
'''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 :  Optional[int]    							=       ["gpt2"]
_lowercase :  Union[str, Any]    							=       "gpt2"

if is_tf_available():






							class  lowerCAmelCase__   (  tf.Module     ):






												def __init__(   self				,							__SCREAMING_SNAKE_CASE		):


																		"""simple docstring"""





																		super().__init__()
																		lowercase_	:     int													=       tokenizer
																		lowercase_	:     List[str]													=       AutoConfig.from_pretrained(__SCREAMING_SNAKE_CASE		)
																		lowercase_	:     Optional[int]													=       TFGPTaLMHeadModel.from_config(__SCREAMING_SNAKE_CASE		)






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


																		"""simple docstring"""





																		lowercase_	:     Dict													=       self.tokenizer(__SCREAMING_SNAKE_CASE		)
																		lowercase_	:     Dict													=       tokenized['''input_ids'''].to_tensor()

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

																		lowercase_	:     Any													=       self.model(input_ids=__SCREAMING_SNAKE_CASE				,							attention_mask=__SCREAMING_SNAKE_CASE		)['''logits''']

																		return outputs






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






					def 	_snake_case    (   self		):


											"""simple docstring"""





											super().setUp()

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

											lowercase_	:     Optional[Any]													=       [
											    '''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_	:     Optional[int]													=       list(zip(self.test_sentences				,							self.test_sentences[::-1]		)		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





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

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






					@slow
					def 	_snake_case    (   self		):


											"""simple docstring"""





											for tf_tokenizer in self.tf_tokenizers:
																	lowercase_	:     str													=       tf.function(__SCREAMING_SNAKE_CASE		)
																	for test_inputs in self.test_sentences:
																							lowercase_	:     str													=       tf.constant(__SCREAMING_SNAKE_CASE		)
																							lowercase_	:     Tuple													=       compiled_tokenizer(__SCREAMING_SNAKE_CASE		)
																							lowercase_	:     List[str]													=       tf_tokenizer(__SCREAMING_SNAKE_CASE		)

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






					@slow
					def 	_snake_case    (   self		):


											"""simple docstring"""





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


											"""simple docstring"""





											for tf_tokenizer in self.tf_tokenizers:
																	lowercase_	:     Dict													=       tf.convert_to_tensor([self.test_sentences[0]]		)
																	lowercase_	:     Optional[int]													=       tf_tokenizer(__SCREAMING_SNAKE_CASE		)  # Build model with some sample inputs

																	lowercase_	:     Tuple													=       tf_tokenizer.get_config()
																	lowercase_	:     int													=       TFGPTaTokenizer.from_config(__SCREAMING_SNAKE_CASE		)
																	lowercase_	:     List[Any]													=       model_from_config(__SCREAMING_SNAKE_CASE		)

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






					@slow
					def 	_snake_case    (   self		):


											"""simple docstring"""





											for tf_tokenizer in self.tf_tokenizers:
																	# for the test to run
																	lowercase_	:     Union[str, Any]													=       12_31_23

																	for max_length in [3, 5, 10_24]:
																							lowercase_	:     Dict													=       tf.convert_to_tensor([self.test_sentences[0]]		)
																							lowercase_	:     Dict													=       tf_tokenizer(__SCREAMING_SNAKE_CASE				,							max_length=__SCREAMING_SNAKE_CASE		)

																							lowercase_	:     Optional[Any]													=       out['''input_ids'''].numpy().shape[1]

																							assert out_length == max_length




import gc
import threading
import time

import psutil
import torch




class    _UpperCamelCase      :


			"""simple docstring"""






			def __init__(   self						)     ->					str:

							'''simple docstring'''


							__lowercase 			=							psutil.Process()
							__lowercase 			=							False





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


							__lowercase 			=							-1

							while True:
											__lowercase 			=							max(self.process.memory_info().rss      ,   self.cpu_memory_peak						)

											# can't sleep or will not catch the peak right (this comment is here on purpose)
											if not self.peak_monitoring:
															break





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


							__lowercase 			=							True
							__lowercase 			=							threading.Thread(target=self.peak_monitor						)
							__lowercase 			=							True
							self.thread.start()





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


							__lowercase 			=							False
							self.thread.join()
							return self.cpu_memory_peak


__a  :     List[str]       =  PeakCPUMemory()





def 				UpperCAmelCase						(    ):







				"""simple docstring"""



				__lowercase 			=							{'''time''': time.time()}

				gc.collect()
				torch.cuda.empty_cache()

				# CPU mem
				__lowercase 			=							psutil.Process().memory_info().rss
				cpu_peak_tracker.start()

				# GPU mem
				for i in range(torch.cuda.device_count()							):
								__lowercase 			=							torch.cuda.memory_allocated(lowercase							)
				torch.cuda.reset_peak_memory_stats()

				return measures





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



				__lowercase 			=							{'''time''': time.time() - start_measures['''time''']}

				gc.collect()
				torch.cuda.empty_cache()

				# CPU mem
				__lowercase 			=							(psutil.Process().memory_info().rss - start_measures['''cpu''']) / 2**20
				__lowercase 			=							(cpu_peak_tracker.stop() - start_measures['''cpu''']) / 2**20

				# GPU mem
				for i in range(torch.cuda.device_count()							):
								__lowercase 			=							(torch.cuda.memory_allocated(lowercase							) - start_measures[str(lowercase							)]) / 2**20
								__lowercase 			=							(torch.cuda.max_memory_allocated(lowercase							) - start_measures[str(lowercase							)]) / 2**20

				return measures





def 				UpperCAmelCase						(    lowercase							,			lowercase							):







				"""simple docstring"""



				print(F"{description}:"							)
				print(F"- Time: {measures['time']:.2f}s"							)
				for i in range(torch.cuda.device_count()							):
								print(F"- GPU {i} allocated: {measures[str(lowercase							)]:.2f}MiB"							)
								__lowercase 			=							measures[F"{i}-peak"]
								print(F"- GPU {i} peak: {peak:.2f}MiB"							)
				print(F"- CPU RAM allocated: {measures['cpu']:.2f}MiB"							)
				print(F"- CPU RAM peak: {measures['cpu-peak']:.2f}MiB"							)