

'''simple docstring'''


import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
_snake_case   :   Union[str, Any]     							=	logging.get_logger('transformers.models.speecht5')

_snake_case   :   Dict     							=	{
    'speech_encoder_prenet.layer_norm': 'speecht5.encoder.prenet.feature_projection.layer_norm',
    'speech_encoder_prenet.post_extract_proj': 'speecht5.encoder.prenet.feature_projection.projection',
    'speech_encoder_prenet.pos_conv.0': 'speecht5.encoder.prenet.pos_conv_embed.conv',
    'speech_encoder_prenet.mask_emb': 'speecht5.encoder.prenet.masked_spec_embed',
}
_snake_case   :   Any     							=	{
    'text_encoder_prenet.encoder_prenet.0': 'speecht5.encoder.prenet.embed_tokens',
    'text_encoder_prenet.encoder_prenet.1.alpha': 'speecht5.encoder.prenet.encode_positions.alpha',
}
_snake_case   :   List[Any]     							=	{
    'speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0': 'speecht5.decoder.prenet.layers.0',
    'speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0': 'speecht5.decoder.prenet.layers.1',
    'speech_decoder_prenet.decoder_prenet.0.1': 'speecht5.decoder.prenet.final_layer',
    'speech_decoder_prenet.decoder_prenet.1.alpha': 'speecht5.decoder.prenet.encode_positions.alpha',
    'speech_decoder_prenet.spkembs_layer.0': 'speecht5.decoder.prenet.speaker_embeds_layer',
}
_snake_case   :   Union[str, Any]     							=	{
    'speech_decoder_postnet.feat_out': 'speech_decoder_postnet.feat_out',
    'speech_decoder_postnet.prob_out': 'speech_decoder_postnet.prob_out',
    'speech_decoder_postnet.postnet.postnet.0.0': 'speech_decoder_postnet.layers.0.conv',
    'speech_decoder_postnet.postnet.postnet.0.1': 'speech_decoder_postnet.layers.0.batch_norm',
    'speech_decoder_postnet.postnet.postnet.1.0': 'speech_decoder_postnet.layers.1.conv',
    'speech_decoder_postnet.postnet.postnet.1.1': 'speech_decoder_postnet.layers.1.batch_norm',
    'speech_decoder_postnet.postnet.postnet.2.0': 'speech_decoder_postnet.layers.2.conv',
    'speech_decoder_postnet.postnet.postnet.2.1': 'speech_decoder_postnet.layers.2.batch_norm',
    'speech_decoder_postnet.postnet.postnet.3.0': 'speech_decoder_postnet.layers.3.conv',
    'speech_decoder_postnet.postnet.postnet.3.1': 'speech_decoder_postnet.layers.3.batch_norm',
    'speech_decoder_postnet.postnet.postnet.4.0': 'speech_decoder_postnet.layers.4.conv',
    'speech_decoder_postnet.postnet.postnet.4.1': 'speech_decoder_postnet.layers.4.batch_norm',
}
_snake_case   :   str     							=	{
    'text_decoder_prenet.embed_tokens': 'speecht5.decoder.prenet.embed_tokens',
}
_snake_case   :   Tuple     							=	{
    'text_decoder_postnet.output_projection': 'text_decoder_postnet.lm_head',
}
_snake_case   :   int     							=	{
    'encoder.layers.*.self_attn.k_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj',
    'encoder.layers.*.self_attn.v_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj',
    'encoder.layers.*.self_attn.q_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj',
    'encoder.layers.*.self_attn.out_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj',
    'encoder.layers.*.self_attn_layer_norm': 'speecht5.encoder.wrapped_encoder.layers.*.layer_norm',
    'encoder.layers.*.fc1': 'speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense',
    'encoder.layers.*.fc2': 'speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense',
    'encoder.layers.*.final_layer_norm': 'speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'speecht5.encoder.wrapped_encoder.layer_norm',
    'encoder.pos_emb.pe_k': 'speecht5.encoder.wrapped_encoder.embed_positions.pe_k',
}
_snake_case   :   Optional[int]     							=	{
    'decoder.layers.*.self_attn.k_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj',
    'decoder.layers.*.self_attn.v_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj',
    'decoder.layers.*.self_attn.q_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj',
    'decoder.layers.*.self_attn.out_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj',
    'decoder.layers.*.self_attn_layer_norm': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm',
    'decoder.layers.*.encoder_attn.k_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj',
    'decoder.layers.*.encoder_attn.v_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj',
    'decoder.layers.*.encoder_attn.q_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj',
    'decoder.layers.*.encoder_attn.out_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj',
    'decoder.layers.*.encoder_attn_layer_norm': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm',
    'decoder.layers.*.fc1': 'speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense',
    'decoder.layers.*.fc2': 'speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense',
    'decoder.layers.*.final_layer_norm': 'speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm',
}
_snake_case   :   Tuple     							=	{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
_snake_case   :   Any     							=	{
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
_snake_case   :   str     							=	{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
_snake_case   :   List[Any]     							=	[]
_snake_case   :   Optional[int]     							=	[
    'encoder.version',
    'encoder.layers.*.norm_k.weight',
    'encoder.layers.*.norm_k.bias',
    'decoder.version',
    'decoder.layers.*.norm_k.weight',
    'decoder.layers.*.norm_k.bias',
    'decoder.pos_emb.pe_k',
    'speech_encoder_prenet.embed_positions._float_tensor',
    'text_decoder_prenet.embed_positions._float_tensor',
]
_snake_case   :   Optional[int]     							=	IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'speech_decoder_prenet.*',
    'speech_decoder_postnet.*',
]
_snake_case   :   List[str]     							=	IGNORE_KEYS + [
    'encoder.proj',
    'speech_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]
_snake_case   :   Optional[int]     							=	IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]
def 	snake_case_					(UpperCamelCase :						List[str]				,		UpperCamelCase :						List[str]				,		UpperCamelCase :						List[Any]				,		UpperCamelCase :						Optional[int]				,		UpperCamelCase :						List[Any]   ):





