
"""simple docstring"""




from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers import (
					    TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
					    TFTransfoXLForSequenceClassification,
					    TFTransfoXLLMHeadModel,
					    TFTransfoXLModel,
					)



class 			lowerCAmelCase_		:


				"""simple docstring"""



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     parent
											SCREAMING_SNAKE_CASE__		: str     =     13
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     7
											SCREAMING_SNAKE_CASE__		: List[Any]     =     30
											SCREAMING_SNAKE_CASE__		: Any     =     self.seq_length + self.mem_len
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     15
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     True
											SCREAMING_SNAKE_CASE__		: List[str]     =     True
											SCREAMING_SNAKE_CASE__		: Any     =     99
											SCREAMING_SNAKE_CASE__		: Tuple     =     [10, 50, 80]
											SCREAMING_SNAKE_CASE__		: Tuple     =     32
											SCREAMING_SNAKE_CASE__		: Dict     =     32
											SCREAMING_SNAKE_CASE__		: Dict     =     4
											SCREAMING_SNAKE_CASE__		: List[str]     =     8
											SCREAMING_SNAKE_CASE__		: Any     =     1_28
											SCREAMING_SNAKE_CASE__		: Any     =     2
											SCREAMING_SNAKE_CASE__		: str     =     2
											SCREAMING_SNAKE_CASE__		: str     =     None
											SCREAMING_SNAKE_CASE__		: int     =     1
											SCREAMING_SNAKE_CASE__		: Any     =     0
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     3
											SCREAMING_SNAKE_CASE__		: Tuple     =     self.vocab_size - 1
											SCREAMING_SNAKE_CASE__		: Tuple     =     0.01



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: List[Any]     =     ids_tensor([self.batch_size, self.seq_length]     , self.vocab_size					)
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     ids_tensor([self.batch_size, self.seq_length]     , self.vocab_size					)

											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     None
											if self.use_labels:
																		SCREAMING_SNAKE_CASE__		: Any     =     ids_tensor([self.batch_size, self.seq_length]     , self.vocab_size					)

											SCREAMING_SNAKE_CASE__		: int     =     TransfoXLConfig(
											    vocab_size=self.vocab_size     , mem_len=self.mem_len     , clamp_len=self.clamp_len     , cutoffs=self.cutoffs     , d_model=self.hidden_size     , d_embed=self.d_embed     , n_head=self.num_attention_heads     , d_head=self.d_head     , d_inner=self.d_inner     , div_val=self.div_val     , n_layer=self.num_hidden_layers     , eos_token_id=self.eos_token_id     , pad_token_id=self.vocab_size - 1     , init_range=self.init_range     , num_labels=self.num_labels     , )

											return (config, input_ids_a, input_ids_a, lm_labels)



				def 			__magic_name__					(self					)      ->	int:



											"""simple docstring"""




											random.seed(self.seed					)
											tf.random.set_seed(self.seed					)



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: int     =     TFTransfoXLModel(SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: List[str]     =     model(SCREAMING_SNAKE_CASE__					).to_tuple()

											SCREAMING_SNAKE_CASE__		: Any     =     {"""input_ids""": input_ids_a, """mems""": mems_a}

											SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: Tuple     =     model(SCREAMING_SNAKE_CASE__					).to_tuple()

											self.parent.assertEqual(hidden_states_a.shape     , (self.batch_size, self.seq_length, self.hidden_size)					)
											self.parent.assertEqual(hidden_states_a.shape     , (self.batch_size, self.seq_length, self.hidden_size)					)
											self.parent.assertListEqual(
											    [mem.shape for mem in mems_a]     , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers     , )
											self.parent.assertListEqual(
											    [mem.shape for mem in mems_a]     , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers     , )



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Any     =     TFTransfoXLLMHeadModel(SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: Any     =     model(SCREAMING_SNAKE_CASE__					).to_tuple()

											SCREAMING_SNAKE_CASE__		: List[str]     =     {"""input_ids""": input_ids_a, """labels""": lm_labels}
											SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: Optional[Any]     =     model(SCREAMING_SNAKE_CASE__					).to_tuple()

											SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: Tuple     =     model([input_ids_a, mems_a]					).to_tuple()

											SCREAMING_SNAKE_CASE__		: List[Any]     =     {"""input_ids""": input_ids_a, """mems""": mems_a, """labels""": lm_labels}

											SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: List[str]     =     model(SCREAMING_SNAKE_CASE__					).to_tuple()

											self.parent.assertEqual(lm_logits_a.shape     , (self.batch_size, self.seq_length, self.vocab_size)					)
											self.parent.assertListEqual(
											    [mem.shape for mem in mems_a]     , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers     , )

											self.parent.assertEqual(lm_logits_a.shape     , (self.batch_size, self.seq_length, self.vocab_size)					)
											self.parent.assertListEqual(
											    [mem.shape for mem in mems_a]     , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers     , )



