


"""simple docstring"""


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						(     _UpperCAmelCase	):
							'''simple docstring'''







							__UpperCAmelCase		:					List[Any]          =None
							__UpperCAmelCase		:					Optional[Any]          =None

							@property
							def     snake_case       (  self			):
										return self.feat_extract_tester.prepare_feat_extract_dict()

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.feature_extraction_class(**self.feat_extract_dict			)
										self.assertTrue(hasattr(A_					,     "feature_size"			)			)
										self.assertTrue(hasattr(A_					,     "sampling_rate"			)			)
										self.assertTrue(hasattr(A_					,     "padding_value"			)			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.feat_extract_tester.prepare_inputs_for_common()
										__lowerCAmelCase       					=    self.feature_extraction_class(**self.feat_extract_dict			)
										__lowerCAmelCase       					=    feat_extract.model_input_names[0]

										__lowerCAmelCase       					=    BatchFeature({input_name: speech_inputs}			)

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

										__lowerCAmelCase       					=    self.feat_extract_tester.prepare_inputs_for_common(equal_length=A_			)
										__lowerCAmelCase       					=    BatchFeature({input_name: speech_inputs}					,     tensor_type="np"			)

										__lowerCAmelCase       					=    processed_features[input_name]

										if len(batch_features_input.shape			) < 3:
													__lowerCAmelCase       					=    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     snake_case       (  self			):
										__lowerCAmelCase       					=    self.feat_extract_tester.prepare_inputs_for_common(equal_length=A_			)
										__lowerCAmelCase       					=    self.feature_extraction_class(**self.feat_extract_dict			)
										__lowerCAmelCase       					=    feat_extract.model_input_names[0]

										__lowerCAmelCase       					=    BatchFeature({input_name: speech_inputs}					,     tensor_type="pt"			)

										__lowerCAmelCase       					=    processed_features[input_name]

										if len(batch_features_input.shape			) < 3:
													__lowerCAmelCase       					=    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     snake_case       (  self			):
										__lowerCAmelCase       					=    self.feat_extract_tester.prepare_inputs_for_common(equal_length=A_			)
										__lowerCAmelCase       					=    self.feature_extraction_class(**self.feat_extract_dict			)
										__lowerCAmelCase       					=    feat_extract.model_input_names[0]

										__lowerCAmelCase       					=    BatchFeature({input_name: speech_inputs}					,     tensor_type="tf"			)

										__lowerCAmelCase       					=    processed_features[input_name]

										if len(batch_features_input.shape			) < 3:
													__lowerCAmelCase       					=    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     snake_case       (  self					,     __a=False			):
										def _inputs_have_equal_length(__a			):
													__lowerCAmelCase       					=    len(input[0]			)
													for input_slice in input[1:]:
																if len(A_			) != length:
																			return False
													return True

										def _inputs_are_equal(__a					,     __a			):
													if len(A_			) != len(A_			):
																return False

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

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

										__lowerCAmelCase       					=    BatchFeature({input_name: speech_inputs}			)

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

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

										__lowerCAmelCase       					=    feat_extract.pad(A_					,     padding="longest"			)
										__lowerCAmelCase       					=    input_a[input_name]

										__lowerCAmelCase       					=    feat_extract.pad(A_					,     padding="max_length"					,     max_length=len(speech_inputs[-1]			)			)
										__lowerCAmelCase       					=    input_a[input_name]

										__lowerCAmelCase       					=    feat_extract.pad(A_					,     padding="longest"					,     return_tensors="np"			)
										__lowerCAmelCase       					=    input_a[input_name]

