'''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__     (				_snake_case	):


	def 				lowercase__  (self : List[str]      )     ->			List[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= tempfile.mkdtemp()
					SCREAMING_SNAKE_CASE  :						str      					= 8

					# DPR tok
					SCREAMING_SNAKE_CASE  :						str      					= [
					    '''[UNK]''',
					    '''[CLS]''',
					    '''[SEP]''',
					    '''[PAD]''',
					    '''[MASK]''',
					    '''want''',
					    '''##want''',
					    '''##ed''',
					    '''wa''',
					    '''un''',
					    '''runn''',
					    '''##ing''',
					    ''',''',
					    '''low''',
					    '''lowest''',
					]
					SCREAMING_SNAKE_CASE  :						str      					= os.path.join(self.tmpdirname,					'''dpr_tokenizer'''      )
					os.makedirs(__UpperCAmelCase,					exist_ok=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= os.path.join(__UpperCAmelCase,					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
					SCREAMING_SNAKE_CASE  :						Tuple      					= [
					    '''l''',
					    '''o''',
					    '''w''',
					    '''e''',
					    '''r''',
					    '''s''',
					    '''t''',
					    '''i''',
					    '''d''',
					    '''n''',
					    '''\u0120''',
					    '''\u0120l''',
					    '''\u0120n''',
					    '''\u0120lo''',
					    '''\u0120low''',
					    '''er''',
					    '''\u0120lowest''',
					    '''\u0120newer''',
					    '''\u0120wider''',
					    '''<unk>''',
					]
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= dict(zip(__UpperCAmelCase,					range(len(__UpperCAmelCase      )      )      )      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
					SCREAMING_SNAKE_CASE  :						Tuple      					= {'''unk_token''': '''<unk>'''}

					SCREAMING_SNAKE_CASE  :						Dict      					= os.path.join(self.tmpdirname,					'''bart_tokenizer'''      )
					os.makedirs(__UpperCAmelCase,					exist_ok=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= os.path.join(__UpperCAmelCase,					BART_VOCAB_FILES_NAMES['''vocab_file''']      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= os.path.join(__UpperCAmelCase,					BART_VOCAB_FILES_NAMES['''merges_file''']      )
					with open(self.vocab_file,					'''w''',					encoding='''utf-8'''      ) as fp:
									fp.write(json.dumps(__UpperCAmelCase      ) + '''\n'''      )
					with open(self.merges_file,					'''w''',					encoding='''utf-8'''      ) as fp:
									fp.write('''\n'''.join(__UpperCAmelCase      )      )


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

					"""simple docstring"""

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


	def 				lowercase__  (self : str      )     ->			Dict:

					"""simple docstring"""

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


	def 				lowercase__  (self : str      )     ->			Dict:

					"""simple docstring"""

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


	def 				lowercase__  (self : Dict      )     ->			List[Any]:

					"""simple docstring"""

					shutil.rmtree(self.tmpdirname      )


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= 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 				lowercase__  (self : List[Any]      )     ->			Optional[int]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.get_dummy_dataset()
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= 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:
									SCREAMING_SNAKE_CASE  :						List[Any]      					= dataset
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= RagRetriever(
									    __UpperCAmelCase,					question_encoder_tokenizer=self.get_dpr_tokenizer(),					generator_tokenizer=self.get_bart_tokenizer(),					)
					return retriever


	def 				lowercase__  (self : Optional[Any],					__UpperCAmelCase : bool      )     ->			Dict:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.get_dummy_dataset()
					SCREAMING_SNAKE_CASE  :						int      					= RagConfig(
					    retrieval_vector_size=self.retrieval_vector_size,					question_encoder=DPRConfig().to_dict(),					generator=BartConfig().to_dict(),					index_name='''custom''',					)
					if from_disk:
									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= os.path.join(self.tmpdirname,					'''dataset'''      )
									SCREAMING_SNAKE_CASE  :						str      					= 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
									SCREAMING_SNAKE_CASE  :						List[str]      					= RagRetriever(
									    __UpperCAmelCase,					question_encoder_tokenizer=self.get_dpr_tokenizer(),					generator_tokenizer=self.get_bart_tokenizer(),					)
					else:
									SCREAMING_SNAKE_CASE  :						List[str]      					= RagRetriever(
									    __UpperCAmelCase,					question_encoder_tokenizer=self.get_dpr_tokenizer(),					generator_tokenizer=self.get_bart_tokenizer(),					index=CustomHFIndex(config.retrieval_vector_size,					__UpperCAmelCase      ),					)
					return retriever


	def 				lowercase__  (self : Any      )     ->			Optional[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= 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      )

