




"""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


_lowerCamelCase  :			str        =       logging.get_logger(__name__)

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

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

class        lowercase      (    __UpperCAmelCase):
				__lowerCAmelCase    :    Tuple         =   VOCAB_FILES_NAMES
				__lowerCAmelCase    :    List[Any]         =   CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
				__lowerCAmelCase    :    Any         =   CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__lowerCAmelCase    :    str         =   CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION

class        lowercase      (    __UpperCAmelCase):
				__lowerCAmelCase    :    int         =   VOCAB_FILES_NAMES
				__lowerCAmelCase    :    Any         =   QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
				__lowerCAmelCase    :    str         =   QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__lowerCAmelCase    :    List[str]         =   QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
_lowerCamelCase  :			Dict        =       collections.namedtuple(
    'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text']
)

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


_lowerCamelCase  :			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    ```\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n    ```\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    Returns:\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        lowercase      :







				def __call__(							self   :					Optional[Any]      ,     _lowerCamelCase   :					Union[str, 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   :					Any      ,     ):



					"""simple docstring"""

					if titles is None and texts is None:
						return super().__call__(
						    __UpperCamelCase      ,     padding=__UpperCamelCase      ,     truncation=__UpperCamelCase      ,     max_length=__UpperCamelCase      ,     return_tensors=__UpperCamelCase      ,     return_attention_mask=__UpperCamelCase      ,     **__UpperCamelCase      ,     )
					elif titles is None or texts is None:
						A_  :				Union[str, Any]	   =		titles if texts is None else texts
						return super().__call__(
						    __UpperCamelCase      ,     __UpperCamelCase      ,     padding=__UpperCamelCase      ,     truncation=__UpperCamelCase      ,     max_length=__UpperCamelCase      ,     return_tensors=__UpperCamelCase      ,     return_attention_mask=__UpperCamelCase      ,     **__UpperCamelCase      ,     )
					A_  :				str	   =		titles if not isinstance(__UpperCamelCase      ,     __UpperCamelCase       ) else [titles]
					A_  :				List[str]	   =		texts if not isinstance(__UpperCamelCase      ,     __UpperCamelCase       ) else [texts]
					A_  :				Dict	   =		len(__UpperCamelCase       )
					A_  :				Any	   =		questions if not isinstance(__UpperCamelCase      ,     __UpperCamelCase       ) else [questions] * n_passages
					if len(__UpperCamelCase       ) != len(__UpperCamelCase       ):
						raise ValueError(
						    F"""There should be as many titles than texts but got {len(__UpperCamelCase       )} titles and {len(__UpperCamelCase       )} texts."""       )
					A_  :				List[str]	   =		super().__call__(__UpperCamelCase      ,     __UpperCamelCase      ,     padding=__UpperCamelCase      ,     truncation=__UpperCamelCase       )['''input_ids''']
					A_  :				Dict	   =		super().__call__(__UpperCamelCase      ,     add_special_tokens=__UpperCamelCase      ,     padding=__UpperCamelCase      ,     truncation=__UpperCamelCase       )['''input_ids''']
					A_  :				List[Any]	   =		{
					    '''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(__UpperCamelCase      ,     __UpperCamelCase       )
					    ]
					}
					if return_attention_mask is not False:
						A_  :				Dict	   =		[]
						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_  :				List[Any]	   =		attention_mask
					return self.pad(__UpperCamelCase      ,     padding=__UpperCamelCase      ,     max_length=__UpperCamelCase      ,     return_tensors=__UpperCamelCase       )







				def 					a_  (							self   :					Optional[int]      ,     _lowerCamelCase   :					BatchEncoding      ,     _lowerCamelCase   :					DPRReaderOutput      ,     _lowerCamelCase   :					int = 16      ,     _lowerCamelCase   :					int = 64      ,     _lowerCamelCase   :					int = 4      ,     ):



					"""simple docstring"""

