
from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


__lowerCamelCase							=       logging.get_logger(__name__)


if is_vision_available():
   import PIL






class   UpperCAmelCase		(						A_ ):
   A__					:		str						     =     ["pixel_values"]



   def __init__(self		:					List[str]    ,						snake_case__		:					bool = True    ,						snake_case__		:					Dict[str, int] = None    ,						snake_case__		:					PILImageResampling = PILImageResampling.BICUBIC    ,						snake_case__		:					bool = True    ,						snake_case__		:					Dict[str, int] = None    ,						snake_case__		:					bool = True    ,						snake_case__		:					Union[int, float] = 1 / 2_55    ,						snake_case__		:					bool = True    ,						snake_case__		:					Optional[Union[float, List[float]]] = None    ,						snake_case__		:					Optional[Union[float, List[float]]] = None    ,						snake_case__		:					bool = True    ,						**snake_case__		:					List[Any]    ,						)		->	None:


          '''simple docstring'''


          super().__init__(**snake_case__ )
          snake_case       :   Any							=      size if size is not None else {"shortest_edge": 2_24}
          snake_case       :   str							=      get_size_dict(snake_case__    ,						default_to_square=snake_case__ )
          snake_case       :   Dict							=      crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
          snake_case       :   Optional[int]							=      get_size_dict(snake_case__    ,						default_to_square=snake_case__    ,						param_name="crop_size" )

          snake_case       :   List[str]							=      do_resize
          snake_case       :   List[str]							=      size
          snake_case       :   Optional[int]							=      resample
          snake_case       :   List[str]							=      do_center_crop
          snake_case       :   List[Any]							=      crop_size
          snake_case       :   Any							=      do_rescale
          snake_case       :   Union[str, Any]							=      rescale_factor
          snake_case       :   Dict							=      do_normalize
          snake_case       :   Tuple							=      image_mean if image_mean is not None else OPENAI_CLIP_MEAN
          snake_case       :   Union[str, Any]							=      image_std if image_std is not None else OPENAI_CLIP_STD
          snake_case       :   Union[str, Any]							=      do_convert_rgb



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str]    ,						snake_case__		:					np.ndarray    ,						snake_case__		:					Dict[str, int]    ,						snake_case__		:					PILImageResampling = PILImageResampling.BICUBIC    ,						snake_case__		:					Optional[Union[str, ChannelDimension]] = None    ,						**snake_case__		:					Optional[int]    ,						)		->	np.ndarray:


          '''simple docstring'''


          snake_case       :   List[str]							=      get_size_dict(snake_case__    ,						default_to_square=snake_case__ )
          if "shortest_edge" not in size:
                 raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}""" )
          snake_case       :   Dict							=      get_resize_output_image_size(snake_case__    ,						size=size["shortest_edge"]    ,						default_to_square=snake_case__ )
          return resize(snake_case__    ,						size=snake_case__    ,						resample=snake_case__    ,						data_format=snake_case__    ,						**snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple    ,						snake_case__		:					np.ndarray    ,						snake_case__		:					Dict[str, int]    ,						snake_case__		:					Optional[Union[str, ChannelDimension]] = None    ,						**snake_case__		:					Optional[Any]    ,						)		->	np.ndarray:


          '''simple docstring'''


          snake_case       :   Union[str, Any]							=      get_size_dict(snake_case__ )
          if "height" not in size or "width" not in size:
                 raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}""" )
          return center_crop(snake_case__    ,						size=(size["height"], size["width"])    ,						data_format=snake_case__    ,						**snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple    ,						snake_case__		:					np.ndarray    ,						snake_case__		:					Union[int, float]    ,						snake_case__		:					Optional[Union[str, ChannelDimension]] = None    ,						**snake_case__		:					Optional[int]    ,						)		->	Any:


          '''simple docstring'''


          return rescale(snake_case__    ,						scale=snake_case__    ,						data_format=snake_case__    ,						**snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict    ,						snake_case__		:					np.ndarray    ,						snake_case__		:					Union[float, List[float]]    ,						snake_case__		:					Union[float, List[float]]    ,						snake_case__		:					Optional[Union[str, ChannelDimension]] = None    ,						**snake_case__		:					Optional[int]    ,						)		->	np.ndarray:


