
from manim import *





class 				UpperCAmelCase (    __SCREAMING_SNAKE_CASE    ):




	'''simple docstring'''





	def 							UpperCamelCase_    (			self   :					str					):
					__A       =					Rectangle(height=0.5				,width=0.5					)
					__A       =					Rectangle(height=0.46				,width=0.46					).set_stroke(width=0					)

					__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(1					)]
					__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.align_to(A				,A					)
					gpu.set_x(gpu.get_x() - 1					)

					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.play(
					    Create(A				,run_time=1					)				,Create(A				,run_time=1					)				,Create(A				,run_time=1					)				,)

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

					__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]					)

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

					self.add(A					)

					__A       =					[]
					__A       =					[]
					__A       =					[]
					for i, rect in enumerate(A					):
									__A       =					Rectangle(height=0.46				,width=0.46					).set_stroke(width=0.0					).set_fill(A				,opacity=0.7					)
									cpu_target.move_to(A					)
									cpu_target.generate_target()
									__A       =					0.46 / 4
									__A       =					0.46 / 3

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

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

					self.play(*A					)
					self.play(*A					)

					self.wait()







from typing import TYPE_CHECKING

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


a__      :     str         =		{
    'configuration_roformer': ['ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoFormerConfig', 'RoFormerOnnxConfig'],
    'tokenization_roformer': ['RoFormerTokenizer'],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      a__      :     List[Any]         =		['RoFormerTokenizerFast']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      a__      :     List[str]         =		[
          'ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'RoFormerForCausalLM',
          'RoFormerForMaskedLM',
          'RoFormerForMultipleChoice',
          'RoFormerForQuestionAnswering',
          'RoFormerForSequenceClassification',
          'RoFormerForTokenClassification',
          'RoFormerLayer',
          'RoFormerModel',
          'RoFormerPreTrainedModel',
          'load_tf_weights_in_roformer',
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      a__      :     List[Any]         =		[
          'TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFRoFormerForCausalLM',
          'TFRoFormerForMaskedLM',
          'TFRoFormerForMultipleChoice',
          'TFRoFormerForQuestionAnswering',
          'TFRoFormerForSequenceClassification',
          'TFRoFormerForTokenClassification',
          'TFRoFormerLayer',
          'TFRoFormerModel',
          'TFRoFormerPreTrainedModel',
      ]


try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      a__      :     Dict         =		[
          'FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'FlaxRoFormerForMaskedLM',
          'FlaxRoFormerForMultipleChoice',
          'FlaxRoFormerForQuestionAnswering',
          'FlaxRoFormerForSequenceClassification',
          'FlaxRoFormerForTokenClassification',
          'FlaxRoFormerModel',
          'FlaxRoFormerPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
      from .tokenization_roformer import RoFormerTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roformer_fast import RoFormerTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roformer import (
                ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                RoFormerForCausalLM,
                RoFormerForMaskedLM,
                RoFormerForMultipleChoice,
                RoFormerForQuestionAnswering,
                RoFormerForSequenceClassification,
                RoFormerForTokenClassification,
                RoFormerLayer,
                RoFormerModel,
                RoFormerPreTrainedModel,
                load_tf_weights_in_roformer,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roformer import (
                TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRoFormerForCausalLM,
                TFRoFormerForMaskedLM,
                TFRoFormerForMultipleChoice,
                TFRoFormerForQuestionAnswering,
                TFRoFormerForSequenceClassification,
                TFRoFormerForTokenClassification,
                TFRoFormerLayer,
                TFRoFormerModel,
                TFRoFormerPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roformer import (
                FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                FlaxRoFormerForMaskedLM,
                FlaxRoFormerForMultipleChoice,
                FlaxRoFormerForQuestionAnswering,
                FlaxRoFormerForSequenceClassification,
                FlaxRoFormerForTokenClassification,
                FlaxRoFormerModel,
                FlaxRoFormerPreTrainedModel,
            )


else:
      import sys

      a__      :     Union[str, Any]         =		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



