
import functools
import operator

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


__magic_name__  :  Any     			=   logging.get_logger(__name__)

__magic_name__  :  Tuple     			=   {
    """facebook/hubert-base-ls960""": """https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json""",
    # See all Hubert models at https://huggingface.co/models?filter=hubert
}


class 							lowerCamelCase       (		__UpperCAmelCase					):



  """simple docstring"""





  lowerCAmelCase_								=		"""hubert"""

  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=True						,    __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="sum"						,    __UpperCamelCase=False						,    __UpperCamelCase=False						,    __UpperCamelCase=256						,    __UpperCamelCase=0						,    __UpperCamelCase=1						,    __UpperCamelCase=2						,    **__UpperCamelCase						,    ):
      super().__init__(**__UpperCamelCase						,    pad_token_id=__UpperCamelCase						,    bos_token_id=__UpperCamelCase						,    eos_token_id=__UpperCamelCase					)
      A_								=					hidden_size
      A_								=					feat_extract_norm
      A_								=					feat_extract_activation
      A_								=					list(__UpperCamelCase					)
      A_								=					list(__UpperCamelCase					)
      A_								=					list(__UpperCamelCase					)
      A_								=					conv_bias
      A_								=					num_conv_pos_embeddings
      A_								=					num_conv_pos_embedding_groups
      A_								=					len(self.conv_dim					)
      A_								=					num_hidden_layers
      A_								=					intermediate_size
      A_								=					hidden_act
      A_								=					num_attention_heads
      A_								=					hidden_dropout
      A_								=					attention_dropout
      A_								=					activation_dropout
      A_								=					feat_proj_layer_norm
      A_								=					feat_proj_dropout
      A_								=					final_dropout
      A_								=					layerdrop
      A_								=					layer_norm_eps
      A_								=					initializer_range
      A_								=					vocab_size
      A_								=					do_stable_layer_norm
      A_								=					use_weighted_layer_sum
      A_								=					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_								=					apply_spec_augment
      A_								=					mask_time_prob
      A_								=					mask_time_length
      A_								=					mask_time_min_masks
      A_								=					mask_feature_prob
      A_								=					mask_feature_length
      A_								=					mask_feature_min_masks

      # ctc loss
      A_								=					ctc_loss_reduction
      A_								=					ctc_zero_infinity

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








'''simple docstring'''







import logging

# standard libraries
import sys

import numpy as np
import pytest  # type: ignore

# Custom/local libraries
from matrix import matrix_operation as matop

_snake_case					: List[str]     			=					[[12, 10], [3, 9]]
_snake_case					: Any     			=					[[3, 4], [7, 4]]
_snake_case					: List[str]     			=					[[3, 0, 2], [2, 0, -2], [0, 1, 1]]
_snake_case					: Any     			=					[[3, 0, -2], [2, 0, 2], [0, 1, 1]]
_snake_case					: Any     			=					[[3, 0, 2], [2, 0, -2], [0, 1, 1], [2, 0, -2]]
_snake_case					: Optional[Any]     			=					[1]
_snake_case					: str     			=					[2]

_snake_case					: Optional[int]     			=					logging.getLogger()
_snake_case					: Optional[Any]     			=					logging.DEBUG
_snake_case					: Tuple     			=					logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)



@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2")						,      [(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]	)
def       _a		(						_SCREAMING_SNAKE_CASE		: List[Any]						,      _SCREAMING_SNAKE_CASE		: Tuple	):
						if (np.array(_SCREAMING_SNAKE_CASE	)).shape < (2, 2) or (np.array(_SCREAMING_SNAKE_CASE	)).shape < (2, 2):
												with pytest.raises(_SCREAMING_SNAKE_CASE	):
																		logger.info(F'\n\t{test_addition.__name__} returned integer'	)
																		matop.add(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)
						elif (np.array(_SCREAMING_SNAKE_CASE	)).shape == (np.array(_SCREAMING_SNAKE_CASE	)).shape:
												logger.info(F'\n\t{test_addition.__name__} with same matrix dims'	)
												_SCREAMING_SNAKE_CASE					=			(np.array(_SCREAMING_SNAKE_CASE	) + np.array(_SCREAMING_SNAKE_CASE	)).tolist()
												_SCREAMING_SNAKE_CASE					=			matop.add(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)
												assert theo == act
						else:
												with pytest.raises(_SCREAMING_SNAKE_CASE	):
																		logger.info(F'\n\t{test_addition.__name__} with different matrix dims'	)
																		matop.add(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)



