






"""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						):


   '''simple docstring'''

   def 				_SCREAMING_SNAKE_CASE				(	self :     Any,       _lowerCamelCase :     Optional[int]  ):

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


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any]  ):

     '''simple docstring'''
     __A									= '''sshleifer/tiny-gpt2'''
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       multi_process=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase  )
     __A									= benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result  )
     self.check_results_dict_not_empty(results.memory_inference_result  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any]  ):

     '''simple docstring'''
     __A									= '''sgugger/tiny-distilbert-classification'''
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       multi_process=_lowerCamelCase,       only_pretrain_model=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase  )
     __A									= benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result  )
     self.check_results_dict_not_empty(results.memory_inference_result  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Any  ):

     '''simple docstring'''
     __A									= '''sshleifer/tiny-gpt2'''
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       torchscript=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       multi_process=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase  )
     __A									= 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 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any]  ):

     '''simple docstring'''
     __A									= '''sshleifer/tiny-gpt2'''
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       fpaa=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       multi_process=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase  )
     __A									= benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result  )
     self.check_results_dict_not_empty(results.memory_inference_result  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict  ):

     '''simple docstring'''
     __A									= '''sshleifer/tiny-gpt2'''
     __A									= AutoConfig.from_pretrained(_lowerCamelCase  )
     # set architectures equal to `None`
     __A									= None
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       multi_process=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase,       configs=[config]  )
     __A									= benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result  )
     self.check_results_dict_not_empty(results.memory_inference_result  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     int  ):

     '''simple docstring'''
     __A									= '''sshleifer/tiny-gpt2'''
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       multi_process=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase  )
     __A									= 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 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int]  ):

     '''simple docstring'''
     __A									= '''sshleifer/tiny-gpt2'''
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       fpaa=_lowerCamelCase,       multi_process=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase  )
     __A									= benchmark.run()
     self.check_results_dict_not_empty(results.time_train_result  )
     self.check_results_dict_not_empty(results.memory_train_result  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     str  ):

     '''simple docstring'''
     __A									= '''sshleifer/tiny-gpt2'''
     __A									= AutoConfig.from_pretrained(_lowerCamelCase  )
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       multi_process=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase,       configs=[config]  )
     __A									= benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result  )
     self.check_results_dict_not_empty(results.memory_inference_result  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any]  ):

     '''simple docstring'''
     __A									= '''sshleifer/tinier_bart'''
     __A									= AutoConfig.from_pretrained(_lowerCamelCase  )
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       multi_process=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase,       configs=[config]  )
     __A									= benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result  )
     self.check_results_dict_not_empty(results.memory_inference_result  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any]  ):

     '''simple docstring'''
     __A									= '''sshleifer/tiny-gpt2'''
     __A									= AutoConfig.from_pretrained(_lowerCamelCase  )
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       multi_process=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase,       configs=[config]  )
     __A									= benchmark.run()
     self.check_results_dict_not_empty(results.time_train_result  )
     self.check_results_dict_not_empty(results.memory_train_result  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict  ):

     '''simple docstring'''
     __A									= '''sshleifer/tinier_bart'''
     __A									= AutoConfig.from_pretrained(_lowerCamelCase  )
     __A									= PyTorchBenchmarkArguments(
         models=[MODEL_ID],       training=_lowerCamelCase,       inference=_lowerCamelCase,       sequence_lengths=[8],       batch_sizes=[1],       multi_process=_lowerCamelCase,       )
     __A									= PyTorchBenchmark(_lowerCamelCase,       configs=[config]  )
     __A									= benchmark.run()
     self.check_results_dict_not_empty(results.time_train_result  )
     self.check_results_dict_not_empty(results.memory_train_result  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any]  ):

     '''simple docstring'''
     __A									= '''sshleifer/tiny-gpt2'''
     with tempfile.TemporaryDirectory() as tmp_dir:
       __A									= 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									= 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 				_SCREAMING_SNAKE_CASE				(	self :     Union[str, Any]  ):

     '''simple docstring'''
     __A									= '''sshleifer/tiny-gpt2'''

     def _check_summary_is_not_empty(_lowerCamelCase :     List[Any]  ):
       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									= 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									= PyTorchBenchmark(_lowerCamelCase  )
       __A									= 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()  )












"""simple docstring"""


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import ViTImageProcessor


class 					snake_case		(							unittest.TestCase						):


   '''simple docstring'''

   def __init__(	self :     List[str],       _lowerCamelCase :     Optional[Any],       _lowerCamelCase :     Union[str, Any]=13,       _lowerCamelCase :     Any=3,       _lowerCamelCase :     Optional[int]=2_24,       _lowerCamelCase :     str=30,       _lowerCamelCase :     Dict=4_00,       _lowerCamelCase :     Union[str, Any]=True,       _lowerCamelCase :     Any=None,       _lowerCamelCase :     Optional[Any]=True,       _lowerCamelCase :     Any=[0.5, 0.5, 0.5],       _lowerCamelCase :     List[str]=[0.5, 0.5, 0.5],       ):

