






import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class      UpperCamelCase (				snake_case__ ):







		"""simple docstring"""



		snake_case    =   None
		snake_case    =   None
		@property
		def       A(  self		:       Union[str, Any]		)    ->   Any:


						'''simple docstring'''
						return self.feat_extract_tester.prepare_feat_extract_dict()
		def       A(  self		:       str		)    ->   Union[str, Any]:


						'''simple docstring'''
						A	 =      self.feature_extraction_class(**self.feat_extract_dict		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE						,'feature_size'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE						,'sampling_rate'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE						,'padding_value'		)		)
		def       A(  self		:       Dict		)    ->   Dict:


						'''simple docstring'''
						A	 =      self.feat_extract_tester.prepare_inputs_for_common()
						A	 =      self.feature_extraction_class(**self.feat_extract_dict		)
						A	 =      feat_extract.model_input_names[0]

						A	 =      BatchFeature({input_name: speech_inputs}		)

						self.assertTrue(all(len(_SCREAMING_SNAKE_CASE		) == len(_SCREAMING_SNAKE_CASE		) for x, y in zip(_SCREAMING_SNAKE_CASE						,processed_features[input_name]		)		)		)

						A	 =      self.feat_extract_tester.prepare_inputs_for_common(equal_length=_SCREAMING_SNAKE_CASE		)
						A	 =      BatchFeature({input_name: speech_inputs}						,tensor_type='np'		)

						A	 =      processed_features[input_name]

						if len(batch_features_input.shape		) < 3:
										A	 =      batch_features_input[:, :, None]

						self.assertTrue(
						    batch_features_input.shape
						    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]		), self.feat_extract_tester.feature_size)		)
		@require_torch
		def       A(  self		:       Optional[int]		)    ->   Any:


						'''simple docstring'''
						A	 =      self.feat_extract_tester.prepare_inputs_for_common(equal_length=_SCREAMING_SNAKE_CASE		)
						A	 =      self.feature_extraction_class(**self.feat_extract_dict		)
						A	 =      feat_extract.model_input_names[0]

						A	 =      BatchFeature({input_name: speech_inputs}						,tensor_type='pt'		)

						A	 =      processed_features[input_name]

						if len(batch_features_input.shape		) < 3:
										A	 =      batch_features_input[:, :, None]

						self.assertTrue(
						    batch_features_input.shape
						    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]		), self.feat_extract_tester.feature_size)		)
		@require_tf
		def       A(  self		:       List[str]		)    ->   int:


						'''simple docstring'''
						A	 =      self.feat_extract_tester.prepare_inputs_for_common(equal_length=_SCREAMING_SNAKE_CASE		)
						A	 =      self.feature_extraction_class(**self.feat_extract_dict		)
						A	 =      feat_extract.model_input_names[0]

						A	 =      BatchFeature({input_name: speech_inputs}						,tensor_type='tf'		)

						A	 =      processed_features[input_name]

						if len(batch_features_input.shape		) < 3:
										A	 =      batch_features_input[:, :, None]

						self.assertTrue(
						    batch_features_input.shape
						    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]		), self.feat_extract_tester.feature_size)		)
		def       A(  self		:       Optional[Any]						,_SCREAMING_SNAKE_CASE		:       Optional[int]=False		)    ->   int:


						'''simple docstring'''
						def _inputs_have_equal_length(_SCREAMING_SNAKE_CASE		:       int		):
										A	 =      len(input[0]		)
										for input_slice in input[1:]:
														if len(_SCREAMING_SNAKE_CASE		) != length:
																		return False
										return True

						def _inputs_are_equal(_SCREAMING_SNAKE_CASE		:       Any						,_SCREAMING_SNAKE_CASE		:       Optional[int]		):
										if len(_SCREAMING_SNAKE_CASE		) != len(_SCREAMING_SNAKE_CASE		):
														return False

										for input_slice_a, input_slice_a in zip(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
														if not np.allclose(np.asarray(_SCREAMING_SNAKE_CASE		)						,np.asarray(_SCREAMING_SNAKE_CASE		)						,atol=1E-3		):
																		return False
										return True

						A	 =      self.feature_extraction_class(**self.feat_extract_dict		)
						A	 =      self.feat_extract_tester.prepare_inputs_for_common(numpify=_SCREAMING_SNAKE_CASE		)
						A	 =      feat_extract.model_input_names[0]

						A	 =      BatchFeature({input_name: speech_inputs}		)

