_			,				)
					self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.num_choices)			)




	def 		_lowercase    (			self    :     Any			)			->	Dict:
					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 					lowercase__(							UpperCAmelCase					,		UpperCAmelCase					,		unittest.TestCase				):



	"""simple docstring"""




	a     :str       	=      (
	    (
	        MegatronBertModel,
	        MegatronBertForMaskedLM,
	        MegatronBertForCausalLM,
	        MegatronBertForMultipleChoice,
	        MegatronBertForNextSentencePrediction,
	        MegatronBertForPreTraining,
	        MegatronBertForQuestionAnswering,
	        MegatronBertForSequenceClassification,
	        MegatronBertForTokenClassification,
	    )
	    if is_torch_available()
	    else ()
	)
	a     :Tuple       	=      (
	    {
	        'feature-extraction': MegatronBertModel,
	        'fill-mask': MegatronBertForMaskedLM,
	        'question-answering': MegatronBertForQuestionAnswering,
	        'text-classification': MegatronBertForSequenceClassification,
	        'text-generation': MegatronBertForCausalLM,
	        'token-classification': MegatronBertForTokenClassification,
	        'zero-shot': MegatronBertForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	a     :List[str]       	=      True
	# test_resize_embeddings = False
	a     :str       	=      False
	def 		_lowercase    (			self    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     List[Any]=False			)			->	int:
					lowercase_	    =   super()._prepare_for_class(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				return_labels=SCREAMING_SNAKE_CASE_			)

					if return_labels:
									if model_class in get_values(SCREAMING_SNAKE_CASE_			):
													lowercase_	    =   torch.zeros(
													    (self.model_tester.batch_size, self.model_tester.seq_length)			,				dtype=torch.long			,				device=SCREAMING_SNAKE_CASE_			)
													lowercase_	    =   torch.zeros(
													    self.model_tester.batch_size			,				dtype=torch.long			,				device=SCREAMING_SNAKE_CASE_			)
					return inputs_dict
	def 		_lowercase    (			self    :     Tuple			)			->	List[Any]:
					lowercase_	    =   MegatronBertModelTester(self			)
					lowercase_	    =   ConfigTester(self			,				config_class=SCREAMING_SNAKE_CASE_			,				hidden_size=3_7			)
	def 		_lowercase    (			self    :     int			)			->	Optional[int]:
					self.config_tester.run_common_tests()
	def 		_lowercase    (			self    :     Optional[Any]			)			->	Optional[Any]:
					lowercase_	    =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_megatron_bert_model(*SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     int			)			->	Any:
					lowercase_	    =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_megatron_bert_for_masked_lm(*SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     int			)			->	int:
					lowercase_	    =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_megatron_bert_for_multiple_choice(*SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     Optional[int]			)			->	str:
					lowercase_	    =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_megatron_bert_for_next_sequence_prediction(*SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     Dict			)			->	Optional[int]:
					lowercase_	    =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_megatron_bert_for_pretraining(*SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     Tuple			)			->	Optional[Any]:
					lowercase_	    =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_megatron_bert_for_question_answering(*SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     List[Any]			)			->	Optional[int]:
					lowercase_	    =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_megatron_bert_for_sequence_classification(*SCREAMING_SNAKE_CASE_			)




	def 		_lowercase    (			self    :     Tuple			)			->	List[Any]:
					lowercase_	    =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_megatron_bert_for_token_classification(*SCREAMING_SNAKE_CASE_			)




def 							a		(				snake_case__:   Any   ):


				'''simple docstring'''
				return torch.tensor(
				    snake_case__		,  dtype=torch.long		,  device=snake_case__		,  )


__a         =   1E-4


@require_torch
@require_sentencepiece
@require_tokenizers
class 					lowercase__(							unittest.TestCase				):



	"""simple docstring"""




	@slow
	@unittest.skip('''Model is not available.'''			)
	def 		_lowercase    (			self    :     Union[str, Any]			)			->	Dict:
					lowercase_	    =   '''nvidia/megatron-bert-uncased-345m'''
					if "MYDIR" in os.environ:
									lowercase_	    =   os.path.join(os.environ['''MYDIR''']			,				SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   MegatronBertModel.from_pretrained(SCREAMING_SNAKE_CASE_			)
					model.to(SCREAMING_SNAKE_CASE_			)
					model.half()
					lowercase_	    =   _long_tensor([[1_0_1, 7_1_1_0, 1_0_0_5, 1_0_5_6, 2_0_2_3, 1_1_3_3_3, 1_7_4_1_3, 1_0_2_9, 1_0_2]]			)
					with torch.no_grad():
									lowercase_	    =   model(SCREAMING_SNAKE_CASE_			)[0]
					lowercase_	    =   torch.Size((1, 9, 1_0_2_4)			)
					self.assertEqual(output.shape			,				SCREAMING_SNAKE_CASE_			)

