



"""simple docstring"""


import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__A   :							Any										=   logging.get_logger(__name__)


__A   :							int										=   {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

__A   :							Dict										=   {
    "vocab_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"
    },
    "merges_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"
    },
    "tokenizer_config_file": {
        "facebook/blenderbot_small-90M": (
            "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"
        )
    },
}

__A   :							int										=   {"facebook/blenderbot_small-90M": 512}







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Optional[int]				):






				'''simple docstring'''



				_UpperCAmelCase					=    set()
				_UpperCAmelCase					=    word[0]
				for char in word[1:]:
								pairs.add((prev_char, char)				)
								_UpperCAmelCase					=    char

				_UpperCAmelCase					=    set(_SCREAMING_SNAKE_CASE				)
				return pairs
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     :    Union[str, Any]				,   __UpperCamelCase     :    int				,   __UpperCamelCase     :    Optional[int]				,   __UpperCamelCase     :    Union[str, Any]="__start__"				,   __UpperCamelCase     :    str="__end__"				,   __UpperCamelCase     :    int="__unk__"				,   __UpperCamelCase     :    Any="__null__"				,   **__UpperCamelCase     :    Optional[int]				,   )->Union[str, Any]:
							super().__init__(unk_token=__UpperCamelCase				,   bos_token=__UpperCamelCase				,   eos_token=__UpperCamelCase				,   pad_token=__UpperCamelCase				,   **__UpperCamelCase   )

							with open(__UpperCamelCase				,   encoding='''utf-8'''   ) as vocab_handle:
											_UpperCAmelCase					=    json.load(__UpperCamelCase   )
							_UpperCAmelCase					=    {v: k for k, v in self.encoder.items()}
							with open(__UpperCamelCase				,   encoding='''utf-8'''   ) as merges_handle:
											_UpperCAmelCase					=    merges_handle.read().split('''\n'''   )[1:-1]
							_UpperCAmelCase					=    [tuple(merge.split()   ) for merge in merges]
							_UpperCAmelCase					=    dict(zip(__UpperCamelCase				,   range(len(__UpperCamelCase   )   )   )   )
							_UpperCAmelCase					=    {}



			@property
			def  lowercase__						(		self     :    str   )->int:
							return len(self.encoder   )



			def  lowercase__						(		self     :    Union[str, Any]   )->Dict:
							return dict(self.encoder				,   **self.added_tokens_encoder   )



			def  lowercase__						(		self     :    Optional[int]				,   __UpperCamelCase     :    str   )->str:
							if token in self.cache:
											return self.cache[token]
							_UpperCAmelCase					=    re.sub('''([.,!?()])'''				,   r''' \1'''				,   __UpperCamelCase   )
							_UpperCAmelCase					=    re.sub('''(\')'''				,   r''' \1 '''				,   __UpperCamelCase   )
							_UpperCAmelCase					=    re.sub(r'''\s{2,}'''				,   ''' '''				,   __UpperCamelCase   )
							if "\n" in token:
											_UpperCAmelCase					=    token.replace('''\n'''				,   ''' __newln__'''   )

							_UpperCAmelCase					=    token.split(''' '''   )
							_UpperCAmelCase					=    []
							for token in tokens:
											if not len(__UpperCamelCase   ):
															continue

											_UpperCAmelCase					=    token.lower()
											_UpperCAmelCase					=    tuple(__UpperCamelCase   )
											_UpperCAmelCase					=    tuple(list(word[:-1]   ) + [word[-1] + '''</w>''']   )
											_UpperCAmelCase					=    get_pairs(__UpperCamelCase   )

											if not pairs:
															words.append(__UpperCamelCase   )
															continue

											while True:
															_UpperCAmelCase					=    min(__UpperCamelCase				,   key=lambda __UpperCamelCase   : self.bpe_ranks.get(__UpperCamelCase				,   float('''inf'''   )   )   )
															if bigram not in self.bpe_ranks:
																			break
															_UpperCAmelCase		,						_UpperCAmelCase					=    bigram
															_UpperCAmelCase					=    []
															_UpperCAmelCase					=    0

															while i < len(__UpperCamelCase   ):
																			try:
																							_UpperCAmelCase					=    word.index(__UpperCamelCase				,   __UpperCamelCase   )
																							new_word.extend(word[i:j]   )
																							_UpperCAmelCase					=    j
																			except ValueError:
																							new_word.extend(word[i:]   )
																							break

																			if word[i] == first and i < len(__UpperCamelCase   ) - 1 and word[i + 1] == second:
																							new_word.append(first + second   )
																							i += 2
																			else:
																							new_word.append(word[i]   )
																							i += 1
															_UpperCAmelCase					=    tuple(__UpperCamelCase   )
															_UpperCAmelCase					=    new_word
															if len(__UpperCamelCase   ) == 1:
																			break
															else:
																			_UpperCAmelCase					=    get_pairs(__UpperCamelCase   )
											_UpperCAmelCase					=    '''@@ '''.join(__UpperCamelCase   )
											_UpperCAmelCase					=    word[:-4]

											_UpperCAmelCase					=    word
											words.append(__UpperCamelCase   )
							return " ".join(__UpperCamelCase   )



			def  lowercase__						(		self     :    Dict				,   __UpperCamelCase     :    str   )->List[str]:
							_UpperCAmelCase					=    []

							_UpperCAmelCase					=    re.findall(r'''\S+\n?'''				,   __UpperCamelCase   )

							for token in words:
											split_tokens.extend(list(self.bpe(__UpperCamelCase   ).split(''' '''   )   )   )
							return split_tokens



			def  lowercase__						(		self     :    Union[str, Any]				,   __UpperCamelCase     :    str   )->int:
							_UpperCAmelCase					=    token.lower()
							return self.encoder.get(__UpperCamelCase				,   self.encoder.get(self.unk_token   )   )



			def  lowercase__						(		self     :    Optional[int]				,   __UpperCamelCase     :    int   )->str:
							return self.decoder.get(__UpperCamelCase				,   self.unk_token   )



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



			def  lowercase__						(		self     :    List[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''']   )
							_UpperCAmelCase					=    os.path.join(
							    __UpperCamelCase				,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']   )

							with open(__UpperCamelCase				,   '''w'''				,   encoding='''utf-8'''   ) as f:
											f.write(json.dumps(self.encoder				,   indent=2				,   sort_keys=__UpperCamelCase				,   ensure_ascii=__UpperCamelCase   ) + '''\n'''   )

							_UpperCAmelCase					=    0
							with open(__UpperCamelCase				,   '''w'''				,   encoding='''utf-8'''   ) as writer:
											writer.write('''#version: 0.2\n'''   )
											for bpe_tokens, token_index in sorted(self.bpe_ranks.items()				,   key=lambda __UpperCamelCase   : kv[1]   ):
															if index != token_index:
																			logger.warning(
																			    F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
																			    ''' Please check that the tokenizer is not corrupted!'''   )
																			_UpperCAmelCase					=    token_index
															writer.write(''' '''.join(__UpperCamelCase   ) + '''\n'''   )
															index += 1

							return vocab_file, merge_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)



