




"""simple docstring"""

import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
		from transformers.pipelines.conversational import Conversation

_lowerCamelCase  :			List[str]        =       logging.get_logger(__name__)


_lowerCamelCase  :			Tuple        =       {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

_lowerCamelCase  :			List[str]        =       {
    'vocab_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'},
    'merges_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'},
    'tokenizer_config_file': {
        'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'
    },
}

_lowerCamelCase  :			str        =       {'facebook/blenderbot-3B': 128}

class        lowercase      (    __UpperCAmelCase):
				__lowerCAmelCase    :    str         =   VOCAB_FILES_NAMES
				__lowerCAmelCase    :    List[str]         =   PRETRAINED_VOCAB_FILES_MAP
				__lowerCAmelCase    :    Optional[int]         =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__lowerCAmelCase    :    Optional[Any]         =   ["""input_ids""", """attention_mask"""]
				__lowerCAmelCase    :    Optional[int]         =   BlenderbotTokenizer







				def __init__(							self   :					Any      ,     _lowerCamelCase   :					Dict=None      ,     _lowerCamelCase   :					str=None      ,     _lowerCamelCase   :					Optional[Any]=None      ,     _lowerCamelCase   :					int="replace"      ,     _lowerCamelCase   :					Optional[Any]="<s>"      ,     _lowerCamelCase   :					Optional[Any]="</s>"      ,     _lowerCamelCase   :					Optional[int]="</s>"      ,     _lowerCamelCase   :					Optional[int]="<s>"      ,     _lowerCamelCase   :					Union[str, Any]="<unk>"      ,     _lowerCamelCase   :					int="<pad>"      ,     _lowerCamelCase   :					str="<mask>"      ,     _lowerCamelCase   :					int=False      ,     _lowerCamelCase   :					str=True      ,     **_lowerCamelCase   :					Optional[Any]      ,     ):



					"""simple docstring"""

					super().__init__(
					    _lowerCamelCase      ,     _lowerCamelCase      ,     tokenizer_file=_lowerCamelCase      ,     errors=_lowerCamelCase      ,     bos_token=_lowerCamelCase      ,     eos_token=_lowerCamelCase      ,     sep_token=_lowerCamelCase      ,     cls_token=_lowerCamelCase      ,     unk_token=_lowerCamelCase      ,     pad_token=_lowerCamelCase      ,     mask_token=_lowerCamelCase      ,     add_prefix_space=_lowerCamelCase      ,     trim_offsets=_lowerCamelCase      ,     **_lowerCamelCase      ,     )

					A_  :				Union[str, Any]	   =		json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
					if pre_tok_state.get('''add_prefix_space'''      ,     _lowerCamelCase       ) != add_prefix_space:
						A_  :				Dict	   =		getattr(_lowerCamelCase      ,     pre_tok_state.pop('''type'''       )       )
						A_  :				Tuple	   =		add_prefix_space
						A_  :				List[str]	   =		pre_tok_class(**_lowerCamelCase       )

					A_  :				Tuple	   =		add_prefix_space

					A_  :				List[Any]	   =		'''post_processor'''
					A_  :				List[str]	   =		getattr(self.backend_tokenizer      ,     _lowerCamelCase      ,     _lowerCamelCase       )
					if tokenizer_component_instance:
						A_  :				Optional[int]	   =		json.loads(tokenizer_component_instance.__getstate__()       )

						# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
						if "sep" in state:
							A_  :				Optional[Any]	   =		tuple(state['''sep''']       )
						if "cls" in state:
							A_  :				str	   =		tuple(state['''cls''']       )

						A_  :				List[str]	   =		False

						if state.get('''add_prefix_space'''      ,     _lowerCamelCase       ) != add_prefix_space:
							A_  :				List[str]	   =		add_prefix_space
							A_  :				Union[str, Any]	   =		True

						if state.get('''trim_offsets'''      ,     _lowerCamelCase       ) != trim_offsets:
							A_  :				Tuple	   =		trim_offsets
							A_  :				Any	   =		True

						if changes_to_apply:
							A_  :				Tuple	   =		getattr(_lowerCamelCase      ,     state.pop('''type'''       )       )
							A_  :				Tuple	   =		component_class(**_lowerCamelCase       )
							setattr(self.backend_tokenizer      ,     _lowerCamelCase      ,     _lowerCamelCase       )







