



"""simple docstring"""


import os
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


__A   :							Dict										=   logging.get_logger(__name__)

__A   :							Optional[Any]										=   "▁"

__A   :							Tuple										=   {"vocab_file": "sentencepiece.bpe.model"}

__A   :							Optional[Any]										=   {
    "vocab_file": {
        "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model",
        "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model",
        "xlm-roberta-large-finetuned-conll02-dutch": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll02-spanish": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll03-english": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll03-german": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"
        ),
    }
}

__A   :							Any										=   {
    "xlm-roberta-base": 512,
    "xlm-roberta-large": 512,
    "xlm-roberta-large-finetuned-conll02-dutch": 512,
    "xlm-roberta-large-finetuned-conll02-spanish": 512,
    "xlm-roberta-large-finetuned-conll03-english": 512,
    "xlm-roberta-large-finetuned-conll03-german": 512,
}
class 			_a (					lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__			      =	VOCAB_FILES_NAMES
			UpperCamelCase__			      =	PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase__			      =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase__			      =	["""input_ids""", """attention_mask"""]



			def __init__(		self     :    Optional[Any]				,   __UpperCamelCase     :    int				,   __UpperCamelCase     :    Dict="<s>"				,   __UpperCamelCase     :    Optional[int]="</s>"				,   __UpperCamelCase     :    Union[str, Any]="</s>"				,   __UpperCamelCase     :    Optional[Any]="<s>"				,   __UpperCamelCase     :    List[str]="<unk>"				,   __UpperCamelCase     :    Optional[Any]="<pad>"				,   __UpperCamelCase     :    Tuple="<mask>"				,   __UpperCamelCase     :    Optional[Dict[str, Any]] = None				,   **__UpperCamelCase     :    Optional[int]				,   )->None:
							# Mask token behave like a normal word, i.e. include the space before it
							_UpperCAmelCase					=    AddedToken(__UpperCamelCase				,   lstrip=__UpperCamelCase				,   rstrip=__UpperCamelCase   ) if isinstance(__UpperCamelCase				,   __UpperCamelCase   ) else mask_token

							_UpperCAmelCase					=    {} if sp_model_kwargs is None else sp_model_kwargs

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

							_UpperCAmelCase					=    spm.SentencePieceProcessor(**self.sp_model_kwargs   )
							self.sp_model.Load(str(__UpperCamelCase   )   )
							_UpperCAmelCase					=    vocab_file

							# Original fairseq vocab and spm vocab must be "aligned":
							# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
							# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
							# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
							# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

							# Mimic fairseq token-to-id alignment for the first 4 token
							_UpperCAmelCase					=    {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

							# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
							_UpperCAmelCase					=    1

							_UpperCAmelCase					=    len(self.sp_model   ) + self.fairseq_offset
							_UpperCAmelCase					=    {v: k for k, v in self.fairseq_tokens_to_ids.items()}



			def __getstate__(		self     :    str   )->Union[str, Any]:
							_UpperCAmelCase					=    self.__dict__.copy()
							_UpperCAmelCase					=    None
							_UpperCAmelCase					=    self.sp_model.serialized_model_proto()
							return state



			def __setstate__(		self     :    List[Any]				,   __UpperCamelCase     :    Optional[Any]   )->Any:
							_UpperCAmelCase					=    d

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

							_UpperCAmelCase					=    spm.SentencePieceProcessor(**self.sp_model_kwargs   )
							self.sp_model.LoadFromSerializedProto(self.sp_model_proto   )



			def  lowercase__						(		self     :    int				,   __UpperCamelCase     :    List[int]				,   __UpperCamelCase     :    Optional[List[int]] = None   )->List[int]:

							if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							_UpperCAmelCase					=    [self.cls_token_id]
							_UpperCAmelCase					=    [self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep



			def  lowercase__						(		self     :    Any				,   __UpperCamelCase     :    List[int]				,   __UpperCamelCase     :    Optional[List[int]] = None				,   __UpperCamelCase     :    bool = False   )->List[int]:

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

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



			def  lowercase__						(		self     :    Optional[int]				,   __UpperCamelCase     :    List[int]				,   __UpperCamelCase     :    Optional[List[int]] = None   )->List[int]:
							_UpperCAmelCase					=    [self.sep_token_id]
							_UpperCAmelCase					=    [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]



