




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_12,
    "facebook/dpr-ctx_encoder-multiset-base": 5_12,
}
__A								=       {
    "facebook/dpr-question_encoder-single-nq-base": 5_12,
    "facebook/dpr-question_encoder-multiset-base": 5_12,
}
__A								=       {
    "facebook/dpr-reader-single-nq-base": 5_12,
    "facebook/dpr-reader-multiset-base": 5_12,
}


__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        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			snake_case_  						=			VOCAB_FILES_NAMES
			snake_case_  						=			CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
			snake_case_  						=			CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			snake_case_  						=			CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
			snake_case_  						=			DPRContextEncoderTokenizer







class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			snake_case_  						=			VOCAB_FILES_NAMES
			snake_case_  						=			QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
			snake_case_  						=			QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			snake_case_  						=			QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
			snake_case_  						=			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"\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(__magic_name__					)
class        __lowerCAmelCase					:

			"""simple docstring"""







			def __call__( self     ,		lowerCamelCase__     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = False     ,		lowerCamelCase__ = False     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		**lowerCamelCase__     ,		)       -> BatchEncoding:



							'''simple docstring'''
							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:
											__lowerCamelCase									=       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__     ,		)
							__lowerCamelCase									=       titles if not isinstance(lowerCamelCase__     ,		lowerCamelCase__				) else [titles]
							__lowerCamelCase									=       texts if not isinstance(lowerCamelCase__     ,		lowerCamelCase__				) else [texts]
							__lowerCamelCase									=       len(lowerCamelCase__				)
							__lowerCamelCase									=       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."""
							__lowerCamelCase									=       super().__call__(lowerCamelCase__     ,		lowerCamelCase__     ,		padding=lowerCamelCase__     ,		truncation=lowerCamelCase__				)['input_ids']
							__lowerCamelCase									=       super().__call__(lowerCamelCase__     ,		add_special_tokens=lowerCamelCase__     ,		padding=lowerCamelCase__     ,		truncation=lowerCamelCase__				)['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(lowerCamelCase__     ,		lowerCamelCase__				)
							    ]
							}
							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(lowerCamelCase__     ,		padding=lowerCamelCase__     ,		max_length=lowerCamelCase__     ,		return_tensors=lowerCamelCase__				)


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__ = 16     ,		lowerCamelCase__ = 64     ,		lowerCamelCase__ = 4     ,		)       -> List[DPRSpanPrediction]:



							'''simple docstring'''
							__lowerCamelCase									=       reader_input['input_ids']
							__lowerCamelCase			,		__lowerCamelCase			,		__lowerCamelCase									=       reader_output[:3]
							__lowerCamelCase									=       len(lowerCamelCase__				)
							__lowerCamelCase									=       sorted(range(lowerCamelCase__				)     ,		reverse=lowerCamelCase__     ,		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(lowerCamelCase__				)

											__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=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  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		)       -> List[DPRSpanPrediction]:



							'''simple docstring'''
							__lowerCamelCase									=       []
							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)				)
							__lowerCamelCase									=       sorted(lowerCamelCase__     ,		key=lambda lowerCamelCase__				: x[1]     ,		reverse=lowerCamelCase__				)
							__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(lowerCamelCase__				) == top_spans:
															break
							return chosen_span_intervals







@add_end_docstrings(__magic_name__					)
class        __lowerCAmelCase					(			__magic_name__     ,							__magic_name__					):

			"""simple docstring"""







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








# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser







def      lowerCamelCase_						(			)	->			Union[str, Any]:





				"""simple docstring"""


				__lowerCamelCase									=       ArgumentParser('Accelerate CLI tool'					,	usage='accelerate <command> [<args>]'					,	allow_abbrev=UpperCamelCase__ )
				__lowerCamelCase									=       parser.add_subparsers(help='accelerate command helpers' )

				# Register commands
				get_config_parser(subparsers=UpperCamelCase__ )
				env_command_parser(subparsers=UpperCamelCase__ )
				launch_command_parser(subparsers=UpperCamelCase__ )
				tpu_command_parser(subparsers=UpperCamelCase__ )
				test_command_parser(subparsers=UpperCamelCase__ )

				# Let's go
				__lowerCamelCase									=       parser.parse_args()

				if not hasattr(UpperCamelCase__					,	'func' ):
								parser.print_help()
								exit(1 )

				# Run
				args.func(UpperCamelCase__ )


if __name__ == "__main__":
					main()


