


'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase__  			=				{
    '''configuration_xlm_roberta''': [
        '''XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XLMRobertaConfig''',
        '''XLMRobertaOnnxConfig''',
    ],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__  			=				['''XLMRobertaTokenizer''']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__  			=				['''XLMRobertaTokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__  			=				[
					    '''XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''XLMRobertaForCausalLM''',
					    '''XLMRobertaForMaskedLM''',
					    '''XLMRobertaForMultipleChoice''',
					    '''XLMRobertaForQuestionAnswering''',
					    '''XLMRobertaForSequenceClassification''',
					    '''XLMRobertaForTokenClassification''',
					    '''XLMRobertaModel''',
					    '''XLMRobertaPreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__  			=				[
					    '''TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFXLMRobertaForCausalLM''',
					    '''TFXLMRobertaForMaskedLM''',
					    '''TFXLMRobertaForMultipleChoice''',
					    '''TFXLMRobertaForQuestionAnswering''',
					    '''TFXLMRobertaForSequenceClassification''',
					    '''TFXLMRobertaForTokenClassification''',
					    '''TFXLMRobertaModel''',
					    '''TFXLMRobertaPreTrainedModel''',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__  			=				[
					    '''FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''FlaxXLMRobertaForMaskedLM''',
					    '''FlaxXLMRobertaForCausalLM''',
					    '''FlaxXLMRobertaForMultipleChoice''',
					    '''FlaxXLMRobertaForQuestionAnswering''',
					    '''FlaxXLMRobertaForSequenceClassification''',
					    '''FlaxXLMRobertaForTokenClassification''',
					    '''FlaxXLMRobertaModel''',
					    '''FlaxXLMRobertaPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_xlm_roberta import (
					    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    XLMRobertaConfig,
					    XLMRobertaOnnxConfig,
					)

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_xlm_roberta import XLMRobertaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_xlm_roberta import (
										    XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    XLMRobertaForCausalLM,
										    XLMRobertaForMaskedLM,
										    XLMRobertaForMultipleChoice,
										    XLMRobertaForQuestionAnswering,
										    XLMRobertaForSequenceClassification,
										    XLMRobertaForTokenClassification,
										    XLMRobertaModel,
										    XLMRobertaPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_xlm_roberta import (
										    TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFXLMRobertaForCausalLM,
										    TFXLMRobertaForMaskedLM,
										    TFXLMRobertaForMultipleChoice,
										    TFXLMRobertaForQuestionAnswering,
										    TFXLMRobertaForSequenceClassification,
										    TFXLMRobertaForTokenClassification,
										    TFXLMRobertaModel,
										    TFXLMRobertaPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_xlm_roberta import (
										    FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    FlaxXLMRobertaForCausalLM,
										    FlaxXLMRobertaForMaskedLM,
										    FlaxXLMRobertaForMultipleChoice,
										    FlaxXLMRobertaForQuestionAnswering,
										    FlaxXLMRobertaForSequenceClassification,
										    FlaxXLMRobertaForTokenClassification,
										    FlaxXLMRobertaModel,
										    FlaxXLMRobertaPreTrainedModel,
										)

else:
					import sys

					UpperCAmelCase__  			=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)







'''simple docstring'''





from __future__ import annotations

import math

UpperCAmelCase__  			=				'''2020.9.26'''
UpperCAmelCase__  			=				'''xcodz-dot, cclaus, dhruvmanila'''






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: float,_SCREAMING_SNAKE_CASE					: float,_SCREAMING_SNAKE_CASE					: float,_SCREAMING_SNAKE_CASE					: float,_SCREAMING_SNAKE_CASE					: float ):






			"""simple docstring"""




			if not all(isinstance(_SCREAMING_SNAKE_CASE,(float, int) ) for val in locals().values() ):
						__A=	f"""Input values must either be float or int: {list(locals().values() )}"""
						raise TypeError(_SCREAMING_SNAKE_CASE )
			__A=	((x * distance) / (z + distance)) * scale
			__A=	((y * distance) / (z + distance)) * scale
			return projected_x, projected_y






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: float,_SCREAMING_SNAKE_CASE					: float,_SCREAMING_SNAKE_CASE					: float,_SCREAMING_SNAKE_CASE					: str,_SCREAMING_SNAKE_CASE					: float ):






			"""simple docstring"""




			if not isinstance(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE ):
						raise TypeError('Axis must be a str' )
			__A=	locals()
			del input_variables["axis"]
			if not all(isinstance(_SCREAMING_SNAKE_CASE,(float, int) ) for val in input_variables.values() ):
						__A=	(
						    'Input values except axis must either be float or int: '
						    f"""{list(input_variables.values() )}"""
						)
						raise TypeError(_SCREAMING_SNAKE_CASE )
			__A=	(angle % 360) / 450 * 180 / math.pi
			if axis == "z":
						__A=	x * math.cos(_SCREAMING_SNAKE_CASE ) - y * math.sin(_SCREAMING_SNAKE_CASE )
						__A=	y * math.cos(_SCREAMING_SNAKE_CASE ) + x * math.sin(_SCREAMING_SNAKE_CASE )
						__A=	z
			elif axis == "x":
						__A=	y * math.cos(_SCREAMING_SNAKE_CASE ) - z * math.sin(_SCREAMING_SNAKE_CASE )
						__A=	z * math.cos(_SCREAMING_SNAKE_CASE ) + y * math.sin(_SCREAMING_SNAKE_CASE )
						__A=	x
			elif axis == "y":
						__A=	x * math.cos(_SCREAMING_SNAKE_CASE ) - z * math.sin(_SCREAMING_SNAKE_CASE )
						__A=	z * math.cos(_SCREAMING_SNAKE_CASE ) + x * math.sin(_SCREAMING_SNAKE_CASE )
						__A=	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) = }""")