			@property
			def  lowercase__						(		self     :    Dict   )->Optional[Any]:
							return len(self.sp_model   ) + self.fairseq_offset + 1  # Add the <mask> token



			def  lowercase__						(		self     :    Union[str, Any]   )->Optional[int]:
							_UpperCAmelCase					=    {self.convert_ids_to_tokens(__UpperCamelCase   ): i for i in range(self.vocab_size   )}
							vocab.update(self.added_tokens_encoder   )
							return vocab



			def  lowercase__						(		self     :    int				,   __UpperCamelCase     :    str   )->List[str]:
							return self.sp_model.encode(__UpperCamelCase				,   out_type=__UpperCamelCase   )



			def  lowercase__						(		self     :    Optional[int]				,   __UpperCamelCase     :    str   )->Dict:
							if token in self.fairseq_tokens_to_ids:
											return self.fairseq_tokens_to_ids[token]
							_UpperCAmelCase					=    self.sp_model.PieceToId(__UpperCamelCase   )

							# Need to return unknown token if the SP model returned 0
							return spm_id + self.fairseq_offset if spm_id else self.unk_token_id



			def  lowercase__						(		self     :    Dict				,   __UpperCamelCase     :    str   )->Union[str, Any]:
							if index in self.fairseq_ids_to_tokens:
											return self.fairseq_ids_to_tokens[index]
							return self.sp_model.IdToPiece(index - self.fairseq_offset   )



			def  lowercase__						(		self     :    Dict				,   __UpperCamelCase     :    List[str]   )->int:
							_UpperCAmelCase					=    ''''''.join(__UpperCamelCase   ).replace(__UpperCamelCase				,   ''' '''   ).strip()
							return out_string



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

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

							return (out_vocab_file,)





"""simple docstring"""


import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

__A   :							Tuple										=   (
    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
)

__A   :							Optional[int]										=   (
    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
)

__A   :							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  lowercase	(      _SCREAMING_SNAKE_CASE   :						Optional[int]					,     _SCREAMING_SNAKE_CASE   :						List[Any]				):






				'''simple docstring'''



				for tf_name, hf_name in patterns:
								_UpperCAmelCase					=    k.replace(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)
				return k







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						dict					,     _SCREAMING_SNAKE_CASE   :						dict				):






				'''simple docstring'''



				_UpperCAmelCase					=    BigBirdPegasusConfig(**_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    BigBirdPegasusForConditionalGeneration(_SCREAMING_SNAKE_CASE				)
				_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(_SCREAMING_SNAKE_CASE				) for ending in KEYS_TO_IGNORE]
								if any(_SCREAMING_SNAKE_CASE				):
												continue
								_UpperCAmelCase					=    DECODER_PATTERNS
								_UpperCAmelCase					=    rename_state_dict_key(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)
								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(_SCREAMING_SNAKE_CASE				)
								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(_SCREAMING_SNAKE_CASE				) for ending in KEYS_TO_IGNORE]
								if any(_SCREAMING_SNAKE_CASE				):
												continue
								_UpperCAmelCase					=    REMAINING_PATTERNS
								_UpperCAmelCase					=    rename_state_dict_key(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)
								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(_SCREAMING_SNAKE_CASE				)
								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(_SCREAMING_SNAKE_CASE					,     strict=_SCREAMING_SNAKE_CASE				)
				_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  lowercase	(      _SCREAMING_SNAKE_CASE   :						Any				):






				'''simple docstring'''



				_UpperCAmelCase					=    tf.train.list_variables(_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    {}
				_UpperCAmelCase					=    ['''global_step''']
				for name, shape in tqdm(_SCREAMING_SNAKE_CASE					,     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(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)
								_UpperCAmelCase					=    array
				return tf_weights







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						str					,     _SCREAMING_SNAKE_CASE   :						str					,     _SCREAMING_SNAKE_CASE   :						dict				):






				'''simple docstring'''



				_UpperCAmelCase					=    get_tf_weights_as_numpy(_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    convert_bigbird_pegasus(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)
				torch_model.save_pretrained(_SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
					__A   :							Tuple										=   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.")
					__A   :							Dict										=   parser.parse_args()
					__A   :							Tuple										=   {}
					convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)



