





import json
import os
from typing import Optional

import numpy as np

from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer


_a 						=      logging.get_logger(__name__)






class 					__A    (    UpperCAmelCase__ ):

			'''simple docstring'''







			lowerCAmelCase_							     =						"""AutoTokenizer"""
			lowerCAmelCase_							     =						["""tokenizer"""]

			lowerCAmelCase_							     =						{
			    """semantic_prompt""": 1,
			    """coarse_prompt""": 2,
			    """fine_prompt""": 2,
			}





			def __init__(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase=None							):



				'''simple docstring'''







				super().__init__(_a							)

				lowerCamelCase__								=		speaker_embeddings





			@classmethod
			def 		__lowerCamelCase				(    cls       ,  __lowerCAmelCase       ,  __lowerCAmelCase="speaker_embeddings_path.json"       ,  **__lowerCAmelCase							):



				'''simple docstring'''







				if speaker_embeddings_dict_path is not None:
					lowerCamelCase__								=		get_file_from_repo(
					    _a       ,  _a       ,  subfolder=kwargs.pop('''subfolder'''       ,  _a							)       ,  cache_dir=kwargs.pop('''cache_dir'''       ,  _a							)       ,  force_download=kwargs.pop('''force_download'''       ,  _a							)       ,  proxies=kwargs.pop('''proxies'''       ,  _a							)       ,  resume_download=kwargs.pop('''resume_download'''       ,  _a							)       ,  local_files_only=kwargs.pop('''local_files_only'''       ,  _a							)       ,  use_auth_token=kwargs.pop('''use_auth_token'''       ,  _a							)       ,  revision=kwargs.pop('''revision'''       ,  _a							)       ,  )
					if speaker_embeddings_path is None:
						logger.warning(
						    F'`{os.path.join(_a       ,  _a							)}` does not exists\n                    , no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json\n                    dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`.'							)
						lowerCamelCase__								=		None
					else:
						with open(_a							) as speaker_embeddings_json:
							lowerCamelCase__								=		json.load(_a							)
				else:
					lowerCamelCase__								=		None

				lowerCamelCase__								=		AutoTokenizer.from_pretrained(_a       ,  **_a							)

				return cls(tokenizer=_a       ,  speaker_embeddings=_a							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase="speaker_embeddings_path.json"       ,  __lowerCAmelCase="speaker_embeddings"       ,  __lowerCAmelCase = False       ,  **__lowerCAmelCase       ,  ):



				'''simple docstring'''







				if self.speaker_embeddings is not None:
					os.makedirs(os.path.join(_a       ,  _a       ,  '''v2'''							)       ,  exist_ok=_a							)

					lowerCamelCase__								=		{}

					lowerCamelCase__								=		save_directory

					for prompt_key in self.speaker_embeddings:
						if prompt_key != "repo_or_path":
							lowerCamelCase__								=		self._load_voice_preset(_a							)

							lowerCamelCase__								=		{}
							for key in self.speaker_embeddings[prompt_key]:
								np.save(
								    os.path.join(
								        embeddings_dict['''repo_or_path''']       ,  _a       ,  F'{prompt_key}_{key}'							)       ,  voice_preset[key]       ,  allow_pickle=_a       ,  )
								lowerCamelCase__								=		os.path.join(_a       ,  F'{prompt_key}_{key}.npy'							)

							lowerCamelCase__								=		tmp_dict

					with open(os.path.join(_a       ,  _a							)       ,  '''w'''							) as fp:
						json.dump(_a       ,  _a							)

				super().save_pretrained(_a       ,  _a       ,  **_a							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase = None       ,  **__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		self.speaker_embeddings[voice_preset]

				lowerCamelCase__								=		{}
				for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
					if key not in voice_preset_paths:
						raise ValueError(
						    F'Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}].'							)

					lowerCamelCase__								=		get_file_from_repo(
					    self.speaker_embeddings.get('''repo_or_path'''       ,  '''/'''							)       ,  voice_preset_paths[key]       ,  subfolder=kwargs.pop('''subfolder'''       ,  _a							)       ,  cache_dir=kwargs.pop('''cache_dir'''       ,  _a							)       ,  force_download=kwargs.pop('''force_download'''       ,  _a							)       ,  proxies=kwargs.pop('''proxies'''       ,  _a							)       ,  resume_download=kwargs.pop('''resume_download'''       ,  _a							)       ,  local_files_only=kwargs.pop('''local_files_only'''       ,  _a							)       ,  use_auth_token=kwargs.pop('''use_auth_token'''       ,  _a							)       ,  revision=kwargs.pop('''revision'''       ,  _a							)       ,  )
					if path is None:
						raise ValueError(
						    F'`{os.path.join(self.speaker_embeddings.get("repo_or_path"       ,  "/"							)       ,  voice_preset_paths[key]							)}` does not exists\n                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}\n                    embeddings.'							)

					lowerCamelCase__								=		np.load(_a							)

				return voice_preset_dict





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase = None							):



