import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
							from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments







@require_torch
class     __lowerCAmelCase					(    unittest.TestCase		):



			def 					_lowerCamelCase (		self     :						Optional[Any]				,	A     :						Any)     ->					str:



						"""simple docstring"""






						for model_result in results.values():
									for batch_size, sequence_length in zip(model_result['bs']				,	model_result['ss']):
												_UpperCAmelCase	 =     model_result['result'][batch_size][sequence_length]
												self.assertIsNotNone(A)



			def 					_lowerCamelCase (		self     :						Union[str, Any])     ->					Any:



						"""simple docstring"""






						_UpperCAmelCase	 =     'sshleifer/tiny-gpt2'
						_UpperCAmelCase	 =     PyTorchBenchmarkArguments(
						    models=[MODEL_ID]				,	training=A				,	inference=A				,	sequence_lengths=[8]				,	batch_sizes=[1]				,	multi_process=A				,	)
						_UpperCAmelCase	 =     PyTorchBenchmark(A)
						_UpperCAmelCase	 =     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)     ->					int:



						"""simple docstring"""






						_UpperCAmelCase	 =     'sgugger/tiny-distilbert-classification'
						_UpperCAmelCase	 =     PyTorchBenchmarkArguments(
						    models=[MODEL_ID]				,	training=A				,	inference=A				,	sequence_lengths=[8]				,	batch_sizes=[1]				,	multi_process=A				,	only_pretrain_model=A				,	)
						_UpperCAmelCase	 =     PyTorchBenchmark(A)
						_UpperCAmelCase	 =     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     :						List[str])     ->					Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase	 =     'sshleifer/tiny-gpt2'
						_UpperCAmelCase	 =     PyTorchBenchmarkArguments(
						    models=[MODEL_ID]				,	training=A				,	inference=A				,	torchscript=A				,	sequence_lengths=[8]				,	batch_sizes=[1]				,	multi_process=A				,	)
						_UpperCAmelCase	 =     PyTorchBenchmark(A)
						_UpperCAmelCase	 =     benchmark.run()
						self.check_results_dict_not_empty(results.time_inference_result)
						self.check_results_dict_not_empty(results.memory_inference_result)



			@unittest.skipIf(torch_device == 'cpu'				,	'Cant do half precision')
			def 					_lowerCamelCase (		self     :						Any)     ->					Tuple:



						"""simple docstring"""






						_UpperCAmelCase	 =     'sshleifer/tiny-gpt2'
						_UpperCAmelCase	 =     PyTorchBenchmarkArguments(
						    models=[MODEL_ID]				,	training=A				,	inference=A				,	fpaa=A				,	sequence_lengths=[8]				,	batch_sizes=[1]				,	multi_process=A				,	)
						_UpperCAmelCase	 =     PyTorchBenchmark(A)
						_UpperCAmelCase	 =     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)     ->					Tuple:



						"""simple docstring"""






						_UpperCAmelCase	 =     'sshleifer/tiny-gpt2'
						_UpperCAmelCase	 =     AutoConfig.from_pretrained(A)
						# set architectures equal to `None`
						_UpperCAmelCase	 =     None
						_UpperCAmelCase	 =     PyTorchBenchmarkArguments(
						    models=[MODEL_ID]				,	training=A				,	inference=A				,	sequence_lengths=[8]				,	batch_sizes=[1]				,	multi_process=A				,	)
						_UpperCAmelCase	 =     PyTorchBenchmark(A				,	configs=[config])
						_UpperCAmelCase	 =     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)     ->					int:



						"""simple docstring"""






						_UpperCAmelCase	 =     'sshleifer/tiny-gpt2'
						_UpperCAmelCase	 =     PyTorchBenchmarkArguments(
						    models=[MODEL_ID]				,	training=A				,	inference=A				,	sequence_lengths=[8]				,	batch_sizes=[1]				,	multi_process=A				,	)
						_UpperCAmelCase	 =     PyTorchBenchmark(A)
						_UpperCAmelCase	 =     benchmark.run()
						self.check_results_dict_not_empty(results.time_train_result)
						self.check_results_dict_not_empty(results.memory_train_result)



			@unittest.skipIf(torch_device == 'cpu'				,	'Can\'t do half precision')
			def 					_lowerCamelCase (		self     :						Any)     ->					Dict:



						"""simple docstring"""






