


'''simple docstring'''
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
    import torch

    from transformers import XLMRobertaModel






@require_sentencepiece
@require_tokenizers
@require_torch
class        lowerCAmelCase_  (     unittest.TestCase	):






       @slow
       def   UpperCamelCase						( self   )->      List[Any]:
            _A				=							XLMRobertaModel.from_pretrained('xlm-roberta-base'   )
            _A				=							torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]]   )
            # The dog is cute and lives in the garden house

            _A				=							torch.Size((1, 12, 768)   )  # batch_size, sequence_length, embedding_vector_dim
            _A				=							torch.tensor(
                [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]   )
            #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
            #  xlmr.eval()
            #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
            with torch.no_grad():
                 _A				=							model(_UpperCamelCase   )['last_hidden_state'].detach()
            self.assertEqual(output.shape			,     _UpperCamelCase   )
            # compare the actual values for a slice of last dim
            self.assertTrue(torch.allclose(output[:, :, -1]			,     _UpperCamelCase			,     atol=1e-3   )   )






       @slow
       def   UpperCamelCase						( self   )->      Union[str, Any]:
            _A				=							XLMRobertaModel.from_pretrained('xlm-roberta-large'   )
            _A				=							torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]]   )
            # The dog is cute and lives in the garden house

            _A				=							torch.Size((1, 12, 1024)   )  # batch_size, sequence_length, embedding_vector_dim
            _A				=							torch.tensor(
                [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]]   )
            #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
            #  xlmr.eval()
            #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
            with torch.no_grad():
                 _A				=							model(_UpperCamelCase   )['last_hidden_state'].detach()
            self.assertEqual(output.shape			,     _UpperCamelCase   )
            # compare the actual values for a slice of last dim
            self.assertTrue(torch.allclose(output[:, :, -1]			,     _UpperCamelCase			,     atol=1e-3   )   )









'''simple docstring'''
import colorsys

from PIL import Image  # type: ignore

def 					lowerCamelCase_ ( __UpperCamelCase					:       float							,    __UpperCamelCase					:       float							,    __UpperCamelCase					:       int		)					->	float:







     """simple docstring"""
     _A				=							x
     _A				=							y
     for step in range(__UpperCamelCase		):  # noqa: B007
          _A				=							a * a - b * b + x
          _A				=							2 * a * b + y
          _A				=							a_new

          # divergence happens for all complex number with an absolute value
          # greater than 4
          if a * a + b * b > 4:
               break
     return step / (max_step - 1)

def 					lowerCamelCase_ ( __UpperCamelCase					:       float		)					->	tuple:







     """simple docstring"""
     if distance == 1:
          return (0, 0, 0)
     else:
          return (2_5_5, 2_5_5, 2_5_5)

def 					lowerCamelCase_ ( __UpperCamelCase					:       float		)					->	tuple:







     """simple docstring"""
     if distance == 1:
          return (0, 0, 0)
     else:
          return tuple(round(i * 2_5_5		) for i in colorsys.hsv_to_rgb(__UpperCamelCase							,    1							,    1		)		)

def 					lowerCamelCase_ ( __UpperCamelCase					:       int = 8_0_0							,    __UpperCamelCase					:       int = 6_0_0							,    __UpperCamelCase					:       float = -0.6							,    __UpperCamelCase					:       float = 0							,    __UpperCamelCase					:       float = 3.2							,    __UpperCamelCase					:       int = 5_0							,    __UpperCamelCase					:       bool = True							,    )					->	Image.Image:







     """simple docstring"""
     _A				=							Image.new('RGB'							,    (image_width, image_height)		)
     _A				=							img.load()

     # loop through the image-coordinates
     for image_x in range(__UpperCamelCase		):
          for image_y in range(__UpperCamelCase		):
               # determine the figure-coordinates based on the image-coordinates
               _A				=							figure_width / image_width * image_height
               _A				=							figure_center_x + (image_x / image_width - 0.5) * figure_width
               _A				=							figure_center_y + (image_y / image_height - 0.5) * figure_height

               _A				=							get_distance(__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase		)

               # color the corresponding pixel based on the selected coloring-function
               if use_distance_color_coding:
                    _A				=							get_color_coded_rgb(__UpperCamelCase		)
               else:
                    _A				=							get_black_and_white_rgb(__UpperCamelCase		)

     return img


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    # colored version, full figure
    lowerCAmelCase						=					get_image()

    # uncomment for colored version, different section, zoomed in
    # img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
    # figure_width = 0.8)

    # uncomment for black and white version, full figure
    # img = get_image(use_distance_color_coding = False)

    # uncomment to save the image
    # img.save("mandelbrot.png")

    img.show()





