




"""simple docstring"""







import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow






@require_torch
class       SCREAMING_SNAKE_CASE__					(					unittest.TestCase					):


						def   __lowercase (							self				:			Optional[Any]					):
										lowerCAmelCase  =				"""ylacombe/bark-small"""
										lowerCAmelCase  =				tempfile.mkdtemp()
										lowerCAmelCase  =				"""en_speaker_1"""
										lowerCAmelCase  =				"""This is a test string"""
										lowerCAmelCase  =				"""speaker_embeddings_path.json"""
										lowerCAmelCase  =				"""speaker_embeddings"""


						def   __lowercase (							self				:			Tuple						,						**lowerCAmelCase				:			Optional[Any]					):
										return AutoTokenizer.from_pretrained(self.checkpoint						,						**lowerCAmelCase					)


						def   __lowercase (							self				:			str					):
										shutil.rmtree(self.tmpdirname					)


						def   __lowercase (							self				:			str					):
										lowerCAmelCase  =				self.get_tokenizer()

										lowerCAmelCase  =				BarkProcessor(tokenizer=lowerCAmelCase					)

										processor.save_pretrained(self.tmpdirname					)
										lowerCAmelCase  =				BarkProcessor.from_pretrained(self.tmpdirname					)

										self.assertEqual(processor.tokenizer.get_vocab()						,						tokenizer.get_vocab()					)


						@slow
						def   __lowercase (							self				:			str					):
										lowerCAmelCase  =				BarkProcessor.from_pretrained(
										    pretrained_processor_name_or_path=self.checkpoint						,						speaker_embeddings_dict_path=self.speaker_embeddings_dict_path						,						)
										processor.save_pretrained(
										    self.tmpdirname						,						speaker_embeddings_dict_path=self.speaker_embeddings_dict_path						,						speaker_embeddings_directory=self.speaker_embeddings_directory						,						)

										lowerCAmelCase  =				self.get_tokenizer(bos_token="""(BOS)"""						,						eos_token="""(EOS)"""					)

										lowerCAmelCase  =				BarkProcessor.from_pretrained(
										    self.tmpdirname						,						self.speaker_embeddings_dict_path						,						bos_token="""(BOS)"""						,						eos_token="""(EOS)"""						,						)

										self.assertEqual(processor.tokenizer.get_vocab()						,						tokenizer_add_kwargs.get_vocab()					)


						def   __lowercase (							self				:			str					):
										lowerCAmelCase  =				BarkProcessor.from_pretrained(
										    pretrained_processor_name_or_path=self.checkpoint						,						speaker_embeddings_dict_path=self.speaker_embeddings_dict_path						,						)

										lowerCAmelCase  =				35
										lowerCAmelCase  =				2
										lowerCAmelCase  =				8

										lowerCAmelCase  =				{
										    """semantic_prompt""": np.ones(lowerCAmelCase					),
										    """coarse_prompt""": np.ones((nb_codebooks_coarse, seq_len)					),
										    """fine_prompt""": np.ones((nb_codebooks_total, seq_len)					),
										}

										# test providing already loaded voice_preset
										lowerCAmelCase  =				processor(text=self.input_string						,						voice_preset=lowerCAmelCase					)

										lowerCAmelCase  =				inputs["""history_prompt"""]
										for key in voice_preset:
														self.assertListEqual(voice_preset[key].tolist()						,						processed_voice_preset.get(lowerCAmelCase						,						np.array([]					)					).tolist()					)

										# test loading voice preset from npz file
										lowerCAmelCase  =				os.path.join(self.tmpdirname						,						"""file.npz"""					)
										np.savez(lowerCAmelCase						,						**lowerCAmelCase					)
										lowerCAmelCase  =				processor(text=self.input_string						,						voice_preset=lowerCAmelCase					)
										lowerCAmelCase  =				inputs["""history_prompt"""]

										for key in voice_preset:
														self.assertListEqual(voice_preset[key].tolist()						,						processed_voice_preset.get(lowerCAmelCase						,						np.array([]					)					).tolist()					)

										# test loading voice preset from the hub
										lowerCAmelCase  =				processor(text=self.input_string						,						voice_preset=self.voice_preset					)




						def   __lowercase (							self				:			str					):
										lowerCAmelCase  =				self.get_tokenizer()

										lowerCAmelCase  =				BarkProcessor(tokenizer=lowerCAmelCase					)

										lowerCAmelCase  =				processor(text=self.input_string					)

										lowerCAmelCase  =				tokenizer(
										    self.input_string						,						padding="""max_length"""						,						max_length=256						,						add_special_tokens=lowerCAmelCase						,						return_attention_mask=lowerCAmelCase						,						return_token_type_ids=lowerCAmelCase						,						)

										for key in encoded_tok.keys():
														self.assertListEqual(encoded_tok[key]						,						encoded_processor[key].squeeze().tolist()					)











"""simple docstring"""







# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.utils import ComputeEnvironment

from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file  # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment  # noqa: F401
from .sagemaker import get_sagemaker_input


a      =							'Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine'





def 							lowercase					()     ->  Optional[Any]:


				'''simple docstring'''






				lowerCAmelCase  =				_ask_options(
				    """In which compute environment are you running?"""      ,       ["""This machine""", """AWS (Amazon SageMaker)"""]      ,       _convert_compute_environment      ,       )
				if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
								lowerCAmelCase  =				get_sagemaker_input()
				else:
								lowerCAmelCase  =				get_cluster_input()
				return config





def 							lowercase					(snake_case__   : List[str]=None )     ->  int:


				'''simple docstring'''






				if subparsers is not None:
								lowerCAmelCase  =				subparsers.add_parser("""config"""      ,       description=snake_case__ )
				else:
								lowerCAmelCase  =				argparse.ArgumentParser("""Accelerate config command"""      ,       description=snake_case__ )

				parser.add_argument(
				    """--config_file"""      ,       default=snake_case__      ,       help=(
				        """The path to use to store the config file. Will default to a file named default_config.yaml in the cache """
				        """location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have """
				        """such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed """
				        """with 'huggingface'."""
				    )      ,       )

				if subparsers is not None:
								parser.set_defaults(func=snake_case__ )
				return parser





def 							lowercase					(snake_case__   : str )     ->  Optional[Any]:


				'''simple docstring'''






				lowerCAmelCase  =				get_user_input()
				if args.config_file is not None:
								lowerCAmelCase  =				args.config_file
				else:
								if not os.path.isdir(snake_case__ ):
												os.makedirs(snake_case__ )
								lowerCAmelCase  =				default_yaml_config_file

				if config_file.endswith(""".json""" ):
								config.to_json_file(snake_case__ )
				else:
								config.to_yaml_file(snake_case__ )
				print(f'''accelerate configuration saved at {config_file}''' )





def 							lowercase					()     ->  Optional[int]:


				'''simple docstring'''






				lowerCAmelCase  =				config_command_parser()
				lowerCAmelCase  =				parser.parse_args()
				config_command(snake_case__ )


if __name__ == "__main__":
		main()





