






'''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:   Tuple						=					logging.get_logger(__name__)

A:   Dict						=					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:   List[Any]						=					_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)






def 						_UpperCAmelCase     (	a		:						str				)      ->      List[Any]:





						"""simple docstring"""
						for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
												if class_name in processors:
																		lowercase_   : Optional[Any]   	=      model_type_to_module_name(a				)

																		lowercase_   : Tuple   	=      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.
						lowercase_   : str   	=      importlib.import_module('transformers'				)
						if hasattr(a    ,      a				):
												return getattr(a    ,      a				)

						return None






class 				__magic_name__  :







	"""simple docstring"""



	def __init__(				self							)    ->	str:
							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(_lowercase							)
	def 							lowerCamelCase__		(				cls	,   _lowercase	,   **_lowercase							)    ->	List[str]:
							lowercase_   : str   	=      kwargs.pop('config'	,   _lowercase							)
							lowercase_   : Union[str, Any]   	=      kwargs.pop('trust_remote_code'	,   _lowercase							)
							lowercase_   : int   	=      True

							lowercase_   : Union[str, Any]   	=      None
							lowercase_   : Any   	=      None

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

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

																			lowercase_   : Dict   	=      config_dict.get('processor_class'	,   _lowercase							)
																			if "AutoProcessor" in config_dict.get('auto_map'	,   {}							):
																									lowercase_   : Optional[int]   	=      config_dict['auto_map']['AutoProcessor']

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

													# And check if the config contains the processor class.
													lowercase_   : int   	=      getattr(_lowercase	,   'processor_class'	,   _lowercase							)
													if hasattr(_lowercase	,   'auto_map'							) and "AutoProcessor" in config.auto_map:
																			lowercase_   : List[str]   	=      config.auto_map['AutoProcessor']

							if processor_class is not None:
													lowercase_   : Tuple   	=      processor_class_from_name(_lowercase							)

							lowercase_   : List[str]   	=      processor_auto_map is not None
							lowercase_   : Union[str, Any]   	=      processor_class is not None or type(_lowercase							) in PROCESSOR_MAPPING
							lowercase_   : int   	=      resolve_trust_remote_code(
							    _lowercase	,   _lowercase	,   _lowercase	,   _lowercase							)

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

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

													try:
																			return AutoFeatureExtractor.from_pretrained(
																			    _lowercase	,   trust_remote_code=_lowercase	,   **_lowercase							)
													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__		(				_lowercase	,   _lowercase							)    ->	Any:
							PROCESSOR_MAPPING.register(_lowercase	,   _lowercase							)








'''simple docstring'''

from collections.abc import Callable
from math import log
from os import name, system
from random import gauss, seed
from typing import TypeVar






def 						_UpperCAmelCase     (	a		:						float    ,      a		:						float    ,      a		:						int				)      ->      list:





						"""simple docstring"""
						seed(1				)
						return [gauss(a    ,      a				) for _ in range(a				)]






def 						_UpperCAmelCase     (	a		:						int    ,      a		:						list				)      ->      list:





						"""simple docstring"""
						return [k for k in range(a				) for _ in range(instance_count[k]				)]






def 						_UpperCAmelCase     (	a		:						int    ,      a		:						list				)      ->      float:





						"""simple docstring"""
						return sum(a				) / instance_count






def 						_UpperCAmelCase     (	a		:						int    ,      a		:						int				)      ->      float:





						"""simple docstring"""
						return instance_count / total_count






def 						_UpperCAmelCase     (	a		:						list    ,      a		:						list    ,      a		:						int				)      ->      float:





						"""simple docstring"""
						lowercase_   : str   	=      []  # An empty list to store all squared differences
						# iterate over number of elements in items
						for i in range(len(a				)				):
												# for loop iterates over number of elements in inner layer of items
												for j in range(len(items[i]				)				):
																		# appending squared differences to 'squared_diff' list
																		squared_diff.append((items[i][j] - means[i]) ** 2				)

    # one divided by (the number of all instances - number of classes) multiplied by
    # sum of all squared differences
						lowercase_   : Tuple   	=      len(a				)  # Number of classes in dataset
						return 1 / (total_count - n_classes) * sum(a				)






def 						_UpperCAmelCase     (	a		:						list    ,      a		:						list    ,      a		:						float    ,      a		:						list				)      ->      list:





						"""simple docstring"""
						lowercase_   : str   	=      []
						# for loop iterates over number of elements in list
						for i in range(len(a				)				):
												# for loop iterates over number of inner items of each element
												for j in range(len(x_items[i]				)				):
																		lowercase_   : Any   	=      []  # to store all discriminant values of each item as a list
																		# for loop iterates over number of classes we have in our dataset
																		for k in range(len(a				)				):
																								# appending values of discriminants for each class to 'temp' list
																								temp.append(
																								    x_items[i][j] * (means[k] / variance)
																								    - (means[k] ** 2 / (2 * variance))
																								    + log(probabilities[k]				)				)
																		# appending discriminant values of each item to 'results' list
																		results.append(a				)

						return [result.index(max(a				)				) for result in results]






def 						_UpperCAmelCase     (	a		:						list    ,      a		:						list				)      ->      float:





						"""simple docstring"""
						lowercase_   : Optional[Any]   	=      sum(1 for i, j in zip(a    ,      a				) if i == j				)
						# percentage of accuracy equals to number of correct predictions divided by number
						# of all data and multiplied by 100
						return (correct / len(a				)) * 1_0_0


