






"""simple docstring"""
import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

lowerCamelCase_							      = (
    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_							      = (
    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_							      = [
    '''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 							snake_case (  A__      ,A__		):
			for tf_name, hf_name in patterns:
						UpperCAmelCase_	:					Tuple     	=   k.replace(A__      ,A__		)
			return k

def 							snake_case (  A__      ,A__		):
			UpperCAmelCase_	:					Optional[int]     	=   BigBirdPegasusConfig(**A__		)
			UpperCAmelCase_	:					List[Any]     	=   BigBirdPegasusForConditionalGeneration(A__		)
			UpperCAmelCase_	:					List[str]     	=   torch_model.state_dict()
			UpperCAmelCase_	:					Any     	=   {}

			# separating decoder weights
			UpperCAmelCase_	:					Union[str, Any]     	=   {k: tf_weights[k] for k in tf_weights if k.startswith("pegasus/decoder"		)}
			UpperCAmelCase_	:					Dict     	=   {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_	:					List[Any]     	=   [k.endswith(A__		) for ending in KEYS_TO_IGNORE]
						if any(A__		):
									continue
						UpperCAmelCase_	:					Any     	=   DECODER_PATTERNS
						UpperCAmelCase_	:					int     	=   rename_state_dict_key(A__      ,A__		)
						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_	:					Optional[Any]     	=   v.T
						UpperCAmelCase_	:					str     	=   torch.from_numpy(A__		)
						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_	:					str     	=   [k.endswith(A__		) for ending in KEYS_TO_IGNORE]
						if any(A__		):
									continue
						UpperCAmelCase_	:					Optional[int]     	=   REMAINING_PATTERNS
						UpperCAmelCase_	:					Tuple     	=   rename_state_dict_key(A__      ,A__		)
						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_	:					int     	=   torch.from_numpy(A__		)
						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_	:					Any     	=   mapping["model.embed_positions.weight"]
			UpperCAmelCase_	:					List[Any]     	=   mapping.pop("model.embed_positions.weight"		)
			UpperCAmelCase_	,      UpperCAmelCase_	:					Tuple     	=   torch_model.load_state_dict(A__      ,strict=A__		)
			UpperCAmelCase_	:					Optional[Any]     	=   [
			    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 							snake_case (  A__		):
			UpperCAmelCase_	:					List[str]     	=   tf.train.list_variables(A__		)
			UpperCAmelCase_	:					List[Any]     	=   {}
			UpperCAmelCase_	:					Optional[int]     	=   ["global_step"]
			for name, shape in tqdm(A__      ,desc="converting tf checkpoint to dict"		):
						UpperCAmelCase_	:					Union[str, Any]     	=   any(pat in name for pat in ignore_name		)
						if skip_key:
									continue
						UpperCAmelCase_	:					Dict     	=   tf.train.load_variable(A__      ,A__		)
						UpperCAmelCase_	:					int     	=   array
			return tf_weights

def 							snake_case (  A__      ,A__      ,A__		):
			UpperCAmelCase_	:					Union[str, Any]     	=   get_tf_weights_as_numpy(A__		)
			UpperCAmelCase_	:					Union[str, Any]     	=   convert_bigbird_pegasus(A__      ,A__		)
			torch_model.save_pretrained(A__		)


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







"""simple docstring"""


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

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


if is_sentencepiece_available():
	from .tokenization_xlnet import XLNetTokenizer
else:
	A	:  Optional[Any]            =     None


A	:  Any            =     logging.get_logger(__name__)

A	:  Union[str, Any]            =     {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

A	:  List[Any]            =     {
    "vocab_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json",
    },
}

A	:  Union[str, Any]            =     {
    "xlnet-base-cased": None,
    "xlnet-large-cased": None,
}

A	:  Optional[int]            =     "▁"

# Segments (not really needed)
A	:  str            =     0
A	:  Dict            =     1
A	:  List[str]            =     2
A	:  Tuple            =     3
A	:  Tuple            =     4
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Optional[Any]							 =VOCAB_FILES_NAMES
							__UpperCAmelCase		:       Optional[int]							 =PRETRAINED_VOCAB_FILES_MAP
							__UpperCAmelCase		:       Dict							 =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__UpperCAmelCase		:       List[Any]							 ="""left"""
							__UpperCAmelCase		:       List[str]							 =XLNetTokenizer

							def __init__(	self       ,  __a=None       ,  __a=None       ,  __a=False       ,  __a=True       ,  __a=False       ,  __a="<s>"       ,  __a="</s>"       ,  __a="<unk>"       ,  __a="<sep>"       ,  __a="<pad>"       ,  __a="<cls>"       ,  __a="<mask>"       ,  __a=["<eop>", "<eod>"]       ,  **__a       ,  ):
										# Mask token behave like a normal word, i.e. include the space before it
										__lowerCAmelCase							       =     AddedToken(__a       ,  lstrip=__a       ,  rstrip=__a      ) if isinstance(__a       ,  __a      ) else mask_token

										super().__init__(
										    vocab_file=__a       ,  tokenizer_file=__a       ,  do_lower_case=__a       ,  remove_space=__a       ,  keep_accents=__a       ,  bos_token=__a       ,  eos_token=__a       ,  unk_token=__a       ,  sep_token=__a       ,  pad_token=__a       ,  cls_token=__a       ,  mask_token=__a       ,  additional_special_tokens=__a       ,  **__a       ,  )

										__lowerCAmelCase							       =     3
										__lowerCAmelCase							       =     do_lower_case
										__lowerCAmelCase							       =     remove_space
										__lowerCAmelCase							       =     keep_accents
										__lowerCAmelCase							       =     vocab_file
										__lowerCAmelCase							       =     False if not self.vocab_file else True

							def      snake_case							(	self       ,  __a       ,  __a = None      ):
										__lowerCAmelCase							       =     [self.sep_token_id]
										__lowerCAmelCase							       =     [self.cls_token_id]
										if token_ids_a is None:
													return token_ids_a + sep + cls
										return token_ids_a + sep + token_ids_a + sep + cls

							def      snake_case							(	self       ,  __a       ,  __a = None      ):
										__lowerCAmelCase							       =     [self.sep_token_id]
										__lowerCAmelCase							       =     [2]

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



							def      snake_case							(	self       ,  __a       ,  __a = None      ):
										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,)