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: str     =     TFTransfoXLForSequenceClassification(SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: Dict     =     model(SCREAMING_SNAKE_CASE__					)
											self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.num_labels)					)



				def 			__magic_name__					(self					)      ->	str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     self.prepare_config_and_inputs()
											((SCREAMING_SNAKE_CASE__)   ,   (SCREAMING_SNAKE_CASE__)   ,   (SCREAMING_SNAKE_CASE__)   ,   (SCREAMING_SNAKE_CASE__))		: Union[str, Any]     =     config_and_inputs
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     {"""input_ids""": input_ids_a}
											return config, inputs_dict






@require_tf
class 			lowerCAmelCase_		(a__				,      a__				,      unittest.TestCase						):


				"""simple docstring"""



				__UpperCamelCase   :		Any       =     (
				    (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
				)
				__UpperCamelCase   :		Any       =     () if is_tf_available() else ()
				__UpperCamelCase   :		str       =     (
				    {
				        '''feature-extraction''': TFTransfoXLModel,
				        '''text-classification''': TFTransfoXLForSequenceClassification,
				        '''text-generation''': TFTransfoXLLMHeadModel,
				        '''zero-shot''': TFTransfoXLForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)
				# TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
				__UpperCamelCase   :		Optional[int]       =     False
				__UpperCamelCase   :		Any       =     False
				__UpperCamelCase   :		Any       =     False
				__UpperCamelCase   :		Union[str, Any]       =     False



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



											"""simple docstring"""




											if pipeline_test_casse_name == "TextGenerationPipelineTests":
																		# Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
																		# `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
																		# tokenizer.
																		return True

											return False



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Dict     =     TFTransfoXLModelTester(self					)
											SCREAMING_SNAKE_CASE__		: List[str]     =     ConfigTester(self     , config_class=SCREAMING_SNAKE_CASE__     , d_embed=37					)



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



											"""simple docstring"""




											self.config_tester.run_common_tests()



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



											"""simple docstring"""




											self.model_tester.set_seed()
											SCREAMING_SNAKE_CASE__		: Tuple     =     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_transfo_xl_model(*SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self					)      ->	Any:



											"""simple docstring"""




											self.model_tester.set_seed()
											SCREAMING_SNAKE_CASE__		: Dict     =     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_transfo_xl_lm_head(*SCREAMING_SNAKE_CASE__					)



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: str     =     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self					)      ->	Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: int     =     self.model_tester.prepare_config_and_inputs_for_common()
											SCREAMING_SNAKE_CASE__		: Dict     =     [TFTransfoXLForSequenceClassification]

											for model_class in self.all_model_classes:
																		SCREAMING_SNAKE_CASE__		: List[Any]     =     model_class(SCREAMING_SNAKE_CASE__					)
																		assert isinstance(model.get_input_embeddings()     , tf.keras.layers.Layer					)
																		if model_class in list_other_models_with_output_ebd:
																									SCREAMING_SNAKE_CASE__		: int     =     model.get_output_embeddings()
																									assert isinstance(SCREAMING_SNAKE_CASE__     , tf.keras.layers.Layer					)
																									SCREAMING_SNAKE_CASE__		: str     =     model.get_bias()
																									assert name is None
																		else:
																									SCREAMING_SNAKE_CASE__		: int     =     model.get_output_embeddings()
																									assert x is None
																									SCREAMING_SNAKE_CASE__		: List[Any]     =     model.get_bias()
																									assert name is None



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



											"""simple docstring"""




											pass



				@slow
				def 			__magic_name__					(self					)      ->	str:



											"""simple docstring"""




											for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     TFTransfoXLModel.from_pretrained(SCREAMING_SNAKE_CASE__					)
																		self.assertIsNotNone(SCREAMING_SNAKE_CASE__					)



				@unittest.skip(reason="""This model doesn't play well with fit() due to not returning a single loss."""					)
				def 			__magic_name__					(self					)      ->	str:



											"""simple docstring"""




											pass






@require_tf
class 			lowerCAmelCase_		(unittest.TestCase						):


				"""simple docstring"""



