
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				=logging.get_logger(__name__)

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

__A				={
    '''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				={
    '''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				={
    '''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				={
    '''facebook/dpr-ctx_encoder-single-nq-base''': 5_1_2,
    '''facebook/dpr-ctx_encoder-multiset-base''': 5_1_2,
}
__A				={
    '''facebook/dpr-question_encoder-single-nq-base''': 5_1_2,
    '''facebook/dpr-question_encoder-multiset-base''': 5_1_2,
}
__A				={
    '''facebook/dpr-reader-single-nq-base''': 5_1_2,
    '''facebook/dpr-reader-multiset-base''': 5_1_2,
}


__A				={
    '''facebook/dpr-ctx_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-ctx_encoder-multiset-base''': {'''do_lower_case''': True},
}
__A				={
    '''facebook/dpr-question_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-question_encoder-multiset-base''': {'''do_lower_case''': True},
}
__A				={
    '''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_						):
	lowerCAmelCase__         = VOCAB_FILES_NAMES
	lowerCAmelCase__         = CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase__         = CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase__         = CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
	lowerCAmelCase__         = DPRContextEncoderTokenizer




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):
	lowerCAmelCase__         = VOCAB_FILES_NAMES
	lowerCAmelCase__         = QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase__         = QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase__         = QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
	lowerCAmelCase__         = DPRQuestionEncoderTokenizer



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

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


__A				=R'''
    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.
    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),
    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`
    with the format:

    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>

    Args:
        questions (`str` or `List[str]`):
            The questions to be encoded. You can specify one question for many passages. In this case, the question
            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in
            `titles` or `texts`.
        titles (`str` or `List[str]`):
            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.
        texts (`str` or `List[str]`):
            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.
        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
            Activates and controls padding. Accepts the following values:

            - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
              if provided).
            - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided.
            - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different
              lengths).
        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
            Activates and controls truncation. Accepts the following values:

            - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to
              the maximum acceptable input length for the model if that argument is not provided. This will truncate
              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch
              of pairs) is provided.
            - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the first
              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the
              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths
              greater than the model maximum admissible input size).
        max_length (`int`, *optional*):
                Controls the maximum length to use by one of the truncation/padding parameters.

                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
                is required by one of the truncation/padding parameters. If the model has no specific maximum input
                length (like XLNet) truncation/padding to a maximum length will be deactivated.
        return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors instead of list of python integers. Acceptable values are:

                - `\'tf\'`: Return TensorFlow `tf.constant` objects.
                - `\'pt\'`: Return PyTorch `torch.Tensor` objects.
                - `\'np\'`: Return Numpy `np.ndarray` objects.
        return_attention_mask (`bool`, *optional*):
            Whether or not to return the attention mask. If not set, will return the attention mask according to the
            specific tokenizer\'s default, defined by the `return_outputs` attribute.

            [What are attention masks?](../glossary#attention-mask)

    Return:
        `Dict[str, List[List[int]]]`: A dictionary with the following keys:

        - `input_ids`: List of token ids to be fed to a model.
        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.
    '''




@add_start_docstrings(snake_case_						)
class     _SCREAMING_SNAKE_CASE     :



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



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

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



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  )				->       List[DPRSpanPrediction]:
					lowerCamelCase_						=			[]
					for start_index, start_score in enumerate(lowercase ):
									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) )
					lowerCamelCase_						=			sorted(lowercase						,  key=lambda lowercase : x[1]						,  reverse=lowercase )
					lowerCamelCase_						=			[]
					for (start_index, end_index), score in scores:
									assert start_index <= end_index, f'Wrong span indices: [{start_index}:{end_index}]'
									lowerCamelCase_						=			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(lowercase ) == top_spans:
													break
					return chosen_span_intervals




