






import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

A__	:			Dict			   = (
    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__	:			Tuple			   = (
    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__	:			int			   = [
    '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__	:			Union[str, 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__	:			Optional[int]			   = {}
							convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)












from pathlib import PurePosixPath
from typing import Optional

import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo

from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class       _UpperCAmelCase					(       A__	):

				"""simple docstring"""




				lowercase__											=       """"""
				lowercase__											=       """hf-legacy"""  # "hf://"" is reserved for hffs




				def __init__(     self						:    List[str],  lowerCamelCase						:    Optional[DatasetInfo] = None,  lowerCamelCase						:    Optional[str] = None,  **lowerCamelCase						:    str,  ):

								'''simple docstring'''
								super().__init__(self,  **lowerCamelCase  )
								lowercase__    =			repo_info
								lowercase__    =			token
								lowercase__    =			None




				def 				lowercase__   (     self						:    Optional[Any]  ):

								'''simple docstring'''
								if self.dir_cache is None:
												lowercase__    =			{}
												for hf_file in self.repo_info.siblings:
																# TODO(QL): add sizes
																lowercase__    =			{
																    '''name''': hf_file.rfilename,
																    '''size''': None,
																    '''type''': '''file''',
																}
																self.dir_cache.update(
																    {
																        str(lowerCamelCase  ): {'''name''': str(lowerCamelCase  ), '''size''': None, '''type''': '''directory'''}
																        for d in list(PurePosixPath(hf_file.rfilename  ).parents  )[:-1]
																    }  )




				def 				lowercase__   (     self						:    Any,  lowerCamelCase						:    str,  lowerCamelCase						:    str = "rb",  **lowerCamelCase						:    Any,  ):

								'''simple docstring'''
								if not isinstance(self.repo_info,  lowerCamelCase  ):
												raise NotImplementedError(F"""Open is only implemented for dataset repositories, but got {self.repo_info}"""  )
								lowercase__    =			hf_hub_url(self.repo_info.id,  lowerCamelCase,  revision=self.repo_info.sha  )
								return fsspec.open(
								    lowerCamelCase,  mode=lowerCamelCase,  headers=get_authentication_headers_for_url(lowerCamelCase,  use_auth_token=self.token  ),  client_kwargs={'''trust_env''': True},  ).open()




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

								'''simple docstring'''
								self._get_dirs()
								lowercase__    =			self._strip_protocol(lowerCamelCase  )
								if path in self.dir_cache:
												return self.dir_cache[path]
								else:
												raise FileNotFoundError(lowerCamelCase  )




				def 				lowercase__   (     self						:    Optional[Any],  lowerCamelCase						:    List[str],  lowerCamelCase						:    Optional[int]=False,  **lowerCamelCase						:    str  ):

								'''simple docstring'''
								self._get_dirs()
								lowercase__    =			PurePosixPath(path.strip('''/'''  )  )
								lowercase__    =			{}
								for p, f in self.dir_cache.items():
												lowercase__    =			PurePosixPath(p.strip('''/'''  )  )
												lowercase__    =			p.parent
												if root == path:
																lowercase__    =			f
								lowercase__    =			list(paths.values()  )
								if detail:
												return out
								else:
												return sorted(f['''name'''] for f in out  )