					A_  :				Any	   =		reader_input['''input_ids''']
					A_   , A_   , A_  :				List[Any]	   =		reader_output[:3]
					A_  :				Union[str, Any]	   =		len(__UpperCamelCase       )
					A_  :				Dict	   =		sorted(range(__UpperCamelCase       )      ,     reverse=__UpperCamelCase      ,     key=relevance_logits.__getitem__       )
					A_  :				Dict	   =		[]
					for doc_id in sorted_docs:
						A_  :				List[Any]	   =		list(input_ids[doc_id]       )
						# assuming question & title information is at the beginning of the sequence
						A_  :				Tuple	   =		sequence_ids.index(self.sep_token_id      ,     2       ) + 1  # second sep id
						if sequence_ids[-1] == self.pad_token_id:
							A_  :				Dict	   =		sequence_ids.index(self.pad_token_id       )
						else:
							A_  :				Optional[Any]	   =		len(__UpperCamelCase       )

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







				def 					a_  (							self   :					Tuple      ,     _lowerCamelCase   :					List[int]      ,     _lowerCamelCase   :					List[int]      ,     _lowerCamelCase   :					int      ,     _lowerCamelCase   :					int      ,     ):



					"""simple docstring"""

					A_  :				int	   =		[]
					for start_index, start_score in enumerate(__UpperCamelCase       ):
						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_  :				int	   =		sorted(__UpperCamelCase      ,     key=lambda _lowerCamelCase       : x[1]      ,     reverse=__UpperCamelCase       )
					A_  :				str	   =		[]
					for (start_index, end_index), score in scores:
						if start_index > end_index:
							raise ValueError(F"""Wrong span indices: [{start_index}:{end_index}]"""       )
						A_  :				Optional[Any]	   =		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(__UpperCamelCase       ) == top_spans:
							break
					return chosen_span_intervals

@add_end_docstrings(__UpperCAmelCase)
class        lowercase      (    __UpperCAmelCase     ,	__UpperCAmelCase):
				__lowerCAmelCase    :    List[str]         =   VOCAB_FILES_NAMES
				__lowerCAmelCase    :    Tuple         =   READER_PRETRAINED_VOCAB_FILES_MAP
				__lowerCAmelCase    :    Optional[int]         =   READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__lowerCAmelCase    :    Dict         =   READER_PRETRAINED_INIT_CONFIGURATION
				__lowerCAmelCase    :    int         =   ["""input_ids""", """attention_mask"""]









"""simple docstring"""

import argparse
from collections import OrderedDict
from pathlib import Path

import torch

from transformers import (
    VisualBertConfig,
    VisualBertForMultipleChoice,
    VisualBertForPreTraining,
    VisualBertForQuestionAnswering,
    VisualBertForVisualReasoning,
)
from transformers.utils import logging


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

_lowerCamelCase  :			Any        =       [
    ('bert.bert', 'visual_bert'),
    ('bert.cls', 'cls'),
    ('bert.classifier', 'cls'),
    ('token_type_embeddings_visual', 'visual_token_type_embeddings'),
    ('position_embeddings_visual', 'visual_position_embeddings'),
    ('projection', 'visual_projection'),
]

_lowerCamelCase  :			int        =       [
    'nlvr2_coco_pre_trained.th',
    'nlvr2_fine_tuned.th',
    'nlvr2_pre_trained.th',
    'vcr_coco_pre_train.th',
    'vcr_fine_tune.th',
    'vcr_pre_train.th',
    'vqa_coco_pre_trained.th',
    'vqa_fine_tuned.th',
    'vqa_pre_trained.th',
]

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	A_  :				Union[str, Any]	   =		torch.load(_UpperCAmelCase  ,      map_location='''cpu'''	)
	return sd

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase=rename_keys_prefix	):




	"""simple docstring"""






