


"""simple docstring"""




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 			__snake_case   (unittest.TestCase  ):




							def   __a   (     self:    Any     ,							A_:    List[str]   ):
										for model_result in results.values():
													for batch_size, sequence_length in zip(model_result["""bs"""]     ,							model_result["""ss"""]   ):
																__lowerCamelCase          =      model_result["""result"""][batch_size][sequence_length]
																self.assertIsNotNone(A_   )




							def   __a   (     self:    List[Any]   ):
										__lowerCamelCase          =      """sshleifer/tiny-gpt2"""
										__lowerCamelCase          =      PyTorchBenchmarkArguments(
										    models=[MODEL_ID]     ,							training=A_     ,							inference=A_     ,							sequence_lengths=[8]     ,							batch_sizes=[1]     ,							multi_process=A_     ,							)
										__lowerCamelCase          =      PyTorchBenchmark(A_   )
										__lowerCamelCase          =      benchmark.run()
										self.check_results_dict_not_empty(results.time_inference_result   )
										self.check_results_dict_not_empty(results.memory_inference_result   )




							def   __a   (     self:    Tuple   ):
										__lowerCamelCase          =      """sgugger/tiny-distilbert-classification"""
										__lowerCamelCase          =      PyTorchBenchmarkArguments(
										    models=[MODEL_ID]     ,							training=A_     ,							inference=A_     ,							sequence_lengths=[8]     ,							batch_sizes=[1]     ,							multi_process=A_     ,							only_pretrain_model=A_     ,							)
										__lowerCamelCase          =      PyTorchBenchmark(A_   )
										__lowerCamelCase          =      benchmark.run()
										self.check_results_dict_not_empty(results.time_inference_result   )
										self.check_results_dict_not_empty(results.memory_inference_result   )




							def   __a   (     self:    List[str]   ):
										__lowerCamelCase          =      """sshleifer/tiny-gpt2"""
										__lowerCamelCase          =      PyTorchBenchmarkArguments(
										    models=[MODEL_ID]     ,							training=A_     ,							inference=A_     ,							torchscript=A_     ,							sequence_lengths=[8]     ,							batch_sizes=[1]     ,							multi_process=A_     ,							)
										__lowerCamelCase          =      PyTorchBenchmark(A_   )
										__lowerCamelCase          =      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   __a   (     self:    Optional[int]   ):
										__lowerCamelCase          =      """sshleifer/tiny-gpt2"""
										__lowerCamelCase          =      PyTorchBenchmarkArguments(
										    models=[MODEL_ID]     ,							training=A_     ,							inference=A_     ,							fpaa=A_     ,							sequence_lengths=[8]     ,							batch_sizes=[1]     ,							multi_process=A_     ,							)
										__lowerCamelCase          =      PyTorchBenchmark(A_   )
										__lowerCamelCase          =      benchmark.run()
										self.check_results_dict_not_empty(results.time_inference_result   )
										self.check_results_dict_not_empty(results.memory_inference_result   )




							def   __a   (     self:    Optional[Any]   ):
										__lowerCamelCase          =      """sshleifer/tiny-gpt2"""
										__lowerCamelCase          =      AutoConfig.from_pretrained(A_   )
										# set architectures equal to `None`
										__lowerCamelCase          =      None
										__lowerCamelCase          =      PyTorchBenchmarkArguments(
										    models=[MODEL_ID]     ,							training=A_     ,							inference=A_     ,							sequence_lengths=[8]     ,							batch_sizes=[1]     ,							multi_process=A_     ,							)
										__lowerCamelCase          =      PyTorchBenchmark(A_     ,							configs=[config]   )
										__lowerCamelCase          =      benchmark.run()
										self.check_results_dict_not_empty(results.time_inference_result   )
										self.check_results_dict_not_empty(results.memory_inference_result   )