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

										__lowerCAmelCase       					=    feat_extract.pad(
										    A_					,     padding="max_length"					,     max_length=A_					,     return_tensors="np"			)
										__lowerCAmelCase       					=    input_a[input_name]

										self.assertFalse(_inputs_have_equal_length(A_			)			)
										self.assertTrue(_inputs_have_equal_length(A_			)			)
										self.assertTrue(_inputs_have_equal_length(A_			)			)
										self.assertTrue(_inputs_are_equal(A_					,     A_			)			)
										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
										__lowerCAmelCase       					=    feat_extract.pad(A_					,     pad_to_multiple_of=10			)
										__lowerCAmelCase       					=    input_a[input_name]

										__lowerCAmelCase       					=    feat_extract.pad(A_					,     padding="longest"					,     pad_to_multiple_of=10			)
										__lowerCAmelCase       					=    input_a[input_name]

										__lowerCAmelCase       					=    feat_extract.pad(
										    A_					,     padding="max_length"					,     pad_to_multiple_of=10					,     max_length=A_			)
										__lowerCAmelCase       					=    input_a[input_name]

										__lowerCAmelCase       					=    feat_extract.pad(
										    A_					,     padding="max_length"					,     pad_to_multiple_of=10					,     max_length=A_					,     return_tensors="np"					,     )
										__lowerCAmelCase       					=    input_a[input_name]

										self.assertTrue(all(len(A_			) % 10 == 0 for x in input_a			)			)
										self.assertTrue(_inputs_are_equal(A_					,     A_			)			)

										__lowerCAmelCase       					=    pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
										self.assertTrue(all(len(A_			) == 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
										__lowerCAmelCase       					=    (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     snake_case       (  self					,     __a=False			):
										def _inputs_have_equal_length(__a			):
													__lowerCAmelCase       					=    len(input[0]			)
													for input_slice in input[1:]:
																if len(A_			) != length:
																			return False
													return True

										def _inputs_are_equal(__a					,     __a			):
													if len(A_			) != len(A_			):
																return False

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

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

										__lowerCAmelCase       					=    BatchFeature({input_name: speech_inputs}			)

										# truncate to smallest
										__lowerCAmelCase       					=    feat_extract.pad(
										    A_					,     padding="max_length"					,     max_length=len(speech_inputs[0]			)					,     truncation=A_			)
										__lowerCAmelCase       					=    input_a[input_name]

										__lowerCAmelCase       					=    feat_extract.pad(A_					,     padding="max_length"					,     max_length=len(speech_inputs[0]			)			)
										__lowerCAmelCase       					=    input_a[input_name]

										self.assertTrue(_inputs_have_equal_length(A_			)			)
										self.assertFalse(_inputs_have_equal_length(A_			)			)

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

										__lowerCAmelCase       					=    feat_extract.pad(
										    A_					,     padding="max_length"					,     max_length=len(speech_inputs[0]			)					,     return_tensors="np"			)
										__lowerCAmelCase       					=    input_a[input_name]

										self.assertTrue(_inputs_have_equal_length(A_			)			)
										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(A_			)			)

										# truncate to middle
										__lowerCAmelCase       					=    feat_extract.pad(
										    A_					,     padding="max_length"					,     max_length=len(speech_inputs[1]			)					,     truncation=A_					,     return_tensors="np"					,     )
										__lowerCAmelCase       					=    input_a[input_name]

										__lowerCAmelCase       					=    feat_extract.pad(
										    A_					,     padding="max_length"					,     max_length=len(speech_inputs[1]			)					,     truncation=A_			)
										__lowerCAmelCase       					=    input_a[input_name]

										__lowerCAmelCase       					=    feat_extract.pad(
										    A_					,     padding="max_length"					,     max_length=len(speech_inputs[1]			)					,     return_tensors="np"			)
										__lowerCAmelCase       					=    input_a[input_name]

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

										# 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(A_			)			)
										self.assertTrue(len(input_a[-1]			) == len(speech_inputs[-1]			)			)

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

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

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

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

										# test truncation for `pad_to_multiple_of` for List[int] + numpy
										__lowerCAmelCase       					=    12
										__lowerCAmelCase       					=    feat_extract.pad(
										    A_					,     padding="max_length"					,     max_length=len(speech_inputs[0]			)					,     pad_to_multiple_of=A_					,     truncation=A_					,     )
										__lowerCAmelCase       					=    input_a[input_name]

