
'''simple docstring'''
import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

lowerCAmelCase					:    Union[str, Any]           = (
    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					:    Optional[Any]           = (
    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					:    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 		A_(     A						:		Optional[int]		,     A						:		List[str]):
							for tf_name, hf_name in patterns:
														UpperCamelCase   			=     k.replace(A		,     A)
							return k







def 		A_(     A						:		dict		,     A						:		dict):
							UpperCamelCase   			=     BigBirdPegasusConfig(**A)
							UpperCamelCase   			=     BigBirdPegasusForConditionalGeneration(A)
							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(A) for ending in KEYS_TO_IGNORE]
														if any(A):
																					continue
														UpperCamelCase   			=     DECODER_PATTERNS
														UpperCamelCase   			=     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   			=     v.T
														UpperCamelCase   			=     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   			=     [k.endswith(A) for ending in KEYS_TO_IGNORE]
														if any(A):
																					continue
														UpperCamelCase   			=     REMAINING_PATTERNS
														UpperCamelCase   			=     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   			=     v.T
														UpperCamelCase   			=     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   			=     mapping['model.embed_positions.weight']
							UpperCamelCase   			=     mapping.pop('model.embed_positions.weight')
							UpperCamelCase			,	UpperCamelCase   			=     torch_model.load_state_dict(A		,     strict=A)
							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 		A_(     A						:		List[str]):
							UpperCamelCase   			=     tf.train.list_variables(A)
							UpperCamelCase   			=     {}
							UpperCamelCase   			=     ['global_step']
							for name, shape in tqdm(A		,     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(A		,     A)
														UpperCamelCase   			=     array
							return tf_weights







def 		A_(     A						:		str		,     A						:		str		,     A						:		dict):
							UpperCamelCase   			=     get_tf_weights_as_numpy(A)
							UpperCamelCase   			=     convert_bigbird_pegasus(A		,     A)
							torch_model.save_pretrained(A)


if __name__ == "__main__":
						lowerCAmelCase					:    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.')
						lowerCAmelCase					:    Dict           = parser.parse_args()
						lowerCAmelCase					:    Any           = {}
						convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)







"""simple docstring"""
import warnings
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_A							=  logging.get_logger(__name__)

class       lowerCamelCase	(       lowerCAmelCase__   ):


  '''simple docstring'''







  SCREAMING_SNAKE_CASE					      =  ['input_ids', 'attention_mask']



  def __init__(self							,       _lowerCamelCase="</s>"							,       _lowerCamelCase="<unk>"							,       _lowerCamelCase="<pad>"							,       _lowerCamelCase=125							,       _lowerCamelCase=None							,       **_lowerCamelCase							,       ):


        """simple docstring"""

        if extra_ids > 0 and additional_special_tokens is None:
              UpperCAmelCase__			:							List[str]							=    [F"""<extra_id_{i}>""" for i in range(_lowerCamelCase	)]
        elif extra_ids > 0 and additional_special_tokens is not None:
              # Check that we have the right number of extra_id special tokens
              UpperCAmelCase__			:							int							=    len(set(filter(lambda _lowerCamelCase	: bool("""extra_id""" in str(_lowerCamelCase	)	)							,       _lowerCamelCase	)	)	)
              if extra_tokens != extra_ids:
                    raise ValueError(
                        F"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
                        """ provided to ByT5Tokenizer. In this case the additional_special_tokens must include the"""
                        """ extra_ids tokens"""	)

        UpperCAmelCase__			:							List[str]							=    AddedToken(_lowerCamelCase							,       lstrip=_lowerCamelCase							,       rstrip=_lowerCamelCase	) if isinstance(_lowerCamelCase							,       _lowerCamelCase	) else pad_token
        UpperCAmelCase__			:							int							=    AddedToken(_lowerCamelCase							,       lstrip=_lowerCamelCase							,       rstrip=_lowerCamelCase	) if isinstance(_lowerCamelCase							,       _lowerCamelCase	) else eos_token
        UpperCAmelCase__			:							Optional[int]							=    AddedToken(_lowerCamelCase							,       lstrip=_lowerCamelCase							,       rstrip=_lowerCamelCase	) if isinstance(_lowerCamelCase							,       _lowerCamelCase	) else unk_token

        super().__init__(
            eos_token=_lowerCamelCase							,       unk_token=_lowerCamelCase							,       pad_token=_lowerCamelCase							,       extra_ids=_lowerCamelCase							,       additional_special_tokens=_lowerCamelCase							,       **_lowerCamelCase							,       )

