



'''simple docstring'''






from manim import *

class 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






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

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

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

								self.add(_snake_case		)

								UpperCAmelCase :       List[Any] 							=		[mem.copy() for i in range(6		)]
								UpperCAmelCase :       Union[str, Any] 							=		VGroup(*_snake_case		).arrange(_snake_case		,			buff=0		)

								UpperCAmelCase :       List[str] 							=		Text('''Model'''		,			font_size=24		)
								UpperCAmelCase :       List[str] 							=		Group(_snake_case		,			_snake_case		).arrange(_snake_case		,			buff=0.5		,			aligned_edge=_snake_case		)
								model.move_to([3, -1.0, 0]		)

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

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

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

								UpperCAmelCase :       int 							=		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(_snake_case		,			run_time=2.5		)		,			Write(_snake_case		)		,			Write(_snake_case		)		)

								self.add(_snake_case		)

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

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

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

								self.play(*_snake_case		)
								self.play(*_snake_case		)

								self.wait()











from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case_ : Dict    		=       {"configuration_mbart": ["MBART_PRETRAINED_CONFIG_ARCHIVE_MAP", "MBartConfig", "MBartOnnxConfig"]}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : Tuple    		=       ["MBartTokenizer"]

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : str    		=       ["MBartTokenizerFast"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : List[Any]    		=       [
        "MBART_PRETRAINED_MODEL_ARCHIVE_LIST",
        "MBartForCausalLM",
        "MBartForConditionalGeneration",
        "MBartForQuestionAnswering",
        "MBartForSequenceClassification",
        "MBartModel",
        "MBartPreTrainedModel",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : Any    		=       [
        "TFMBartForConditionalGeneration",
        "TFMBartModel",
        "TFMBartPreTrainedModel",
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : List[str]    		=       [
        "FlaxMBartForConditionalGeneration",
        "FlaxMBartForQuestionAnswering",
        "FlaxMBartForSequenceClassification",
        "FlaxMBartModel",
        "FlaxMBartPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_mbart import MBartTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_mbart_fast import MBartTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mbart import (
            MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
            MBartForCausalLM,
            MBartForConditionalGeneration,
            MBartForQuestionAnswering,
            MBartForSequenceClassification,
            MBartModel,
            MBartPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_mbart import (
            FlaxMBartForConditionalGeneration,
            FlaxMBartForQuestionAnswering,
            FlaxMBartForSequenceClassification,
            FlaxMBartModel,
            FlaxMBartPreTrainedModel,
        )

else:
    import sys

    snake_case_ : Any    		=       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






