

from manim import *






class      lowerCamelCase_      (	_lowercase	):




	def        lowerCAmelCase_       (     self						:     str							):
		__A :			Optional[Any]		    =			Rectangle(height=0.5					,      width=0.5							)
		__A :			Any		    =			Rectangle(height=0.4_6					,      width=0.4_6							).set_stroke(width=0							)

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

		__A :			List[str]		    =			[mem.copy() for i in range(4							)]
		__A :			List[str]		    =			VGroup(*__A							).arrange(__A					,      buff=0							)
		__A :			List[Any]		    =			Text("""GPU"""					,      font_size=24							)
		__A :			Optional[int]		    =			Group(__A					,      __A							).arrange(__A					,      buff=0.5					,      aligned_edge=__A							)
		gpu.move_to([-1, -1, 0]							)
		self.add(__A							)

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

		__A :			Tuple		    =			Text("""Model"""					,      font_size=24							)
		__A :			Dict		    =			Group(__A					,      __A							).arrange(__A					,      buff=0.5					,      aligned_edge=__A							)
		model.move_to([3, -1.0, 0]							)
		self.add(__A							)

		__A :			Optional[int]		    =			[]
		for i, rect in enumerate(__A							):
			rect.set_stroke(__A							)
			# target = fill.copy().set_fill(YELLOW, opacity=0.7)
			# target.move_to(rect)
			# self.add(target)

			__A :			Tuple		    =			Rectangle(height=0.4_6 / 4					,      width=0.4_6 / 3							).set_stroke(width=0.0							).set_fill(__A					,      opacity=0.7							)

			if i == 0:
				cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT							)					,      buff=0.0_2					,      direction=__A							)
				cpu_target.set_x(cpu_target.get_x() + 0.1							)
			elif i == 3:
				cpu_target.next_to(cpu_targs[0]					,      direction=__A					,      buff=0.0							)
			else:
				cpu_target.next_to(cpu_targs[i - 1]					,      direction=__A					,      buff=0.0							)
			self.add(__A							)
			cpu_targs.append(__A							)

		__A :			Union[str, Any]		    =			[mem.copy() for i in range(6							)]
		__A :			Optional[Any]		    =			VGroup(*__A							).arrange(__A					,      buff=0							)

		__A :			Union[str, Any]		    =			Text("""Loaded Checkpoint"""					,      font_size=24							)
		__A :			Union[str, Any]		    =			Group(__A					,      __A							).arrange(__A					,      aligned_edge=__A					,      buff=0.4							)
		checkpoint.move_to([3, 0.5, 0]							)

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

		__A :			List[Any]		    =			MarkupText(
		    F"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model"""					,      font_size=18					,      )

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

		self.add(__A					,      __A							)

		__A :			Optional[Any]		    =			MarkupText(
		    F"""<span fgcolor='{BLUE}'>●</span> Checkpoint"""					,      font_size=18					,      )

		blue_text.next_to(__A					,      DOWN * 2.4					,      aligned_edge=key_text.get_left()							)

		__A :			int		    =			MarkupText(
		    F"""Next, a <i><span fgcolor=\"{BLUE}\">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor=\"{BLUE}\">single shard</span>."""					,      font_size=24					,      )
		step_a.move_to([2, 2, 0]							)
		self.play(Write(__A							)					,      Write(__A							)							)

		self.play(Write(__A					,      run_time=1							)					,      Create(__A					,      run_time=1							)							)

		__A :			str		    =			[]
		__A :			Union[str, Any]		    =			[]
		for i, rect in enumerate(__A							):
			__A :			Any		    =			fill.copy().set_fill(__A					,      opacity=0.7							)
			target.move_to(__A							)
			first_animations.append(GrowFromCenter(__A					,      run_time=1							)							)

			__A :			str		    =			target.copy()
			cpu_target.generate_target()
			if i < 5:
				cpu_target.target.move_to(cpu_left_col_base[i + 1]							)
			else:
				cpu_target.target.move_to(cpu_right_col_base[i - 5]							)
			second_animations.append(MoveToTarget(__A					,      run_time=1.5							)							)

		self.play(*__A							)
		self.play(*__A							)
		self.wait()








import functools
import operator

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


UpperCAmelCase_							:					List[str]		 =   logging.get_logger(__name__)