						_UpperCAmelCase	 =     'sshleifer/tiny-gpt2'
						_UpperCAmelCase	 =     PyTorchBenchmarkArguments(
						    models=[MODEL_ID]				,	training=A				,	inference=A				,	sequence_lengths=[8]				,	batch_sizes=[1]				,	fpaa=A				,	multi_process=A				,	)
						_UpperCAmelCase	 =     PyTorchBenchmark(A)
						_UpperCAmelCase	 =     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     :						Optional[int])     ->					List[str]:



						"""simple docstring"""






						_UpperCAmelCase	 =     'sshleifer/tiny-gpt2'
						_UpperCAmelCase	 =     AutoConfig.from_pretrained(A)
						_UpperCAmelCase	 =     PyTorchBenchmarkArguments(
						    models=[MODEL_ID]				,	training=A				,	inference=A				,	sequence_lengths=[8]				,	batch_sizes=[1]				,	multi_process=A				,	)
						_UpperCAmelCase	 =     PyTorchBenchmark(A				,	configs=[config])
						_UpperCAmelCase	 =     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     :						List[Any])     ->					Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase	 =     'sshleifer/tinier_bart'
						_UpperCAmelCase	 =     AutoConfig.from_pretrained(A)
						_UpperCAmelCase	 =     PyTorchBenchmarkArguments(
						    models=[MODEL_ID]				,	training=A				,	inference=A				,	sequence_lengths=[8]				,	batch_sizes=[1]				,	multi_process=A				,	)
						_UpperCAmelCase	 =     PyTorchBenchmark(A				,	configs=[config])
						_UpperCAmelCase	 =     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     :						List[str])     ->					Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase	 =     'sshleifer/tiny-gpt2'
						_UpperCAmelCase	 =     AutoConfig.from_pretrained(A)
						_UpperCAmelCase	 =     PyTorchBenchmarkArguments(
						    models=[MODEL_ID]				,	training=A				,	inference=A				,	sequence_lengths=[8]				,	batch_sizes=[1]				,	multi_process=A				,	)
						_UpperCAmelCase	 =     PyTorchBenchmark(A				,	configs=[config])
						_UpperCAmelCase	 =     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)     ->					Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase	 =     'sshleifer/tinier_bart'
						_UpperCAmelCase	 =     AutoConfig.from_pretrained(A)
						_UpperCAmelCase	 =     PyTorchBenchmarkArguments(
						    models=[MODEL_ID]				,	training=A				,	inference=A				,	sequence_lengths=[8]				,	batch_sizes=[1]				,	multi_process=A				,	)
						_UpperCAmelCase	 =     PyTorchBenchmark(A				,	configs=[config])
						_UpperCAmelCase	 =     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     :						List[str])     ->					Tuple:



						"""simple docstring"""






						_UpperCAmelCase	 =     'sshleifer/tiny-gpt2'
						with tempfile.TemporaryDirectory() as tmp_dir:
									_UpperCAmelCase	 =     PyTorchBenchmarkArguments(
									    models=[MODEL_ID]				,	training=A				,	inference=A				,	save_to_csv=A				,	sequence_lengths=[8]				,	batch_sizes=[1]				,	inference_time_csv_file=os.path.join(A				,	'inf_time.csv')				,	train_memory_csv_file=os.path.join(A				,	'train_mem.csv')				,	inference_memory_csv_file=os.path.join(A				,	'inf_mem.csv')				,	train_time_csv_file=os.path.join(A				,	'train_time.csv')				,	env_info_csv_file=os.path.join(A				,	'env.csv')				,	multi_process=A				,	)
									_UpperCAmelCase	 =     PyTorchBenchmark(A)
									benchmark.run()
									self.assertTrue(Path(os.path.join(A				,	'inf_time.csv')).exists())
									self.assertTrue(Path(os.path.join(A				,	'train_time.csv')).exists())
									self.assertTrue(Path(os.path.join(A				,	'inf_mem.csv')).exists())
									self.assertTrue(Path(os.path.join(A				,	'train_mem.csv')).exists())
									self.assertTrue(Path(os.path.join(A				,	'env.csv')).exists())



			def 					_lowerCamelCase (		self     :						Dict)     ->					List[Any]:



						"""simple docstring"""






