




"""simple docstring"""




import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch


if is_faiss_available():
   import faiss




@require_faiss
class       a__	( UpperCamelCase_				):







     def       lowerCAmelCase_    (     self			)  ->   Optional[int]:



          '''simple docstring'''



          a     =				tempfile.mkdtemp()
          a     =				8

          # DPR tok
          a     =				[
              """[UNK]""",
              """[CLS]""",
              """[SEP]""",
              """[PAD]""",
              """[MASK]""",
              """want""",
              """##want""",
              """##ed""",
              """wa""",
              """un""",
              """runn""",
              """##ing""",
              """,""",
              """low""",
              """lowest""",
          ]
          a     =				os.path.join(self.tmpdirname  ,		"dpr_tokenizer"			)
          os.makedirs(_a  ,		exist_ok=_a			)
          a     =				os.path.join(_a  ,		DPR_VOCAB_FILES_NAMES["vocab_file"]			)
          with open(self.vocab_file  ,		"w"  ,		encoding="utf-8"			) as vocab_writer:
               vocab_writer.write("".join([x + "\n" for x in vocab_tokens]			)			)

          # BART tok
          a     =				[
              """l""",
              """o""",
              """w""",
              """e""",
              """r""",
              """s""",
              """t""",
              """i""",
              """d""",
              """n""",
              """\u0120""",
              """\u0120l""",
              """\u0120n""",
              """\u0120lo""",
              """\u0120low""",
              """er""",
              """\u0120lowest""",
              """\u0120newer""",
              """\u0120wider""",
              """<unk>""",
          ]
          a     =				dict(zip(_a  ,		range(len(_a			)			)			)			)
          a     =				["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
          a     =				{"""unk_token""": """<unk>"""}

          a     =				os.path.join(self.tmpdirname  ,		"bart_tokenizer"			)
          os.makedirs(_a  ,		exist_ok=_a			)
          a     =				os.path.join(_a  ,		BART_VOCAB_FILES_NAMES["vocab_file"]			)
          a     =				os.path.join(_a  ,		BART_VOCAB_FILES_NAMES["merges_file"]			)
          with open(self.vocab_file  ,		"w"  ,		encoding="utf-8"			) as fp:
               fp.write(json.dumps(_a			) + "\n"			)
          with open(self.merges_file  ,		"w"  ,		encoding="utf-8"			) as fp:
               fp.write("\n".join(_a			)			)







     def       lowerCAmelCase_    (     self			)  ->   DPRQuestionEncoderTokenizer:



          '''simple docstring'''



          return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname  ,		"dpr_tokenizer"			)			)







     def       lowerCAmelCase_    (     self			)  ->   DPRContextEncoderTokenizer:



          '''simple docstring'''



          return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname  ,		"dpr_tokenizer"			)			)







     def       lowerCAmelCase_    (     self			)  ->   BartTokenizer:



          '''simple docstring'''



          return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname  ,		"bart_tokenizer"			)			)







     def       lowerCAmelCase_    (     self			)  ->   Union[str, Any]:



          '''simple docstring'''



          shutil.rmtree(self.tmpdirname			)







     def       lowerCAmelCase_    (     self			)  ->   Union[str, Any]:



          '''simple docstring'''



          a     =				Dataset.from_dict(
              {
                  "id": ["0", "1"],
                  "text": ["foo", "bar"],
                  "title": ["Foo", "Bar"],
                  "embeddings": [np.ones(self.retrieval_vector_size			), 2 * np.ones(self.retrieval_vector_size			)],
              }			)
          dataset.add_faiss_index("embeddings"  ,		string_factory="Flat"  ,		metric_type=faiss.METRIC_INNER_PRODUCT			)
          return dataset







     def       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''simple docstring'''



          a     =				self.get_dummy_dataset()
          a     =				RagConfig(
              retrieval_vector_size=self.retrieval_vector_size  ,		question_encoder=DPRConfig().to_dict()  ,		generator=BartConfig().to_dict()  ,		)
          with patch("transformers.models.rag.retrieval_rag.load_dataset"			) as mock_load_dataset:
               a     =				dataset
               a     =				RagRetriever(
                   _a  ,		question_encoder_tokenizer=self.get_dpr_tokenizer()  ,		generator_tokenizer=self.get_bart_tokenizer()  ,		)
          return retriever







     def       lowerCAmelCase_    (     self  ,		A			)  ->   str:



          '''simple docstring'''



          a     =				self.get_dummy_dataset()
          a     =				RagConfig(
              retrieval_vector_size=self.retrieval_vector_size  ,		question_encoder=DPRConfig().to_dict()  ,		generator=BartConfig().to_dict()  ,		index_name="custom"  ,		)
          if from_disk:
               a     =				os.path.join(self.tmpdirname  ,		"dataset"			)
               a     =				os.path.join(self.tmpdirname  ,		"index.faiss"			)
               dataset.get_index("embeddings"			).save(os.path.join(self.tmpdirname  ,		"index.faiss"			)			)
               dataset.drop_index("embeddings"			)
               dataset.save_to_disk(os.path.join(self.tmpdirname  ,		"dataset"			)			)
               del dataset
               a     =				RagRetriever(
                   _a  ,		question_encoder_tokenizer=self.get_dpr_tokenizer()  ,		generator_tokenizer=self.get_bart_tokenizer()  ,		)
          else:
               a     =				RagRetriever(
                   _a  ,		question_encoder_tokenizer=self.get_dpr_tokenizer()  ,		generator_tokenizer=self.get_bart_tokenizer()  ,		index=CustomHFIndex(config.retrieval_vector_size  ,		_a			)  ,		)
          return retriever







     def       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''simple docstring'''



          a     =				Dataset.from_dict(
              {
                  "id": ["0", "1"],
                  "text": ["foo", "bar"],
                  "title": ["Foo", "Bar"],
                  "embeddings": [np.ones(self.retrieval_vector_size + 1			), 2 * np.ones(self.retrieval_vector_size + 1			)],
              }			)
          dataset.add_faiss_index("embeddings"  ,		string_factory="Flat"  ,		metric_type=faiss.METRIC_INNER_PRODUCT			)

          a     =				os.path.join(self.tmpdirname  ,		"hf_bert_base.hnswSQ8_correct_phi_128.c_index"			)
          dataset.save_faiss_index("embeddings"  ,		index_file_name + ".index.dpr"			)
          pickle.dump(dataset["id"]  ,		open(index_file_name + ".index_meta.dpr"  ,		"wb"			)			)

          a     =				os.path.join(self.tmpdirname  ,		"psgs_w100.tsv.pkl"			)
          a     =				{sample["""id"""]: [sample["""text"""], sample["""title"""]] for sample in dataset}
          pickle.dump(_a  ,		open(_a  ,		"wb"			)			)

          a     =				RagConfig(
              retrieval_vector_size=self.retrieval_vector_size  ,		question_encoder=DPRConfig().to_dict()  ,		generator=BartConfig().to_dict()  ,		index_name="legacy"  ,		index_path=self.tmpdirname  ,		)
          a     =				RagRetriever(
              _a  ,		question_encoder_tokenizer=self.get_dpr_tokenizer()  ,		generator_tokenizer=self.get_bart_tokenizer()			)
          return retriever







     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''simple docstring'''



          a     =				1
          a     =				self.get_dummy_canonical_hf_index_retriever()
          a     =				np.array(
              [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)]  ,		dtype=np.floataa			)
          a     =				retriever.retrieve(_a  ,		n_docs=_a			)
          self.assertEqual(retrieved_doc_embeds.shape  ,		(2, n_docs, self.retrieval_vector_size)			)
          self.assertEqual(len(_a			)  ,		2			)
          self.assertEqual(sorted(doc_dicts[0]			)  ,		["embeddings", "id", "text", "title"]			)
          self.assertEqual(len(doc_dicts[0]["id"]			)  ,		_a			)
          self.assertEqual(doc_dicts[0]["id"][0]  ,		"1"			)  # max inner product is reached with second doc
          self.assertEqual(doc_dicts[1]["id"][0]  ,		"0"			)  # max inner product is reached with first doc
          self.assertListEqual(doc_ids.tolist()  ,		[[1], [0]]			)







     def       lowerCAmelCase_    (     self			)  ->   Any:



          '''simple docstring'''



          a     =				self.get_dummy_canonical_hf_index_retriever()
          with tempfile.TemporaryDirectory() as tmp_dirname:
               with patch("transformers.models.rag.retrieval_rag.load_dataset"			) as mock_load_dataset:
                    a     =				self.get_dummy_dataset()
                    retriever.save_pretrained(_a			)
                    a     =				RagRetriever.from_pretrained(_a			)
                    self.assertIsInstance(_a  ,		_a			)
                    a     =				np.array(
                        [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)]  ,		dtype=np.floataa			)
                    a     =				retriever.retrieve(_a  ,		n_docs=1			)
                    self.assertTrue(out is not None			)







     def       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



          a     =				1
          a     =				self.get_dummy_custom_hf_index_retriever(from_disk=_a			)
          a     =				np.array(
              [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)]  ,		dtype=np.floataa			)
          a     =				retriever.retrieve(_a  ,		n_docs=_a			)
          self.assertEqual(retrieved_doc_embeds.shape  ,		(2, n_docs, self.retrieval_vector_size)			)
          self.assertEqual(len(_a			)  ,		2			)
          self.assertEqual(sorted(doc_dicts[0]			)  ,		["embeddings", "id", "text", "title"]			)
          self.assertEqual(len(doc_dicts[0]["id"]			)  ,		_a			)
          self.assertEqual(doc_dicts[0]["id"][0]  ,		"1"			)  # max inner product is reached with second doc
          self.assertEqual(doc_dicts[1]["id"][0]  ,		"0"			)  # max inner product is reached with first doc
          self.assertListEqual(doc_ids.tolist()  ,		[[1], [0]]			)







     def       lowerCAmelCase_    (     self			)  ->   str:



          '''simple docstring'''



          a     =				self.get_dummy_custom_hf_index_retriever(from_disk=_a			)
          with tempfile.TemporaryDirectory() as tmp_dirname:
               retriever.save_pretrained(_a			)
               a     =				RagRetriever.from_pretrained(_a			)
               self.assertIsInstance(_a  ,		_a			)
               a     =				np.array(
                   [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)]  ,		dtype=np.floataa			)
               a     =				retriever.retrieve(_a  ,		n_docs=1			)
               self.assertTrue(out is not None			)







     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



          a     =				1
          a     =				self.get_dummy_custom_hf_index_retriever(from_disk=_a			)
          a     =				np.array(
              [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)]  ,		dtype=np.floataa			)
          a     =				retriever.retrieve(_a  ,		n_docs=_a			)
          self.assertEqual(retrieved_doc_embeds.shape  ,		(2, n_docs, self.retrieval_vector_size)			)
          self.assertEqual(len(_a			)  ,		2			)
          self.assertEqual(sorted(doc_dicts[0]			)  ,		["embeddings", "id", "text", "title"]			)
          self.assertEqual(len(doc_dicts[0]["id"]			)  ,		_a			)
          self.assertEqual(doc_dicts[0]["id"][0]  ,		"1"			)  # max inner product is reached with second doc
          self.assertEqual(doc_dicts[1]["id"][0]  ,		"0"			)  # max inner product is reached with first doc
          self.assertListEqual(doc_ids.tolist()  ,		[[1], [0]]			)







     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''simple docstring'''



          a     =				self.get_dummy_custom_hf_index_retriever(from_disk=_a			)
          with tempfile.TemporaryDirectory() as tmp_dirname:
               retriever.save_pretrained(_a			)
               a     =				RagRetriever.from_pretrained(_a			)
               self.assertIsInstance(_a  ,		_a			)
               a     =				np.array(
                   [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)]  ,		dtype=np.floataa			)
               a     =				retriever.retrieve(_a  ,		n_docs=1			)
               self.assertTrue(out is not None			)







