





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 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					)						->			List[str]:
 # Load configuration defined in the metadata file
 with open(_lowerCAmelCase					) as metadata_file:
  UpperCamelCase     : Optional[int]	     =       json.load(_lowerCAmelCase					)
 UpperCamelCase     : Optional[int]	     =       LukeConfig(use_entity_aware_attention=_lowerCAmelCase							,       **metadata["model_config"]					)

 # Load in the weights from the checkpoint_path
 UpperCamelCase     : Dict	     =       torch.load(_lowerCAmelCase							,       map_location="cpu"					)

 # Load the entity vocab file
 UpperCamelCase     : List[str]	     =       load_entity_vocab(_lowerCAmelCase					)

 UpperCamelCase     : Any	     =       RobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]					)

 # Add special tokens to the token vocabulary for downstream tasks
 UpperCamelCase     : Any	     =       AddedToken("<ent>"							,       lstrip=_lowerCAmelCase							,       rstrip=_lowerCAmelCase					)
 UpperCamelCase     : Optional[int]	     =       AddedToken("<ent2>"							,       lstrip=_lowerCAmelCase							,       rstrip=_lowerCAmelCase					)
 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(_lowerCAmelCase					)
 with open(os.path.join(_lowerCAmelCase							,       LukeTokenizer.vocab_files_names["entity_vocab_file"]					)							,       "w"					) as f:
  json.dump(_lowerCAmelCase							,       _lowerCAmelCase					)

 UpperCamelCase     : List[Any]	     =       LukeTokenizer.from_pretrained(_lowerCAmelCase					)

 # Initialize the embeddings of the special tokens
 UpperCamelCase     : Dict	     =       state_dict["embeddings.word_embeddings.weight"]
 UpperCamelCase     : Any	     =       word_emb[tokenizer.convert_tokens_to_ids(["@"]					)[0]].unsqueeze(0					)
 UpperCamelCase     : List[str]	     =       word_emb[tokenizer.convert_tokens_to_ids(["#"]					)[0]].unsqueeze(0					)
 UpperCamelCase     : Dict	     =       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     : str	     =       F"""encoder.layer.{layer_index}.attention.self."""
   UpperCamelCase     : Tuple	     =       state_dict[prefix + matrix_name]
   UpperCamelCase     : Optional[int]	     =       state_dict[prefix + matrix_name]
   UpperCamelCase     : List[str]	     =       state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
 UpperCamelCase     : int	     =       state_dict["entity_embeddings.entity_embeddings.weight"]
 UpperCamelCase     : List[Any]	     =       entity_emb[entity_vocab["[MASK]"]]

 UpperCamelCase     : Optional[int]	     =       LukeModel(config=_lowerCAmelCase					).eval()

 UpperCamelCase     ,   UpperCamelCase     : Tuple	     =       model.load_state_dict(_lowerCAmelCase							,       strict=_lowerCAmelCase					)
 if not (len(_lowerCAmelCase					) == 1 and missing_keys[0] == "embeddings.position_ids"):
  raise ValueError(F"""Missing keys {", ".join(_lowerCAmelCase					)}. 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     : List[str]	     =       LukeTokenizer.from_pretrained(_lowerCAmelCase							,       task="entity_classification"					)

 UpperCamelCase     : List[Any]	     =       (
     "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     : Any	     =       (39, 42)
 UpperCamelCase     : Optional[int]	     =       tokenizer(_lowerCAmelCase							,       entity_spans=[span]							,       add_prefix_space=_lowerCAmelCase							,       return_tensors="pt"					)

 UpperCamelCase     : Any	     =       model(**_lowerCAmelCase					)

 # Verify word hidden states
 if model_size == "large":
  UpperCamelCase     : Optional[Any]	     =       torch.Size((1, 42, 1024)					)
  UpperCamelCase     : Optional[int]	     =       torch.tensor(
      [[0.0_133, 0.0_865, 0.0_095], [0.3_093, -0.2_576, -0.7_418], [-0.1_720, -0.2_117, -0.2_869]]					)
 else:  # base
  UpperCamelCase     : Any	     =       torch.Size((1, 42, 768)					)
  UpperCamelCase     : List[str]	     =       torch.tensor([[0.0_037, 0.1_368, -0.0_091], [0.1_099, 0.3_329, -0.1_095], [0.0_765, 0.5_335, 0.1_179]]					)

 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]							,       _lowerCAmelCase							,       atol=1e-4					):
  raise ValueError

 # Verify entity hidden states
 if model_size == "large":
  UpperCamelCase     : List[Any]	     =       torch.Size((1, 1, 1024)					)
  UpperCamelCase     : Optional[int]	     =       torch.tensor([[0.0_466, -0.0_106, -0.0_179]]					)
 else:  # base
  UpperCamelCase     : Optional[Any]	     =       torch.Size((1, 1, 768)					)
  UpperCamelCase     : Tuple	     =       torch.tensor([[0.1_457, 0.1_044, 0.0_174]]					)

