




import functools
import operator

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


_snake_case										=       logging.get_logger(__name__)

_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 						lowercase							(   a__		):
     _a							 =						'''wavlm'''

     def __init__(  self		,     _a=32		,     _a=768		,     _a=12		,     _a=12		,     _a=3072		,     _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=(512, 512, 512, 512, 512, 512, 512)		,     _a=(5, 2, 2, 2, 2, 2, 2)		,     _a=(10, 3, 3, 3, 3, 2, 2)		,     _a=False		,     _a=128		,     _a=16		,     _a=320		,     _a=800		,     _a=False		,     _a=True		,     _a=0.05		,     _a=10		,     _a=2		,     _a=0.0		,     _a=10		,     _a=320		,     _a=2		,     _a=0.1		,     _a=100		,     _a=256		,     _a=256		,     _a=0.1		,     _a="mean"		,     _a=False		,     _a=False		,     _a=256		,     _a=(512, 512, 512, 512, 1500)		,     _a=(5, 3, 3, 1, 1)		,     _a=(1, 2, 3, 1, 1)		,     _a=512		,     _a=80		,     _a=0		,     _a=1		,     _a=2		,     _a=False		,     _a=3		,     _a=2		,     _a=3		,     _a=None		,     **_a		,     )						->							Optional[Any]:
           super().__init__(**SCREAMING_SNAKE_CASE__		,     pad_token_id=SCREAMING_SNAKE_CASE__		,     bos_token_id=SCREAMING_SNAKE_CASE__		,     eos_token_id=SCREAMING_SNAKE_CASE__				)
           _A							:     Dict			=						hidden_size
           _A							:     Any			=						feat_extract_norm
           _A							:     str			=						feat_extract_activation
           _A							:     Any			=						list(SCREAMING_SNAKE_CASE__				)
           _A							:     Any			=						list(SCREAMING_SNAKE_CASE__				)
           _A							:     List[str]			=						list(SCREAMING_SNAKE_CASE__				)
           _A							:     List[Any]			=						conv_bias
           _A							:     Dict			=						num_buckets
           _A							:     List[str]			=						max_bucket_distance
           _A							:     Union[str, Any]			=						num_conv_pos_embeddings
           _A							:     Optional[int]			=						num_conv_pos_embedding_groups
           _A							:     Optional[Any]			=						len(self.conv_dim				)
           _A							:     Optional[int]			=						num_hidden_layers
           _A							:     Tuple			=						intermediate_size
           _A							:     List[str]			=						hidden_act
           _A							:     Tuple			=						num_attention_heads
           _A							:     Any			=						hidden_dropout
           _A							:     int			=						attention_dropout
           _A							:     List[Any]			=						activation_dropout
           _A							:     Tuple			=						feat_proj_dropout
           _A							:     Any			=						final_dropout
           _A							:     Optional[int]			=						layerdrop
           _A							:     int			=						layer_norm_eps
           _A							:     Optional[Any]			=						initializer_range
           _A							:     Optional[Any]			=						num_ctc_classes
           _A							:     List[str]			=						vocab_size
           _A							:     Optional[Any]			=						do_stable_layer_norm
           _A							:     List[str]			=						use_weighted_layer_sum
           _A							:     Optional[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							:     List[str]			=						mask_time_prob
           _A							:     Any			=						mask_time_length
           _A							:     Tuple			=						mask_time_min_masks
           _A							:     Optional[int]			=						mask_feature_prob
           _A							:     List[str]			=						mask_feature_length

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

           # ctc loss
           _A							:     List[Any]			=						ctc_loss_reduction
           _A							:     Tuple			=						ctc_zero_infinity

           # adapter
           _A							:     Dict			=						add_adapter
           _A							:     List[str]			=						adapter_kernel_size
           _A							:     Optional[int]			=						adapter_stride
           _A							:     List[str]			=						num_adapter_layers
           _A							:     List[str]			=						output_hidden_size or hidden_size

           # SequenceClassification-specific parameter. Feel free to ignore for other classes.
           _A							:     Optional[Any]			=						classifier_proj_size

           # XVector-specific parameters. Feel free to ignore for other classes.
           _A							:     List[str]			=						list(SCREAMING_SNAKE_CASE__				)
           _A							:     int			=						list(SCREAMING_SNAKE_CASE__				)
           _A							:     Union[str, Any]			=						list(SCREAMING_SNAKE_CASE__				)
           _A							:     int			=						xvector_output_dim



     @property
     def 					a__	(  self				)						->							List[str]:
           return functools.reduce(operator.mul		,     self.conv_stride		,     1				)



"""simple docstring"""




import mpmath  # for roots of unity
import numpy as np



class 	lowerCAmelCase_						:


