





'''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					(      snake_case_       ):
      # to overwrite at feature extractactor specific tests
      _lowercase:      Dict											=       None
      _lowercase:      Tuple											=       None




      @property
      def     lowercase__   (      self      :			Optional[Any]  )    ->				Optional[Any]:
            return self.feat_extract_tester.prepare_feat_extract_dict()




      def     lowercase__   (      self      :			Optional[Any]  )    ->				List[str]:
            _lowerCAmelCase												=							self.feature_extraction_class(**self.feat_extract_dict  )
            self.assertTrue(hasattr(__snake_case      ,      """feature_size"""  )  )
            self.assertTrue(hasattr(__snake_case      ,      """sampling_rate"""  )  )
            self.assertTrue(hasattr(__snake_case      ,      """padding_value"""  )  )




      def     lowercase__   (      self      :			Dict  )    ->				int:
            _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(__snake_case  ) == len(__snake_case  ) for x, y in zip(__snake_case      ,      processed_features[input_name]  )  )  )

            _lowerCAmelCase												=							self.feat_extract_tester.prepare_inputs_for_common(equal_length=__snake_case  )
            _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     lowercase__   (      self      :			Tuple  )    ->				int:
            _lowerCAmelCase												=							self.feat_extract_tester.prepare_inputs_for_common(equal_length=__snake_case  )
            _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     lowercase__   (      self      :			Optional[int]  )    ->				Optional[Any]:
            _lowerCAmelCase												=							self.feat_extract_tester.prepare_inputs_for_common(equal_length=__snake_case  )
            _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     lowercase__   (      self      :			int      ,      __snake_case      :			Optional[Any]=False  )    ->				Dict:
            def _inputs_have_equal_length(__snake_case      :			Optional[int]  ):
                  _lowerCAmelCase												=							len(input[0]  )
                  for input_slice in input[1:]:
                        if len(__snake_case  ) != length:
                              return False
                  return True

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

                  for input_slice_a, input_slice_a in zip(__snake_case      ,      __snake_case  ):
                        if not np.allclose(np.asarray(__snake_case  )      ,      np.asarray(__snake_case  )      ,      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=__snake_case  )
            _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(__snake_case      ,      padding=__snake_case  )
            _lowerCAmelCase												=							input_a[input_name]

            _lowerCAmelCase												=							feat_extract.pad(__snake_case      ,      padding="""longest"""  )
            _lowerCAmelCase												=							input_a[input_name]

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

            _lowerCAmelCase												=							feat_extract.pad(__snake_case      ,      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(__snake_case  ):
                  feat_extract.pad(__snake_case      ,      padding="""max_length"""  )[input_name]

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

            self.assertFalse(_inputs_have_equal_length(__snake_case  )  )
            self.assertTrue(_inputs_have_equal_length(__snake_case  )  )
            self.assertTrue(_inputs_have_equal_length(__snake_case  )  )
            self.assertTrue(_inputs_are_equal(__snake_case      ,      __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
            _lowerCAmelCase												=							feat_extract.pad(__snake_case      ,      pad_to_multiple_of=10  )
            _lowerCAmelCase												=							input_a[input_name]

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

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

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

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

            _lowerCAmelCase												=							pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
            self.assertTrue(all(len(__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
            _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     lowercase__   (      self      :			int      ,      __snake_case      :			Union[str, Any]=False  )    ->				int:
            def _inputs_have_equal_length(__snake_case      :			Tuple  ):
                  _lowerCAmelCase												=							len(input[0]  )
                  for input_slice in input[1:]:
                        if len(__snake_case  ) != length:
                              return False
                  return True

            def _inputs_are_equal(__snake_case      :			str      ,      __snake_case      :			Any  ):
                  if len(__snake_case  ) != len(__snake_case  ):
                        return False

                  for input_slice_a, input_slice_a in zip(__snake_case      ,      __snake_case  ):
                        if not np.allclose(np.asarray(__snake_case  )      ,      np.asarray(__snake_case  )      ,      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=__snake_case  )
            _lowerCAmelCase												=							feat_extract.model_input_names[0]

            _lowerCAmelCase												=							BatchFeature({input_name: speech_inputs}  )

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

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

            self.assertTrue(_inputs_have_equal_length(__snake_case  )  )
            self.assertFalse(_inputs_have_equal_length(__snake_case  )  )

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

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

            self.assertTrue(_inputs_have_equal_length(__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(__snake_case  )  )

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

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

            _lowerCAmelCase												=							feat_extract.pad(
                __snake_case      ,      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(__snake_case  )  )
            self.assertTrue(_inputs_have_equal_length(__snake_case  )  )
            self.assertTrue(_inputs_are_equal(__snake_case      ,      __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(__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(__snake_case  ):
                  feat_extract.pad(__snake_case      ,      truncation=__snake_case  )[input_name]

