


'''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					:  List[str]											=      [
    # 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_			(    A__					:   List[Any]					):
  '''simple docstring'''





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







def 				UpperCamelCase_			(    A__					:   Optional[int]							,    A__					:   Tuple					):
  '''simple docstring'''





  lowerCAmelCase_			:      Dict			=   DEFAULTS.copy()
  cfg_kwargs.update(__snake_case					)
  lowerCAmelCase_			:      Any			=   PegasusConfig(**__snake_case					)
  lowerCAmelCase_			:      Optional[Any]			=   PegasusForConditionalGeneration(__snake_case					)
  lowerCAmelCase_			:      Optional[Any]			=   torch_model.model.state_dict()
  lowerCAmelCase_			:      Tuple			=   {}
  for k, v in tf_weights.items():
    lowerCAmelCase_			:      Any			=   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:
      lowerCAmelCase_			:      Any			=   v.T
    lowerCAmelCase_			:      Dict			=   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
  lowerCAmelCase_			:      List[str]			=   torch.zeros_like(mapping["""shared.weight"""][cfg.pad_token_id + 1]					)
  lowerCAmelCase_			:      List[str]			=   mapping["""shared.weight"""]
  lowerCAmelCase_			:      Tuple			=   mapping["""shared.weight"""]
  lowerCAmelCase_			:      Optional[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					)
  lowerCAmelCase_,							lowerCAmelCase_			:      Optional[Any]			=   torch_model.model.load_state_dict(__snake_case							,    strict=__snake_case					)
  lowerCAmelCase_			:      Union[str, 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 				UpperCamelCase_			(    A__					:   str="./ckpt/aeslc/model.ckpt-32000"					):
  '''simple docstring'''





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







def 				UpperCamelCase_			(    A__					:   Optional[Any]							,    A__					:   str					):
  '''simple docstring'''





  lowerCAmelCase_			:      Any			=   Path(__snake_case					).parent.name
  lowerCAmelCase_			:      Tuple			=   task_specific_params[f'summarization_{dataset}']["""max_position_embeddings"""]
  lowerCAmelCase_			:      List[str]			=   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
  lowerCAmelCase_			:      str			=   get_tf_weights_as_numpy(__snake_case					)
  lowerCAmelCase_			:      List[Any]			=   task_specific_params[f'summarization_{dataset}']
  if dataset == "large":
    lowerCAmelCase_			:      Tuple			=   task_specific_params
  lowerCAmelCase_			:      Dict			=   convert_pegasus(__snake_case							,    __snake_case					)
  torch_model.save_pretrained(__snake_case					)
  lowerCAmelCase_			:      Optional[int]			=   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					:  Dict											=      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					:  Union[str, Any]											=      parser.parse_args()
  if args.save_dir is None:
    __A					:  Optional[int]											=      Path(args.tf_ckpt_path).parent.name
    __A					:  Optional[int]											=      os.path.join("pegasus", dataset)
  convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)





"""simple docstring"""





import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken



@torch.no_grad()
def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case,     __snake_case,     __snake_case	)							->		str:


					"""simple docstring"""





					with open(__snake_case	) as metadata_file:
										_UpperCamelCase							 =  json.load(__snake_case	)
					_UpperCamelCase							 =  LukeConfig(use_entity_aware_attention=__snake_case,     **metadata['''model_config''']	)

					# Load in the weights from the checkpoint_path
					_UpperCamelCase							 =  torch.load(__snake_case,     map_location='''cpu'''	)

					# Load the entity vocab file
					_UpperCamelCase							 =  load_entity_vocab(__snake_case	)

					_UpperCamelCase							 =  RobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name''']	)

					# Add special tokens to the token vocabulary for downstream tasks
					_UpperCamelCase							 =  AddedToken('''<ent>''',     lstrip=__snake_case,     rstrip=__snake_case	)
					_UpperCamelCase							 =  AddedToken('''<ent2>''',     lstrip=__snake_case,     rstrip=__snake_case	)
					tokenizer.add_special_tokens({'''additional_special_tokens''': [entity_token_a, entity_token_a]}	)
					config.vocab_size += 2

					print(F'''Saving tokenizer to {pytorch_dump_folder_path}'''	)
					tokenizer.save_pretrained(__snake_case	)
					with open(os.path.join(__snake_case,     LukeTokenizer.vocab_files_names['''entity_vocab_file''']	),     '''w'''	) as f:
										json.dump(__snake_case,     __snake_case	)

					_UpperCamelCase							 =  LukeTokenizer.from_pretrained(__snake_case	)

					# Initialize the embeddings of the special tokens
					_UpperCamelCase							 =  state_dict['''embeddings.word_embeddings.weight''']
					_UpperCamelCase							 =  word_emb[tokenizer.convert_tokens_to_ids(['''@''']	)[0]].unsqueeze(0	)
					_UpperCamelCase							 =  word_emb[tokenizer.convert_tokens_to_ids(['''#''']	)[0]].unsqueeze(0	)
					_UpperCamelCase							 =  torch.cat([word_emb, ent_emb, enta_emb]	)

