




import functools
import operator

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


UpperCAmelCase_		:				List[Any]									=     logging.get_logger(__name__)

UpperCAmelCase_		:				int									=     {
    "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 		__A      (			UpperCamelCase__			):
						UpperCamelCase     						=					"""unispeech"""

						def __init__(		self   :List[str]    , __snake_case   :Optional[int]=32    , __snake_case   :Any=7_68    , __snake_case   :Any=12    , __snake_case   :int=12    , __snake_case   :Optional[Any]=30_72    , __snake_case   :Any="gelu"    , __snake_case   :Any=0.1    , __snake_case   :Any=0.1    , __snake_case   :str=0.1    , __snake_case   :Dict=0.0    , __snake_case   :Optional[int]=0.0    , __snake_case   :Tuple=0.1    , __snake_case   :List[Any]=0.1    , __snake_case   :int=0.02    , __snake_case   :int=1E-5    , __snake_case   :Union[str, Any]="group"    , __snake_case   :int="gelu"    , __snake_case   :List[Any]=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)    , __snake_case   :int=(5, 2, 2, 2, 2, 2, 2)    , __snake_case   :List[Any]=(10, 3, 3, 3, 3, 2, 2)    , __snake_case   :str=False    , __snake_case   :str=1_28    , __snake_case   :Dict=16    , __snake_case   :Dict=False    , __snake_case   :Dict=True    , __snake_case   :Tuple=0.05    , __snake_case   :List[Any]=10    , __snake_case   :Any=2    , __snake_case   :Tuple=0.0    , __snake_case   :Optional[int]=10    , __snake_case   :Dict=0    , __snake_case   :List[Any]=3_20    , __snake_case   :str=2    , __snake_case   :int=0.1    , __snake_case   :Union[str, Any]=1_00    , __snake_case   :Union[str, Any]=2_56    , __snake_case   :List[Any]=2_56    , __snake_case   :Union[str, Any]=0.1    , __snake_case   :Tuple="mean"    , __snake_case   :int=False    , __snake_case   :int=False    , __snake_case   :List[str]=2_56    , __snake_case   :List[str]=80    , __snake_case   :Optional[Any]=0    , __snake_case   :int=1    , __snake_case   :str=2    , __snake_case   :Optional[int]=0.5    , **__snake_case   :Union[str, Any]    , ):
										'''simple docstring'''

										super().__init__(**__snake_case    , pad_token_id=__snake_case    , bos_token_id=__snake_case    , eos_token_id=__snake_case )
										__magic_name__	:	str								=hidden_size
										__magic_name__	:	Optional[int]								=feat_extract_norm
										__magic_name__	:	List[str]								=feat_extract_activation
										__magic_name__	:	Any								=list(__snake_case )
										__magic_name__	:	int								=list(__snake_case )
										__magic_name__	:	int								=list(__snake_case )
										__magic_name__	:	int								=conv_bias
										__magic_name__	:	List[str]								=num_conv_pos_embeddings
										__magic_name__	:	str								=num_conv_pos_embedding_groups
										__magic_name__	:	Union[str, Any]								=len(self.conv_dim )
										__magic_name__	:	int								=num_hidden_layers
										__magic_name__	:	Union[str, Any]								=intermediate_size
										__magic_name__	:	Tuple								=hidden_act
										__magic_name__	:	Optional[int]								=num_attention_heads
										__magic_name__	:	Dict								=hidden_dropout
										__magic_name__	:	Optional[int]								=attention_dropout
										__magic_name__	:	Tuple								=activation_dropout
										__magic_name__	:	List[str]								=feat_proj_dropout
										__magic_name__	:	Optional[int]								=final_dropout
										__magic_name__	:	Optional[int]								=layerdrop
										__magic_name__	:	Optional[int]								=layer_norm_eps
										__magic_name__	:	List[Any]								=initializer_range
										__magic_name__	:	Tuple								=num_ctc_classes
										__magic_name__	:	Any								=vocab_size
										__magic_name__	:	Optional[Any]								=do_stable_layer_norm
										__magic_name__	:	Dict								=use_weighted_layer_sum
										__magic_name__	:	Dict								=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
										__magic_name__	:	Optional[Any]								=apply_spec_augment
										__magic_name__	:	List[str]								=mask_time_prob
										__magic_name__	:	Any								=mask_time_length
										__magic_name__	:	List[str]								=mask_time_min_masks
										__magic_name__	:	int								=mask_feature_prob
										__magic_name__	:	int								=mask_feature_length
										__magic_name__	:	Tuple								=mask_feature_min_masks