     def       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



          a     =				1
          a     =				self.get_dummy_legacy_index_retriever()
          a     =				np.array(
              [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)]  ,		dtype=np.floataa			)
          a     =				retriever.retrieve(_a  ,		n_docs=_a			)
          self.assertEqual(retrieved_doc_embeds.shape  ,		(2, n_docs, self.retrieval_vector_size)			)
          self.assertEqual(len(_a			)  ,		2			)
          self.assertEqual(sorted(doc_dicts[0]			)  ,		["text", "title"]			)
          self.assertEqual(len(doc_dicts[0]["text"]			)  ,		_a			)
          self.assertEqual(doc_dicts[0]["text"][0]  ,		"bar"			)  # max inner product is reached with second doc
          self.assertEqual(doc_dicts[1]["text"][0]  ,		"foo"			)  # max inner product is reached with first doc
          self.assertListEqual(doc_ids.tolist()  ,		[[1], [0]]			)







     def       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



          a     =				self.get_dummy_legacy_index_retriever()
          with tempfile.TemporaryDirectory() as tmp_dirname:
               retriever.save_pretrained(_a			)
               a     =				RagRetriever.from_pretrained(_a			)
               self.assertIsInstance(_a  ,		_a			)
               a     =				np.array(
                   [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)]  ,		dtype=np.floataa			)
               a     =				retriever.retrieve(_a  ,		n_docs=1			)
               self.assertTrue(out is not None			)







     @require_torch
     @require_tokenizers
     @require_sentencepiece
     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          import torch

          a     =				1
          a     =				self.get_dummy_canonical_hf_index_retriever()
          a     =				[[5, 7], [10, 11]]
          a     =				np.array(
              [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)]  ,		dtype=np.floataa			)
          a     =				retriever(_a  ,		_a  ,		prefix=retriever.config.generator.prefix  ,		n_docs=_a			)
          a     =				(
              out["""context_input_ids"""],
              out["""context_attention_mask"""],
              out["""retrieved_doc_embeds"""],
          )
          self.assertEqual(retrieved_doc_embeds.shape  ,		(2, n_docs, self.retrieval_vector_size)			)
          self.assertIsInstance(_a  ,		_a			)
          self.assertIsInstance(_a  ,		_a			)
          self.assertIsInstance(_a  ,		np.ndarray			)

          a     =				retriever(
              _a  ,		_a  ,		prefix=retriever.config.generator.prefix  ,		n_docs=_a  ,		return_tensors="pt"  ,		)
          a     =				(  # noqa: F841
              out["""context_input_ids"""],
              out["""context_attention_mask"""],
              out["""retrieved_doc_embeds"""],
              out["""doc_ids"""],
          )
          self.assertEqual(retrieved_doc_embeds.shape  ,		(2, n_docs, self.retrieval_vector_size)			)
          self.assertIsInstance(_a  ,		torch.Tensor			)
          self.assertIsInstance(_a  ,		torch.Tensor			)
          self.assertIsInstance(_a  ,		torch.Tensor			)







     @require_torch
     @require_tokenizers
     @require_sentencepiece
     def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



          '''simple docstring'''



          a     =				self.get_dpr_ctx_encoder_tokenizer()
          a     =				1
          a     =				self.get_dummy_custom_hf_index_retriever(from_disk=_a			)
          retriever.set_ctx_encoder_tokenizer(_a			)

          a     =				[[5, 7], [10, 11]]
          a     =				np.array(
              [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)]  ,		dtype=np.floataa			)
          a     =				retriever(_a  ,		_a  ,		prefix=retriever.config.generator.prefix  ,		n_docs=_a			)

          self.assertEqual(
              len(_a			)  ,		6			)  # check whether the retriever output consist of 6 attributes including tokenized docs
          self.assertEqual(
              all(k in out for k in ("tokenized_doc_ids", "tokenized_doc_attention_mask")			)  ,		_a			)  # check for doc token related keys in dictionary.







from manim import *




class       a__	( UpperCamelCase__				):







     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



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

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

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

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

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

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

          a     =				[]
          a     =				[]

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

               a     =				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			)

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

          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(A  ,		A			)

          a     =				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			)

          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(A			)			)

          a     =				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			)			)

          a     =				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]			)

          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(A  ,		run_time=3			)			)

          a     =				{"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]			)			)

          a     =				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			)

               a     =				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:
                         a     =				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]			)			)

               a     =				a_c
               a     =				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			)  ,		)

          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(A  ,		run_time=3			)  ,		MoveToTarget(A			)			)

          self.wait()

