
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					:      Tuple    ,     a					:      Dict    ,     a					:      Tuple=13    ,     a					:      Optional[Any]=7    ,     a					:      Union[str, Any]=True    ,     a					:      Union[str, Any]=True    ,     a					:      Optional[int]=True    ,     a					:      Optional[int]=True    ,     a					:      Any=99    ,     a					:      Tuple=32    ,     a					:      Tuple=5    ,     a					:      str=4    ,     a					:      Tuple=37    ,     a					:      List[Any]="gelu"    ,     a					:      Optional[int]=0.1    ,     a					:      List[Any]=0.1    ,     a					:      Dict=512    ,     a					:      Optional[int]=16    ,     a					:      int=2    ,     a					:      str=0.0_2    ,     a					:      Dict=4    ,     ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[Any]         =		parent
							lowerCAmelCase__      :      Optional[int]         =		batch_size
							lowerCAmelCase__      :      List[str]         =		seq_length
							lowerCAmelCase__      :      Optional[Any]         =		is_training
							lowerCAmelCase__      :      Dict         =		use_attention_mask
							lowerCAmelCase__      :      List[str]         =		use_token_type_ids
							lowerCAmelCase__      :      List[str]         =		use_labels
							lowerCAmelCase__      :      List[str]         =		vocab_size
							lowerCAmelCase__      :      Tuple         =		hidden_size
							lowerCAmelCase__      :      Optional[Any]         =		num_hidden_layers
							lowerCAmelCase__      :      List[str]         =		num_attention_heads
							lowerCAmelCase__      :      Tuple         =		intermediate_size
							lowerCAmelCase__      :      str         =		hidden_act
							lowerCAmelCase__      :      str         =		hidden_dropout_prob
							lowerCAmelCase__      :      List[Any]         =		attention_probs_dropout_prob
							lowerCAmelCase__      :      Optional[int]         =		max_position_embeddings
							lowerCAmelCase__      :      List[Any]         =		type_vocab_size
							lowerCAmelCase__      :      Optional[Any]         =		type_sequence_label_size
							lowerCAmelCase__      :      Any         =		initializer_range
							lowerCAmelCase__      :      List[Any]         =		num_choices






				def 							_lowerCamelCase		(						self					:      str   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Union[str, Any]         =		ids_tensor([self.batch_size, self.seq_length]    ,     self.vocab_size   )

							lowerCAmelCase__      :      Optional[int]         =		None
							if self.use_attention_mask:
										lowerCAmelCase__      :      Any         =		random_attention_mask([self.batch_size, self.seq_length]   )

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

							lowerCAmelCase__      :      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=a    ,     initializer_range=self.initializer_range    ,     )

							return config, input_ids, token_type_ids, attention_mask






				def 							_lowerCamelCase		(						self					:      str   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[int]         =		self.prepare_config_and_inputs()
							lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      :      Optional[int]         =		config_and_inputs
							lowerCAmelCase__      :      int         =		{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
							return config, inputs_dict







@require_flax
class      A__							(     __magic_name__						,   unittest.TestCase	):
				lowercase			    =  True

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






				def 							_lowerCamelCase		(						self					:      Optional[int]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[int]         =		FlaxRoFormerModelTester(self   )






				@slow
				def 							_lowerCamelCase		(						self					:      Dict   ):






							'''simple docstring'''







							for model_class_name in self.all_model_classes:
										lowerCAmelCase__      :      Optional[Any]         =		model_class_name.from_pretrained('junnyu/roformer_chinese_small'    ,     from_pt=a   )
										lowerCAmelCase__      :      Optional[Any]         =		model(np.ones((1, 1)   )   )
										self.assertIsNotNone(a   )







@require_flax
class      A__							(     unittest.TestCase	):