        UpperCAmelCase__			:							int							=    extra_ids

        UpperCAmelCase__			:							List[str]							=    2**8  # utf is 8 bits

        # define special tokens dict
        UpperCAmelCase__			:							Dict[int, str]							=    {
            self.pad_token: 0,
            self.eos_token: 1,
            self.unk_token: 2,
        }
        UpperCAmelCase__			:							str							=    len(self.special_tokens_encoder	)
        UpperCAmelCase__			:							Union[str, Any]							=    len(_lowerCamelCase	)
        for i, token in enumerate(_lowerCamelCase	):
              UpperCAmelCase__			:							Optional[Any]							=    self.vocab_size + i - n
        UpperCAmelCase__			:							Dict[str, int]							=    {v: k for k, v in self.special_tokens_encoder.items()}



  @property
  def  _a      (self	):


        """simple docstring"""

        return self._utf_vocab_size + self._num_special_tokens + self._extra_ids



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase = None							,       _lowerCamelCase = False	):


        """simple docstring"""

        if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=_lowerCamelCase							,       token_ids_a=_lowerCamelCase							,       already_has_special_tokens=_lowerCamelCase	)

        # normal case: some special tokens
        if token_ids_a is None:
              return ([0] * len(_lowerCamelCase	)) + [1]
        return ([0] * len(_lowerCamelCase	)) + [1] + ([0] * len(_lowerCamelCase	)) + [1]



  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        if len(_lowerCamelCase	) > 0 and token_ids[-1] == self.eos_token_id:
              warnings.warn(
                  F"""This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"""
                  """ eos tokens being added."""	)
              return token_ids
        else:
              return token_ids + [self.eos_token_id]



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase = None	):


        """simple docstring"""

        UpperCAmelCase__			:							List[str]							=    [self.eos_token_id]

        if token_ids_a is None:
              return len(token_ids_a + eos	) * [0]
        return len(token_ids_a + eos + token_ids_a + eos	) * [0]



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase = None	):


        """simple docstring"""

        UpperCAmelCase__			:							Tuple							=    self._add_eos_if_not_present(_lowerCamelCase	)
        if token_ids_a is None:
              return token_ids_a
        else:
              UpperCAmelCase__			:							List[str]							=    self._add_eos_if_not_present(_lowerCamelCase	)
              return token_ids_a + token_ids_a



  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        UpperCAmelCase__			:							Optional[Any]							=    [chr(_lowerCamelCase	) for i in text.encode("""utf-8"""	)]
        return tokens



  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        if token in self.special_tokens_encoder:
              UpperCAmelCase__			:							List[str]							=    self.special_tokens_encoder[token]
        elif token in self.added_tokens_encoder:
              UpperCAmelCase__			:							Tuple							=    self.added_tokens_encoder[token]
        elif len(_lowerCamelCase	) != 1:
              UpperCAmelCase__			:							Dict							=    self.unk_token_id
        else:
              UpperCAmelCase__			:							Tuple							=    ord(_lowerCamelCase	) + self._num_special_tokens
        return token_id



  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        if index in self.special_tokens_decoder:
              UpperCAmelCase__			:							Optional[Any]							=    self.special_tokens_decoder[index]
        else:
              UpperCAmelCase__			:							Dict							=    chr(index - self._num_special_tokens	)
        return token



  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        UpperCAmelCase__			:							List[Any]							=    B""""""
        for token in tokens:
              if token in self.special_tokens_decoder:
                    UpperCAmelCase__			:							Tuple							=    self.special_tokens_decoder[token].encode("""utf-8"""	)
              elif token in self.added_tokens_decoder:
                    UpperCAmelCase__			:							Optional[Any]							=    self.special_tokens_decoder[token].encode("""utf-8"""	)
              elif token in self.special_tokens_encoder:
                    UpperCAmelCase__			:							str							=    token.encode("""utf-8"""	)
              elif token in self.added_tokens_encoder:
                    UpperCAmelCase__			:							List[Any]							=    token.encode("""utf-8"""	)
              else:
                    UpperCAmelCase__			:							Optional[int]							=    bytes([ord(_lowerCamelCase	)]	)
              bstring += tok_string
        UpperCAmelCase__			:							int							=    bstring.decode("""utf-8"""							,       errors="""ignore"""	)
        return string




  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase = None	):


        """simple docstring"""

        return ()

