


'''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


__lowercase	:						List[str]       =	['gpt2']
__lowercase	:						int       =	'gpt2'

if is_tf_available():





	class 							__UpperCamelCase				(		tf.Module					):




		def __init__(   self						,				__a							):

							'''simple docstring'''





							super().__init__()
							__a			:       Dict					    =  tokenizer
							__a			:       List[Any]					    =  AutoConfig.from_pretrained(__a							)
							__a			:       List[Any]					    =  TFGPTaLMHeadModel.from_config(__a							)





		@tf.function(input_signature=(tf.TensorSpec((None,)						,				tf.string						,				name='text'							),)							)
		def       __UpperCAmelCase							(   self						,				__a							):

							'''simple docstring'''





							__a			:       Optional[Any]					    =  self.tokenizer(__a							)
							__a			:       Optional[int]					    =  tokenized['input_ids'].to_tensor()

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

							__a			:       str					    =  self.model(input_ids=__a						,				attention_mask=__a							)['logits']

							return outputs





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




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						super().setUp()

						__a			:       List[str]					    =  [GPTaTokenizer.from_pretrained(__a							) for checkpoint in (TOKENIZER_CHECKPOINTS)]
						__a			:       Any					    =  [TFGPTaTokenizer.from_pretrained(__a							) for checkpoint in TOKENIZER_CHECKPOINTS]
						assert len(self.tokenizers							) == len(self.tf_tokenizers							)

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




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





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

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

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




	@slow
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						for tf_tokenizer in self.tf_tokenizers:
											__a			:       int					    =  tf.function(__a							)
											for test_inputs in self.test_sentences:
																__a			:       Dict					    =  tf.constant(__a							)
																__a			:       List[str]					    =  compiled_tokenizer(__a							)
																__a			:       str					    =  tf_tokenizer(__a							)

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




	@slow
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						for tf_tokenizer in self.tf_tokenizers:
											__a			:       int					    =  ModelToSave(tokenizer=__a							)
											__a			:       List[Any]					    =  tf.convert_to_tensor([self.test_sentences[0]]							)
											__a			:       Optional[Any]					    =  model.serving(__a							)  # Build model with some sample inputs
											with TemporaryDirectory() as tempdir:
																__a			:       int					    =  Path(__a							) / 'saved.model'
																tf.saved_model.save(__a						,				__a						,				signatures={'serving_default': model.serving}							)
																__a			:       List[Any]					    =  tf.saved_model.load(__a							)
											__a			:       List[Any]					    =  loaded_model.signatures['serving_default'](__a							)['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							):

						'''simple docstring'''





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

											__a			:       Union[str, Any]					    =  tf_tokenizer.get_config()
											__a			:       List[Any]					    =  TFGPTaTokenizer.from_config(__a							)
											__a			:       Tuple					    =  model_from_config(__a							)

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





	@slow
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						for tf_tokenizer in self.tf_tokenizers:
											# for the test to run
											__a			:       List[Any]					    =  12_3123

											for max_length in [3, 5, 1024]:
																__a			:       Union[str, Any]					    =  tf.convert_to_tensor([self.test_sentences[0]]							)
																__a			:       Dict					    =  tf_tokenizer(__a						,				max_length=__a							)

																__a			:       Dict					    =  out['input_ids'].numpy().shape[1]

																assert out_length == max_length







'''simple docstring'''




print((lambda quine: quine % quine)("""print((lambda quine: quine %% quine)(%r))"""))





