




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


_lowerCamelCase	:		str							       =   ["""gpt2"""]
_lowerCamelCase	:		str							       =   """gpt2"""

if is_tf_available():


  class 				UpperCamelCase_    ( tf.Module     ):

      '''simple docstring'''






      def __init__(			self      :	Tuple					, UpperCAmelCase__      :	Union[str, Any])						->List[Any]:





             '''simple docstring'''

             super().__init__()
             A__       =    tokenizer
             A__       =    AutoConfig.from_pretrained(UpperCAmelCase__)
             A__       =    TFGPTaLMHeadModel.from_config(UpperCAmelCase__)





      @tf.function(input_signature=(tf.TensorSpec((None,)					, tf.string					, name='''text'''),))
      def        SCREAMING_SNAKE_CASE							(			self      :	List[str]					, UpperCAmelCase__      :	List[str])						->Optional[int]:





             '''simple docstring'''

             A__       =    self.tokenizer(UpperCAmelCase__)
             A__       =    tokenized['''input_ids'''].to_tensor()

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

             A__       =    self.model(input_ids=UpperCAmelCase__					, attention_mask=UpperCAmelCase__)['''logits''']

             return outputs


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

    '''simple docstring'''






    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any])						->Dict:





           '''simple docstring'''

           super().setUp()

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

           A__       =    [
               '''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''',
           ]
           A__       =    list(zip(self.test_sentences					, self.test_sentences[::-1]))


    def        SCREAMING_SNAKE_CASE							(			self      :	List[str])						->Dict:





           '''simple docstring'''

           for tokenizer, tf_tokenizer in zip(self.tokenizers					, self.tf_tokenizers):
                  for test_inputs in self.test_sentences:
                         A__       =    tokenizer([test_inputs]					, return_tensors='''tf''')
                         A__       =    tf_tokenizer([test_inputs])

                         for key in python_outputs.keys():
                                # convert them to numpy to avoid messing with ragged tensors
                                A__       =    python_outputs[key].numpy()
                                A__       =    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      :	int)						->int:





           '''simple docstring'''

           for tf_tokenizer in self.tf_tokenizers:
                  A__       =    tf.function(UpperCAmelCase__)
                  for test_inputs in self.test_sentences:
                         A__       =    tf.constant(UpperCAmelCase__)
                         A__       =    compiled_tokenizer(UpperCAmelCase__)
                         A__       =    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      :	Optional[Any])						->Optional[Any]:





           '''simple docstring'''

           for tf_tokenizer in self.tf_tokenizers:
                  A__       =    ModelToSave(tokenizer=UpperCAmelCase__)
                  A__       =    tf.convert_to_tensor([self.test_sentences[0]])
                  A__       =    model.serving(UpperCAmelCase__)  # Build model with some sample inputs
                  with TemporaryDirectory() as tempdir:
                         A__       =    Path(UpperCAmelCase__) / '''saved.model'''
                         tf.saved_model.save(UpperCAmelCase__					, UpperCAmelCase__					, signatures={'''serving_default''': model.serving})
                         A__       =    tf.saved_model.load(UpperCAmelCase__)
                  A__       =    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      :	List[str])						->Tuple:





           '''simple docstring'''

           for tf_tokenizer in self.tf_tokenizers:
                  A__       =    tf.convert_to_tensor([self.test_sentences[0]])
                  A__       =    tf_tokenizer(UpperCAmelCase__)  # Build model with some sample inputs

                  A__       =    tf_tokenizer.get_config()
                  A__       =    TFGPTaTokenizer.from_config(UpperCAmelCase__)
                  A__       =    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      :	Dict)						->List[Any]:





           '''simple docstring'''

           for tf_tokenizer in self.tf_tokenizers:
                  # for the test to run
                  A__       =    123_123

                  for max_length in [3, 5, 1_024]:
                         A__       =    tf.convert_to_tensor([self.test_sentences[0]])
                         A__       =    tf_tokenizer(UpperCAmelCase__					, max_length=UpperCAmelCase__)

                         A__       =    out['''input_ids'''].numpy().shape[1]

                         assert out_length == max_length








from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

_lowerCamelCase	:		str							       =   typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
_lowerCamelCase	:		Tuple							       =   typing.Union[np.floataa, int, float]  # noqa: UP007







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       VectorOut:



       """simple docstring"""

       return np.sqrt(np.sum((np.asarray(lowercase_						) - np.asarray(lowercase_						)) ** 2						)						)







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       VectorOut:



       """simple docstring"""

       return sum((va - va) ** 2 for va, va in zip(lowercase_ ,				lowercase_						)						) ** (1 / 2)


if __name__ == "__main__":







  def SCREAMING_SNAKE_CASE				(       )   ->       None:



         """simple docstring"""

         from timeit import timeit

         print('''Without Numpy'''						)
         print(
             timeit(
                 '''euclidean_distance_no_np([1, 2, 3], [4, 5, 6])''' ,				number=10_000 ,				globals=globals() ,				)						)
         print('''With Numpy'''						)
         print(
             timeit(
                 '''euclidean_distance([1, 2, 3], [4, 5, 6])''' ,				number=10_000 ,				globals=globals() ,				)						)
  benchmark()


