





'''simple docstring'''


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


lowerCAmelCase_   :   List[Any]       =					logging.get_logger(__name__)






class       UpperCamelCase__  (		__lowerCAmelCase     ):
	lowerCAmelCase__					:			Union[str, Any]								=						"AutoTokenizer"
	lowerCAmelCase__					:			Any								=						["tokenizer"]

	lowerCAmelCase__					:			Union[str, Any]								=						{
	    "semantic_prompt": 1,
	    "coarse_prompt": 2,
	    "fine_prompt": 2,
	}







	def __init__(				self			:   Tuple , lowerCamelCase			:   int , lowerCamelCase			:   Optional[Any]=None		):




				'''simple docstring'''
				super().__init__(lowerCamelCase		)

				a__     = speaker_embeddings







	@classmethod
	def 						__a   (				cls			:   int , lowerCamelCase			:   Tuple , lowerCamelCase			:   List[Any]="speaker_embeddings_path.json" , **lowerCamelCase			:   Union[str, Any]		):




				'''simple docstring'''

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

				a__     = AutoTokenizer.from_pretrained(lowerCamelCase , **lowerCamelCase		)

				return cls(tokenizer=lowerCamelCase , speaker_embeddings=lowerCamelCase		)







	def 						__a   (				self			:   Any , lowerCamelCase			:   Union[str, Any] , lowerCamelCase			:   Any="speaker_embeddings_path.json" , lowerCamelCase			:   int="speaker_embeddings" , lowerCamelCase			:   bool = False , **lowerCamelCase			:   Union[str, Any] , ):




				'''simple docstring'''
				if self.speaker_embeddings is not None:
							os.makedirs(os.path.join(lowerCamelCase , lowerCamelCase , "v2"		) , exist_ok=lowerCamelCase		)

							a__     = {}

							a__     = save_directory

							for prompt_key in self.speaker_embeddings:
										if prompt_key != "repo_or_path":
													a__     = self._load_voice_preset(lowerCamelCase		)

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

													a__     = tmp_dict

							with open(os.path.join(lowerCamelCase , lowerCamelCase		) , "w"		) as fp:
										json.dump(lowerCamelCase , lowerCamelCase		)

				super().save_pretrained(lowerCamelCase , lowerCamelCase , **lowerCamelCase		)







	def 						__a   (				self			:   int , lowerCamelCase			:   str = None , **lowerCamelCase			:   List[Any]		):




				'''simple docstring'''
				a__     = self.speaker_embeddings[voice_preset]

				a__     = {}
				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}].'''		)

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

							a__     = np.load(lowerCamelCase		)

				return voice_preset_dict







	def 						__a   (				self			:   int , lowerCamelCase			:   Optional[dict] = 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			:   Optional[int] , lowerCamelCase			:   int=None , lowerCamelCase			:   Any=None , lowerCamelCase			:   List[Any]="pt" , lowerCamelCase			:   List[str]=2_5_6 , lowerCamelCase			:   Dict=False , lowerCamelCase			:   Union[str, Any]=True , lowerCamelCase			:   Any=False , **lowerCamelCase			:   Dict , ):




				'''simple docstring'''
				if voice_preset is not None and not isinstance(lowerCamelCase , lowerCamelCase		):
							if (
							    isinstance(lowerCamelCase , lowerCamelCase		)
							    and self.speaker_embeddings is not None
							    and voice_preset in self.speaker_embeddings
							):
										a__     = self._load_voice_preset(lowerCamelCase		)

							else:
										if isinstance(lowerCamelCase , lowerCamelCase		) and not voice_preset.endswith(".npz"		):
													a__     = voice_preset + ".npz"

										a__     = np.load(lowerCamelCase		)

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

				a__     = self.tokenizer(
				    lowerCamelCase , return_tensors=lowerCamelCase , padding="max_length" , max_length=lowerCamelCase , return_attention_mask=lowerCamelCase , return_token_type_ids=lowerCamelCase , add_special_tokens=lowerCamelCase , **lowerCamelCase , )

				if voice_preset is not None:
							a__     = voice_preset

				return encoded_text










'''simple docstring'''


import argparse
import os
import re

import packaging.version


lowerCAmelCase_   :   Optional[int]       =					"examples/"
lowerCAmelCase_   :   Optional[int]       =					{
    "examples": (re.compile(R"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
    "init": (re.compile(R"^__version__\s+=\s+\"([^\"]+)\"\s*$", re.MULTILINE), "__version__ = \"VERSION\"\n"),
    "setup": (re.compile(R"^(\s*)version\s*=\s*\"[^\"]+\",", re.MULTILINE), R"\1version=\"VERSION\","),
    "doc": (re.compile(R"^(\s*)release\s*=\s*\"[^\"]+\"$", re.MULTILINE), "release = \"VERSION\"\n"),
}
lowerCAmelCase_   :   Optional[int]       =					{
    "init": "src/transformers/__init__.py",
    "setup": "setup.py",
}
lowerCAmelCase_   :   List[Any]       =					"README.md"







