



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      UpperCAmelCase__			(				__snake_case=""				) ->			str:
  _A							     =     tempfile.mkdtemp()
  return os.path.join(__UpperCAmelCase    ,					str(uuid.uuida()				) + suffix				)



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





 """simple docstring"""



 def lowercase_		(    self       )						->				str:




   """simple docstring"""







   _A							     =     torch.rand(1_2						,     dtype=torch.floataa       ) - 0.5
   _A							     =     AgentAudio(a       )
   _A							     =     str(agent_type.to_string()       )

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

   del agent_type

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

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



 def lowercase_		(    self       )						->				int:




   """simple docstring"""







   _A							     =     torch.rand(1_2						,     dtype=torch.floataa       ) - 0.5
   _A							     =     get_new_path(suffix='''.wav'''       )
   sf.write(a						,     a						,     1_6_0_0_0       )

   _A							     =     AgentAudio(a       )

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







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





 """simple docstring"""



 def lowercase_		(    self       )						->				Optional[Any]:




   """simple docstring"""







   _A							     =     torch.randint(0						,     2_5_6						,     (6_4, 6_4, 3)       )
   _A							     =     AgentImage(a       )
   _A							     =     str(agent_type.to_string()       )

   # Ensure that the tensor and the agent_type's tensor are the same
   self.assertTrue(torch.allclose(a						,     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(a       )       )
 def lowercase_		(    self       )						->				Any:




   """simple docstring"""







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

   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(a       )       )



 def lowercase_		(    self       )						->				List[str]:




   """simple docstring"""







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

   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(a       )       )







class 		_snake_case   (      unittest.TestCase					):





 """simple docstring"""



 def lowercase_		(    self       )						->				Any:




   """simple docstring"""







   _A							     =     '''Hey!'''
   _A							     =     AgentText(a       )

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






'''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


a   	=    logging.get_logger(__name__)

def     __magic_name__							(				__UpperCAmelCase=None				,						__UpperCAmelCase=None   )    ->       Dict:

   '''simple docstring'''







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







@dataclass
class __a			:
   __UpperCamelCase :			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'
           )
       }, )

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

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

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





   def   UpperCAmelCase__  (       self					:      List[Any]     ):

      '''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   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

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





   @property
   def   UpperCAmelCase__  (       self					:      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   UpperCAmelCase__  (       self					:      Tuple     ):

      '''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





