




import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

__a							     =					(
    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							     =					(
    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							     =					[
    '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 lowerCamelCase__  (     _lowercase					,		_lowercase ):

     '''simple docstring'''


     for tf_name, hf_name in patterns:
          UpperCAmelCase_     :				int  =     k.replace(_lowercase					,		_lowercase )
     return k







def lowerCamelCase__  (     _lowercase					,		_lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				Union[str, Any]  =     BigBirdPegasusConfig(**_lowercase )
     UpperCAmelCase_     :				Union[str, Any]  =     BigBirdPegasusForConditionalGeneration(_lowercase )
     UpperCAmelCase_     :				Union[str, Any]  =     torch_model.state_dict()
     UpperCAmelCase_     :				Dict  =     {}

     # separating decoder weights
     UpperCAmelCase_     :				Any  =     {k: tf_weights[k] for k in tf_weights if k.startswith('''pegasus/decoder''' )}
     UpperCAmelCase_     :				Optional[Any]  =     {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_     :				Optional[Any]  =     [k.endswith(_lowercase ) for ending in KEYS_TO_IGNORE]
          if any(_lowercase ):
               continue
          UpperCAmelCase_     :				Optional[int]  =     DECODER_PATTERNS
          UpperCAmelCase_     :				Union[str, Any]  =     rename_state_dict_key(_lowercase					,		_lowercase )
          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_     :				int  =     v.T
          UpperCAmelCase_     :				List[Any]  =     torch.from_numpy(_lowercase )
          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_     :				Optional[int]  =     [k.endswith(_lowercase ) for ending in KEYS_TO_IGNORE]
          if any(_lowercase ):
               continue
          UpperCAmelCase_     :				str  =     REMAINING_PATTERNS
          UpperCAmelCase_     :				Union[str, Any]  =     rename_state_dict_key(_lowercase					,		_lowercase )
          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_     :				int  =     v.T
          UpperCAmelCase_     :				Tuple  =     torch.from_numpy(_lowercase )
          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_     :				Tuple  =     mapping['''model.embed_positions.weight''']
     UpperCAmelCase_     :				str  =     mapping.pop('''model.embed_positions.weight''' )
     UpperCAmelCase_,       UpperCAmelCase_     :				List[Any]  =     torch_model.load_state_dict(_lowercase					,		strict=_lowercase )
     UpperCAmelCase_     :				str  =     [
         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 lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				List[str]  =     tf.train.list_variables(_lowercase )
     UpperCAmelCase_     :				List[Any]  =     {}
     UpperCAmelCase_     :				List[str]  =     ['''global_step''']
     for name, shape in tqdm(_lowercase					,		desc='''converting tf checkpoint to dict''' ):
          UpperCAmelCase_     :				Any  =     any(pat in name for pat in ignore_name )
          if skip_key:
               continue
          UpperCAmelCase_     :				Tuple  =     tf.train.load_variable(_lowercase					,		_lowercase )
          UpperCAmelCase_     :				List[Any]  =     array
     return tf_weights







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				Dict  =     get_tf_weights_as_numpy(_lowercase )
     UpperCAmelCase_     :				Any  =     convert_bigbird_pegasus(_lowercase					,		_lowercase )
     torch_model.save_pretrained(_lowercase )


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



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:
		UpperCAmelCase   :    Union[str, Any]     =		None


UpperCAmelCase   :    Any     =		logging.get_logger(__name__)


UpperCAmelCase   :    Optional[Any]     =		"▁"

UpperCAmelCase   :    str     =		{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase   :    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"
    },
}

UpperCAmelCase   :    List[Any]     =		{
    "google/pegasus-xsum": 5_12,
}







class        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					str			       =		VOCAB_FILES_NAMES
		UpperCamelCase     :					int			       =		PRETRAINED_VOCAB_FILES_MAP
		UpperCamelCase     :					List[Any]			       =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		UpperCamelCase     :					Any			       =		PegasusTokenizer
		UpperCamelCase     :					Optional[Any]			       =		["input_ids", "attention_mask"]







		def __init__( self		,      A=None		,      A=None		,      A="<pad>"		,      A="</s>"		,      A="<unk>"		,      A="<mask_2>"		,      A="<mask_1>"		,      A=None		,      A=1_03		,      **A		,      )  -> Any:







					'''simple docstring'''







					lowerCamelCase    		=  offset

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

								lowerCamelCase    		=  (
								    ([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(A	)		,      self.offset - 1	)
								]

								if len(set(A	)	) != len(A	):
											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}.'	)
								lowerCamelCase    		=  additional_special_tokens_extended
					else:
								lowerCamelCase    		=  [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__(
					    A		,      tokenizer_file=A		,      pad_token=A		,      eos_token=A		,      unk_token=A		,      mask_token=A		,      mask_token_sent=A		,      offset=A		,      additional_special_tokens=A		,      **A		,      )
					lowerCamelCase    		=  vocab_file
					lowerCamelCase    		=  False if not self.vocab_file else True







		def 					__A   ( self		,      A	)  -> Dict:







					'''simple docstring'''







					lowerCamelCase    		=  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 					__A   ( self		,      A		,      A = None		,      A = False	)  -> List[int]:







					'''simple docstring'''







					if already_has_special_tokens:
								return self._special_token_mask(A	)
					elif token_ids_a is None:
								return self._special_token_mask(A	) + [1]
					else:
								return self._special_token_mask(token_ids_a + token_ids_a	) + [1]







		def 					__A   ( self		,      A		,      A=None	)  -> List[int]:







					'''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 					__A   ( self		,      A		,      A = None	)  -> Tuple[str]:







					'''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(A	):
								logger.error(F'Vocabulary path ({save_directory}) should be a directory'	)
								return
					lowerCamelCase    		=  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	):
								copyfile(self.vocab_file		,      A	)

					return (out_vocab_file,)

