
'''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 	_snake_case						(      A_       :    Any=""   ):
  """simple docstring"""







  a_      :	Dict      =							tempfile.mkdtemp()
  return os.path.join(A_     ,		str(uuid.uuida()   ) + suffix   )


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

     """simple docstring"""

     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[Any]      =							torch.rand(12							,		dtype=torch.floataa ) - 0.5
       a_      :	int      =							AgentAudio(lowerCAmelCase_ )
       a_      :	Optional[int]      =							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
       a_      , a_      :	List[str]      =							sf.read(lowerCAmelCase_ )
       self.assertTrue(torch.allclose(lowerCAmelCase_							,		torch.tensor(lowerCAmelCase_ )							,		atol=1E-4 ) )






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	str      =							torch.rand(12							,		dtype=torch.floataa ) - 0.5
       a_      :	str      =							get_new_path(suffix=""".wav""" )
       sf.write(lowerCAmelCase_							,		lowerCAmelCase_							,		1_60_00 )

       a_      :	Optional[int]      =							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    ):

     """simple docstring"""

     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Dict      =							torch.randint(0							,		2_56							,		(64, 64, 3) )
       a_      :	List[str]      =							AgentImage(lowerCAmelCase_ )
       a_      :	Dict      =							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 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Any      =							Path(get_tests_dir("""fixtures/tests_samples/COCO""" ) ) / """000000039769.png"""
       a_      :	Any      =							Image.open(lowerCAmelCase_ )
       a_      :	List[Any]      =							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 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							Path(get_tests_dir("""fixtures/tests_samples/COCO""" ) ) / """000000039769.png"""
       a_      :	Union[str, Any]      =							Image.open(lowerCAmelCase_ )
       a_      :	Union[str, Any]      =							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    ):

     """simple docstring"""

     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							"""Hey!"""
       a_      :	Tuple      =							AgentText(lowerCAmelCase_ )

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




'''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  _UpperCAmelCase ( unittest.TestCase    ):

     """simple docstring"""

     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       for model_result in results.values():
         for batch_size, sequence_length in zip(model_result["""bs"""]							,		model_result["""ss"""] ):
           a_      :	str      =							model_result["""result"""][batch_size][sequence_length]
           self.assertIsNotNone(lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[int]      =							"""sshleifer/tiny-gpt2"""
       a_      :	Any      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		multi_process=lowerCAmelCase_							,		)
       a_      :	List[Any]      =							PyTorchBenchmark(lowerCAmelCase_ )
       a_      :	List[Any]      =							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 ):

       '''simple docstring'''

       a_      :	Optional[int]      =							"""sgugger/tiny-distilbert-classification"""
       a_      :	str      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		multi_process=lowerCAmelCase_							,		only_pretrain_model=lowerCAmelCase_							,		)
       a_      :	Optional[Any]      =							PyTorchBenchmark(lowerCAmelCase_ )
       a_      :	Optional[Any]      =							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 ):

       '''simple docstring'''

       a_      :	List[str]      =							"""sshleifer/tiny-gpt2"""
       a_      :	Any      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		torchscript=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		multi_process=lowerCAmelCase_							,		)
       a_      :	Optional[Any]      =							PyTorchBenchmark(lowerCAmelCase_ )
       a_      :	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(torch_device == """cpu"""							,		"""Cant do half precision""" )
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	int      =							"""sshleifer/tiny-gpt2"""
       a_      :	Tuple      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		fpaa=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		multi_process=lowerCAmelCase_							,		)
       a_      :	List[Any]      =							PyTorchBenchmark(lowerCAmelCase_ )
       a_      :	Dict      =							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 ):

       '''simple docstring'''

       a_      :	Dict      =							"""sshleifer/tiny-gpt2"""
       a_      :	Dict      =							AutoConfig.from_pretrained(lowerCAmelCase_ )
       # set architectures equal to `None`
       a_      :	Any      =							None
       a_      :	List[Any]      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		multi_process=lowerCAmelCase_							,		)
       a_      :	Optional[Any]      =							PyTorchBenchmark(lowerCAmelCase_							,		configs=[config] )
       a_      :	Any      =							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 ):

