





'''simple docstring'''


from __future__ import annotations

import math

a__			:					int       						=						'2020.9.26'
a__			:					Optional[Any]       						=						'xcodz-dot, cclaus, dhruvmanila'
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			float			,	SCREAMING_SNAKE_CASE_ :			float			,	SCREAMING_SNAKE_CASE_ :			float			,	SCREAMING_SNAKE_CASE_ :			float			,	SCREAMING_SNAKE_CASE_ :			float )							->     tuple[float, float]:

       """simple docstring"""


       if not all(isinstance(SCREAMING_SNAKE_CASE_			,	(float, int) ) for val in locals().values() ):
              UpperCAmelCase  =						f"Input values must either be float or int: {list(locals().values() )}"
              raise TypeError(SCREAMING_SNAKE_CASE_ )
       UpperCAmelCase  =						((x * distance) / (z + distance)) * scale
       UpperCAmelCase  =						((y * distance) / (z + distance)) * scale
       return projected_x, projected_y
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			float			,	SCREAMING_SNAKE_CASE_ :			float			,	SCREAMING_SNAKE_CASE_ :			float			,	SCREAMING_SNAKE_CASE_ :			str			,	SCREAMING_SNAKE_CASE_ :			float )							->     tuple[float, float, float]:

       """simple docstring"""


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





"""simple docstring"""




import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
						import tensorflow as tf

if is_keras_nlp_available():
						from transformers.models.gpta import TFGPTaTokenizer


A_				:      Union[str, Any]           =			["gpt2"]
A_				:      Optional[int]           =			"gpt2"

if is_tf_available():





						class  lowerCamelCase    (tf.Module      ):

									def __init__(       self					:    Dict     ,					__UpperCAmelCase					:    str							) ->     Any:
												super().__init__()
												SCREAMING_SNAKE_CASE__										=   tokenizer
												SCREAMING_SNAKE_CASE__										=   AutoConfig.from_pretrained(__UpperCAmelCase							)
												SCREAMING_SNAKE_CASE__										=   TFGPTaLMHeadModel.from_config(__UpperCAmelCase							)





									@tf.function(input_signature=(tf.TensorSpec((None,)     ,					tf.string     ,					name="""text"""							),)							)
									def        SCREAMING_SNAKE_CASE					(       self					:    Tuple     ,					__UpperCAmelCase					:    Tuple							) ->     List[Any]:
												SCREAMING_SNAKE_CASE__										=   self.tokenizer(__UpperCAmelCase							)
												SCREAMING_SNAKE_CASE__										=   tokenized["""input_ids"""].to_tensor()

												SCREAMING_SNAKE_CASE__										=   tf.cast(input_ids_dense > 0     ,					tf.intaa							)
												# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

												SCREAMING_SNAKE_CASE__										=   self.model(input_ids=__UpperCAmelCase     ,					attention_mask=__UpperCAmelCase							)["""logits"""]

												return outputs





@require_tf
@require_keras_nlp
class  lowerCamelCase    (unittest.TestCase      ):

			def        SCREAMING_SNAKE_CASE					(       self					:    Union[str, Any]							) ->     Optional[int]:
						super().setUp()

						SCREAMING_SNAKE_CASE__										=   [GPTaTokenizer.from_pretrained(__UpperCAmelCase							) for checkpoint in (TOKENIZER_CHECKPOINTS)]
						SCREAMING_SNAKE_CASE__										=   [TFGPTaTokenizer.from_pretrained(__UpperCAmelCase							) for checkpoint in TOKENIZER_CHECKPOINTS]
						assert len(self.tokenizers							) == len(self.tf_tokenizers							)

