



'''simple docstring'''





from __future__ import annotations

import time

import numpy as np

UpperCamelCase__  :			Optional[Any]          =					[8, 5, 9, 7]
UpperCamelCase__  :			Optional[Any]          =					[
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
UpperCamelCase__  :			List[Any]          =					[
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]


class 				_lowerCAmelCase     :


 """simple docstring"""

 def __init__(	self ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  )  ->  None:
    A_   :						Optional[int]		= claim_vector
    A_   :						Union[str, Any]		= allocated_resources_table
    A_   :						str		= maximum_claim_table







 def 	UpperCAmelCase_    (	self   )  ->  list[int]:
    return [
        sum(p_item[i] for p_item in self.__allocated_resources_table   )
        for i in range(len(self.__allocated_resources_table[0]   )   )
    ]







 def 	UpperCAmelCase_    (	self   )  ->  list[int]:
    return np.array(self.__claim_vector   ) - np.array(
        self.__processes_resource_summation()   )







 def 	UpperCAmelCase_    (	self   )  ->  list[list[int]]:
    return [
        list(np.array(self.__maximum_claim_table[i]   ) - np.array(lowerCAmelCase__   )   )
        for i, allocated_resource in enumerate(self.__allocated_resources_table   )
    ]







 def 	UpperCAmelCase_    (	self   )  ->  dict[int, list[int]]:
    return {self.__need().index(lowerCAmelCase__   ): i for i in self.__need()}







 def 	UpperCAmelCase_    (	self ,  **_lowerCamelCase   )  ->  None:
    A_   :						Any		= self.__need()
    A_   :						Dict		= self.__allocated_resources_table
    A_   :						Any		= self.__available_resources()
    A_   :						List[Any]		= self.__need_index_manager()
    for kw, val in kwargs.items():
       if kw and val is True:
          self.__pretty_data()
    print("""_""" * 50 + """\n"""   )
    while need_list:
       A_   :						Optional[int]		= False
       for each_need in need_list:
          A_   :						Tuple		= True
          for index, need in enumerate(lowerCAmelCase__   ):
             if need > available_resources[index]:
                A_   :						Optional[int]		= False
                break
          if execution:
             A_   :						Optional[int]		= True
             # get the original index of the process from ind_ctrl db
             for original_need_index, need_clone in need_index_manager.items():
                if each_need == need_clone:
                   A_   :						List[str]		= original_need_index
             print(F"Process {process_number + 1} is executing."   )
             # remove the process run from stack
             need_list.remove(lowerCAmelCase__   )
             # update available/freed resources stack
             A_   :						Optional[int]		= np.array(lowerCAmelCase__   ) + np.array(
                 alloc_resources_table[process_number]   )
             print(
                 """Updated available resource stack for processes: """
                 + """ """.join([str(lowerCAmelCase__   ) for x in available_resources]   )   )
             break
       if safe:
          print("""The process is in a safe state.\n"""   )
       else:
          print("""System in unsafe state. Aborting...\n"""   )
          break







 def 	UpperCAmelCase_    (	self   )  ->  List[Any]:
    print(""" """ * 9 + """Allocated Resource Table"""   )
    for item in self.__allocated_resources_table:
       print(
           F"P{self.__allocated_resources_table.index(lowerCAmelCase__   ) + 1}"
           + """ """.join(F"{it:>8}" for it in item   )
           + """\n"""   )
    print(""" """ * 9 + """System Resource Table"""   )
    for item in self.__maximum_claim_table:
       print(
           F"P{self.__maximum_claim_table.index(lowerCAmelCase__   ) + 1}"
           + """ """.join(F"{it:>8}" for it in item   )
           + """\n"""   )
    print(
        """Current Usage by Active Processes: """
        + """ """.join(str(lowerCAmelCase__   ) for x in self.__claim_vector   )   )
    print(
        """Initial Available Resources:       """
        + """ """.join(str(lowerCAmelCase__   ) for x in self.__available_resources()   )   )
    time.sleep(1   )


if __name__ == "__main__":
  import doctest

  doctest.testmod()

import functools
import operator

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


__a  :     str       =  logging.get_logger(__name__)

__a  :     int       =  {
    """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    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			__a  : Any			 =						'''wavlm'''





