'''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			:      int					      =				[
    # 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      UpperCamelCase_						(					snake_case_       :       Optional[int]      )					->     Tuple:






 '''simple docstring'''

 for pegasus_name, hf_name in PATTERNS:
  __lowerCAmelCase											=						k.replace(lowerCAmelCase_						,		lowerCAmelCase_      )
 return k







def      UpperCamelCase_						(					snake_case_       :       Union[str, Any]						,		snake_case_       :       List[Any]      )					->     List[str]:






 '''simple docstring'''

 __lowerCAmelCase											=						DEFAULTS.copy()
 cfg_kwargs.update(lowerCAmelCase_      )
 __lowerCAmelCase											=						PegasusConfig(**lowerCAmelCase_      )
 __lowerCAmelCase											=						PegasusForConditionalGeneration(lowerCAmelCase_      )
 __lowerCAmelCase											=						torch_model.model.state_dict()
 __lowerCAmelCase											=						{}
 for k, v in tf_weights.items():
  __lowerCAmelCase											=						rename_state_dict_key(lowerCAmelCase_      )
  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:
   __lowerCAmelCase											=						v.T
  __lowerCAmelCase											=						torch.tensor(lowerCAmelCase_						,		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
 __lowerCAmelCase											=						torch.zeros_like(mapping["""shared.weight"""][cfg.pad_token_id + 1]      )
 __lowerCAmelCase											=						mapping["""shared.weight"""]
 __lowerCAmelCase											=						mapping["""shared.weight"""]
 __lowerCAmelCase											=						{k: torch.zeros_like(lowerCAmelCase_      ) for k, v in sd.items() if k.endswith("""bias"""      ) and k not in mapping}
 mapping.update(**lowerCAmelCase_      )
 __lowerCAmelCase							,       __lowerCAmelCase											=						torch_model.model.load_state_dict(lowerCAmelCase_						,		strict=lowerCAmelCase_      )
 __lowerCAmelCase											=						[
     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      UpperCamelCase_						(					snake_case_       :       Tuple="./ckpt/aeslc/model.ckpt-32000"      )					->     Tuple:






 '''simple docstring'''

 __lowerCAmelCase											=						tf.train.list_variables(lowerCAmelCase_      )
 __lowerCAmelCase											=						{}
 __lowerCAmelCase											=						["""Adafactor""", """global_step"""]
 for name, shape in tqdm(lowerCAmelCase_						,		desc="""converting tf checkpoint to dict"""      ):
  __lowerCAmelCase											=						any(pat in name for pat in ignore_name      )
  if skip_key:
   continue
  __lowerCAmelCase											=						tf.train.load_variable(lowerCAmelCase_						,		lowerCAmelCase_      )
  __lowerCAmelCase											=						array
 return tf_weights







def      UpperCamelCase_						(					snake_case_       :       Optional[int]						,		snake_case_       :       Tuple      )					->     List[str]:






 '''simple docstring'''

 __lowerCAmelCase											=						Path(lowerCAmelCase_      ).parent.name
 __lowerCAmelCase											=						task_specific_params[f"""summarization_{dataset}"""]["""max_position_embeddings"""]
 __lowerCAmelCase											=						PegasusTokenizer.from_pretrained("""sshleifer/pegasus"""						,		model_max_length=lowerCAmelCase_      )
 assert tok.model_max_length == desired_max_model_length
 tok.save_pretrained(lowerCAmelCase_      )

 # convert model
 __lowerCAmelCase											=						get_tf_weights_as_numpy(lowerCAmelCase_      )
 __lowerCAmelCase											=						task_specific_params[f"""summarization_{dataset}"""]
 if dataset == "large":
  __lowerCAmelCase											=						task_specific_params
 __lowerCAmelCase											=						convert_pegasus(lowerCAmelCase_						,		lowerCAmelCase_      )
 torch_model.save_pretrained(lowerCAmelCase_      )
 __lowerCAmelCase											=						torch_model.state_dict()
 sd.pop("""model.decoder.embed_positions.weight"""      )
 sd.pop("""model.encoder.embed_positions.weight"""      )
 torch.save(lowerCAmelCase_						,		Path(lowerCAmelCase_      ) / """pytorch_model.bin"""      )


if __name__ == "__main__":
 _A			:      Any					      =				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			:      List[str]					      =				parser.parse_args()
 if args.save_dir is None:
  _A			:      int					      =				Path(args.tf_ckpt_path).parent.name
  _A			:      Tuple					      =				os.path.join('''pegasus''', dataset)
 convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)










"""simple docstring"""







from collections import defaultdict
from math import ceil, sqrt




def    UpperCAmelCase__       (lowerCAmelCase_ = 100_0000  ,						lowerCAmelCase_ = 10   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				defaultdict(lowerCAmelCase_   )

					for outer_width in range(3  ,						(t_limit // 4) + 2   ):
										if outer_width * outer_width > t_limit:
															__SCREAMING_SNAKE_CASE						   =				max(
															    ceil(sqrt(outer_width * outer_width - t_limit   )   )  ,						1   )
										else:
															__SCREAMING_SNAKE_CASE						   =				1

										hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

										for hole_width in range(lowerCAmelCase_  ,						outer_width - 1  ,						2   ):
															count[outer_width * outer_width - hole_width * hole_width] += 1

					return sum(1 for n in count.values() if 1 <= n <= 10   )


if __name__ == "__main__":
		print(F"{solution() = }")
