import importlib
import inspect
import json
import os
from collections import OrderedDict

# 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 ...image_processing_utils import ImageProcessingMixin
from ...tokenization_utils import TOKENIZER_CONFIG_FILE
from ...utils import 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,
)
from .feature_extraction_auto import AutoFeatureExtractor
from .image_processing_auto import AutoImageProcessor
from .tokenization_auto import AutoTokenizer


_A				:							int							= logging.get_logger(__name__)

_A				:							Optional[int]							= OrderedDict(
    [
        ("""align""", """AlignProcessor"""),
        ("""altclip""", """AltCLIPProcessor"""),
        ("""bark""", """BarkProcessor"""),
        ("""blip""", """BlipProcessor"""),
        ("""blip-2""", """Blip2Processor"""),
        ("""bridgetower""", """BridgeTowerProcessor"""),
        ("""chinese_clip""", """ChineseCLIPProcessor"""),
        ("""clap""", """ClapProcessor"""),
        ("""clip""", """CLIPProcessor"""),
        ("""clipseg""", """CLIPSegProcessor"""),
        ("""flava""", """FlavaProcessor"""),
        ("""git""", """GitProcessor"""),
        ("""groupvit""", """CLIPProcessor"""),
        ("""hubert""", """Wav2Vec2Processor"""),
        ("""instructblip""", """InstructBlipProcessor"""),
        ("""layoutlmv2""", """LayoutLMv2Processor"""),
        ("""layoutlmv3""", """LayoutLMv3Processor"""),
        ("""markuplm""", """MarkupLMProcessor"""),
        ("""mctct""", """MCTCTProcessor"""),
        ("""mgp-str""", """MgpstrProcessor"""),
        ("""oneformer""", """OneFormerProcessor"""),
        ("""owlvit""", """OwlViTProcessor"""),
        ("""pix2struct""", """Pix2StructProcessor"""),
        ("""sam""", """SamProcessor"""),
        ("""sew""", """Wav2Vec2Processor"""),
        ("""sew-d""", """Wav2Vec2Processor"""),
        ("""speech_to_text""", """Speech2TextProcessor"""),
        ("""speech_to_text_2""", """Speech2Text2Processor"""),
        ("""speecht5""", """SpeechT5Processor"""),
        ("""trocr""", """TrOCRProcessor"""),
        ("""tvlt""", """TvltProcessor"""),
        ("""unispeech""", """Wav2Vec2Processor"""),
        ("""unispeech-sat""", """Wav2Vec2Processor"""),
        ("""vilt""", """ViltProcessor"""),
        ("""vision-text-dual-encoder""", """VisionTextDualEncoderProcessor"""),
        ("""wav2vec2""", """Wav2Vec2Processor"""),
        ("""wav2vec2-conformer""", """Wav2Vec2Processor"""),
        ("""wavlm""", """Wav2Vec2Processor"""),
        ("""whisper""", """WhisperProcessor"""),
        ("""xclip""", """XCLIPProcessor"""),
    ]
)

_A				:							str							= _LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)


def        __snake_case       (						lowerCAmelCase_  )							->	List[str]:
    for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
        if class_name in processors:
            SCREAMING_SNAKE_CASE__       							= model_type_to_module_name(lowerCAmelCase_  )

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

    for processor in PROCESSOR_MAPPING._extra_content.values():
        if getattr(lowerCAmelCase_   ,       '''__name__'''   ,       lowerCAmelCase_  ) == class_name:
            return processor

    # 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.
    SCREAMING_SNAKE_CASE__       							= importlib.import_module('''transformers'''  )
    if hasattr(lowerCAmelCase_   ,       lowerCAmelCase_  ):
        return getattr(lowerCAmelCase_   ,       lowerCAmelCase_  )

    return None



class 				__snake_case  :


    '''simple docstring'''







    def __init__(					self    ):





        '''simple docstring'''

        raise EnvironmentError(
            '''AutoProcessor is designed to be instantiated '''
            '''using the `AutoProcessor.from_pretrained(pretrained_model_name_or_path)` method.'''    )



    @classmethod
    @replace_list_option_in_docstrings(A_    )
    def    lowercase_				(					cls      ,							A_      ,							**A_    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= kwargs.pop('''config'''      ,							A_    )
        SCREAMING_SNAKE_CASE__       							= kwargs.pop('''trust_remote_code'''      ,							A_    )
        SCREAMING_SNAKE_CASE__       							= True

