"""simple docstring"""






from manim import *


class     UpperCAmelCase						(_UpperCAmelCase  ):



    """simple docstring"""

    def _snake_case			(   self							):
       lowercase__:   Union[str, Any]					=      Rectangle(height=0.5    ,			width=0.5							)
       lowercase__:   Union[str, Any]					=      Rectangle(height=0.46    ,			width=0.46							).set_stroke(width=0							)

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

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

       self.add(_UpperCAmelCase							)

       lowercase__:   Union[str, Any]					=      [mem.copy() for i in range(6							)]
       lowercase__:   Dict					=      VGroup(*_UpperCAmelCase							).arrange(_UpperCAmelCase    ,			buff=0							)

       lowercase__:   Optional[int]					=      Text('''Model'''    ,			font_size=24							)
       lowercase__:   Tuple					=      Group(_UpperCAmelCase    ,			_UpperCAmelCase							).arrange(_UpperCAmelCase    ,			buff=0.5    ,			aligned_edge=_UpperCAmelCase							)
       model.move_to([3, -1.0, 0]							)

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

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

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

       lowercase__:   Optional[Any]					=      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(_UpperCAmelCase    ,			run_time=2.5							)    ,			Write(_UpperCAmelCase							)    ,			Write(_UpperCAmelCase							)							)

       self.add(_UpperCAmelCase							)

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

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

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

       self.play(*_UpperCAmelCase							)
       self.play(*_UpperCAmelCase							)

       self.wait()

"""simple docstring"""






import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
       import jax.numpy as jnp

       from transformers.models.roformer.modeling_flax_roformer import (
           FlaxRoFormerForMaskedLM,
           FlaxRoFormerForMultipleChoice,
           FlaxRoFormerForQuestionAnswering,
           FlaxRoFormerForSequenceClassification,
           FlaxRoFormerForTokenClassification,
           FlaxRoFormerModel,
       )


class     UpperCAmelCase						(unittest.TestCase  ):



    """simple docstring"""

    def __init__(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase=13    ,			_UpperCAmelCase=7    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=99    ,			_UpperCAmelCase=32    ,			_UpperCAmelCase=5    ,			_UpperCAmelCase=4    ,			_UpperCAmelCase=37    ,			_UpperCAmelCase="gelu"    ,			_UpperCAmelCase=0.1    ,			_UpperCAmelCase=0.1    ,			_UpperCAmelCase=512    ,			_UpperCAmelCase=16    ,			_UpperCAmelCase=2    ,			_UpperCAmelCase=0.02    ,			_UpperCAmelCase=4    ,			):
       lowercase__:   Dict					=      parent
       lowercase__:   List[str]					=      batch_size
       lowercase__:   Optional[Any]					=      seq_length
       lowercase__:   List[Any]					=      is_training
       lowercase__:   int					=      use_attention_mask
       lowercase__:   Tuple					=      use_token_type_ids
       lowercase__:   Union[str, Any]					=      use_labels
       lowercase__:   str					=      vocab_size
       lowercase__:   str					=      hidden_size
       lowercase__:   str					=      num_hidden_layers
       lowercase__:   Optional[int]					=      num_attention_heads
       lowercase__:   List[str]					=      intermediate_size
       lowercase__:   List[str]					=      hidden_act
       lowercase__:   Tuple					=      hidden_dropout_prob
       lowercase__:   int					=      attention_probs_dropout_prob
       lowercase__:   int					=      max_position_embeddings
       lowercase__:   Union[str, Any]					=      type_vocab_size
       lowercase__:   List[Any]					=      type_sequence_label_size
       lowercase__:   Any					=      initializer_range
       lowercase__:   str					=      num_choices






    def _snake_case			(   self							):
       lowercase__:   int					=      ids_tensor([self.batch_size, self.seq_length]    ,			self.vocab_size							)

       lowercase__:   List[Any]					=      None
       if self.use_attention_mask:
          lowercase__:   Tuple					=      random_attention_mask([self.batch_size, self.seq_length]							)

       lowercase__:   List[Any]					=      None
       if self.use_token_type_ids:
          lowercase__:   str					=      ids_tensor([self.batch_size, self.seq_length]    ,			self.type_vocab_size							)

       lowercase__:   Optional[int]					=      RoFormerConfig(
           vocab_size=self.vocab_size    ,			hidden_size=self.hidden_size    ,			num_hidden_layers=self.num_hidden_layers    ,			num_attention_heads=self.num_attention_heads    ,			intermediate_size=self.intermediate_size    ,			hidden_act=self.hidden_act    ,			hidden_dropout_prob=self.hidden_dropout_prob    ,			attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,			max_position_embeddings=self.max_position_embeddings    ,			type_vocab_size=self.type_vocab_size    ,			is_decoder=_UpperCAmelCase    ,			initializer_range=self.initializer_range    ,			)

       return config, input_ids, token_type_ids, attention_mask






    def _snake_case			(   self							):
       lowercase__:   str					=      self.prepare_config_and_inputs()
       lowercase__, lowercase__, lowercase__, lowercase__:   Optional[Any]					=      config_and_inputs
       lowercase__:   Dict					=      {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
       return config, inputs_dict



@require_flax
class     UpperCAmelCase						(_UpperCAmelCase    ,unittest.TestCase  ):



    """simple docstring"""

    _UpperCAmelCase	:List[str]						    = True

    _UpperCAmelCase	:Union[str, Any]						    = (
        (
            FlaxRoFormerModel,
            FlaxRoFormerForMaskedLM,
            FlaxRoFormerForSequenceClassification,
            FlaxRoFormerForTokenClassification,
            FlaxRoFormerForMultipleChoice,
            FlaxRoFormerForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )






    def _snake_case			(   self							):
       lowercase__:   str					=      FlaxRoFormerModelTester(self							)






    @slow
    def _snake_case			(   self							):
       for model_class_name in self.all_model_classes:
          lowercase__:   Dict					=      model_class_name.from_pretrained('''junnyu/roformer_chinese_small'''    ,			from_pt=_UpperCAmelCase							)
          lowercase__:   int					=      model(np.ones((1, 1)							)							)
          self.assertIsNotNone(_UpperCAmelCase							)



@require_flax
class     UpperCAmelCase						(unittest.TestCase  ):



    """simple docstring"""

    @slow
    def _snake_case			(   self							):
       lowercase__:   Any					=      FlaxRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''							)
       lowercase__:   Optional[int]					=      jnp.array([[0, 1, 2, 3, 4, 5]]							)
       lowercase__:   List[Any]					=      model(_UpperCAmelCase							)[0]

       lowercase__:   str					=      50000

       lowercase__:   Tuple					=      (1, 6, vocab_size)
       self.assertEqual(output.shape    ,			_UpperCAmelCase							)

       lowercase__:   List[Any]					=      jnp.array(
           [[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]]							)

       self.assertTrue(jnp.allclose(output[:, :3, :3]    ,			_UpperCAmelCase    ,			atol=1e-4							)							)
