






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 						A_							(      unittest.TestCase  ):







				def __init__(							self							:						List[Any]						,    __lowerCamelCase							:						Tuple						,    __lowerCamelCase							:						Any=1_3						,    __lowerCamelCase							:						str=7						,    __lowerCamelCase							:						Optional[int]=True						,    __lowerCamelCase							:						Optional[Any]=True						,    __lowerCamelCase							:						Union[str, Any]=True						,    __lowerCamelCase							:						Any=True						,    __lowerCamelCase							:						Dict=9_9						,    __lowerCamelCase							:						Any=3_2						,    __lowerCamelCase							:						List[str]=5						,    __lowerCamelCase							:						Tuple=4						,    __lowerCamelCase							:						List[Any]=3_7						,    __lowerCamelCase							:						Any="gelu"						,    __lowerCamelCase							:						List[str]=0.1						,    __lowerCamelCase							:						Optional[Any]=0.1						,    __lowerCamelCase							:						Tuple=5_1_2						,    __lowerCamelCase							:						List[Any]=1_6						,    __lowerCamelCase							:						List[str]=2						,    __lowerCamelCase							:						Any=0.02						,    __lowerCamelCase							:						List[str]=4						,    )				->					List[Any]:
										__magic_name__            =     parent
										__magic_name__            =     batch_size
										__magic_name__            =     seq_length
										__magic_name__            =     is_training
										__magic_name__            =     use_attention_mask
										__magic_name__            =     use_token_type_ids
										__magic_name__            =     use_labels
										__magic_name__            =     vocab_size
										__magic_name__            =     hidden_size
										__magic_name__            =     num_hidden_layers
										__magic_name__            =     num_attention_heads
										__magic_name__            =     intermediate_size
										__magic_name__            =     hidden_act
										__magic_name__            =     hidden_dropout_prob
										__magic_name__            =     attention_probs_dropout_prob
										__magic_name__            =     max_position_embeddings
										__magic_name__            =     type_vocab_size
										__magic_name__            =     type_sequence_label_size
										__magic_name__            =     initializer_range
										__magic_name__            =     num_choices







				def     _snake_case					(							self							:						Tuple	)				->					str:
										__magic_name__            =     ids_tensor([self.batch_size, self.seq_length]						,    self.vocab_size	)

										__magic_name__            =     None
										if self.use_attention_mask:
																__magic_name__            =     random_attention_mask([self.batch_size, self.seq_length]	)

										__magic_name__            =     None
										if self.use_token_type_ids:
																__magic_name__            =     ids_tensor([self.batch_size, self.seq_length]						,    self.type_vocab_size	)

										__magic_name__            =     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							:						int	)				->					Optional[int]:
										__magic_name__            =     self.prepare_config_and_inputs()
										__magic_name__      ,    __magic_name__      ,    __magic_name__      ,    __magic_name__            =     config_and_inputs
										__magic_name__            =     {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
										return config, inputs_dict





@require_flax
class 						A_							(      _A					,							unittest.TestCase  ):
				UpperCAmelCase__        =				True

				UpperCAmelCase__        =				(
				    (
				        FlaxRoFormerModel,
				        FlaxRoFormerForMaskedLM,
				        FlaxRoFormerForSequenceClassification,
				        FlaxRoFormerForTokenClassification,
				        FlaxRoFormerForMultipleChoice,
				        FlaxRoFormerForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)







				def     _snake_case					(							self							:						Union[str, Any]	)				->					Union[str, Any]:
										__magic_name__            =     FlaxRoFormerModelTester(self	)







				@slow
				def     _snake_case					(							self							:						str	)				->					Optional[int]:






										for model_class_name in self.all_model_classes:
																__magic_name__            =     model_class_name.from_pretrained("junnyu/roformer_chinese_small"						,    from_pt=UpperCamelCase__	)
																__magic_name__            =     model(np.ones((1, 1)	)	)
																self.assertIsNotNone(UpperCamelCase__	)





@require_flax
class 						A_							(      unittest.TestCase  ):







				@slow
				def     _snake_case					(							self							:						Any	)				->					Any:
										__magic_name__            =     FlaxRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base"	)
										__magic_name__            =     jnp.array([[0, 1, 2, 3, 4, 5]]	)
										__magic_name__            =     model(UpperCamelCase__	)[0]

										__magic_name__            =     5_0_0_0_0

										__magic_name__            =     (1, 6, vocab_size)
										self.assertEqual(output.shape						,    UpperCamelCase__	)

										__magic_name__            =     jnp.array(
										    [[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]]	)

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











"""simple docstring"""

import colorsys

from PIL import Image  # type: ignore






def _lowerCAmelCase	(  __lowerCamelCase:float ,						__lowerCamelCase:float ,						__lowerCamelCase:int   ):

						'''simple docstring'''






						__magic_name__            =     x
						__magic_name__            =     y
						for step in range(__lowerCamelCase   ):  # noqa: B007
												__magic_name__            =     a * a - b * b + x
												__magic_name__            =     2 * a * b + y
												__magic_name__            =     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	(  __lowerCamelCase:float   ):

						'''simple docstring'''






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






def _lowerCAmelCase	(  __lowerCamelCase:float   ):

						'''simple docstring'''






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






def _lowerCAmelCase	(  __lowerCamelCase:int = 8_0_0 ,						__lowerCamelCase:int = 6_0_0 ,						__lowerCamelCase:float = -0.6 ,						__lowerCamelCase:float = 0 ,						__lowerCamelCase:float = 3.2 ,						__lowerCamelCase:int = 5_0 ,						__lowerCamelCase:bool = True ,						):

						'''simple docstring'''






						__magic_name__            =     Image.new("RGB" ,						(image_width, image_height)   )
						__magic_name__            =     img.load()

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

																		__magic_name__            =     get_distance(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase   )

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

						return img


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			# colored version, full figure
			lowercase    			=       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()



