



'''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 				snake_case (unittest.TestCase   ):



       def 							_a      (				self      )  ->							Dict:
           lowercase__		       =						"""ylacombe/bark-small"""
           lowercase__		       =						tempfile.mkdtemp()
           lowercase__		       =						"""en_speaker_1"""
           lowercase__		       =						"""This is a test string"""
           lowercase__		       =						"""speaker_embeddings_path.json"""
           lowercase__		       =						"""speaker_embeddings"""



       def 							_a      (				self     ,**UpperCAmelCase_      )  ->							Dict:
           return AutoTokenizer.from_pretrained(self.checkpoint     ,**lowercase_      )



       def 							_a      (				self      )  ->							Tuple:
           shutil.rmtree(self.tmpdirname      )



       def 							_a      (				self      )  ->							List[str]:
           lowercase__		       =						self.get_tokenizer()

           lowercase__		       =						BarkProcessor(tokenizer=lowercase_      )

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

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



       @slow
       def 							_a      (				self      )  ->							Tuple:
           lowercase__		       =						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     ,)

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

           lowercase__		       =						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 							_a      (				self      )  ->							Union[str, Any]:
           lowercase__		       =						BarkProcessor.from_pretrained(
               pretrained_processor_name_or_path=self.checkpoint     ,speaker_embeddings_dict_path=self.speaker_embeddings_dict_path     ,)

           lowercase__		       =						35
           lowercase__		       =						2
           lowercase__		       =						8

           lowercase__		       =						{
               """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
           lowercase__		       =						processor(text=self.input_string     ,voice_preset=lowercase_      )

           lowercase__		       =						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
           lowercase__		       =						os.path.join(self.tmpdirname     ,"file.npz"      )
           np.savez(lowercase_     ,**lowercase_      )
           lowercase__		       =						processor(text=self.input_string     ,voice_preset=lowercase_      )
           lowercase__		       =						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
           lowercase__		       =						processor(text=self.input_string     ,voice_preset=self.voice_preset      )







       def 							_a      (				self      )  ->							Optional[Any]:
           lowercase__		       =						self.get_tokenizer()

           lowercase__		       =						BarkProcessor(tokenizer=lowercase_      )

           lowercase__		       =						processor(text=self.input_string      )

           lowercase__		       =						tokenizer(
               self.input_string     ,padding="max_length"     ,max_length=256     ,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()      )









'''simple docstring'''





