






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 			A						(							unittest.TestCase			):







    """simple docstring"""





    def 						snake_case__					(				self			:	Dict		)->   int:





        '''simple docstring'''







        A__   				=       'ylacombe/bark-small'
        A__   				=       tempfile.mkdtemp()
        A__   				=       'en_speaker_1'
        A__   				=       'This is a test string'
        A__   				=       'speaker_embeddings_path.json'
        A__   				=       'speaker_embeddings'


    def 						snake_case__					(				self			:	Tuple,**lowercase_			:	str		)->   int:





        '''simple docstring'''







        return AutoTokenizer.from_pretrained(self.checkpoint,**__lowercase		)


    def 						snake_case__					(				self			:	Optional[int]		)->   Dict:





        '''simple docstring'''







        shutil.rmtree(self.tmpdirname		)


    def 						snake_case__					(				self			:	Any		)->   List[str]:





        '''simple docstring'''







        A__   				=       self.get_tokenizer()

        A__   				=       BarkProcessor(tokenizer=__lowercase		)

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

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


    @slow
    def 						snake_case__					(				self			:	Optional[int]		)->   Dict:





        '''simple docstring'''







        A__   				=       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,)

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

        A__   				=       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 						snake_case__					(				self			:	Optional[Any]		)->   List[str]:





        '''simple docstring'''







        A__   				=       BarkProcessor.from_pretrained(
            pretrained_processor_name_or_path=self.checkpoint,speaker_embeddings_dict_path=self.speaker_embeddings_dict_path,)

        A__   				=       3_5
        A__   				=       2
        A__   				=       8

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

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

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

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

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

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


    def 						snake_case__					(				self			:	List[str]		)->   int:





        '''simple docstring'''







        A__   				=       self.get_tokenizer()

        A__   				=       BarkProcessor(tokenizer=__lowercase		)

        A__   				=       processor(text=self.input_string		)

        A__   				=       tokenizer(
            self.input_string,padding='max_length',max_length=2_5_6,add_special_tokens=__lowercase,return_attention_mask=__lowercase,return_token_type_ids=__lowercase,)

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




# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


lowercase_			=  Path(__file__).resolve().parents[3] / "src"
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

lowercase_			=  {"base": "patrickvonplaten/wav2vec2_tiny_random", "robust": "patrickvonplaten/wav2vec2_tiny_random_robust"}

lowercase_			=  "zero2"
lowercase_			=  "zero3"
lowercase_			=  [ZEROa, ZEROa]
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Optional[int]   ,      SCREAMING_SNAKE_CASE__      :     Tuple   ,      SCREAMING_SNAKE_CASE__      :     List[Any]				)							->				Dict:





    '''simple docstring'''






    A__   				=       parameterized.to_safe_name('_'.join(str(SCREAMING_SNAKE_CASE__				) for x in param.args				)				)
    return f'{func.__name__}_{param_based_name}'


# Cartesian-product of zero stages with models to test
lowercase_			=  list(itertools.product(stages, models.keys()))


@slow
@require_deepspeed
@require_torch_gpu
class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    @parameterized.expand(lowercase_,name_func=lowercase_		)
    def 						snake_case__					(				self			:	int,lowercase_			:	str,lowercase_			:	Any		)->   Optional[int]:





        '''simple docstring'''







        self.run_and_check(
            stage=lowercase_,model=lowercase_,distributed=lowercase_,fpaa=lowercase_,)


    @require_torch_multi_gpu
    @parameterized.expand(lowercase_,name_func=lowercase_		)
    def 						snake_case__					(				self			:	Union[str, Any],lowercase_			:	Optional[Any],lowercase_			:	List[Any]		)->   int:





        '''simple docstring'''







        self.run_and_check(
            stage=lowercase_,model=lowercase_,distributed=lowercase_,fpaa=lowercase_,)


    @parameterized.expand(lowercase_,name_func=lowercase_		)
    def 						snake_case__					(				self			:	List[str],lowercase_			:	List[str],lowercase_			:	List[Any]		)->   Any:





        '''simple docstring'''







        self.run_and_check(
            stage=lowercase_,model=lowercase_,distributed=lowercase_,fpaa=lowercase_,)


    @require_torch_multi_gpu
    @parameterized.expand(lowercase_,name_func=lowercase_		)
    def 						snake_case__					(				self			:	Dict,lowercase_			:	Optional[Any],lowercase_			:	List[Any]		)->   Optional[int]:





        '''simple docstring'''







        self.run_and_check(
            stage=lowercase_,model=lowercase_,distributed=lowercase_,fpaa=lowercase_,)


    def 						snake_case__					(				self			:	Tuple,lowercase_			:	Any		)->   Union[str, Any]:





        '''simple docstring'''







        pass


    def 						snake_case__					(				self			:	int,lowercase_			:	str,lowercase_			:	str,lowercase_			:	int = 1_0,lowercase_			:	bool = True,lowercase_			:	bool = True,lowercase_			:	bool = True,)->   Union[str, Any]:





        '''simple docstring'''







        A__   				=       models[model]

        A__   				=       self.run_trainer(
            stage=lowercase_,model_name=lowercase_,eval_steps=lowercase_,num_train_epochs=1,distributed=lowercase_,fpaa=lowercase_,)

        self.do_checks(lowercase_		)

        return output_dir


    def 						snake_case__					(				self			:	Union[str, Any],lowercase_			:	str,lowercase_			:	str,lowercase_			:	int = 1_0,lowercase_			:	int = 1,lowercase_			:	bool = True,lowercase_			:	bool = True,)->   Any:





        '''simple docstring'''







        A__   				=       self.get_auto_remove_tmp_dir('./xxx',after=lowercase_		)
        A__   				=       F'\n            --model_name_or_path {model_name}\n            --dataset_name hf-internal-testing/librispeech_asr_dummy\n            --dataset_config_name clean\n            --train_split_name validation\n            --validation_split_name validation\n            --output_dir {output_dir}\n            --num_train_epochs {str(lowercase_		)}\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 2\n            --evaluation_strategy steps\n            --learning_rate 5e-4\n            --warmup_steps 8\n            --orthography timit\n            --preprocessing_num_workers 1\n            --group_by_length\n            --freeze_feature_extractor\n            --report_to none\n            --save_steps 0\n            --eval_steps {eval_steps}\n            --report_to none\n        '.split()

        if fpaa:
            args.extend(['--fp16']		)

        # currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
        # hence the separate config files
        A__   				=       F'--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'.split()
        A__   				=       [F'{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py']
        A__   				=       self.get_launcher(lowercase_		)

        A__   				=       launcher + script + args + ds_args
        # keep for quick debug
        # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
        execute_subprocess_async(lowercase_,env=self.get_env()		)

        return output_dir


    def 						snake_case__					(				self			:	Any,lowercase_			:	int=False		)->   Tuple:





        '''simple docstring'''







        A__   				=       min(2,get_gpu_count()		) if distributed else 1
        return F'deepspeed --num_nodes 1 --num_gpus {num_gpus}'.split()