        SCREAMING_SNAKE_CASE__       							= None
        SCREAMING_SNAKE_CASE__       							= None

        # First, let's see if we have a preprocessor config.
        # Filter the kwargs for `get_file_from_repo`.
        SCREAMING_SNAKE_CASE__       							= {
            key: kwargs[key] for key in inspect.signature(A_    ).parameters.keys() if key in kwargs
        }
        # Let's start by checking whether the processor class is saved in an image processor
        SCREAMING_SNAKE_CASE__       							= get_file_from_repo(
            A_      ,							A_      ,							**A_    )
        if preprocessor_config_file is not None:
            SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__       							= ImageProcessingMixin.get_image_processor_dict(A_      ,							**A_    )
            SCREAMING_SNAKE_CASE__       							= config_dict.get('''processor_class'''      ,							A_    )
            if "AutoProcessor" in config_dict.get('''auto_map'''      ,							{}    ):
                SCREAMING_SNAKE_CASE__       							= config_dict['''auto_map''']['''AutoProcessor''']

        # If not found, let's check whether the processor class is saved in a feature extractor config
        if preprocessor_config_file is not None and processor_class is None:
            SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__       							= FeatureExtractionMixin.get_feature_extractor_dict(A_      ,							**A_    )
            SCREAMING_SNAKE_CASE__       							= config_dict.get('''processor_class'''      ,							A_    )
            if "AutoProcessor" in config_dict.get('''auto_map'''      ,							{}    ):
                SCREAMING_SNAKE_CASE__       							= config_dict['''auto_map''']['''AutoProcessor''']

        if processor_class is None:
            # Next, let's check whether the processor class is saved in a tokenizer
            SCREAMING_SNAKE_CASE__       							= get_file_from_repo(
                A_      ,							A_      ,							**A_    )
            if tokenizer_config_file is not None:
                with open(A_      ,							encoding='''utf-8'''    ) as reader:
                    SCREAMING_SNAKE_CASE__       							= json.load(A_    )

                SCREAMING_SNAKE_CASE__       							= config_dict.get('''processor_class'''      ,							A_    )
                if "AutoProcessor" in config_dict.get('''auto_map'''      ,							{}    ):
                    SCREAMING_SNAKE_CASE__       							= config_dict['''auto_map''']['''AutoProcessor''']

        if processor_class is None:
            # Otherwise, load config, if it can be loaded.
            if not isinstance(A_      ,							A_    ):
                SCREAMING_SNAKE_CASE__       							= AutoConfig.from_pretrained(
                    A_      ,							trust_remote_code=A_      ,							**A_    )

            # And check if the config contains the processor class.
            SCREAMING_SNAKE_CASE__       							= getattr(A_      ,							'''processor_class'''      ,							A_    )
            if hasattr(A_      ,							'''auto_map'''    ) and "AutoProcessor" in config.auto_map:
                SCREAMING_SNAKE_CASE__       							= config.auto_map['''AutoProcessor''']

        if processor_class is not None:
            SCREAMING_SNAKE_CASE__       							= processor_class_from_name(A_    )

        SCREAMING_SNAKE_CASE__       							= processor_auto_map is not None
        SCREAMING_SNAKE_CASE__       							= processor_class is not None or type(A_    ) in PROCESSOR_MAPPING
        SCREAMING_SNAKE_CASE__       							= resolve_trust_remote_code(
            A_      ,							A_      ,							A_      ,							A_    )

        if has_remote_code and trust_remote_code:
            SCREAMING_SNAKE_CASE__       							= get_class_from_dynamic_module(
                A_      ,							A_      ,							**A_    )
            SCREAMING_SNAKE_CASE__       							= kwargs.pop('''code_revision'''      ,							A_    )
            if os.path.isdir(A_    ):
                processor_class.register_for_auto_class()
            return processor_class.from_pretrained(
                A_      ,							trust_remote_code=A_      ,							**A_    )
        elif processor_class is not None:
            return processor_class.from_pretrained(
                A_      ,							trust_remote_code=A_      ,							**A_    )
        # Last try: we use the PROCESSOR_MAPPING.
        elif type(A_    ) in PROCESSOR_MAPPING:
            return PROCESSOR_MAPPING[type(A_    )].from_pretrained(A_      ,							**A_    )

        # At this stage, there doesn't seem to be a `Processor` class available for this model, so let's try a
        # tokenizer.
        try:
            return AutoTokenizer.from_pretrained(
                A_      ,							trust_remote_code=A_      ,							**A_    )
        except Exception:
            try:
                return AutoImageProcessor.from_pretrained(
                    A_      ,							trust_remote_code=A_      ,							**A_    )
            except Exception:
                pass

            try:
                return AutoFeatureExtractor.from_pretrained(
                    A_      ,							trust_remote_code=A_      ,							**A_    )
            except Exception:
                pass

        raise ValueError(
            f'''Unrecognized processing class in {pretrained_model_name_or_path}. Can\'t instantiate a processor, a '''
            '''tokenizer, an image processor or a feature extractor for this model. Make sure the repository contains'''
            '''the files of at least one of those processing classes.'''    )




    @staticmethod
    def    lowercase_				(					A_      ,							A_    ):





        '''simple docstring'''

        PROCESSOR_MAPPING.register(A_      ,							A_    )







import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A				:							Any							= logging.get_logger(__name__)

