






"""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    __lowerCamelCase    (  unittest.TestCase  ):

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       UpperCamelCase__         =    'ylacombe/bark-small'
       UpperCamelCase__         =    tempfile.mkdtemp()
       UpperCamelCase__         =    'en_speaker_1'
       UpperCamelCase__         =    'This is a test string'
       UpperCamelCase__         =    'speaker_embeddings_path.json'
       UpperCamelCase__         =    'speaker_embeddings'

   def 						SCREAMING_SNAKE_CASE__ (					self ,				**snake_case_      )		->       Optional[Any]:
       return AutoTokenizer.from_pretrained(self.checkpoint ,				**snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       shutil.rmtree(self.tmpdirname      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       UpperCamelCase__         =    self.get_tokenizer()

       UpperCamelCase__         =    BarkProcessor(tokenizer=snake_case_      )

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

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

   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[Any]:
       UpperCamelCase__         =    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 ,				)

       UpperCamelCase__         =    self.get_tokenizer(bos_token='(BOS)' ,				eos_token='(EOS)'      )

       UpperCamelCase__         =    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 						SCREAMING_SNAKE_CASE__ (					self      )		->       int:
       UpperCamelCase__         =    BarkProcessor.from_pretrained(
           pretrained_processor_name_or_path=self.checkpoint ,				speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,				)

       UpperCamelCase__         =    35
       UpperCamelCase__         =    2
       UpperCamelCase__         =    8

       UpperCamelCase__         =    {
           'semantic_prompt': np.ones(snake_case_      ),
           'coarse_prompt': np.ones((nb_codebooks_coarse, seq_len)      ),
           'fine_prompt': np.ones((nb_codebooks_total, seq_len)      ),
       }

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

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

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

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

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







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[str]:
       UpperCamelCase__         =    self.get_tokenizer()

       UpperCamelCase__         =    BarkProcessor(tokenizer=snake_case_      )

       UpperCamelCase__         =    processor(text=self.input_string      )

       UpperCamelCase__         =    tokenizer(
           self.input_string ,				padding='max_length' ,				max_length=256 ,				add_special_tokens=snake_case_ ,				return_attention_mask=snake_case_ ,				return_token_type_ids=snake_case_ ,				)

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












"""simple docstring"""





# Copyright 2022 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
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


A__					: Optional[Any]=	"""Run commands across TPU VMs for initial setup before running `accelerate launch`."""







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE=None					)					->	Dict:
    """simple docstring"""

    if subparsers is not None:
        UpperCamelCase__         =    subparsers.add_parser('tpu-config' ,	description=_description					)
    else:
        UpperCamelCase__         =    argparse.ArgumentParser('Accelerate tpu-config command' ,	description=_description					)
    # Core arguments
    UpperCamelCase__         =    parser.add_argument_group(
        'Config Arguments' ,	'Arguments that can be configured through `accelerate config`.'					)
    config_args.add_argument(
        '--config_file' ,	type=SCREAMING_SNAKE_CASE ,	default=SCREAMING_SNAKE_CASE ,	help='Path to the config file to use for accelerate.' ,	)
    config_args.add_argument(
        '--tpu_name' ,	default=SCREAMING_SNAKE_CASE ,	help='The name of the TPU to use. If not specified, will use the TPU specified in the config file.' ,	)
    config_args.add_argument(
        '--tpu_zone' ,	default=SCREAMING_SNAKE_CASE ,	help='The zone of the TPU to use. If not specified, will use the zone specified in the config file.' ,	)
    UpperCamelCase__         =    parser.add_argument_group('TPU Arguments' ,	'Arguments for options ran inside the TPU.'					)
    pod_args.add_argument(
        '--use_alpha' ,	action='store_true' ,	help='Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`.' ,	)
    pod_args.add_argument(
        '--command_file' ,	default=SCREAMING_SNAKE_CASE ,	help='The path to the file containing the commands to run on the pod on startup.' ,	)
    pod_args.add_argument(
        '--command' ,	action='append' ,	nargs='+' ,	help='A command to run on the pod. Can be passed multiple times.' ,	)
    pod_args.add_argument(
        '--install_accelerate' ,	action='store_true' ,	help='Whether to install accelerate on the pod. Defaults to False.' ,	)
    pod_args.add_argument(
        '--accelerate_version' ,	default='latest' ,	help='The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify \'dev\' to install from GitHub.' ,	)
    pod_args.add_argument(
        '--debug' ,	action='store_true' ,	help='If set, will print the command that would be run instead of running it.'					)

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







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	Dict:
    """simple docstring"""

    UpperCamelCase__         =    None

    # Get the default from the config file if it exists.
    if args.config_file is not None or os.path.isfile(SCREAMING_SNAKE_CASE					):
        UpperCamelCase__         =    load_config_from_file(args.config_file					)
        if not args.command_file and defaults.command_file is not None and not args.command:
            UpperCamelCase__         =    defaults.command_file
        if not args.command and defaults.commands is not None:
            UpperCamelCase__         =    defaults.commands
        if not args.tpu_name:
            UpperCamelCase__         =    defaults.tpu_name
        if not args.tpu_zone:
            UpperCamelCase__         =    defaults.tpu_zone
    if args.accelerate_version == "dev":
        UpperCamelCase__         =    'git+https://github.com/huggingface/accelerate.git'
    elif args.accelerate_version == "latest":
        UpperCamelCase__         =    'accelerate -U'
    elif isinstance(parse(args.accelerate_version					) ,	SCREAMING_SNAKE_CASE					):
        UpperCamelCase__         =    F'accelerate=={args.accelerate_version}'

    if not args.command_file and not args.command:
        raise ValueError('You must specify either a command file or a command to run on the pod.'					)

    if args.command_file:
        with open(args.command_file ,	'r'					) as f:
            UpperCamelCase__         =    [f.read().splitlines()]

    # To turn list of lists into list of strings
    if isinstance(args.command[0] ,	SCREAMING_SNAKE_CASE					):
        UpperCamelCase__         =    [line for cmd in args.command for line in cmd]
    # Default to the shared folder and install accelerate
    UpperCamelCase__         =    ['cd /usr/share']
    if args.install_accelerate:
        new_cmd += [F'pip install {args.accelerate_version}']
    new_cmd += args.command
    UpperCamelCase__         =    '; '.join(SCREAMING_SNAKE_CASE					)

    # Then send it to gcloud
    # Eventually try to use google-api-core to do this instead of subprocess
    UpperCamelCase__         =    ['gcloud']
    if args.use_alpha:
        cmd += ["alpha"]
    cmd += [
        "compute",
        "tpus",
        "tpu-vm",
        "ssh",
        args.tpu_name,
        "--zone",
        args.tpu_zone,
        "--command",
        args.command,
        "--worker",
        "all",
    ]
    if args.debug:
        print(F'Running {" ".join(SCREAMING_SNAKE_CASE					)}'					)
        return
    subprocess.run(SCREAMING_SNAKE_CASE					)
    print('Successfully setup pod.'					)







def   lowerCAmelCase_( )					->	int:
    """simple docstring"""

    UpperCamelCase__         =    tpu_command_parser()
    UpperCamelCase__         =    parser.parse_args()

    tpu_command_launcher(SCREAMING_SNAKE_CASE					)




