





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 torch

from transformers import AutoTokenizer



class 				__snake_case  (     __SCREAMING_SNAKE_CASE						):


    '''simple docstring'''







    def __init__(					self      ,							A_    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= AutoTokenizer.from_pretrained(A_    )



    def    lowercase_				(					self      ,							A_    ):





        '''simple docstring'''

        if isinstance(A_      ,							A_    ) and all(isinstance(_x      ,							A_    ) for _x in x    ):
            SCREAMING_SNAKE_CASE__       							= None
            for l in x:
                SCREAMING_SNAKE_CASE__       							= self.tokenizer(
                    A_      ,							padding='''max_length'''      ,							max_length=3_84      ,							truncation=A_      ,							return_tensors='''pt'''      ,							)
                SCREAMING_SNAKE_CASE__       							= torch.tensor([len(A_    )]    )
                if d is not None:
                    for k in d.keys():
                        SCREAMING_SNAKE_CASE__       							= torch.cat((d[k], t[k])      ,							0    )
                else:
                    SCREAMING_SNAKE_CASE__       							= t

            SCREAMING_SNAKE_CASE__       							= torch.tensor(self.tokenizer.convert_tokens_to_ids('''[E]'''    )    )
            SCREAMING_SNAKE_CASE__       							= torch.tensor(self.tokenizer.convert_tokens_to_ids('''[/E]'''    )    )

        elif isinstance(A_      ,							A_    ) and all(isinstance(_x      ,							A_    ) for _x in x    ):
            SCREAMING_SNAKE_CASE__       							= self.tokenizer(
                A_      ,							padding='''max_length'''      ,							max_length=3_84      ,							truncation=A_      ,							return_tensors='''pt'''      ,							)

        else:
            raise Exception(
                '''Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of'''
                ''' strings` for supports are supported.'''    )

        return d




    def    lowercase_				(					self      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_=0.70    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= []
        for idx in range(len(W_query['''input_ids''']    )    ):
            SCREAMING_SNAKE_CASE__       							= SCREAMING_SNAKE_CASE__       							= range(p_start.shape[1]    )

            SCREAMING_SNAKE_CASE__       							= []
            for start_id in start_indexes:
                for end_id in end_indexes:
                    if start_id < end_id:
                        output.append(
                            (
                                start_id,
                                end_id,
                                p_start[idx][start_id].item(),
                                p_end[idx][end_id].item(),
                            )    )

            output.sort(key=lambda A_    : (tup[2] * tup[3])      ,							reverse=A_    )
            SCREAMING_SNAKE_CASE__       							= []
            for k in range(len(A_    )    ):
                if output[k][2] * output[k][3] >= thresh:
                    SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__       							= output[k][0], output[k][1]
                    SCREAMING_SNAKE_CASE__       							= self.tokenizer.decode(W_query['''input_ids'''][idx][c_start_pos:c_end_pos]    )
                    temp.append((decoded, output[k][2] * output[k][3])    )

            final_outputs.append(A_    )

        return final_outputs