	A_  :				Any	   =		OrderedDict()
	A_  :				Union[str, Any]	   =		torch.arange(config.max_position_embeddings	).expand((1, -1)	)
	# detector_d = OrderedDict()
	for key in d:
		if "detector" in key:
			# detector_d[key.replace('detector.','')] = d[key]
			continue
		A_  :				List[Any]	   =		key
		for name_pair in rename_keys_prefix:
			A_  :				Any	   =		new_key.replace(name_pair[0]  ,      name_pair[1]	)
		A_  :				Dict	   =		d[key]
		if key == "bert.cls.predictions.decoder.weight":
			# Old bert code didn't have `decoder.bias`, but was added separately
			A_  :				Any	   =		new_d['''cls.predictions.bias''']
	return new_d

@torch.no_grad()
def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase	):




	"""simple docstring"""






	assert (
	    checkpoint_path.split('''/'''	)[-1] in ACCEPTABLE_CHECKPOINTS
	), f"""The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}."""

	# Get Config
	if "pre" in checkpoint_path:
		A_  :				List[Any]	   =		'''pretraining'''
		if "vcr" in checkpoint_path:
			A_  :				Optional[Any]	   =		{'''visual_embedding_dim''': 512}
		elif "vqa_advanced" in checkpoint_path:
			A_  :				Tuple	   =		{'''visual_embedding_dim''': 2048}
		elif "vqa" in checkpoint_path:
			A_  :				str	   =		{'''visual_embedding_dim''': 2048}
		elif "nlvr" in checkpoint_path:
			A_  :				List[Any]	   =		{'''visual_embedding_dim''': 1024}
		else:
			raise NotImplementedError(f"""No implementation found for `{checkpoint_path}`."""	)
	else:
		if "vcr" in checkpoint_path:
			A_  :				Any	   =		{'''visual_embedding_dim''': 512}
			A_  :				int	   =		'''multichoice'''
		elif "vqa_advanced" in checkpoint_path:
			A_  :				Any	   =		{'''visual_embedding_dim''': 2048}
			A_  :				Any	   =		'''vqa_advanced'''
		elif "vqa" in checkpoint_path:
			A_  :				int	   =		{'''visual_embedding_dim''': 2048, '''num_labels''': 3129}
			A_  :				Any	   =		'''vqa'''
		elif "nlvr" in checkpoint_path:
			A_  :				List[Any]	   =		{
			    '''visual_embedding_dim''': 1024,
			    '''num_labels''': 2,
			}
			A_  :				List[Any]	   =		'''nlvr'''

	A_  :				Dict	   =		VisualBertConfig(**_UpperCAmelCase	)

	# Load State Dict
	A_  :				List[str]	   =		load_state_dict(_UpperCAmelCase	)

	A_  :				List[str]	   =		get_new_dict(_UpperCAmelCase  ,      _UpperCAmelCase	)

	if model_type == "pretraining":
		A_  :				List[Any]	   =		VisualBertForPreTraining(_UpperCAmelCase	)
	elif model_type == "vqa":
		A_  :				Optional[Any]	   =		VisualBertForQuestionAnswering(_UpperCAmelCase	)
	elif model_type == "nlvr":
		A_  :				Any	   =		VisualBertForVisualReasoning(_UpperCAmelCase	)
	elif model_type == "multichoice":
		A_  :				Optional[int]	   =		VisualBertForMultipleChoice(_UpperCAmelCase	)

	model.load_state_dict(_UpperCAmelCase	)
	# Save Checkpoints
	Path(_UpperCAmelCase	).mkdir(exist_ok=_UpperCAmelCase	)
	model.save_pretrained(_UpperCAmelCase	)


if __name__ == "__main__":
		_lowerCamelCase  :			Dict        =       argparse.ArgumentParser()
		# Required parameters
		parser.add_argument('orig_checkpoint_path', type=str, help='A path to .th on local filesystem.')
		parser.add_argument('pytorch_dump_folder_path', type=str, help='Path to the output PyTorch model.')
		_lowerCamelCase  :			Optional[int]        =       parser.parse_args()
		convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)





