
'''simple docstring'''






import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
					import os

					# The slow tests are often failing with OOM error on GPU
					# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
					# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
					A         =   'platform'

					import jax
					import jax.numpy as jnp

					from transformers import BlenderbotTokenizer
					from transformers.models.blenderbot.modeling_flax_blenderbot import (
					    FlaxBlenderbotForConditionalGeneration,
					    FlaxBlenderbotModel,
					    shift_tokens_right,
					)





def  UpperCAmelCase				(	UpperCAmelCase__				:				Union[str, Any]							,				UpperCAmelCase__				:				Union[str, Any]							,				UpperCAmelCase__				:				str=None							,				UpperCAmelCase__				:				str=None							,				UpperCAmelCase__				:				Union[str, Any]=None							,				UpperCAmelCase__				:				Union[str, Any]=None							,				UpperCAmelCase__				:				List[Any]=None							,				UpperCAmelCase__				:				int=None							,				):




				if attention_mask is None:
								lowerCamelCase       :      Tuple          =			np.where(input_ids != config.pad_token_id							,				1							,				0)
				if decoder_attention_mask is None:
								lowerCamelCase       :      Dict          =			np.where(decoder_input_ids != config.pad_token_id							,				1							,				0)
				if head_mask is None:
								lowerCamelCase       :      Optional[Any]          =			np.ones((config.encoder_layers, config.encoder_attention_heads))
				if decoder_head_mask is None:
								lowerCamelCase       :      Optional[int]          =			np.ones((config.decoder_layers, config.decoder_attention_heads))
				if cross_attn_head_mask is None:
								lowerCamelCase       :      Any          =			np.ones((config.decoder_layers, config.decoder_attention_heads))
				return {
				    "input_ids": input_ids,
				    "decoder_input_ids": decoder_input_ids,
				    "attention_mask": attention_mask,
				    "decoder_attention_mask": attention_mask,
				}




class      __snake_case		:
					def __init__(  self,     A,     A=13,     A=7,     A=True,     A=False,     A=99,     A=16,     A=2,     A=4,     A=4,     A="gelu",     A=0.1,     A=0.1,     A=32,     A=2,     A=1,     A=0,     A=0.02,     ):

									"""simple docstring"""


									lowerCamelCase       :      Dict          =			parent
									lowerCamelCase       :      List[str]          =			batch_size
									lowerCamelCase       :      Optional[Any]          =			seq_length
									lowerCamelCase       :      Optional[Any]          =			is_training
									lowerCamelCase       :      str          =			use_labels
									lowerCamelCase       :      Union[str, Any]          =			vocab_size
									lowerCamelCase       :      Union[str, Any]          =			hidden_size
									lowerCamelCase       :      Optional[Any]          =			num_hidden_layers
									lowerCamelCase       :      str          =			num_attention_heads
									lowerCamelCase       :      Optional[int]          =			intermediate_size
									lowerCamelCase       :      Tuple          =			hidden_act
									lowerCamelCase       :      Any          =			hidden_dropout_prob
									lowerCamelCase       :      int          =			attention_probs_dropout_prob
									lowerCamelCase       :      Optional[Any]          =			max_position_embeddings
									lowerCamelCase       :      List[str]          =			eos_token_id
									lowerCamelCase       :      Union[str, Any]          =			pad_token_id
									lowerCamelCase       :      Tuple          =			bos_token_id
									lowerCamelCase       :      Optional[Any]          =			initializer_range
					def      UpperCAmelCase_				(  self   ):

									"""simple docstring"""


									lowerCamelCase       :      List[str]          =			np.clip(ids_tensor([self.batch_size, self.seq_length - 1],     self.vocab_size   ),     3,     self.vocab_size   )
									lowerCamelCase       :      Any          =			np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1),     dtype=np.intaa   )),     -1   )

									lowerCamelCase       :      List[str]          =			shift_tokens_right(_UpperCAmelCase,     1,     2   )

									lowerCamelCase       :      Optional[int]          =			BlenderbotConfig(
									    vocab_size=self.vocab_size,     d_model=self.hidden_size,     encoder_layers=self.num_hidden_layers,     decoder_layers=self.num_hidden_layers,     encoder_attention_heads=self.num_attention_heads,     decoder_attention_heads=self.num_attention_heads,     encoder_ffn_dim=self.intermediate_size,     decoder_ffn_dim=self.intermediate_size,     dropout=self.hidden_dropout_prob,     attention_dropout=self.attention_probs_dropout_prob,     max_position_embeddings=self.max_position_embeddings,     eos_token_id=self.eos_token_id,     bos_token_id=self.bos_token_id,     pad_token_id=self.pad_token_id,     initializer_range=self.initializer_range,     use_cache=_UpperCAmelCase,     )
									lowerCamelCase       :      Dict          =			prepare_blenderbot_inputs_dict(_UpperCAmelCase,     _UpperCAmelCase,     _UpperCAmelCase   )
									return config, inputs_dict
					def      UpperCAmelCase_				(  self   ):