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= 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'''      )      )

					SCREAMING_SNAKE_CASE  :						str      					= os.path.join(self.tmpdirname,					'''psgs_w100.tsv.pkl'''      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= {sample['''id''']: [sample['''text'''], sample['''title''']] for sample in dataset}
					pickle.dump(__UpperCAmelCase,					open(__UpperCAmelCase,					'''wb'''      )      )

					SCREAMING_SNAKE_CASE  :						str      					= RagConfig(
					    retrieval_vector_size=self.retrieval_vector_size,					question_encoder=DPRConfig().to_dict(),					generator=BartConfig().to_dict(),					index_name='''legacy''',					index_path=self.tmpdirname,					)
					SCREAMING_SNAKE_CASE  :						List[str]      					= RagRetriever(
					    __UpperCAmelCase,					question_encoder_tokenizer=self.get_dpr_tokenizer(),					generator_tokenizer=self.get_bart_tokenizer()      )
					return retriever


	def 				lowercase__  (self : Optional[int]      )     ->			Dict:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= 1
					SCREAMING_SNAKE_CASE  :						List[Any]      					= self.get_dummy_canonical_hf_index_retriever()
					SCREAMING_SNAKE_CASE  :						Any      					= np.array(
					    [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )],					dtype=np.floataa      )
					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						int      					= retriever.retrieve(__UpperCAmelCase,					n_docs=__UpperCAmelCase      )
					self.assertEqual(retrieved_doc_embeds.shape,					(2, n_docs, self.retrieval_vector_size)      )
					self.assertEqual(len(__UpperCAmelCase      ),					2      )
					self.assertEqual(sorted(doc_dicts[0]      ),					['''embeddings''', '''id''', '''text''', '''title''']      )
					self.assertEqual(len(doc_dicts[0]['''id''']      ),					__UpperCAmelCase      )
					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 				lowercase__  (self : List[Any]      )     ->			Dict:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= 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:
													SCREAMING_SNAKE_CASE  :						Any      					= self.get_dummy_dataset()
													retriever.save_pretrained(__UpperCAmelCase      )
													SCREAMING_SNAKE_CASE  :						Optional[int]      					= RagRetriever.from_pretrained(__UpperCAmelCase      )
													self.assertIsInstance(__UpperCAmelCase,					__UpperCAmelCase      )
													SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= np.array(
													    [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )],					dtype=np.floataa      )
													SCREAMING_SNAKE_CASE  :						Tuple      					= retriever.retrieve(__UpperCAmelCase,					n_docs=1      )
													self.assertTrue(out is not None      )


	def 				lowercase__  (self : int      )     ->			str:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= 1
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.get_dummy_custom_hf_index_retriever(from_disk=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= np.array(
					    [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )],					dtype=np.floataa      )
					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Optional[int]      					= retriever.retrieve(__UpperCAmelCase,					n_docs=__UpperCAmelCase      )
					self.assertEqual(retrieved_doc_embeds.shape,					(2, n_docs, self.retrieval_vector_size)      )
					self.assertEqual(len(__UpperCAmelCase      ),					2      )
					self.assertEqual(sorted(doc_dicts[0]      ),					['''embeddings''', '''id''', '''text''', '''title''']      )
					self.assertEqual(len(doc_dicts[0]['''id''']      ),					__UpperCAmelCase      )
					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 				lowercase__  (self : Any      )     ->			Tuple:

					"""simple docstring"""

