
"""simple docstring"""





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


_a														=		[
    # 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       lowerCamelCase__		(			__snake_case	)							->		Optional[int]:


					"""simple docstring"""





					for pegasus_name, hf_name in PATTERNS:
										_UpperCamelCase							 =  k.replace(__snake_case,     __snake_case	)
					return k



def       lowerCamelCase__		(			__snake_case,     __snake_case	)							->		PegasusForConditionalGeneration:


					"""simple docstring"""





					_UpperCamelCase							 =  DEFAULTS.copy()
					cfg_kwargs.update(__snake_case	)
					_UpperCamelCase							 =  PegasusConfig(**__snake_case	)
					_UpperCamelCase							 =  PegasusForConditionalGeneration(__snake_case	)
					_UpperCamelCase							 =  torch_model.model.state_dict()
					_UpperCamelCase							 =  {}
					for k, v in tf_weights.items():
										_UpperCamelCase							 =  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:
															_UpperCamelCase							 =  v.T
										_UpperCamelCase							 =  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
					_UpperCamelCase							 =  torch.zeros_like(mapping['''shared.weight'''][cfg.pad_token_id + 1]	)
					_UpperCamelCase							 =  mapping['''shared.weight''']
					_UpperCamelCase							 =  mapping['''shared.weight''']
					_UpperCamelCase							 =  {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	)
					_UpperCamelCase ,  _UpperCamelCase							 =  torch_model.model.load_state_dict(__snake_case,     strict=__snake_case	)
					_UpperCamelCase							 =  [
					    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       lowerCamelCase__		(			__snake_case="./ckpt/aeslc/model.ckpt-32000"	)							->		Dict:


					"""simple docstring"""





					_UpperCamelCase							 =  tf.train.list_variables(__snake_case	)
					_UpperCamelCase							 =  {}
					_UpperCamelCase							 =  ['''Adafactor''', '''global_step''']
					for name, shape in tqdm(__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(__snake_case,     __snake_case	)
										_UpperCamelCase							 =  array
					return tf_weights



def       lowerCamelCase__		(			__snake_case,     __snake_case	)							->		List[str]:


					"""simple docstring"""





					_UpperCamelCase							 =  Path(__snake_case	).parent.name
					_UpperCamelCase							 =  task_specific_params[F'''summarization_{dataset}''']['''max_position_embeddings''']
					_UpperCamelCase							 =  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
					_UpperCamelCase							 =  get_tf_weights_as_numpy(__snake_case	)
					_UpperCamelCase							 =  task_specific_params[F'''summarization_{dataset}''']
					if dataset == "large":
										_UpperCamelCase							 =  task_specific_params
					_UpperCamelCase							 =  convert_pegasus(__snake_case,     __snake_case	)
					torch_model.save_pretrained(__snake_case	)
					_UpperCamelCase							 =  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__":
				_a														=		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.""")
				_a														=		parser.parse_args()
				if args.save_dir is None:
								_a														=		Path(args.tf_ckpt_path).parent.name
								_a														=		os.path.join("""pegasus""", dataset)
				convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)








"""simple docstring"""





from __future__ import annotations



def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case	)							->		int | float:


					"""simple docstring"""





					if len(__snake_case	) == 0:
										raise ValueError('''find_max() arg is an empty sequence'''	)
					if (
					    left >= len(__snake_case	)
					    or left < -len(__snake_case	)
					    or right >= len(__snake_case	)
					    or right < -len(__snake_case	)
					):
										raise IndexError('''list index out of range'''	)
					if left == right:
										return nums[left]
					_UpperCamelCase							 =  (left + right) >> 1  # the middle
					_UpperCamelCase							 =  find_max(__snake_case,     __snake_case,     __snake_case	)  # find max in range[left, mid]
					_UpperCamelCase							 =  find_max(__snake_case,     mid + 1,     __snake_case	)  # find max in range[mid + 1, right]

					return left_max if left_max >= right_max else right_max


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)