UpperCAmelCase_							:					str		 =   {
    '''microsoft/unispeech-large-1500h-cv''': (
        '''https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json'''
    ),
    # See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}






class      lowerCamelCase_      (	_lowercase	):
	_lowercase		:   Dict     					=				'''unispeech'''




	def __init__(     self						:     str					,      __A						:     Tuple=32					,      __A						:     List[str]=768					,      __A						:     Dict=12					,      __A						:     Union[str, Any]=12					,      __A						:     Tuple=3072					,      __A						:     Any="gelu"					,      __A						:     int=0.1					,      __A						:     Optional[int]=0.1					,      __A						:     List[Any]=0.1					,      __A						:     Any=0.0					,      __A						:     List[str]=0.0					,      __A						:     int=0.1					,      __A						:     List[Any]=0.1					,      __A						:     List[str]=0.0_2					,      __A						:     List[str]=1e-5					,      __A						:     List[Any]="group"					,      __A						:     int="gelu"					,      __A						:     Any=(512, 512, 512, 512, 512, 512, 512)					,      __A						:     Union[str, Any]=(5, 2, 2, 2, 2, 2, 2)					,      __A						:     Tuple=(10, 3, 3, 3, 3, 2, 2)					,      __A						:     Optional[int]=False					,      __A						:     Any=128					,      __A						:     Union[str, Any]=16					,      __A						:     Optional[Any]=False					,      __A						:     str=True					,      __A						:     Dict=0.0_5					,      __A						:     Optional[Any]=10					,      __A						:     Dict=2					,      __A						:     int=0.0					,      __A						:     List[str]=10					,      __A						:     str=0					,      __A						:     List[str]=320					,      __A						:     List[Any]=2					,      __A						:     Tuple=0.1					,      __A						:     Optional[int]=100					,      __A						:     Any=256					,      __A						:     Dict=256					,      __A						:     Tuple=0.1					,      __A						:     List[str]="mean"					,      __A						:     int=False					,      __A						:     List[str]=False					,      __A						:     List[Any]=256					,      __A						:     str=80					,      __A						:     Tuple=0					,      __A						:     Tuple=1					,      __A						:     int=2					,      __A						:     Dict=0.5					,      **__A						:     List[Any]					,      ):
		super().__init__(**__A					,      pad_token_id=__A					,      bos_token_id=__A					,      eos_token_id=__A							)
		__A :			Dict		    =			hidden_size
		__A :			Optional[Any]		    =			feat_extract_norm
		__A :			List[Any]		    =			feat_extract_activation
		__A :			str		    =			list(__A							)
		__A :			Optional[Any]		    =			list(__A							)
		__A :			Optional[int]		    =			list(__A							)
		__A :			List[Any]		    =			conv_bias
		__A :			Optional[int]		    =			num_conv_pos_embeddings
		__A :			List[Any]		    =			num_conv_pos_embedding_groups
		__A :			int		    =			len(self.conv_dim							)
		__A :			Optional[Any]		    =			num_hidden_layers
		__A :			List[str]		    =			intermediate_size
		__A :			Union[str, Any]		    =			hidden_act
		__A :			Optional[int]		    =			num_attention_heads
		__A :			Tuple		    =			hidden_dropout
		__A :			Optional[Any]		    =			attention_dropout
		__A :			Union[str, Any]		    =			activation_dropout
		__A :			Dict		    =			feat_proj_dropout
		__A :			Optional[int]		    =			final_dropout
		__A :			Dict		    =			layerdrop
		__A :			Optional[int]		    =			layer_norm_eps
		__A :			Optional[Any]		    =			initializer_range
		__A :			Optional[int]		    =			num_ctc_classes
		__A :			Dict		    =			vocab_size
		__A :			List[str]		    =			do_stable_layer_norm
		__A :			Tuple		    =			use_weighted_layer_sum
		__A :			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 :			Tuple		    =			apply_spec_augment
		__A :			Union[str, Any]		    =			mask_time_prob
		__A :			Optional[Any]		    =			mask_time_length
		__A :			List[Any]		    =			mask_time_min_masks
		__A :			List[Any]		    =			mask_feature_prob
		__A :			Any		    =			mask_feature_length
		__A :			List[Any]		    =			mask_feature_min_masks

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

		# ctc loss
		__A :			List[str]		    =			ctc_loss_reduction
		__A :			Any		    =			ctc_zero_infinity

		# pretraining loss
		__A :			Union[str, Any]		    =			replace_prob




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





