



"""simple docstring"""


import os
from typing import Optional

import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class 			_a (					lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__			      =	""""""
			UpperCamelCase__			      =	(
			    None  # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
			)
			UpperCamelCase__			      =	None  # compression type in fsspec. ex: "gzip"
			UpperCamelCase__			      =	None  # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz



			def __init__(		self     :    Union[str, Any]				,   __UpperCamelCase     :    str = ""				,   __UpperCamelCase     :    Optional[str] = None				,   __UpperCamelCase     :    Optional[dict] = None				,   **__UpperCamelCase     :    List[str]   )->Optional[Any]:
							super().__init__(self				,   **__UpperCamelCase   )
							# always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
							_UpperCAmelCase					=    fsspec.open(
							    __UpperCamelCase				,   mode='''rb'''				,   protocol=__UpperCamelCase				,   compression=self.compression				,   client_kwargs={
							        '''requote_redirect_url''': False,  # see https://github.com/huggingface/datasets/pull/5459
							        '''trust_env''': True,  # Enable reading proxy env variables.
							        **(target_options or {}).pop('''client_kwargs'''				,   {}   ),  # To avoid issues if it was already passed.
							    }				,   **(target_options or {})				,   )
							_UpperCAmelCase					=    os.path.basename(self.file.path.split('''::'''   )[0]   )
							_UpperCAmelCase					=    (
							    self.compressed_name[: self.compressed_name.rindex('''.'''   )]
							    if '''.''' in self.compressed_name
							    else self.compressed_name
							)
							_UpperCAmelCase					=    None



			@classmethod
			def  lowercase__						(		cls     :    Dict				,   __UpperCamelCase     :    List[Any]   )->Optional[int]:
							# compressed file paths are always relative to the archive root
							return super()._strip_protocol(__UpperCamelCase   ).lstrip('''/'''   )



			def  lowercase__						(		self     :    Tuple   )->int:
							if self.dir_cache is None:
											_UpperCAmelCase					=    {**self.file.fs.info(self.file.path   ), '''name''': self.uncompressed_name}
											_UpperCAmelCase					=    {f['''name''']: f}



			def  lowercase__						(		self     :    Tuple				,   __UpperCamelCase     :    str   )->List[Any]:
							return self.file.open().read()



			def  lowercase__						(		self     :    Optional[Any]				,   __UpperCamelCase     :    str				,   __UpperCamelCase     :    str = "rb"				,   __UpperCamelCase     :    Any=None				,   __UpperCamelCase     :    Tuple=True				,   __UpperCamelCase     :    Tuple=None				,   **__UpperCamelCase     :    Any				,   )->Tuple:
							_UpperCAmelCase					=    self._strip_protocol(__UpperCamelCase   )
							if mode != "rb":
											raise ValueError(F'Tried to read with mode {mode} on file {self.file.path} opened with mode \'rb\''   )
							return self.file.open()



class 			_a (					lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__			      =	"""bz2"""
			UpperCamelCase__			      =	"""bz2"""
			UpperCamelCase__			      =	""".bz2"""



class 			_a (					lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__			      =	"""gzip"""
			UpperCamelCase__			      =	"""gzip"""
			UpperCamelCase__			      =	""".gz"""



class 			_a (					lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__			      =	"""lz4"""
			UpperCamelCase__			      =	"""lz4"""
			UpperCamelCase__			      =	""".lz4"""



class 			_a (					lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__			      =	"""xz"""
			UpperCamelCase__			      =	"""xz"""
			UpperCamelCase__			      =	""".xz"""



class 			_a (					lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__			      =	"""zstd"""
			UpperCamelCase__			      =	"""zstd"""
			UpperCamelCase__			      =	""".zst"""



			def __init__(		self     :    Dict				,   __UpperCamelCase     :    str				,   __UpperCamelCase     :    str = "rb"				,   __UpperCamelCase     :    Optional[str] = None				,   __UpperCamelCase     :    Optional[dict] = None				,   __UpperCamelCase     :    int = DEFAULT_BLOCK_SIZE				,   **__UpperCamelCase     :    List[str]				,   )->Any:
							super().__init__(
							    fo=__UpperCamelCase				,   mode=__UpperCamelCase				,   target_protocol=__UpperCamelCase				,   target_options=__UpperCamelCase				,   block_size=__UpperCamelCase				,   **__UpperCamelCase				,   )
							# We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
							#
							# File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
							#     out.close = close
							# AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
							#
							# see https://github.com/intake/filesystem_spec/issues/725
							_UpperCAmelCase					=    self.file.__enter__
							class 			_a :
										"""simple docstring"""



										def __init__(		self     :    Optional[int]				,   __UpperCamelCase     :    List[Any]   )->List[str]:
														_UpperCAmelCase					=    file_



										def __enter__(		self     :    Dict   )->Any:
														self._file.__enter__()
														return self



										def __exit__(		self     :    Any				,   *__UpperCamelCase     :    Optional[Any]				,   **__UpperCamelCase     :    Any   )->Any:
														self._file.__exit__(*__UpperCamelCase				,   **__UpperCamelCase   )



										def __iter__(		self     :    Tuple   )->Union[str, Any]:
														return iter(self._file   )



										def  lowercase__						(		self     :    Union[str, Any]   )->Optional[int]:
														return next(self._file   )



										def __getattr__(		self     :    Optional[Any]				,   __UpperCamelCase     :    Optional[int]   )->Tuple:
														return getattr(self._file				,   __UpperCamelCase   )

							def fixed_enter(*__UpperCamelCase     :    Optional[int]				,   **__UpperCamelCase     :    str   ):
											return WrappedFile(_enter(*__UpperCamelCase				,   **__UpperCamelCase   )   )

							_UpperCAmelCase					=    fixed_enter








"""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)