				'''simple docstring'''







				for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
					if key not in voice_preset:
						raise ValueError(F'Voice preset unrecognized, missing {key} as a key.'							)

					if not isinstance(voice_preset[key]       ,  np.ndarray							):
						raise ValueError(F'{key} voice preset must be a {str(self.preset_shape[key]							)}D ndarray.'							)

					if len(voice_preset[key].shape							) != self.preset_shape[key]:
						raise ValueError(F'{key} voice preset must be a {str(self.preset_shape[key]							)}D ndarray.'							)





			def __call__(    self       ,  __lowerCAmelCase=None       ,  __lowerCAmelCase=None       ,  __lowerCAmelCase="pt"       ,  __lowerCAmelCase=2_5_6       ,  __lowerCAmelCase=False       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=False       ,  **__lowerCAmelCase       ,  ):



				'''simple docstring'''







				if voice_preset is not None and not isinstance(_a       ,  _a							):
					if (
					    isinstance(_a       ,  _a							)
					    and self.speaker_embeddings is not None
					    and voice_preset in self.speaker_embeddings
					):
						lowerCamelCase__								=		self._load_voice_preset(_a							)

					else:
						if isinstance(_a       ,  _a							) and not voice_preset.endswith('''.npz'''							):
							lowerCamelCase__								=		voice_preset + '''.npz'''

						lowerCamelCase__								=		np.load(_a							)

				if voice_preset is not None:
					self._validate_voice_preset_dict(_a       ,  **_a							)
					lowerCamelCase__								=		BatchFeature(data=_a       ,  tensor_type=_a							)

				lowerCamelCase__								=		self.tokenizer(
				    _a       ,  return_tensors=_a       ,  padding='''max_length'''       ,  max_length=_a       ,  return_attention_mask=_a       ,  return_token_type_ids=_a       ,  add_special_tokens=_a       ,  **_a       ,  )

				if voice_preset is not None:
					lowerCamelCase__								=		voice_preset

				return encoded_text









"""simple docstring"""




import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase							:			Dict     							=							logging.get_logger(__name__)



def     a__ (				snake_case__				)						->					Dict:
			lowerCamelCase			      =					torch.load(snake_case__       , map_location="""cpu"""				)
			if "model" in sd.keys():
						lowerCamelCase			      =					torch.load(snake_case__       , map_location="""cpu"""				)["""model"""]

			# pop unnecessary weights
			lowerCamelCase			      =					[
			    """decoder.version""",
			    """decoder.output_projection.weight""",
			]
			for key in keys_to_delete:
						if key in sd:
									sd.pop(snake_case__				)

			lowerCamelCase			      =					{
			    """decoder.project_in_dim.weight""": """decoder.project_in.weight""",
			    """decoder.project_out_dim.weight""": """decoder.project_out.weight""",
			    """decoder.layer_norm.weight""": """decoder.final_layer_norm.weight""",
			    """decoder.layer_norm.bias""": """decoder.final_layer_norm.bias""",
			}
			for old_key, new_key in keys_to_rename.items():
						if old_key in sd:
									lowerCamelCase			      =					sd.pop(snake_case__				)

			lowerCamelCase			      =					list(sd.keys()				)
			for key in keys:
						if ".qkv_proj." in key:
									lowerCamelCase			      =					sd[key]
									# We split QKV in separate Q,K,V

									lowerCamelCase			      =					key.replace(""".qkv_proj."""       , """.q_proj."""				)
									lowerCamelCase			      =					key.replace(""".qkv_proj."""       , """.k_proj."""				)
									lowerCamelCase			      =					key.replace(""".qkv_proj."""       , """.v_proj."""				)

									lowerCamelCase			      =					value.shape[0]
									assert depth % 3 == 0
									# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
									# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
									lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase			      =					torch.split(snake_case__       , depth // 3       , dim=0				)

									lowerCamelCase			      =					q
									lowerCamelCase			      =					k
									lowerCamelCase			      =					v
									del sd[key]

			return sd



@torch.no_grad()
def     a__ (				snake_case__       , snake_case__       , snake_case__=None				)						->					Tuple:
			lowerCamelCase			      =					load_checkpoint(snake_case__				)

			if config is not None:
						lowerCamelCase			      =					OPTConfig.from_pretrained(snake_case__				)
			else:
						lowerCamelCase			      =					OPTConfig()

			lowerCamelCase			      =					OPTModel(snake_case__				).half().eval()
			model.load_state_dict(snake_case__				)

			# Check results
			Path(snake_case__				).mkdir(exist_ok=snake_case__				)
			model.save_pretrained(snake_case__				)


if __name__ == "__main__":
					lowerCAmelCase							:			List[str]     							=							argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--fairseq_path""",
					    type=str,
					    help=(
					        """path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"""
					        """ https://huggingface.co/models?other=opt_metasq"""
					    ),
					)
					parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
					parser.add_argument("""--hf_config""", default=None, type=str, help="""Define HF config.""")
					lowerCAmelCase							:			Optional[Any]     							=							parser.parse_args()
					convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)