A:   Union[str, Any]						=					TypeVar("num")






def 						_UpperCAmelCase     (	a		:						Callable[[object], num]    ,      a		:						str    ,      a		:						str    ,      a		:						Callable[[num], bool] = lambda a				: True    ,      a		:						str | None = None    ,      )      ->      num:





						"""simple docstring"""
						while True:
												try:
																		lowercase_   : int   	=      input_type(input(a				).strip() or default				)
																		if condition(a				):
																								return user_input
																		else:
																								print(f"{user_input}: {err_msg}"				)
																								continue
												except ValueError:
																		print(
																		    f"{user_input}: Incorrect input type, expected {input_type.__name__!r}"				)






def 						_UpperCAmelCase     (	)      ->      List[str]:





						"""simple docstring"""
						while True:
												print(' Linear Discriminant Analysis '.center(5_0    ,      '*'				)				)
												print('*' * 5_0    ,      '\n'				)
												print('First of all we should specify the number of classes that'				)
												print('we want to generate as training dataset'				)
												# Trying to get number of classes
												lowercase_   : Dict   	=      valid_input(
												    input_type=a    ,      condition=lambda a				: x > 0    ,      input_msg='Enter the number of classes (Data Groupings): '    ,      err_msg='Number of classes should be positive!'    ,      )

												print('-' * 1_0_0				)

												# Trying to get the value of standard deviation
												lowercase_   : List[Any]   	=      valid_input(
												    input_type=a    ,      condition=lambda a				: x >= 0    ,      input_msg=(
												        'Enter the value of standard deviation'
												        '(Default value is 1.0 for all classes): '
												    )    ,      err_msg='Standard deviation should not be negative!'    ,      default='1.0'    ,      )

												print('-' * 1_0_0				)

												# Trying to get number of instances in classes and theirs means to generate
												# dataset
												lowercase_   : Any   	=      []  # An empty list to store instance counts of classes in dataset
												for i in range(a				):
																		lowercase_   : Union[str, Any]   	=      valid_input(
																		    input_type=a    ,      condition=lambda a				: x > 0    ,      input_msg=(f"Enter The number of instances for class_{i+1}: ")    ,      err_msg='Number of instances should be positive!'    ,      )
																		counts.append(a				)
												print('-' * 1_0_0				)

												# An empty list to store values of user-entered means of classes
												lowercase_   : int   	=      []
												for a in range(a				):
																		lowercase_   : Union[str, Any]   	=      valid_input(
																		    input_type=a    ,      input_msg=(f"Enter the value of mean for class_{a+1}: ")    ,      err_msg='This is an invalid value.'    ,      )
																		user_means.append(a				)
												print('-' * 1_0_0				)

												print('Standard deviation: '    ,      a				)
												# print out the number of instances in classes in separated line
												for i, count in enumerate(a    ,      1				):
																		print(f"Number of instances in class_{i} is: {count}"				)
												print('-' * 1_0_0				)

												# print out mean values of classes separated line
												for i, user_mean in enumerate(a    ,      1				):
																		print(f"Mean of class_{i} is: {user_mean}"				)
												print('-' * 1_0_0				)

												# Generating training dataset drawn from gaussian distribution
												lowercase_   : Optional[int]   	=      [
												    gaussian_distribution(user_means[j]    ,      a    ,      counts[j]				)
												    for j in range(a				)
												]
												print('Generated Normal Distribution: \n'    ,      a				)
												print('-' * 1_0_0				)

												# Generating Ys to detecting corresponding classes
												lowercase_   : Optional[int]   	=      y_generator(a    ,      a				)
												print('Generated Corresponding Ys: \n'    ,      a				)
												print('-' * 1_0_0				)

												# Calculating the value of actual mean for each class
												lowercase_   : str   	=      [calculate_mean(counts[k]    ,      x[k]				) for k in range(a				)]
												# for loop iterates over number of elements in 'actual_means' list and print
												# out them in separated line
												for i, actual_mean in enumerate(a    ,      1				):
																		print(f"Actual(Real) mean of class_{i} is: {actual_mean}"				)
												print('-' * 1_0_0				)

												# Calculating the value of probabilities for each class
												lowercase_   : Union[str, Any]   	=      [
												    calculate_probabilities(counts[i]    ,      sum(a				)				) for i in range(a				)
												]

												# for loop iterates over number of elements in 'probabilities' list and print
												# out them in separated line
												for i, probability in enumerate(a    ,      1				):
																		print(f"Probability of class_{i} is: {probability}"				)
												print('-' * 1_0_0				)

												# Calculating the values of variance for each class
												lowercase_   : List[str]   	=      calculate_variance(a    ,      a    ,      sum(a				)				)
												print('Variance: '    ,      a				)
												print('-' * 1_0_0				)

												# Predicting Y values
												# storing predicted Y values in 'pre_indexes' variable
												lowercase_   : Tuple   	=      predict_y_values(a    ,      a    ,      a    ,      a				)
												print('-' * 1_0_0				)

												# Calculating Accuracy of the model
												print(f"Accuracy: {accuracy(a    ,      a				)}"				)
												print('-' * 1_0_0				)
												print(' DONE '.center(1_0_0    ,      '+'				)				)

												if input('Press any key to restart or \'q\' for quit: '				).strip().lower() == "q":
																		print('\n' + 'GoodBye!'.center(1_0_0    ,      '-'				) + '\n'				)
																		break
												system('cls' if name == 'nt' else 'clear'				)  # noqa: S605


if __name__ == "__main__":
						main()
