
"""simple docstring"""






import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
				import tensorflow as tf

				from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments




@require_tf
class 			_SCREAMING_SNAKE_CASE					(			unittest.TestCase							):






			def  __lowerCAmelCase (					self       ,   __A       )			->     List[Any]:





						for model_result in results.values():
									for batch_size, sequence_length in zip(model_result["""bs"""]       ,   model_result["""ss"""]       ):
												lowerCAmelCase_    :Union[str, Any]			     =  model_result['''result'''][batch_size][sequence_length]
												self.assertIsNotNone(UpperCamelCase__       )






			def  __lowerCAmelCase (					self       )			->     int:
						lowerCAmelCase_    :List[Any]			     =  '''sshleifer/tiny-gpt2'''
						lowerCAmelCase_    :List[Any]			     =  TensorFlowBenchmarkArguments(
						    models=[MODEL_ID]       ,   training=UpperCamelCase__       ,   inference=UpperCamelCase__       ,   sequence_lengths=[8]       ,   batch_sizes=[1]       ,   eager_mode=UpperCamelCase__       ,   multi_process=UpperCamelCase__       ,   )
						lowerCAmelCase_    :str			     =  TensorFlowBenchmark(UpperCamelCase__       )
						lowerCAmelCase_    :List[Any]			     =  benchmark.run()
						self.check_results_dict_not_empty(results.time_inference_result       )
						self.check_results_dict_not_empty(results.memory_inference_result       )






			def  __lowerCAmelCase (					self       )			->     int:
						lowerCAmelCase_    :str			     =  '''sgugger/tiny-distilbert-classification'''
						lowerCAmelCase_    :Union[str, Any]			     =  TensorFlowBenchmarkArguments(
						    models=[MODEL_ID]       ,   training=UpperCamelCase__       ,   inference=UpperCamelCase__       ,   sequence_lengths=[8]       ,   batch_sizes=[1]       ,   multi_process=UpperCamelCase__       ,   only_pretrain_model=UpperCamelCase__       ,   )
						lowerCAmelCase_    :List[str]			     =  TensorFlowBenchmark(UpperCamelCase__       )
						lowerCAmelCase_    :Optional[Any]			     =  benchmark.run()
						self.check_results_dict_not_empty(results.time_inference_result       )
						self.check_results_dict_not_empty(results.memory_inference_result       )






			def  __lowerCAmelCase (					self       )			->     Any:
						lowerCAmelCase_    :Union[str, Any]			     =  '''sshleifer/tiny-gpt2'''
						lowerCAmelCase_    :int			     =  TensorFlowBenchmarkArguments(
						    models=[MODEL_ID]       ,   training=UpperCamelCase__       ,   inference=UpperCamelCase__       ,   sequence_lengths=[8]       ,   batch_sizes=[1]       ,   multi_process=UpperCamelCase__       ,   )
						lowerCAmelCase_    :Union[str, Any]			     =  TensorFlowBenchmark(UpperCamelCase__       )
						lowerCAmelCase_    :int			     =  benchmark.run()
						self.check_results_dict_not_empty(results.time_inference_result       )
						self.check_results_dict_not_empty(results.memory_inference_result       )






			def  __lowerCAmelCase (					self       )			->     Any:
						lowerCAmelCase_    :Optional[int]			     =  '''sshleifer/tiny-gpt2'''
						lowerCAmelCase_    :int			     =  AutoConfig.from_pretrained(UpperCamelCase__       )
						lowerCAmelCase_    :Optional[int]			     =  TensorFlowBenchmarkArguments(
						    models=[MODEL_ID]       ,   training=UpperCamelCase__       ,   inference=UpperCamelCase__       ,   sequence_lengths=[8]       ,   batch_sizes=[1]       ,   eager_mode=UpperCamelCase__       ,   multi_process=UpperCamelCase__       ,   )
						lowerCAmelCase_    :str			     =  TensorFlowBenchmark(UpperCamelCase__       ,   [config]       )
						lowerCAmelCase_    :Optional[int]			     =  benchmark.run()
						self.check_results_dict_not_empty(results.time_inference_result       )
						self.check_results_dict_not_empty(results.memory_inference_result       )






			def  __lowerCAmelCase (					self       )			->     str:
						lowerCAmelCase_    :Dict			     =  '''sshleifer/tiny-gpt2'''
						lowerCAmelCase_    :Optional[int]			     =  AutoConfig.from_pretrained(UpperCamelCase__       )
						lowerCAmelCase_    :Optional[Any]			     =  TensorFlowBenchmarkArguments(
						    models=[MODEL_ID]       ,   training=UpperCamelCase__       ,   inference=UpperCamelCase__       ,   sequence_lengths=[8]       ,   batch_sizes=[1]       ,   multi_process=UpperCamelCase__       ,   )
						lowerCAmelCase_    :List[str]			     =  TensorFlowBenchmark(UpperCamelCase__       ,   [config]       )
						lowerCAmelCase_    :int			     =  benchmark.run()
						self.check_results_dict_not_empty(results.time_inference_result       )
						self.check_results_dict_not_empty(results.memory_inference_result       )