						A	 =      self.feat_extract_tester.seq_length_diff
						A	 =      self.feat_extract_tester.max_seq_length + pad_diff
						A	 =      self.feat_extract_tester.min_seq_length
						A	 =      self.feat_extract_tester.batch_size
						A	 =      self.feat_extract_tester.feature_size

						# test padding for List[int] + numpy
						A	 =      feat_extract.pad(_SCREAMING_SNAKE_CASE						,padding=_SCREAMING_SNAKE_CASE		)
						A	 =      input_a[input_name]

						A	 =      feat_extract.pad(_SCREAMING_SNAKE_CASE						,padding='longest'		)
						A	 =      input_a[input_name]

						A	 =      feat_extract.pad(_SCREAMING_SNAKE_CASE						,padding='max_length'						,max_length=len(speech_inputs[-1]		)		)
						A	 =      input_a[input_name]

						A	 =      feat_extract.pad(_SCREAMING_SNAKE_CASE						,padding='longest'						,return_tensors='np'		)
						A	 =      input_a[input_name]

						# max_length parameter has to be provided when setting `padding="max_length"`
						with self.assertRaises(_SCREAMING_SNAKE_CASE		):
										feat_extract.pad(_SCREAMING_SNAKE_CASE						,padding='max_length'		)[input_name]

						A	 =      feat_extract.pad(
						    _SCREAMING_SNAKE_CASE						,padding='max_length'						,max_length=_SCREAMING_SNAKE_CASE						,return_tensors='np'		)
						A	 =      input_a[input_name]

						self.assertFalse(_inputs_have_equal_length(_SCREAMING_SNAKE_CASE		)		)
						self.assertTrue(_inputs_have_equal_length(_SCREAMING_SNAKE_CASE		)		)
						self.assertTrue(_inputs_have_equal_length(_SCREAMING_SNAKE_CASE		)		)
						self.assertTrue(_inputs_are_equal(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)		)
						self.assertTrue(len(input_a[0]		) == pad_min_length		)
						self.assertTrue(len(input_a[1]		) == pad_min_length + pad_diff		)
						self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0]		))		)
						self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length)		)

						if feature_size > 1:
										self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size		)

						# test padding for `pad_to_multiple_of` for List[int] + numpy
						A	 =      feat_extract.pad(_SCREAMING_SNAKE_CASE						,pad_to_multiple_of=1_0		)
						A	 =      input_a[input_name]

						A	 =      feat_extract.pad(_SCREAMING_SNAKE_CASE						,padding='longest'						,pad_to_multiple_of=1_0		)
						A	 =      input_a[input_name]

						A	 =      feat_extract.pad(
						    _SCREAMING_SNAKE_CASE						,padding='max_length'						,pad_to_multiple_of=1_0						,max_length=_SCREAMING_SNAKE_CASE		)
						A	 =      input_a[input_name]

						A	 =      feat_extract.pad(
						    _SCREAMING_SNAKE_CASE						,padding='max_length'						,pad_to_multiple_of=1_0						,max_length=_SCREAMING_SNAKE_CASE						,return_tensors='np'						,)
						A	 =      input_a[input_name]

						self.assertTrue(all(len(_SCREAMING_SNAKE_CASE		) % 1_0 == 0 for x in input_a		)		)
						self.assertTrue(_inputs_are_equal(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)		)

						A	 =      pad_max_length if pad_max_length % 1_0 == 0 else (pad_max_length // 1_0 + 1) * 1_0
						self.assertTrue(all(len(_SCREAMING_SNAKE_CASE		) == expected_mult_pad_length for x in input_a		)		)
						self.assertEqual(input_a.shape[:2]						,(batch_size, expected_mult_pad_length)		)

						if feature_size > 1:
										self.assertTrue(input_a.shape[2] == feature_size		)

						# Check padding value is correct
						A	 =      (np.ones(self.feat_extract_tester.feature_size		) * feat_extract.padding_value).sum()
						self.assertTrue(
						    abs(np.asarray(input_a[0]		)[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)		)
						    < 1E-3		)
						self.assertTrue(
						    abs(
						        np.asarray(input_a[1]		)[pad_min_length + pad_diff :].sum()
						        - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff)		)
						    < 1E-3		)
						self.assertTrue(
						    abs(
						        np.asarray(input_a[2]		)[pad_min_length + 2 * pad_diff :].sum()
						        - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff)		)
						    < 1E-3		)
						self.assertTrue(
						    abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)		) < 1E-3		)
						self.assertTrue(
						    abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length)		)
						    < 1E-3		)
		def       A(  self		:       str						,_SCREAMING_SNAKE_CASE		:       List[Any]=False		)    ->   List[Any]:


						'''simple docstring'''
						def _inputs_have_equal_length(_SCREAMING_SNAKE_CASE		:       List[str]		):
										A	 =      len(input[0]		)
										for input_slice in input[1:]:
														if len(_SCREAMING_SNAKE_CASE		) != length:
																		return False
										return True

						def _inputs_are_equal(_SCREAMING_SNAKE_CASE		:       Any						,_SCREAMING_SNAKE_CASE		:       List[str]		):
										if len(_SCREAMING_SNAKE_CASE		) != len(_SCREAMING_SNAKE_CASE		):
														return False

										for input_slice_a, input_slice_a in zip(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
														if not np.allclose(np.asarray(_SCREAMING_SNAKE_CASE		)						,np.asarray(_SCREAMING_SNAKE_CASE		)						,atol=1E-3		):
																		return False
										return True

						A	 =      self.feature_extraction_class(**self.feat_extract_dict		)
						A	 =      self.feat_extract_tester.prepare_inputs_for_common(numpify=_SCREAMING_SNAKE_CASE		)
						A	 =      feat_extract.model_input_names[0]

						A	 =      BatchFeature({input_name: speech_inputs}		)

						# truncate to smallest
						A	 =      feat_extract.pad(
						    _SCREAMING_SNAKE_CASE						,padding='max_length'						,max_length=len(speech_inputs[0]		)						,truncation=_SCREAMING_SNAKE_CASE		)
						A	 =      input_a[input_name]

						A	 =      feat_extract.pad(_SCREAMING_SNAKE_CASE						,padding='max_length'						,max_length=len(speech_inputs[0]		)		)
						A	 =      input_a[input_name]

						self.assertTrue(_inputs_have_equal_length(_SCREAMING_SNAKE_CASE		)		)
						self.assertFalse(_inputs_have_equal_length(_SCREAMING_SNAKE_CASE		)		)

						# truncate to smallest with np
						A	 =      feat_extract.pad(
						    _SCREAMING_SNAKE_CASE						,padding='max_length'						,max_length=len(speech_inputs[0]		)						,return_tensors='np'						,truncation=_SCREAMING_SNAKE_CASE						,)
						A	 =      input_a[input_name]

						A	 =      feat_extract.pad(
						    _SCREAMING_SNAKE_CASE						,padding='max_length'						,max_length=len(speech_inputs[0]		)						,return_tensors='np'		)
						A	 =      input_a[input_name]

						self.assertTrue(_inputs_have_equal_length(_SCREAMING_SNAKE_CASE		)		)
						self.assertTrue(input_a.shape[1] == len(speech_inputs[0]		)		)

						# since truncation forces padding to be smaller than longest input
						# function can't return `np.ndarray`, but has to return list
						self.assertFalse(_inputs_have_equal_length(_SCREAMING_SNAKE_CASE		)		)

						# truncate to middle
						A	 =      feat_extract.pad(
						    _SCREAMING_SNAKE_CASE						,padding='max_length'						,max_length=len(speech_inputs[1]		)						,truncation=_SCREAMING_SNAKE_CASE						,return_tensors='np'						,)
						A	 =      input_a[input_name]

						A	 =      feat_extract.pad(
						    _SCREAMING_SNAKE_CASE						,padding='max_length'						,max_length=len(speech_inputs[1]		)						,truncation=_SCREAMING_SNAKE_CASE		)
						A	 =      input_a[input_name]

						A	 =      feat_extract.pad(
						    _SCREAMING_SNAKE_CASE						,padding='max_length'						,max_length=len(speech_inputs[1]		)						,return_tensors='np'		)
						A	 =      input_a[input_name]

						self.assertTrue(input_a.shape[1] == len(speech_inputs[1]		)		)
						self.assertTrue(_inputs_have_equal_length(_SCREAMING_SNAKE_CASE		)		)
						self.assertTrue(_inputs_have_equal_length(_SCREAMING_SNAKE_CASE		)		)
						self.assertTrue(_inputs_are_equal(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)		)

						# since truncation forces padding to be smaller than longest input
						# function can't return `np.ndarray`, but has to return list
						self.assertFalse(_inputs_have_equal_length(_SCREAMING_SNAKE_CASE		)		)
						self.assertTrue(len(input_a[-1]		) == len(speech_inputs[-1]		)		)

						# padding has to be max_length when setting `truncation=True`
						with self.assertRaises(_SCREAMING_SNAKE_CASE		):
										feat_extract.pad(_SCREAMING_SNAKE_CASE						,truncation=_SCREAMING_SNAKE_CASE		)[input_name]

