




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


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

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

__lowerCAmelCase		:				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'
        ),
    },
}
__lowerCAmelCase		:				Union[str, 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'
        ),
    },
}
__lowerCAmelCase		:				Any       			=		{
    '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'
        ),
    },
}

__lowerCAmelCase		:				Union[str, Any]       			=		{
    'facebook/dpr-ctx_encoder-single-nq-base': 512,
    'facebook/dpr-ctx_encoder-multiset-base': 512,
}
__lowerCAmelCase		:				Tuple       			=		{
    'facebook/dpr-question_encoder-single-nq-base': 512,
    'facebook/dpr-question_encoder-multiset-base': 512,
}
__lowerCAmelCase		:				Union[str, Any]       			=		{
    'facebook/dpr-reader-single-nq-base': 512,
    'facebook/dpr-reader-multiset-base': 512,
}


__lowerCAmelCase		:				Union[str, Any]       			=		{
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
__lowerCAmelCase		:				Tuple       			=		{
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
__lowerCAmelCase		:				str       			=		{
    'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}




class     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							Optional[Any]							 =					VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE_       :							Dict							 =					CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE_       :							Optional[int]							 =					CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE_       :							List[str]							 =					CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
				SCREAMING_SNAKE_CASE_       :							Dict							 =					DPRContextEncoderTokenizer




class     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							int							 =					VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE_       :							str							 =					QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE_       :							Union[str, Any]							 =					QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE_       :							Dict							 =					QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
				SCREAMING_SNAKE_CASE_       :							Optional[int]							 =					DPRQuestionEncoderTokenizer

__lowerCAmelCase		:				List[str]       			=		collections.namedtuple(
    'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text']
)

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


__lowerCAmelCase		:				Optional[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    [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     snake_case__							:
				"""simple docstring"""



				def __call__(      self				:     Tuple					,      __lowerCamelCase				:     List[Any]					,      __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				:     Optional[int]					,      )					->     BatchEncoding:
							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:
										a    =			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					,      )
							a    =			titles if not isinstance(__lowerCamelCase					,      __lowerCamelCase							) else [titles]
							a    =			texts if not isinstance(__lowerCamelCase					,      __lowerCamelCase							) else [texts]
							a    =			len(__lowerCamelCase							)
							a    =			questions if not isinstance(__lowerCamelCase					,      __lowerCamelCase							) else [questions] * n_passages
							assert len(__lowerCamelCase							) == len(
							    __lowerCamelCase							), f"""There should be as many titles than texts but got {len(__lowerCamelCase							)} titles and {len(__lowerCamelCase							)} texts."""
							a    =			super().__call__(__lowerCamelCase					,      __lowerCamelCase					,      padding=__lowerCamelCase					,      truncation=__lowerCamelCase							)["input_ids"]
							a    =			super().__call__(__lowerCamelCase					,      add_special_tokens=__lowerCamelCase					,      padding=__lowerCamelCase					,      truncation=__lowerCamelCase							)["input_ids"]
							a    =			{
							    "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:
										a    =			[]
										for input_ids in encoded_inputs["input_ids"]:
													attention_mask.append([int(input_id != self.pad_token_id							) for input_id in input_ids]							)
										a    =			attention_mask
							return self.pad(__lowerCamelCase					,      padding=__lowerCamelCase					,      max_length=__lowerCamelCase					,      return_tensors=__lowerCamelCase							)





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

										a    =			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 							__UpperCAmelCase							(      self				:     List[str]					,      __lowerCamelCase				:     List[int]					,      __lowerCamelCase				:     List[int]					,      __lowerCamelCase				:     int					,      __lowerCamelCase				:     int					,      )					->     List[DPRSpanPrediction]:
							a    =			[]
							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)							)
							a    =			sorted(__lowerCamelCase					,      key=lambda __lowerCamelCase							: x[1]					,      reverse=__lowerCamelCase							)
							a    =			[]
							for (start_index, end_index), score in scores:
										assert start_index <= end_index, f"""Wrong span indices: [{start_index}:{end_index}]"""
										a    =			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(__lowerCamelCase							) == top_spans:
													break
							return chosen_span_intervals