@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2")						,      [(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]	)
def       _a		(						_SCREAMING_SNAKE_CASE		: Optional[Any]						,      _SCREAMING_SNAKE_CASE		: List[str]	):
						if (np.array(_SCREAMING_SNAKE_CASE	)).shape < (2, 2) or (np.array(_SCREAMING_SNAKE_CASE	)).shape < (2, 2):
												with pytest.raises(_SCREAMING_SNAKE_CASE	):
																		logger.info(F'\n\t{test_subtraction.__name__} returned integer'	)
																		matop.subtract(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)
						elif (np.array(_SCREAMING_SNAKE_CASE	)).shape == (np.array(_SCREAMING_SNAKE_CASE	)).shape:
												logger.info(F'\n\t{test_subtraction.__name__} with same matrix dims'	)
												_SCREAMING_SNAKE_CASE					=			(np.array(_SCREAMING_SNAKE_CASE	) - np.array(_SCREAMING_SNAKE_CASE	)).tolist()
												_SCREAMING_SNAKE_CASE					=			matop.subtract(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)
												assert theo == act
						else:
												with pytest.raises(_SCREAMING_SNAKE_CASE	):
																		logger.info(F'\n\t{test_subtraction.__name__} with different matrix dims'	)
																		assert matop.subtract(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)



@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2")						,      [(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]	)
def       _a		(						_SCREAMING_SNAKE_CASE		: Any						,      _SCREAMING_SNAKE_CASE		: List[str]	):
						if (np.array(_SCREAMING_SNAKE_CASE	)).shape < (2, 2) or (np.array(_SCREAMING_SNAKE_CASE	)).shape < (2, 2):
												logger.info(F'\n\t{test_multiplication.__name__} returned integer'	)
												with pytest.raises(_SCREAMING_SNAKE_CASE	):
																		matop.add(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)
						elif (np.array(_SCREAMING_SNAKE_CASE	)).shape == (np.array(_SCREAMING_SNAKE_CASE	)).shape:
												logger.info(F'\n\t{test_multiplication.__name__} meets dim requirements'	)
												_SCREAMING_SNAKE_CASE					=			(np.matmul(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)).tolist()
												_SCREAMING_SNAKE_CASE					=			matop.multiply(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)
												assert theo == act
						else:
												with pytest.raises(_SCREAMING_SNAKE_CASE	):
																		logger.info(
																		    F'\n\t{test_multiplication.__name__} does not meet dim requirements'	)
																		assert matop.subtract(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)



@pytest.mark.mat_ops
def       _a		(						):
						_SCREAMING_SNAKE_CASE					=			(3.5 * np.array(_SCREAMING_SNAKE_CASE	)).tolist()
						_SCREAMING_SNAKE_CASE					=			matop.scalar_multiply(_SCREAMING_SNAKE_CASE						,      3.5	)
						assert theo == act



@pytest.mark.mat_ops
def       _a		(						):
						_SCREAMING_SNAKE_CASE					=			(np.identity(5	)).tolist()
						_SCREAMING_SNAKE_CASE					=			matop.identity(5	)
						assert theo == act




@pytest.mark.mat_ops
@pytest.mark.parametrize("mat"						,      [mat_a, mat_b, mat_c, mat_d, mat_e, mat_f]	)
def       _a		(						_SCREAMING_SNAKE_CASE		: Tuple	):
						if (np.array(_SCREAMING_SNAKE_CASE	)).shape < (2, 2):
												with pytest.raises(_SCREAMING_SNAKE_CASE	):
																		logger.info(F'\n\t{test_transpose.__name__} returned integer'	)
																		matop.transpose(_SCREAMING_SNAKE_CASE	)
						else:
												_SCREAMING_SNAKE_CASE					=			(np.transpose(_SCREAMING_SNAKE_CASE	)).tolist()
												_SCREAMING_SNAKE_CASE					=			matop.transpose(_SCREAMING_SNAKE_CASE						,      return_map=_SCREAMING_SNAKE_CASE	)
												assert theo == act