



"""simple docstring"""
import functools
import operator

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


SCREAMING_SNAKE_CASE_				= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_				= {
    """microsoft/wavlm-base""": """https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json""",
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}




class 			snake_case_						(						a_				):
							__lowerCAmelCase    					=					"wavlm"






							def __init__(   self			,     a_=3_2			,     a_=7_6_8			,     a_=1_2			,     a_=1_2			,     a_=3_0_7_2			,     a_="gelu"			,     a_=0.1			,     a_=0.1			,     a_=0.1			,     a_=0.0			,     a_=0.1			,     a_=0.1			,     a_=0.02			,     a_=1e-5			,     a_="group"			,     a_="gelu"			,     a_=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)			,     a_=(5, 2, 2, 2, 2, 2, 2)			,     a_=(1_0, 3, 3, 3, 3, 2, 2)			,     a_=False			,     a_=1_2_8			,     a_=1_6			,     a_=3_2_0			,     a_=8_0_0			,     a_=False			,     a_=True			,     a_=0.05			,     a_=1_0			,     a_=2			,     a_=0.0			,     a_=1_0			,     a_=3_2_0			,     a_=2			,     a_=0.1			,     a_=1_0_0			,     a_=2_5_6			,     a_=2_5_6			,     a_=0.1			,     a_="mean"			,     a_=False			,     a_=False			,     a_=2_5_6			,     a_=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0)			,     a_=(5, 3, 3, 1, 1)			,     a_=(1, 2, 3, 1, 1)			,     a_=5_1_2			,     a_=8_0			,     a_=0			,     a_=1			,     a_=2			,     a_=False			,     a_=3			,     a_=2			,     a_=3			,     a_=None			,     **a_			,     ):
														super().__init__(**a_			,     pad_token_id=a_			,     bos_token_id=a_			,     eos_token_id=a_      )
														a_     :      Optional[int] 						=     hidden_size
														a_     :      str 						=     feat_extract_norm
														a_     :      str 						=     feat_extract_activation
														a_     :      int 						=     list(a_      )
														a_     :      Dict 						=     list(a_      )
														a_     :      List[Any] 						=     list(a_      )
														a_     :      int 						=     conv_bias
														a_     :      int 						=     num_buckets
														a_     :      List[Any] 						=     max_bucket_distance
														a_     :      Union[str, Any] 						=     num_conv_pos_embeddings
														a_     :      Dict 						=     num_conv_pos_embedding_groups
														a_     :      List[Any] 						=     len(self.conv_dim      )
														a_     :      str 						=     num_hidden_layers
														a_     :      List[Any] 						=     intermediate_size
														a_     :      Tuple 						=     hidden_act
														a_     :      Optional[int] 						=     num_attention_heads
														a_     :      str 						=     hidden_dropout
														a_     :      Tuple 						=     attention_dropout
														a_     :      List[Any] 						=     activation_dropout
														a_     :      int 						=     feat_proj_dropout
														a_     :      Dict 						=     final_dropout
														a_     :      List[Any] 						=     layerdrop
														a_     :      int 						=     layer_norm_eps
														a_     :      Optional[int] 						=     initializer_range
														a_     :      List[str] 						=     num_ctc_classes
														a_     :      List[Any] 						=     vocab_size
														a_     :      Dict 						=     do_stable_layer_norm
														a_     :      Tuple 						=     use_weighted_layer_sum
														a_     :      Union[str, Any] 						=     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
														a_     :      Optional[Any] 						=     apply_spec_augment
														a_     :      Dict 						=     mask_time_prob
														a_     :      str 						=     mask_time_length
														a_     :      Any 						=     mask_time_min_masks
														a_     :      Union[str, Any] 						=     mask_feature_prob
														a_     :      Dict 						=     mask_feature_length

														# parameters for pretraining with codevector quantized representations
														a_     :      List[str] 						=     num_codevectors_per_group
														a_     :      Any 						=     num_codevector_groups
														a_     :      List[Any] 						=     contrastive_logits_temperature
														a_     :      List[str] 						=     num_negatives
														a_     :      Optional[Any] 						=     codevector_dim
														a_     :      Optional[int] 						=     proj_codevector_dim
														a_     :      int 						=     diversity_loss_weight

														# ctc loss
														a_     :      int 						=     ctc_loss_reduction
														a_     :      int 						=     ctc_zero_infinity

														# adapter
														a_     :      List[Any] 						=     add_adapter
														a_     :      Tuple 						=     adapter_kernel_size
														a_     :      str 						=     adapter_stride
														a_     :      List[Any] 						=     num_adapter_layers
														a_     :      Union[str, Any] 						=     output_hidden_size or hidden_size

														# SequenceClassification-specific parameter. Feel free to ignore for other classes.
														a_     :      Union[str, Any] 						=     classifier_proj_size

														# XVector-specific parameters. Feel free to ignore for other classes.
														a_     :      List[str] 						=     list(a_      )
														a_     :      Tuple 						=     list(a_      )
														a_     :      str 						=     list(a_      )
														a_     :      int 						=     xvector_output_dim






							@property
							def snake_case_  (   self      ):
														return functools.reduce(operator.mul			,     self.conv_stride			,     1      )




"""simple docstring"""
def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__ = 10, SCREAMING_SNAKE_CASE__ = 1_000, SCREAMING_SNAKE_CASE__ = True	)			->      int:
		assert (
		    isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
		    and isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
		    and isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
		), "Invalid type of value(s) specified to function!"

		if min_val > max_val:
				raise ValueError("Invalid value for min_val or max_val (min_value < max_value)"	)
		return min_val if option else max_val



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      int:
		return int((number_a + number_a) / 2	)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      None:
		assert (
		    isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	) and isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	) and isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
		), 'argument values must be type of "int"'

		if lower > higher:
				raise ValueError("argument value for lower and higher must be(lower > higher)"	)

		if not lower < to_guess < higher:
				raise ValueError(
				    "guess value must be within the range of lower and higher value"	)

		def answer(SCREAMING_SNAKE_CASE__	) -> str:
				if number > to_guess:
						return "high"
				elif number < to_guess:
						return "low"
				else:
						return "same"

		print("started..."	)

		a_     :      List[str] 						=     lower
		a_     :      Dict 						=     higher

		a_     :      str 						=     []

		while True:
				a_     :      List[str] 						=     get_avg(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
				last_numbers.append(SCREAMING_SNAKE_CASE__	)

				if answer(SCREAMING_SNAKE_CASE__	) == "low":
						a_     :      Optional[Any] 						=     number
				elif answer(SCREAMING_SNAKE_CASE__	) == "high":
						a_     :      Union[str, Any] 						=     number
				else:
						break

		print(F"""guess the number : {last_numbers[-1]}"""	)
		print(F"""details : {last_numbers!s}"""	)



def        lowerCAmelCase_						(							)			->      None:
		a_     :      str 						=     int(input("Enter lower value : "	).strip()	)
		a_     :      Dict 						=     int(input("Enter high value : "	).strip()	)
		a_     :      Optional[Any] 						=     int(input("Enter value to guess : "	).strip()	)
		guess_the_number(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)


if __name__ == "__main__":
					main()