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


	def 				lowercase__  (self : int      )     ->			Dict:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= 1
					SCREAMING_SNAKE_CASE  :						Tuple      					= self.get_dummy_custom_hf_index_retriever(from_disk=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= np.array(
					    [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )],					dtype=np.floataa      )
					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						List[str]      					= retriever.retrieve(__UpperCAmelCase,					n_docs=__UpperCAmelCase      )
					self.assertEqual(retrieved_doc_embeds.shape,					(2, n_docs, self.retrieval_vector_size)      )
					self.assertEqual(len(__UpperCAmelCase      ),					2      )
					self.assertEqual(sorted(doc_dicts[0]      ),					['''embeddings''', '''id''', '''text''', '''title''']      )
					self.assertEqual(len(doc_dicts[0]['''id''']      ),					__UpperCAmelCase      )
					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 				lowercase__  (self : List[Any]      )     ->			Optional[int]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= self.get_dummy_custom_hf_index_retriever(from_disk=__UpperCAmelCase      )
					with tempfile.TemporaryDirectory() as tmp_dirname:
									retriever.save_pretrained(__UpperCAmelCase      )
									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= RagRetriever.from_pretrained(__UpperCAmelCase      )
									self.assertIsInstance(__UpperCAmelCase,					__UpperCAmelCase      )
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= np.array(
									    [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )],					dtype=np.floataa      )
									SCREAMING_SNAKE_CASE  :						Dict      					= retriever.retrieve(__UpperCAmelCase,					n_docs=1      )
									self.assertTrue(out is not None      )


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						int      					= 1
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.get_dummy_legacy_index_retriever()
					SCREAMING_SNAKE_CASE  :						List[str]      					= np.array(
					    [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )],					dtype=np.floataa      )
					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= retriever.retrieve(__UpperCAmelCase,					n_docs=__UpperCAmelCase      )
					self.assertEqual(retrieved_doc_embeds.shape,					(2, n_docs, self.retrieval_vector_size)      )
					self.assertEqual(len(__UpperCAmelCase      ),					2      )
					self.assertEqual(sorted(doc_dicts[0]      ),					['''text''', '''title''']      )
					self.assertEqual(len(doc_dicts[0]['''text''']      ),					__UpperCAmelCase      )
					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 				lowercase__  (self : int      )     ->			Tuple:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= self.get_dummy_legacy_index_retriever()
					with tempfile.TemporaryDirectory() as tmp_dirname:
									retriever.save_pretrained(__UpperCAmelCase      )
									SCREAMING_SNAKE_CASE  :						List[str]      					= RagRetriever.from_pretrained(__UpperCAmelCase      )
									self.assertIsInstance(__UpperCAmelCase,					__UpperCAmelCase      )
									SCREAMING_SNAKE_CASE  :						Optional[Any]      					= np.array(
									    [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )],					dtype=np.floataa      )
									SCREAMING_SNAKE_CASE  :						str      					= retriever.retrieve(__UpperCAmelCase,					n_docs=1      )
									self.assertTrue(out is not None      )


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

					"""simple docstring"""

					import torch

					SCREAMING_SNAKE_CASE  :						List[Any]      					= 1
					SCREAMING_SNAKE_CASE  :						Dict      					= self.get_dummy_canonical_hf_index_retriever()
					SCREAMING_SNAKE_CASE  :						Dict      					= [[5, 7], [10, 11]]
					SCREAMING_SNAKE_CASE  :						List[Any]      					= np.array(
					    [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )],					dtype=np.floataa      )
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= retriever(__UpperCAmelCase,					__UpperCAmelCase,					prefix=retriever.config.generator.prefix,					n_docs=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						int      					= (
					    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(__UpperCAmelCase,					__UpperCAmelCase      )
					self.assertIsInstance(__UpperCAmelCase,					__UpperCAmelCase      )
					self.assertIsInstance(__UpperCAmelCase,					np.ndarray      )

					SCREAMING_SNAKE_CASE  :						List[str]      					= retriever(
					    __UpperCAmelCase,					__UpperCAmelCase,					prefix=retriever.config.generator.prefix,					n_docs=__UpperCAmelCase,					return_tensors='''pt''',					)
					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						int      					= (  # 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(__UpperCAmelCase,					torch.Tensor      )
					self.assertIsInstance(__UpperCAmelCase,					torch.Tensor      )
					self.assertIsInstance(__UpperCAmelCase,					torch.Tensor      )