									"""simple docstring"""


									lowerCamelCase      ,			lowerCamelCase       :      str          =			self.prepare_config_and_inputs()
									return config, inputs_dict
					def      UpperCAmelCase_				(  self,     A,     A,     A   ):

									"""simple docstring"""


									lowerCamelCase       :      List[Any]          =			20
									lowerCamelCase       :      str          =			model_class_name(_UpperCAmelCase   )

									lowerCamelCase       :      Union[str, Any]          =			model.encode(inputs_dict['input_ids']   )

									lowerCamelCase      ,			lowerCamelCase       :      List[str]          =			(
									    inputs_dict['decoder_input_ids'],
									    inputs_dict['decoder_attention_mask'],
									)

									lowerCamelCase       :      List[str]          =			model.init_cache(decoder_input_ids.shape[0],     _UpperCAmelCase,     _UpperCAmelCase   )
									lowerCamelCase       :      Tuple          =			jnp.ones((decoder_input_ids.shape[0], max_decoder_length),     dtype='i4'   )

									lowerCamelCase       :      Tuple          =			jnp.broadcast_to(
									    jnp.arange(decoder_input_ids.shape[-1] - 1   )[None, :],     (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),     )
									lowerCamelCase       :      int          =			model.decode(
									    decoder_input_ids[:, :-1],     _UpperCAmelCase,     decoder_attention_mask=_UpperCAmelCase,     past_key_values=_UpperCAmelCase,     decoder_position_ids=_UpperCAmelCase,     )

									lowerCamelCase       :      str          =			jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],     dtype='i4'   )
									lowerCamelCase       :      Optional[Any]          =			model.decode(
									    decoder_input_ids[:, -1:],     _UpperCAmelCase,     decoder_attention_mask=_UpperCAmelCase,     past_key_values=outputs_cache.past_key_values,     decoder_position_ids=_UpperCAmelCase,     )

									lowerCamelCase       :      Optional[Any]          =			model.decode(_UpperCAmelCase,     _UpperCAmelCase   )

									lowerCamelCase       :      Any          =			np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])   )   )
									self.parent.assertTrue(diff < 1e-3,     msg=F'''Max diff is {diff}'''   )




					def      UpperCAmelCase_				(  self,     A,     A,     A   ):

									"""simple docstring"""


									lowerCamelCase       :      Dict          =			20
									lowerCamelCase       :      Any          =			model_class_name(_UpperCAmelCase   )

									lowerCamelCase       :      Any          =			model.encode(inputs_dict['input_ids']   )

									lowerCamelCase      ,			lowerCamelCase       :      str          =			(
									    inputs_dict['decoder_input_ids'],
									    inputs_dict['decoder_attention_mask'],
									)

									lowerCamelCase       :      List[str]          =			jnp.concatenate(
									    [
									        decoder_attention_mask,
									        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])   ),
									    ],     axis=-1,     )

									lowerCamelCase       :      Any          =			model.init_cache(decoder_input_ids.shape[0],     _UpperCAmelCase,     _UpperCAmelCase   )
									lowerCamelCase       :      int          =			jnp.broadcast_to(
									    jnp.arange(decoder_input_ids.shape[-1] - 1   )[None, :],     (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),     )

									lowerCamelCase       :      int          =			model.decode(
									    decoder_input_ids[:, :-1],     _UpperCAmelCase,     decoder_attention_mask=_UpperCAmelCase,     past_key_values=_UpperCAmelCase,     decoder_position_ids=_UpperCAmelCase,     )
									lowerCamelCase       :      Union[str, Any]          =			jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],     dtype='i4'   )
									lowerCamelCase       :      Tuple          =			model.decode(
									    decoder_input_ids[:, -1:],     _UpperCAmelCase,     past_key_values=outputs_cache.past_key_values,     decoder_attention_mask=_UpperCAmelCase,     decoder_position_ids=_UpperCAmelCase,     )

									lowerCamelCase       :      List[str]          =			model.decode(_UpperCAmelCase,     _UpperCAmelCase,     decoder_attention_mask=_UpperCAmelCase   )

									lowerCamelCase       :      Optional[Any]          =			np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])   )   )
									self.parent.assertTrue(diff < 1e-3,     msg=F'''Max diff is {diff}'''   )





