


"""simple docstring"""




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					(   _SCREAMING_SNAKE_CASE     ,			unittest.TestCase       ):


		'''simple docstring'''



		__UpperCAmelCase		:			Any										=				MgpstrTokenizer
		__UpperCAmelCase		:			Dict										=				False
		__UpperCAmelCase		:			Tuple										=				{}
		__UpperCAmelCase		:			Optional[Any]										=				False

		def 			__UpperCAmelCase       (  self      ):
				super().setUp()

				# fmt: off
				__a           =			['''[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
				__a           =			dict(zip(A_							, range(len(A_      )      )      )      )

				__a           =			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 			__UpperCAmelCase       (  self							, **_a      ):
				return MgpstrTokenizer.from_pretrained(self.tmpdirname							, **A_      )

		def 			__UpperCAmelCase       (  self							, _a      ):
				__a           =			'''tester'''
				__a           =			'''tester'''
				return input_text, output_text

		@unittest.skip('''MGP-STR always lower cases letters.'''      )
		def 			__UpperCAmelCase       (  self      ):
				pass

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.get_tokenizers(do_lower_case=A_      )
				for tokenizer in tokenizers:
						with self.subTest(f'''{tokenizer.__class__.__name__}'''      ):
								__a           =			'''[SPECIAL_TOKEN]'''

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

								__a           =			tokenizer.decode(A_							, skip_special_tokens=A_      )
								self.assertTrue(special_token not in decoded      )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.get_tokenizers()
				for tokenizer in tokenizers:
						with self.subTest(f'''{tokenizer.__class__.__name__}'''      ):
								__a      ,     __a           =			self.get_input_output_texts(A_      )

								__a           =			tokenizer.tokenize(A_      )
								__a           =			tokenizer.convert_tokens_to_ids(A_      )
								__a           =			tokenizer.encode(A_							, add_special_tokens=A_      )
								self.assertListEqual(A_							, A_      )

								__a           =			tokenizer.convert_ids_to_tokens(A_      )
								self.assertNotEqual(len(A_      )							, 0      )
								__a           =			tokenizer.decode(A_      )
								self.assertIsInstance(A_							, A_      )

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

		@unittest.skip('''MGP-STR tokenizer only handles one sequence.'''      )
		def 			__UpperCAmelCase       (  self      ):
				pass





		@unittest.skip('''inputs cannot be pretokenized in MgpstrTokenizer'''      )
		def 			__UpperCAmelCase       (  self      ):
				pass








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 				lowercase					(		unittest.TestCase   ):



		def __UpperCamelCase ( self				,      A_       )  ->					List[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 __UpperCamelCase ( self       )  ->					Tuple:
				"""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 __UpperCamelCase ( self       )  ->					Any:
				"""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 __UpperCamelCase ( self       )  ->					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 __UpperCamelCase ( self       )  ->					List[str]:
				"""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 __UpperCamelCase ( self       )  ->					str:
				"""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 __UpperCamelCase ( self       )  ->					List[str]:
				"""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 __UpperCamelCase ( self       )  ->					Tuple:
				"""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 __UpperCamelCase ( self       )  ->					Tuple:
				"""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 __UpperCamelCase ( self       )  ->					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 __UpperCamelCase ( self       )  ->					Optional[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 __UpperCamelCase ( self       )  ->					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 __UpperCamelCase ( self       )  ->					str:
				"""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 __UpperCamelCase ( self       )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     'sshleifer/tiny-gpt2'

				def _check_summary_is_not_empty(A_       ):
						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()       )
