



'''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 	lowerCamelCase_							(					unittest.TestCase			):







						def 					__lowercase    (  self   :      Any				,    lowerCAmelCase__   :      int				):

													"""simple docstring"""
													for model_result in results.values():
																				for batch_size, sequence_length in zip(model_result['''bs''']				,    model_result['''ss''']				):
																											SCREAMING_SNAKE_CASE					:      Dict     					=			model_result['''result'''][batch_size][sequence_length]
																											self.assertIsNotNone(lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Any     					=			'''sshleifer/tiny-gpt2'''
													SCREAMING_SNAKE_CASE					:      List[str]     					=			TensorFlowBenchmarkArguments(
													    models=[MODEL_ID]				,    training=lowerCAmelCase__				,    inference=lowerCAmelCase__				,    sequence_lengths=[8]				,    batch_sizes=[1]				,    eager_mode=lowerCAmelCase__				,    multi_process=lowerCAmelCase__				,    )
													SCREAMING_SNAKE_CASE					:      Any     					=			TensorFlowBenchmark(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      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 					__lowercase    (  self   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			'''sgugger/tiny-distilbert-classification'''
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			TensorFlowBenchmarkArguments(
													    models=[MODEL_ID]				,    training=lowerCAmelCase__				,    inference=lowerCAmelCase__				,    sequence_lengths=[8]				,    batch_sizes=[1]				,    multi_process=lowerCAmelCase__				,    only_pretrain_model=lowerCAmelCase__				,    )
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			TensorFlowBenchmark(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      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 					__lowercase    (  self   :      Union[str, Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			'''sshleifer/tiny-gpt2'''
													SCREAMING_SNAKE_CASE					:      Any     					=			TensorFlowBenchmarkArguments(
													    models=[MODEL_ID]				,    training=lowerCAmelCase__				,    inference=lowerCAmelCase__				,    sequence_lengths=[8]				,    batch_sizes=[1]				,    multi_process=lowerCAmelCase__				,    )
													SCREAMING_SNAKE_CASE					:      int     					=			TensorFlowBenchmark(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      str     					=			benchmark.run()
													self.check_results_dict_not_empty(results.time_inference_result				)
													self.check_results_dict_not_empty(results.memory_inference_result				)







						def 					__lowercase    (  self   :      List[str]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			'''sshleifer/tiny-gpt2'''
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			AutoConfig.from_pretrained(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Tuple     					=			TensorFlowBenchmarkArguments(
													    models=[MODEL_ID]				,    training=lowerCAmelCase__				,    inference=lowerCAmelCase__				,    sequence_lengths=[8]				,    batch_sizes=[1]				,    eager_mode=lowerCAmelCase__				,    multi_process=lowerCAmelCase__				,    )
													SCREAMING_SNAKE_CASE					:      str     					=			TensorFlowBenchmark(lowerCAmelCase__				,    [config]				)
													SCREAMING_SNAKE_CASE					:      str     					=			benchmark.run()
													self.check_results_dict_not_empty(results.time_inference_result				)
													self.check_results_dict_not_empty(results.memory_inference_result				)







						def 					__lowercase    (  self   :      str				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			'''sshleifer/tiny-gpt2'''
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			AutoConfig.from_pretrained(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Tuple     					=			TensorFlowBenchmarkArguments(
													    models=[MODEL_ID]				,    training=lowerCAmelCase__				,    inference=lowerCAmelCase__				,    sequence_lengths=[8]				,    batch_sizes=[1]				,    multi_process=lowerCAmelCase__				,    )
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			TensorFlowBenchmark(lowerCAmelCase__				,    [config]				)
													SCREAMING_SNAKE_CASE					:      Tuple     					=			benchmark.run()
													self.check_results_dict_not_empty(results.time_inference_result				)
													self.check_results_dict_not_empty(results.memory_inference_result				)







						def 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			'''sshleifer/tiny-gpt2'''
													SCREAMING_SNAKE_CASE					:      int     					=			TensorFlowBenchmarkArguments(
													    models=[MODEL_ID]				,    training=lowerCAmelCase__				,    inference=lowerCAmelCase__				,    sequence_lengths=[8]				,    batch_sizes=[1]				,    multi_process=lowerCAmelCase__				,    )
													SCREAMING_SNAKE_CASE					:      Dict     					=			TensorFlowBenchmark(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			benchmark.run()
													self.check_results_dict_not_empty(results.time_train_result				)
													self.check_results_dict_not_empty(results.memory_train_result				)