							'''simple docstring'''


							for attribute in key.split('''.'''   ):
														_a			  =       getattr(UpperCAmelCase__				,		UpperCAmelCase__   )

							if weight_type is not None:
														_a			  =       getattr(UpperCAmelCase__				,		UpperCAmelCase__   ).shape
							else:
														_a			  =       hf_pointer.shape

							if hf_shape != value.shape:
														raise ValueError(
														    f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
														    f' {value.shape} for {full_name}'   )

							if weight_type == "weight":
														_a			  =       value
							elif weight_type == "weight_g":
														_a			  =       value
							elif weight_type == "weight_v":
														_a			  =       value
							elif weight_type == "bias":
														_a			  =       value
							elif weight_type == "running_mean":
														_a			  =       value
							elif weight_type == "running_var":
														_a			  =       value
							elif weight_type == "num_batches_tracked":
														_a			  =       value
							else:
														_a			  =       value

							logger.info(f'{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.'   )
def 	snake_case_					(UpperCamelCase :						Optional[int]				,		UpperCamelCase :						str   ):





							'''simple docstring'''


							for key in ignore_keys:
														if key.endswith('''.*'''   ):
																					if name.startswith(key[:-1]   ):
																												return True
														elif ".*." in key:
																					_a  ,							_a			  =       key.split('''.*.'''   )
																					if prefix in name and suffix in name:
																												return True
														elif key in name:
																					return True
							return False
def 	snake_case_					(UpperCamelCase :						Optional[int]				,		UpperCamelCase :						List[Any]				,		UpperCamelCase :						str   ):





							'''simple docstring'''


							_a			  =       []

							if task == "s2t":
														_a			  =       hf_model.speechta.encoder.prenet.feature_encoder
														_a			  =       MAPPING_S2T
														_a			  =       IGNORE_KEYS_S2T
							elif task == "t2s":
														_a			  =       None
														_a			  =       MAPPING_T2S
														_a			  =       IGNORE_KEYS_T2S
							elif task == "s2s":
														_a			  =       hf_model.speechta.encoder.prenet.feature_encoder
														_a			  =       MAPPING_S2S
														_a			  =       IGNORE_KEYS_S2S
							else:
														raise ValueError(f'Unsupported task: {task}'   )

							for name, value in fairseq_dict.items():
														if should_ignore(UpperCAmelCase__				,		UpperCAmelCase__   ):
																					logger.info(f'{name} was ignored'   )
																					continue

														_a			  =       False
														if "conv_layers" in name:
																					load_conv_layer(
																					    UpperCAmelCase__				,		UpperCAmelCase__				,		UpperCAmelCase__				,		UpperCAmelCase__				,		hf_model.config.feat_extract_norm == '''group'''				,		)
																					_a			  =       True
														else:
																					for key, mapped_key in MAPPING.items():
																												# mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

																												if "*" in key:
																																			_a  ,							_a			  =       key.split('''.*.'''   )
																																			if prefix in name and suffix in name:
																																										_a			  =       suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
																												if key in name:
																																			_a			  =       True
																																			if "*" in mapped_key:
																																										_a			  =       name.split(UpperCAmelCase__   )[0].split('''.'''   )[-2]
																																										_a			  =       mapped_key.replace('''*'''				,		UpperCAmelCase__   )
																																			if "weight_g" in name:
																																										_a			  =       '''weight_g'''
																																			elif "weight_v" in name:
																																										_a			  =       '''weight_v'''
																																			elif "bias" in name:
																																										_a			  =       '''bias'''
																																			elif "weight" in name:
																																										_a			  =       '''weight'''
																																			elif "running_mean" in name:
																																										_a			  =       '''running_mean'''
																																			elif "running_var" in name:
																																										_a			  =       '''running_var'''
																																			elif "num_batches_tracked" in name:
																																										_a			  =       '''num_batches_tracked'''
																																			else:
																																										_a			  =       None
																																			set_recursively(UpperCAmelCase__				,		UpperCAmelCase__				,		UpperCAmelCase__				,		UpperCAmelCase__				,		UpperCAmelCase__   )
																												continue
														if not is_used:
																					unused_weights.append(UpperCAmelCase__   )

							logger.warning(f'Unused weights: {unused_weights}'   )
def 	snake_case_					(UpperCamelCase :						int				,		UpperCamelCase :						int				,		UpperCamelCase :						int				,		UpperCamelCase :						Union[str, Any]				,		UpperCamelCase :						Union[str, Any]   ):





							'''simple docstring'''


							_a			  =       full_name.split('''conv_layers.'''   )[-1]
							_a			  =       name.split('''.'''   )
							_a			  =       int(items[0]   )
							_a			  =       int(items[1]   )

							if type_id == 0:
														if "bias" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																												raise ValueError(
																												    f'{full_name} has size {value.shape}, but'
																												    f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'   )
																					_a			  =       value
																					logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'   )
														elif "weight" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																												raise ValueError(
																												    f'{full_name} has size {value.shape}, but'
																												    f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'   )
																					_a			  =       value
																					logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'   )
							elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
														if "bias" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																												raise ValueError(
																												    f'{full_name} has size {value.shape}, but'
																												    f' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'   )
																					_a			  =       value
																					logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'   )
														elif "weight" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																												raise ValueError(
																												    f'{full_name} has size {value.shape}, but'
																												    f' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'   )
																					_a			  =       value
																					logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'   )
							else:
														unused_weights.append(UpperCAmelCase__   )
@torch.no_grad()
def 	snake_case_					(UpperCamelCase :						Optional[int]				,		UpperCamelCase :						str				,		UpperCamelCase :						Tuple				,		UpperCamelCase :						int=None				,		UpperCamelCase :						str=None				,		UpperCamelCase :						Tuple=None				,		):





							'''simple docstring'''


							if config_path is not None:
														_a			  =       SpeechTaConfig.from_pretrained(UpperCAmelCase__   )
							else:
														_a			  =       SpeechTaConfig()

							if task == "s2t":
														_a			  =       config.max_text_positions
														_a			  =       SpeechTaForSpeechToText(UpperCAmelCase__   )
							elif task == "t2s":
														_a			  =       1876
														_a			  =       600
														_a			  =       config.max_speech_positions
														_a			  =       SpeechTaForTextToSpeech(UpperCAmelCase__   )
							elif task == "s2s":
														_a			  =       1876
														_a			  =       config.max_speech_positions
														_a			  =       SpeechTaForSpeechToSpeech(UpperCAmelCase__   )
							else:
														raise ValueError(f'Unknown task name: {task}'   )

							if vocab_path:
														_a			  =       SpeechTaTokenizer(UpperCAmelCase__				,		model_max_length=config.max_text_positions   )

														# Mask token behaves like a normal word, i.e. include the space before it
														_a			  =       AddedToken('''<mask>'''				,		lstrip=UpperCAmelCase__				,		rstrip=UpperCAmelCase__   )
														_a			  =       mask_token
														tokenizer.add_special_tokens({'''mask_token''': mask_token}   )
														tokenizer.add_tokens(['''<ctc_blank>''']   )

							_a			  =       SpeechTaFeatureExtractor()
							_a			  =       SpeechTaProcessor(tokenizer=UpperCAmelCase__				,		feature_extractor=UpperCAmelCase__   )
							processor.save_pretrained(UpperCAmelCase__   )

