


"""simple docstring"""
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 import BertTokenizer


__SCREAMING_SNAKE_CASE  :						Tuple    							=      logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE  :						Union[str, Any]    							=      {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__SCREAMING_SNAKE_CASE  :						Optional[int]    							=      {
    '''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'''
        ),
    },
}
__SCREAMING_SNAKE_CASE  :						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'''
        ),
    },
}
__SCREAMING_SNAKE_CASE  :						Union[str, 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'''
        ),
    },
}

__SCREAMING_SNAKE_CASE  :						int    							=      {
    '''facebook/dpr-ctx_encoder-single-nq-base''': 5_1_2,
    '''facebook/dpr-ctx_encoder-multiset-base''': 5_1_2,
}
__SCREAMING_SNAKE_CASE  :						List[str]    							=      {
    '''facebook/dpr-question_encoder-single-nq-base''': 5_1_2,
    '''facebook/dpr-question_encoder-multiset-base''': 5_1_2,
}
__SCREAMING_SNAKE_CASE  :						Optional[Any]    							=      {
    '''facebook/dpr-reader-single-nq-base''': 5_1_2,
    '''facebook/dpr-reader-multiset-base''': 5_1_2,
}


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


class 							lowerCamelCase_(   a__    ):





					'''simple docstring'''



					lowercase__   :       Union[str, Any]										= VOCAB_FILES_NAMES
					lowercase__   :       List[Any]										= CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
					lowercase__   :       List[str]										= CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowercase__   :       List[str]										= CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION


class 							lowerCamelCase_(   a__    ):





					'''simple docstring'''



					lowercase__   :       Tuple										= VOCAB_FILES_NAMES
					lowercase__   :       str										= QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
					lowercase__   :       List[Any]										= QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowercase__   :       str										= QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
__SCREAMING_SNAKE_CASE  :						Tuple    							=      collections.namedtuple(
    '''DPRSpanPrediction''', ['''span_score''', '''relevance_score''', '''doc_id''', '''start_index''', '''end_index''', '''text''']
)

__SCREAMING_SNAKE_CASE  :						List[str]    							=      collections.namedtuple('''DPRReaderOutput''', ['''start_logits''', '''end_logits''', '''relevance_logits'''])


__SCREAMING_SNAKE_CASE  :						Union[str, Any]    							=      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)

    Returns:
        `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 							lowerCamelCase_:





					'''simple docstring'''



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

									if titles is None and texts is None:
													return super().__call__(
													    _A				,       padding=_A				,       truncation=_A				,       max_length=_A				,       return_tensors=_A				,       return_attention_mask=_A				,       **_A				,       )
									elif titles is None or texts is None:
													_lowerCamelCase        =				titles if texts is None else texts
													return super().__call__(
													    _A				,       _A				,       padding=_A				,       truncation=_A				,       max_length=_A				,       return_tensors=_A				,       return_attention_mask=_A				,       **_A				,       )
									_lowerCamelCase        =				titles if not isinstance(_A				,       _A   ) else [titles]
									_lowerCamelCase        =				texts if not isinstance(_A				,       _A   ) else [texts]
									_lowerCamelCase        =				len(_A   )
									_lowerCamelCase        =				questions if not isinstance(_A				,       _A   ) else [questions] * n_passages
									if len(_A   ) != len(_A   ):
													raise ValueError(
													    F"""There should be as many titles than texts but got {len(_A   )} titles and {len(_A   )} texts."""   )
									_lowerCamelCase        =				super().__call__(_A				,       _A				,       padding=_A				,       truncation=_A   )['input_ids']
									_lowerCamelCase        =				super().__call__(_A				,       add_special_tokens=_A				,       padding=_A				,       truncation=_A   )['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(_A				,       _A   )
									    ]
									}
									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(_A				,       padding=_A				,       max_length=_A				,       return_tensors=_A   )

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__ = 1_6				,       lowerCamelCase__ = 6_4				,       lowerCamelCase__ = 4				,       ):
									_lowerCamelCase        =				reader_input['input_ids']
									_lowerCamelCase        =				reader_output[:3]
									_lowerCamelCase        =				len(_A   )
									_lowerCamelCase        =				sorted(range(_A   )				,       reverse=_A				,       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(_A   )