						_UpperCAmelCase	 =     'sshleifer/tiny-gpt2'

						def _check_summary_is_not_empty(A     :						List[Any]):
									self.assertTrue(hasattr(A				,	'sequential'))
									self.assertTrue(hasattr(A				,	'cumulative'))
									self.assertTrue(hasattr(A				,	'current'))
									self.assertTrue(hasattr(A				,	'total'))

						with tempfile.TemporaryDirectory() as tmp_dir:
									_UpperCAmelCase	 =     PyTorchBenchmarkArguments(
									    models=[MODEL_ID]				,	training=A				,	inference=A				,	sequence_lengths=[8]				,	batch_sizes=[1]				,	log_filename=os.path.join(A				,	'log.txt')				,	log_print=A				,	trace_memory_line_by_line=A				,	multi_process=A				,	)
									_UpperCAmelCase	 =     PyTorchBenchmark(A)
									_UpperCAmelCase	 =     benchmark.run()
									_check_summary_is_not_empty(result.inference_summary)
									_check_summary_is_not_empty(result.train_summary)
									self.assertTrue(Path(os.path.join(A				,	'log.txt')).exists())





import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class     __lowerCAmelCase					(    A       ,		unittest.TestCase		):
			UpperCamelCase           =  MgpstrTokenizer
			UpperCamelCase           =  False
			UpperCamelCase           =  {}
			UpperCamelCase           =  False



			def 					_lowerCamelCase (		self     :						int)     ->					List[Any]:



						"""simple docstring"""






						super().setUp()

						# fmt: off
						_UpperCAmelCase	 =     ['[GO]', '[s]', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
						# fmt: on
						_UpperCAmelCase	 =     dict(zip(A				,	range(len(A))))

						_UpperCAmelCase	 =     os.path.join(self.tmpdirname				,	VOCAB_FILES_NAMES['vocab_file'])
						with open(self.vocab_file				,	'w'				,	encoding='utf-8') as fp:
									fp.write(json.dumps(A) + '\n')



			def 					_lowerCamelCase (		self     :						Dict				,	**A     :						List[Any])     ->					Optional[Any]:



						"""simple docstring"""






						return MgpstrTokenizer.from_pretrained(self.tmpdirname				,	**A)



			def 					_lowerCamelCase (		self     :						List[str]				,	A     :						Optional[int])     ->					Dict:



						"""simple docstring"""






						_UpperCAmelCase	 =     'tester'
						_UpperCAmelCase	 =     'tester'
						return input_text, output_text



			@unittest.skip('MGP-STR always lower cases letters.')
			def 					_lowerCamelCase (		self     :						Optional[Any])     ->					int:



						"""simple docstring"""






						pass



			def 					_lowerCamelCase (		self     :						Optional[int])     ->					Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.get_tokenizers(do_lower_case=A)
						for tokenizer in tokenizers:
									with self.subTest(F"{tokenizer.__class__.__name__}"):
												_UpperCAmelCase	 =     '[SPECIAL_TOKEN]'

												tokenizer.add_special_tokens({'cls_token': special_token})
												_UpperCAmelCase	 =     tokenizer.encode([special_token]				,	add_special_tokens=A)
												self.assertEqual(len(A)				,	1)

												_UpperCAmelCase	 =     tokenizer.decode(A				,	skip_special_tokens=A)
												self.assertTrue(special_token not in decoded)



			def 					_lowerCamelCase (		self     :						Any)     ->					str:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.get_tokenizers()
						for tokenizer in tokenizers:
									with self.subTest(F"{tokenizer.__class__.__name__}"):
												_UpperCAmelCase ,  _UpperCAmelCase	 =     self.get_input_output_texts(A)

												_UpperCAmelCase	 =     tokenizer.tokenize(A)
												_UpperCAmelCase	 =     tokenizer.convert_tokens_to_ids(A)
												_UpperCAmelCase	 =     tokenizer.encode(A				,	add_special_tokens=A)
												self.assertListEqual(A				,	A)

												_UpperCAmelCase	 =     tokenizer.convert_ids_to_tokens(A)
												self.assertNotEqual(len(A)				,	0)
												_UpperCAmelCase	 =     tokenizer.decode(A)
												self.assertIsInstance(A				,	A)

												self.assertEqual(text_a.replace(' '				,	'')				,	A)



			@unittest.skip('MGP-STR tokenizer only handles one sequence.')
			def 					_lowerCamelCase (		self     :						str)     ->					List[str]:



						"""simple docstring"""






						pass



			@unittest.skip('inputs cannot be pretokenized in MgpstrTokenizer')
			def 					_lowerCamelCase (		self     :						Dict)     ->					Tuple:



						"""simple docstring"""






						pass