				@property
				# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
				def 					a_  (							self   :					int       ):



					"""simple docstring"""

					if self._mask_token is None:
						if self.verbose:
							logger.error('''Using mask_token, but it is not set yet.'''       )
						return None
					return str(self._mask_token       )







				@mask_token.setter
				def 					a_  (							self   :					List[Any]      ,     _lowerCamelCase   :					Dict       ):



					"""simple docstring"""

					A_  :				int	   =		AddedToken(_lowerCamelCase      ,     lstrip=_lowerCamelCase      ,     rstrip=_lowerCamelCase       ) if isinstance(_lowerCamelCase      ,     _lowerCamelCase       ) else value
					A_  :				List[str]	   =		value







				def 					a_  (							self   :					int      ,     *_lowerCamelCase   :					str      ,     **_lowerCamelCase   :					Optional[int]       ):



					"""simple docstring"""

					A_  :				Optional[Any]	   =		kwargs.get('''is_split_into_words'''      ,     _lowerCamelCase       )
					assert self.add_prefix_space or not is_split_into_words, (
					    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
					    "to use it with pretokenized inputs."
					)

					return super()._batch_encode_plus(*_lowerCamelCase      ,     **_lowerCamelCase       )







				def 					a_  (							self   :					str      ,     *_lowerCamelCase   :					List[str]      ,     **_lowerCamelCase   :					Optional[Any]       ):



					"""simple docstring"""

					A_  :				Any	   =		kwargs.get('''is_split_into_words'''      ,     _lowerCamelCase       )

					assert self.add_prefix_space or not is_split_into_words, (
					    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
					    "to use it with pretokenized inputs."
					)

					return super()._encode_plus(*_lowerCamelCase      ,     **_lowerCamelCase       )







				def 					a_  (							self   :					Any      ,     _lowerCamelCase   :					str      ,     _lowerCamelCase   :					Optional[str] = None       ):



					"""simple docstring"""

					A_  :				Optional[int]	   =		self._tokenizer.model.save(_lowerCamelCase      ,     name=_lowerCamelCase       )
					return tuple(_lowerCamelCase       )







				def 					a_  (							self   :					Union[str, Any]      ,     _lowerCamelCase   :					List[int]      ,     _lowerCamelCase   :					Optional[List[int]] = None       ):



					"""simple docstring"""

					A_  :				Optional[int]	   =		[self.sep_token_id]
					A_  :				Union[str, Any]	   =		[self.cls_token_id]

					if token_ids_a is None:
						return len(cls + token_ids_a + sep       ) * [0]
					return len(cls + token_ids_a + sep + sep + token_ids_a + sep       ) * [0]







				def 					a_  (							self   :					Optional[int]      ,     _lowerCamelCase   :					List[int]      ,     _lowerCamelCase   :					Optional[List[int]] = None       ):



					"""simple docstring"""

					return token_ids_a + [self.eos_token_id]







				def 					a_  (							self   :					Dict      ,     _lowerCamelCase   :					"Conversation"       ):



					"""simple docstring"""

					A_  :				List[str]	   =		[]
					for is_user, text in conversation.iter_texts():
						if is_user:
							# We need to space prefix as it's being done within blenderbot
							inputs.append(''' ''' + text       )
						else:
							# Generated responses should contain them already.
							inputs.append(_lowerCamelCase       )

					A_  :				Tuple	   =		'''  '''.join(_lowerCamelCase       )
					A_  :				Union[str, Any]	   =		self.encode(_lowerCamelCase       )
					if len(_lowerCamelCase       ) > self.model_max_length:
						A_  :				Dict	   =		input_ids[-self.model_max_length :]
						logger.warning(F"""Trimmed input from conversation as it was longer than {self.model_max_length} tokens."""       )
					return input_ids











"""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)





