
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_  :	List[Any]						=					logging.get_logger(__name__)

lowerCamelCase_  :	int						=					{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

lowerCamelCase_  :	Optional[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_  :	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"""
        ),
    },
}
lowerCamelCase_  :	str						=					{
    """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_  :	Any						=					{
    """facebook/dpr-ctx_encoder-single-nq-base""": 5_12,
    """facebook/dpr-ctx_encoder-multiset-base""": 5_12,
}
lowerCamelCase_  :	str						=					{
    """facebook/dpr-question_encoder-single-nq-base""": 5_12,
    """facebook/dpr-question_encoder-multiset-base""": 5_12,
}
lowerCamelCase_  :	Tuple						=					{
    """facebook/dpr-reader-single-nq-base""": 5_12,
    """facebook/dpr-reader-multiset-base""": 5_12,
}


lowerCamelCase_  :	Optional[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_  :	Any						=					{
    """facebook/dpr-reader-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-reader-multiset-base""": {"""do_lower_case""": True},
}

class     _UpperCamelCase			(   _A					):






			'''simple docstring'''

			__UpperCamelCase :  int    =	VOCAB_FILES_NAMES
			__UpperCamelCase :  str    =	CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
			__UpperCamelCase :  str    =	CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__UpperCamelCase :  Any    =	CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
			__UpperCamelCase :  Union[str, Any]    =	DPRContextEncoderTokenizer




class     _UpperCamelCase			(   _A					):






			'''simple docstring'''

			__UpperCamelCase :  str    =	VOCAB_FILES_NAMES
			__UpperCamelCase :  List[str]    =	QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
			__UpperCamelCase :  List[str]    =	QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__UpperCamelCase :  Dict    =	QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
			__UpperCamelCase :  Tuple    =	DPRQuestionEncoderTokenizer




lowerCamelCase_  :	Optional[Any]						=					collections.namedtuple(
    """DPRSpanPrediction""", ["""span_score""", """relevance_score""", """doc_id""", """start_index""", """end_index""", """text"""]
)

lowerCamelCase_  :	Any						=					collections.namedtuple("""DPRReaderOutput""", ["""start_logits""", """end_logits""", """relevance_logits"""])


lowerCamelCase_  :	Dict						=					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(_A					)
class     _UpperCamelCase			:






			'''simple docstring'''

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

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

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




			def  lowerCAmelCase__		(    self	:    Tuple  ,		snake_case_	:    List[int]  ,		snake_case_	:    List[int]  ,		snake_case_	:    int  ,		snake_case_	:    int  ,		):
							UpperCamelCase_:							Dict		=  []
							for start_index, start_score in enumerate(snake_case_ ):
											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) )
							UpperCamelCase_:							List[str]		=  sorted(snake_case_  ,		key=lambda snake_case_ : x[1]  ,		reverse=snake_case_ )
							UpperCamelCase_:							Optional[Any]		=  []
							for (start_index, end_index), score in scores:
											assert start_index <= end_index, f'''Wrong span indices: [{start_index}:{end_index}]'''
											UpperCamelCase_:							List[Any]		=  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(snake_case_ ) == top_spans:
															break
							return chosen_span_intervals




@add_end_docstrings(_A					)
class     _UpperCamelCase			(   _A       ,    _A					):






			'''simple docstring'''

			__UpperCamelCase :  Optional[int]    =	VOCAB_FILES_NAMES
			__UpperCamelCase :  Tuple    =	READER_PRETRAINED_VOCAB_FILES_MAP
			__UpperCamelCase :  Dict    =	READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__UpperCamelCase :  Optional[int]    =	READER_PRETRAINED_INIT_CONFIGURATION
			__UpperCamelCase :  str    =	["""input_ids""", """attention_mask"""]
			__UpperCamelCase :  int    =	DPRReaderTokenizer








import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()







def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )					->		Union[str, Any]:
				# Construct model
				if gpta_config_file == "":
								UpperCamelCase_:							Union[str, Any]		=  GPTaConfig()
				else:
								UpperCamelCase_:							Optional[Any]		=  GPTaConfig.from_json_file(lowerCamelCase  )
				UpperCamelCase_:							List[Any]		=  GPTaModel(lowerCamelCase  )

				# Load weights from numpy
				load_tf_weights_in_gpta(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )

				# Save pytorch-model
				UpperCamelCase_:							int		=  pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
				UpperCamelCase_:							Any		=  pytorch_dump_folder_path + """/""" + CONFIG_NAME
				print(F'''Save PyTorch model to {pytorch_weights_dump_path}'''  )
				torch.save(model.state_dict()       ,      lowerCamelCase  )
				print(F'''Save configuration file to {pytorch_config_dump_path}'''  )
				with open(lowerCamelCase       ,      """w"""       ,      encoding="""utf-8"""  ) as f:
								f.write(config.to_json_string()  )


if __name__ == "__main__":
							lowerCamelCase_  :	int 				=					argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--gpt2_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
							)
							parser.add_argument(
							    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
							)
							parser.add_argument(
							    """--gpt2_config_file""",
							    default="""""",
							    type=str,
							    help=(
							        """An optional config json file corresponding to the pre-trained OpenAI model. \n"""
							        """This specifies the model architecture."""
							    ),
							)
							lowerCamelCase_  :	List[str] 				=					parser.parse_args()
							convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)






