






import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


SCREAMING_SNAKE_CASE__   :      Tuple				=		[
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ["""memory_attention""", """encoder_attn"""],
    ["""attention""", """attn"""],
    ["""/""", """."""],
    [""".LayerNorm.gamma""", """_layer_norm.weight"""],
    [""".LayerNorm.beta""", """_layer_norm.bias"""],
    ["""r.layer_""", """r.layers."""],
    ["""output_proj""", """out_proj"""],
    ["""ffn.dense_1.""", """fc2."""],
    ["""ffn.dense.""", """fc1."""],
    ["""ffn_layer_norm""", """final_layer_norm"""],
    ["""kernel""", """weight"""],
    ["""encoder_layer_norm.""", """encoder.layer_norm."""],
    ["""decoder_layer_norm.""", """decoder.layer_norm."""],
    ["""embeddings.weights""", """shared.weight"""],
]

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				for pegasus_name, hf_name in PATTERNS:
								__magic_name__		:Union[str, Any]   =   k.replace(snake_case,	snake_case			)
				return k

def   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Dict   =   DEFAULTS.copy()
				cfg_kwargs.update(snake_case			)
				__magic_name__		:Optional[Any]   =   PegasusConfig(**snake_case			)
				__magic_name__		:List[Any]   =   PegasusForConditionalGeneration(snake_case			)
				__magic_name__		:Union[str, Any]   =   torch_model.model.state_dict()
				__magic_name__		:Any   =   {}
				for k, v in tf_weights.items():
								__magic_name__		:int   =   rename_state_dict_key(snake_case			)
								if new_k not in sd:
												raise ValueError(f'''could not find new key {new_k} in state dict. (converted from {k})'''			)

								if "dense" in k or "proj" in new_k:
												__magic_name__		:Tuple   =   v.T
								__magic_name__		:Optional[int]   =   torch.tensor(snake_case,	dtype=sd[new_k].dtype			)
								assert v.shape == sd[new_k].shape, f'''{new_k}, {k}, {v.shape}, {sd[new_k].shape}'''
				# make sure embedding.padding_idx is respected
				__magic_name__		:Tuple   =   torch.zeros_like(mapping['''shared.weight'''][cfg.pad_token_id + 1]			)
				__magic_name__		:int   =   mapping['''shared.weight''']
				__magic_name__		:Optional[Any]   =   mapping['''shared.weight''']
				__magic_name__		:int   =   {k: torch.zeros_like(snake_case			) for k, v in sd.items() if k.endswith('''bias'''			) and k not in mapping}
				mapping.update(**snake_case			)
				__magic_name__  ,					__magic_name__		:Dict   =   torch_model.model.load_state_dict(snake_case,	strict=snake_case			)
				__magic_name__		:Optional[Any]   =   [
				    k for k in missing if k not in ['''encoder.embed_positions.weight''', '''decoder.embed_positions.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       (			snake_case="./ckpt/aeslc/model.ckpt-32000"			):







				"""simple docstring"""






				__magic_name__		:Optional[int]   =   tf.train.list_variables(snake_case			)
				__magic_name__		:Optional[Any]   =   {}
				__magic_name__		:Optional[int]   =   ['''Adafactor''', '''global_step''']
				for name, shape in tqdm(snake_case,	desc='''converting tf checkpoint to dict'''			):
								__magic_name__		:Optional[Any]   =   any(pat in name for pat in ignore_name			)
								if skip_key:
												continue
								__magic_name__		:Optional[int]   =   tf.train.load_variable(snake_case,	snake_case			)
								__magic_name__		:List[Any]   =   array
				return tf_weights

def   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:List[str]   =   Path(snake_case			).parent.name
				__magic_name__		:Union[str, Any]   =   task_specific_params[f'''summarization_{dataset}''']['''max_position_embeddings''']
				__magic_name__		:int   =   PegasusTokenizer.from_pretrained('''sshleifer/pegasus''',	model_max_length=snake_case			)
				assert tok.model_max_length == desired_max_model_length
				tok.save_pretrained(snake_case			)

				# convert model
				__magic_name__		:Optional[int]   =   get_tf_weights_as_numpy(snake_case			)
				__magic_name__		:Optional[Any]   =   task_specific_params[f'''summarization_{dataset}''']
				if dataset == "large":
								__magic_name__		:Union[str, Any]   =   task_specific_params
				__magic_name__		:Optional[int]   =   convert_pegasus(snake_case,	snake_case			)
				torch_model.save_pretrained(snake_case			)
				__magic_name__		:List[str]   =   torch_model.state_dict()
				sd.pop('''model.decoder.embed_positions.weight'''			)
				sd.pop('''model.encoder.embed_positions.weight'''			)
				torch.save(snake_case,	Path(snake_case			) / '''pytorch_model.bin'''			)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   :      int				=		argparse.ArgumentParser()
					# Required parameters
					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.""")
					SCREAMING_SNAKE_CASE__   :      Union[str, Any]				=		parser.parse_args()
					if args.save_dir is None:
										SCREAMING_SNAKE_CASE__   :      Union[str, Any]				=		Path(args.tf_ckpt_path).parent.name
										SCREAMING_SNAKE_CASE__   :      Tuple				=		os.path.join("""pegasus""", dataset)
					convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)














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     lowerCamelCase_					(   lowerCamelCase			):
				a__       							=	''''''
				a__       							=	'''hf-legacy'''  # "hf://"" is reserved for hffs







				def __init__(    self							,       __lowerCAmelCase = None							,       __lowerCAmelCase = None							,       **__lowerCAmelCase							,       ):
								"""simple docstring"""
								super().__init__(self							,       **__lowerCAmelCase  )
								__magic_name__		:List[Any]   =   repo_info
								__magic_name__		:Dict   =   token
								__magic_name__		:Optional[Any]   =   None







				def       A						(    self  ):
								"""simple docstring"""
								if self.dir_cache is None:
												__magic_name__		:Any   =   {}
												for hf_file in self.repo_info.siblings:
																# TODO(QL): add sizes
																__magic_name__		:Optional[int]   =   {
																    '''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       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase = "rb"							,       **__lowerCAmelCase							,       ):
								"""simple docstring"""
								if not isinstance(self.repo_info							,       __lowerCAmelCase  ):
												raise NotImplementedError(F'''Open is only implemented for dataset repositories, but got {self.repo_info}'''  )
								__magic_name__		:Union[str, Any]   =   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       A						(    self							,       __lowerCAmelCase							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								self._get_dirs()
								__magic_name__		:str   =   self._strip_protocol(__lowerCAmelCase  )
								if path in self.dir_cache:
												return self.dir_cache[path]
								else:
												raise FileNotFoundError(__lowerCAmelCase  )







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase=False							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								self._get_dirs()
								__magic_name__		:Union[str, Any]   =   PurePosixPath(path.strip('''/'''  )  )
								__magic_name__		:Dict   =   {}
								for p, f in self.dir_cache.items():
												__magic_name__		:int   =   PurePosixPath(p.strip('''/'''  )  )
												__magic_name__		:Tuple   =   p.parent
												if root == path:
																__magic_name__		:Optional[Any]   =   f
								__magic_name__		:List[Any]   =   list(paths.values()  )
								if detail:
												return out
								else:
												return sorted(f['''name'''] for f in out  )






