
'''simple docstring'''






import functools
import operator

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


A			      =      logging.get_logger(__name__)

A			      =      {
    'asapp/sew-tiny-100k': 'https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json',
    # See all SEW models at https://huggingface.co/models?filter=sew
}



class     __snake_case		(   _snake_case):
					_lowerCAmelCase			     =	'sew'
					def __init__(     self,    A=32,    A=768,    A=12,    A=12,    A=3072,    A=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=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512),    A=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1),    A=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1),    A=False,    A=128,    A=16,    A=True,    A=0.05,    A=10,    A=2,    A=0.0,    A=10,    A=0,    A="mean",    A=False,    A=False,    A=256,    A=0,    A=1,    A=2,    **A,    ):

									"""simple docstring"""


									super().__init__(**A,    pad_token_id=A,    bos_token_id=A,    eos_token_id=A      )
									lowerCamelCase					:			List[str]									= hidden_size
									lowerCamelCase					:			List[str]									= feat_extract_norm
									lowerCamelCase					:			Optional[Any]									= feat_extract_activation
									lowerCamelCase					:			Optional[int]									= list(A      )
									lowerCamelCase					:			Dict									= list(A      )
									lowerCamelCase					:			List[Any]									= list(A      )
									lowerCamelCase					:			Optional[Any]									= conv_bias
									lowerCamelCase					:			Optional[int]									= num_conv_pos_embeddings
									lowerCamelCase					:			Union[str, Any]									= num_conv_pos_embedding_groups
									lowerCamelCase					:			Tuple									= len(self.conv_dim      )
									lowerCamelCase					:			List[Any]									= num_hidden_layers
									lowerCamelCase					:			Any									= intermediate_size
									lowerCamelCase					:			Any									= squeeze_factor
									lowerCamelCase					:			Optional[Any]									= hidden_act
									lowerCamelCase					:			List[str]									= num_attention_heads
									lowerCamelCase					:			int									= hidden_dropout
									lowerCamelCase					:			Any									= attention_dropout
									lowerCamelCase					:			int									= activation_dropout
									lowerCamelCase					:			Any									= feat_proj_dropout
									lowerCamelCase					:			List[Any]									= final_dropout
									lowerCamelCase					:			str									= layerdrop
									lowerCamelCase					:			Union[str, Any]									= layer_norm_eps
									lowerCamelCase					:			str									= initializer_range
									lowerCamelCase					:			int									= vocab_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)`,'
													    F'''but is `len(config.conv_dim) = {len(self.conv_dim      )}`, `len(config.conv_stride)'''
													    F'''= {len(self.conv_stride      )}`, `len(config.conv_kernel) = {len(self.conv_kernel      )}`.'''      )

									# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
									lowerCamelCase					:			Union[str, Any]									= apply_spec_augment
									lowerCamelCase					:			List[Any]									= mask_time_prob
									lowerCamelCase					:			Union[str, Any]									= mask_time_length
									lowerCamelCase					:			List[str]									= mask_time_min_masks
									lowerCamelCase					:			Any									= mask_feature_prob
									lowerCamelCase					:			Dict									= mask_feature_length
									lowerCamelCase					:			List[str]									= mask_feature_min_masks

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

									# sequence classification
									lowerCamelCase					:			Optional[Any]									= use_weighted_layer_sum
									lowerCamelCase					:			str									= classifier_proj_size




					@property
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


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


'''simple docstring'''






from manim import *




class     __snake_case		(   a__):
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[int]									= Rectangle(height=0.5,    width=0.5      )
									lowerCamelCase					:			List[Any]									= Rectangle(height=0.46,    width=0.46      ).set_stroke(width=0      )

									lowerCamelCase					:			List[str]									= [mem.copy() for i in range(6      )]
									lowerCamelCase					:			List[Any]									= [mem.copy() for i in range(6      )]
									lowerCamelCase					:			str									= VGroup(*A      ).arrange(A,    buff=0      )
									lowerCamelCase					:			Any									= VGroup(*A      ).arrange(A,    buff=0      )
									lowerCamelCase					:			Dict									= VGroup(A,    A      ).arrange(A,    buff=0      )
									lowerCamelCase					:			str									= Text('CPU',    font_size=24      )
									lowerCamelCase					:			int									= Group(A,    A      ).arrange(A,    buff=0.5,    aligned_edge=A      )
									cpu.move_to([-2.5, -0.5, 0]      )
									self.add(A      )

									lowerCamelCase					:			Optional[int]									= [mem.copy() for i in range(1      )]
									lowerCamelCase					:			Union[str, Any]									= VGroup(*A      ).arrange(A,    buff=0      )
									lowerCamelCase					:			Optional[Any]									= Text('GPU',    font_size=24      )
									lowerCamelCase					:			Tuple									= Group(A,    A      ).arrange(A,    buff=0.5,    aligned_edge=A      )
									gpu.align_to(A,    A      )
									gpu.set_x(gpu.get_x() - 1      )

									self.add(A      )

									lowerCamelCase					:			Optional[int]									= [mem.copy() for i in range(6      )]
									lowerCamelCase					:			Optional[Any]									= VGroup(*A      ).arrange(A,    buff=0      )

									lowerCamelCase					:			Any									= Text('Model',    font_size=24      )
									lowerCamelCase					:			Tuple									= Group(A,    A      ).arrange(A,    buff=0.5,    aligned_edge=A      )
									model.move_to([3, -1.0, 0]      )

									self.play(
									    Create(A,    run_time=1      ),    Create(A,    run_time=1      ),    Create(A,    run_time=1      ),    )

									lowerCamelCase					:			str									= MarkupText(
									    F'''First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.''',    font_size=24,    )

									lowerCamelCase					:			Any									= Square(side_length=2.2      )
									key.move_to([-5, 2, 0]      )

									lowerCamelCase					:			Tuple									= MarkupText(
									    F'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model''',    font_size=18,    )

									key_text.move_to([-5, 2.4, 0]      )

									step_a.move_to([2, 2, 0]      )
									self.play(Write(A,    run_time=2.5      ),    Write(A      ),    Write(A      )      )

									self.add(A      )

									lowerCamelCase					:			str									= []
									lowerCamelCase					:			Optional[int]									= []
									lowerCamelCase					:			Optional[Any]									= []
									for i, rect in enumerate(A      ):
													lowerCamelCase					:			List[str]									= Rectangle(height=0.46,    width=0.46      ).set_stroke(width=0.0      ).set_fill(A,    opacity=0.7      )
													cpu_target.move_to(A      )
													cpu_target.generate_target()
													lowerCamelCase					:			int									= 0.46 / 4
													lowerCamelCase					:			Optional[int]									= 0.46 / 3

													if i == 0:
																	cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT      ),    buff=0.02,    direction=A      )
																	cpu_target.target.set_x(cpu_target.target.get_x() + 0.1      )
													elif i == 3:
																	cpu_target.target.next_to(cpu_targs[0].target,    direction=A,    buff=0.0      )
													else:
																	cpu_target.target.next_to(cpu_targs[i - 1].target,    direction=A,    buff=0.0      )
													cpu_targs.append(A      )

													first_animations.append(rect.animate(run_time=0.5      ).set_stroke(A      )      )
													second_animations.append(MoveToTarget(A,    run_time=1.5      )      )

									self.play(*A      )
									self.play(*A      )

									self.wait()
