



import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
	from transformers.pipelines.conversational import Conversation

a	:   Tuple       			=       logging.get_logger(__name__)


a	:   List[Any]       			=       {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

a	:   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'
    },
}

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







@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def       lowerCAmelCase_     ():





			"""simple docstring"""


			UpperCAmelCase_:						Dict					=			(
			    list(range(ord("""!"""   )						,		ord("""~"""   ) + 1   )   ) + list(range(ord("""¡"""   )						,		ord("""¬"""   ) + 1   )   ) + list(range(ord("""®"""   )						,		ord("""ÿ"""   ) + 1   )   )
			)
			UpperCAmelCase_:						List[Any]					=			bs[:]
			UpperCAmelCase_:						Optional[int]					=			0
			for b in range(2**8   ):
						if b not in bs:
									bs.append(_snake_case   )
									cs.append(2**8 + n   )
									n += 1
			UpperCAmelCase_:						List[Any]					=			[chr(_snake_case   ) for n in cs]
			return dict(zip(_snake_case						,		_snake_case   )   )







def       lowerCAmelCase_     (lowerCAmelCase__:						Tuple   ):





			"""simple docstring"""


			UpperCAmelCase_:						Union[str, Any]					=			set()
			UpperCAmelCase_:						Optional[Any]					=			word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)   )
						UpperCAmelCase_:						Tuple					=			char
			return pairs







class 							_a							(					a__ ):
			A							=     VOCAB_FILES_NAMES
			A							=     PRETRAINED_VOCAB_FILES_MAP
			A							=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			A							=     ['''input_ids''', '''attention_mask''']

			def __init__(self,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_="replace",					SCREAMING_SNAKE_CASE_="<s>",					SCREAMING_SNAKE_CASE_="</s>",					SCREAMING_SNAKE_CASE_="</s>",					SCREAMING_SNAKE_CASE_="<s>",					SCREAMING_SNAKE_CASE_="<unk>",					SCREAMING_SNAKE_CASE_="<pad>",					SCREAMING_SNAKE_CASE_="<mask>",					SCREAMING_SNAKE_CASE_=False,					**SCREAMING_SNAKE_CASE_,					)    ->					Union[str, Any]:
						UpperCAmelCase_:						List[str]					=			AddedToken(SCREAMING_SNAKE_CASE__,					lstrip=SCREAMING_SNAKE_CASE__,					rstrip=SCREAMING_SNAKE_CASE__ ) if isinstance(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__ ) else bos_token
						UpperCAmelCase_:						Dict					=			AddedToken(SCREAMING_SNAKE_CASE__,					lstrip=SCREAMING_SNAKE_CASE__,					rstrip=SCREAMING_SNAKE_CASE__ ) if isinstance(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__ ) else eos_token
						UpperCAmelCase_:						str					=			AddedToken(SCREAMING_SNAKE_CASE__,					lstrip=SCREAMING_SNAKE_CASE__,					rstrip=SCREAMING_SNAKE_CASE__ ) if isinstance(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__ ) else sep_token
						UpperCAmelCase_:						Any					=			AddedToken(SCREAMING_SNAKE_CASE__,					lstrip=SCREAMING_SNAKE_CASE__,					rstrip=SCREAMING_SNAKE_CASE__ ) if isinstance(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__ ) else cls_token
						UpperCAmelCase_:						Union[str, Any]					=			AddedToken(SCREAMING_SNAKE_CASE__,					lstrip=SCREAMING_SNAKE_CASE__,					rstrip=SCREAMING_SNAKE_CASE__ ) if isinstance(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__ ) else unk_token
						UpperCAmelCase_:						Any					=			AddedToken(SCREAMING_SNAKE_CASE__,					lstrip=SCREAMING_SNAKE_CASE__,					rstrip=SCREAMING_SNAKE_CASE__ ) if isinstance(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__ ) else pad_token

						# Mask token behave like a normal word, i.e. include the space before it
						UpperCAmelCase_:						int					=			AddedToken(SCREAMING_SNAKE_CASE__,					lstrip=SCREAMING_SNAKE_CASE__,					rstrip=SCREAMING_SNAKE_CASE__ ) if isinstance(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__ ) else mask_token

