



import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
			# Create universe of discourse in Python using linspace ()
			lowerCAmelCase__		:					Optional[int]							=np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)

			# Create two fuzzy sets by defining any membership function
			# (trapmf(), gbellmf(), gaussmf(), etc).
			lowerCAmelCase__		:					Optional[int]							=[0, 25, 50]
			lowerCAmelCase__		:					str							=[25, 50, 75]
			lowerCAmelCase__		:					List[Any]							=fuzz.membership.trimf(X, abca)
			lowerCAmelCase__		:					int							=fuzz.membership.trimf(X, abca)

			# Compute the different operations using inbuilt functions.
			lowerCAmelCase__		:					str							=np.ones(75)
			lowerCAmelCase__		:					List[Any]							=np.zeros((75,))
			# 1. Union = max(µA(x), µB(x))
			lowerCAmelCase__		:					Any							=fuzz.fuzzy_or(X, young, X, middle_aged)[1]
			# 2. Intersection = min(µA(x), µB(x))
			lowerCAmelCase__		:					Any							=fuzz.fuzzy_and(X, young, X, middle_aged)[1]
			# 3. Complement (A) = (1- min(µA(x))
			lowerCAmelCase__		:					Any							=fuzz.fuzzy_not(young)
			# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
			lowerCAmelCase__		:					str							=fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
			# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
			lowerCAmelCase__		:					Any							=young + middle_aged - (young * middle_aged)
			# 6. Algebraic Product = (µA(x) * µB(x))
			lowerCAmelCase__		:					Any							=young * middle_aged
			# 7. Bounded Sum = min[1,(µA(x), µB(x))]
			lowerCAmelCase__		:					Union[str, Any]							=fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
			# 8. Bounded difference = min[0,(µA(x), µB(x))]
			lowerCAmelCase__		:					Any							=fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]

			# max-min composition
			# max-product composition

			# Plot each set A, set B and each operation result using plot() and subplot().
			from matplotlib import pyplot as plt

			plt.figure()

			plt.subplot(4, 3, 1)
			plt.plot(X, young)
			plt.title('''Young''')
			plt.grid(True)

			plt.subplot(4, 3, 2)
			plt.plot(X, middle_aged)
			plt.title('''Middle aged''')
			plt.grid(True)

			plt.subplot(4, 3, 3)
			plt.plot(X, union)
			plt.title('''union''')
			plt.grid(True)

			plt.subplot(4, 3, 4)
			plt.plot(X, intersection)
			plt.title('''intersection''')
			plt.grid(True)

			plt.subplot(4, 3, 5)
			plt.plot(X, complement_a)
			plt.title('''complement_a''')
			plt.grid(True)

			plt.subplot(4, 3, 6)
			plt.plot(X, difference)
			plt.title('''difference a/b''')
			plt.grid(True)

			plt.subplot(4, 3, 7)
			plt.plot(X, alg_sum)
			plt.title('''alg_sum''')
			plt.grid(True)

			plt.subplot(4, 3, 8)
			plt.plot(X, alg_product)
			plt.title('''alg_product''')
			plt.grid(True)

			plt.subplot(4, 3, 9)
			plt.plot(X, bdd_sum)
			plt.title('''bdd_sum''')
			plt.grid(True)

			plt.subplot(4, 3, 10)
			plt.plot(X, bdd_difference)
			plt.title('''bdd_difference''')
			plt.grid(True)

			plt.subplots_adjust(hspace=0.5)
			plt.show()











import functools
import operator

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


lowerCAmelCase__		:					Dict							=logging.get_logger(__name__)

lowerCAmelCase__		:					Optional[int]							={
    '''asapp/sew-tiny-100k''': '''https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json''',
    # See all SEW models at https://huggingface.co/models?filter=sew
}

class        UpperCAmelCase_  (       UpperCamelCase_	):

						'''simple docstring'''




						UpperCamelCase__      :			Optional[Any]      =  '''sew'''






						def __init__(   self				,							_A=32				,							_A=768				,							_A=12				,							_A=12				,							_A=3_072				,							_A=2				,							_A="gelu"				,							_A=0.1				,							_A=0.1				,							_A=0.1				,							_A=0.0				,							_A=0.1				,							_A=0.1				,							_A=0.0_2				,							_A=1e-5				,							_A="group"				,							_A="gelu"				,							_A=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)				,							_A=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)				,							_A=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)				,							_A=False				,							_A=128				,							_A=16				,							_A=True				,							_A=0.0_5				,							_A=10				,							_A=2				,							_A=0.0				,							_A=10				,							_A=0				,							_A="mean"				,							_A=False				,							_A=False				,							_A=256				,							_A=0				,							_A=1				,							_A=2				,							**_A				,							):







													'''simple docstring'''

													super().__init__(**_A				,							pad_token_id=_A				,							bos_token_id=_A				,							eos_token_id=_A		)
													__SCREAMING_SNAKE_CASE									= hidden_size
													__SCREAMING_SNAKE_CASE									= feat_extract_norm
													__SCREAMING_SNAKE_CASE									= feat_extract_activation
													__SCREAMING_SNAKE_CASE									= list(_A		)
													__SCREAMING_SNAKE_CASE									= list(_A		)
													__SCREAMING_SNAKE_CASE									= list(_A		)
													__SCREAMING_SNAKE_CASE									= conv_bias
													__SCREAMING_SNAKE_CASE									= num_conv_pos_embeddings
													__SCREAMING_SNAKE_CASE									= num_conv_pos_embedding_groups
													__SCREAMING_SNAKE_CASE									= len(self.conv_dim		)
													__SCREAMING_SNAKE_CASE									= num_hidden_layers
													__SCREAMING_SNAKE_CASE									= intermediate_size
													__SCREAMING_SNAKE_CASE									= squeeze_factor
													__SCREAMING_SNAKE_CASE									= hidden_act
													__SCREAMING_SNAKE_CASE									= num_attention_heads
													__SCREAMING_SNAKE_CASE									= hidden_dropout
													__SCREAMING_SNAKE_CASE									= attention_dropout
													__SCREAMING_SNAKE_CASE									= activation_dropout
													__SCREAMING_SNAKE_CASE									= feat_proj_dropout
													__SCREAMING_SNAKE_CASE									= final_dropout
													__SCREAMING_SNAKE_CASE									= layerdrop
													__SCREAMING_SNAKE_CASE									= layer_norm_eps
													__SCREAMING_SNAKE_CASE									= initializer_range
													__SCREAMING_SNAKE_CASE									= vocab_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)`,'
																				    f"""but is `len(config.conv_dim) = {len(self.conv_dim		)}`, `len(config.conv_stride)"""
																				    f"""= {len(self.conv_stride		)}`, `len(config.conv_kernel) = {len(self.conv_kernel		)}`."""		)

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

													# ctc loss
													__SCREAMING_SNAKE_CASE									= ctc_loss_reduction
													__SCREAMING_SNAKE_CASE									= ctc_zero_infinity

													# sequence classification
													__SCREAMING_SNAKE_CASE									= use_weighted_layer_sum
													__SCREAMING_SNAKE_CASE									= classifier_proj_size






						@property
						def 	_A     (   self		):







													'''simple docstring'''

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