					lowercase_	    =   [-0.60_40, -0.25_17, -0.10_25, 0.34_20, -0.67_58, -0.00_17, -0.10_89, -0.19_90, 0.57_28]
					for ii in range(3			):
									for jj in range(3			):
													lowercase_	    =   output[0, ii, jj]
													lowercase_	    =   expected[3 * ii + jj]
													lowercase_	    =   '''ii={} jj={} a={} b={}'''.format(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
													self.assertTrue(math.isclose(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				rel_tol=SCREAMING_SNAKE_CASE_			,				abs_tol=SCREAMING_SNAKE_CASE_			)			,				msg=SCREAMING_SNAKE_CASE_			)







import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer


__A			:					str						=			logging.get_logger(__name__)

__A			:					Optional[Any]						=			{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

__A			:					Tuple						=			{
    'vocab_file': {
        'facebook/dpr-ctx_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-ctx_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-ctx_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-ctx_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}
__A			:					Optional[Any]						=			{
    'vocab_file': {
        'facebook/dpr-question_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-question_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-question_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-question_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}
__A			:					str						=			{
    'vocab_file': {
        'facebook/dpr-reader-single-nq-base': (
            'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-reader-multiset-base': (
            'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-reader-single-nq-base': (
            'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-reader-multiset-base': (
            'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}

__A			:					Optional[Any]						=			{
    'facebook/dpr-ctx_encoder-single-nq-base': 5_1_2,
    'facebook/dpr-ctx_encoder-multiset-base': 5_1_2,
}
__A			:					List[str]						=			{
    'facebook/dpr-question_encoder-single-nq-base': 5_1_2,
    'facebook/dpr-question_encoder-multiset-base': 5_1_2,
}
__A			:					Any						=			{
    'facebook/dpr-reader-single-nq-base': 5_1_2,
    'facebook/dpr-reader-multiset-base': 5_1_2,
}


__A			:					str						=			{
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
__A			:					Any						=			{
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
__A			:					Dict						=			{
    'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}





class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      VOCAB_FILES_NAMES
   lowerCamelCase__				=      CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase__				=      CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase__				=      CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION





class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      VOCAB_FILES_NAMES
   lowerCamelCase__				=      QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase__				=      QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase__				=      QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION




__A			:					Optional[int]						=			collections.namedtuple(
    'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text']
)

__A			:					List[Any]						=			collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


__A			:					List[Any]						=			r'\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    ```\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n    ```\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `\'tf\'`: Return TensorFlow `tf.constant` objects.\n                - `\'pt\'`: Return PyTorch `torch.Tensor` objects.\n                - `\'np\'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer\'s default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Returns:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    '





@add_start_docstrings(__snake_case		)
class 			_SCREAMING_SNAKE_CASE :







   '''simple docstring'''




   def __call__(       self					:			int     , __lowerCamelCase					:			Dict     , __lowerCamelCase					:			Optional[str] = None     , __lowerCamelCase					:			Optional[str] = None     , __lowerCamelCase					:			Union[bool, str] = False     , __lowerCamelCase					:			Union[bool, str] = False     , __lowerCamelCase					:			Optional[int] = None     , __lowerCamelCase					:			Optional[Union[str, TensorType]] = None     , __lowerCamelCase					:			Optional[bool] = None     , **__lowerCamelCase					:			Any     , ):
       if titles is None and texts is None:
           return super().__call__(
               __lowerCamelCase     , padding=__lowerCamelCase     , truncation=__lowerCamelCase     , max_length=__lowerCamelCase     , return_tensors=__lowerCamelCase     , return_attention_mask=__lowerCamelCase     , **__lowerCamelCase     , )
       elif titles is None or texts is None:
           SCREAMING_SNAKE_CASE										=   titles if texts is None else texts
           return super().__call__(
               __lowerCamelCase     , __lowerCamelCase     , padding=__lowerCamelCase     , truncation=__lowerCamelCase     , max_length=__lowerCamelCase     , return_tensors=__lowerCamelCase     , return_attention_mask=__lowerCamelCase     , **__lowerCamelCase     , )
       SCREAMING_SNAKE_CASE										=   titles if not isinstance(__lowerCamelCase     , __lowerCamelCase			) else [titles]
       SCREAMING_SNAKE_CASE										=   texts if not isinstance(__lowerCamelCase     , __lowerCamelCase			) else [texts]
       SCREAMING_SNAKE_CASE										=   len(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   questions if not isinstance(__lowerCamelCase     , __lowerCamelCase			) else [questions] * n_passages
       if len(__lowerCamelCase			) != len(__lowerCamelCase			):
           raise ValueError(
               f"There should be as many titles than texts but got {len(__lowerCamelCase			)} titles and {len(__lowerCamelCase			)} texts."			)
       SCREAMING_SNAKE_CASE										=   super().__call__(__lowerCamelCase     , __lowerCamelCase     , padding=__lowerCamelCase     , truncation=__lowerCamelCase			)["input_ids"]
       SCREAMING_SNAKE_CASE										=   super().__call__(__lowerCamelCase     , add_special_tokens=__lowerCamelCase     , padding=__lowerCamelCase     , truncation=__lowerCamelCase			)["input_ids"]
       SCREAMING_SNAKE_CASE										=   {
           "input_ids": [
               (encoded_question_and_title + encoded_text)[:max_length]
               if max_length is not None and truncation
               else encoded_question_and_title + encoded_text
               for encoded_question_and_title, encoded_text in zip(__lowerCamelCase     , __lowerCamelCase			)
           ]
       }
       if return_attention_mask is not False:
           SCREAMING_SNAKE_CASE										=   []
           for input_ids in encoded_inputs["input_ids"]:
               attention_mask.append([int(input_id != self.pad_token_id			) for input_id in input_ids]			)
           SCREAMING_SNAKE_CASE										=   attention_mask
       return self.pad(__lowerCamelCase     , padding=__lowerCamelCase     , max_length=__lowerCamelCase     , return_tensors=__lowerCamelCase			)