							def   __a   (     self:    List[Any]   ):
										__lowerCamelCase          =      """sshleifer/tiny-gpt2"""
										__lowerCamelCase          =      PyTorchBenchmarkArguments(
										    models=[MODEL_ID]     ,							training=A_     ,							inference=A_     ,							sequence_lengths=[8]     ,							batch_sizes=[1]     ,							multi_process=A_     ,							)
										__lowerCamelCase          =      PyTorchBenchmark(A_   )
										__lowerCamelCase          =      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   __a   (     self:    int   ):
										__lowerCamelCase          =      """sshleifer/tiny-gpt2"""
										__lowerCamelCase          =      PyTorchBenchmarkArguments(
										    models=[MODEL_ID]     ,							training=A_     ,							inference=A_     ,							sequence_lengths=[8]     ,							batch_sizes=[1]     ,							fpaa=A_     ,							multi_process=A_     ,							)
										__lowerCamelCase          =      PyTorchBenchmark(A_   )
										__lowerCamelCase          =      benchmark.run()
										self.check_results_dict_not_empty(results.time_train_result   )
										self.check_results_dict_not_empty(results.memory_train_result   )




							def   __a   (     self:    Optional[int]   ):
										__lowerCamelCase          =      """sshleifer/tiny-gpt2"""
										__lowerCamelCase          =      AutoConfig.from_pretrained(A_   )
										__lowerCamelCase          =      PyTorchBenchmarkArguments(
										    models=[MODEL_ID]     ,							training=A_     ,							inference=A_     ,							sequence_lengths=[8]     ,							batch_sizes=[1]     ,							multi_process=A_     ,							)
										__lowerCamelCase          =      PyTorchBenchmark(A_     ,							configs=[config]   )
										__lowerCamelCase          =      benchmark.run()
										self.check_results_dict_not_empty(results.time_inference_result   )
										self.check_results_dict_not_empty(results.memory_inference_result   )




							def   __a   (     self:    List[str]   ):
										__lowerCamelCase          =      """sshleifer/tinier_bart"""
										__lowerCamelCase          =      AutoConfig.from_pretrained(A_   )
										__lowerCamelCase          =      PyTorchBenchmarkArguments(
										    models=[MODEL_ID]     ,							training=A_     ,							inference=A_     ,							sequence_lengths=[8]     ,							batch_sizes=[1]     ,							multi_process=A_     ,							)
										__lowerCamelCase          =      PyTorchBenchmark(A_     ,							configs=[config]   )
										__lowerCamelCase          =      benchmark.run()
										self.check_results_dict_not_empty(results.time_inference_result   )
										self.check_results_dict_not_empty(results.memory_inference_result   )




							def   __a   (     self:    List[Any]   ):
										__lowerCamelCase          =      """sshleifer/tiny-gpt2"""
										__lowerCamelCase          =      AutoConfig.from_pretrained(A_   )
										__lowerCamelCase          =      PyTorchBenchmarkArguments(
										    models=[MODEL_ID]     ,							training=A_     ,							inference=A_     ,							sequence_lengths=[8]     ,							batch_sizes=[1]     ,							multi_process=A_     ,							)
										__lowerCamelCase          =      PyTorchBenchmark(A_     ,							configs=[config]   )
										__lowerCamelCase          =      benchmark.run()
										self.check_results_dict_not_empty(results.time_train_result   )
										self.check_results_dict_not_empty(results.memory_train_result   )




							def   __a   (     self:    Optional[int]   ):
										__lowerCamelCase          =      """sshleifer/tinier_bart"""
										__lowerCamelCase          =      AutoConfig.from_pretrained(A_   )
										__lowerCamelCase          =      PyTorchBenchmarkArguments(
										    models=[MODEL_ID]     ,							training=A_     ,							inference=A_     ,							sequence_lengths=[8]     ,							batch_sizes=[1]     ,							multi_process=A_     ,							)
										__lowerCamelCase          =      PyTorchBenchmark(A_     ,							configs=[config]   )
										__lowerCamelCase          =      benchmark.run()
										self.check_results_dict_not_empty(results.time_train_result   )
										self.check_results_dict_not_empty(results.memory_train_result   )