										__lowerCAmelCase       					=    feat_extract.pad(
										    A_					,     padding="max_length"					,     max_length=len(speech_inputs[0]			)					,     pad_to_multiple_of=A_					,     )
										__lowerCAmelCase       					=    input_a[input_name]

										# retrieve expected_length as multiple of pad_to_multiple_of
										__lowerCAmelCase       					=    len(speech_inputs[0]			)
										if expected_length % pad_to_multiple_of != 0:
													__lowerCAmelCase       					=    ((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(A_			)			)
										self.assertFalse(_inputs_have_equal_length(A_			)			)

							def     snake_case       (  self			):
										self._check_padding(numpify=A_			)

							def     snake_case       (  self			):
										self._check_padding(numpify=A_			)

							def     snake_case       (  self			):
										self._check_truncation(numpify=A_			)

							def     snake_case       (  self			):
										self._check_truncation(numpify=A_			)

							@require_torch
							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.feature_extraction_class(**self.feat_extract_dict			)
										__lowerCAmelCase       					=    self.feat_extract_tester.prepare_inputs_for_common()
										__lowerCAmelCase       					=    feat_extract.model_input_names[0]

										__lowerCAmelCase       					=    BatchFeature({input_name: speech_inputs}			)

										__lowerCAmelCase       					=    feat_extract.pad(A_					,     padding="longest"					,     return_tensors="np"			)[input_name]
										__lowerCAmelCase       					=    feat_extract.pad(A_					,     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     snake_case       (  self			):
										__lowerCAmelCase       					=    self.feature_extraction_class(**self.feat_extract_dict			)
										__lowerCAmelCase       					=    self.feat_extract_tester.prepare_inputs_for_common()
										__lowerCAmelCase       					=    feat_extract.model_input_names[0]

										__lowerCAmelCase       					=    BatchFeature({input_name: speech_inputs}			)

										__lowerCAmelCase       					=    feat_extract.pad(A_					,     padding="longest"					,     return_tensors="np"			)[input_name]
										__lowerCAmelCase       					=    feat_extract.pad(A_					,     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     snake_case       (  self			):
										__lowerCAmelCase       					=    self.feat_extract_dict
										__lowerCAmelCase       					=    True
										__lowerCAmelCase       					=    self.feature_extraction_class(**A_			)
										__lowerCAmelCase       					=    self.feat_extract_tester.prepare_inputs_for_common()
										__lowerCAmelCase       					=    [len(A_			) for x in speech_inputs]
										__lowerCAmelCase       					=    feat_extract.model_input_names[0]

										__lowerCAmelCase       					=    BatchFeature({input_name: speech_inputs}			)

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



							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.feat_extract_dict
										__lowerCAmelCase       					=    True
										__lowerCAmelCase       					=    self.feature_extraction_class(**A_			)
										__lowerCAmelCase       					=    self.feat_extract_tester.prepare_inputs_for_common()
										__lowerCAmelCase       					=    [len(A_			) for x in speech_inputs]
										__lowerCAmelCase       					=    feat_extract.model_input_names[0]

										__lowerCAmelCase       					=    BatchFeature({input_name: speech_inputs}			)
										__lowerCAmelCase       					=    min(A_			)

										__lowerCAmelCase       					=    feat_extract.pad(
										    A_					,     padding="max_length"					,     max_length=A_					,     truncation=A_					,     return_tensors="np"			)
										self.assertIn("attention_mask"					,     A_			)
										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]			)








"""simple docstring"""


from manim import *
class 						_UpperCamelCase						(     lowerCAmelCase__	):
							'''simple docstring'''







							def     snake_case       (  self			):
										__lowerCAmelCase       					=    Rectangle(height=0.5					,     width=0.5			)
										__lowerCAmelCase       					=    Rectangle(height=0.4_6					,     width=0.4_6			).set_stroke(width=0			)