			def  __lowerCAmelCase (					self       )			->     int:
						lowerCAmelCase_    :str			     =  '''sshleifer/tiny-gpt2'''
						lowerCAmelCase_    :int			     =  TensorFlowBenchmarkArguments(
						    models=[MODEL_ID]       ,   training=UpperCamelCase__       ,   inference=UpperCamelCase__       ,   sequence_lengths=[8]       ,   batch_sizes=[1]       ,   multi_process=UpperCamelCase__       ,   )
						lowerCAmelCase_    :Optional[int]			     =  TensorFlowBenchmark(UpperCamelCase__       )
						lowerCAmelCase_    :Tuple			     =  benchmark.run()
						self.check_results_dict_not_empty(results.time_train_result       )
						self.check_results_dict_not_empty(results.memory_train_result       )






			def  __lowerCAmelCase (					self       )			->     str:
						lowerCAmelCase_    :List[str]			     =  '''sshleifer/tiny-gpt2'''
						lowerCAmelCase_    :int			     =  AutoConfig.from_pretrained(UpperCamelCase__       )
						lowerCAmelCase_    :Tuple			     =  TensorFlowBenchmarkArguments(
						    models=[MODEL_ID]       ,   training=UpperCamelCase__       ,   inference=UpperCamelCase__       ,   sequence_lengths=[8]       ,   batch_sizes=[1]       ,   multi_process=UpperCamelCase__       ,   )
						lowerCAmelCase_    :Dict			     =  TensorFlowBenchmark(UpperCamelCase__       ,   [config]       )
						lowerCAmelCase_    :str			     =  benchmark.run()
						self.check_results_dict_not_empty(results.time_train_result       )
						self.check_results_dict_not_empty(results.memory_train_result       )






			def  __lowerCAmelCase (					self       )			->     int:
						lowerCAmelCase_    :List[str]			     =  '''patrickvonplaten/t5-tiny-random'''
						lowerCAmelCase_    :Union[str, Any]			     =  AutoConfig.from_pretrained(UpperCamelCase__       )
						lowerCAmelCase_    :Union[str, Any]			     =  TensorFlowBenchmarkArguments(
						    models=[MODEL_ID]       ,   training=UpperCamelCase__       ,   inference=UpperCamelCase__       ,   sequence_lengths=[8]       ,   batch_sizes=[1]       ,   multi_process=UpperCamelCase__       ,   )
						lowerCAmelCase_    :Dict			     =  TensorFlowBenchmark(UpperCamelCase__       ,   configs=[config]       )
						lowerCAmelCase_    :Dict			     =  benchmark.run()
						self.check_results_dict_not_empty(results.time_inference_result       )
						self.check_results_dict_not_empty(results.memory_inference_result       )






			@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices("""GPU"""       )       ) == 0       ,   """Cannot do xla on CPU."""       )
			def  __lowerCAmelCase (					self       )			->     Union[str, Any]:
						lowerCAmelCase_    :Optional[Any]			     =  '''sshleifer/tiny-gpt2'''
						lowerCAmelCase_    :Tuple			     =  TensorFlowBenchmarkArguments(
						    models=[MODEL_ID]       ,   training=UpperCamelCase__       ,   inference=UpperCamelCase__       ,   sequence_lengths=[8]       ,   batch_sizes=[1]       ,   use_xla=UpperCamelCase__       ,   multi_process=UpperCamelCase__       ,   )
						lowerCAmelCase_    :Any			     =  TensorFlowBenchmark(UpperCamelCase__       )
						lowerCAmelCase_    :List[Any]			     =  benchmark.run()
						self.check_results_dict_not_empty(results.time_inference_result       )
						self.check_results_dict_not_empty(results.memory_inference_result       )






			def  __lowerCAmelCase (					self       )			->     Tuple:
						lowerCAmelCase_    :Optional[int]			     =  '''sshleifer/tiny-gpt2'''
						with tempfile.TemporaryDirectory() as tmp_dir:
									lowerCAmelCase_    :Tuple			     =  TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]       ,   inference=UpperCamelCase__       ,   save_to_csv=UpperCamelCase__       ,   sequence_lengths=[8]       ,   batch_sizes=[1]       ,   inference_time_csv_file=os.path.join(UpperCamelCase__       ,   """inf_time.csv"""       )       ,   inference_memory_csv_file=os.path.join(UpperCamelCase__       ,   """inf_mem.csv"""       )       ,   env_info_csv_file=os.path.join(UpperCamelCase__       ,   """env.csv"""       )       ,   multi_process=UpperCamelCase__       ,   )
									lowerCAmelCase_    :Optional[Any]			     =  TensorFlowBenchmark(UpperCamelCase__       )
									benchmark.run()
									self.assertTrue(Path(os.path.join(UpperCamelCase__       ,   """inf_time.csv"""       )       ).exists()       )
									self.assertTrue(Path(os.path.join(UpperCamelCase__       ,   """inf_mem.csv"""       )       ).exists()       )
									self.assertTrue(Path(os.path.join(UpperCamelCase__       ,   """env.csv"""       )       ).exists()       )