	@require_torch
	@require_tokenizers
	@require_sentencepiece
	def 				lowercase__  (self : Tuple      )     ->			Any:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.get_dpr_ctx_encoder_tokenizer()
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= 1
					SCREAMING_SNAKE_CASE  :						List[str]      					= self.get_dummy_custom_hf_index_retriever(from_disk=__UpperCAmelCase      )
					retriever.set_ctx_encoder_tokenizer(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Dict      					= [[5, 7], [10, 11]]
					SCREAMING_SNAKE_CASE  :						List[Any]      					= np.array(
					    [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )],					dtype=np.floataa      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= retriever(__UpperCAmelCase,					__UpperCAmelCase,					prefix=retriever.config.generator.prefix,					n_docs=__UpperCAmelCase      )

					self.assertEqual(
					    len(__UpperCAmelCase      ),					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''')      ),					__UpperCAmelCase      )  # check for doc token related keys in dictionary.









'''simple docstring'''

from manim import *


class 	a__     (				_lowercase	):


	def 				lowercase__  (self : Optional[Any]      )     ->			str:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[Any]      					= Rectangle(height=0.5,					width=0.5      )
					SCREAMING_SNAKE_CASE  :						str      					= Rectangle(height=0.46,					width=0.46      ).set_stroke(width=0      )

					SCREAMING_SNAKE_CASE  :						List[str]      					= [mem.copy() for i in range(6      )]
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= [mem.copy() for i in range(6      )]
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= VGroup(*__UpperCAmelCase      ).arrange(__UpperCAmelCase,					buff=0      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= VGroup(*__UpperCAmelCase      ).arrange(__UpperCAmelCase,					buff=0      )
					SCREAMING_SNAKE_CASE  :						List[Any]      					= VGroup(__UpperCAmelCase,					__UpperCAmelCase      ).arrange(__UpperCAmelCase,					buff=0      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= Text('''CPU''',					font_size=24      )
					SCREAMING_SNAKE_CASE  :						Dict      					= Group(__UpperCAmelCase,					__UpperCAmelCase      ).arrange(__UpperCAmelCase,					buff=0.5,					aligned_edge=__UpperCAmelCase      )
					cpu.move_to([-2.5, -0.5, 0]      )
					self.add(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Any      					= [mem.copy() for i in range(1      )]
					SCREAMING_SNAKE_CASE  :						Dict      					= VGroup(*__UpperCAmelCase      ).arrange(__UpperCAmelCase,					buff=0      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= Text('''GPU''',					font_size=24      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= Group(__UpperCAmelCase,					__UpperCAmelCase      ).arrange(__UpperCAmelCase,					buff=0.5,					aligned_edge=__UpperCAmelCase      )
					gpu.align_to(__UpperCAmelCase,					__UpperCAmelCase      )
					gpu.set_x(gpu.get_x() - 1      )

					self.add(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						List[Any]      					= [mem.copy() for i in range(6      )]
					SCREAMING_SNAKE_CASE  :						Any      					= VGroup(*__UpperCAmelCase      ).arrange(__UpperCAmelCase,					buff=0      )

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= Text('''Model''',					font_size=24      )
					SCREAMING_SNAKE_CASE  :						Dict      					= Group(__UpperCAmelCase,					__UpperCAmelCase      ).arrange(__UpperCAmelCase,					buff=0.5,					aligned_edge=__UpperCAmelCase      )
					model.move_to([3, -1.0, 0]      )

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

					SCREAMING_SNAKE_CASE  :						Tuple      					= MarkupText(
					    F'''First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.''',					font_size=24,					)

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= Square(side_length=2.2      )
					key.move_to([-5, 2, 0]      )

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= MarkupText(
					    F'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model''',					font_size=18,					)

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

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

					self.add(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						List[str]      					= []
					SCREAMING_SNAKE_CASE  :						List[str]      					= []
					SCREAMING_SNAKE_CASE  :						Dict      					= []
					for i, rect in enumerate(__UpperCAmelCase      ):
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= Rectangle(height=0.46,					width=0.46      ).set_stroke(width=0.0      ).set_fill(__UpperCAmelCase,					opacity=0.7      )
									cpu_target.move_to(__UpperCAmelCase      )
									cpu_target.generate_target()
									SCREAMING_SNAKE_CASE  :						List[str]      					= 0.46 / 4
									SCREAMING_SNAKE_CASE  :						Any      					= 0.46 / 3

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

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

					self.play(*__UpperCAmelCase      )
					self.play(*__UpperCAmelCase      )

					self.wait()




