




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,
)


_lowerCAmelCase						:   Union[str, Any]		=   logging.get_logger(__name__)

_lowerCAmelCase						:   Dict		=   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"),
    ]
)

_lowerCAmelCase						:   str		=   _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)



def 						lowerCAmelCase		(		_lowerCAmelCase							:   str	):




 """simple docstring"""







 for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
  if class_name in extractors:
   UpperCAmelCase__							 =						model_type_to_module_name(_lowerCAmelCase	)

   UpperCAmelCase__							 =						importlib.import_module(F'''.{module_name}'''						,							"transformers.models"	)
   try:
    return getattr(_lowerCAmelCase						,							_lowerCAmelCase	)
   except AttributeError:
    continue

 for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
  if getattr(_lowerCAmelCase						,							"__name__"						,							_lowerCAmelCase	) == 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.
 UpperCAmelCase__							 =						importlib.import_module("transformers"	)
 if hasattr(_lowerCAmelCase						,							_lowerCAmelCase	):
  return getattr(_lowerCAmelCase						,							_lowerCAmelCase	)

 return None



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Union[str, os.PathLike]						,							_lowerCAmelCase							:   Optional[Union[str, os.PathLike]] = None						,							_lowerCAmelCase							:   bool = False						,							_lowerCAmelCase							:   bool = False						,							_lowerCAmelCase							:   Optional[Dict[str, str]] = None						,							_lowerCAmelCase							:   Optional[Union[bool, str]] = None						,							_lowerCAmelCase							:   Optional[str] = None						,							_lowerCAmelCase							:   bool = False						,							**_lowerCAmelCase							:   int						,							):




 """simple docstring"""







 UpperCAmelCase__							 =						get_file_from_repo(
     _lowerCAmelCase						,							_lowerCAmelCase						,							cache_dir=_lowerCAmelCase						,							force_download=_lowerCAmelCase						,							resume_download=_lowerCAmelCase						,							proxies=_lowerCAmelCase						,							use_auth_token=_lowerCAmelCase						,							revision=_lowerCAmelCase						,							local_files_only=_lowerCAmelCase						,							)
 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(_lowerCAmelCase						,							encoding="utf-8"	) as reader:
  return json.load(_lowerCAmelCase	)


class 			_UpperCamelCase      :
      def __init__(			self   :str      )     ->			Optional[Any]:
       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(lowerCamelCase      )
      def 	UpperCAmelCase_						(			cls   :List[str] ,      lowerCamelCase   :Union[str, Any] ,      **lowerCamelCase   :Any      )     ->			Tuple:
       UpperCAmelCase__							 =						kwargs.pop("config" ,      lowerCamelCase      )
       UpperCAmelCase__							 =						kwargs.pop("trust_remote_code" ,      lowerCamelCase      )
       UpperCAmelCase__							 =						True

       UpperCAmelCase__ , UpperCAmelCase__							 =						FeatureExtractionMixin.get_feature_extractor_dict(lowerCamelCase ,      **lowerCamelCase      )
       UpperCAmelCase__							 =						config_dict.get("feature_extractor_type" ,      lowerCamelCase      )
       UpperCAmelCase__							 =						None
       if "AutoFeatureExtractor" in config_dict.get("auto_map" ,      {}      ):
        UpperCAmelCase__							 =						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(lowerCamelCase ,      lowerCamelCase      ):
         UpperCAmelCase__							 =						AutoConfig.from_pretrained(lowerCamelCase ,      **lowerCamelCase      )
        # It could be in `config.feature_extractor_type``
        UpperCAmelCase__							 =						getattr(lowerCamelCase ,      "feature_extractor_type" ,      lowerCamelCase      )
        if hasattr(lowerCamelCase ,      "auto_map"      ) and "AutoFeatureExtractor" in config.auto_map:
         UpperCAmelCase__							 =						config.auto_map["AutoFeatureExtractor"]

       if feature_extractor_class is not None:
        UpperCAmelCase__							 =						feature_extractor_class_from_name(lowerCamelCase      )

