


'''simple docstring'''

from __future__ import annotations





def __snake_case      (   UpperCAmelCase_		:		str      ,       UpperCAmelCase_		:		list[str] | None = None      ,       UpperCAmelCase_		:		dict[str, float] | None = None      ,       UpperCAmelCase_		:		bool = False      ,       ):
	lowerCamelCase_  				=						cipher_alphabet or [chr(UpperCAmelCase_	) 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)
		lowerCamelCase_  				=						{
		    "a": 0.0_8497,
		    "b": 0.0_1492,
		    "c": 0.0_2202,
		    "d": 0.0_4253,
		    "e": 0.1_1162,
		    "f": 0.0_2228,
		    "g": 0.0_2015,
		    "h": 0.0_6094,
		    "i": 0.0_7546,
		    "j": 0.0_0153,
		    "k": 0.0_1292,
		    "l": 0.0_4025,
		    "m": 0.0_2406,
		    "n": 0.0_6749,
		    "o": 0.0_7507,
		    "p": 0.0_1929,
		    "q": 0.0_0095,
		    "r": 0.0_7587,
		    "s": 0.0_6327,
		    "t": 0.0_9356,
		    "u": 0.0_2758,
		    "v": 0.0_0978,
		    "w": 0.0_2560,
		    "x": 0.0_0150,
		    "y": 0.0_1994,
		    "z": 0.0_0077,
		}
	else:
		# Custom frequencies dictionary
		lowerCamelCase_  				=						frequencies_dict

	if not case_sensitive:
		lowerCamelCase_  				=						ciphertext.lower()

	# Chi squared statistic values
	lowerCamelCase_  				=						{}

	# cycle through all of the shifts
	for shift in range(len(UpperCAmelCase_	)	):
		lowerCamelCase_  				=						""

		# decrypt the message with the shift
		for letter in ciphertext:
			try:
				# Try to index the letter in the alphabet
				lowerCamelCase_  				=						(alphabet_letters.index(letter.lower()	) - shift) % len(
				    UpperCAmelCase_	)
				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

		lowerCamelCase_  				=						0.0

		# Loop through each letter in the decoded message with the shift
		for letter in decrypted_with_shift:
			if case_sensitive:
				lowerCamelCase_  				=						letter.lower()
				if letter in frequencies:
					# Get the amount of times the letter occurs in the message
					lowerCamelCase_  				=						decrypted_with_shift.lower().count(UpperCAmelCase_	)

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

					# Complete the chi squared statistic formula
					lowerCamelCase_  				=						((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
					lowerCamelCase_  				=						decrypted_with_shift.count(UpperCAmelCase_	)

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

					# Complete the chi squared statistic formula
					lowerCamelCase_  				=						((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
		lowerCamelCase_  				=						(
		    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(UpperCAmelCase_		:		int	) -> tuple[float, str]:
		return chi_squared_statistic_values[key]

	lowerCamelCase_  				=						min(
	    UpperCAmelCase_      ,       key=UpperCAmelCase_      ,       )

	# Get all the data from the most likely cipher (key, decoded message)
	(
	    (
	    lowerCamelCase_
	)				,(
	    lowerCamelCase_
	)				,
	)  				=						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,
	)







'''simple docstring'''

import functools
import operator

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


a_						:       List[str]      			=				logging.get_logger(__name__)

a_						:       Dict      			=				{
    """microsoft/unispeech-large-1500h-cv""": (
        """https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json"""
    ),
    # See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}





class   snake_case					(					lowercase    ):





  """simple docstring"""




  _lowerCamelCase						=						"unispeech"