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

						with open(SCREAMING_SNAKE_CASE__,					encoding="""utf-8""" ) as vocab_handle:
									UpperCAmelCase_:						int					=			json.load(SCREAMING_SNAKE_CASE__ )
						UpperCAmelCase_:						Dict					=			{v: k for k, v in self.encoder.items()}
						UpperCAmelCase_:						List[Any]					=			errors  # how to handle errors in decoding
						UpperCAmelCase_:						List[str]					=			bytes_to_unicode()
						UpperCAmelCase_:						List[str]					=			{v: k for k, v in self.byte_encoder.items()}
						with open(SCREAMING_SNAKE_CASE__,					encoding="""utf-8""" ) as merges_handle:
									UpperCAmelCase_:						Dict					=			merges_handle.read().split("""\n""" )[1:-1]
						UpperCAmelCase_:						Union[str, Any]					=			[tuple(merge.split() ) for merge in bpe_merges]
						UpperCAmelCase_:						Optional[Any]					=			dict(zip(SCREAMING_SNAKE_CASE__,					range(len(SCREAMING_SNAKE_CASE__ ) ) ) )
						UpperCAmelCase_:						Optional[int]					=			{}
						UpperCAmelCase_:						List[str]					=			add_prefix_space

						# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
						UpperCAmelCase_:						Tuple					=			re.compile(R"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""" )

			@property
			# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
			def 			__snake_case    (self )    ->					int:
						return len(self.encoder )

			def 			__snake_case    (self )    ->					int:
						return dict(self.encoder,					**self.added_tokens_encoder )

			def 			__snake_case    (self,					SCREAMING_SNAKE_CASE_ )    ->					Tuple:




						if token in self.cache:
									return self.cache[token]
						UpperCAmelCase_:						int					=			tuple(SCREAMING_SNAKE_CASE__ )
						UpperCAmelCase_:						Union[str, Any]					=			get_pairs(SCREAMING_SNAKE_CASE__ )

						if not pairs:
									return token

						while True:
									UpperCAmelCase_:						Optional[int]					=			min(SCREAMING_SNAKE_CASE__,					key=lambda SCREAMING_SNAKE_CASE_ : self.bpe_ranks.get(SCREAMING_SNAKE_CASE__,					float("""inf""" ) ) )
									if bigram not in self.bpe_ranks:
												break
									UpperCAmelCase_:						List[Any]					=			bigram
									UpperCAmelCase_:						Any					=			[]
									UpperCAmelCase_:						Optional[Any]					=			0
									while i < len(SCREAMING_SNAKE_CASE__ ):
												try:
															UpperCAmelCase_:						Tuple					=			word.index(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__ )
												except ValueError:
															new_word.extend(word[i:] )
															break
												else:
															new_word.extend(word[i:j] )
															UpperCAmelCase_:						Dict					=			j

												if word[i] == first and i < len(SCREAMING_SNAKE_CASE__ ) - 1 and word[i + 1] == second:
															new_word.append(first + second )
															i += 2
												else:
															new_word.append(word[i] )
															i += 1
									UpperCAmelCase_:						str					=			tuple(SCREAMING_SNAKE_CASE__ )
									UpperCAmelCase_:						List[Any]					=			new_word
									if len(SCREAMING_SNAKE_CASE__ ) == 1:
												break
									else:
												UpperCAmelCase_:						Tuple					=			get_pairs(SCREAMING_SNAKE_CASE__ )
						UpperCAmelCase_:						List[str]					=			""" """.join(SCREAMING_SNAKE_CASE__ )
						UpperCAmelCase_:						Union[str, Any]					=			word
						return word

			def 			__snake_case    (self,					SCREAMING_SNAKE_CASE_ )    ->					Union[str, Any]:
						UpperCAmelCase_:						Dict					=			[]
						for token in re.findall(self.pat,					SCREAMING_SNAKE_CASE__ ):
									UpperCAmelCase_:						List[Any]					=			"""""".join(
									    self.byte_encoder[b] for b in token.encode("""utf-8""" ) )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
									bpe_tokens.extend(bpe_token for bpe_token in self.bpe(SCREAMING_SNAKE_CASE__ ).split(""" """ ) )
						return bpe_tokens

			def 			__snake_case    (self,					SCREAMING_SNAKE_CASE_ )    ->					Optional[int]:
						return self.encoder.get(SCREAMING_SNAKE_CASE__,					self.encoder.get(self.unk_token ) )

			def 			__snake_case    (self,					SCREAMING_SNAKE_CASE_ )    ->					str:
						return self.decoder.get(SCREAMING_SNAKE_CASE__ )