							def   __a   (     self:    str   ):
										__lowerCamelCase          =      """sshleifer/tiny-gpt2"""
										with tempfile.TemporaryDirectory() as tmp_dir:
													__lowerCamelCase          =      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_     ,							)
													__lowerCamelCase          =      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   __a   (     self:    Dict   ):
										__lowerCamelCase          =      """sshleifer/tiny-gpt2"""

										def _check_summary_is_not_empty(A_:    List[str]   ):
													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:
													__lowerCamelCase          =      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_     ,							)
													__lowerCamelCase          =      PyTorchBenchmark(A_   )
													__lowerCamelCase          =      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()   )








"""simple docstring"""




__magic_name__						: Optional[Any]    =				'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'





def       a_ (	lowercase__ :bytes     ):
			# Make sure the supplied data is a bytes-like object
			if not isinstance(lowercase__,			lowercase__     ):
						__lowerCamelCase          =      f'a bytes-like object is required, not \'{data.__class__.__name__}\''
						raise TypeError(lowercase__     )

			__lowerCamelCase          =      """""".join(bin(lowercase__     )[2:].zfill(8     ) for byte in data     )

			__lowerCamelCase          =      len(lowercase__     ) % 6 != 0

			if padding_needed:
						# The padding that will be added later
						__lowerCamelCase          =      B"""=""" * ((6 - len(lowercase__     ) % 6) // 2)

						# Append binary_stream with arbitrary binary digits (0's by default) to make its
						# length a multiple of 6.
						binary_stream += "0" * (6 - len(lowercase__     ) % 6)
			else:
						__lowerCamelCase          =      B""""""

			# Encode every 6 binary digits to their corresponding Base64 character
			return (
			    "".join(
			        B64_CHARSET[int(binary_stream[index : index + 6],			2     )]
			        for index in range(0,			len(lowercase__     ),			6     )     ).encode()
			    + padding
			)





def       a_ (	lowercase__ :str     ):
			# Make sure encoded_data is either a string or a bytes-like object
			if not isinstance(lowercase__,			lowercase__     ) and not isinstance(lowercase__,			lowercase__     ):
						__lowerCamelCase          =      (
						    """argument should be a bytes-like object or ASCII string, """
						    f'not \'{encoded_data.__class__.__name__}\''
						)
						raise TypeError(lowercase__     )

			# In case encoded_data is a bytes-like object, make sure it contains only
			# ASCII characters so we convert it to a string object
			if isinstance(lowercase__,			lowercase__     ):
						try:
									__lowerCamelCase          =      encoded_data.decode("""utf-8"""     )
						except UnicodeDecodeError:
									raise ValueError("""base64 encoded data should only contain ASCII characters"""     )

			__lowerCamelCase          =      encoded_data.count("""="""     )

			# Check if the encoded string contains non base64 characters
			if padding:
						assert all(
						    char in B64_CHARSET for char in encoded_data[:-padding]     ), "Invalid base64 character(s) found."
			else:
						assert all(
						    char in B64_CHARSET for char in encoded_data     ), "Invalid base64 character(s) found."

			# Check the padding
			assert len(lowercase__     ) % 4 == 0 and padding < 3, "Incorrect padding"

			if padding:
						# Remove padding if there is one
						__lowerCamelCase          =      encoded_data[:-padding]

						__lowerCamelCase          =      """""".join(
						    bin(B64_CHARSET.index(lowercase__     )     )[2:].zfill(6     ) for char in encoded_data     )[: -padding * 2]
			else:
						__lowerCamelCase          =      """""".join(
						    bin(B64_CHARSET.index(lowercase__     )     )[2:].zfill(6     ) for char in encoded_data     )

			__lowerCamelCase          =      [
			    int(binary_stream[index : index + 8],			2     )
			    for index in range(0,			len(lowercase__     ),			8     )
			]

			return bytes(lowercase__     )


if __name__ == "__main__":
					import doctest

					doctest.testmod()