						SCREAMING_SNAKE_CASE__										=   [
						    """This is a straightforward English test sentence.""",
						    """This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.""",
						    """Now we're going to add some Chinese: 一 二 三 一二三""",
						    """And some much more rare Chinese: 齉 堃 齉堃""",
						    """Je vais aussi écrire en français pour tester les accents""",
						    """Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ""",
						]
						SCREAMING_SNAKE_CASE__										=   list(zip(self.test_sentences     ,					self.test_sentences[::-1]							)							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple							) ->     str:
						for tokenizer, tf_tokenizer in zip(self.tokenizers     ,					self.tf_tokenizers							):
									for test_inputs in self.test_sentences:
												SCREAMING_SNAKE_CASE__										=   tokenizer([test_inputs]     ,					return_tensors="""tf"""							)
												SCREAMING_SNAKE_CASE__										=   tf_tokenizer([test_inputs]							)

												for key in python_outputs.keys():
															# convert them to numpy to avoid messing with ragged tensors
															SCREAMING_SNAKE_CASE__										=   python_outputs[key].numpy()
															SCREAMING_SNAKE_CASE__										=   tf_outputs[key].numpy()

															self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape							)							)
															self.assertTrue(tf.reduce_all(tf.cast(__UpperCAmelCase     ,					tf.intaa							) == tf_outputs_values							)							)

			@slow
			def        SCREAMING_SNAKE_CASE					(       self					:    Union[str, Any]							) ->     str:
						for tf_tokenizer in self.tf_tokenizers:
									SCREAMING_SNAKE_CASE__										=   tf.function(__UpperCAmelCase							)
									for test_inputs in self.test_sentences:
												SCREAMING_SNAKE_CASE__										=   tf.constant(__UpperCAmelCase							)
												SCREAMING_SNAKE_CASE__										=   compiled_tokenizer(__UpperCAmelCase							)
												SCREAMING_SNAKE_CASE__										=   tf_tokenizer(__UpperCAmelCase							)

												for key in eager_outputs.keys():
															self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]							)							)

			@slow
			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     List[str]:
						for tf_tokenizer in self.tf_tokenizers:
									SCREAMING_SNAKE_CASE__										=   ModelToSave(tokenizer=__UpperCAmelCase							)
									SCREAMING_SNAKE_CASE__										=   tf.convert_to_tensor([self.test_sentences[0]]							)
									SCREAMING_SNAKE_CASE__										=   model.serving(__UpperCAmelCase							)  # Build model with some sample inputs
									with TemporaryDirectory() as tempdir:
												SCREAMING_SNAKE_CASE__										=   Path(__UpperCAmelCase							) / """saved.model"""
												tf.saved_model.save(__UpperCAmelCase     ,					__UpperCAmelCase     ,					signatures={"""serving_default""": model.serving}							)
												SCREAMING_SNAKE_CASE__										=   tf.saved_model.load(__UpperCAmelCase							)
									SCREAMING_SNAKE_CASE__										=   loaded_model.signatures["""serving_default"""](__UpperCAmelCase							)["""output_0"""]
									# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
									self.assertTrue(tf.reduce_all(out == loaded_output							)							)

			@slow
			def        SCREAMING_SNAKE_CASE					(       self					:    str							) ->     List[Any]:
						for tf_tokenizer in self.tf_tokenizers:
									SCREAMING_SNAKE_CASE__										=   tf.convert_to_tensor([self.test_sentences[0]]							)
									SCREAMING_SNAKE_CASE__										=   tf_tokenizer(__UpperCAmelCase							)  # Build model with some sample inputs

									SCREAMING_SNAKE_CASE__										=   tf_tokenizer.get_config()
									SCREAMING_SNAKE_CASE__										=   TFGPTaTokenizer.from_config(__UpperCAmelCase							)
									SCREAMING_SNAKE_CASE__										=   model_from_config(__UpperCAmelCase							)

									for key in from_config_output.keys():
												self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]							)							)





			@slow
			def        SCREAMING_SNAKE_CASE					(       self					:    int							) ->     Optional[int]:
						for tf_tokenizer in self.tf_tokenizers:
									# for the test to run
									SCREAMING_SNAKE_CASE__										=   1_2_3_1_2_3

									for max_length in [3, 5, 1_0_2_4]:
												SCREAMING_SNAKE_CASE__										=   tf.convert_to_tensor([self.test_sentences[0]]							)
												SCREAMING_SNAKE_CASE__										=   tf_tokenizer(__UpperCAmelCase     ,					max_length=__UpperCAmelCase							)

												SCREAMING_SNAKE_CASE__										=   out["""input_ids"""].numpy().shape[1]

												assert out_length == max_length