@require_flax
class      __snake_case		( unittest.TestCase):
					_lowerCAmelCase        =						99
					def      UpperCAmelCase_				(  self   ):

									"""simple docstring"""


									lowerCamelCase       :      List[str]          =			np.array(
									    [
									        [71, 82, 18, 33, 46, 91, 2],
									        [68, 34, 26, 58, 30, 82, 2],
									        [5, 97, 17, 39, 94, 40, 2],
									        [76, 83, 94, 25, 70, 78, 2],
									        [87, 59, 41, 35, 48, 66, 2],
									        [55, 13, 16, 58, 5, 2, 1],  # note padding
									        [64, 27, 31, 51, 12, 75, 2],
									        [52, 64, 86, 17, 83, 39, 2],
									        [48, 61, 9, 24, 71, 82, 2],
									        [26, 1, 60, 48, 22, 13, 2],
									        [21, 5, 62, 28, 14, 76, 2],
									        [45, 98, 37, 86, 59, 48, 2],
									        [70, 70, 50, 9, 28, 0, 2],
									    ],     dtype=np.intaa,     )

									lowerCamelCase       :      Optional[Any]          =			input_ids.shape[0]
									lowerCamelCase       :      Union[str, Any]          =			BlenderbotConfig(
									    vocab_size=self.vocab_size,     d_model=24,     encoder_layers=2,     decoder_layers=2,     encoder_attention_heads=2,     decoder_attention_heads=2,     encoder_ffn_dim=32,     decoder_ffn_dim=32,     max_position_embeddings=48,     eos_token_id=2,     pad_token_id=1,     bos_token_id=0,     )
									return config, input_ids, batch_size
					def      UpperCAmelCase_				(  self   ):

									"""simple docstring"""


									lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase       :      str          =			self._get_config_and_data()
									lowerCamelCase       :      str          =			FlaxBlenderbotForConditionalGeneration(_UpperCAmelCase   )
									lowerCamelCase       :      Optional[Any]          =			lm_model(input_ids=_UpperCAmelCase   )
									lowerCamelCase       :      List[str]          =			(batch_size, input_ids.shape[1], config.vocab_size)
									self.assertEqual(outputs['logits'].shape,     _UpperCAmelCase   )
					def      UpperCAmelCase_				(  self   ):

									"""simple docstring"""


									lowerCamelCase       :      List[Any]          =			BlenderbotConfig(
									    vocab_size=self.vocab_size,     d_model=14,     encoder_layers=2,     decoder_layers=2,     encoder_attention_heads=2,     decoder_attention_heads=2,     encoder_ffn_dim=8,     decoder_ffn_dim=8,     max_position_embeddings=48,     )
									lowerCamelCase       :      int          =			FlaxBlenderbotForConditionalGeneration(_UpperCAmelCase   )
									lowerCamelCase       :      Optional[Any]          =			np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]],     dtype=np.intaa   )
									lowerCamelCase       :      int          =			np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]],     dtype=np.intaa   )
									lowerCamelCase       :      int          =			lm_model(input_ids=_UpperCAmelCase,     decoder_input_ids=_UpperCAmelCase   )
									lowerCamelCase       :      Dict          =			(*summary.shape, config.vocab_size)
									self.assertEqual(outputs['logits'].shape,     _UpperCAmelCase   )




					def      UpperCAmelCase_				(  self   ):

									"""simple docstring"""


									lowerCamelCase       :      Dict          =			np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]],     dtype=np.intaa   )
									lowerCamelCase       :      str          =			shift_tokens_right(_UpperCAmelCase,     1,     2   )
									lowerCamelCase       :      Union[str, Any]          =			np.equal(_UpperCAmelCase,     1   ).astype(np.floataa   ).sum()
									lowerCamelCase       :      Tuple          =			np.equal(_UpperCAmelCase,     1   ).astype(np.floataa   ).sum()
									self.assertEqual(shifted.shape,     input_ids.shape   )
									self.assertEqual(_UpperCAmelCase,     n_pad_before - 1   )
									self.assertTrue(np.equal(shifted[:, 0],     2   ).all()   )