										__lowerCAmelCase       					=    Rectangle(height=0.2_5					,     width=0.2_5			)

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

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

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

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

										__lowerCAmelCase       					=    []
										__lowerCAmelCase       					=    []

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

													__lowerCAmelCase       					=    Rectangle(height=0.4_6					,     width=0.4_6			).set_stroke(width=0.0			).set_fill(__a					,     opacity=0.8			)
													cpu_target.move_to(cpu_left_col_base[i]			)
													model_cpu_arr.append(__a			)

										self.add(*__a					,     *__a			)

										__lowerCAmelCase       					=    [meta_mem.copy() for i in range(6			)]
										__lowerCAmelCase       					=    [meta_mem.copy() for i in range(6			)]
										__lowerCAmelCase       					=    VGroup(*__a			).arrange(__a					,     buff=0			)
										__lowerCAmelCase       					=    VGroup(*__a			).arrange(__a					,     buff=0			)
										__lowerCAmelCase       					=    VGroup(__a					,     __a			).arrange(__a					,     buff=0			)
										__lowerCAmelCase       					=    Text("Disk"					,     font_size=24			)
										__lowerCAmelCase       					=    Group(__a					,     __a			).arrange(__a					,     buff=0.5					,     aligned_edge=__a			)
										disk.move_to([-4, -1.2_5, 0]			)
										self.add(__a					,     __a			)

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

										__lowerCAmelCase       					=    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			)

										__lowerCAmelCase       					=    MarkupText(
										    f"<span fgcolor='{BLUE}'>●</span> Checkpoint"					,     font_size=18					,     )

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

										__lowerCAmelCase       					=    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(__a			)			)

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

										self.play(Write(__a			)			)

										input.generate_target()
										input.target.next_to(model_arr[0]					,     direction=__a					,     buff=0.0_2			)
										self.play(MoveToTarget(__a			)			)

										self.play(FadeOut(__a			)			)

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

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

										__lowerCAmelCase       					=    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(__a					,     run_time=3			)			)

										__lowerCAmelCase       					=    {"run_time": 1, "fade_in": True, "fade_out": True, "buff": 0.0_2}

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

										__lowerCAmelCase       					=    a.copy()
										for i in range(6			):
													a_c.next_to(model_arr[i].get_right() + 0.0_2					,     __a					,     buff=0.2			)

													input.generate_target()
													input.target.move_to(model_arr[i].get_right() + 0.0_2			)

													__lowerCAmelCase       					=    AnimationGroup(
													    FadeOut(__a					,     run_time=0.5			)					,     MoveToTarget(__a					,     run_time=0.5			)					,     FadeIn(__a					,     run_time=0.5			)					,     lag_ratio=0.2			)

													self.play(__a			)

													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:
																			__lowerCAmelCase       					=    0.7

																self.play(
																    Circumscribe(model_arr[i]					,     **__a			)					,     Circumscribe(cpu_left_col_base[i]					,     **__a			)					,     Circumscribe(cpu_left_col_base[i + 1]					,     color=__a					,     **__a			)					,     Circumscribe(gpu_rect[0]					,     color=__a					,     **__a			)					,     Circumscribe(model_arr[i + 1]					,     color=__a					,     **__a			)					,     )
																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 + 0.0_2					,     buff=0.2			)

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

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

													__lowerCAmelCase       					=    a_c
													__lowerCAmelCase       					=    a_c.copy()

										input.generate_target()
										input.target.next_to(model_base[-1]					,     RIGHT + 0.0_2					,     buff=0.5			)
										self.play(
										    FadeOut(__a			)					,     FadeOut(__a					,     run_time=0.5			)					,     )

										__lowerCAmelCase       					=    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(__a					,     run_time=3			)					,     MoveToTarget(__a			)			)

										self.wait()




