

"""simple docstring"""


from manim import *

class      __a							(UpperCamelCase_):






     '''simple docstring'''

     def 					_a			(						self  )  ->      Any:


           """simple docstring"""


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

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	[mem.copy() for i in range(1  )]
           SCREAMING_SNAKE_CASE__   :      str      		=	VGroup(*_a  ).arrange(_a	,    buff=0  )
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	Text("""GPU"""	,    font_size=24  )
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	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  )

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

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	Text("""Model"""	,    font_size=24  )
           SCREAMING_SNAKE_CASE__   :      int      		=	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  )	,    )

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

           SCREAMING_SNAKE_CASE__   :      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(_a	,    run_time=2.5  )	,    Write(_a  )	,    Write(_a  )  )

           self.add(_a  )

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	[]
           SCREAMING_SNAKE_CASE__   :      str      		=	[]
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	[]
           for i, rect in enumerate(_a  ):
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	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()
                 SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	0.46 / 4
                 SCREAMING_SNAKE_CASE__   :      List[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=_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()







"""simple docstring"""


from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
  import numpy as np
  import tensorflow as tf

  from transformers import TFCamembertModel

@require_tf
@require_sentencepiece
@require_tokenizers
class      __a							(unittest.TestCase):






     '''simple docstring'''

     @slow
     def 					_a			(						self  )  ->      Union[str, Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	TFCamembertModel.from_pretrained("""jplu/tf-camembert-base"""  )

           SCREAMING_SNAKE_CASE__   :      Any      		=	tf.convert_to_tensor(
               [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]]	,    dtype=tf.intaa	,    )  # J'aime le camembert !"

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	model(_a  )["""last_hidden_state"""]
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	tf.TensorShape((1, 10, 768)  )
           self.assertEqual(output.shape	,    _a  )
           # compare the actual values for a slice.
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	tf.convert_to_tensor(
               [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]]	,    dtype=tf.floataa	,    )
           # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
           # camembert.eval()
           # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

           self.assertTrue(np.allclose(output[:, :3, :3].numpy()	,    expected_slice.numpy()	,    atol=1E-4  )  )




