
import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase__   :					Optional[Any]												=				get_tests_dir('fixtures/test_sentencepiece_no_bos.model')



@require_sentencepiece
@require_tokenizers
class 			lowerCAmelCase_		(_lowercase				,      unittest.TestCase						):


				"""simple docstring"""



				__UpperCamelCase   :		Tuple       =     PegasusTokenizer
				__UpperCamelCase   :		Optional[int]       =     PegasusTokenizerFast
				__UpperCamelCase   :		str       =     True
				__UpperCamelCase   :		List[Any]       =     True



				def 			__magic_name__					(self					)      ->	Any:



											"""simple docstring"""




											super().setUp()

											# We have a SentencePiece fixture for testing
											SCREAMING_SNAKE_CASE__		: List[Any]     =     PegasusTokenizer(A_					)
											tokenizer.save_pretrained(self.tmpdirname					)



				@cached_property
				def 			__magic_name__					(self					)      ->	Dict:



											"""simple docstring"""




											return PegasusTokenizer.from_pretrained("""google/pegasus-large"""					)



				def 			__magic_name__					(self     , **SCREAMING_SNAKE_CASE__					)      ->	PegasusTokenizer:



											"""simple docstring"""




											return PegasusTokenizer.from_pretrained(self.tmpdirname     , **A_					)



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	str:



											"""simple docstring"""




											return ("This is a test", "This is a test")



				def 			__magic_name__					(self					)      ->	int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: int     =     """</s>"""
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     1

											self.assertEqual(self.get_tokenizer()._convert_token_to_id(A_					)     , A_					)
											self.assertEqual(self.get_tokenizer()._convert_id_to_token(A_					)     , A_					)



				def 			__magic_name__					(self					)      ->	int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: List[Any]     =     list(self.get_tokenizer().get_vocab().keys()					)

											self.assertEqual(vocab_keys[0]     , """<pad>"""					)
											self.assertEqual(vocab_keys[1]     , """</s>"""					)
											self.assertEqual(vocab_keys[-1]     , """v"""					)
											self.assertEqual(len(A_					)     , 11_03					)



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



											"""simple docstring"""




											self.assertEqual(self.get_tokenizer().vocab_size     , 11_03					)



				def 			__magic_name__					(self					)      ->	str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: List[Any]     =     self.rust_tokenizer_class.from_pretrained(self.tmpdirname					)
											SCREAMING_SNAKE_CASE__		: Tuple     =     self.tokenizer_class.from_pretrained(self.tmpdirname					)
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     (
											    """Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"""
											    """ </s> <pad> <pad> <pad>"""
											)
											SCREAMING_SNAKE_CASE__		: List[str]     =     rust_tokenizer([raw_input_str]     , return_tensors=A_     , add_special_tokens=A_					).input_ids[0]
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     py_tokenizer([raw_input_str]     , return_tensors=A_     , add_special_tokens=A_					).input_ids[0]
											self.assertListEqual(A_     , A_					)



				def 			__magic_name__					(self					)      ->	Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Any     =     self._large_tokenizer
											# <mask_1> masks whole sentence while <mask_2> masks single word
											SCREAMING_SNAKE_CASE__		: List[Any]     =     """<mask_1> To ensure a <mask_2> flow of bank resolutions."""
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     [2, 4_13, 6_15, 1_14, 3, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     tokenizer([raw_input_str]     , return_tensors=A_					).input_ids[0]
											self.assertListEqual(A_     , A_					)



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[int]     =     self._large_tokenizer
											# The tracebacks for the following asserts are **better** without messages or self.assertEqual
											assert tokenizer.vocab_size == 9_61_03
											assert tokenizer.pad_token_id == 0
											assert tokenizer.eos_token_id == 1
											assert tokenizer.offset == 1_03
											assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_05
											assert tokenizer.unk_token == "<unk>"
											assert tokenizer.model_max_length == 10_24
											SCREAMING_SNAKE_CASE__		: str     =     """To ensure a smooth flow of bank resolutions."""
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     [4_13, 6_15, 1_14, 22_91, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
											SCREAMING_SNAKE_CASE__		: Tuple     =     tokenizer([raw_input_str]     , return_tensors=A_					).input_ids[0]
											self.assertListEqual(A_     , A_					)
											assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]					) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]



				@require_torch
				def 			__magic_name__					(self					)      ->	Any:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: str     =     ["""This is going to be way too long.""" * 1_50, """short example"""]
											SCREAMING_SNAKE_CASE__		: int     =     ["""not super long but more than 5 tokens""", """tiny"""]
											SCREAMING_SNAKE_CASE__		: List[Any]     =     self._large_tokenizer(A_     , padding=A_     , truncation=A_     , return_tensors="""pt"""					)
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     self._large_tokenizer(
											    text_target=A_     , max_length=5     , padding=A_     , truncation=A_     , return_tensors="""pt"""					)

											assert batch.input_ids.shape == (2, 10_24)
											assert batch.attention_mask.shape == (2, 10_24)
											assert targets["input_ids"].shape == (2, 5)
											assert len(A_					) == 2  # input_ids, attention_mask.



