
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


_UpperCAmelCase    			=					logging.get_logger(__name__)

_UpperCAmelCase    			=					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"),
    ]
)

_UpperCAmelCase    			=					_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)


def 		_lowerCamelCase       (   _a  ):
  """simple docstring"""




  for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
    if class_name in processors:
      _lowerCamelCase      =						model_type_to_module_name(_a  )

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

  for processor in PROCESSOR_MAPPING._extra_content.values():
    if getattr(_a			,   '''__name__'''			,   _a  ) == 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.
  _lowerCamelCase      =						importlib.import_module('''transformers'''  )
  if hasattr(_a			,   _a  ):
    return getattr(_a			,   _a  )

  return None






class 		__magic_name__				:


  """simple docstring"""





  def __init__(					self		):
    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(lowerCAmelCase__		)
  def     _UpperCAmelCase  (					cls	,		a__	,		**a__		):
    _lowerCamelCase      =						kwargs.pop('''config'''	,		lowerCAmelCase__		)
    _lowerCamelCase      =						kwargs.pop('''trust_remote_code'''	,		lowerCAmelCase__		)
    _lowerCamelCase      =						True

    _lowerCamelCase      =						None
    _lowerCamelCase      =						None

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

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

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

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

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

    if processor_class is not None:
      _lowerCamelCase      =						processor_class_from_name(lowerCAmelCase__		)

    _lowerCamelCase      =						processor_auto_map is not None
    _lowerCamelCase      =						processor_class is not None or type(lowerCAmelCase__		) in PROCESSOR_MAPPING
    _lowerCamelCase      =						resolve_trust_remote_code(
        lowerCAmelCase__	,		lowerCAmelCase__	,		lowerCAmelCase__	,		lowerCAmelCase__		)

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

    # 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(
          lowerCAmelCase__	,		trust_remote_code=lowerCAmelCase__	,		**lowerCAmelCase__		)
    except Exception:
      try:
        return AutoImageProcessor.from_pretrained(
            lowerCAmelCase__	,		trust_remote_code=lowerCAmelCase__	,		**lowerCAmelCase__		)
      except Exception:
        pass

      try:
        return AutoFeatureExtractor.from_pretrained(
            lowerCAmelCase__	,		trust_remote_code=lowerCAmelCase__	,		**lowerCAmelCase__		)
      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     _UpperCAmelCase  (					a__	,		a__		):
    PROCESSOR_MAPPING.register(lowerCAmelCase__	,		lowerCAmelCase__		)





"""simple docstring"""


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


__UpperCamelCase										=     logging.get_logger(__name__)

__UpperCamelCase										=     {
    '''uw-madison/nystromformer-512''': '''https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json''',
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}




class 					UpperCamelCase   (							lowerCAmelCase__  ):
       SCREAMING_SNAKE_CASE_						      = "nystromformer"







       def __init__(					self,				lowerCAmelCase__=3_0000,				lowerCAmelCase__=768,				lowerCAmelCase__=12,				lowerCAmelCase__=12,				lowerCAmelCase__=3072,				lowerCAmelCase__="gelu_new",				lowerCAmelCase__=0.1,				lowerCAmelCase__=0.1,				lowerCAmelCase__=510,				lowerCAmelCase__=2,				lowerCAmelCase__=64,				lowerCAmelCase__=64,				lowerCAmelCase__=65,				lowerCAmelCase__=False,				lowerCAmelCase__=0.02,				lowerCAmelCase__=1e-5,				lowerCAmelCase__=1,				lowerCAmelCase__=0,				lowerCAmelCase__=2,				**lowerCAmelCase__,				) -> Optional[Any]:
            snake_case_   			=   vocab_size
            snake_case_   			=   max_position_embeddings
            snake_case_   			=   hidden_size
            snake_case_   			=   num_hidden_layers
            snake_case_   			=   num_attention_heads
            snake_case_   			=   intermediate_size
            snake_case_   			=   hidden_act
            snake_case_   			=   hidden_dropout_prob
            snake_case_   			=   attention_probs_dropout_prob
            snake_case_   			=   initializer_range
            snake_case_   			=   type_vocab_size
            snake_case_   			=   segment_means_seq_len
            snake_case_   			=   num_landmarks
            snake_case_   			=   conv_kernel_size
            snake_case_   			=   inv_coeff_init_option
            snake_case_   			=   layer_norm_eps
            super().__init__(pad_token_id=lowerCAmelCase__,				bos_token_id=lowerCAmelCase__,				eos_token_id=lowerCAmelCase__,				**lowerCAmelCase__)