 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]							,       _lowerCAmelCase							,       atol=1e-4					):
  raise ValueError

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



def 		A_    (					_lowerCAmelCase					)						->			Any:
 UpperCamelCase     : Tuple	     =       {}
 with open(_lowerCAmelCase							,       "r"							,       encoding="utf-8"					) as f:
  for index, line in enumerate(_lowerCAmelCase					):
   UpperCamelCase     ,   UpperCamelCase     : Optional[Any]	     =       line.rstrip().split("\t"					)
   UpperCamelCase     : Optional[Any]	     =       index

 return entity_vocab


if __name__ == "__main__":
    __lowerCamelCase	:					List[str]			  =					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."""
    )
    __lowerCamelCase	:					List[Any]			  =					parser.parse_args()
    convert_luke_checkpoint(
        args.checkpoint_path,
        args.metadata_path,
        args.entity_vocab_path,
        args.pytorch_dump_folder_path,
        args.model_size,
    )







import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
__lowerCamelCase	:					Dict			  =					logging.get_logger("""transformers.models.speecht5""")



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					)						->			Tuple:
 hf_model.apply_weight_norm()

 UpperCamelCase     : int	     =       checkpoint["input_conv.weight_g"]
 UpperCamelCase     : Dict	     =       checkpoint["input_conv.weight_v"]
 UpperCamelCase     : List[Any]	     =       checkpoint["input_conv.bias"]

 for i in range(len(config.upsample_rates					)					):
  UpperCamelCase     : Any	     =       checkpoint[F"""upsamples.{i}.1.weight_g"""]
  UpperCamelCase     : List[Any]	     =       checkpoint[F"""upsamples.{i}.1.weight_v"""]
  UpperCamelCase     : Optional[Any]	     =       checkpoint[F"""upsamples.{i}.1.bias"""]

 for i in range(len(config.upsample_rates					) * len(config.resblock_kernel_sizes					)					):
  for j in range(len(config.resblock_dilation_sizes					)					):
   UpperCamelCase     : Union[str, Any]	     =       checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_g"""]
   UpperCamelCase     : int	     =       checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_v"""]
   UpperCamelCase     : str	     =       checkpoint[F"""blocks.{i}.convs1.{j}.1.bias"""]

   UpperCamelCase     : Union[str, Any]	     =       checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_g"""]
   UpperCamelCase     : int	     =       checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_v"""]
   UpperCamelCase     : Optional[Any]	     =       checkpoint[F"""blocks.{i}.convs2.{j}.1.bias"""]

 UpperCamelCase     : Tuple	     =       checkpoint["output_conv.1.weight_g"]
 UpperCamelCase     : Tuple	     =       checkpoint["output_conv.1.weight_v"]
 UpperCamelCase     : int	     =       checkpoint["output_conv.1.bias"]

 hf_model.remove_weight_norm()



@torch.no_grad()
def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase=None							,       _lowerCAmelCase=None							,       )						->			Tuple:
 if config_path is not None:
  UpperCamelCase     : List[Any]	     =       SpeechTaHifiGanConfig.from_pretrained(_lowerCAmelCase					)
 else:
  UpperCamelCase     : Optional[int]	     =       SpeechTaHifiGanConfig()

 UpperCamelCase     : List[str]	     =       SpeechTaHifiGan(_lowerCAmelCase					)

 UpperCamelCase     : str	     =       torch.load(_lowerCAmelCase					)
 load_weights(orig_checkpoint["model"]["generator"]							,       _lowerCAmelCase							,       _lowerCAmelCase					)

 UpperCamelCase     : List[Any]	     =       np.load(_lowerCAmelCase					)
 UpperCamelCase     : List[str]	     =       stats[0].reshape(-1					)
 UpperCamelCase     : Tuple	     =       stats[1].reshape(-1					)
 UpperCamelCase     : Any	     =       torch.from_numpy(_lowerCAmelCase					).float()
 UpperCamelCase     : Any	     =       torch.from_numpy(_lowerCAmelCase					).float()

 model.save_pretrained(_lowerCAmelCase					)

 if repo_id:
  print("Pushing to the hub..."					)
  model.push_to_hub(_lowerCAmelCase					)


if __name__ == "__main__":
    __lowerCamelCase	:					Union[str, Any]			  =					argparse.ArgumentParser()
    parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to original checkpoint""")
    parser.add_argument("""--stats_path""", required=True, default=None, type=str, help="""Path to stats.npy file""")
    parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
    parser.add_argument(
        """--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
    )
    parser.add_argument(
        """--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
    )

    __lowerCamelCase	:					Tuple			  =					parser.parse_args()
    convert_hifigan_checkpoint(
        args.checkpoint_path,
        args.stats_path,
        args.pytorch_dump_folder_path,
        args.config_path,
        args.push_to_hub,
    )
