


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 	_a							(      tf.Module      ):

										def __init__(							self:     int   ,						UpperCamelCase_:     Optional[Any]							)      ->       str:





												"""simple docstring"""







												super().__init__()
												lowercase__						=				tokenizer
												lowercase__						=				AutoConfig.from_pretrained(UpperCamelCase_							)
												lowercase__						=				TFGPTaLMHeadModel.from_config(UpperCamelCase_							)


										@tf.function(input_signature=(tf.TensorSpec((None,)   ,						tf.string   ,						name='''text'''							),)							)
										def 				lowerCamelCase_			(							self:     Optional[Any]   ,						UpperCamelCase_:     int							)      ->       Any:





												"""simple docstring"""







												lowercase__						=				self.tokenizer(UpperCamelCase_							)
												lowercase__						=				tokenized['''input_ids'''].to_tensor()

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

												lowercase__						=				self.model(input_ids=UpperCamelCase_   ,						attention_mask=UpperCamelCase_							)['''logits''']

												return outputs


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

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





									"""simple docstring"""







									super().setUp()

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

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

							def 				lowerCamelCase_			(							self:     List[str]							)      ->       List[Any]:





									"""simple docstring"""







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

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





									"""simple docstring"""







									for tf_tokenizer in self.tf_tokenizers:
											lowercase__						=				tf.function(UpperCamelCase_							)
											for test_inputs in self.test_sentences:
													lowercase__						=				tf.constant(UpperCamelCase_							)
													lowercase__						=				compiled_tokenizer(UpperCamelCase_							)
													lowercase__						=				tf_tokenizer(UpperCamelCase_							)

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

							@slow
							def 				lowerCamelCase_			(							self:     Dict							)      ->       str:





									"""simple docstring"""







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





									"""simple docstring"""







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

											lowercase__						=				tf_tokenizer.get_config()
											lowercase__						=				TFGPTaTokenizer.from_config(UpperCamelCase_							)
											lowercase__						=				model_from_config(UpperCamelCase_							)

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


							@slow
							def 				lowerCamelCase_			(							self:     Tuple							)      ->       str:





									"""simple docstring"""







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

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

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

													assert out_length == max_length






import logging

from transformers import PretrainedConfig


lowerCAmelCase	=							logging.getLogger(__name__)


lowerCAmelCase	=							{
    'bertabs-finetuned-cnndm': 'https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json',
}


class 	_a							(      UpperCamelCase__      ):
							_lowercase     :      List[Any]  				=			'''bertabs'''

							def __init__(							self:     List[str]   ,						UpperCamelCase_:     Dict=30_522   ,						UpperCamelCase_:     Union[str, Any]=512   ,						UpperCamelCase_:     Optional[int]=6   ,						UpperCamelCase_:     int=512   ,						UpperCamelCase_:     Optional[int]=8   ,						UpperCamelCase_:     List[Any]=512   ,						UpperCamelCase_:     Tuple=0.2   ,						UpperCamelCase_:     List[Any]=6   ,						UpperCamelCase_:     Tuple=768   ,						UpperCamelCase_:     List[Any]=8   ,						UpperCamelCase_:     Union[str, Any]=2_048   ,						UpperCamelCase_:     str=0.2   ,						**UpperCamelCase_:     Any   ,						)      ->       List[str]:





									"""simple docstring"""







									super().__init__(**UpperCamelCase_							)

									lowercase__						=				vocab_size
									lowercase__						=				max_pos

									lowercase__						=				enc_layers
									lowercase__						=				enc_hidden_size
									lowercase__						=				enc_heads
									lowercase__						=				enc_ff_size
									lowercase__						=				enc_dropout

									lowercase__						=				dec_layers
									lowercase__						=				dec_hidden_size
									lowercase__						=				dec_heads
									lowercase__						=				dec_ff_size
									lowercase__						=				dec_dropout


