



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__   			=   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   			=   {
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''adapter_layer''': '''encoder.layers.*.adapter_layer''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
    '''pooling_layer.linear''': '''projector''',
    '''pooling_layer.projection''': '''classifier''',
}
SCREAMING_SNAKE_CASE__   			=   [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''projector''',
    '''classifier''',
]


def A    (							__UpperCamelCase       )    ->   Optional[int]:
			A__	  =     {}
			with open(__UpperCamelCase			,						'r'       ) as file:
						for line_number, line in enumerate(__UpperCamelCase       ):
									A__	  =     line.strip()
									if line:
												A__	  =     line.split()
												A__	  =     line_number
												A__	  =     words[0]
												A__	  =     value
			return result


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   Tuple:
			for attribute in key.split('.'       ):
						A__	  =     getattr(__UpperCamelCase			,						__UpperCamelCase       )

			A__	  =     None
			for param_key in PARAM_MAPPING.keys():
						if full_name.endswith(__UpperCamelCase       ):
									A__	  =     PARAM_MAPPING[full_name.split('.'       )[-1]]
									A__	  =     'param'

			if weight_type is not None and weight_type != "param":
						A__	  =     getattr(__UpperCamelCase			,						__UpperCamelCase       ).shape
			elif weight_type is not None and weight_type == "param":
						A__	  =     hf_pointer
						for attribute in hf_param_name.split('.'       ):
									A__	  =     getattr(__UpperCamelCase			,						__UpperCamelCase       )
						A__	  =     shape_pointer.shape

						# let's reduce dimension
						A__	  =     value[0]
			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 == "param":
						for attribute in hf_param_name.split('.'       ):
									A__	  =     getattr(__UpperCamelCase			,						__UpperCamelCase       )
						A__	  =     value
			else:
						A__	  =     value

			logger.info(f'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''       )


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   Tuple:
			A__	  =     None
			for param_key in PARAM_MAPPING.keys():
						if full_name.endswith(__UpperCamelCase       ):
									A__	  =     PARAM_MAPPING[full_name.split('.'       )[-1]]
									A__	  =     'param'

			if weight_type is not None and weight_type != "param":
						A__	  =     '.'.join([key, weight_type]       )
			elif weight_type is not None and weight_type == "param":
						A__	  =     '.'.join([key, hf_param_name]       )
			else:
						A__	  =     key

			A__	  =     value if 'lm_head' in full_key else value[0]


SCREAMING_SNAKE_CASE__   			=   {
    '''W_a''': '''linear_1.weight''',
    '''W_b''': '''linear_2.weight''',
    '''b_a''': '''linear_1.bias''',
    '''b_b''': '''linear_2.bias''',
    '''ln_W''': '''norm.weight''',
    '''ln_b''': '''norm.bias''',
}


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase=None			,						__UpperCamelCase=None       )    ->   Union[str, Any]:
			A__	  =     False
			for key, mapped_key in MAPPING.items():
						A__	  =     'wav2vec2.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
						if key in name or key.split('w2v_model.'       )[-1] == name.split('.'       )[0]:
									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:
												# TODO: don't match quantizer.weight_proj
												A__	  =     'weight'
									else:
												A__	  =     None
									if hf_dict is not None:
												rename_dict(__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )
									else:
												set_recursively(__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )
									return is_used
			return is_used


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   List[str]:
			A__	  =     []
			A__	  =     fairseq_model.state_dict()

			A__	  =     hf_model.wavaveca.feature_extractor

			for name, value in fairseq_dict.items():
						A__	  =     False
						if "conv_layers" in name:
									load_conv_layer(
									    __UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						hf_model.config.feat_extract_norm == 'group'			,						)
									A__	  =     True
						else:
									A__	  =     load_wavaveca_layer(__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )
						if not is_used:
									unused_weights.append(__UpperCamelCase       )

			logger.warning(f'''Unused weights: {unused_weights}'''       )