@require_flax
class      __snake_case		( UpperCAmelCase__     , unittest.TestCase     , UpperCAmelCase__):
					_lowerCAmelCase        =						True
					_lowerCAmelCase        =						(
					    (
					        FlaxBlenderbotModel,
					        FlaxBlenderbotForConditionalGeneration,
					    )
					    if is_flax_available()
					    else ()
					)
					_lowerCAmelCase        =						(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()
					def      UpperCAmelCase_				(  self   ):

									"""simple docstring"""


									lowerCamelCase       :      Optional[Any]          =			FlaxBlenderbotModelTester(self   )
					def      UpperCAmelCase_				(  self   ):

									"""simple docstring"""


									lowerCamelCase      ,			lowerCamelCase       :      List[str]          =			self.model_tester.prepare_config_and_inputs()
									for model_class in self.all_model_classes:
													self.model_tester.check_use_cache_forward(_UpperCAmelCase,     _UpperCAmelCase,     _UpperCAmelCase   )
					def      UpperCAmelCase_				(  self   ):

									"""simple docstring"""


									lowerCamelCase      ,			lowerCamelCase       :      Optional[int]          =			self.model_tester.prepare_config_and_inputs()
									for model_class in self.all_model_classes:
													self.model_tester.check_use_cache_forward_with_attn_mask(_UpperCAmelCase,     _UpperCAmelCase,     _UpperCAmelCase   )
					def      UpperCAmelCase_				(  self   ):

									"""simple docstring"""


									lowerCamelCase      ,			lowerCamelCase       :      Dict          =			self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__   ):
																	lowerCamelCase       :      Optional[int]          =			self._prepare_for_class(_UpperCAmelCase,     _UpperCAmelCase   )
																	lowerCamelCase       :      Dict          =			model_class(_UpperCAmelCase   )

																	@jax.jit
																	def encode_jitted(A,     A=None,     **A   ):
																					return model.encode(input_ids=_UpperCAmelCase,     attention_mask=_UpperCAmelCase   )

																	with self.subTest('JIT Enabled'   ):
																					lowerCamelCase       :      Optional[int]          =			encode_jitted(**_UpperCAmelCase   ).to_tuple()

																	with self.subTest('JIT Disabled'   ):
																					with jax.disable_jit():
																									lowerCamelCase       :      Optional[int]          =			encode_jitted(**_UpperCAmelCase   ).to_tuple()

																	self.assertEqual(len(_UpperCAmelCase   ),     len(_UpperCAmelCase   )   )
																	for jitted_output, output in zip(_UpperCAmelCase,     _UpperCAmelCase   ):
																					self.assertEqual(jitted_output.shape,     output.shape   )
					def      UpperCAmelCase_				(  self   ):

									"""simple docstring"""


									lowerCamelCase      ,			lowerCamelCase       :      List[str]          =			self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__   ):
																	lowerCamelCase       :      Optional[Any]          =			model_class(_UpperCAmelCase   )
																	lowerCamelCase       :      Any          =			model.encode(inputs_dict['input_ids'],     inputs_dict['attention_mask']   )

																	lowerCamelCase       :      Optional[int]          =			{
																	    'decoder_input_ids': inputs_dict['decoder_input_ids'],
																	    'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
																	    'encoder_outputs': encoder_outputs,
																	}

																	@jax.jit
																	def decode_jitted(A,     A,     A   ):
																					return model.decode(
																					    decoder_input_ids=_UpperCAmelCase,     decoder_attention_mask=_UpperCAmelCase,     encoder_outputs=_UpperCAmelCase,     )

																	with self.subTest('JIT Enabled'   ):
																					lowerCamelCase       :      Dict          =			decode_jitted(**_UpperCAmelCase   ).to_tuple()

																	with self.subTest('JIT Disabled'   ):
																					with jax.disable_jit():
																									lowerCamelCase       :      int          =			decode_jitted(**_UpperCAmelCase   ).to_tuple()

																	self.assertEqual(len(_UpperCAmelCase   ),     len(_UpperCAmelCase   )   )
																	for jitted_output, output in zip(_UpperCAmelCase,     _UpperCAmelCase   ):
																					self.assertEqual(jitted_output.shape,     output.shape   )
					@slow
					def      UpperCAmelCase_				(  self   ):

									"""simple docstring"""


									for model_class_name in self.all_model_classes:
													lowerCamelCase       :      str          =			model_class_name.from_pretrained('facebook/blenderbot-400M-distill'   )
													# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
													lowerCamelCase       :      Union[str, Any]          =			np.ones((1, 1)   ) * model.config.eos_token_id
													lowerCamelCase       :      Dict          =			model(_UpperCAmelCase   )
													self.assertIsNotNone(_UpperCAmelCase   )




