




from transformers import (
    DPRContextEncoderTokenizer,
    DPRContextEncoderTokenizerFast,
    DPRQuestionEncoderTokenizer,
    DPRQuestionEncoderTokenizerFast,
    DPRReaderOutput,
    DPRReaderTokenizer,
    DPRReaderTokenizerFast,
)
from transformers.testing_utils import require_tokenizers, slow
from transformers.tokenization_utils_base import BatchEncoding

from ..bert.test_tokenization_bert import BertTokenizationTest


@require_tokenizers
class 				__SCREAMING_SNAKE_CASE							(		lowerCamelCase  ):
					_lowerCamelCase		  = DPRContextEncoderTokenizer
					_lowerCamelCase		  = DPRContextEncoderTokenizerFast
					_lowerCamelCase		  = True


@require_tokenizers
class 				__SCREAMING_SNAKE_CASE							(		lowerCamelCase  ):
					_lowerCamelCase		  = DPRQuestionEncoderTokenizer
					_lowerCamelCase		  = DPRQuestionEncoderTokenizerFast
					_lowerCamelCase		  = True


@require_tokenizers
class 				__SCREAMING_SNAKE_CASE							(		lowerCamelCase  ):
					_lowerCamelCase		  = DPRReaderTokenizer
					_lowerCamelCase		  = DPRReaderTokenizerFast
					_lowerCamelCase		  = True

					@slow
					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		List[Any]							   =							self.tokenizer_class.from_pretrained('bert-base-uncased'					)

						lowerCamelCase_							:		str							   =							tokenizer.encode('question sequence' ,       add_special_tokens=__lowercase					)
						lowerCamelCase_							:		Optional[Any]							   =							tokenizer.encode('title sequence' ,       add_special_tokens=__lowercase					)
						lowerCamelCase_							:		Optional[int]							   =							tokenizer.encode('text sequence ' * 4 ,       add_special_tokens=__lowercase					)
						lowerCamelCase_							:		List[str]							   =							[[101] + text_a + [102] + text_a + [102] + text_a]
						lowerCamelCase_							:		Dict							   =							BatchEncoding({'input_ids': input_ids}					)

						lowerCamelCase_							:		List[Any]							   =							[[0] * len(input_ids[0]					)]
						lowerCamelCase_							:		Union[str, Any]							   =							[[0] * len(input_ids[0]					)]
						lowerCamelCase_							:		Any							   =							[0]
						lowerCamelCase_							:		Optional[int]							   =							DPRReaderOutput(__lowercase ,       __lowercase ,       __lowercase					)

						lowerCamelCase_   ,			lowerCamelCase_							:		Tuple							   =							8, 9
						lowerCamelCase_							:		List[Any]							   =							10
						lowerCamelCase_							:		Union[str, Any]							   =							10
						lowerCamelCase_							:		Tuple							   =							tokenizer.decode_best_spans(__lowercase ,       __lowercase					)
						self.assertEqual(predicted_spans[0].start_index ,       __lowercase					)
						self.assertEqual(predicted_spans[0].end_index ,       __lowercase					)
						self.assertEqual(predicted_spans[0].doc_id ,       0					)





					@slow
					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[int]							   =							self.tokenizer_class.from_pretrained('bert-base-uncased'					)

						lowerCamelCase_							:		Dict							   =							tokenizer.encode('question sequence' ,       add_special_tokens=__lowercase					)
						lowerCamelCase_							:		int							   =							tokenizer.encode('title sequence' ,       add_special_tokens=__lowercase					)
						lowerCamelCase_							:		Dict							   =							tokenizer.encode('text sequence' ,       add_special_tokens=__lowercase					)
						lowerCamelCase_							:		Dict							   =							[101] + text_a + [102] + text_a + [102] + text_a
						lowerCamelCase_							:		Optional[Any]							   =							tokenizer(questions=['question sequence'] ,       titles=['title sequence'] ,       texts=['text sequence']					)
						self.assertIn('input_ids' ,       __lowercase					)
						self.assertIn('attention_mask' ,       __lowercase					)
						self.assertListEqual(encoded_input['input_ids'][0] ,       __lowercase					)






'''simple docstring'''
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin






class        SCREAMING_SNAKE_CASE(				__A   ,      __A   ,      __A     ):

       @register_to_config
       def __init__(    self					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__ = False					,		)    ->					Optional[int]:

           """simple docstring"""



           super().__init__()

           __lowercase							=						nn.Linear(lowerCamelCase__					,		lowerCamelCase__					,		bias=lowerCamelCase__     )

           __lowercase							=						nn.Embedding(lowerCamelCase__					,		lowerCamelCase__     )
           __lowercase							=						False

           __lowercase							=						nn.Dropout(p=lowerCamelCase__     )

           __lowercase							=						TaConfig(
               d_model=lowerCamelCase__					,		num_heads=lowerCamelCase__					,		d_kv=lowerCamelCase__					,		d_ff=lowerCamelCase__					,		feed_forward_proj=lowerCamelCase__					,		dropout_rate=lowerCamelCase__					,		is_decoder=lowerCamelCase__					,		is_encoder_decoder=lowerCamelCase__					,		)
           __lowercase							=						nn.ModuleList()
           for lyr_num in range(lowerCamelCase__     ):
               __lowercase							=						TaBlock(lowerCamelCase__     )
               self.encoders.append(lowerCamelCase__     )

           __lowercase							=						TaLayerNorm(lowerCamelCase__     )
           __lowercase							=						nn.Dropout(p=lowerCamelCase__     )




       def     snake_case__			(    self					,		lowerCamelCase__					,		lowerCamelCase__     )    ->					str:

           """simple docstring"""



           __lowercase							=						self.input_proj(lowerCamelCase__     )

           # terminal relative positional encodings
           __lowercase							=						encoder_inputs.shape[1]
           __lowercase							=						torch.arange(lowerCamelCase__					,		device=encoder_inputs.device     )

           __lowercase							=						encoder_inputs_mask.sum(-1     )
           __lowercase							=						torch.roll(input_positions.unsqueeze(0     )					,		tuple(seq_lens.tolist()     )					,		dims=0     )
           x += self.position_encoding(lowerCamelCase__     )

           __lowercase							=						self.dropout_pre(lowerCamelCase__     )

           # inverted the attention mask
           __lowercase							=						encoder_inputs.size()
           __lowercase							=						self.get_extended_attention_mask(lowerCamelCase__					,		lowerCamelCase__     )

           for lyr in self.encoders:
               __lowercase							=						lyr(lowerCamelCase__					,		lowerCamelCase__     )[0]
           __lowercase							=						self.layer_norm(lowerCamelCase__     )

           return self.dropout_post(lowerCamelCase__     ), encoder_inputs_mask