_A				:							Any							= {
    """facebook/hubert-base-ls960""": """https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json""",
    # See all Hubert models at https://huggingface.co/models?filter=hubert
}



class 				__snake_case  (     __SCREAMING_SNAKE_CASE						):


    '''simple docstring'''







    lowerCamelCase__						:		int							=  """hubert"""



    def __init__(					self      ,							A_=32      ,							A_=7_68      ,							A_=12      ,							A_=12      ,							A_=30_72      ,							A_="gelu"      ,							A_=0.1      ,							A_=0.1      ,							A_=0.1      ,							A_=True      ,							A_=0.0      ,							A_=0.1      ,							A_=0.1      ,							A_=0.02      ,							A_=1E-5      ,							A_="group"      ,							A_="gelu"      ,							A_=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)      ,							A_=(5, 2, 2, 2, 2, 2, 2)      ,							A_=(10, 3, 3, 3, 3, 2, 2)      ,							A_=False      ,							A_=1_28      ,							A_=16      ,							A_=False      ,							A_=True      ,							A_=0.05      ,							A_=10      ,							A_=2      ,							A_=0.0      ,							A_=10      ,							A_=0      ,							A_="sum"      ,							A_=False      ,							A_=False      ,							A_=2_56      ,							A_=0      ,							A_=1      ,							A_=2      ,							**A_      ,							):





        '''simple docstring'''

        super().__init__(**A_      ,							pad_token_id=A_      ,							bos_token_id=A_      ,							eos_token_id=A_    )
        SCREAMING_SNAKE_CASE__       							= hidden_size
        SCREAMING_SNAKE_CASE__       							= feat_extract_norm
        SCREAMING_SNAKE_CASE__       							= feat_extract_activation
        SCREAMING_SNAKE_CASE__       							= list(A_    )
        SCREAMING_SNAKE_CASE__       							= list(A_    )
        SCREAMING_SNAKE_CASE__       							= list(A_    )
        SCREAMING_SNAKE_CASE__       							= conv_bias
        SCREAMING_SNAKE_CASE__       							= num_conv_pos_embeddings
        SCREAMING_SNAKE_CASE__       							= num_conv_pos_embedding_groups
        SCREAMING_SNAKE_CASE__       							= len(self.conv_dim    )
        SCREAMING_SNAKE_CASE__       							= num_hidden_layers
        SCREAMING_SNAKE_CASE__       							= intermediate_size
        SCREAMING_SNAKE_CASE__       							= hidden_act
        SCREAMING_SNAKE_CASE__       							= num_attention_heads
        SCREAMING_SNAKE_CASE__       							= hidden_dropout
        SCREAMING_SNAKE_CASE__       							= attention_dropout
        SCREAMING_SNAKE_CASE__       							= activation_dropout
        SCREAMING_SNAKE_CASE__       							= feat_proj_layer_norm
        SCREAMING_SNAKE_CASE__       							= feat_proj_dropout
        SCREAMING_SNAKE_CASE__       							= final_dropout
        SCREAMING_SNAKE_CASE__       							= layerdrop
        SCREAMING_SNAKE_CASE__       							= layer_norm_eps
        SCREAMING_SNAKE_CASE__       							= initializer_range
        SCREAMING_SNAKE_CASE__       							= vocab_size
        SCREAMING_SNAKE_CASE__       							= do_stable_layer_norm
        SCREAMING_SNAKE_CASE__       							= use_weighted_layer_sum
        SCREAMING_SNAKE_CASE__       							= classifier_proj_size

        if (
            (len(self.conv_stride    ) != self.num_feat_extract_layers)
            or (len(self.conv_kernel    ) != self.num_feat_extract_layers)
            or (len(self.conv_dim    ) != self.num_feat_extract_layers)
        ):
            raise ValueError(
                '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
                ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
                f''' {len(self.conv_dim    )}`, `len(config.conv_stride) = {len(self.conv_stride    )}`,'''
                f''' `len(config.conv_kernel) = {len(self.conv_kernel    )}`.'''    )

        # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
        SCREAMING_SNAKE_CASE__       							= apply_spec_augment
        SCREAMING_SNAKE_CASE__       							= mask_time_prob
        SCREAMING_SNAKE_CASE__       							= mask_time_length
        SCREAMING_SNAKE_CASE__       							= mask_time_min_masks
        SCREAMING_SNAKE_CASE__       							= mask_feature_prob
        SCREAMING_SNAKE_CASE__       							= mask_feature_length
        SCREAMING_SNAKE_CASE__       							= mask_feature_min_masks

        # ctc loss
        SCREAMING_SNAKE_CASE__       							= ctc_loss_reduction
        SCREAMING_SNAKE_CASE__       							= ctc_zero_infinity




    @property
    def    lowercase_				(					self    ):





        '''simple docstring'''

        return functools.reduce(operator.mul      ,							self.conv_stride      ,							1    )