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





					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       ):
									_lowerCamelCase        =				[]
									for start_index, start_score in enumerate(_A   ):
													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(_A				,       key=lambda lowerCamelCase__   : x[1]				,       reverse=_A   )
									_lowerCamelCase        =				[]
									for (start_index, end_index), score in scores:
													if start_index > end_index:
																	raise ValueError(F"""Wrong span indices: [{start_index}:{end_index}]"""   )
													_lowerCamelCase        =				end_index - start_index + 1
													if length > max_answer_length:
																	raise ValueError(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(_A   ) == top_spans:
																	break
									return chosen_span_intervals


@add_end_docstrings(a__    )
class 							lowerCamelCase_(   a__,				a__    ):





					'''simple docstring'''



					lowercase__   :       str										= VOCAB_FILES_NAMES
					lowercase__   :       Tuple										= READER_PRETRAINED_VOCAB_FILES_MAP
					lowercase__   :       Tuple										= READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowercase__   :       Optional[int]										= READER_PRETRAINED_INIT_CONFIGURATION
					lowercase__   :       Tuple										= ['input_ids', 'attention_mask']





"""simple docstring"""
import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken



@torch.no_grad()
def        lowerCAmelCase_(   lowercase_     :							int						,   lowercase_     :							List[Any]						,   lowercase_     :							List[Any]						,   lowercase_     :							Tuple						,   lowercase_     :							List[str]							)	->       Dict:
				# Load configuration defined in the metadata file
				with open(lowercase_							) as metadata_file:
								_lowerCamelCase        =				json.load(lowercase_							)
				_lowerCamelCase        =				LukeConfig(use_entity_aware_attention=lowercase_						,   **metadata['''model_config''']							)

				# Load in the weights from the checkpoint_path
				_lowerCamelCase        =				torch.load(lowercase_						,   map_location='''cpu'''							)

				# Load the entity vocab file
				_lowerCamelCase        =				load_entity_vocab(lowercase_							)

				_lowerCamelCase        =				RobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name''']							)

				# Add special tokens to the token vocabulary for downstream tasks
				_lowerCamelCase        =				AddedToken('''<ent>'''						,   lstrip=lowercase_						,   rstrip=lowercase_							)
				_lowerCamelCase        =				AddedToken('''<ent2>'''						,   lstrip=lowercase_						,   rstrip=lowercase_							)
				tokenizer.add_special_tokens({'''additional_special_tokens''': [entity_token_a, entity_token_a]}							)
				config.vocab_size += 2

				print(F"""Saving tokenizer to {pytorch_dump_folder_path}"""							)
				tokenizer.save_pretrained(lowercase_							)
				with open(os.path.join(lowercase_						,   LukeTokenizer.vocab_files_names['''entity_vocab_file''']							)						,   '''w'''							) as f:
								json.dump(lowercase_						,   lowercase_							)

				_lowerCamelCase        =				LukeTokenizer.from_pretrained(lowercase_							)

				# Initialize the embeddings of the special tokens
				_lowerCamelCase        =				state_dict['''embeddings.word_embeddings.weight''']
				_lowerCamelCase        =				word_emb[tokenizer.convert_tokens_to_ids(['''@''']							)[0]].unsqueeze(0							)
				_lowerCamelCase        =				word_emb[tokenizer.convert_tokens_to_ids(['''#''']							)[0]].unsqueeze(0							)
				_lowerCamelCase        =				torch.cat([word_emb, ent_emb, enta_emb]							)

				# Initialize the query layers of the entity-aware self-attention mechanism
				for layer_index in range(config.num_hidden_layers							):
								for matrix_name in ["query.weight", "query.bias"]:
												_lowerCamelCase        =				F"""encoder.layer.{layer_index}.attention.self."""
												_lowerCamelCase        =				state_dict[prefix + matrix_name]
												_lowerCamelCase        =				state_dict[prefix + matrix_name]
												_lowerCamelCase        =				state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
				_lowerCamelCase        =				state_dict['''entity_embeddings.entity_embeddings.weight''']
				_lowerCamelCase        =				entity_emb[entity_vocab['''[MASK]''']]

				_lowerCamelCase        =				LukeModel(config=lowercase_							).eval()

				_lowerCamelCase  ,     _lowerCamelCase        =				model.load_state_dict(lowercase_						,   strict=lowercase_							)
				if not (len(lowercase_							) == 1 and missing_keys[0] == "embeddings.position_ids"):
								raise ValueError(F"""Missing keys {", ".join(lowercase_							)}. Expected only missing embeddings.position_ids"""							)
				if not (all(key.startswith('''entity_predictions'''							) or key.startswith('''lm_head'''							) for key in unexpected_keys							)):
								raise ValueError(
								    '''Unexpected keys'''
								    F""" {", ".join([key for key in unexpected_keys if not (key.startswith("entity_predictions"							) or key.startswith("lm_head"							))]							)}"""							)