				@slow
				def 							_lowerCamelCase		(						self					:      List[str]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Dict         =		FlaxRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base'   )
							lowerCAmelCase__      :      Tuple         =		jnp.array([[0, 1, 2, 3, 4, 5]]   )
							lowerCAmelCase__      :      Any         =		model(a   )[0]

							lowerCAmelCase__      :      Optional[int]         =		50_000

							lowerCAmelCase__      :      Dict         =		(1, 6, vocab_size)
							self.assertEqual(output.shape    ,     a   )

							lowerCAmelCase__      :      Dict         =		jnp.array(
							    [[[-0.1_2_0_5, -1.0_2_6_5, 0.2_9_2_2], [-1.5_1_3_4, 0.1_9_7_4, 0.1_5_1_9], [-5.0_1_3_5, -3.9_0_0_3, -0.8_4_0_4]]]   )

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

from __future__ import annotations

import math

lowerCamelCase__          =		"""2020.9.26"""
lowerCamelCase__          =		"""xcodz-dot, cclaus, dhruvmanila"""
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		tuple[float, float]:
			if not all(isinstance(SCREAMING_SNAKE_CASE_       ,							(float, int)     ) for val in locals().values()     ):
						lowerCAmelCase__      :      List[str]         =		F'''Input values must either be float or int: {list(locals().values()     )}'''
						raise TypeError(SCREAMING_SNAKE_CASE_     )
			lowerCAmelCase__      :      Tuple         =		((x * distance) / (z + distance)) * scale
			lowerCAmelCase__      :      Optional[int]         =		((y * distance) / (z + distance)) * scale
			return projected_x, projected_y
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		tuple[float, float, float]:
			if not isinstance(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     ):
						raise TypeError('Axis must be a str'     )
			lowerCAmelCase__      :      Optional[int]         =		locals()
			del input_variables["axis"]
			if not all(isinstance(SCREAMING_SNAKE_CASE_       ,							(float, int)     ) for val in input_variables.values()     ):
						lowerCAmelCase__      :      List[Any]         =		(
						    'Input values except axis must either be float or int: '
						    F'''{list(input_variables.values()     )}'''
						)
						raise TypeError(SCREAMING_SNAKE_CASE_     )
			lowerCAmelCase__      :      int         =		(angle % 360) / 450 * 180 / math.pi
			if axis == "z":
						lowerCAmelCase__      :      Tuple         =		x * math.cos(SCREAMING_SNAKE_CASE_     ) - y * math.sin(SCREAMING_SNAKE_CASE_     )
						lowerCAmelCase__      :      List[str]         =		y * math.cos(SCREAMING_SNAKE_CASE_     ) + x * math.sin(SCREAMING_SNAKE_CASE_     )
						lowerCAmelCase__      :      Optional[int]         =		z
			elif axis == "x":
						lowerCAmelCase__      :      Dict         =		y * math.cos(SCREAMING_SNAKE_CASE_     ) - z * math.sin(SCREAMING_SNAKE_CASE_     )
						lowerCAmelCase__      :      List[str]         =		z * math.cos(SCREAMING_SNAKE_CASE_     ) + y * math.sin(SCREAMING_SNAKE_CASE_     )
						lowerCAmelCase__      :      Tuple         =		x
			elif axis == "y":
						lowerCAmelCase__      :      str         =		x * math.cos(SCREAMING_SNAKE_CASE_     ) - z * math.sin(SCREAMING_SNAKE_CASE_     )
						lowerCAmelCase__      :      Optional[int]         =		z * math.cos(SCREAMING_SNAKE_CASE_     ) + x * math.sin(SCREAMING_SNAKE_CASE_     )
						lowerCAmelCase__      :      List[Any]         =		y
			else:
						raise ValueError('not a valid axis, choose one of \'x\', \'y\', \'z\''     )

			return new_x, new_y, new_z


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					print(F"""{convert_to_ad(1.0, 2.0, 3.0, 10.0, 10.0) = }""")
					print(F"""{rotate(1.0, 2.0, 3.0, "y", 90.0) = }""")