     '''simple docstring'''
     __A									= size if size is not None else {'''height''': 18, '''width''': 18}
     __A									= parent
     __A									= batch_size
     __A									= num_channels
     __A									= image_size
     __A									= min_resolution
     __A									= max_resolution
     __A									= do_resize
     __A									= size
     __A									= do_normalize
     __A									= image_mean
     __A									= image_std






   def 				_SCREAMING_SNAKE_CASE				(	self :     int  ):

     '''simple docstring'''
     return {
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "do_normalize": self.do_normalize,
         "do_resize": self.do_resize,
         "size": self.size,
     }





@require_torch
@require_vision
class 					snake_case		(							_lowerCAmelCase   ,	unittest.TestCase						):


   '''simple docstring'''

   A_						:	str						 = ViTImageProcessor if is_vision_available() else None


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int]  ):

     '''simple docstring'''
     __A									= EfficientFormerImageProcessorTester(self  )


   @property
   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict  ):

     '''simple docstring'''
     return self.image_proc_tester.prepare_image_processor_dict()


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any]  ):

     '''simple docstring'''
     __A									= self.image_processing_class(**self.image_processor_dict  )
     self.assertTrue(hasattr(_lowerCamelCase,       '''image_mean'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,       '''image_std'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,       '''do_normalize'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,       '''do_resize'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,       '''size'''  )  )


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

     '''simple docstring'''
     pass


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any]  ):

     '''simple docstring'''
     # Initialize image_processor
     __A									= self.image_processing_class(**self.image_processor_dict  )
     # create random PIL images
     __A									= prepare_image_inputs(self.image_proc_tester,       equal_resolution=_lowerCamelCase  )
     for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase,       Image.Image  )

     # Test not batched input
     __A									= image_processor(image_inputs[0],       return_tensors='''pt'''  ).pixel_values
     self.assertEqual(
         encoded_images.shape,       (
             1,
             self.image_proc_tester.num_channels,
             self.image_proc_tester.size['''height'''],
             self.image_proc_tester.size['''width'''],
         ),       )

     # Test batched
     __A									= image_processor(_lowerCamelCase,       return_tensors='''pt'''  ).pixel_values
     self.assertEqual(
         encoded_images.shape,       (
             self.image_proc_tester.batch_size,
             self.image_proc_tester.num_channels,
             self.image_proc_tester.size['''height'''],
             self.image_proc_tester.size['''width'''],
         ),       )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Tuple  ):

     '''simple docstring'''
     # Initialize image_processor
     __A									= self.image_processing_class(**self.image_processor_dict  )
     # create random numpy tensors
     __A									= prepare_image_inputs(self.image_proc_tester,       equal_resolution=_lowerCamelCase,       numpify=_lowerCamelCase  )
     for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase,       np.ndarray  )

     # Test not batched input
     __A									= image_processor(image_inputs[0],       return_tensors='''pt'''  ).pixel_values
     self.assertEqual(
         encoded_images.shape,       (
             1,
             self.image_proc_tester.num_channels,
             self.image_proc_tester.size['''height'''],
             self.image_proc_tester.size['''width'''],
         ),       )

     # Test batched
     __A									= image_processor(_lowerCamelCase,       return_tensors='''pt'''  ).pixel_values
     self.assertEqual(
         encoded_images.shape,       (
             self.image_proc_tester.batch_size,
             self.image_proc_tester.num_channels,
             self.image_proc_tester.size['''height'''],
             self.image_proc_tester.size['''width'''],
         ),       )






   def 				_SCREAMING_SNAKE_CASE				(	self :     str  ):

     '''simple docstring'''
     # Initialize image_processor
     __A									= self.image_processing_class(**self.image_processor_dict  )
     # create random PyTorch tensors
     __A									= prepare_image_inputs(self.image_proc_tester,       equal_resolution=_lowerCamelCase,       torchify=_lowerCamelCase  )
     for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase,       torch.Tensor  )

     # Test not batched input
     __A									= image_processor(image_inputs[0],       return_tensors='''pt'''  ).pixel_values
     self.assertEqual(
         encoded_images.shape,       (
             1,
             self.image_proc_tester.num_channels,
             self.image_proc_tester.size['''height'''],
             self.image_proc_tester.size['''width'''],
         ),       )

     # Test batched
     __A									= image_processor(_lowerCamelCase,       return_tensors='''pt'''  ).pixel_values
     self.assertEqual(
         encoded_images.shape,       (
             self.image_proc_tester.batch_size,
             self.image_proc_tester.num_channels,
             self.image_proc_tester.size['''height'''],
             self.image_proc_tester.size['''width'''],
         ),       )