				@unittest.skip("""Skip test until #12651 is resolved."""					)
				@slow
				def 			__magic_name__					(self					)      ->	Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     TFTransfoXLLMHeadModel.from_pretrained("""transfo-xl-wt103"""					)
											# fmt: off
											SCREAMING_SNAKE_CASE__		: Dict     =     tf.convert_to_tensor([[33,12_97,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,22,17_06,17,2_00_98,5,32_15,21,37,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,62_24,8_31,1_60_02,2,8,6_03,7_89_67,2_95_46,23,8_03,20,25,4_16,5,8,2_32,4,2_77,6,18_55,46_01,3,2_95_46,54,8,36_09,5,5_72_11,49,4,1,2_77,18,8,17_55,1_56_91,3,3_41,25,4_16,6_93,4_25_73,71,17,4_01,94,31,1_79_19,2,2_95_46,78_73,18,1,4_35,23,1_10_11,7_55,5,51_67,3,79_83,98,84,2,2_95_46,32_67,8,36_09,4,1,48_65,10_75,2,60_87,71,6,3_46,8,58_54,3,2_95_46,8_24,14_00,18_68,2,19,1_60,2,3_11,8,54_96,2,2_09_20,17,25,1_50_97,3,24,24,0]]     , dtype=tf.intaa					)  # noqa: E231
											# fmt: on
											#  In 1991 , the remains of Russian Tsar Nicholas II and his family
											#  ( except for Alexei and Maria ) are discovered .
											#  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
											#  remainder of the story . 1883 Western Siberia ,
											#  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
											#  Rasputin has a vision and denounces one of the men as a horse thief . Although his
											#  father initially slaps him for making such an accusation , Rasputin watches as the
											#  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
											#  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
											#  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

											# fmt: off
											SCREAMING_SNAKE_CASE__		: Any     =     [33,12_97,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,22,17_06,17,2_00_98,5,32_15,21,37,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,62_24,8_31,1_60_02,2,8,6_03,7_89_67,2_95_46,23,8_03,20,25,4_16,5,8,2_32,4,2_77,6,18_55,46_01,3,2_95_46,54,8,36_09,5,5_72_11,49,4,1,2_77,18,8,17_55,1_56_91,3,3_41,25,4_16,6_93,4_25_73,71,17,4_01,94,31,1_79_19,2,2_95_46,78_73,18,1,4_35,23,1_10_11,7_55,5,51_67,3,79_83,98,84,2,2_95_46,32_67,8,36_09,4,1,48_65,10_75,2,60_87,71,6,3_46,8,58_54,3,2_95_46,8_24,14_00,18_68,2,19,1_60,2,3_11,8,54_96,2,2_09_20,17,25,1_50_97,3,24,24,0,33,1,18_57,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,28,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,0]  # noqa: E231
											# fmt: on
											#  In 1991, the remains of Russian Tsar Nicholas II and his family (
											#  except for Alexei and Maria ) are discovered. The voice of young son,
											#  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
											#  1883 Western Siberia, a young Grigori Rasputin is asked by his father
											#  and a group of men to perform magic. Rasputin has a vision and
											#  denounces one of the men as a horse thief. Although his father initially
											#  slaps him for making such an accusation, Rasputin watches as the man
											#  is chased outside and beaten. Twenty years later, Rasputin sees a vision
											#  of the Virgin Mary, prompting him to become a priest.
											#  Rasputin quickly becomes famous, with people, even a bishop, begging for
											#  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
											# Nicholas II and his family were discovered. The voice of <unk> young son,
											# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     model.generate(SCREAMING_SNAKE_CASE__     , max_length=2_00     , do_sample=SCREAMING_SNAKE_CASE__					)
											self.assertListEqual(output_ids[0].numpy().tolist()     , SCREAMING_SNAKE_CASE__					)






"""simple docstring"""




from manim import *



class 			lowerCAmelCase_		(a__						):


				"""simple docstring"""



				def 			__magic_name__					(self					)      ->	Any:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Dict     =     Rectangle(height=0.5     , width=0.5					)
											SCREAMING_SNAKE_CASE__		: Dict     =     Rectangle(height=0.46     , width=0.46					).set_stroke(width=0					)

											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     Rectangle(height=0.25     , width=0.25					)

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

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

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

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

											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     []
											SCREAMING_SNAKE_CASE__		: Any     =     []

											for i, rect in enumerate(SCREAMING_SNAKE_CASE__					):
																		SCREAMING_SNAKE_CASE__		: List[str]     =     fill.copy().set_fill(SCREAMING_SNAKE_CASE__     , opacity=0.8					)
																		target.move_to(SCREAMING_SNAKE_CASE__					)
																		model_arr.append(SCREAMING_SNAKE_CASE__					)

