






'''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


lowerCAmelCase__          =						['gpt2']
lowerCAmelCase__          =						'gpt2'

if is_tf_available():







		class       __lowercase					(tf.Module    ):

			def __init__(      self   :						int  ,					UpperCAmelCase_   :						int):
					super().__init__()
					UpperCamelCase__				:					Any					=					tokenizer
					UpperCamelCase__				:					Optional[int]					=					AutoConfig.from_pretrained(UpperCAmelCase_)
					UpperCamelCase__				:					str					=					TFGPTaLMHeadModel.from_config(UpperCAmelCase_)





			@tf.function(input_signature=(tf.TensorSpec((None,)  ,					tf.string  ,					name='text'),))
			def      __UpperCamelCase							(      self   :						Any  ,					UpperCAmelCase_   :						int):
					UpperCamelCase__				:					str					=					self.tokenizer(UpperCAmelCase_)
					UpperCamelCase__				:					Dict					=					tokenized['input_ids'].to_tensor()

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

					UpperCamelCase__				:					Union[str, Any]					=					self.model(input_ids=UpperCAmelCase_  ,					attention_mask=UpperCAmelCase_)['logits']

					return outputs







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

	def      __UpperCamelCase							(      self   :						int):
			super().setUp()

			UpperCamelCase__				:					Tuple					=					[GPTaTokenizer.from_pretrained(UpperCAmelCase_) for checkpoint in (TOKENIZER_CHECKPOINTS)]
			UpperCamelCase__				:					int					=					[TFGPTaTokenizer.from_pretrained(UpperCAmelCase_) for checkpoint in TOKENIZER_CHECKPOINTS]
			assert len(self.tokenizers) == len(self.tf_tokenizers)

			UpperCamelCase__				:					Dict					=					[
			    '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',
			]
			UpperCamelCase__				:					Tuple					=					list(zip(self.test_sentences  ,					self.test_sentences[::-1]))

	def      __UpperCamelCase							(      self   :						str):
			for tokenizer, tf_tokenizer in zip(self.tokenizers  ,					self.tf_tokenizers):
					for test_inputs in self.test_sentences:
							UpperCamelCase__				:					int					=					tokenizer([test_inputs]  ,					return_tensors='tf')
							UpperCamelCase__				:					List[str]					=					tf_tokenizer([test_inputs])

							for key in python_outputs.keys():
									# convert them to numpy to avoid messing with ragged tensors
									UpperCamelCase__				:					Optional[int]					=					python_outputs[key].numpy()
									UpperCamelCase__				:					Optional[int]					=					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      __UpperCamelCase							(      self   :						List[Any]):
			for tf_tokenizer in self.tf_tokenizers:
					UpperCamelCase__				:					Dict					=					tf.function(UpperCAmelCase_)
					for test_inputs in self.test_sentences:
							UpperCamelCase__				:					int					=					tf.constant(UpperCAmelCase_)
							UpperCamelCase__				:					Optional[int]					=					compiled_tokenizer(UpperCAmelCase_)
							UpperCamelCase__				:					List[Any]					=					tf_tokenizer(UpperCAmelCase_)

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

	@slow
	def      __UpperCamelCase							(      self   :						Union[str, Any]):
			for tf_tokenizer in self.tf_tokenizers:
					UpperCamelCase__				:					Union[str, Any]					=					ModelToSave(tokenizer=UpperCAmelCase_)
					UpperCamelCase__				:					Optional[int]					=					tf.convert_to_tensor([self.test_sentences[0]])
					UpperCamelCase__				:					List[str]					=					model.serving(UpperCAmelCase_)  # Build model with some sample inputs
					with TemporaryDirectory() as tempdir:
							UpperCamelCase__				:					Dict					=					Path(UpperCAmelCase_) / 'saved.model'
							tf.saved_model.save(UpperCAmelCase_  ,					UpperCAmelCase_  ,					signatures={'serving_default': model.serving})
							UpperCamelCase__				:					Optional[int]					=					tf.saved_model.load(UpperCAmelCase_)
					UpperCamelCase__				:					Optional[int]					=					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      __UpperCamelCase							(      self   :						Dict):
			for tf_tokenizer in self.tf_tokenizers:
					UpperCamelCase__				:					Tuple					=					tf.convert_to_tensor([self.test_sentences[0]])
					UpperCamelCase__				:					int					=					tf_tokenizer(UpperCAmelCase_)  # Build model with some sample inputs

					UpperCamelCase__				:					Tuple					=					tf_tokenizer.get_config()
					UpperCamelCase__				:					Any					=					TFGPTaTokenizer.from_config(UpperCAmelCase_)
					UpperCamelCase__				:					Optional[Any]					=					model_from_config(UpperCAmelCase_)

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





	@slow
	def      __UpperCamelCase							(      self   :						List[Any]):
			for tf_tokenizer in self.tf_tokenizers:
					# for the test to run
					UpperCamelCase__				:					List[Any]					=					123_123

					for max_length in [3, 5, 1_024]:
							UpperCamelCase__				:					Tuple					=					tf.convert_to_tensor([self.test_sentences[0]])
							UpperCamelCase__				:					List[Any]					=					tf_tokenizer(UpperCAmelCase_  ,					max_length=UpperCAmelCase_)

							UpperCamelCase__				:					Union[str, Any]					=					out['input_ids'].numpy().shape[1]

							assert out_length == max_length












'''simple docstring'''




def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_)							->     list[str]:
		return [sentence[i : i + ngram_size] for i in range(len(lowerCamelCase_) - ngram_size + 1)]


if __name__ == "__main__":
		from doctest import testmod

		testmod()




