



"""simple docstring"""






import math

import torch
from torch import nn
from torch.nn import init

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer

class 			__SCREAMING_SNAKE_CASE      ( nn.Linear  ):


					'''simple docstring'''





					def __init__(			self				:    List[Any],    lowerCamelCase				:    int,    lowerCamelCase				:    int,    lowerCamelCase				:    bool = True,    lowerCamelCase				:    str = "constant",    lowerCamelCase				:    float = 0.0,    lowerCamelCase				:    str = "topK",    )->   Any:
								super(lowerCamelCase,    self	).__init__(in_features=lowerCamelCase,    out_features=lowerCamelCase,    bias=lowerCamelCase	)
								assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
								lowerCamelCase__						:						Optional[int]						      =pruning_method

								if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
											lowerCamelCase__						:						List[Any]						      =mask_scale
											lowerCamelCase__						:						str						      =mask_init
											lowerCamelCase__						:						Any						      =nn.Parameter(torch.empty(self.weight.size()	)	)
											self.init_mask()



					def 						snake_case			(			self				:    Union[str, Any]	)->   Optional[int]:
								if self.mask_init == "constant":
											init.constant_(self.mask_scores,    val=self.mask_scale	)
								elif self.mask_init == "uniform":
											init.uniform_(self.mask_scores,    a=-self.mask_scale,    b=self.mask_scale	)
								elif self.mask_init == "kaiming":
											init.kaiming_uniform_(self.mask_scores,    a=math.sqrt(5	)	)




					def 						snake_case			(			self				:    int,    lowerCamelCase				:    torch.tensor,    lowerCamelCase				:    float	)->   Union[str, Any]:
								# Get the mask
								if self.pruning_method == "topK":
											lowerCamelCase__						:						Dict						      =TopKBinarizer.apply(self.mask_scores,    lowerCamelCase	)
								elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
											lowerCamelCase__						:						int						      ='''sigmoied''' in self.pruning_method
											lowerCamelCase__						:						Optional[Any]						      =ThresholdBinarizer.apply(self.mask_scores,    lowerCamelCase,    lowerCamelCase	)
								elif self.pruning_method == "magnitude":
											lowerCamelCase__						:						Optional[Any]						      =MagnitudeBinarizer.apply(self.weight,    lowerCamelCase	)
								elif self.pruning_method == "l0":
											lowerCamelCase__      ,  lowerCamelCase__      ,  lowerCamelCase__						:						str						      =-0.1, 1.1, 2 / 3
											if self.training:
														lowerCamelCase__						:						Any						      =torch.zeros_like(self.mask_scores	).uniform_().clamp(0.0_001,    0.9_999	)
														lowerCamelCase__						:						List[str]						      =torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b	)
											else:
														lowerCamelCase__						:						List[Any]						      =torch.sigmoid(self.mask_scores	)
											lowerCamelCase__						:						Optional[int]						      =s * (r - l) + l
											lowerCamelCase__						:						List[Any]						      =s_bar.clamp(min=0.0,    max=1.0	)
								# Mask weights with computed mask
								lowerCamelCase__						:						int						      =mask * self.weight
								# Compute output (linear layer) with masked weights
								return nn.functional.linear(lowerCamelCase,    lowerCamelCase,    self.bias	)









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   UpperCAmelCase							(		A__     )							->					Dict:
	for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
		if class_name in processors:
			_snake_case					:							str					     =   model_type_to_module_name(A__     )

			_snake_case					:							Dict					     =   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.
	_snake_case					:							Optional[int]					     =   importlib.import_module("""transformers"""     )
	if hasattr(A__				,  A__     ):
		return getattr(A__				,  A__     )

	return None





class 		__SCREAMING_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(SCREAMING_SNAKE_CASE__      )
				def 		__lowerCamelCase(				cls						,	SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							List[str]					     =   kwargs.pop("""config"""						,	SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Tuple					     =   kwargs.pop("""trust_remote_code"""						,	SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Optional[int]					     =   True

					_snake_case					:							Optional[int]					     =   None
					_snake_case					:							List[Any]					     =   None

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

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

							_snake_case					:							Optional[int]					     =   config_dict.get("""processor_class"""						,	SCREAMING_SNAKE_CASE__      )
							if "AutoProcessor" in config_dict.get("""auto_map"""						,	{}      ):
								_snake_case					:							Tuple					     =   config_dict["""auto_map"""]["""AutoProcessor"""]

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

						# And check if the config contains the processor class.
						_snake_case					:							Optional[Any]					     =   getattr(SCREAMING_SNAKE_CASE__						,	"""processor_class"""						,	SCREAMING_SNAKE_CASE__      )
						if hasattr(SCREAMING_SNAKE_CASE__						,	"""auto_map"""      ) and "AutoProcessor" in config.auto_map:
							_snake_case					:							List[str]					     =   config.auto_map["""AutoProcessor"""]

					if processor_class is not None:
						_snake_case					:							Any					     =   processor_class_from_name(SCREAMING_SNAKE_CASE__      )

					_snake_case					:							Optional[Any]					     =   processor_auto_map is not None
					_snake_case					:							Optional[int]					     =   processor_class is not None or type(SCREAMING_SNAKE_CASE__      ) in PROCESSOR_MAPPING
					_snake_case					:							List[Any]					     =   resolve_trust_remote_code(
					    SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )

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

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

						try:
							return AutoFeatureExtractor.from_pretrained(
							    SCREAMING_SNAKE_CASE__						,	trust_remote_code=SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__      )
						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 		__lowerCamelCase(				SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					PROCESSOR_MAPPING.register(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )