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   Optional[Any]:
			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 A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase=None			,						__UpperCamelCase=None			,						__UpperCamelCase=True			,						__UpperCamelCase=False       )    ->   int:
			if config_path is not None:
						A__	  =     WavaVecaConfig.from_pretrained(__UpperCamelCase       )
			else:
						A__	  =     WavaVecaConfig()

			if is_seq_class:
						A__	  =     read_txt_into_dict(__UpperCamelCase       )
						A__	  =     idalabel
						A__	  =     WavaVecaForSequenceClassification(__UpperCamelCase       )
						A__	  =     WavaVecaFeatureExtractor(
						    feature_size=1			,						sampling_rate=16_000			,						padding_value=0			,						do_normalize=__UpperCamelCase			,						return_attention_mask=__UpperCamelCase			,						)
						feature_extractor.save_pretrained(__UpperCamelCase       )

			elif is_finetuned:
						if dict_path:
									A__	  =     Dictionary.load(__UpperCamelCase       )

									# important change bos & pad token id since CTC symbol is <pad> and
									# not <s> as in fairseq
									A__	  =     target_dict.pad_index
									A__	  =     target_dict.bos_index
									A__	  =     target_dict.eos_index
									A__	  =     len(target_dict.symbols       )
									A__	  =     os.path.join(__UpperCamelCase			,						'vocab.json'       )
									if not os.path.isdir(__UpperCamelCase       ):
												logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(__UpperCamelCase       )       )
												return
									os.makedirs(__UpperCamelCase			,						exist_ok=__UpperCamelCase       )
									A__	  =     target_dict.indices

									# fairseq has the <pad> and <s> switched
									A__	  =     0
									A__	  =     1
									with open(__UpperCamelCase			,						'w'			,						encoding='utf-8'       ) as vocab_handle:
												json.dump(__UpperCamelCase			,						__UpperCamelCase       )
									A__	  =     WavaVecaCTCTokenizer(
									    __UpperCamelCase			,						unk_token=target_dict.unk_word			,						pad_token=target_dict.pad_word			,						bos_token=target_dict.bos_word			,						eos_token=target_dict.eos_word			,						word_delimiter_token='|'			,						do_lower_case=__UpperCamelCase			,						)
									A__	  =     True if config.feat_extract_norm == 'layer' else False
									A__	  =     WavaVecaFeatureExtractor(
									    feature_size=1			,						sampling_rate=16_000			,						padding_value=0			,						do_normalize=__UpperCamelCase			,						return_attention_mask=__UpperCamelCase			,						)
									A__	  =     WavaVecaProcessor(feature_extractor=__UpperCamelCase			,						tokenizer=__UpperCamelCase       )
									processor.save_pretrained(__UpperCamelCase       )

						A__	  =     WavaVecaForCTC(__UpperCamelCase       )
			else:
						A__	  =     WavaVecaForPreTraining(__UpperCamelCase       )

			if is_finetuned or is_seq_class:
						A__  ,			A__  ,			A__	  =     fairseq.checkpoint_utils.load_model_ensemble_and_task(
						    [checkpoint_path]			,						arg_overrides={'data': '/'.join(dict_path.split('/'       )[:-1]       )}       )
			else:
						A__	  =     argparse.Namespace(task='audio_pretraining'       )
						A__	  =     fairseq.tasks.setup_task(__UpperCamelCase       )

						A__  ,			A__  ,			A__	  =     fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]			,						task=__UpperCamelCase       )

			A__	  =     model[0].eval()

			recursively_load_weights(__UpperCamelCase			,						__UpperCamelCase			,						not is_finetuned       )

			hf_wavavec.save_pretrained(__UpperCamelCase       )


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__   			=   argparse.ArgumentParser()
							parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
							parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
							parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
							parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
							parser.add_argument(
							    '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
							)
							parser.add_argument(
							    '''--is_seq_class''',
							    action='''store_true''',
							    help='''Whether the model to convert is a fine-tuned sequence classification model or not''',
							)
							SCREAMING_SNAKE_CASE__   			=   parser.parse_args()

							SCREAMING_SNAKE_CASE__   			=   not args.not_finetuned and not args.is_seq_class
							convert_wavaveca_checkpoint(
							    args.checkpoint_path,
							    args.pytorch_dump_folder_path,
							    args.config_path,
							    args.dict_path,
							    is_finetuned,
							    args.is_seq_class,
							)










"""simple docstring"""

from manim import *





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''

 def      lowerCAmelCase_				(					self			:			Union[str, Any]						):
       _A     =  Rectangle(height=0.5       ,				width=0.5						)
       _A     =  Rectangle(height=0.46       ,				width=0.46						).set_stroke(width=0						)

       _A     =  Rectangle(height=0.25       ,				width=0.25						)

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

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

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

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

       _A     =  []
       _A     =  []

       for i, rect in enumerate(_UpperCAmelCase						):
             _A     =  fill.copy().set_fill(_UpperCAmelCase       ,				opacity=0.8						)
             target.move_to(_UpperCAmelCase						)
             model_arr.append(_UpperCAmelCase						)

             _A     =  Rectangle(height=0.46       ,				width=0.46						).set_stroke(width=0.0						).set_fill(_UpperCAmelCase       ,				opacity=0.8						)
             cpu_target.move_to(cpu_left_col_base[i]						)
             model_cpu_arr.append(_UpperCAmelCase						)

       self.add(*_UpperCAmelCase       ,				*_UpperCAmelCase						)

       _A     =  [meta_mem.copy() for i in range(6						)]
       _A     =  [meta_mem.copy() for i in range(6						)]
       _A     =  VGroup(*_UpperCAmelCase						).arrange(_UpperCAmelCase       ,				buff=0						)
       _A     =  VGroup(*_UpperCAmelCase						).arrange(_UpperCAmelCase       ,				buff=0						)
       _A     =  VGroup(_UpperCAmelCase       ,				_UpperCAmelCase						).arrange(_UpperCAmelCase       ,				buff=0						)
       _A     =  Text('Disk'       ,				font_size=24						)
       _A     =  Group(_UpperCAmelCase       ,				_UpperCAmelCase						).arrange(_UpperCAmelCase       ,				buff=0.5       ,				aligned_edge=_UpperCAmelCase						)
       disk.move_to([-4, -1.25, 0]						)
       self.add(_UpperCAmelCase       ,				_UpperCAmelCase						)

       _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(_UpperCAmelCase       ,				_UpperCAmelCase						)

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

       blue_text.next_to(_UpperCAmelCase       ,				DOWN * 2.4       ,				aligned_edge=key_text.get_left()						)
       self.add(_UpperCAmelCase						)

       _A     =  MarkupText(
           F'''Now watch as an input is passed through the model\nand how the memory is utilized and handled.'''       ,				font_size=24       ,				)
       step_a.move_to([2, 2, 0]						)

       self.play(Write(_UpperCAmelCase						)						)

       _A     =  Square(0.3						)
       input.set_fill(_UpperCAmelCase       ,				opacity=1.0						)
       input.set_stroke(width=0.0						)
       input.next_to(model_base[0]       ,				_UpperCAmelCase       ,				buff=0.5						)

       self.play(Write(_UpperCAmelCase						)						)

       input.generate_target()
       input.target.next_to(model_arr[0]       ,				direction=_UpperCAmelCase       ,				buff=0.02						)
       self.play(MoveToTarget(_UpperCAmelCase						)						)

       self.play(FadeOut(_UpperCAmelCase						)						)

       _A     =  Arrow(start=_UpperCAmelCase       ,				end=_UpperCAmelCase       ,				color=_UpperCAmelCase       ,				buff=0.5						)
       a.next_to(model_arr[0].get_left()       ,				_UpperCAmelCase       ,				buff=0.2						)

       model_cpu_arr[0].generate_target()
       model_cpu_arr[0].target.move_to(gpu_rect[0]						)

       _A     =  MarkupText(
           F'''As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back.'''       ,				font_size=24       ,				)
       step_a.move_to([2, 2, 0]						)

       self.play(Write(_UpperCAmelCase       ,				run_time=3						)						)