							_a			  =       torch.load(UpperCAmelCase__   )
							recursively_load_weights(fairseq_checkpoint['''model''']				,		UpperCAmelCase__				,		UpperCAmelCase__   )

							model.save_pretrained(UpperCAmelCase__   )

							if repo_id:
														print('''Pushing to the hub...'''   )
														processor.push_to_hub(UpperCAmelCase__   )
														model.push_to_hub(UpperCAmelCase__   )


if __name__ == "__main__":
				_snake_case   :   Any     							=	argparse.ArgumentParser()
				parser.add_argument(
				    '--task',
				    default='s2t',
				    type=str,
				    help='Type of the SpeechT5 model you\'d like to convert. Should be one of \'s2t\', \'t2s\', \'s2s\'.',
				)
				parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to fairseq checkpoint')
				parser.add_argument('--vocab_path', default=None, type=str, help='Path to SentencePiece model')
				parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
				parser.add_argument(
				    '--pytorch_dump_folder_path', required=True, default=None, type=str, help='Path to the output PyTorch model.'
				)
				parser.add_argument(
				    '--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
				)

				_snake_case   :   Dict     							=	parser.parse_args()
				convert_speechta_checkpoint(
				    args.task,
				    args.checkpoint_path,
				    args.pytorch_dump_folder_path,
				    args.config_path,
				    args.vocab_path,
				    args.push_to_hub,
				)







'''simple docstring'''


from manim import *

class     A					(	_a					):







							def      __lowerCAmelCase    (						self :						Tuple      )    ->							Dict:


														"""simple docstring"""


														_a			  =       Rectangle(height=0.5							,    width=0.5      )
														_a			  =       Rectangle(height=0.4_6							,    width=0.4_6      ).set_stroke(width=0      )

														_a			  =       [mem.copy() for i in range(6      )]
														_a			  =       [mem.copy() for i in range(6      )]
														_a			  =       VGroup(*lowerCAmelCase_      ).arrange(lowerCAmelCase_							,    buff=0      )
														_a			  =       VGroup(*lowerCAmelCase_      ).arrange(lowerCAmelCase_							,    buff=0      )
														_a			  =       VGroup(lowerCAmelCase_							,    lowerCAmelCase_      ).arrange(lowerCAmelCase_							,    buff=0      )
														_a			  =       Text('''CPU'''							,    font_size=24      )
														_a			  =       Group(lowerCAmelCase_							,    lowerCAmelCase_      ).arrange(lowerCAmelCase_							,    buff=0.5							,    aligned_edge=lowerCAmelCase_      )
														cpu.move_to([-2.5, -0.5, 0]      )
														self.add(lowerCAmelCase_      )

														_a			  =       [mem.copy() for i in range(4      )]
														_a			  =       VGroup(*lowerCAmelCase_      ).arrange(lowerCAmelCase_							,    buff=0      )
														_a			  =       Text('''GPU'''							,    font_size=24      )
														_a			  =       Group(lowerCAmelCase_							,    lowerCAmelCase_      ).arrange(lowerCAmelCase_							,    buff=0.5							,    aligned_edge=lowerCAmelCase_      )
														gpu.move_to([-1, -1, 0]      )
														self.add(lowerCAmelCase_      )

														_a			  =       [mem.copy() for i in range(6      )]
														_a			  =       VGroup(*lowerCAmelCase_      ).arrange(lowerCAmelCase_							,    buff=0      )

														_a			  =       Text('''Model'''							,    font_size=24      )
														_a			  =       Group(lowerCAmelCase_							,    lowerCAmelCase_      ).arrange(lowerCAmelCase_							,    buff=0.5							,    aligned_edge=lowerCAmelCase_      )
														model.move_to([3, -1.0, 0]      )
														self.add(lowerCAmelCase_      )

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

																					_a			  =       Rectangle(height=0.4_6 / 4							,    width=0.4_6 / 3      ).set_stroke(width=0.0      ).set_fill(lowerCAmelCase_							,    opacity=0.7      )

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

														_a			  =       [mem.copy() for i in range(6      )]
														_a			  =       VGroup(*lowerCAmelCase_      ).arrange(lowerCAmelCase_							,    buff=0      )

														_a			  =       Text('''Loaded Checkpoint'''							,    font_size=24      )
														_a			  =       Group(lowerCAmelCase_							,    lowerCAmelCase_      ).arrange(lowerCAmelCase_							,    aligned_edge=lowerCAmelCase_							,    buff=0.4      )
														checkpoint.move_to([3, 0.5, 0]      )

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

														_a			  =       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(lowerCAmelCase_							,    lowerCAmelCase_      )

														_a			  =       MarkupText(
														    F'<span fgcolor=\'{BLUE}\'>●</span> Checkpoint'							,    font_size=18							,    )

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

														_a			  =       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(lowerCAmelCase_      )							,    Write(lowerCAmelCase_      )      )

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

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

																					_a			  =       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(lowerCAmelCase_							,    run_time=1.5      )      )

														self.play(*lowerCAmelCase_      )
														self.play(*lowerCAmelCase_      )
														self.wait()