       UpperCAmelCase__							 =						feature_extractor_auto_map is not None
       UpperCAmelCase__							 =						feature_extractor_class is not None or type(lowerCamelCase      ) in FEATURE_EXTRACTOR_MAPPING
       UpperCAmelCase__							 =						resolve_trust_remote_code(
           lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase      )

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

       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 	UpperCAmelCase_						(			lowerCamelCase   :Optional[Any] ,      lowerCamelCase   :Any      )     ->			Tuple:
       FEATURE_EXTRACTOR_MAPPING.register(lowerCamelCase ,      lowerCamelCase      )











import torch

from transformers import AutoModel
class 			_UpperCamelCase      (				torch.nn.Module		):
      def __init__(			self   :str ,      lowerCamelCase   :Tuple="sayef/fsner-bert-base-uncased"      )     ->			int:
       super(lowerCamelCase ,      self      ).__init__()

       UpperCAmelCase__							 =						AutoModel.from_pretrained(lowerCamelCase ,      return_dict=lowerCamelCase      )
       UpperCAmelCase__							 =						torch.nn.CosineSimilarity(3 ,      1e-08      )
       UpperCAmelCase__							 =						torch.nn.Softmax(dim=1      )
      def 	UpperCAmelCase_						(			self   :Union[str, Any] ,      **lowerCamelCase   :Tuple      )     ->			Dict:
       return self.bert(**lowerCamelCase      ).last_hidden_state
      def 	UpperCAmelCase_						(			self   :Any ,      lowerCamelCase   :Union[str, Any]      )     ->			Union[str, Any]:
       return token_embeddings.sum(2 ,      keepdim=lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Dict ,      lowerCamelCase   :List[Any] ,      lowerCamelCase   :int ,      lowerCamelCase   :Union[str, Any]=1      )     ->			Dict:
       return self.softmax(T * self.cos(lowerCamelCase ,      lowerCamelCase      )      )







      def 	UpperCAmelCase_						(			self   :Union[str, Any] ,      lowerCamelCase   :Any ,      lowerCamelCase   :Any      )     ->			Union[str, Any]:
       UpperCAmelCase__							 =						W_supports["sizes"].tolist()
       UpperCAmelCase__							 =						W_supports["start_token_id"].item()
       UpperCAmelCase__							 =						W_supports["end_token_id"].item()

       del W_supports["sizes"]
       del W_supports["start_token_id"]
       del W_supports["end_token_id"]

       UpperCAmelCase__							 =						self.BERT(**lowerCamelCase      )
       UpperCAmelCase__							 =						self.BERT(**lowerCamelCase      )

       UpperCAmelCase__							 =						None
       UpperCAmelCase__							 =						None

       UpperCAmelCase__							 =						W_supports["input_ids"] == start_token_id
       UpperCAmelCase__							 =						W_supports["input_ids"] == end_token_id

       for i, size in enumerate(lowerCamelCase      ):
        if i == 0:
         UpperCAmelCase__							 =						0
        else:
         UpperCAmelCase__							 =						support_sizes[i - 1]

        UpperCAmelCase__							 =						S[s : s + size][start_token_masks[s : s + size]]
        UpperCAmelCase__							 =						S[s : s + size][end_token_masks[s : s + size]]

        UpperCAmelCase__							 =						torch.matmul(q[i] ,      s_start.T      ).sum(1      ).softmax(0      )
        UpperCAmelCase__							 =						torch.matmul(q[i] ,      s_end.T      ).sum(1      ).softmax(0      )

        if p_starts is not None:
         UpperCAmelCase__							 =						torch.vstack((p_starts, p_start)      )
         UpperCAmelCase__							 =						torch.vstack((p_ends, p_end)      )
        else:
         UpperCAmelCase__							 =						p_start
         UpperCAmelCase__							 =						p_end

       return p_starts, p_ends





