"""simple docstring"""






import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


lowerCAmelCase__  :  Any						     =	[
    # tf -> hf
    ('/', '.'),
    ('layer_', 'layers.'),
    ('kernel', 'weight'),
    ('beta', 'bias'),
    ('gamma', 'weight'),
    ('pegasus', 'model'),
]
lowerCAmelCase__  :  int						     =	[
    ('.output.dense', '.fc2'),
    ('intermediate.LayerNorm', 'final_layer_norm'),
    ('intermediate.dense', 'fc1'),
]

lowerCAmelCase__  :  int						     =	(
    INIT_COMMON
    + [
        ('attention.self.LayerNorm', 'self_attn_layer_norm'),
        ('attention.output.dense', 'self_attn.out_proj'),
        ('attention.self', 'self_attn'),
        ('attention.encdec.LayerNorm', 'encoder_attn_layer_norm'),
        ('attention.encdec_output.dense', 'encoder_attn.out_proj'),
        ('attention.encdec', 'encoder_attn'),
        ('key', 'k_proj'),
        ('value', 'v_proj'),
        ('query', 'q_proj'),
        ('decoder.LayerNorm', 'decoder.layernorm_embedding'),
    ]
    + END_COMMON
)

lowerCAmelCase__  :  Dict						     =	(
    INIT_COMMON
    + [
        ('embeddings.word_embeddings', 'shared.weight'),
        ('embeddings.position_embeddings', 'embed_positions.weight'),
        ('attention.self.LayerNorm', 'self_attn_layer_norm'),
        ('attention.output.dense', 'self_attn.output'),
        ('attention.self', 'self_attn.self'),
        ('encoder.LayerNorm', 'encoder.layernorm_embedding'),
    ]
    + END_COMMON
)

lowerCAmelCase__  :  Union[str, Any]						     =	[
    'encdec/key/bias',
    'encdec/query/bias',
    'encdec/value/bias',
    'self/key/bias',
    'self/query/bias',
    'self/value/bias',
    'encdec_output/dense/bias',
    'attention/output/dense/bias',
]



def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				for tf_name, hf_name in patterns:
								UpperCAmelCase__             =							k.replace(lowerCamelCase					,			lowerCamelCase       )
				return k



def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							BigBirdPegasusConfig(**lowerCamelCase       )
				UpperCAmelCase__             =							BigBirdPegasusForConditionalGeneration(lowerCamelCase       )
				UpperCAmelCase__             =							torch_model.state_dict()
				UpperCAmelCase__             =							{}

				# separating decoder weights
				UpperCAmelCase__             =							{k: tf_weights[k] for k in tf_weights if k.startswith('pegasus/decoder'       )}
				UpperCAmelCase__             =							{k: tf_weights[k] for k in tf_weights if not k.startswith('pegasus/decoder'       )}

				for k, v in tqdm(decoder_weights.items()					,			'tf -> hf conversion'       ):
								UpperCAmelCase__             =							[k.endswith(lowerCamelCase       ) for ending in KEYS_TO_IGNORE]
								if any(lowerCamelCase       ):
												continue
								UpperCAmelCase__             =							DECODER_PATTERNS
								UpperCAmelCase__             =							rename_state_dict_key(lowerCamelCase					,			lowerCamelCase       )
								if new_k not in state_dict:
												raise ValueError(f'''could not find new key {new_k} in state dict. (converted from {k})'''       )
								if any(True if i in k else False for i in ['dense', 'query', 'key', 'value']       ):
												UpperCAmelCase__             =							v.T
								UpperCAmelCase__             =							torch.from_numpy(lowerCamelCase       )
								assert v.shape == state_dict[new_k].shape, f'''{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'''

				for k, v in tqdm(remaining_weights.items()					,			'tf -> hf conversion'       ):
								UpperCAmelCase__             =							[k.endswith(lowerCamelCase       ) for ending in KEYS_TO_IGNORE]
								if any(lowerCamelCase       ):
												continue
								UpperCAmelCase__             =							REMAINING_PATTERNS
								UpperCAmelCase__             =							rename_state_dict_key(lowerCamelCase					,			lowerCamelCase       )
								if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
												raise ValueError(f'''could not find new key {new_k} in state dict. (converted from {k})'''       )
								if any(True if i in k else False for i in ['dense', 'query', 'key', 'value']       ):
												UpperCAmelCase__             =							v.T
								UpperCAmelCase__             =							torch.from_numpy(lowerCamelCase       )
								if k != "pegasus/embeddings/position_embeddings":
												assert v.shape == state_dict[new_k].shape, f'''{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'''

