


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


__snake_case      = [
    # 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     (		SCREAMING_SNAKE_CASE_ :		List[Any]						):





			"""simple docstring"""




			for pegasus_name, hf_name in PATTERNS:
						UpperCamelCase								=       k.replace(a__							,	a__						)
			return k






def 				_lowercase     (		SCREAMING_SNAKE_CASE_ :		str							,	SCREAMING_SNAKE_CASE_ :		Optional[Any]						):





			"""simple docstring"""




			UpperCamelCase								=       DEFAULTS.copy()
			cfg_kwargs.update(a__						)
			UpperCamelCase								=       PegasusConfig(**a__						)
			UpperCamelCase								=       PegasusForConditionalGeneration(a__						)
			UpperCamelCase								=       torch_model.model.state_dict()
			UpperCamelCase								=       {}
			for k, v in tf_weights.items():
						UpperCamelCase								=       rename_state_dict_key(a__						)
						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(a__							,	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(a__						) for k, v in sd.items() if k.endswith("""bias"""						) and k not in mapping}
			mapping.update(**a__						)
			UpperCamelCase	,	UpperCamelCase								=       torch_model.model.load_state_dict(a__							,	strict=a__						)
			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 				_lowercase     (		SCREAMING_SNAKE_CASE_ :		Tuple="./ckpt/aeslc/model.ckpt-32000"						):





			"""simple docstring"""




			UpperCamelCase								=       tf.train.list_variables(a__						)
			UpperCamelCase								=       {}
			UpperCamelCase								=       ["""Adafactor""", """global_step"""]
			for name, shape in tqdm(a__							,	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(a__							,	a__						)
						UpperCamelCase								=       array
			return tf_weights






def 				_lowercase     (		SCREAMING_SNAKE_CASE_ :		Union[str, Any]							,	SCREAMING_SNAKE_CASE_ :		List[Any]						):





			"""simple docstring"""




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

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


if __name__ == "__main__":
			__snake_case      = 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.")
			__snake_case      = parser.parse_args()
			if args.save_dir is None:
						__snake_case      = Path(args.tf_ckpt_path).parent.name
						__snake_case      = os.path.join("pegasus", dataset)
			convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)







'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase					:			Union[str, Any]       =	logging.get_logger(__name__)

UpperCAmelCase					:			Any       =	{
    'uw-madison/mra-base-512-4': 'https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json',
}







class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 lowerCAmelCase__       = "mra"
 def __init__(						self   :			Tuple   ,				__SCREAMING_SNAKE_CASE   :			Dict=50_265   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=768   ,				__SCREAMING_SNAKE_CASE   :			int=12   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=12   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=3_072   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]="gelu"   ,				__SCREAMING_SNAKE_CASE   :			List[str]=0.1   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=0.1   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=512   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=1   ,				__SCREAMING_SNAKE_CASE   :			Any=0.02   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=1E-5   ,				__SCREAMING_SNAKE_CASE   :			str="absolute"   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=4   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]="full"   ,				__SCREAMING_SNAKE_CASE   :			str=0   ,				__SCREAMING_SNAKE_CASE   :			Dict=0   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=1   ,				__SCREAMING_SNAKE_CASE   :			Dict=0   ,				__SCREAMING_SNAKE_CASE   :			Dict=2   ,				**__SCREAMING_SNAKE_CASE   :			Optional[int]   ,				)    ->					Tuple:


      """simple docstring"""




      super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE   ,				bos_token_id=__SCREAMING_SNAKE_CASE   ,				eos_token_id=__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = vocab_size
      __SCREAMING_SNAKE_CASE      = max_position_embeddings
      __SCREAMING_SNAKE_CASE      = hidden_size
      __SCREAMING_SNAKE_CASE      = num_hidden_layers
      __SCREAMING_SNAKE_CASE      = num_attention_heads
      __SCREAMING_SNAKE_CASE      = intermediate_size
      __SCREAMING_SNAKE_CASE      = hidden_act
      __SCREAMING_SNAKE_CASE      = hidden_dropout_prob
      __SCREAMING_SNAKE_CASE      = attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE      = initializer_range
      __SCREAMING_SNAKE_CASE      = type_vocab_size
      __SCREAMING_SNAKE_CASE      = layer_norm_eps
      __SCREAMING_SNAKE_CASE      = position_embedding_type
      __SCREAMING_SNAKE_CASE      = block_per_row
      __SCREAMING_SNAKE_CASE      = approx_mode
      __SCREAMING_SNAKE_CASE      = initial_prior_first_n_blocks
      __SCREAMING_SNAKE_CASE      = initial_prior_diagonal_n_blocks