import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


SCREAMING_SNAKE_CASE__      					=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__      					=     OrderedDict(
    [
        ("audio-spectrogram-transformer", "ASTFeatureExtractor"),
        ("beit", "BeitFeatureExtractor"),
        ("chinese_clip", "ChineseCLIPFeatureExtractor"),
        ("clap", "ClapFeatureExtractor"),
        ("clip", "CLIPFeatureExtractor"),
        ("clipseg", "ViTFeatureExtractor"),
        ("conditional_detr", "ConditionalDetrFeatureExtractor"),
        ("convnext", "ConvNextFeatureExtractor"),
        ("cvt", "ConvNextFeatureExtractor"),
        ("data2vec-audio", "Wav2Vec2FeatureExtractor"),
        ("data2vec-vision", "BeitFeatureExtractor"),
        ("deformable_detr", "DeformableDetrFeatureExtractor"),
        ("deit", "DeiTFeatureExtractor"),
        ("detr", "DetrFeatureExtractor"),
        ("dinat", "ViTFeatureExtractor"),
        ("donut-swin", "DonutFeatureExtractor"),
        ("dpt", "DPTFeatureExtractor"),
        ("encodec", "EncodecFeatureExtractor"),
        ("flava", "FlavaFeatureExtractor"),
        ("glpn", "GLPNFeatureExtractor"),
        ("groupvit", "CLIPFeatureExtractor"),
        ("hubert", "Wav2Vec2FeatureExtractor"),
        ("imagegpt", "ImageGPTFeatureExtractor"),
        ("layoutlmv2", "LayoutLMv2FeatureExtractor"),
        ("layoutlmv3", "LayoutLMv3FeatureExtractor"),
        ("levit", "LevitFeatureExtractor"),
        ("maskformer", "MaskFormerFeatureExtractor"),
        ("mctct", "MCTCTFeatureExtractor"),
        ("mobilenet_v1", "MobileNetV1FeatureExtractor"),
        ("mobilenet_v2", "MobileNetV2FeatureExtractor"),
        ("mobilevit", "MobileViTFeatureExtractor"),
        ("nat", "ViTFeatureExtractor"),
        ("owlvit", "OwlViTFeatureExtractor"),
        ("perceiver", "PerceiverFeatureExtractor"),
        ("poolformer", "PoolFormerFeatureExtractor"),
        ("regnet", "ConvNextFeatureExtractor"),
        ("resnet", "ConvNextFeatureExtractor"),
        ("segformer", "SegformerFeatureExtractor"),
        ("sew", "Wav2Vec2FeatureExtractor"),
        ("sew-d", "Wav2Vec2FeatureExtractor"),
        ("speech_to_text", "Speech2TextFeatureExtractor"),
        ("speecht5", "SpeechT5FeatureExtractor"),
        ("swiftformer", "ViTFeatureExtractor"),
        ("swin", "ViTFeatureExtractor"),
        ("swinv2", "ViTFeatureExtractor"),
        ("table-transformer", "DetrFeatureExtractor"),
        ("timesformer", "VideoMAEFeatureExtractor"),
        ("tvlt", "TvltFeatureExtractor"),
        ("unispeech", "Wav2Vec2FeatureExtractor"),
        ("unispeech-sat", "Wav2Vec2FeatureExtractor"),
        ("van", "ConvNextFeatureExtractor"),
        ("videomae", "VideoMAEFeatureExtractor"),
        ("vilt", "ViltFeatureExtractor"),
        ("vit", "ViTFeatureExtractor"),
        ("vit_mae", "ViTFeatureExtractor"),
        ("vit_msn", "ViTFeatureExtractor"),
        ("wav2vec2", "Wav2Vec2FeatureExtractor"),
        ("wav2vec2-conformer", "Wav2Vec2FeatureExtractor"),
        ("wavlm", "Wav2Vec2FeatureExtractor"),
        ("whisper", "WhisperFeatureExtractor"),
        ("xclip", "CLIPFeatureExtractor"),
        ("yolos", "YolosFeatureExtractor"),
    ]
)

SCREAMING_SNAKE_CASE__      					=     _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)




def        lowerCamelCase  (				_snake_case  :						str       ):





    '''simple docstring'''


    for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
        if class_name in extractors:
            lowercase__		       =						model_type_to_module_name(_snake_case       )

            lowercase__		       =						importlib.import_module(f'''.{module_name}'''     ,"transformers.models"       )
            try:
                return getattr(_snake_case     ,_snake_case       )
            except AttributeError:
                continue

    for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
        if getattr(_snake_case     ,"__name__"     ,_snake_case       ) == class_name:
            return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
    lowercase__		       =						importlib.import_module("transformers"       )
    if hasattr(_snake_case     ,_snake_case       ):
        return getattr(_snake_case     ,_snake_case       )

    return None




def        lowerCamelCase  (				_snake_case  :						Union[str, os.PathLike]     ,_snake_case  :						Optional[Union[str, os.PathLike]] = None     ,_snake_case  :						bool = False     ,_snake_case  :						bool = False     ,_snake_case  :						Optional[Dict[str, str]] = None     ,_snake_case  :						Optional[Union[bool, str]] = None     ,_snake_case  :						Optional[str] = None     ,_snake_case  :						bool = False     ,**_snake_case  :						Union[str, Any]     ,):