																		SCREAMING_SNAKE_CASE__		: List[str]     =     Rectangle(height=0.46     , width=0.46					).set_stroke(width=0.0					).set_fill(SCREAMING_SNAKE_CASE__     , opacity=0.8					)
																		cpu_target.move_to(cpu_left_col_base[i]					)
																		model_cpu_arr.append(SCREAMING_SNAKE_CASE__					)

											self.add(*SCREAMING_SNAKE_CASE__     , *SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: Tuple     =     [meta_mem.copy() for i in range(6					)]
											SCREAMING_SNAKE_CASE__		: List[str]     =     [meta_mem.copy() for i in range(6					)]
											SCREAMING_SNAKE_CASE__		: Any     =     VGroup(*SCREAMING_SNAKE_CASE__					).arrange(SCREAMING_SNAKE_CASE__     , buff=0					)
											SCREAMING_SNAKE_CASE__		: int     =     VGroup(*SCREAMING_SNAKE_CASE__					).arrange(SCREAMING_SNAKE_CASE__     , buff=0					)
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     VGroup(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					).arrange(SCREAMING_SNAKE_CASE__     , buff=0					)
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     Text("""Disk"""     , font_size=24					)
											SCREAMING_SNAKE_CASE__		: Any     =     Group(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					).arrange(SCREAMING_SNAKE_CASE__     , buff=0.5     , aligned_edge=SCREAMING_SNAKE_CASE__					)
											disk.move_to([-4, -1.25, 0]					)
											self.add(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)

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

											SCREAMING_SNAKE_CASE__		: List[str]     =     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(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: Any     =     MarkupText(
											    F'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint'''     , font_size=18     , )

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

											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     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(SCREAMING_SNAKE_CASE__					)					)

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

											self.play(Write(SCREAMING_SNAKE_CASE__					)					)

											input.generate_target()
											input.target.next_to(model_arr[0]     , direction=SCREAMING_SNAKE_CASE__     , buff=0.02					)
											self.play(MoveToTarget(SCREAMING_SNAKE_CASE__					)					)

											self.play(FadeOut(SCREAMING_SNAKE_CASE__					)					)

											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     Arrow(start=SCREAMING_SNAKE_CASE__     , end=SCREAMING_SNAKE_CASE__     , color=SCREAMING_SNAKE_CASE__     , buff=0.5					)
											a.next_to(model_arr[0].get_left()     , SCREAMING_SNAKE_CASE__     , buff=0.2					)

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

											SCREAMING_SNAKE_CASE__		: Tuple     =     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(SCREAMING_SNAKE_CASE__     , run_time=3					)					)

											SCREAMING_SNAKE_CASE__		: Tuple     =     {"""run_time""": 1, """fade_in""": True, """fade_out""": True, """buff""": 0.02}

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

											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     a.copy()
											for i in range(6					):
																		a_c.next_to(model_arr[i].get_right() + 0.02     , SCREAMING_SNAKE_CASE__     , buff=0.2					)

																		input.generate_target()
																		input.target.move_to(model_arr[i].get_right() + 0.02					)

																		SCREAMING_SNAKE_CASE__		: Optional[Any]     =     AnimationGroup(
																		    FadeOut(SCREAMING_SNAKE_CASE__     , run_time=0.5					)     , MoveToTarget(SCREAMING_SNAKE_CASE__     , run_time=0.5					)     , FadeIn(SCREAMING_SNAKE_CASE__     , run_time=0.5					)     , lag_ratio=0.2					)

																		self.play(SCREAMING_SNAKE_CASE__					)

																		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:
																																SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     0.7

																									self.play(
																									    Circumscribe(model_arr[i]     , **SCREAMING_SNAKE_CASE__					)     , Circumscribe(cpu_left_col_base[i]     , **SCREAMING_SNAKE_CASE__					)     , Circumscribe(cpu_left_col_base[i + 1]     , color=SCREAMING_SNAKE_CASE__     , **SCREAMING_SNAKE_CASE__					)     , Circumscribe(gpu_rect[0]     , color=SCREAMING_SNAKE_CASE__     , **SCREAMING_SNAKE_CASE__					)     , Circumscribe(model_arr[i + 1]     , color=SCREAMING_SNAKE_CASE__     , **SCREAMING_SNAKE_CASE__					)     , )
																									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 + 0.02     , buff=0.2					)

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

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

																		SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     a_c
																		SCREAMING_SNAKE_CASE__		: Optional[Any]     =     a_c.copy()

											input.generate_target()
											input.target.next_to(model_base[-1]     , RIGHT + 0.02     , buff=0.5					)
											self.play(
											    FadeOut(SCREAMING_SNAKE_CASE__					)     , FadeOut(SCREAMING_SNAKE_CASE__     , run_time=0.5					)     , )

											SCREAMING_SNAKE_CASE__		: Dict     =     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(SCREAMING_SNAKE_CASE__     , run_time=3					)     , MoveToTarget(SCREAMING_SNAKE_CASE__					)					)

											self.wait()