						# padding has to be max_length when setting `truncation=True`
						with self.assertRaises(_SCREAMING_SNAKE_CASE		):
										feat_extract.pad(_SCREAMING_SNAKE_CASE						,padding='longest'						,truncation=_SCREAMING_SNAKE_CASE		)[input_name]

						# padding has to be max_length when setting `truncation=True`
						with self.assertRaises(_SCREAMING_SNAKE_CASE		):
										feat_extract.pad(_SCREAMING_SNAKE_CASE						,padding='longest'						,truncation=_SCREAMING_SNAKE_CASE		)[input_name]

						# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
						with self.assertRaises(_SCREAMING_SNAKE_CASE		):
										feat_extract.pad(_SCREAMING_SNAKE_CASE						,padding='max_length'						,truncation=_SCREAMING_SNAKE_CASE		)[input_name]

						# test truncation for `pad_to_multiple_of` for List[int] + numpy
						A	 =      1_2
						A	 =      feat_extract.pad(
						    _SCREAMING_SNAKE_CASE						,padding='max_length'						,max_length=len(speech_inputs[0]		)						,pad_to_multiple_of=_SCREAMING_SNAKE_CASE						,truncation=_SCREAMING_SNAKE_CASE						,)
						A	 =      input_a[input_name]

						A	 =      feat_extract.pad(
						    _SCREAMING_SNAKE_CASE						,padding='max_length'						,max_length=len(speech_inputs[0]		)						,pad_to_multiple_of=_SCREAMING_SNAKE_CASE						,)
						A	 =      input_a[input_name]

						# retrieve expected_length as multiple of pad_to_multiple_of
						A	 =      len(speech_inputs[0]		)
						if expected_length % pad_to_multiple_of != 0:
										A	 =      ((len(speech_inputs[0]		) // pad_to_multiple_of) + 1) * pad_to_multiple_of

						self.assertTrue(len(input_a[0]		) == expected_length		)
						self.assertTrue(_inputs_have_equal_length(_SCREAMING_SNAKE_CASE		)		)
						self.assertFalse(_inputs_have_equal_length(_SCREAMING_SNAKE_CASE		)		)
		def       A(  self		:       str		)    ->   Optional[int]:


						'''simple docstring'''
						self._check_padding(numpify=_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       Optional[Any]		)    ->   Tuple:


						'''simple docstring'''
						self._check_padding(numpify=_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       Tuple		)    ->   Union[str, Any]:


						'''simple docstring'''
						self._check_truncation(numpify=_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       List[str]		)    ->   Any:


						'''simple docstring'''
						self._check_truncation(numpify=_SCREAMING_SNAKE_CASE		)
		@require_torch
		def       A(  self		:       int		)    ->   Dict:


						'''simple docstring'''
						A	 =      self.feature_extraction_class(**self.feat_extract_dict		)
						A	 =      self.feat_extract_tester.prepare_inputs_for_common()
						A	 =      feat_extract.model_input_names[0]

						A	 =      BatchFeature({input_name: speech_inputs}		)

						A	 =      feat_extract.pad(_SCREAMING_SNAKE_CASE						,padding='longest'						,return_tensors='np'		)[input_name]
						A	 =      feat_extract.pad(_SCREAMING_SNAKE_CASE						,padding='longest'						,return_tensors='pt'		)[input_name]

						self.assertTrue(abs(input_np.astype(np.floataa		).sum() - input_pt.numpy().astype(np.floataa		).sum()		) < 1E-2		)
		@require_tf
		def       A(  self		:       Optional[Any]		)    ->   Optional[Any]:


						'''simple docstring'''
						A	 =      self.feature_extraction_class(**self.feat_extract_dict		)
						A	 =      self.feat_extract_tester.prepare_inputs_for_common()
						A	 =      feat_extract.model_input_names[0]

						A	 =      BatchFeature({input_name: speech_inputs}		)

						A	 =      feat_extract.pad(_SCREAMING_SNAKE_CASE						,padding='longest'						,return_tensors='np'		)[input_name]
						A	 =      feat_extract.pad(_SCREAMING_SNAKE_CASE						,padding='longest'						,return_tensors='tf'		)[input_name]

						self.assertTrue(abs(input_np.astype(np.floataa		).sum() - input_tf.numpy().astype(np.floataa		).sum()		) < 1E-2		)
		def       A(  self		:       List[str]		)    ->   Tuple:


						'''simple docstring'''
						A	 =      self.feat_extract_dict
						A	 =      True
						A	 =      self.feature_extraction_class(**_SCREAMING_SNAKE_CASE		)
						A	 =      self.feat_extract_tester.prepare_inputs_for_common()
						A	 =      [len(_SCREAMING_SNAKE_CASE		) for x in speech_inputs]
						A	 =      feat_extract.model_input_names[0]

						A	 =      BatchFeature({input_name: speech_inputs}		)

						A	 =      feat_extract.pad(_SCREAMING_SNAKE_CASE						,padding='longest'						,return_tensors='np'		)
						self.assertIn('attention_mask'						,_SCREAMING_SNAKE_CASE		)
						self.assertListEqual(list(processed.attention_mask.shape		)						,list(processed[input_name].shape[:2]		)		)
						self.assertListEqual(processed.attention_mask.sum(-1		).tolist()						,_SCREAMING_SNAKE_CASE		)







		def       A(  self		:       int		)    ->   int:


						'''simple docstring'''
						A	 =      self.feat_extract_dict
						A	 =      True
						A	 =      self.feature_extraction_class(**_SCREAMING_SNAKE_CASE		)
						A	 =      self.feat_extract_tester.prepare_inputs_for_common()
						A	 =      [len(_SCREAMING_SNAKE_CASE		) for x in speech_inputs]
						A	 =      feat_extract.model_input_names[0]

						A	 =      BatchFeature({input_name: speech_inputs}		)
						A	 =      min(_SCREAMING_SNAKE_CASE		)

						A	 =      feat_extract.pad(
						    _SCREAMING_SNAKE_CASE						,padding='max_length'						,max_length=_SCREAMING_SNAKE_CASE						,truncation=_SCREAMING_SNAKE_CASE						,return_tensors='np'		)
						self.assertIn('attention_mask'						,_SCREAMING_SNAKE_CASE		)
						self.assertListEqual(
						    list(processed_pad.attention_mask.shape		)						,[processed_pad[input_name].shape[0], max_length]		)
						self.assertListEqual(
						    processed_pad.attention_mask[:, :max_length].sum(-1		).tolist()						,[max_length for x in speech_inputs]		)













from manim import *
class      UpperCamelCase (				snake_case__ ):







		"""simple docstring"""



		def       A(  self		:       Dict		)    ->   Tuple:


						'''simple docstring'''
						A	 =      Rectangle(height=0.5						,width=0.5		)
						A	 =      Rectangle(height=0.46						,width=0.46		).set_stroke(width=0		)

						A	 =      [mem.copy() for i in range(6		)]
						A	 =      [mem.copy() for i in range(6		)]
						A	 =      VGroup(*_SCREAMING_SNAKE_CASE		).arrange(_SCREAMING_SNAKE_CASE						,buff=0		)
						A	 =      VGroup(*_SCREAMING_SNAKE_CASE		).arrange(_SCREAMING_SNAKE_CASE						,buff=0		)
						A	 =      VGroup(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		).arrange(_SCREAMING_SNAKE_CASE						,buff=0		)
						A	 =      Text('CPU'						,font_size=2_4		)
						A	 =      Group(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		).arrange(_SCREAMING_SNAKE_CASE						,buff=0.5						,aligned_edge=_SCREAMING_SNAKE_CASE		)
						cpu.move_to([-2.5, -0.5, 0]		)
						self.add(_SCREAMING_SNAKE_CASE		)

						A	 =      [mem.copy() for i in range(1		)]
						A	 =      VGroup(*_SCREAMING_SNAKE_CASE		).arrange(_SCREAMING_SNAKE_CASE						,buff=0		)
						A	 =      Text('GPU'						,font_size=2_4		)
						A	 =      Group(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		).arrange(_SCREAMING_SNAKE_CASE						,buff=0.5						,aligned_edge=_SCREAMING_SNAKE_CASE		)
						gpu.align_to(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)
						gpu.set_x(gpu.get_x() - 1		)

						self.add(_SCREAMING_SNAKE_CASE		)

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

						A	 =      Text('Model'						,font_size=2_4		)
						A	 =      Group(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		).arrange(_SCREAMING_SNAKE_CASE						,buff=0.5						,aligned_edge=_SCREAMING_SNAKE_CASE		)
						model.move_to([3, -1.0, 0]		)

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

						A	 =      MarkupText(
						    f'First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.'						,font_size=2_4						,)

						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=1_8						,)

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

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

						self.add(_SCREAMING_SNAKE_CASE		)

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

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

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

						self.play(*_SCREAMING_SNAKE_CASE		)
						self.play(*_SCREAMING_SNAKE_CASE		)

						self.wait()