					@unittest.skipUnless(jax_device != 'cpu',     '3B test too slow on CPU.'   )
					@slow
					def      UpperCAmelCase_				(  self   ):

									"""simple docstring"""


									lowerCamelCase       :      int          =			{'num_beams': 1, 'early_stopping': True, 'min_length': 15, 'max_length': 25}
									lowerCamelCase       :      Any          =			{'skip_special_tokens': True, 'clean_up_tokenization_spaces': True}

									lowerCamelCase       :      List[Any]          =			FlaxBlenderbotForConditionalGeneration.from_pretrained('facebook/blenderbot-3B',     from_pt=_UpperCAmelCase   )
									lowerCamelCase       :      Union[str, Any]          =			BlenderbotTokenizer.from_pretrained('facebook/blenderbot-3B'   )

									lowerCamelCase       :      Tuple          =			['Sam']
									lowerCamelCase       :      Union[str, Any]          =			tokenizer(_UpperCAmelCase,     return_tensors='jax'   )

									lowerCamelCase       :      str          =			model.generate(**_UpperCAmelCase,     **_UpperCAmelCase   )
									lowerCamelCase       :      List[Any]          =			'Sam is a great name. It means \"sun\" in Gaelic.'

									lowerCamelCase       :      Union[str, Any]          =			tokenizer.batch_decode(_UpperCAmelCase,     **_UpperCAmelCase   )
									assert generated_txt[0].strip() == tgt_text




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_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


A			:						Optional[Any]        =  logging.get_logger(__name__)

A			:						str        =  {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

A			:						Any        =  {
    '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			:						Dict        =  {
    '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			:						Optional[int]        =  {
    '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			:						int        =  {
    'facebook/dpr-ctx_encoder-single-nq-base': 5_1_2,
    'facebook/dpr-ctx_encoder-multiset-base': 5_1_2,
}
A			:						Optional[Any]        =  {
    '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			:						Union[str, Any]        =  {
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
A			:						Tuple        =  {
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
A			:						Any        =  {
    'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}



class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					A__     =					VOCAB_FILES_NAMES
					A__     =					CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
					A__     =					CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					A__     =					CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
					A__     =					DPRContextEncoderTokenizer






class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					A__     =					VOCAB_FILES_NAMES
					A__     =					QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
					A__     =					QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					A__     =					QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
					A__     =					DPRQuestionEncoderTokenizer






A			:						Any        =  collections.namedtuple(
    'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text']
)

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


A			:						int        =  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    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\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    Return:\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(UpperCAmelCase__				)
class 				A       :
					'''simple docstring'''






					def __call__(self       :		str ,   _UpperCAmelCase       :		str ,   _UpperCAmelCase       :		Optional[str] = None ,   _UpperCAmelCase       :		Optional[str] = None ,   _UpperCAmelCase       :		Union[bool, str] = False ,   _UpperCAmelCase       :		Union[bool, str] = False ,   _UpperCAmelCase       :		Optional[int] = None ,   _UpperCAmelCase       :		Optional[Union[str, TensorType]] = None ,   _UpperCAmelCase       :		Optional[bool] = None ,   **_UpperCAmelCase       :		Any ,   )		->  BatchEncoding:




							"""simple docstring"""