@add_end_docstrings(_UpperCamelCase   )
class     snake_case__							(_UpperCamelCase    ,       _UpperCamelCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							Union[str, Any]							 =					VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE_       :							List[str]							 =					READER_PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE_       :							Union[str, Any]							 =					READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE_       :							int							 =					READER_PRETRAINED_INIT_CONFIGURATION
				SCREAMING_SNAKE_CASE_       :							int							 =					["""input_ids""", """attention_mask"""]
				SCREAMING_SNAKE_CASE_       :							Optional[Any]							 =					DPRReaderTokenizer











"""simple docstring"""







import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment







class       UpperCamelCase__   (	unittest.TestCase  ):




      """simple docstring"""




      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]       ):
        lowerCAmelCase_   :				Union[str, Any]								= inspect.getfile(accelerate.test_utils       )
        lowerCAmelCase_   :				Dict								= os.path.sep.join(mod_file.split(os.path.sep       )[:-1] + ['scripts', 'test_script.py']       )
        lowerCAmelCase_   :				int								= os.path.sep.join(
            mod_file.split(os.path.sep       )[:-1] + ['scripts', 'test_distributed_data_loop.py']       )
        lowerCAmelCase_   :				Union[str, Any]								= os.path.sep.join(mod_file.split(os.path.sep       )[:-1] + ['scripts', 'test_ops.py']       )



      @require_multi_gpu
      def      SCREAMING_SNAKE_CASE__ (      self				:      Any       ):
        print(F"Found {torch.cuda.device_count()} devices."       )
        lowerCAmelCase_   :				Optional[int]								= ['torchrun', F"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
        with patch_environment(omp_num_threads=1       ):
          execute_subprocess_async(SCREAMING_SNAKE_CASE_    ,			env=os.environ.copy()       )



      @require_multi_gpu
      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[Any]       ):
        print(F"Found {torch.cuda.device_count()} devices."       )
        lowerCAmelCase_   :				int								= ['torchrun', F"--nproc_per_node={torch.cuda.device_count()}", self.operation_file_path]
        print(F"Command: {cmd}"       )
        with patch_environment(omp_num_threads=1       ):
          execute_subprocess_async(SCREAMING_SNAKE_CASE_    ,			env=os.environ.copy()       )



      @require_multi_gpu
      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]       ):
        lowerCAmelCase_   :				List[Any]								= ['torchrun', F"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__       )]
        with patch_environment(omp_num_threads=1       ):
          execute_subprocess_async(SCREAMING_SNAKE_CASE_    ,			env=os.environ.copy()       )





      @require_multi_gpu
      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]       ):
        print(F"Found {torch.cuda.device_count()} devices, using 2 devices only"       )
        lowerCAmelCase_   :				Any								= ['torchrun', F"--nproc_per_node={torch.cuda.device_count()}", self.data_loop_file_path]
        with patch_environment(omp_num_threads=1    ,			cuda_visible_devices='0,1'       ):
          execute_subprocess_async(SCREAMING_SNAKE_CASE_    ,			env=os.environ.copy()       )


if __name__ == "__main__":
     lowercase__		:				Dict          =	Accelerator()
     lowercase__		:				List[Any]          =	(accelerator.state.process_index + 2, 1_0)
     lowercase__		:				Any          =	torch.randint(0, 1_0, shape).to(accelerator.device)

     lowercase__		:				List[Any]          =	""""""

     lowercase__		:				Union[str, Any]          =	accelerator.pad_across_processes(tensor)
     if tensora.shape[0] != accelerator.state.num_processes + 1:
          error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
     if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
          error_msg += "Tensors have different values."
     if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
          error_msg += "Padding was not done with the right value (0)."

     lowercase__		:				Optional[Any]          =	accelerator.pad_across_processes(tensor, pad_first=True)
     if tensora.shape[0] != accelerator.state.num_processes + 1:
          error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
     lowercase__		:				Any          =	accelerator.state.num_processes - accelerator.state.process_index - 1
     if not torch.equal(tensora[index:], tensor):
          error_msg += "Tensors have different values."
     if not torch.all(tensora[:index] == 0):
          error_msg += "Padding was not done with the right value (0)."

     # Raise error at the end to make sure we don't stop at the first failure.
     if len(error_msg) > 0:
          raise ValueError(error_msg)