    '''simple docstring'''


    lowercase__		       =						get_file_from_repo(
        _snake_case     ,_snake_case     ,cache_dir=_snake_case     ,force_download=_snake_case     ,resume_download=_snake_case     ,proxies=_snake_case     ,use_auth_token=_snake_case     ,revision=_snake_case     ,local_files_only=_snake_case     ,)
    if resolved_config_file is None:
        logger.info(
            "Could not locate the feature extractor configuration file, will try to use the model config instead."       )
        return {}

    with open(_snake_case     ,encoding="utf-8"       ) as reader:
        return json.load(_snake_case       )



class 				snake_case :



       def __init__(				self      )  ->							List[str]:
           raise EnvironmentError(
               "AutoFeatureExtractor is designed to be instantiated "
               "using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method."      )



       @classmethod
       @replace_list_option_in_docstrings(UpperCAmelCase_      )
       def 							_a      (				cls     ,UpperCAmelCase_     ,**UpperCAmelCase_      )  ->							Tuple:
           lowercase__		       =						kwargs.pop("config"     ,UpperCAmelCase_      )
           lowercase__		       =						kwargs.pop("trust_remote_code"     ,UpperCAmelCase_      )
           lowercase__		       =						True

           lowercase__       ,       lowercase__		       =						FeatureExtractionMixin.get_feature_extractor_dict(UpperCAmelCase_     ,**UpperCAmelCase_      )
           lowercase__		       =						config_dict.get("feature_extractor_type"     ,UpperCAmelCase_      )
           lowercase__		       =						None
           if "AutoFeatureExtractor" in config_dict.get("auto_map"     ,{}      ):
               lowercase__		       =						config_dict["auto_map"]["AutoFeatureExtractor"]

           # If we don't find the feature extractor class in the feature extractor config, let's try the model config.
           if feature_extractor_class is None and feature_extractor_auto_map is None:
               if not isinstance(UpperCAmelCase_     ,UpperCAmelCase_      ):
                   lowercase__		       =						AutoConfig.from_pretrained(UpperCAmelCase_     ,**UpperCAmelCase_      )
               # It could be in `config.feature_extractor_type``
               lowercase__		       =						getattr(UpperCAmelCase_     ,"feature_extractor_type"     ,UpperCAmelCase_      )
               if hasattr(UpperCAmelCase_     ,"auto_map"      ) and "AutoFeatureExtractor" in config.auto_map:
                   lowercase__		       =						config.auto_map["AutoFeatureExtractor"]

           if feature_extractor_class is not None:
               lowercase__		       =						feature_extractor_class_from_name(UpperCAmelCase_      )

           lowercase__		       =						feature_extractor_auto_map is not None
           lowercase__		       =						feature_extractor_class is not None or type(UpperCAmelCase_      ) in FEATURE_EXTRACTOR_MAPPING
           lowercase__		       =						resolve_trust_remote_code(
               UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )

           if has_remote_code and trust_remote_code:
               lowercase__		       =						get_class_from_dynamic_module(
                   UpperCAmelCase_     ,UpperCAmelCase_     ,**UpperCAmelCase_      )
               lowercase__		       =						kwargs.pop("code_revision"     ,UpperCAmelCase_      )
               if os.path.isdir(UpperCAmelCase_      ):
                   feature_extractor_class.register_for_auto_class()
               return feature_extractor_class.from_dict(UpperCAmelCase_     ,**UpperCAmelCase_      )
           elif feature_extractor_class is not None:
               return feature_extractor_class.from_dict(UpperCAmelCase_     ,**UpperCAmelCase_      )
           # Last try: we use the FEATURE_EXTRACTOR_MAPPING.
           elif type(UpperCAmelCase_      ) in FEATURE_EXTRACTOR_MAPPING:
               lowercase__		       =						FEATURE_EXTRACTOR_MAPPING[type(UpperCAmelCase_      )]
               return feature_extractor_class.from_dict(UpperCAmelCase_     ,**UpperCAmelCase_      )

           raise ValueError(
               F'''Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a '''
               F'''`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following '''
               F'''`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys()      )}'''      )







       @staticmethod
       def 							_a      (				UpperCAmelCase_     ,UpperCAmelCase_      )  ->							Any:
           FEATURE_EXTRACTOR_MAPPING.register(UpperCAmelCase_     ,UpperCAmelCase_      )




