


'''simple docstring'''



import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


_lowercase      :      Union[str, Any]            =					logging.get_logger(__name__)


def 				lowerCamelCase__				(	A       :      int=None				,       A       :      List[Any]=None					):




						'''simple docstring'''






						return field(default_factory=lambda: default				,       metadata=A					)






@dataclass
class 	UpperCamelCase__:
				__magic_name__    :				List[str]  		=	list_field(
				    default=[]						,    metadata={
				        "help": (
				            "Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"
				            " of all available models"
				        )
				    }						,    )

				__magic_name__    :				List[int]  		=	list_field(
				    default=[8]						,    metadata={"help": "List of batch sizes for which memory and time performance will be evaluated"}	)

				__magic_name__    :				List[int]  		=	list_field(
				    default=[8, 32, 128, 512]						,    metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"}						,    )

				__magic_name__    :				bool  		=	field(
				    default=lowerCAmelCase						,    metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."}						,    )
				__magic_name__    :				bool  		=	field(
				    default=lowerCAmelCase						,    metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."}						,    )
				__magic_name__    :				bool  		=	field(
				    default=lowerCAmelCase						,    metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."}	)
				__magic_name__    :				bool  		=	field(default=lowerCAmelCase						,    metadata={"help": "Use FP16 to accelerate inference."}	)
				__magic_name__    :				bool  		=	field(default=lowerCAmelCase						,    metadata={"help": "Benchmark training of model"}	)
				__magic_name__    :				bool  		=	field(default=lowerCAmelCase						,    metadata={"help": "Verbose memory tracing"}	)
				__magic_name__    :				bool  		=	field(
				    default=lowerCAmelCase						,    metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."}						,    )
				__magic_name__    :				bool  		=	field(
				    default=lowerCAmelCase						,    metadata={
				        "help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
				    }						,    )
				__magic_name__    :				bool  		=	field(default=lowerCAmelCase						,    metadata={"help": "Trace memory line by line"}	)
				__magic_name__    :				bool  		=	field(default=lowerCAmelCase						,    metadata={"help": "Save result to a CSV file"}	)
				__magic_name__    :				bool  		=	field(default=lowerCAmelCase						,    metadata={"help": "Save all print statements in a log file"}	)
				__magic_name__    :				bool  		=	field(default=lowerCAmelCase						,    metadata={"help": "Whether to print environment information"}	)
				__magic_name__    :				bool  		=	field(
				    default=lowerCAmelCase						,    metadata={
				        "help": (
				            "Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"
				            " multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"
				            " for debugging / testing and on TPU."
				        )
				    }						,    )
				__magic_name__    :				str  		=	field(
				    default=f'inference_time_{round(time()	)}.csv'						,    metadata={"help": "CSV filename used if saving time results to csv."}						,    )
				__magic_name__    :				str  		=	field(
				    default=f'inference_memory_{round(time()	)}.csv'						,    metadata={"help": "CSV filename used if saving memory results to csv."}						,    )
				__magic_name__    :				str  		=	field(
				    default=f'train_time_{round(time()	)}.csv'						,    metadata={"help": "CSV filename used if saving time results to csv for training."}						,    )
				__magic_name__    :				str  		=	field(
				    default=f'train_memory_{round(time()	)}.csv'						,    metadata={"help": "CSV filename used if saving memory results to csv for training."}						,    )
				__magic_name__    :				str  		=	field(
				    default=f'env_info_{round(time()	)}.csv'						,    metadata={"help": "CSV filename used if saving environment information."}						,    )
				__magic_name__    :				str  		=	field(
				    default=f'log_{round(time()	)}.csv'						,    metadata={"help": "Log filename used if print statements are saved in log."}						,    )
				__magic_name__    :				int  		=	field(default=3						,    metadata={"help": "Times an experiment will be run."}	)
				__magic_name__    :				bool  		=	field(
				    default=lowerCAmelCase						,    metadata={
				        "help": (
				            "Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"
				            " model weights."
				        )
				    }						,    )




				def 					a__(		self      :			List[Any]				)->					Tuple:

										"""simple docstring"""




										warnings.warn(
										    F"""The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"""
										    ''' are deprecated in general and it is advised to use external Benchmarking libraries '''
										    ''' to benchmark Transformer models.'''				,   lowerCAmelCase				,   )




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

										"""simple docstring"""




										return json.dumps(dataclasses.asdict(self				)				,   indent=2				)




				@property
				def 					a__(		self      :			List[str]				)->					List[str]:

										"""simple docstring"""




										if len(self.models				) <= 0:
																raise ValueError(
																    '''Please make sure you provide at least one model name / model identifier, *e.g.* `--models'''
																    ''' bert-base-cased` or `args.models = [\'bert-base-cased\'].'''				)
										return self.models





				@property
				def 					a__(		self      :			Union[str, Any]				)->					Union[str, Any]:

										"""simple docstring"""




										if not self.multi_process:
																return False
										elif self.is_tpu:
																logger.info('''Multiprocessing is currently not possible on TPU.'''				)
																return False
										else:
																return True









'''simple docstring'''



import os
import tempfile
import unittest
import uuid
from pathlib import Path

from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available


if is_torch_available():
	import torch

if is_soundfile_availble():
	import soundfile as sf

if is_vision_available():
	from PIL import Image


def 				lowerCamelCase__				(	A       :      str=""					):




						'''simple docstring'''






						UpperCAmelCase					    =     tempfile.mkdtemp()
						return os.path.join(A				,       str(uuid.uuida()					) + suffix					)






@require_soundfile
@require_torch
class 	UpperCamelCase__(       unittest.TestCase	):




				def 					a__(		self      :			int				)->					int:

										"""simple docstring"""




										UpperCAmelCase					    =     torch.rand(12				,   dtype=torch.floataa				) - 0.5
										UpperCAmelCase					    =     AgentAudio(lowerCAmelCase				)
										UpperCAmelCase					    =     str(agent_type.to_string()				)

										# Ensure that the tensor and the agent_type's tensor are the same
										self.assertTrue(torch.allclose(lowerCAmelCase				,   agent_type.to_raw()				,   atol=1E-4				)				)

										del agent_type

										# Ensure the path remains even after the object deletion
										self.assertTrue(os.path.exists(lowerCAmelCase				)				)

										# Ensure that the file contains the same value as the original tensor
										UpperCAmelCase    ,    UpperCAmelCase					    =     sf.read(lowerCAmelCase				)
										self.assertTrue(torch.allclose(lowerCAmelCase				,   torch.tensor(lowerCAmelCase				)				,   atol=1E-4				)				)





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

										"""simple docstring"""




										UpperCAmelCase					    =     torch.rand(12				,   dtype=torch.floataa				) - 0.5
										UpperCAmelCase					    =     get_new_path(suffix='''.wav'''				)
										sf.write(lowerCAmelCase				,   lowerCAmelCase				,   16000				)

										UpperCAmelCase					    =     AgentAudio(lowerCAmelCase				)

										self.assertTrue(torch.allclose(lowerCAmelCase				,   agent_type.to_raw()				,   atol=1E-4				)				)
										self.assertEqual(agent_type.to_string()				,   lowerCAmelCase				)







@require_vision
@require_torch
class 	UpperCamelCase__(       unittest.TestCase	):




				def 					a__(		self      :			List[Any]				)->					Any:

										"""simple docstring"""




										UpperCAmelCase					    =     torch.randint(0				,   256				,   (64, 64, 3)				)
										UpperCAmelCase					    =     AgentImage(lowerCAmelCase				)
										UpperCAmelCase					    =     str(agent_type.to_string()				)

										# Ensure that the tensor and the agent_type's tensor are the same
										self.assertTrue(torch.allclose(lowerCAmelCase				,   agent_type._tensor				,   atol=1E-4				)				)

										self.assertIsInstance(agent_type.to_raw()				,   Image.Image				)

										# Ensure the path remains even after the object deletion
										del agent_type
										self.assertTrue(os.path.exists(lowerCAmelCase				)				)




				def 					a__(		self      :			List[Any]				)->					List[Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     Path(get_tests_dir('''fixtures/tests_samples/COCO'''				)				) / '''000000039769.png'''
										UpperCAmelCase					    =     Image.open(lowerCAmelCase				)
										UpperCAmelCase					    =     AgentImage(lowerCAmelCase				)

										self.assertTrue(path.samefile(agent_type.to_string()				)				)
										self.assertTrue(image == agent_type.to_raw()				)

										# Ensure the path remains even after the object deletion
										del agent_type
										self.assertTrue(os.path.exists(lowerCAmelCase				)				)





				def 					a__(		self      :			Optional[Any]				)->					List[str]:

										"""simple docstring"""




										UpperCAmelCase					    =     Path(get_tests_dir('''fixtures/tests_samples/COCO'''				)				) / '''000000039769.png'''
										UpperCAmelCase					    =     Image.open(lowerCAmelCase				)
										UpperCAmelCase					    =     AgentImage(lowerCAmelCase				)

										self.assertFalse(path.samefile(agent_type.to_string()				)				)
										self.assertTrue(image == agent_type.to_raw()				)

										# Ensure the path remains even after the object deletion
										del agent_type
										self.assertTrue(os.path.exists(lowerCAmelCase				)				)







class 	UpperCamelCase__(       unittest.TestCase	):




				def 					a__(		self      :			int				)->					Any:

										"""simple docstring"""




										UpperCAmelCase					    =     '''Hey!'''
										UpperCAmelCase					    =     AgentText(lowerCAmelCase				)

										self.assertEqual(lowerCAmelCase				,   agent_type.to_string()				)
										self.assertEqual(lowerCAmelCase				,   agent_type.to_raw()				)
										self.assertEqual(lowerCAmelCase				,   lowerCAmelCase				)





