


from __future__ import annotations




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_ = None      ,       lowercase_ = None      ,       lowercase_ = False      ,       )		->     tuple[int, float, str]:




						'''simple docstring'''







						__UpperCAmelCase			:							Optional[Any]	    =	cipher_alphabet or [chr(lowercase_      ) for i in range(97      ,       123      )]

						# If the argument is None or the user provided an empty dictionary
						if not frequencies_dict:
												# Frequencies of letters in the english language (how much they show up)
												__UpperCAmelCase			:							Any	    =	{
												    '''a''': 0.0_8_4_9_7,
												    '''b''': 0.0_1_4_9_2,
												    '''c''': 0.0_2_2_0_2,
												    '''d''': 0.0_4_2_5_3,
												    '''e''': 0.1_1_1_6_2,
												    '''f''': 0.0_2_2_2_8,
												    '''g''': 0.0_2_0_1_5,
												    '''h''': 0.0_6_0_9_4,
												    '''i''': 0.0_7_5_4_6,
												    '''j''': 0.0_0_1_5_3,
												    '''k''': 0.0_1_2_9_2,
												    '''l''': 0.0_4_0_2_5,
												    '''m''': 0.0_2_4_0_6,
												    '''n''': 0.0_6_7_4_9,
												    '''o''': 0.0_7_5_0_7,
												    '''p''': 0.0_1_9_2_9,
												    '''q''': 0.0_0_0_9_5,
												    '''r''': 0.0_7_5_8_7,
												    '''s''': 0.0_6_3_2_7,
												    '''t''': 0.0_9_3_5_6,
												    '''u''': 0.0_2_7_5_8,
												    '''v''': 0.0_0_9_7_8,
												    '''w''': 0.0_2_5_6_0,
												    '''x''': 0.0_0_1_5_0,
												    '''y''': 0.0_1_9_9_4,
												    '''z''': 0.0_0_0_7_7,
												}
						else:
												# Custom frequencies dictionary
												__UpperCAmelCase			:							Optional[Any]	    =	frequencies_dict

						if not case_sensitive:
												__UpperCAmelCase			:							Tuple	    =	ciphertext.lower()

						# Chi squared statistic values
						__UpperCAmelCase			:							dict[int, tuple[float, str]]	    =	{}

						# cycle through all of the shifts
						for shift in range(len(lowercase_      )      ):
												__UpperCAmelCase			:							str	    =	''''''

												# decrypt the message with the shift
												for letter in ciphertext:
																		try:
																								# Try to index the letter in the alphabet
																								__UpperCAmelCase			:							Any	    =	(alphabet_letters.index(letter.lower()      ) - shift) % len(
																								    lowercase_      )
																								decrypted_with_shift += (
																								    alphabet_letters[new_key].upper()
																								    if case_sensitive and letter.isupper()
																								    else alphabet_letters[new_key]
																								)
																		except ValueError:
																								# Append the character if it isn't in the alphabet
																								decrypted_with_shift += letter

												__UpperCAmelCase			:							Optional[Any]	    =	0.0

												# Loop through each letter in the decoded message with the shift
												for letter in decrypted_with_shift:
																		if case_sensitive:
																								__UpperCAmelCase			:							int	    =	letter.lower()
																								if letter in frequencies:
																														# Get the amount of times the letter occurs in the message
																														__UpperCAmelCase			:							List[Any]	    =	decrypted_with_shift.lower().count(lowercase_      )

																														# Get the excepcted amount of times the letter should appear based
																														# on letter frequencies
																														__UpperCAmelCase			:							List[Any]	    =	frequencies[letter] * occurrences

																														# Complete the chi squared statistic formula
																														__UpperCAmelCase			:							str	    =	((occurrences - expected) ** 2) / expected

																														# Add the margin of error to the total chi squared statistic
																														chi_squared_statistic += chi_letter_value
																		else:
																								if letter.lower() in frequencies:
																														# Get the amount of times the letter occurs in the message
																														__UpperCAmelCase			:							Any	    =	decrypted_with_shift.count(lowercase_      )