def    _lowerCamelCase  (__lowerCamelCase  :     Dict					,  __lowerCamelCase  :     List[Any]					,  __lowerCamelCase  :     Optional[Any]     )   ->    str:
			with open(__lowerCamelCase					,  "r"					,  encoding="utf-8"					,  newline="\n"     ) as f:
						a__     = f.read()
			a__   ,				a__     = REPLACE_PATTERNS[pattern]
			a__     = replace.replace("VERSION"					,  __lowerCamelCase     )
			a__     = re_pattern.sub(__lowerCamelCase					,  __lowerCamelCase     )
			with open(__lowerCamelCase					,  "w"					,  encoding="utf-8"					,  newline="\n"     ) as f:
						f.write(__lowerCamelCase     )







def    _lowerCamelCase  (__lowerCamelCase  :     Union[str, Any]     )   ->    Dict:
			for folder, directories, fnames in os.walk(__lowerCamelCase     ):
						# Removing some of the folders with non-actively maintained examples from the walk
						if "research_projects" in directories:
									directories.remove("research_projects"     )
						if "legacy" in directories:
									directories.remove("legacy"     )
						for fname in fnames:
									if fname.endswith(".py"     ):
												update_version_in_file(os.path.join(__lowerCamelCase					,  __lowerCamelCase     )					,  __lowerCamelCase					,  pattern="examples"     )







def    _lowerCamelCase  (__lowerCamelCase  :     int					,  __lowerCamelCase  :     Any=False     )   ->    Optional[Any]:
			for pattern, fname in REPLACE_FILES.items():
						update_version_in_file(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase     )
			if not patch:
						update_version_in_examples(__lowerCamelCase     )







def    _lowerCamelCase  ()   ->    Tuple:
			a__     = "🤗 Transformers currently provides the following architectures"
			a__     = "1. Want to contribute a new model?"
			with open(__lowerCamelCase					,  "r"					,  encoding="utf-8"					,  newline="\n"     ) as f:
						a__     = f.readlines()

			# Find the start of the list.
			a__     = 0
			while not lines[start_index].startswith(_start_prompt     ):
						start_index += 1
			start_index += 1

			a__     = start_index
			# Update the lines in the model list.
			while not lines[index].startswith(_end_prompt     ):
						if lines[index].startswith("1."     ):
									a__     = lines[index].replace(
									    "https://huggingface.co/docs/transformers/main/model_doc"					,  "https://huggingface.co/docs/transformers/model_doc"					,  )
						index += 1

			with open(__lowerCamelCase					,  "w"					,  encoding="utf-8"					,  newline="\n"     ) as f:
						f.writelines(__lowerCamelCase     )







def    _lowerCamelCase  ()   ->    Tuple:
			with open(REPLACE_FILES["init"]					,  "r"     ) as f:
						a__     = f.read()
			a__     = REPLACE_PATTERNS["init"][0].search(__lowerCamelCase     ).groups()[0]
			return packaging.version.parse(__lowerCamelCase     )







def    _lowerCamelCase  (__lowerCamelCase  :     Any=False     )   ->    int:
			a__     = get_version()
			if patch and default_version.is_devrelease:
						raise ValueError("Can't create a patch version from the dev branch, checkout a released version!"     )
			if default_version.is_devrelease:
						a__     = default_version.base_version
			elif patch:
						a__     = f'''{default_version.major}.{default_version.minor}.{default_version.micro + 1}'''
			else:
						a__     = f'''{default_version.major}.{default_version.minor + 1}.0'''

			# Now let's ask nicely if that's the right one.
			a__     = input(f'''Which version are you releasing? [{default_version}]'''     )
			if len(__lowerCamelCase     ) == 0:
						a__     = default_version

			print(f'''Updating version to {version}.'''     )
			global_version_update(__lowerCamelCase					,  patch=__lowerCamelCase     )
			if not patch:
						print("Cleaning main README, don't forget to run `make fix-copies`."     )
						clean_main_ref_in_model_list()







def    _lowerCamelCase  ()   ->    Tuple:
			a__     = get_version()
			a__     = f'''{current_version.major}.{current_version.minor + 1}.0.dev0'''
			a__     = current_version.base_version

			# Check with the user we got that right.
			a__     = input(f'''Which version are we developing now? [{dev_version}]'''     )
			if len(__lowerCamelCase     ) == 0:
						a__     = dev_version

			print(f'''Updating version to {version}.'''     )
			global_version_update(__lowerCamelCase     )
			print("Cleaning main README, don't forget to run `make fix-copies`."     )
			clean_main_ref_in_model_list()


if __name__ == "__main__":
		lowerCAmelCase_   :   List[str]       =					argparse.ArgumentParser()
		parser.add_argument("--post_release", action="store_true", help="Whether this is pre or post release.")
		parser.add_argument("--patch", action="store_true", help="Whether or not this is a patch release.")
		lowerCAmelCase_   :   int       =					parser.parse_args()
		if not args.post_release:
				pre_release_work(patch=args.patch)
		elif args.patch:
				print("Nothing to do after a patch :-)")
		else:
				post_release_work()



