


"""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


__UpperCamelCase						=      ['''gpt2''']
__UpperCamelCase						=      '''gpt2'''

if is_tf_available():
	class 					lowerCAmelCase	(					tf.Module    ):




			'''simple docstring'''







			def __init__(	self						,       lowerCAmelCase__				)     ->							Optional[int]:
									super().__init__()
									SCREAMING_SNAKE_CASE													=					tokenizer
									SCREAMING_SNAKE_CASE													=					AutoConfig.from_pretrained(lowerCAmelCase__				)
									SCREAMING_SNAKE_CASE													=					TFGPTaLMHeadModel.from_config(lowerCAmelCase__				)







			@tf.function(input_signature=(tf.TensorSpec((None,)						,       tf.string						,       name='text'				),)				)
			def 							__A						(	self						,       lowerCAmelCase__				)     ->							Dict:
									SCREAMING_SNAKE_CASE													=					self.tokenizer(lowerCAmelCase__				)
									SCREAMING_SNAKE_CASE													=					tokenized['input_ids'].to_tensor()

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

									SCREAMING_SNAKE_CASE													=					self.model(input_ids=lowerCAmelCase__						,       attention_mask=lowerCAmelCase__				)['logits']

									return outputs
@require_tf
@require_keras_nlp
class 					lowerCAmelCase	(					unittest.TestCase    ):




		'''simple docstring'''







		def 							__A						(	self				)     ->							Optional[int]:
								super().setUp()

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

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







		def 							__A						(	self				)     ->							Union[str, Any]:
								for tokenizer, tf_tokenizer in zip(self.tokenizers						,       self.tf_tokenizers				):
														for test_inputs in self.test_sentences:
																				SCREAMING_SNAKE_CASE													=					tokenizer([test_inputs]						,       return_tensors='tf'				)
																				SCREAMING_SNAKE_CASE													=					tf_tokenizer([test_inputs]				)

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







		@slow
		def 							__A						(	self				)     ->							List[Any]:
								for tf_tokenizer in self.tf_tokenizers:
														SCREAMING_SNAKE_CASE													=					tf.function(lowerCAmelCase__				)
														for test_inputs in self.test_sentences:
																				SCREAMING_SNAKE_CASE													=					tf.constant(lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE													=					compiled_tokenizer(lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE													=					tf_tokenizer(lowerCAmelCase__				)

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







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

														SCREAMING_SNAKE_CASE													=					tf_tokenizer.get_config()
														SCREAMING_SNAKE_CASE													=					TFGPTaTokenizer.from_config(lowerCAmelCase__				)
														SCREAMING_SNAKE_CASE													=					model_from_config(lowerCAmelCase__				)

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







		@slow
		def 							__A						(	self				)     ->							Union[str, Any]:
								for tf_tokenizer in self.tf_tokenizers:
														# for the test to run
														SCREAMING_SNAKE_CASE													=					123_123

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

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

																				assert out_length == max_length






"""simple docstring"""




# This is the module that test_patching.py uses to test patch_submodule()

import os  # noqa: this is just for tests
import os as renamed_os  # noqa: this is just for tests
from os import path  # noqa: this is just for tests
from os import path as renamed_path  # noqa: this is just for tests
from os.path import join  # noqa: this is just for tests
from os.path import join as renamed_join  # noqa: this is just for tests


__UpperCamelCase						=      open  # noqa: we just need to have a builtin inside this module to test it properly