       _A     =  {'run_time': 1, 'fade_in': True, 'fade_out': True, 'buff': 0.02}

       self.play(
           Write(_UpperCAmelCase						)       ,				Circumscribe(model_arr[0]       ,				color=_UpperCAmelCase       ,				**_UpperCAmelCase						)       ,				Circumscribe(model_cpu_arr[0]       ,				color=_UpperCAmelCase       ,				**_UpperCAmelCase						)       ,				Circumscribe(gpu_rect[0]       ,				color=_UpperCAmelCase       ,				**_UpperCAmelCase						)       ,				)
       self.play(MoveToTarget(model_cpu_arr[0]						)						)

       _A     =  a.copy()
       for i in range(6						):
             a_c.next_to(model_arr[i].get_right() + 0.02       ,				_UpperCAmelCase       ,				buff=0.2						)

             input.generate_target()
             input.target.move_to(model_arr[i].get_right() + 0.02						)

             _A     =  AnimationGroup(
                 FadeOut(_UpperCAmelCase       ,				run_time=0.5						)       ,				MoveToTarget(_UpperCAmelCase       ,				run_time=0.5						)       ,				FadeIn(_UpperCAmelCase       ,				run_time=0.5						)       ,				lag_ratio=0.2						)

             self.play(_UpperCAmelCase						)

             model_cpu_arr[i].generate_target()
             model_cpu_arr[i].target.move_to(cpu_left_col_base[i]						)

             if i < 5:
                   model_cpu_arr[i + 1].generate_target()
                   model_cpu_arr[i + 1].target.move_to(gpu_rect[0]						)
                   if i >= 1:
                         _A     =  0.7

                   self.play(
                       Circumscribe(model_arr[i]       ,				**_UpperCAmelCase						)       ,				Circumscribe(cpu_left_col_base[i]       ,				**_UpperCAmelCase						)       ,				Circumscribe(cpu_left_col_base[i + 1]       ,				color=_UpperCAmelCase       ,				**_UpperCAmelCase						)       ,				Circumscribe(gpu_rect[0]       ,				color=_UpperCAmelCase       ,				**_UpperCAmelCase						)       ,				Circumscribe(model_arr[i + 1]       ,				color=_UpperCAmelCase       ,				**_UpperCAmelCase						)       ,				)
                   if i < 1:
                         self.play(
                             MoveToTarget(model_cpu_arr[i]						)       ,				MoveToTarget(model_cpu_arr[i + 1]						)       ,				)
                   else:
                         self.play(
                             MoveToTarget(model_cpu_arr[i]       ,				run_time=0.7						)       ,				MoveToTarget(model_cpu_arr[i + 1]       ,				run_time=0.7						)       ,				)
             else:
                   model_cpu_arr[i].generate_target()
                   model_cpu_arr[i].target.move_to(cpu_left_col_base[-1]						)
                   input.generate_target()
                   input.target.next_to(model_arr[-1].get_right()       ,				RIGHT + 0.02       ,				buff=0.2						)

                   self.play(
                       Circumscribe(model_arr[-1]       ,				color=_UpperCAmelCase       ,				**_UpperCAmelCase						)       ,				Circumscribe(cpu_left_col_base[-1]       ,				color=_UpperCAmelCase       ,				**_UpperCAmelCase						)       ,				Circumscribe(gpu_rect[0]       ,				color=_UpperCAmelCase       ,				**_UpperCAmelCase						)       ,				)

                   self.play(MoveToTarget(model_cpu_arr[i]						)						)

             _A     =  a_c
             _A     =  a_c.copy()

       input.generate_target()
       input.target.next_to(model_base[-1]       ,				RIGHT + 0.02       ,				buff=0.5						)
       self.play(
           FadeOut(_UpperCAmelCase						)       ,				FadeOut(_UpperCAmelCase       ,				run_time=0.5						)       ,				)

       _A     =  MarkupText(F'''Inference on a model too large for GPU memory\nis successfully completed.'''       ,				font_size=24						)
       step_a.move_to([2, 2, 0]						)

       self.play(Write(_UpperCAmelCase       ,				run_time=3						)       ,				MoveToTarget(_UpperCAmelCase						)						)

       self.wait()

