






import json
import os
from typing import Optional, Tuple

import regex as re

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


A__      :     Optional[Any]       =					logging.get_logger(__name__)

A__      :     Tuple       =					{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

A__      :     Union[str, Any]       =					{
    'vocab_file': {'ctrl': 'https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json'},
    'merges_file': {'ctrl': 'https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt'},
}

A__      :     Optional[int]       =					{
    'ctrl': 2_56,
}

A__      :     Union[str, Any]       =					{
    'Pregnancy': 16_86_29,
    'Christianity': 76_75,
    'Explain': 10_64_23,
    'Fitness': 6_34_40,
    'Saving': 6_31_63,
    'Ask': 2_71_71,
    'Ass': 9_59_85,
    'Joke': 16_35_09,
    'Questions': 4_56_22,
    'Thoughts': 4_96_05,
    'Retail': 5_23_42,
    'Feminism': 16_43_38,
    'Writing': 1_19_92,
    'Atheism': 19_22_63,
    'Netflix': 4_86_16,
    'Computing': 3_96_39,
    'Opinion': 4_32_13,
    'Alone': 4_49_67,
    'Funny': 5_89_17,
    'Gaming': 4_03_58,
    'Human': 40_88,
    'India': 13_31,
    'Joker': 7_71_38,
    'Diet': 3_62_06,
    'Legal': 1_18_59,
    'Norman': 49_39,
    'Tip': 7_26_89,
    'Weight': 5_23_43,
    'Movies': 4_62_73,
    'Running': 2_34_25,
    'Science': 20_90,
    'Horror': 3_77_93,
    'Confession': 6_05_72,
    'Finance': 1_22_50,
    'Politics': 1_63_60,
    'Scary': 19_19_85,
    'Support': 1_26_54,
    'Technologies': 3_25_16,
    'Teenage': 6_61_60,
    'Event': 3_27_69,
    'Learned': 6_74_60,
    'Notion': 18_27_70,
    'Wikipedia': 3_75_83,
    'Books': 66_65,
    'Extract': 7_60_50,
    'Confessions': 10_27_01,
    'Conspiracy': 7_59_32,
    'Links': 6_36_74,
    'Narcissus': 15_04_25,
    'Relationship': 5_47_66,
    'Relationships': 13_47_96,
    'Reviews': 4_16_71,
    'News': 42_56,
    'Translation': 2_68_20,
    'multilingual': 12_84_06,
}






def     a     (		lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     set()
				lowercase__  					=     word[0]
				for char in word[1:]:
								pairs.add((prev_char, char)							)
								lowercase__  					=     char

				lowercase__  					=     set(lowerCamelCase_							)
				return pairs




class  _UpperCAmelCase   (			A__      ):

				"""simple docstring"""




				lowercase__											=			VOCAB_FILES_NAMES
				lowercase__											=			PRETRAINED_VOCAB_FILES_MAP
				lowercase__											=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowercase__											=			CONTROL_CODES




				def __init__(	self		:       Union[str, Any],    lowerCamelCase		:       Union[str, Any],    lowerCamelCase		:       Dict,    lowerCamelCase		:       Optional[int]="<unk>",    **lowerCamelCase		:       List[str]    ):

								'''simple docstring'''
								super().__init__(unk_token=lowerCamelCase,    **lowerCamelCase    )

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




				@property
				def 							lowercase__  (	self		:       Optional[Any]    ):

								'''simple docstring'''
								return len(self.encoder    )




				def 							lowercase__  (	self		:       Tuple    ):

								'''simple docstring'''
								return dict(self.encoder,    **self.added_tokens_encoder    )




				def 							lowercase__  (	self		:       Any,    lowerCamelCase		:       Dict    ):

								'''simple docstring'''
								if token in self.cache:
												return self.cache[token]
								lowercase__  					=     tuple(lowerCamelCase    )
								lowercase__  					=     tuple(list(word[:-1]    ) + [word[-1] + '''</w>''']    )
								lowercase__  					=     get_pairs(lowerCamelCase    )

								if not pairs:
												return token

								while True:
												lowercase__  					=     min(lowerCamelCase,    key=lambda lowerCamelCase    : self.bpe_ranks.get(lowerCamelCase,    float('''inf'''    )    )    )
												if bigram not in self.bpe_ranks:
																break
												lowercase__					,		lowercase__  					=     bigram
												lowercase__  					=     []
												lowercase__  					=     0
												while i < len(lowerCamelCase    ):
																try:
																				lowercase__  					=     word.index(lowerCamelCase,    lowerCamelCase    )
																except ValueError:
																				new_word.extend(word[i:]    )
																				break
																else:
																				new_word.extend(word[i:j]    )
																				lowercase__  					=     j

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




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

								'''simple docstring'''
								lowercase__  					=     []

								lowercase__  					=     re.findall(R'''\S+\n?''',    lowerCamelCase    )

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




				def 							lowercase__  (	self		:       Tuple,    lowerCamelCase		:       List[str]    ):

								'''simple docstring'''
								return self.encoder.get(lowerCamelCase,    self.encoder.get(self.unk_token    )    )




				def 							lowercase__  (	self		:       str,    lowerCamelCase		:       Union[str, Any]    ):

								'''simple docstring'''
								return self.decoder.get(lowerCamelCase,    self.unk_token    )




				def 							lowercase__  (	self		:       Any,    lowerCamelCase		:       List[str]    ):

								'''simple docstring'''
								lowercase__  					=     ''' '''.join(lowerCamelCase    ).replace('''@@ ''',    ''''''    ).strip()
								return out_string




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

								'''simple docstring'''
								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''']    )
								lowercase__  					=     os.path.join(
								    lowerCamelCase,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']    )

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

								lowercase__  					=     0
								with open(lowerCamelCase,    '''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 lowerCamelCase    : 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!'''    )
																				lowercase__  					=     token_index
																writer.write(''' '''.join(lowerCamelCase    ) + '''\n'''    )
																index += 1

								return vocab_file, merge_file

				# def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
				#     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
				#     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
				#     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
				#     return ''.join(tokens_generated_so_far)












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)