										# parameters for pretraining with codevector quantized representations
										__magic_name__	:	List[Any]								=num_codevectors_per_group
										__magic_name__	:	int								=num_codevector_groups
										__magic_name__	:	Dict								=contrastive_logits_temperature
										__magic_name__	:	str								=feat_quantizer_dropout
										__magic_name__	:	Tuple								=num_negatives
										__magic_name__	:	Union[str, Any]								=codevector_dim
										__magic_name__	:	Dict								=proj_codevector_dim
										__magic_name__	:	Tuple								=diversity_loss_weight

										# ctc loss
										__magic_name__	:	Optional[int]								=ctc_loss_reduction
										__magic_name__	:	Dict								=ctc_zero_infinity

										# pretraining loss
										__magic_name__	:	Optional[Any]								=replace_prob


						@property
						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

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









def 		lowerCAmelCase_   (	lowerCamelCase   ):
				__magic_name__	:	Optional[int]								=[]
				__magic_name__	:	int								=[]
				__magic_name__	:	str								={
				    """^""": 3,
				    """*""": 2,
				    """/""": 2,
				    """%""": 2,
				    """+""": 1,
				    """-""": 1,
				}  # Priority of each operator
				__magic_name__	:	Dict								=len(lowerCamelCase   ) if (len(lowerCamelCase   ) > 7) else 7

				# Print table header for output
				print(
				    """Symbol""".center(8   )	,     """Stack""".center(lowerCamelCase   )	,     """Postfix""".center(lowerCamelCase   )	,     sep=""" | """	,     )
				print("""-""" * (print_width * 3 + 7)   )

				for x in infix:
								if x.isalpha() or x.isdigit():
												post_fix.append(lowerCamelCase   )  # if x is Alphabet / Digit, add it to Postfix
								elif x == "(":
												stack.append(lowerCamelCase   )  # if x is "(" push to Stack
								elif x == ")":  # if x is ")" pop stack until "(" is encountered
												while stack[-1] != "(":
																post_fix.append(stack.pop()   )  # Pop stack & add the content to Postfix
												stack.pop()
								else:
												if len(lowerCamelCase   ) == 0:
																stack.append(lowerCamelCase   )  # If stack is empty, push x to stack
												else:  # while priority of x is not > priority of element in the stack
																while len(lowerCamelCase   ) > 0 and priority[x] <= priority[stack[-1]]:
																				post_fix.append(stack.pop()   )  # pop stack & add to Postfix
																stack.append(lowerCamelCase   )  # push x to stack

								print(
								    x.center(8   )	,     ("""""".join(lowerCamelCase   )).ljust(lowerCamelCase   )	,     ("""""".join(lowerCamelCase   )).ljust(lowerCamelCase   )	,     sep=""" | """	,     )  # Output in tabular format

				while len(lowerCamelCase   ) > 0:  # while stack is not empty
								post_fix.append(stack.pop()   )  # pop stack & add to Postfix
								print(
								    """ """.center(8   )	,     ("""""".join(lowerCamelCase   )).ljust(lowerCamelCase   )	,     ("""""".join(lowerCamelCase   )).ljust(lowerCamelCase   )	,     sep=""" | """	,     )  # Output in tabular format

				return "".join(lowerCamelCase   )  # return Postfix as str




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				__magic_name__	:	Union[str, Any]								=list(infix[::-1]   )  # reverse the infix equation

				for i in range(len(lowerCamelCase   )   ):
								if infix[i] == "(":
												__magic_name__	:	str								=""")"""  # change "(" to ")"
								elif infix[i] == ")":
												__magic_name__	:	int								="""("""  # change ")" to "("

				return (infix_2_postfix("""""".join(lowerCamelCase   )   ))[
				    ::-1
				]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
							UpperCAmelCase_		:				Tuple									=     input("\nEnter an Infix Equation = ")  # Input an Infix equation
							UpperCAmelCase_		:				Dict									=     "".join(Infix.split())  # Remove spaces from the input
							print("\n\t", Infix, "(Infix) -> ", infix_2_prefix(Infix), "(Prefix)")




