


"""simple docstring"""







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			 =   logging.get_logger(__name__)

__a			 =   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			 =   _LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

						for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
												if class_name in processors:
																		UpperCAmelCase__		=    model_type_to_module_name(_UpperCamelCase )

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

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

						return None


class 							lowerCamelCase       :


							"""simple docstring"""






							def __init__(						self :     int				):
													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 					lowerCAmelCase     (						cls :     Optional[int]		, lowerCamelCase_ :     int		, **lowerCamelCase_ :     Optional[Any]				):
													UpperCAmelCase__		=    kwargs.pop('''config'''		, lowerCamelCase_				)
													UpperCAmelCase__		=    kwargs.pop('''trust_remote_code'''		, lowerCamelCase_				)
													UpperCAmelCase__		=    True

													UpperCAmelCase__		=    None
													UpperCAmelCase__		=    None

													# First, let's see if we have a preprocessor config.
													# Filter the kwargs for `get_file_from_repo`.
													UpperCAmelCase__		=    {
													    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
													UpperCAmelCase__		=    get_file_from_repo(
													    lowerCamelCase_		, lowerCamelCase_		, **lowerCamelCase_				)
													if preprocessor_config_file is not None:
																			UpperCAmelCase__	,				UpperCAmelCase__		=    ImageProcessingMixin.get_image_processor_dict(lowerCamelCase_		, **lowerCamelCase_				)
																			UpperCAmelCase__		=    config_dict.get('''processor_class'''		, lowerCamelCase_				)
																			if "AutoProcessor" in config_dict.get('''auto_map'''		, {}				):
																									UpperCAmelCase__		=    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:
																			UpperCAmelCase__	,				UpperCAmelCase__		=    FeatureExtractionMixin.get_feature_extractor_dict(lowerCamelCase_		, **lowerCamelCase_				)
																			UpperCAmelCase__		=    config_dict.get('''processor_class'''		, lowerCamelCase_				)
																			if "AutoProcessor" in config_dict.get('''auto_map'''		, {}				):
																									UpperCAmelCase__		=    config_dict['''auto_map''']['''AutoProcessor''']

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

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

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

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

													if processor_class is not None:
																			UpperCAmelCase__		=    processor_class_from_name(lowerCamelCase_				)

													UpperCAmelCase__		=    processor_auto_map is not None
													UpperCAmelCase__		=    processor_class is not None or type(lowerCamelCase_				) in PROCESSOR_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_				):
																									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 					lowerCAmelCase     (						lowerCamelCase_ :     Any		, lowerCamelCase_ :     List[Any]				):
													PROCESSOR_MAPPING.register(lowerCamelCase_		, lowerCamelCase_				)



"""simple docstring"""







from __future__ import annotations

from collections.abc import Callable






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase = 100	,			):





						"""simple docstring"""

						UpperCAmelCase__		=    x_start
						UpperCAmelCase__		=    fnc(_UpperCamelCase )
						UpperCAmelCase__		=    0.0

						for _ in range(_UpperCamelCase ):
												# Approximates small segments of curve as linear and solve
												# for trapezoidal area
												UpperCAmelCase__		=    (x_end - x_start) / steps + xa
												UpperCAmelCase__		=    fnc(_UpperCamelCase )
												area += abs(fxa + fxa ) * (xa - xa) / 2

												# Increment step
												UpperCAmelCase__		=    xa
												UpperCAmelCase__		=    fxa
						return area


if __name__ == "__main__":






						def 					a__ (       _UpperCamelCase ):





												"""simple docstring"""

												return x**3

						print("f(x) = x^3")
						print("The area between the curve, x = -10, x = 10 and the x axis is:")
						__a			 =   10
						while i <= 10_00_00:
												__a			 =   trapezoidal_area(f, -5, 5, i)
												print(f'''with {i} steps: {area}''')
												i *= 10