




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


SCREAMING_SNAKE_CASE__			: int    =  ['gpt2']
SCREAMING_SNAKE_CASE__			: str    =  'gpt2'

if is_tf_available():




       class 						UpperCamelCase__      (tf.Module	):

           '''simple docstring'''



           def __init__(       self	,    UpperCamelCase__	)	-> Union[str, Any]:
                  super().__init__()
                  lowerCamelCase  : Any     					=   tokenizer
                  lowerCamelCase  : Optional[Any]     					=   AutoConfig.from_pretrained(__lowerCAmelCase	)
                  lowerCamelCase  : Any     					=   TFGPTaLMHeadModel.from_config(__lowerCAmelCase	)







           @tf.function(input_signature=(tf.TensorSpec((None,)	,    tf.string	,    name="text"	),)	)
           def        _lowercase	(       self	,    UpperCamelCase__	)	-> Optional[Any]:
                  lowerCamelCase  : Dict     					=   self.tokenizer(__lowerCAmelCase	)
                  lowerCamelCase  : Union[str, Any]     					=   tokenized["input_ids"].to_tensor()

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

                  lowerCamelCase  : Union[str, Any]     					=   self.model(input_ids=__lowerCAmelCase	,    attention_mask=__lowerCAmelCase	)["logits"]

                  return outputs




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

    '''simple docstring'''



    def        _lowercase	(       self	)	-> List[str]:
           super().setUp()

           lowerCamelCase  : Union[str, Any]     					=   [GPTaTokenizer.from_pretrained(__lowerCAmelCase	) for checkpoint in (TOKENIZER_CHECKPOINTS)]
           lowerCamelCase  : Optional[int]     					=   [TFGPTaTokenizer.from_pretrained(__lowerCAmelCase	) for checkpoint in TOKENIZER_CHECKPOINTS]
           assert len(self.tokenizers	) == len(self.tf_tokenizers	)

           lowerCamelCase  : int     					=   [
               "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",
           ]
           lowerCamelCase  : List[str]     					=   list(zip(self.test_sentences	,    self.test_sentences[::-1]	)	)







    def        _lowercase	(       self	)	-> Optional[int]:
           for tokenizer, tf_tokenizer in zip(self.tokenizers	,    self.tf_tokenizers	):
                  for test_inputs in self.test_sentences:
                         lowerCamelCase  : Union[str, Any]     					=   tokenizer([test_inputs]	,    return_tensors="tf"	)
                         lowerCamelCase  : Any     					=   tf_tokenizer([test_inputs]	)

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

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







    @slow
    def        _lowercase	(       self	)	-> Optional[int]:
           for tf_tokenizer in self.tf_tokenizers:
                  lowerCamelCase  : Dict     					=   tf.function(__lowerCAmelCase	)
                  for test_inputs in self.test_sentences:
                         lowerCamelCase  : List[Any]     					=   tf.constant(__lowerCAmelCase	)
                         lowerCamelCase  : Optional[Any]     					=   compiled_tokenizer(__lowerCAmelCase	)
                         lowerCamelCase  : List[str]     					=   tf_tokenizer(__lowerCAmelCase	)

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







    @slow
    def        _lowercase	(       self	)	-> int:
           for tf_tokenizer in self.tf_tokenizers:
                  lowerCamelCase  : int     					=   ModelToSave(tokenizer=__lowerCAmelCase	)
                  lowerCamelCase  : Optional[Any]     					=   tf.convert_to_tensor([self.test_sentences[0]]	)
                  lowerCamelCase  : List[str]     					=   model.serving(__lowerCAmelCase	)  # Build model with some sample inputs
                  with TemporaryDirectory() as tempdir:
                         lowerCamelCase  : Any     					=   Path(__lowerCAmelCase	) / "saved.model"
                         tf.saved_model.save(__lowerCAmelCase	,    __lowerCAmelCase	,    signatures={"serving_default": model.serving}	)
                         lowerCamelCase  : Dict     					=   tf.saved_model.load(__lowerCAmelCase	)
                  lowerCamelCase  : Dict     					=   loaded_model.signatures["serving_default"](__lowerCAmelCase	)["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        _lowercase	(       self	)	-> Any:
           for tf_tokenizer in self.tf_tokenizers:
                  lowerCamelCase  : Optional[Any]     					=   tf.convert_to_tensor([self.test_sentences[0]]	)
                  lowerCamelCase  : Optional[Any]     					=   tf_tokenizer(__lowerCAmelCase	)  # Build model with some sample inputs

                  lowerCamelCase  : int     					=   tf_tokenizer.get_config()
                  lowerCamelCase  : str     					=   TFGPTaTokenizer.from_config(__lowerCAmelCase	)
                  lowerCamelCase  : Tuple     					=   model_from_config(__lowerCAmelCase	)

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







    @slow
    def        _lowercase	(       self	)	-> Optional[Any]:
           for tf_tokenizer in self.tf_tokenizers:
                  # for the test to run
                  lowerCamelCase  : Dict     					=   12_3123

                  for max_length in [3, 5, 1024]:
                         lowerCamelCase  : str     					=   tf.convert_to_tensor([self.test_sentences[0]]	)
                         lowerCamelCase  : List[str]     					=   tf_tokenizer(__lowerCAmelCase	,    max_length=__lowerCAmelCase	)

                         lowerCamelCase  : List[str]     					=   out["input_ids"].numpy().shape[1]

                         assert out_length == max_length













from __future__ import annotations

from math import pi





def      _UpperCamelCase  (a__ :float ,      a__ :float ,      a__ :float							):






							"""simple docstring"""





							if (inductance, frequency, reactance).count(0							) != 1:
														raise ValueError("""One and only one argument must be 0"""							)
							if inductance < 0:
														raise ValueError("""Inductance cannot be negative"""							)
							if frequency < 0:
														raise ValueError("""Frequency cannot be negative"""							)
							if reactance < 0:
														raise ValueError("""Inductive reactance cannot be negative"""							)
							if inductance == 0:
														return {"inductance": reactance / (2 * pi * frequency)}
							elif frequency == 0:
														return {"frequency": reactance / (2 * pi * inductance)}
							elif reactance == 0:
														return {"reactance": 2 * pi * frequency * inductance}
							else:
														raise ValueError("""Exactly one argument must be 0"""							)


if __name__ == "__main__":
					import doctest

					doctest.testmod()