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

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

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

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

            _lowerCAmelCase												=							feat_extract.pad(
                __snake_case      ,      padding="""max_length"""      ,      max_length=len(speech_inputs[0]  )      ,      pad_to_multiple_of=__snake_case      ,      )
            _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(__snake_case  )  )
            self.assertFalse(_inputs_have_equal_length(__snake_case  )  )




      def     lowercase__   (      self      :			List[Any]  )    ->				List[Any]:
            self._check_padding(numpify=__snake_case  )




      def     lowercase__   (      self      :			Optional[Any]  )    ->				Dict:
            self._check_padding(numpify=__snake_case  )




      def     lowercase__   (      self      :			Tuple  )    ->				Dict:
            self._check_truncation(numpify=__snake_case  )




      def     lowercase__   (      self      :			str  )    ->				Union[str, Any]:
            self._check_truncation(numpify=__snake_case  )




      @require_torch
      def     lowercase__   (      self      :			int  )    ->				List[Any]:
            _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(__snake_case      ,      padding="""longest"""      ,      return_tensors="""np"""  )[input_name]
            _lowerCAmelCase												=							feat_extract.pad(__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     lowercase__   (      self      :			Dict  )    ->				List[str]:
            _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(__snake_case      ,      padding="""longest"""      ,      return_tensors="""np"""  )[input_name]
            _lowerCAmelCase												=							feat_extract.pad(__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     lowercase__   (      self      :			Any  )    ->				str:
            _lowerCAmelCase												=							self.feat_extract_dict
            _lowerCAmelCase												=							True
            _lowerCAmelCase												=							self.feature_extraction_class(**__snake_case  )
            _lowerCAmelCase												=							self.feat_extract_tester.prepare_inputs_for_common()
            _lowerCAmelCase												=							[len(__snake_case  ) for x in speech_inputs]
            _lowerCAmelCase												=							feat_extract.model_input_names[0]

            _lowerCAmelCase												=							BatchFeature({input_name: speech_inputs}  )

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




      def     lowercase__   (      self      :			Any  )    ->				Tuple:
            _lowerCAmelCase												=							self.feat_extract_dict
            _lowerCAmelCase												=							True
            _lowerCAmelCase												=							self.feature_extraction_class(**__snake_case  )
            _lowerCAmelCase												=							self.feat_extract_tester.prepare_inputs_for_common()
            _lowerCAmelCase												=							[len(__snake_case  ) for x in speech_inputs]
            _lowerCAmelCase												=							feat_extract.model_input_names[0]

            _lowerCAmelCase												=							BatchFeature({input_name: speech_inputs}  )
            _lowerCAmelCase												=							min(__snake_case  )

            _lowerCAmelCase												=							feat_extract.pad(
                __snake_case      ,      padding="""max_length"""      ,      max_length=__snake_case      ,      truncation=__snake_case      ,      return_tensors="""np"""  )
            self.assertIn("""attention_mask"""      ,      __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]  )













'''simple docstring'''






from manim import *




class 			UpperCAmelCase					(      snake_case_       ):




      def     lowercase__   (      self      :			Union[str, Any]  )    ->				str:
            _lowerCAmelCase												=							Rectangle(height=0.5      ,      width=0.5  )
            _lowerCAmelCase												=							Rectangle(height=0.46      ,      width=0.46  ).set_stroke(width=0  )

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

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

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

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

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

                  _lowerCAmelCase												=							Rectangle(height=0.46 / 4      ,      width=0.46 / 3  ).set_stroke(width=0.0  ).set_fill(__snake_case      ,      opacity=0.7  )

                  if i == 0:
                        cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT  )      ,      buff=0.02      ,      direction=__snake_case  )
                        cpu_target.set_x(cpu_target.get_x() + 0.1  )
                  elif i == 3:
                        cpu_target.next_to(cpu_targs[0]      ,      direction=__snake_case      ,      buff=0.0  )
                  else:
                        cpu_target.next_to(cpu_targs[i - 1]      ,      direction=__snake_case      ,      buff=0.0  )
                  self.add(__snake_case  )
                  cpu_targs.append(__snake_case  )

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

            _lowerCAmelCase												=							Text("""Loaded Checkpoint"""      ,      font_size=24  )
            _lowerCAmelCase												=							Group(__snake_case      ,      __snake_case  ).arrange(__snake_case      ,      aligned_edge=__snake_case      ,      buff=0.4  )
            checkpoint.move_to([3, 0.5, 0]  )

            _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(__snake_case      ,      __snake_case  )

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

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

            _lowerCAmelCase												=							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(__snake_case  )      ,      Write(__snake_case  )  )

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

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

                  _lowerCAmelCase												=							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(__snake_case      ,      run_time=1.5  )  )

            self.play(*__snake_case  )
            self.play(*__snake_case  )
            self.wait()