			def 			__snake_case    (self,					SCREAMING_SNAKE_CASE_ )    ->					Optional[Any]:
						UpperCAmelCase_:						Optional[Any]					=			"""""".join(SCREAMING_SNAKE_CASE__ )
						UpperCAmelCase_:						Union[str, Any]					=			bytearray([self.byte_decoder[c] for c in text] ).decode("""utf-8""",					errors=self.errors )
						return text

			def 			__snake_case    (self,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_ = None )    ->					Tuple[str]:




						if not os.path.isdir(SCREAMING_SNAKE_CASE__ ):
									logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
									return
						UpperCAmelCase_:						List[Any]					=			os.path.join(
						    SCREAMING_SNAKE_CASE__,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
						UpperCAmelCase_:						Optional[int]					=			os.path.join(
						    SCREAMING_SNAKE_CASE__,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""] )

						with open(SCREAMING_SNAKE_CASE__,					"""w""",					encoding="""utf-8""" ) as f:
									f.write(json.dumps(self.encoder,					indent=2,					sort_keys=SCREAMING_SNAKE_CASE__,					ensure_ascii=SCREAMING_SNAKE_CASE__ ) + """\n""" )

						UpperCAmelCase_:						Union[str, Any]					=			0
						with open(SCREAMING_SNAKE_CASE__,					"""w""",					encoding="""utf-8""" ) as writer:
									writer.write("""#version: 0.2\n""" )
									for bpe_tokens, token_index in sorted(self.bpe_ranks.items(),					key=lambda SCREAMING_SNAKE_CASE_ : kv[1] ):
												if index != token_index:
															logger.warning(
															    f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
															    """ Please check that the tokenizer is not corrupted!""" )
															UpperCAmelCase_:						str					=			token_index
												writer.write(""" """.join(SCREAMING_SNAKE_CASE__ ) + """\n""" )
												index += 1

						return vocab_file, merge_file

			def 			__snake_case    (self,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_ = None,					SCREAMING_SNAKE_CASE_ = False )    ->					List[int]:




						if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=SCREAMING_SNAKE_CASE__,					token_ids_a=SCREAMING_SNAKE_CASE__,					already_has_special_tokens=SCREAMING_SNAKE_CASE__ )

						if token_ids_a is None:
									return [1] + ([0] * len(SCREAMING_SNAKE_CASE__ )) + [1]
						return [1] + ([0] * len(SCREAMING_SNAKE_CASE__ )) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE__ )) + [1]

			def 			__snake_case    (self,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_ = None )    ->					List[int]:
						UpperCAmelCase_:						Optional[int]					=			[self.sep_token_id]
						UpperCAmelCase_:						List[str]					=			[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 			__snake_case    (self,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_=False,					**SCREAMING_SNAKE_CASE_ )    ->					Union[str, Any]:
						UpperCAmelCase_:						Union[str, Any]					=			kwargs.pop("""add_prefix_space""",					self.add_prefix_space )
						if (is_split_into_words or add_prefix_space) and (len(SCREAMING_SNAKE_CASE__ ) > 0 and not text[0].isspace()):
									UpperCAmelCase_:						List[Any]					=			""" """ + text
						return (text, kwargs)

			def 			__snake_case    (self,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_ = None )    ->					int:
						return token_ids_a + [self.eos_token_id]

			def 			__snake_case    (self,					SCREAMING_SNAKE_CASE_ )    ->					List[int]:
						UpperCAmelCase_:						Tuple					=			[]
						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(SCREAMING_SNAKE_CASE__ )

						UpperCAmelCase_:						List[str]					=			"""  """.join(SCREAMING_SNAKE_CASE__ )
						UpperCAmelCase_:						Dict					=			self.encode(SCREAMING_SNAKE_CASE__ )
						if len(SCREAMING_SNAKE_CASE__ ) > self.model_max_length:
									UpperCAmelCase_:						Any					=			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
import os

import torch

from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint

def lowercase_  (   _snake_case						):
							# encoder.embeddings are double copied in original FLAVA
							return sum(param.float().sum() if """encoder.embeddings""" not in key else 0 for key, param in state_dict.items()						)

def lowercase_  (   _snake_case   ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:       Any           =   {}

							for key, value in state_dict.items():
														if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
																					continue