						def 					__lowercase    (  self   :      str				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Any     					=			'''sshleifer/tiny-gpt2'''
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			AutoConfig.from_pretrained(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			TensorFlowBenchmarkArguments(
													    models=[MODEL_ID]				,    training=lowerCAmelCase__				,    inference=lowerCAmelCase__				,    sequence_lengths=[8]				,    batch_sizes=[1]				,    multi_process=lowerCAmelCase__				,    )
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			TensorFlowBenchmark(lowerCAmelCase__				,    [config]				)
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			benchmark.run()
													self.check_results_dict_not_empty(results.time_train_result				)
													self.check_results_dict_not_empty(results.memory_train_result				)







						def 					__lowercase    (  self   :      Union[str, Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			'''patrickvonplaten/t5-tiny-random'''
													SCREAMING_SNAKE_CASE					:      Tuple     					=			AutoConfig.from_pretrained(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Tuple     					=			TensorFlowBenchmarkArguments(
													    models=[MODEL_ID]				,    training=lowerCAmelCase__				,    inference=lowerCAmelCase__				,    sequence_lengths=[8]				,    batch_sizes=[1]				,    multi_process=lowerCAmelCase__				,    )
													SCREAMING_SNAKE_CASE					:      Any     					=			TensorFlowBenchmark(lowerCAmelCase__				,    configs=[config]				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			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 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Any     					=			'''sshleifer/tiny-gpt2'''
													SCREAMING_SNAKE_CASE					:      List[str]     					=			TensorFlowBenchmarkArguments(
													    models=[MODEL_ID]				,    training=lowerCAmelCase__				,    inference=lowerCAmelCase__				,    sequence_lengths=[8]				,    batch_sizes=[1]				,    use_xla=lowerCAmelCase__				,    multi_process=lowerCAmelCase__				,    )
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			TensorFlowBenchmark(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Tuple     					=			benchmark.run()
													self.check_results_dict_not_empty(results.time_inference_result				)
													self.check_results_dict_not_empty(results.memory_inference_result				)







						def 					__lowercase    (  self   :      List[Any]				):

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







						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			'''sshleifer/tiny-gpt2'''

													def _check_summary_is_not_empty(lowerCAmelCase__   :      int				):
																				self.assertTrue(hasattr(lowerCAmelCase__				,    '''sequential'''				)				)
																				self.assertTrue(hasattr(lowerCAmelCase__				,    '''cumulative'''				)				)
																				self.assertTrue(hasattr(lowerCAmelCase__				,    '''current'''				)				)
																				self.assertTrue(hasattr(lowerCAmelCase__				,    '''total'''				)				)

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









'''simple docstring'''



import string
from math import logaa
def 					UpperCAmelCase   (						A  :				str					,		A  :				str				):
							SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			document.translate(
							    str.maketrans(''''''					,		''''''					,		string.punctuation				)				).replace('''\n'''					,		''''''				)
							SCREAMING_SNAKE_CASE					:      Tuple     					=			document_without_punctuation.split(''' '''				)  # word tokenization
							return len([word for word in tokenize_document if word.lower() == term.lower()]				)
def 					UpperCAmelCase   (						A  :				str					,		A  :				str				):
							SCREAMING_SNAKE_CASE					:      int     					=			corpus.lower().translate(
							    str.maketrans(''''''					,		''''''					,		string.punctuation				)				)  # strip all punctuation and replace it with ''
							SCREAMING_SNAKE_CASE					:      Any     					=			corpus_without_punctuation.split('''\n'''				)
							SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			term.lower()
							return (len([doc for doc in docs if term in doc]				), len(A				))
def 					UpperCAmelCase   (						A  :				int					,		A  :				int					,		A  :				str=False				):
							if smoothing:
														if n == 0:
																					raise ValueError('''log10(0) is undefined.'''				)
														return round(1 + logaa(n / (1 + df)				)					,		3				)

							if df == 0:
														raise ZeroDivisionError('''df must be > 0'''				)
							elif n == 0:
														raise ValueError('''log10(0) is undefined.'''				)
							return round(logaa(n / df				)					,		3				)






def 					UpperCAmelCase   (						A  :				int					,		A  :				int				):
							return round(tf * idf					,		3				)