																														# Get the excepcted amount of times the letter should appear based
																														# on letter frequencies
																														__UpperCAmelCase			:							int	    =	frequencies[letter] * occurrences

																														# Complete the chi squared statistic formula
																														__UpperCAmelCase			:							List[str]	    =	((occurrences - expected) ** 2) / expected

																														# Add the margin of error to the total chi squared statistic
																														chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
												__UpperCAmelCase			:							int	    =	(
												    chi_squared_statistic,
												    decrypted_with_shift,
												)

						# Get the most likely cipher by finding the cipher with the smallest chi squared
						# statistic
						def chi_squared_statistic_values_sorting_key(lowercase_      ) -> tuple[float, str]:
												return chi_squared_statistic_values[key]

						__UpperCAmelCase			:							int	    =	min(
						    lowercase_      ,       key=lowercase_      ,       )

						# Get all the data from the most likely cipher (key, decoded message)
						(
						    (
						    __UpperCAmelCase
						)    ,      (
						    __UpperCAmelCase
						)    ,      
						)			:							int	    =	chi_squared_statistic_values[most_likely_cipher]

						# Return the data on the most likely shift
						return (
						    most_likely_cipher,
						    most_likely_cipher_chi_squared_value,
						    decoded_most_likely_cipher,
						)





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase       		=    logging.get_logger(__name__)

lowerCAmelCase       		=    {
    """google/mobilenet_v2_1.4_224""": """https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json""",
    """google/mobilenet_v2_1.0_224""": """https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v2_0.75_160""": """https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json""",
    """google/mobilenet_v2_0.35_96""": """https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json""",
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}




class 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		Optional[Any]										=						'''mobilenet_v2'''


	def __init__(    self				,     lowercase__=3				,     lowercase__=2_2_4				,     lowercase__=1.0				,     lowercase__=8				,     lowercase__=8				,     lowercase__=6				,     lowercase__=3_2				,     lowercase__=True				,     lowercase__=True				,     lowercase__="relu6"				,     lowercase__=True				,     lowercase__=0.8				,     lowercase__=0.0_2				,     lowercase__=0.0_0_1				,     lowercase__=2_5_5				,     **lowercase__				,     ):
							super().__init__(**lowercase__)

							if depth_multiplier <= 0:
													raise ValueError('''depth_multiplier must be greater than zero.''')

							__UpperCAmelCase			:							Optional[int]	    =	num_channels
							__UpperCAmelCase			:							Optional[Any]	    =	image_size
							__UpperCAmelCase			:							Dict	    =	depth_multiplier
							__UpperCAmelCase			:							Optional[Any]	    =	depth_divisible_by
							__UpperCAmelCase			:							Union[str, Any]	    =	min_depth
							__UpperCAmelCase			:							Dict	    =	expand_ratio
							__UpperCAmelCase			:							int	    =	output_stride
							__UpperCAmelCase			:							Tuple	    =	first_layer_is_expansion
							__UpperCAmelCase			:							Union[str, Any]	    =	finegrained_output
							__UpperCAmelCase			:							int	    =	hidden_act
							__UpperCAmelCase			:							Tuple	    =	tf_padding
							__UpperCAmelCase			:							List[str]	    =	classifier_dropout_prob
							__UpperCAmelCase			:							Optional[Any]	    =	initializer_range
							__UpperCAmelCase			:							List[str]	    =	layer_norm_eps
							__UpperCAmelCase			:							Optional[int]	    =	semantic_loss_ignore_index






class 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		Tuple										=						version.parse('''1.11''' )


	@property
	def  A(    self):
							return OrderedDict([('''pixel_values''', {0: '''batch'''})])


	@property
	def  A(    self):
							if self.task == "image-classification":
													return OrderedDict([('''logits''', {0: '''batch'''})])
							else:
													return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})])


	@property
	def  A(    self):
							return 1e-4