  def __init__(					self    ,						UpperCamelCase=32    ,						UpperCamelCase=768    ,						UpperCamelCase=12    ,						UpperCamelCase=12    ,						UpperCamelCase=3072    ,						UpperCamelCase="gelu"    ,						UpperCamelCase=0.1    ,						UpperCamelCase=0.1    ,						UpperCamelCase=0.1    ,						UpperCamelCase=0.0    ,						UpperCamelCase=0.0    ,						UpperCamelCase=0.1    ,						UpperCamelCase=0.1    ,						UpperCamelCase=0.02    ,						UpperCamelCase=1e-5    ,						UpperCamelCase="group"    ,						UpperCamelCase="gelu"    ,						UpperCamelCase=(512, 512, 512, 512, 512, 512, 512)    ,						UpperCamelCase=(5, 2, 2, 2, 2, 2, 2)    ,						UpperCamelCase=(10, 3, 3, 3, 3, 2, 2)    ,						UpperCamelCase=False    ,						UpperCamelCase=128    ,						UpperCamelCase=16    ,						UpperCamelCase=False    ,						UpperCamelCase=True    ,						UpperCamelCase=0.05    ,						UpperCamelCase=10    ,						UpperCamelCase=2    ,						UpperCamelCase=0.0    ,						UpperCamelCase=10    ,						UpperCamelCase=0    ,						UpperCamelCase=320    ,						UpperCamelCase=2    ,						UpperCamelCase=0.1    ,						UpperCamelCase=100    ,						UpperCamelCase=256    ,						UpperCamelCase=256    ,						UpperCamelCase=0.1    ,						UpperCamelCase="mean"    ,						UpperCamelCase=False    ,						UpperCamelCase=False    ,						UpperCamelCase=256    ,						UpperCamelCase=80    ,						UpperCamelCase=0    ,						UpperCamelCase=1    ,						UpperCamelCase=2    ,						UpperCamelCase=0.5    ,						**UpperCamelCase    ,						):







   """simple docstring"""
   super().__init__(**UpperCamelCase    ,						pad_token_id=UpperCamelCase    ,						bos_token_id=UpperCamelCase    ,						eos_token_id=UpperCamelCase  )
   lowerCamelCase_  				=						hidden_size
   lowerCamelCase_  				=						feat_extract_norm
   lowerCamelCase_  				=						feat_extract_activation
   lowerCamelCase_  				=						list(UpperCamelCase  )
   lowerCamelCase_  				=						list(UpperCamelCase  )
   lowerCamelCase_  				=						list(UpperCamelCase  )
   lowerCamelCase_  				=						conv_bias
   lowerCamelCase_  				=						num_conv_pos_embeddings
   lowerCamelCase_  				=						num_conv_pos_embedding_groups
   lowerCamelCase_  				=						len(self.conv_dim  )
   lowerCamelCase_  				=						num_hidden_layers
   lowerCamelCase_  				=						intermediate_size
   lowerCamelCase_  				=						hidden_act
   lowerCamelCase_  				=						num_attention_heads
   lowerCamelCase_  				=						hidden_dropout
   lowerCamelCase_  				=						attention_dropout
   lowerCamelCase_  				=						activation_dropout
   lowerCamelCase_  				=						feat_proj_dropout
   lowerCamelCase_  				=						final_dropout
   lowerCamelCase_  				=						layerdrop
   lowerCamelCase_  				=						layer_norm_eps
   lowerCamelCase_  				=						initializer_range
   lowerCamelCase_  				=						num_ctc_classes
   lowerCamelCase_  				=						vocab_size
   lowerCamelCase_  				=						do_stable_layer_norm
   lowerCamelCase_  				=						use_weighted_layer_sum
   lowerCamelCase_  				=						classifier_proj_size

   if (
       (len(self.conv_stride  ) != self.num_feat_extract_layers)
       or (len(self.conv_kernel  ) != self.num_feat_extract_layers)
       or (len(self.conv_dim  ) != self.num_feat_extract_layers)
   ):
    raise ValueError(
        "Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
        " `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
        f''' {len(self.conv_dim  )}`, `len(config.conv_stride) = {len(self.conv_stride  )}`,'''
        f''' `len(config.conv_kernel) = {len(self.conv_kernel  )}`.'''  )

   # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
   lowerCamelCase_  				=						apply_spec_augment
   lowerCamelCase_  				=						mask_time_prob
   lowerCamelCase_  				=						mask_time_length
   lowerCamelCase_  				=						mask_time_min_masks
   lowerCamelCase_  				=						mask_feature_prob
   lowerCamelCase_  				=						mask_feature_length
   lowerCamelCase_  				=						mask_feature_min_masks

   # parameters for pretraining with codevector quantized representations
   lowerCamelCase_  				=						num_codevectors_per_group
   lowerCamelCase_  				=						num_codevector_groups
   lowerCamelCase_  				=						contrastive_logits_temperature
   lowerCamelCase_  				=						feat_quantizer_dropout
   lowerCamelCase_  				=						num_negatives
   lowerCamelCase_  				=						codevector_dim
   lowerCamelCase_  				=						proj_codevector_dim
   lowerCamelCase_  				=						diversity_loss_weight

   # ctc loss
   lowerCamelCase_  				=						ctc_loss_reduction
   lowerCamelCase_  				=						ctc_zero_infinity

   # pretraining loss
   lowerCamelCase_  				=						replace_prob







  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   return functools.reduce(operator.mul    ,						self.conv_stride    ,						1  )



