


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        snake_case_   (unittest.TestCase      ):
					def  lowerCamelCase__(      self   :Any			,__snake_case   :Dict      )					->    List[str]:
												for model_result in results.values():
																			for batch_size, sequence_length in zip(model_result['bs']			,model_result['ss']      ):
																										a__             =     model_result['result'][batch_size][sequence_length]
																										self.assertIsNotNone(__snake_case      )
					def  lowerCamelCase__(      self   :Optional[Any]      )					->    str:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,eager_mode=__snake_case			,multi_process=__snake_case			,)
												a__             =     TensorFlowBenchmark(__snake_case      )
												a__             =     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   :Optional[int]      )					->    str:
												a__             =     'sgugger/tiny-distilbert-classification'
												a__             =     TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,only_pretrain_model=__snake_case			,)
												a__             =     TensorFlowBenchmark(__snake_case      )
												a__             =     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   :Optional[int]      )					->    Union[str, Any]:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     TensorFlowBenchmark(__snake_case      )
												a__             =     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   :Optional[int]      )					->    Optional[int]:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     AutoConfig.from_pretrained(__snake_case      )
												a__             =     TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,eager_mode=__snake_case			,multi_process=__snake_case			,)
												a__             =     TensorFlowBenchmark(__snake_case			,[config]      )
												a__             =     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      )					->    Optional[int]:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     AutoConfig.from_pretrained(__snake_case      )
												a__             =     TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     TensorFlowBenchmark(__snake_case			,[config]      )
												a__             =     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]      )					->    Tuple:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     TensorFlowBenchmark(__snake_case      )
												a__             =     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[Any]      )					->    Dict:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     AutoConfig.from_pretrained(__snake_case      )
												a__             =     TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     TensorFlowBenchmark(__snake_case			,[config]      )
												a__             =     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]      )					->    Dict:
												a__             =     'patrickvonplaten/t5-tiny-random'
												a__             =     AutoConfig.from_pretrained(__snake_case      )
												a__             =     TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     TensorFlowBenchmark(__snake_case			,configs=[config]      )
												a__             =     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]      )					->    str:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,use_xla=__snake_case			,multi_process=__snake_case			,)
												a__             =     TensorFlowBenchmark(__snake_case      )
												a__             =     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   :Dict      )					->    Tuple:
												a__             =     'sshleifer/tiny-gpt2'
												with tempfile.TemporaryDirectory() as tmp_dir:
																			a__             =     TensorFlowBenchmarkArguments(
																			    models=[MODEL_ID]			,inference=__snake_case			,save_to_csv=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,inference_time_csv_file=os.path.join(__snake_case			,'inf_time.csv'      )			,inference_memory_csv_file=os.path.join(__snake_case			,'inf_mem.csv'      )			,env_info_csv_file=os.path.join(__snake_case			,'env.csv'      )			,multi_process=__snake_case			,)
																			a__             =     TensorFlowBenchmark(__snake_case      )
																			benchmark.run()
																			self.assertTrue(Path(os.path.join(__snake_case			,'inf_time.csv'      )      ).exists()      )
																			self.assertTrue(Path(os.path.join(__snake_case			,'inf_mem.csv'      )      ).exists()      )
																			self.assertTrue(Path(os.path.join(__snake_case			,'env.csv'      )      ).exists()      )



					def  lowerCamelCase__(      self   :Union[str, Any]      )					->    Optional[int]:
												a__             =     'sshleifer/tiny-gpt2'

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

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








import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin







class        snake_case_   (lowerCamelCase_	,						unittest.TestCase      ):
					UpperCAmelCase__					:    Tuple          =			XLMTokenizer
					UpperCAmelCase__					:    List[str]          =			False
					def  lowerCamelCase__(      self   :Any      )					->    Optional[Any]:
												super().setUp()

												# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
												a__             =     [
												    'l',
												    'o',
												    'w',
												    'e',
												    'r',
												    's',
												    't',
												    'i',
												    'd',
												    'n',
												    'w</w>',
												    'r</w>',
												    't</w>',
												    'lo',
												    'low',
												    'er</w>',
												    'low</w>',
												    'lowest</w>',
												    'newer</w>',
												    'wider</w>',
												    '<unk>',
												]
												a__             =     dict(zip(__snake_case			,range(len(__snake_case      )      )      )      )
												a__             =     ['l o 123', 'lo w 1456', 'e r</w> 1789', '']

												a__             =     os.path.join(self.tmpdirname			,VOCAB_FILES_NAMES['vocab_file']      )
												a__             =     os.path.join(self.tmpdirname			,VOCAB_FILES_NAMES['merges_file']      )
												with open(self.vocab_file			,'w'      ) as fp:
																			fp.write(json.dumps(__snake_case      )      )
												with open(self.merges_file			,'w'      ) as fp:
																			fp.write('\n'.join(__snake_case      )      )
					def  lowerCamelCase__(      self   :Any			,__snake_case   :int      )					->    Optional[int]:
												a__             =     'lower newer'
												a__             =     'lower newer'
												return input_text, output_text
					def  lowerCamelCase__(      self   :Tuple      )					->    Tuple:
												a__             =     XLMTokenizer(self.vocab_file			,self.merges_file      )

												a__             =     'lower'
												a__             =     ['low', 'er</w>']
												a__             =     tokenizer.tokenize(__snake_case      )
												self.assertListEqual(__snake_case			,__snake_case      )

												a__             =     tokens + ['<unk>']
												a__             =     [14, 15, 20]
												self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case      )			,__snake_case      )



					@slow
					def  lowerCamelCase__(      self   :List[str]      )					->    Optional[Any]:
												a__             =     XLMTokenizer.from_pretrained('xlm-mlm-en-2048'      )

												a__             =     tokenizer.encode('sequence builders'			,add_special_tokens=__snake_case      )
												a__             =     tokenizer.encode('multi-sequence build'			,add_special_tokens=__snake_case      )

												a__             =     tokenizer.build_inputs_with_special_tokens(__snake_case      )
												a__             =     tokenizer.build_inputs_with_special_tokens(__snake_case			,__snake_case      )

												assert encoded_sentence == [0] + text + [1]
												assert encoded_pair == [0] + text + [1] + text_a + [1]