			def  __lowerCAmelCase (					self       )			->     Union[str, Any]:
						lowerCAmelCase_    :Any			     =  '''sshleifer/tiny-gpt2'''

						def _check_summary_is_not_empty(__A       ):
									self.assertTrue(hasattr(UpperCamelCase__       ,   """sequential"""       )       )
									self.assertTrue(hasattr(UpperCamelCase__       ,   """cumulative"""       )       )
									self.assertTrue(hasattr(UpperCamelCase__       ,   """current"""       )       )
									self.assertTrue(hasattr(UpperCamelCase__       ,   """total"""       )       )

						with tempfile.TemporaryDirectory() as tmp_dir:
									lowerCAmelCase_    :List[str]			     =  TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]       ,   inference=UpperCamelCase__       ,   sequence_lengths=[8]       ,   batch_sizes=[1]       ,   log_filename=os.path.join(UpperCamelCase__       ,   """log.txt"""       )       ,   log_print=UpperCamelCase__       ,   trace_memory_line_by_line=UpperCamelCase__       ,   eager_mode=UpperCamelCase__       ,   multi_process=UpperCamelCase__       ,   )
									lowerCAmelCase_    :List[Any]			     =  TensorFlowBenchmark(UpperCamelCase__       )
									lowerCAmelCase_    :Dict			     =  benchmark.run()
									_check_summary_is_not_empty(result.inference_summary       )
									self.assertTrue(Path(os.path.join(UpperCamelCase__       ,   """log.txt"""       )       ).exists()       )







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		:       Optional[Any]									=     ['gpt2']
__UpperCamelCase		:       str									=     'gpt2'

if is_tf_available():





					class 					lowercase__						(   tf.Module):


											def __init__(       self	:				Optional[Any]    ,		UpperCamelCase__	:				Union[str, Any]						):





																	'''simple docstring'''





																	super().__init__()
																	SCREAMING_SNAKE_CASE							:    Dict							=							tokenizer
																	SCREAMING_SNAKE_CASE							:    int							=							AutoConfig.from_pretrained(UpperCamelCase__						)
																	SCREAMING_SNAKE_CASE							:    Dict							=							TFGPTaLMHeadModel.from_config(UpperCamelCase__						)



											@tf.function(input_signature=(tf.TensorSpec((None,)    ,		tf.string    ,		name='''text'''						),)						)
											def        __A       (       self	:				str    ,		UpperCamelCase__	:				int						):





																	'''simple docstring'''





																	SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.tokenizer(UpperCamelCase__						)
																	SCREAMING_SNAKE_CASE							:    Optional[Any]							=							tokenized['''input_ids'''].to_tensor()

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

																	SCREAMING_SNAKE_CASE							:    List[Any]							=							self.model(input_ids=UpperCamelCase__    ,		attention_mask=UpperCamelCase__						)['''logits''']

																	return outputs





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


						def        __A       (       self	:				int						):





												'''simple docstring'''





												super().setUp()

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

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


						def        __A       (       self	:				str						):





												'''simple docstring'''





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

																								for key in python_outputs.keys():
																														# convert them to numpy to avoid messing with ragged tensors
																														SCREAMING_SNAKE_CASE							:    int							=							python_outputs[key].numpy()
																														SCREAMING_SNAKE_CASE							:    int							=							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        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												for tf_tokenizer in self.tf_tokenizers:
																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							tf.function(UpperCamelCase__						)
																		for test_inputs in self.test_sentences:
																								SCREAMING_SNAKE_CASE							:    Optional[int]							=							tf.constant(UpperCamelCase__						)
																								SCREAMING_SNAKE_CASE							:    List[str]							=							compiled_tokenizer(UpperCamelCase__						)
																								SCREAMING_SNAKE_CASE							:    List[Any]							=							tf_tokenizer(UpperCamelCase__						)

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


						@slow
						def        __A       (       self	:				Optional[int]						):





												'''simple docstring'''





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





												'''simple docstring'''





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

																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							tf_tokenizer.get_config()
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							TFGPTaTokenizer.from_config(UpperCamelCase__						)
																		SCREAMING_SNAKE_CASE							:    str							=							model_from_config(UpperCamelCase__						)

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



						@slow
						def        __A       (       self	:				Optional[int]						):





												'''simple docstring'''





												for tf_tokenizer in self.tf_tokenizers:
																		# for the test to run
																		SCREAMING_SNAKE_CASE							:    Tuple							=							12_3123

																		for max_length in [3, 5, 1024]:
																								SCREAMING_SNAKE_CASE							:    Dict							=							tf.convert_to_tensor([self.test_sentences[0]]						)
																								SCREAMING_SNAKE_CASE							:    Tuple							=							tf_tokenizer(UpperCamelCase__    ,		max_length=UpperCamelCase__						)

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

																								assert out_length == max_length