       '''simple docstring'''

       a_      :	Dict      =							"""sshleifer/tiny-gpt2"""
       a_      :	Tuple      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		multi_process=lowerCAmelCase_							,		)
       a_      :	int      =							PyTorchBenchmark(lowerCAmelCase_ )
       a_      :	Tuple      =							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 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[str]      =							"""sshleifer/tiny-gpt2"""
       a_      :	List[Any]      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		fpaa=lowerCAmelCase_							,		multi_process=lowerCAmelCase_							,		)
       a_      :	List[str]      =							PyTorchBenchmark(lowerCAmelCase_ )
       a_      :	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 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[Any]      =							"""sshleifer/tiny-gpt2"""
       a_      :	List[str]      =							AutoConfig.from_pretrained(lowerCAmelCase_ )
       a_      :	Union[str, Any]      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		multi_process=lowerCAmelCase_							,		)
       a_      :	Tuple      =							PyTorchBenchmark(lowerCAmelCase_							,		configs=[config] )
       a_      :	List[str]      =							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 ):

       '''simple docstring'''

       a_      :	Dict      =							"""sshleifer/tinier_bart"""
       a_      :	Union[str, Any]      =							AutoConfig.from_pretrained(lowerCAmelCase_ )
       a_      :	Dict      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		multi_process=lowerCAmelCase_							,		)
       a_      :	Union[str, Any]      =							PyTorchBenchmark(lowerCAmelCase_							,		configs=[config] )
       a_      :	Optional[Any]      =							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 ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							"""sshleifer/tiny-gpt2"""
       a_      :	Tuple      =							AutoConfig.from_pretrained(lowerCAmelCase_ )
       a_      :	Tuple      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		multi_process=lowerCAmelCase_							,		)
       a_      :	Union[str, Any]      =							PyTorchBenchmark(lowerCAmelCase_							,		configs=[config] )
       a_      :	Tuple      =							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 ):

       '''simple docstring'''

       a_      :	Optional[int]      =							"""sshleifer/tinier_bart"""
       a_      :	int      =							AutoConfig.from_pretrained(lowerCAmelCase_ )
       a_      :	List[Any]      =							PyTorchBenchmarkArguments(
           models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		multi_process=lowerCAmelCase_							,		)
       a_      :	int      =							PyTorchBenchmark(lowerCAmelCase_							,		configs=[config] )
       a_      :	Dict      =							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 ):

       '''simple docstring'''

       a_      :	Optional[Any]      =							"""sshleifer/tiny-gpt2"""
       with tempfile.TemporaryDirectory() as tmp_dir:
         a_      :	List[str]      =							PyTorchBenchmarkArguments(
             models=[MODEL_ID]							,		training=lowerCAmelCase_							,		inference=lowerCAmelCase_							,		save_to_csv=lowerCAmelCase_							,		sequence_lengths=[8]							,		batch_sizes=[1]							,		inference_time_csv_file=os.path.join(lowerCAmelCase_							,		"""inf_time.csv""" )							,		train_memory_csv_file=os.path.join(lowerCAmelCase_							,		"""train_mem.csv""" )							,		inference_memory_csv_file=os.path.join(lowerCAmelCase_							,		"""inf_mem.csv""" )							,		train_time_csv_file=os.path.join(lowerCAmelCase_							,		"""train_time.csv""" )							,		env_info_csv_file=os.path.join(lowerCAmelCase_							,		"""env.csv""" )							,		multi_process=lowerCAmelCase_							,		)
         a_      :	Tuple      =							PyTorchBenchmark(lowerCAmelCase_ )
         benchmark.run()
         self.assertTrue(Path(os.path.join(lowerCAmelCase_							,		"""inf_time.csv""" ) ).exists() )
         self.assertTrue(Path(os.path.join(lowerCAmelCase_							,		"""train_time.csv""" ) ).exists() )
         self.assertTrue(Path(os.path.join(lowerCAmelCase_							,		"""inf_mem.csv""" ) ).exists() )
         self.assertTrue(Path(os.path.join(lowerCAmelCase_							,		"""train_mem.csv""" ) ).exists() )
         self.assertTrue(Path(os.path.join(lowerCAmelCase_							,		"""env.csv""" ) ).exists() )






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Dict      =							"""sshleifer/tiny-gpt2"""

       def _check_summary_is_not_empty(lowerCAmelCase_ ):
         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:
         a_      :	List[Any]      =							PyTorchBenchmarkArguments(
             models=[MODEL_ID]							,		training=lowerCAmelCase_							,		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_							,		multi_process=lowerCAmelCase_							,		)
         a_      :	Dict      =							PyTorchBenchmark(lowerCAmelCase_ )
         a_      :	Tuple      =							benchmark.run()
         _check_summary_is_not_empty(result.inference_summary )
         _check_summary_is_not_empty(result.train_summary )
         self.assertTrue(Path(os.path.join(lowerCAmelCase_							,		"""log.txt""" ) ).exists() )