				UpperCAmelCase__             =							mapping['model.embed_positions.weight']
				UpperCAmelCase__             =							mapping.pop('model.embed_positions.weight'       )
				UpperCAmelCase__      ,	UpperCAmelCase__             =							torch_model.load_state_dict(lowerCamelCase					,			strict=lowerCamelCase       )
				UpperCAmelCase__             =							[
				    k
				    for k in missing
				    if k
				    not in [
				        'final_logits_bias',
				        'model.encoder.embed_tokens.weight',
				        'model.decoder.embed_tokens.weight',
				        'lm_head.weight',
				    ]
				]
				assert unexpected_missing == [], f'''no matches found for the following torch keys {unexpected_missing}'''
				assert extra == [], f'''no matches found for the following tf keys {extra}'''
				return torch_model



def   a_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							tf.train.list_variables(lowerCamelCase       )
				UpperCAmelCase__             =							{}
				UpperCAmelCase__             =							['global_step']
				for name, shape in tqdm(lowerCamelCase					,			desc='converting tf checkpoint to dict'       ):
								UpperCAmelCase__             =							any(pat in name for pat in ignore_name       )
								if skip_key:
												continue
								UpperCAmelCase__             =							tf.train.load_variable(lowerCamelCase					,			lowerCamelCase       )
								UpperCAmelCase__             =							array
				return tf_weights



def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							get_tf_weights_as_numpy(lowerCamelCase       )
				UpperCAmelCase__             =							convert_bigbird_pegasus(lowerCamelCase					,			lowerCamelCase       )
				torch_model.save_pretrained(lowerCamelCase       )


if __name__ == "__main__":
						lowerCAmelCase__  :  int						     =	argparse.ArgumentParser()
						parser.add_argument('--tf_ckpt_path', type=str, help='passed to tf.train.list_variables')
						parser.add_argument('--save_dir', default=None, type=str, help='Path to the output PyTorch model.')
						lowerCAmelCase__  :  str						     =	parser.parse_args()
						lowerCAmelCase__  :  int						     =	{}
						convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)











import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


lowercase  							=					logging.get_logger(__name__)

lowercase  							=					{"vocab_file": "spiece.model"}

lowercase  							=					{
    "vocab_file": {
        "google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model",
        "google/bigbird-roberta-large": (
            "https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"
        ),
        "google/bigbird-base-trivia-itc": (
            "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"
        ),
    }
}

lowercase  							=					{
    "google/bigbird-roberta-base": 4096,
    "google/bigbird-roberta-large": 4096,
    "google/bigbird-base-trivia-itc": 4096,
}




class  UpperCamelCase_      (			snake_case_				):



	'''simple docstring'''




	lowerCAmelCase													= VOCAB_FILES_NAMES
	lowerCAmelCase													= PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase													= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase													= ['''input_ids''', '''attention_mask''']
	lowerCAmelCase													= []




	def __init__(       self       ,			a       ,			a="<unk>"       ,			a="<s>"       ,			a="</s>"       ,			a="<pad>"       ,			a="[SEP]"       ,			a="[MASK]"       ,			a="[CLS]"       ,			a = None       ,			**a       ,			)       ->    None:
								snake_case_				=							AddedToken(a       ,			lstrip=a       ,			rstrip=a					) if isinstance(a       ,			a					) else bos_token
								snake_case_				=							AddedToken(a       ,			lstrip=a       ,			rstrip=a					) if isinstance(a       ,			a					) else eos_token
								snake_case_				=							AddedToken(a       ,			lstrip=a       ,			rstrip=a					) if isinstance(a       ,			a					) else unk_token
								snake_case_				=							AddedToken(a       ,			lstrip=a       ,			rstrip=a					) if isinstance(a       ,			a					) else pad_token
								snake_case_				=							AddedToken(a       ,			lstrip=a       ,			rstrip=a					) if isinstance(a       ,			a					) else cls_token
								snake_case_				=							AddedToken(a       ,			lstrip=a       ,			rstrip=a					) if isinstance(a       ,			a					) else sep_token

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

								snake_case_				=							{} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    bos_token=a       ,			eos_token=a       ,			unk_token=a       ,			pad_token=a       ,			sep_token=a       ,			mask_token=a       ,			cls_token=a       ,			sp_model_kwargs=self.sp_model_kwargs       ,			**a       ,			)

								snake_case_				=							vocab_file

								snake_case_				=							spm.SentencePieceProcessor(**self.sp_model_kwargs					)
								self.sp_model.Load(a					)




	@property
	def 							_UpperCamelCase			(       self					)       ->    Tuple:
								return self.sp_model.get_piece_size()




	def 							_UpperCamelCase			(       self					)       ->    List[Any]:
								snake_case_				=							{self.convert_ids_to_tokens(a					): i for i in range(self.vocab_size					)}
								vocab.update(self.added_tokens_encoder					)
								return vocab




	def __getstate__(       self					)       ->    Optional[int]:
								snake_case_				=							self.__dict__.copy()
								snake_case_				=							None
								return state




	def __setstate__(       self       ,			a					)       ->    Optional[Any]:
								snake_case_				=							d