			def __init__(   self      ,   lowerCAmelCase__=32      ,   lowerCAmelCase__=7_68      ,   lowerCAmelCase__=12      ,   lowerCAmelCase__=12      ,   lowerCAmelCase__=30_72      ,   lowerCAmelCase__="gelu"      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=0.0      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=0.02      ,   lowerCAmelCase__=1E-5      ,   lowerCAmelCase__="group"      ,   lowerCAmelCase__="gelu"      ,   lowerCAmelCase__=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)      ,   lowerCAmelCase__=(5, 2, 2, 2, 2, 2, 2)      ,   lowerCAmelCase__=(10, 3, 3, 3, 3, 2, 2)      ,   lowerCAmelCase__=False      ,   lowerCAmelCase__=1_28      ,   lowerCAmelCase__=16      ,   lowerCAmelCase__=3_20      ,   lowerCAmelCase__=8_00      ,   lowerCAmelCase__=False      ,   lowerCAmelCase__=True      ,   lowerCAmelCase__=0.05      ,   lowerCAmelCase__=10      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__=0.0      ,   lowerCAmelCase__=10      ,   lowerCAmelCase__=3_20      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=1_00      ,   lowerCAmelCase__=2_56      ,   lowerCAmelCase__=2_56      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__="mean"      ,   lowerCAmelCase__=False      ,   lowerCAmelCase__=False      ,   lowerCAmelCase__=2_56      ,   lowerCAmelCase__=(5_12, 5_12, 5_12, 5_12, 15_00)      ,   lowerCAmelCase__=(5, 3, 3, 1, 1)      ,   lowerCAmelCase__=(1, 2, 3, 1, 1)      ,   lowerCAmelCase__=5_12      ,   lowerCAmelCase__=80      ,   lowerCAmelCase__=0      ,   lowerCAmelCase__=1      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__=False      ,   lowerCAmelCase__=3      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__=3      ,   lowerCAmelCase__=None      ,   **lowerCAmelCase__      ,   )     ->					Tuple:

							'''simple docstring'''


							super().__init__(**lowerCAmelCase__      ,   pad_token_id=lowerCAmelCase__      ,   bos_token_id=lowerCAmelCase__      ,   eos_token_id=lowerCAmelCase__						)
							__lowercase 			=							hidden_size
							__lowercase 			=							feat_extract_norm
							__lowercase 			=							feat_extract_activation
							__lowercase 			=							list(lowerCAmelCase__						)
							__lowercase 			=							list(lowerCAmelCase__						)
							__lowercase 			=							list(lowerCAmelCase__						)
							__lowercase 			=							conv_bias
							__lowercase 			=							num_buckets
							__lowercase 			=							max_bucket_distance
							__lowercase 			=							num_conv_pos_embeddings
							__lowercase 			=							num_conv_pos_embedding_groups
							__lowercase 			=							len(self.conv_dim						)
							__lowercase 			=							num_hidden_layers
							__lowercase 			=							intermediate_size
							__lowercase 			=							hidden_act
							__lowercase 			=							num_attention_heads
							__lowercase 			=							hidden_dropout
							__lowercase 			=							attention_dropout
							__lowercase 			=							activation_dropout
							__lowercase 			=							feat_proj_dropout
							__lowercase 			=							final_dropout
							__lowercase 			=							layerdrop
							__lowercase 			=							layer_norm_eps
							__lowercase 			=							initializer_range
							__lowercase 			=							num_ctc_classes
							__lowercase 			=							vocab_size
							__lowercase 			=							do_stable_layer_norm
							__lowercase 			=							use_weighted_layer_sum
							__lowercase 			=							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
							__lowercase 			=							apply_spec_augment
							__lowercase 			=							mask_time_prob
							__lowercase 			=							mask_time_length
							__lowercase 			=							mask_time_min_masks
							__lowercase 			=							mask_feature_prob
							__lowercase 			=							mask_feature_length

							# parameters for pretraining with codevector quantized representations
							__lowercase 			=							num_codevectors_per_group
							__lowercase 			=							num_codevector_groups
							__lowercase 			=							contrastive_logits_temperature
							__lowercase 			=							num_negatives
							__lowercase 			=							codevector_dim
							__lowercase 			=							proj_codevector_dim
							__lowercase 			=							diversity_loss_weight

							# ctc loss
							__lowercase 			=							ctc_loss_reduction
							__lowercase 			=							ctc_zero_infinity

							# adapter
							__lowercase 			=							add_adapter
							__lowercase 			=							adapter_kernel_size
							__lowercase 			=							adapter_stride
							__lowercase 			=							num_adapter_layers
							__lowercase 			=							output_hidden_size or hidden_size

							# SequenceClassification-specific parameter. Feel free to ignore for other classes.
							__lowercase 			=							classifier_proj_size

							# XVector-specific parameters. Feel free to ignore for other classes.
							__lowercase 			=							list(lowerCAmelCase__						)
							__lowercase 			=							list(lowerCAmelCase__						)
							__lowercase 			=							list(lowerCAmelCase__						)
							__lowercase 			=							xvector_output_dim





			@property
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


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