   def      _snake_case		(       self					:			Tuple     , __lowerCamelCase					:			BatchEncoding     , __lowerCamelCase					:			DPRReaderOutput     , __lowerCamelCase					:			int = 16     , __lowerCamelCase					:			int = 64     , __lowerCamelCase					:			int = 4     , ):
       SCREAMING_SNAKE_CASE										=   reader_input["input_ids"]
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   reader_output[:3]
       SCREAMING_SNAKE_CASE										=   len(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   sorted(range(__lowerCamelCase			)     , reverse=__lowerCamelCase     , key=relevance_logits.__getitem__			)
       SCREAMING_SNAKE_CASE										=   []
       for doc_id in sorted_docs:
           SCREAMING_SNAKE_CASE										=   list(input_ids[doc_id]			)
           # assuming question & title information is at the beginning of the sequence
           SCREAMING_SNAKE_CASE										=   sequence_ids.index(self.sep_token_id     , 2			) + 1  # second sep id
           if sequence_ids[-1] == self.pad_token_id:
               SCREAMING_SNAKE_CASE										=   sequence_ids.index(self.pad_token_id			)
           else:
               SCREAMING_SNAKE_CASE										=   len(__lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   self._get_best_spans(
               start_logits=start_logits[doc_id][passage_offset:sequence_len]     , end_logits=end_logits[doc_id][passage_offset:sequence_len]     , max_answer_length=__lowerCamelCase     , top_spans=__lowerCamelCase     , )
           for start_index, end_index in best_spans:
               start_index += passage_offset
               end_index += passage_offset
               nbest_spans_predictions.append(
                   DPRSpanPrediction(
                       span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]     , relevance_score=relevance_logits[doc_id]     , doc_id=__lowerCamelCase     , start_index=__lowerCamelCase     , end_index=__lowerCamelCase     , text=self.decode(sequence_ids[start_index : end_index + 1]			)     , )			)
           if len(__lowerCamelCase			) >= num_spans:
               break
       return nbest_spans_predictions[:num_spans]





   def      _snake_case		(       self					:			Optional[int]     , __lowerCamelCase					:			List[int]     , __lowerCamelCase					:			List[int]     , __lowerCamelCase					:			int     , __lowerCamelCase					:			int     , ):
       SCREAMING_SNAKE_CASE										=   []
       for start_index, start_score in enumerate(__lowerCamelCase			):
           for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]			):
               scores.append(((start_index, start_index + answer_length), start_score + end_score)			)
       SCREAMING_SNAKE_CASE										=   sorted(__lowerCamelCase     , key=lambda __lowerCamelCase			: x[1]     , reverse=__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   []
       for (start_index, end_index), score in scores:
           if start_index > end_index:
               raise ValueError(f"Wrong span indices: [{start_index}:{end_index}]"			)
           SCREAMING_SNAKE_CASE										=   end_index - start_index + 1
           if length > max_answer_length:
               raise ValueError(f"Span is too long: {length} > {max_answer_length}"			)
           if any(
               start_index <= prev_start_index <= prev_end_index <= end_index
               or prev_start_index <= start_index <= end_index <= prev_end_index
               for (prev_start_index, prev_end_index) in chosen_span_intervals			):
               continue
           chosen_span_intervals.append((start_index, end_index)			)

           if len(__lowerCamelCase			) == top_spans:
               break
       return chosen_span_intervals





@add_end_docstrings(__snake_case		)
class 			_SCREAMING_SNAKE_CASE (   __snake_case					,       __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      VOCAB_FILES_NAMES
   lowerCamelCase__				=      READER_PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase__				=      READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase__				=      READER_PRETRAINED_INIT_CONFIGURATION
   lowerCamelCase__				=      ["input_ids", "attention_mask"]