				@slow
				def 			__magic_name__					(self					)      ->	str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: int     =     {"""input_ids""": [[3_89_79, 1_43, 1_84_85, 6_06, 1_30, 2_66_69, 8_76_86, 1_21, 5_41_89, 11_29, 1_11, 2_66_69, 8_76_86, 1_21, 91_14, 1_47_87, 1_21, 1_32_49, 1_58, 5_92, 9_56, 1_21, 1_46_21, 3_15_76, 1_43, 6_26_13, 1_08, 96_88, 9_30, 4_34_30, 1_15_62, 6_26_13, 3_04, 1_08, 1_14_43, 8_97, 1_08, 93_14, 1_74_15, 6_33_99, 1_08, 1_14_43, 76_14, 1_83_16, 1_18, 42_84, 71_48, 1_24_30, 1_43, 14_00, 2_57_03, 1_58, 1_11, 42_84, 71_48, 1_17_72, 1_43, 2_12_97, 10_64, 1_58, 1_22, 2_04, 35_06, 17_54, 11_33, 1_47_87, 15_81, 1_15, 3_32_24, 44_82, 1_11, 13_55, 1_10, 2_91_73, 3_17, 5_08_33, 1_08, 2_01_47, 9_46_65, 1_11, 7_71_98, 1_07, 1], [1_10, 6_26_13, 1_17, 6_38, 1_12, 11_33, 1_21, 2_00_98, 13_55, 7_90_50, 1_38_72, 1_35, 15_96, 5_35_41, 13_52, 1_41, 1_30_39, 55_42, 1_24, 3_02, 5_18, 1_11, 2_68, 29_56, 1_15, 1_49, 44_27, 1_07, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_39, 12_35, 27_99, 1_82_89, 1_77_80, 2_04, 1_09, 94_74, 12_96, 1_07, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
											# fmt: on

											self.tokenizer_integration_test_util(
											    expected_encoding=A_     , model_name="""google/bigbird-pegasus-large-arxiv"""     , revision="""ba85d0851d708441f91440d509690f1ab6353415"""     , )






@require_sentencepiece
@require_tokenizers
class 			lowerCAmelCase_		(_lowercase				,      unittest.TestCase						):


				"""simple docstring"""



				__UpperCamelCase   :		Dict       =     PegasusTokenizer
				__UpperCamelCase   :		Dict       =     PegasusTokenizerFast
				__UpperCamelCase   :		Any       =     True
				__UpperCamelCase   :		Dict       =     True



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



											"""simple docstring"""




											super().setUp()

											# We have a SentencePiece fixture for testing
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     PegasusTokenizer(A_     , offset=0     , mask_token_sent=A_     , mask_token="""[MASK]"""					)
											tokenizer.save_pretrained(self.tmpdirname					)



				@cached_property
				def 			__magic_name__					(self					)      ->	Optional[Any]:



											"""simple docstring"""




											return PegasusTokenizer.from_pretrained("""google/bigbird-pegasus-large-arxiv"""					)



				def 			__magic_name__					(self     , **SCREAMING_SNAKE_CASE__					)      ->	PegasusTokenizer:



											"""simple docstring"""




											return PegasusTokenizer.from_pretrained(self.tmpdirname     , **A_					)



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	Dict:



											"""simple docstring"""




											return ("This is a test", "This is a test")



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Any     =     self.rust_tokenizer_class.from_pretrained(self.tmpdirname					)
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     self.tokenizer_class.from_pretrained(self.tmpdirname					)
											SCREAMING_SNAKE_CASE__		: List[Any]     =     (
											    """Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"""
											    """ <pad> <pad> <pad>"""
											)
											SCREAMING_SNAKE_CASE__		: List[Any]     =     rust_tokenizer([raw_input_str]     , return_tensors=A_     , add_special_tokens=A_					).input_ids[0]
											SCREAMING_SNAKE_CASE__		: Dict     =     py_tokenizer([raw_input_str]     , return_tensors=A_     , add_special_tokens=A_					).input_ids[0]
											self.assertListEqual(A_     , A_					)



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: str     =     ["""This is going to be way too long.""" * 10_00, """short example"""]
											SCREAMING_SNAKE_CASE__		: List[str]     =     ["""not super long but more than 5 tokens""", """tiny"""]
											SCREAMING_SNAKE_CASE__		: int     =     self._large_tokenizer(A_     , padding=A_     , truncation=A_     , return_tensors="""pt"""					)
											SCREAMING_SNAKE_CASE__		: Dict     =     self._large_tokenizer(
											    text_target=A_     , max_length=5     , padding=A_     , truncation=A_     , return_tensors="""pt"""					)

											assert batch.input_ids.shape == (2, 40_96)
											assert batch.attention_mask.shape == (2, 40_96)
											assert targets["input_ids"].shape == (2, 5)
											assert len(A_					) == 2  # input_ids, attention_mask.



				def 			__magic_name__					(self					)      ->	str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: int     =     (
											    """This is an example string that is used to test the original TF implementation against the HF"""
											    """ implementation"""
											)

											SCREAMING_SNAKE_CASE__		: Dict     =     self._large_tokenizer(A_					).input_ids

											self.assertListEqual(
											    A_     , [1_82, 1_17, 1_42, 5_87, 42_11, 1_20, 1_17, 2_63, 1_12, 8_04, 1_09, 8_56, 2_50_16, 31_37, 4_64, 1_09, 2_69_55, 31_37, 1]     , )






"""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()




