




'''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  :				List[Any]									=  ['gpt2']
a  :				Any									=  'gpt2'

if is_tf_available():






							class 						a				(   tf.Module     ):


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


											@tf.function(input_signature=(tf.TensorSpec((None,)       ,				tf.string       ,				name='''text'''					),)					)
											def   A_ (		self    :					Optional[Any]       ,				lowercase_    :					List[Any]					):
																snake_case_									=				self.tokenizer(lowercase_					)
																snake_case_									=				tokenized['''input_ids'''].to_tensor()

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

																snake_case_									=				self.model(input_ids=lowercase_       ,				attention_mask=lowercase_					)['''logits''']

																return outputs






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


				def   A_ (		self    :					Optional[Any]					):
									super().setUp()

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

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


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

																			for key in python_outputs.keys():
																								# convert them to numpy to avoid messing with ragged tensors
																								snake_case_									=				python_outputs[key].numpy()
																								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(lowercase_       ,				tf.intaa					) == tf_outputs_values					)					)


				@slow
				def   A_ (		self    :					List[Any]					):
									for tf_tokenizer in self.tf_tokenizers:
														snake_case_									=				tf.function(lowercase_					)
														for test_inputs in self.test_sentences:
																			snake_case_									=				tf.constant(lowercase_					)
																			snake_case_									=				compiled_tokenizer(lowercase_					)
																			snake_case_									=				tf_tokenizer(lowercase_					)

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


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

														snake_case_									=				tf_tokenizer.get_config()
														snake_case_									=				TFGPTaTokenizer.from_config(lowercase_					)
														snake_case_									=				model_from_config(lowercase_					)

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


				@slow
				def   A_ (		self    :					Dict					):
									for tf_tokenizer in self.tf_tokenizers:
														# for the test to run
														snake_case_									=				12_3123

														for max_length in [3, 5, 1024]:
																			snake_case_									=				tf.convert_to_tensor([self.test_sentences[0]]					)
																			snake_case_									=				tf_tokenizer(lowercase_       ,				max_length=lowercase_					)

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

																			assert out_length == max_length







'''simple docstring'''

from ..utils import DummyObject, requires_backends






class 						a				(   metaclass=_lowerCamelCase     ):
				snake_case_      =		["transformers", "torch", "note_seq"]


				def __init__(		self    :					Union[str, Any]       ,				*lowercase_    :					Optional[int]       ,				**lowercase_    :					int					):
									requires_backends(self       ,				['''transformers''', '''torch''', '''note_seq''']					)


				@classmethod
				def   A_ (		cls    :					List[Any]       ,				*lowercase_    :					Optional[Any]       ,				**lowercase_    :					str					):
									requires_backends(cls       ,				['''transformers''', '''torch''', '''note_seq''']					)


				@classmethod
				def   A_ (		cls    :					Tuple       ,				*lowercase_    :					Union[str, Any]       ,				**lowercase_    :					List[Any]					):
									requires_backends(cls       ,				['''transformers''', '''torch''', '''note_seq''']					)