				"""simple docstring"""



				def __init__(self   ,       SCREAMING_SNAKE_CASE__=None   ,       SCREAMING_SNAKE_CASE__=None    )					->		Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   list(poly_a or [0]    )[:]
											SCREAMING_SNAKE_CASE__						:       Tuple           =   list(poly_b or [0]    )[:]

											# Remove leading zero coefficients
											while self.polyA[-1] == 0:
																		self.polyA.pop()
											SCREAMING_SNAKE_CASE__						:       int           =   len(self.polyA    )

											while self.polyB[-1] == 0:
																		self.polyB.pop()
											SCREAMING_SNAKE_CASE__						:       List[str]           =   len(self.polyB    )

											# Add 0 to make lengths equal a power of 2
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   int(
											    2 ** np.ceil(np.loga(len(self.polyA    ) + len(self.polyB    ) - 1    )    )    )

											while len(self.polyA    ) < self.c_max_length:
																		self.polyA.append(0    )
											while len(self.polyB    ) < self.c_max_length:
																		self.polyB.append(0    )
											# A complex root used for the fourier transform
											SCREAMING_SNAKE_CASE__						:       List[str]           =   complex(mpmath.root(x=1   ,       n=self.c_max_length   ,       k=1    )    )

											# The product
											SCREAMING_SNAKE_CASE__						:       Tuple           =   self.__multiply()



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       str           =   [[x] for x in self.polyA] if which == """A""" else [[x] for x in self.polyB]
											# Corner case
											if len(SCREAMING_SNAKE_CASE__    ) <= 1:
																		return dft[0]
											#
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   self.c_max_length // 2
											while next_ncol > 0:
																		SCREAMING_SNAKE_CASE__						:       Any           =   [[] for i in range(SCREAMING_SNAKE_CASE__    )]
																		SCREAMING_SNAKE_CASE__						:       Tuple           =   self.root**next_ncol

																		# First half of next step
																		SCREAMING_SNAKE_CASE__						:       str           =   1
																		for j in range(self.c_max_length // (next_ncol * 2)    ):
																									for i in range(SCREAMING_SNAKE_CASE__    ):
																																new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j]    )
																									current_root *= root
																		# Second half of next step
																		SCREAMING_SNAKE_CASE__						:       int           =   1
																		for j in range(self.c_max_length // (next_ncol * 2)    ):
																									for i in range(SCREAMING_SNAKE_CASE__    ):
																																new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j]    )
																									current_root *= root
																		# Update
																		SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   new_dft
																		SCREAMING_SNAKE_CASE__						:       Tuple           =   next_ncol // 2
											return dft[0]



				def 	__magic_name__    (self    )					->		int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   self.__dft("""A"""    )
											SCREAMING_SNAKE_CASE__						:       Dict           =   self.__dft("""B"""    )
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   [[dft_a[i] * dft_b[i] for i in range(self.c_max_length    )]]
											del dft_a
											del dft_b

											# Corner Case
											if len(inverce_c[0]    ) <= 1:
																		return inverce_c[0]
											# Inverse DFT
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   2
											while next_ncol <= self.c_max_length:
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   [[] for i in range(SCREAMING_SNAKE_CASE__    )]
																		SCREAMING_SNAKE_CASE__						:       Tuple           =   self.root ** (next_ncol // 2)
																		SCREAMING_SNAKE_CASE__						:       Any           =   1
																		# First half of next step
																		for j in range(self.c_max_length // next_ncol    ):
																									for i in range(next_ncol // 2    ):
																																# Even positions
																																new_inverse_c[i].append(
																																    (
																																        inverce_c[i][j]
																																        + inverce_c[i][j + self.c_max_length // next_ncol]
																																    )
																																    / 2    )
																																# Odd positions
																																new_inverse_c[i + next_ncol // 2].append(
																																    (
																																        inverce_c[i][j]
																																        - inverce_c[i][j + self.c_max_length // next_ncol]
																																    )
																																    / (2 * current_root)    )
																									current_root *= root
																		# Update
																		SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   new_inverse_c
																		next_ncol *= 2
											# Unpack
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   [round(x[0].real   ,       8    ) + round(x[0].imag   ,       8    ) * 1j for x in inverce_c]

											# Remove leading 0's
											while inverce_c[-1] == 0:
																		inverce_c.pop()
											return inverce_c



				def __str__(self    )					->		List[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[str]           =   """A = """ + """ + """.join(
											    F'''{coef}*x^{i}''' for coef, i in enumerate(self.polyA[: self.len_A]    )    )
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   """B = """ + """ + """.join(
											    F'''{coef}*x^{i}''' for coef, i in enumerate(self.polyB[: self.len_B]    )    )
											SCREAMING_SNAKE_CASE__						:       int           =   """A*B = """ + """ + """.join(
											    F'''{coef}*x^{i}''' for coef, i in enumerate(self.product    )    )

											return F'''{a}\n{b}\n{c}'''


# Unit tests
if __name__ == "__main__":
					import doctest

					doctest.testmod()