														SCREAMING_SNAKE_CASE__						:       Optional[int]           =   key.replace("""heads.cmd.mim_head.cls.predictions"""   ,"""mmm_image_head"""						)
														SCREAMING_SNAKE_CASE__						:       Dict           =   key.replace("""heads.cmd.mlm_head.cls.predictions"""   ,"""mmm_text_head"""						)
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   key.replace("""heads.cmd.itm_head.cls"""   ,"""itm_head"""						)
														SCREAMING_SNAKE_CASE__						:       Tuple           =   key.replace("""heads.cmd.itm_head.pooler"""   ,"""itm_head.pooler"""						)
														SCREAMING_SNAKE_CASE__						:       int           =   key.replace("""heads.cmd.clip_head.logit_scale"""   ,"""flava.logit_scale"""						)
														SCREAMING_SNAKE_CASE__						:       Tuple           =   key.replace("""heads.fairseq_mlm.cls.predictions"""   ,"""mlm_head"""						)
														SCREAMING_SNAKE_CASE__						:       str           =   key.replace("""heads.imagenet.mim_head.cls.predictions"""   ,"""mim_head"""						)
														SCREAMING_SNAKE_CASE__						:       List[str]           =   key.replace("""mm_text_projection"""   ,"""flava.text_to_mm_projection"""						)
														SCREAMING_SNAKE_CASE__						:       Dict           =   key.replace("""mm_image_projection"""   ,"""flava.image_to_mm_projection"""						)
														SCREAMING_SNAKE_CASE__						:       str           =   key.replace("""image_encoder.module"""   ,"""flava.image_model"""						)
														SCREAMING_SNAKE_CASE__						:       Tuple           =   key.replace("""text_encoder.module"""   ,"""flava.text_model"""						)
														SCREAMING_SNAKE_CASE__						:       int           =   key.replace("""mm_encoder.module.encoder.cls_token"""   ,"""flava.multimodal_model.cls_token"""						)
														SCREAMING_SNAKE_CASE__						:       Dict           =   key.replace("""mm_encoder.module"""   ,"""flava.multimodal_model"""						)
														SCREAMING_SNAKE_CASE__						:       Any           =   key.replace("""text_projection"""   ,"""flava.text_projection"""						)
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   key.replace("""image_projection"""   ,"""flava.image_projection"""						)

														SCREAMING_SNAKE_CASE__						:       Tuple           =   value.float()

							for key, value in codebook_state_dict.items():
														SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   value

							return upgrade

@torch.no_grad()
def lowercase_  (   _snake_case   ,_snake_case   ,_snake_case   ,_snake_case=None						):
							if config_path is not None:
														SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   FlavaConfig.from_pretrained(_snake_case						)
							else:
														SCREAMING_SNAKE_CASE__						:       List[str]           =   FlavaConfig()

							SCREAMING_SNAKE_CASE__						:       Optional[int]           =   FlavaForPreTraining(_snake_case						).eval()

							SCREAMING_SNAKE_CASE__						:       List[Any]           =   convert_dalle_checkpoint(_snake_case   ,_snake_case   ,save_checkpoint=_snake_case						)

							if os.path.exists(_snake_case						):
														SCREAMING_SNAKE_CASE__						:       List[str]           =   torch.load(_snake_case   ,map_location="""cpu"""						)
							else:
														SCREAMING_SNAKE_CASE__						:       Tuple           =   torch.hub.load_state_dict_from_url(_snake_case   ,map_location="""cpu"""						)

							SCREAMING_SNAKE_CASE__						:       Dict           =   upgrade_state_dict(_snake_case   ,_snake_case						)
							hf_model.load_state_dict(_snake_case						)
							SCREAMING_SNAKE_CASE__						:       Any           =   hf_model.state_dict()
							SCREAMING_SNAKE_CASE__						:       Any           =   count_parameters(_snake_case						)
							SCREAMING_SNAKE_CASE__						:       str           =   count_parameters(_snake_case						) + count_parameters(_snake_case						)

							assert torch.allclose(_snake_case   ,_snake_case   ,atol=1E-3						)

							hf_model.save_pretrained(_snake_case						)


if __name__ == "__main__":
					UpperCAmelCase__      :    List[Any]    							= argparse.ArgumentParser()
					parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
					parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to flava checkpoint')
					parser.add_argument('--codebook_path', default=None, type=str, help='Path to flava codebook checkpoint')
					parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
					UpperCAmelCase__      :    Optional[int]    							= parser.parse_args()

					convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)