@add_end_docstrings(snake_case_						)
class     _SCREAMING_SNAKE_CASE     (      snake_case_			,       snake_case_						):
	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']
	lowerCAmelCase__         = DPRReaderTokenizer




import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field

import torch

from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging


__A				=logging.get_logger(__name__)





def 			lowerCamelCase_ (				):
				# Get the sagemaker specific mp parameters from smp_options variable.
				lowerCamelCase_						=			os.getenv("SM_HP_MP_PARAMETERS"	, "{}"    )
				try:
								# Parse it and check the field "partitions" is included, it is required for model parallel.
								lowerCamelCase_						=			json.loads(lowerCamelCase__    )
								if "partitions" not in smp_options:
												return False
				except json.JSONDecodeError:
								return False

				# Get the sagemaker specific framework parameters from mpi_options variable.
				lowerCamelCase_						=			os.getenv("SM_FRAMEWORK_PARAMS"	, "{}"    )
				try:
								# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
								lowerCamelCase_						=			json.loads(lowerCamelCase__    )
								if not mpi_options.get("sagemaker_mpi_enabled"	, lowerCamelCase__    ):
												return False
				except json.JSONDecodeError:
								return False
				# Lastly, check if the `smdistributed` module is present.
				return importlib.util.find_spec("smdistributed"    ) is not None


if is_sagemaker_model_parallel_available():
			import smdistributed.modelparallel.torch as smp

			smp.init()





@dataclass
class     _SCREAMING_SNAKE_CASE     (      snake_case_						):
	lowerCAmelCase__         = field(
	    default=''			,       metadata={'help': 'Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer'}			,       )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Tuple:
					super().__post_init__()
					warnings.warn(
					    "`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use "
					    "`TrainingArguments` instead."						,  lowercase						,  )



	@cached_property
	def     SCREAMING_SNAKE_CASE_(    self )				->       "torch.device":
					logger.info("PyTorch: setting up devices" )
					if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
									logger.warning(
									    "torch.distributed process group is initialized, but local_rank == -1. "
									    "In order to use Torch DDP, launch your script with `python -m torch.distributed.launch" )
					if self.no_cuda:
									lowerCamelCase_						=			torch.device("cpu" )
									lowerCamelCase_						=			0
					elif is_sagemaker_model_parallel_available():
									lowerCamelCase_						=			smp.local_rank()
									lowerCamelCase_						=			torch.device("cuda"						,  lowercase )
									lowerCamelCase_						=			1
					elif is_sagemaker_dp_enabled():
									import smdistributed.dataparallel.torch.torch_smddp  # noqa: F401

									torch.distributed.init_process_group(backend="smddp"						,  timeout=self.ddp_timeout_delta )
									lowerCamelCase_						=			int(os.getenv("SMDATAPARALLEL_LOCAL_RANK" ) )
									lowerCamelCase_						=			torch.device("cuda"						,  self.local_rank )
									lowerCamelCase_						=			1
					elif self.local_rank == -1:
									# if n_gpu is > 1 we'll use nn.DataParallel.
									# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
									# Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
									# trigger an error that a device index is missing. Index 0 takes into account the
									# GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
									# will use the first GPU in that env, i.e. GPU#1
									lowerCamelCase_						=			torch.device("cuda:0" if torch.cuda.is_available() else "cpu" )
									# Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
									# the default value.
									lowerCamelCase_						=			torch.cuda.device_count()
					else:
									# Here, we'll use torch.distributed.
									# Initializes the distributed backend which will take care of synchronizing nodes/GPUs
									if not torch.distributed.is_initialized():
													torch.distributed.init_process_group(backend="nccl"						,  timeout=self.ddp_timeout_delta )
									lowerCamelCase_						=			torch.device("cuda"						,  self.local_rank )
									lowerCamelCase_						=			1

					if device.type == "cuda":
									torch.cuda.set_device(lowercase )

					return device



	@property
	def     SCREAMING_SNAKE_CASE_(    self )				->       Tuple:
					if is_sagemaker_model_parallel_available():
									return smp.dp_size()

					return super().world_size



	@property
	def     SCREAMING_SNAKE_CASE_(    self )				->       List[str]:
					return not is_sagemaker_model_parallel_available()



	@property
	def     SCREAMING_SNAKE_CASE_(    self )				->       Dict:
					return False