								# for backward compatibility
								if not hasattr(self       ,			'sp_model_kwargs'					):
															snake_case_				=							{}

								snake_case_				=							spm.SentencePieceProcessor(**self.sp_model_kwargs					)
								self.sp_model.Load(self.vocab_file					)




	def 							_UpperCamelCase			(       self       ,			a					)       ->    List[str]:
								return self.sp_model.encode(a       ,			out_type=a					)




	def 							_UpperCamelCase			(       self       ,			a					)       ->    Dict:
								return self.sp_model.piece_to_id(a					)




	def 							_UpperCamelCase			(       self       ,			a					)       ->    Union[str, Any]:
								snake_case_				=							self.sp_model.IdToPiece(a					)
								return token




	def 							_UpperCamelCase			(       self       ,			a					)       ->    List[Any]:
								snake_case_				=							[]
								snake_case_				=							''
								snake_case_				=							False
								for token in tokens:
															# make sure that special tokens are not decoded using sentencepiece model
															if token in self.all_special_tokens:
																						if not prev_is_special:
																													out_string += " "
																						out_string += self.sp_model.decode(a					) + token
																						snake_case_				=							True
																						snake_case_				=							[]
															else:
																						current_sub_tokens.append(a					)
																						snake_case_				=							False
								out_string += self.sp_model.decode(a					)
								return out_string.strip()




	def 							_UpperCamelCase			(       self       ,			a       ,			a = False       ,			a = None       ,			a = True       ,			**a       ,			)       ->    str:
								snake_case_				=							kwargs.pop('use_source_tokenizer'       ,			a					)

								snake_case_				=							self.convert_ids_to_tokens(a       ,			skip_special_tokens=a					)

								# To avoid mixing byte-level and unicode for byte-level BPT
								# we need to build string separately for added tokens and byte-level tokens
								# cf. https://github.com/huggingface/transformers/issues/1133
								snake_case_				=							[]
								snake_case_				=							[]
								for token in filtered_tokens:
															if skip_special_tokens and token in self.all_special_ids:
																						continue
															if token in self.added_tokens_encoder:
																						if current_sub_text:
																													sub_texts.append(self.convert_tokens_to_string(a					)					)
																													snake_case_				=							[]
																						sub_texts.append(a					)
															else:
																						current_sub_text.append(a					)
								if current_sub_text:
															sub_texts.append(self.convert_tokens_to_string(a					)					)

								# Mimic the behavior of the Rust tokenizer:
								# No space before [MASK] and [SEP]
								if spaces_between_special_tokens:
															snake_case_				=							re.sub(R' (\[(MASK|SEP)\])'       ,			R'\1'       ,			' '.join(a					)					)
								else:
															snake_case_				=							''.join(a					)

								snake_case_				=							(
								    clean_up_tokenization_spaces
								    if clean_up_tokenization_spaces is not None
								    else self.clean_up_tokenization_spaces
								)
								if clean_up_tokenization_spaces:
															snake_case_				=							self.clean_up_tokenization(a					)
															return clean_text
								else:
															return text




	def 							_UpperCamelCase			(       self       ,			a       ,			a = None					)       ->    Tuple[str]:
								if not os.path.isdir(a					):
															logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''					)
															return
								snake_case_				=							os.path.join(
								    a       ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']					)

								if os.path.abspath(self.vocab_file					) != os.path.abspath(a					) and os.path.isfile(self.vocab_file					):
															copyfile(self.vocab_file       ,			a					)
								elif not os.path.isfile(self.vocab_file					):
															with open(a       ,			'wb'					) as fi:
																						snake_case_				=							self.sp_model.serialized_model_proto()
																						fi.write(a					)

								return (out_vocab_file,)




	def 							_UpperCamelCase			(       self       ,			a       ,			a = None					)       ->    List[int]:
								if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								snake_case_				=							[self.cls_token_id]
								snake_case_				=							[self.sep_token_id]
								return cls + token_ids_a + sep + token_ids_a + sep




	def 							_UpperCamelCase			(       self       ,			a       ,			a = None       ,			a = False					)       ->    List[int]:
								if already_has_special_tokens:
															return super().get_special_tokens_mask(
															    token_ids_a=a       ,			token_ids_a=a       ,			already_has_special_tokens=a					)

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






	def 							_UpperCamelCase			(       self       ,			a       ,			a = None					)       ->    List[int]:
								snake_case_				=							[self.sep_token_id]
								snake_case_				=							[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					) * [0] + len(token_ids_a + sep					) * [1]