							if titles is None and texts is None:
									return super().__call__(
									    _UpperCAmelCase ,   padding=_UpperCAmelCase ,   truncation=_UpperCAmelCase ,   max_length=_UpperCAmelCase ,   return_tensors=_UpperCAmelCase ,   return_attention_mask=_UpperCAmelCase ,   **_UpperCAmelCase ,   )
							elif titles is None or texts is None:
									lowercase__	     =						titles if texts is None else texts
									return super().__call__(
									    _UpperCAmelCase ,   _UpperCAmelCase ,   padding=_UpperCAmelCase ,   truncation=_UpperCAmelCase ,   max_length=_UpperCAmelCase ,   return_tensors=_UpperCAmelCase ,   return_attention_mask=_UpperCAmelCase ,   **_UpperCAmelCase ,   )
							lowercase__	     =						titles if not isinstance(_UpperCAmelCase ,   _UpperCAmelCase						) else [titles]
							lowercase__	     =						texts if not isinstance(_UpperCAmelCase ,   _UpperCAmelCase						) else [texts]
							lowercase__	     =						len(_UpperCAmelCase						)
							lowercase__	     =						questions if not isinstance(_UpperCAmelCase ,   _UpperCAmelCase						) else [questions] * n_passages
							assert len(_UpperCAmelCase						) == len(
							    _UpperCAmelCase						), f'''There should be as many titles than texts but got {len(_UpperCAmelCase						)} titles and {len(_UpperCAmelCase						)} texts.'''
							lowercase__	     =						super().__call__(_UpperCAmelCase ,   _UpperCAmelCase ,   padding=_UpperCAmelCase ,   truncation=_UpperCAmelCase						)["""input_ids"""]
							lowercase__	     =						super().__call__(_UpperCAmelCase ,   add_special_tokens=_UpperCAmelCase ,   padding=_UpperCAmelCase ,   truncation=_UpperCAmelCase						)["""input_ids"""]
							lowercase__	     =						{
							    """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(_UpperCAmelCase ,   _UpperCAmelCase						)
							    ]
							}
							if return_attention_mask is not False:
									lowercase__	     =						[]
									for input_ids in encoded_inputs["input_ids"]:
											attention_mask.append([int(input_id != self.pad_token_id						) for input_id in input_ids]						)
									lowercase__	     =						attention_mask
							return self.pad(_UpperCAmelCase ,   padding=_UpperCAmelCase ,   max_length=_UpperCAmelCase ,   return_tensors=_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		List[str] ,   _UpperCAmelCase       :		BatchEncoding ,   _UpperCAmelCase       :		DPRReaderOutput ,   _UpperCAmelCase       :		int = 16 ,   _UpperCAmelCase       :		int = 64 ,   _UpperCAmelCase       :		int = 4 ,   )		->  List[DPRSpanPrediction]:




							"""simple docstring"""


							lowercase__	     =						reader_input["""input_ids"""]
							lowercase__     ,      lowercase__     ,      lowercase__	     =						reader_output[:3]
							lowercase__	     =						len(_UpperCAmelCase						)
							lowercase__	     =						sorted(range(_UpperCAmelCase						) ,   reverse=_UpperCAmelCase ,   key=relevance_logits.__getitem__						)
							lowercase__	     =						[]
							for doc_id in sorted_docs:
									lowercase__	     =						list(input_ids[doc_id]						)
									# assuming question & title information is at the beginning of the sequence
									lowercase__	     =						sequence_ids.index(self.sep_token_id ,   2						) + 1  # second sep id
									if sequence_ids[-1] == self.pad_token_id:
											lowercase__	     =						sequence_ids.index(self.pad_token_id						)
									else:
											lowercase__	     =						len(_UpperCAmelCase						)

									lowercase__	     =						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=_UpperCAmelCase ,   top_spans=_UpperCAmelCase ,   )
									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=_UpperCAmelCase ,   start_index=_UpperCAmelCase ,   end_index=_UpperCAmelCase ,   text=self.decode(sequence_ids[start_index : end_index + 1]						) ,   )						)
									if len(_UpperCAmelCase						) >= num_spans:
											break
							return nbest_spans_predictions[:num_spans]


					def     lowerCamelCase__	(self       :		Optional[int] ,   _UpperCAmelCase       :		List[int] ,   _UpperCAmelCase       :		List[int] ,   _UpperCAmelCase       :		int ,   _UpperCAmelCase       :		int ,   )		->  List[DPRSpanPrediction]:




							"""simple docstring"""


							lowercase__	     =						[]
							for start_index, start_score in enumerate(_UpperCAmelCase						):
									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)						)
							lowercase__	     =						sorted(_UpperCAmelCase ,   key=lambda _UpperCAmelCase						: x[1] ,   reverse=_UpperCAmelCase						)
							lowercase__	     =						[]
							for (start_index, end_index), score in scores:
									assert start_index <= end_index, f'''Wrong span indices: [{start_index}:{end_index}]'''
									lowercase__	     =						end_index - start_index + 1
									assert length <= max_answer_length, 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(_UpperCAmelCase						) == top_spans:
											break
							return chosen_span_intervals






@add_end_docstrings(UpperCAmelCase__				)
class 				A       (			UpperCAmelCase__					,    UpperCAmelCase__				):
					'''simple docstring'''






					A__     =					VOCAB_FILES_NAMES
					A__     =					READER_PRETRAINED_VOCAB_FILES_MAP
					A__     =					READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					A__     =					READER_PRETRAINED_INIT_CONFIGURATION
					A__     =					['''input_ids''', '''attention_mask''']
					A__     =					DPRReaderTokenizer