				# Check outputs
				_lowerCamelCase        =				LukeTokenizer.from_pretrained(lowercase_						,   task='''entity_classification'''							)

				_lowerCamelCase        =				(
				    '''Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the'''
				    ''' new world number one avoid a humiliating second- round exit at Wimbledon .'''
				)
				_lowerCamelCase        =				(39, 42)
				_lowerCamelCase        =				tokenizer(lowercase_						,   entity_spans=[span]						,   add_prefix_space=lowercase_						,   return_tensors='''pt'''							)

				_lowerCamelCase        =				model(**lowercase_							)

				# Verify word hidden states
				if model_size == "large":
								_lowerCamelCase        =				torch.Size((1, 42, 10_24)							)
								_lowerCamelCase        =				torch.tensor(
								    [[0.0_1_3_3, 0.0_8_6_5, 0.0_0_9_5], [0.3_0_9_3, -0.2_5_7_6, -0.7_4_1_8], [-0.1_7_2_0, -0.2_1_1_7, -0.2_8_6_9]]							)
				else:  # base
								_lowerCamelCase        =				torch.Size((1, 42, 7_68)							)
								_lowerCamelCase        =				torch.tensor([[0.0_0_3_7, 0.1_3_6_8, -0.0_0_9_1], [0.1_0_9_9, 0.3_3_2_9, -0.1_0_9_5], [0.0_7_6_5, 0.5_3_3_5, 0.1_1_7_9]]							)

				if not (outputs.last_hidden_state.shape == expected_shape):
								raise ValueError(
								    F"""Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}"""							)
				if not torch.allclose(outputs.last_hidden_state[0, :3, :3]						,   lowercase_						,   atol=1e-4							):
								raise ValueError

				# Verify entity hidden states
				if model_size == "large":
								_lowerCamelCase        =				torch.Size((1, 1, 10_24)							)
								_lowerCamelCase        =				torch.tensor([[0.0_4_6_6, -0.0_1_0_6, -0.0_1_7_9]]							)
				else:  # base
								_lowerCamelCase        =				torch.Size((1, 1, 7_68)							)
								_lowerCamelCase        =				torch.tensor([[0.1_4_5_7, 0.1_0_4_4, 0.0_1_7_4]]							)

				if not (outputs.entity_last_hidden_state.shape != expected_shape):
								raise ValueError(
								    F"""Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is"""
								    F""" {expected_shape}"""							)
				if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3]						,   lowercase_						,   atol=1e-4							):
								raise ValueError

				# Finally, save our PyTorch model and tokenizer
				print('''Saving PyTorch model to {}'''.format(lowercase_							)							)
				model.save_pretrained(lowercase_							)



def        lowerCAmelCase_(   lowercase_     :							Optional[Any]							)	->       Any:
				_lowerCamelCase        =				{}
				with open(lowercase_						,   '''r'''						,   encoding='''utf-8'''							) as f:
								for index, line in enumerate(lowercase_							):
												_lowerCamelCase  ,     _lowerCamelCase        =				line.rstrip().split('''\t'''							)
												_lowerCamelCase        =				index

				return entity_vocab


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE  :						List[str]    							=      argparse.ArgumentParser()
			# Required parameters
			parser.add_argument('''--checkpoint_path''', type=str, help='''Path to a pytorch_model.bin file.''')
			parser.add_argument(
			    '''--metadata_path''', default=None, type=str, help='''Path to a metadata.json file, defining the configuration.'''
			)
			parser.add_argument(
			    '''--entity_vocab_path''',
			    default=None,
			    type=str,
			    help='''Path to an entity_vocab.tsv file, containing the entity vocabulary.''',
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to where to dump the output PyTorch model.'''
			)
			parser.add_argument(
			    '''--model_size''', default='''base''', type=str, choices=['''base''', '''large'''], help='''Size of the model to be converted.'''
			)
			__SCREAMING_SNAKE_CASE  :						int    							=      parser.parse_args()
			convert_luke_checkpoint(
			    args.checkpoint_path,
			    args.metadata_path,
			    args.entity_vocab_path,
			    args.pytorch_dump_folder_path,
			    args.model_size,
			)