					# Initialize the query layers of the entity-aware self-attention mechanism
					for layer_index in range(config.num_hidden_layers	):
										for matrix_name in ["query.weight", "query.bias"]:
															_UpperCamelCase							 =  F'''encoder.layer.{layer_index}.attention.self.'''
															_UpperCamelCase							 =  state_dict[prefix + matrix_name]
															_UpperCamelCase							 =  state_dict[prefix + matrix_name]
															_UpperCamelCase							 =  state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
					_UpperCamelCase							 =  state_dict['''entity_embeddings.entity_embeddings.weight''']
					_UpperCamelCase							 =  entity_emb[entity_vocab['''[MASK]''']]

					_UpperCamelCase							 =  LukeModel(config=__snake_case	).eval()

					_UpperCamelCase ,  _UpperCamelCase							 =  model.load_state_dict(__snake_case,     strict=__snake_case	)
					if not (len(__snake_case	) == 1 and missing_keys[0] == "embeddings.position_ids"):
										raise ValueError(F'''Missing keys {", ".join(__snake_case	)}. Expected only missing embeddings.position_ids'''	)
					if not (all(key.startswith('''entity_predictions'''	) or key.startswith('''lm_head'''	) for key in unexpected_keys	)):
										raise ValueError(
										    '''Unexpected keys'''
										    F''' {", ".join([key for key in unexpected_keys if not (key.startswith("entity_predictions"	) or key.startswith("lm_head"	))]	)}'''	)

					# Check outputs
					_UpperCamelCase							 =  LukeTokenizer.from_pretrained(__snake_case,     task='''entity_classification'''	)

					_UpperCamelCase							 =  (
					    '''Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the'''
					    ''' new world number one avoid a humiliating second- round exit at Wimbledon .'''
					)
					_UpperCamelCase							 =  (39, 42)
					_UpperCamelCase							 =  tokenizer(__snake_case,     entity_spans=[span],     add_prefix_space=__snake_case,     return_tensors='''pt'''	)

					_UpperCamelCase							 =  model(**__snake_case	)

					# Verify word hidden states
					if model_size == "large":
										_UpperCamelCase							 =  torch.Size((1, 42, 10_24)	)
										_UpperCamelCase							 =  torch.tensor(
										    [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]	)
					else:  # base
										_UpperCamelCase							 =  torch.Size((1, 42, 7_68)	)
										_UpperCamelCase							 =  torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]]	)

					if not (outputs.last_hidden_state.shape == expected_shape):
										raise ValueError(
										    F'''Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}'''	)
					if not torch.allclose(outputs.last_hidden_state[0, :3, :3],     __snake_case,     atol=1e-4	):
										raise ValueError

					# Verify entity hidden states
					if model_size == "large":
										_UpperCamelCase							 =  torch.Size((1, 1, 10_24)	)
										_UpperCamelCase							 =  torch.tensor([[0.0466, -0.0106, -0.0179]]	)
					else:  # base
										_UpperCamelCase							 =  torch.Size((1, 1, 7_68)	)
										_UpperCamelCase							 =  torch.tensor([[0.1457, 0.1044, 0.0174]]	)

					if not (outputs.entity_last_hidden_state.shape != expected_shape):
										raise ValueError(
										    F'''Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'''
										    F''' {expected_shape}'''	)
					if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3],     __snake_case,     atol=1e-4	):
										raise ValueError

					# Finally, save our PyTorch model and tokenizer
					print('''Saving PyTorch model to {}'''.format(__snake_case	)	)
					model.save_pretrained(__snake_case	)



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


					"""simple docstring"""





					_UpperCamelCase							 =  {}
					with open(__snake_case,     '''r''',     encoding='''utf-8'''	) as f:
										for index, line in enumerate(__snake_case	):
															_UpperCamelCase ,  _UpperCamelCase							 =  line.rstrip().split('''\t'''	)
															_UpperCamelCase							 =  index

					return entity_vocab


if __name__ == "__main__":
				_a														=		argparse.ArgumentParser()
				# Required parameters
				parser.add_argument("""--checkpoint_path""", type=str, help="""Path to a pytorch_model.bin file.""")
				parser.add_argument(
				    """--metadata_path""", default=None, type=str, help="""Path to a metadata.json file, defining the configuration."""
				)
				parser.add_argument(
				    """--entity_vocab_path""",
				    default=None,
				    type=str,
				    help="""Path to an entity_vocab.tsv file, containing the entity vocabulary.""",
				)
				parser.add_argument(
				    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to where to dump the output PyTorch model."""
				)
				parser.add_argument(
				    """--model_size""", default="""base""", type=str, choices=["""base""", """large"""], help="""Size of the model to be converted."""
				)
				_a														=		parser.parse_args()
				convert_luke_checkpoint(
				    args.checkpoint_path,
				    args.metadata_path,
				    args.entity_vocab_path,
				    args.pytorch_dump_folder_path,
				    args.model_size,
				)






