






import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_pegasus import PegasusTokenizer
else:
							A__      :     Optional[Any]       =					None


A__      :     str       =					logging.get_logger(__name__)


A__      :     Optional[int]       =					'▁'

A__      :     List[str]       =					{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

A__      :     Dict       =					{
    'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'},
    'tokenizer_file': {
        'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'
    },
}

A__      :     int       =					{
    'google/pegasus-xsum': 5_12,
}
class  _UpperCAmelCase   (			A__      ):

				"""simple docstring"""




				lowercase__											=			VOCAB_FILES_NAMES
				lowercase__											=			PRETRAINED_VOCAB_FILES_MAP
				lowercase__											=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowercase__											=			PegasusTokenizer
				lowercase__											=			["""input_ids""", """attention_mask"""]




				def __init__(	self		:       Dict,    lowerCamelCase		:       str=None,    lowerCamelCase		:       List[str]=None,    lowerCamelCase		:       int="<pad>",    lowerCamelCase		:       str="</s>",    lowerCamelCase		:       Optional[Any]="<unk>",    lowerCamelCase		:       Any="<mask_2>",    lowerCamelCase		:       Optional[Any]="<mask_1>",    lowerCamelCase		:       int=None,    lowerCamelCase		:       Dict=103,    **lowerCamelCase		:       List[Any],    ):

								'''simple docstring'''
								lowercase__  					=     offset

								if additional_special_tokens is not None:
												if not isinstance(lowerCamelCase,    lowerCamelCase    ):
																raise TypeError(
																    F"""additional_special_tokens should be of type {type(lowerCamelCase    )}, but is"""
																    F""" {type(lowerCamelCase    )}"""    )

												lowercase__  					=     (
												    ([mask_token_sent] + additional_special_tokens)
												    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
												    else additional_special_tokens
												)
												# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
												additional_special_tokens_extended += [
												    F"""<unk_{i}>""" for i in range(len(lowerCamelCase    ),    self.offset - 1    )
												]

												if len(set(lowerCamelCase    )    ) != len(lowerCamelCase    ):
																raise ValueError(
																    '''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
																    F""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."""    )
												lowercase__  					=     additional_special_tokens_extended
								else:
												lowercase__  					=     [mask_token_sent] if mask_token_sent is not None else []
												additional_special_tokens += [F"""<unk_{i}>""" for i in range(2,    self.offset    )]

								super().__init__(
								    lowerCamelCase,    tokenizer_file=lowerCamelCase,    pad_token=lowerCamelCase,    eos_token=lowerCamelCase,    unk_token=lowerCamelCase,    mask_token=lowerCamelCase,    mask_token_sent=lowerCamelCase,    offset=lowerCamelCase,    additional_special_tokens=lowerCamelCase,    **lowerCamelCase,    )
								lowercase__  					=     vocab_file
								lowercase__  					=     False if not self.vocab_file else True




				def 							lowercase__  (	self		:       Optional[Any],    lowerCamelCase		:       int    ):

								'''simple docstring'''
								lowercase__  					=     set(self.all_special_ids    )  # call it once instead of inside list comp
								all_special_ids.remove(self.unk_token_id    )  # <unk> is only sometimes special

								if all_special_ids != set(range(len(self.additional_special_tokens    ) + 3    )    ):
												raise ValueError(
												    '''There should be 3 special tokens: mask_token, pad_token, and eos_token +'''
												    F""" {len(self.additional_special_tokens    )} additional_special_tokens, but got {all_special_ids}"""    )

								return [1 if x in all_special_ids else 0 for x in seq]




				def 							lowercase__  (	self		:       Optional[Any],    lowerCamelCase		:       List,    lowerCamelCase		:       Optional[List] = None,    lowerCamelCase		:       bool = False    ):

								'''simple docstring'''
								if already_has_special_tokens:
												return self._special_token_mask(lowerCamelCase    )
								elif token_ids_a is None:
												return self._special_token_mask(lowerCamelCase    ) + [1]
								else:
												return self._special_token_mask(token_ids_a + token_ids_a    ) + [1]




				def 							lowercase__  (	self		:       Optional[Any],    lowerCamelCase		:       Optional[int],    lowerCamelCase		:       Optional[Any]=None    ):

								'''simple docstring'''
								if token_ids_a is None:
												return token_ids_a + [self.eos_token_id]
								# We don't expect to process pairs, but leave the pair logic for API consistency
								return token_ids_a + token_ids_a + [self.eos_token_id]




				def 							lowercase__  (	self		:       Optional[Any],    lowerCamelCase		:       str,    lowerCamelCase		:       Optional[str] = None    ):

								'''simple docstring'''
								if not self.can_save_slow_tokenizer:
												raise ValueError(
												    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
												    '''tokenizer.'''    )

								if not os.path.isdir(lowerCamelCase    ):
												logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""    )
												return
								lowercase__  					=     os.path.join(
								    lowerCamelCase,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )

								if os.path.abspath(self.vocab_file    ) != os.path.abspath(lowerCamelCase    ):
												copyfile(self.vocab_file,    lowerCamelCase    )

								return (out_vocab_file,)












import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

A__      :     List[str]       =					(
    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__      :     List[str]       =					(
    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__      :     Dict       =					[
    '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_							):






				'''simple docstring'''
				for tf_name, hf_name in patterns:
								lowercase__  					=     k.replace(lowerCamelCase_						,  lowerCamelCase_							)
				return k






def     a     (		lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     BigBirdPegasusConfig(**lowerCamelCase_							)
				lowercase__  					=     BigBirdPegasusForConditionalGeneration(lowerCamelCase_							)
				lowercase__  					=     torch_model.state_dict()
				lowercase__  					=     {}

				# separating decoder weights
				lowercase__  					=     {k: tf_weights[k] for k in tf_weights if k.startswith('''pegasus/decoder'''							)}
				lowercase__  					=     {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'''							):
								lowercase__  					=     [k.endswith(lowerCamelCase_							) for ending in KEYS_TO_IGNORE]
								if any(lowerCamelCase_							):
												continue
								lowercase__  					=     DECODER_PATTERNS
								lowercase__  					=     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''']							):
												lowercase__  					=     v.T
								lowercase__  					=     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'''							):
								lowercase__  					=     [k.endswith(lowerCamelCase_							) for ending in KEYS_TO_IGNORE]
								if any(lowerCamelCase_							):
												continue
								lowercase__  					=     REMAINING_PATTERNS
								lowercase__  					=     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''']							):
												lowercase__  					=     v.T
								lowercase__  					=     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}"""

				lowercase__  					=     mapping['''model.embed_positions.weight''']
				lowercase__  					=     mapping.pop('''model.embed_positions.weight'''							)
				lowercase__					,		lowercase__  					=     torch_model.load_state_dict(lowerCamelCase_						,  strict=lowerCamelCase_							)
				lowercase__  					=     [
				    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_							):






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






def     a     (		lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     get_tf_weights_as_numpy(lowerCamelCase_							)
				lowercase__  					=     convert_bigbird_pegasus(lowerCamelCase_						,  lowerCamelCase_							)
				torch_model.save_pretrained(lowerCamelCase_							)


if __name__ == "__main__":
							A__      :     Optional[Any]       =					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__      :     Tuple       =					parser.parse_args()
							A__      :     int       =					{}
							convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)