          '''simple docstring'''


          return normalize(snake_case__    ,						mean=snake_case__    ,						std=snake_case__    ,						data_format=snake_case__    ,						**snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					ImageInput    ,						snake_case__		:					bool = None    ,						snake_case__		:					Dict[str, int] = None    ,						snake_case__		:					PILImageResampling = None    ,						snake_case__		:					bool = None    ,						snake_case__		:					int = None    ,						snake_case__		:					bool = None    ,						snake_case__		:					float = None    ,						snake_case__		:					bool = None    ,						snake_case__		:					Optional[Union[float, List[float]]] = None    ,						snake_case__		:					Optional[Union[float, List[float]]] = None    ,						snake_case__		:					bool = None    ,						snake_case__		:					Optional[Union[str, TensorType]] = None    ,						snake_case__		:					Optional[ChannelDimension] = ChannelDimension.FIRST    ,						**snake_case__		:					Any    ,						)		->	PIL.Image.Image:


          '''simple docstring'''


          snake_case       :   Optional[int]							=      do_resize if do_resize is not None else self.do_resize
          snake_case       :   Union[str, Any]							=      size if size is not None else self.size
          snake_case       :   List[str]							=      get_size_dict(snake_case__    ,						param_name="size"    ,						default_to_square=snake_case__ )
          snake_case       :   Any							=      resample if resample is not None else self.resample
          snake_case       :   Dict							=      do_center_crop if do_center_crop is not None else self.do_center_crop
          snake_case       :   List[Any]							=      crop_size if crop_size is not None else self.crop_size
          snake_case       :   Optional[int]							=      get_size_dict(snake_case__    ,						param_name="crop_size"    ,						default_to_square=snake_case__ )
          snake_case       :   Any							=      do_rescale if do_rescale is not None else self.do_rescale
          snake_case       :   str							=      rescale_factor if rescale_factor is not None else self.rescale_factor
          snake_case       :   List[str]							=      do_normalize if do_normalize is not None else self.do_normalize
          snake_case       :   Union[str, Any]							=      image_mean if image_mean is not None else self.image_mean
          snake_case       :   str							=      image_std if image_std is not None else self.image_std
          snake_case       :   Union[str, Any]							=      do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

          snake_case       :   int							=      make_list_of_images(snake_case__ )

          if not valid_images(snake_case__ ):
                 raise ValueError(
                     "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                     "torch.Tensor, tf.Tensor or jax.ndarray." )

          if do_resize and size is None:
                 raise ValueError("Size must be specified if do_resize is True." )

          if do_center_crop and crop_size is None:
                 raise ValueError("Crop size must be specified if do_center_crop is True." )

          if do_rescale and rescale_factor is None:
                 raise ValueError("Rescale factor must be specified if do_rescale is True." )

          if do_normalize and (image_mean is None or image_std is None):
                 raise ValueError("Image mean and std must be specified if do_normalize is True." )

          # PIL RGBA images are converted to RGB
          if do_convert_rgb:
                 snake_case       :   Union[str, Any]							=      [convert_to_rgb(snake_case__ ) for image in images]

          # All transformations expect numpy arrays.
          snake_case       :   Optional[int]							=      [to_numpy_array(snake_case__ ) for image in images]

          if do_resize:
                 snake_case       :   Optional[Any]							=      [self.resize(image=snake_case__    ,						size=snake_case__    ,						resample=snake_case__ ) for image in images]

          if do_center_crop:
                 snake_case       :   Tuple							=      [self.center_crop(image=snake_case__    ,						size=snake_case__ ) for image in images]

          if do_rescale:
                 snake_case       :   Optional[Any]							=      [self.rescale(image=snake_case__    ,						scale=snake_case__ ) for image in images]

          if do_normalize:
                 snake_case       :   int							=      [self.normalize(image=snake_case__    ,						mean=snake_case__    ,						std=snake_case__ ) for image in images]

          snake_case       :   str							=      [to_channel_dimension_format(snake_case__    ,						snake_case__ ) for image in images]

          snake_case       :   Optional[Any]							=      {"pixel_values": images}
          return BatchFeature(data=snake_case__    ,						tensor_type=snake_case__ )






import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
   import tensorflow as tf

   from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments





@require_tf
class 		_lowercase						(					unittest.TestCase       ):

 def 						SCREAMING_SNAKE_CASE__   ( self	:							int   ,      snake_case	:							Union[str, Any]			)   ->						Optional[int]:

  """simple docstring"""





  for model_result in results.values():
   for batch_size, sequence_length in zip(model_result['bs']   ,      model_result['ss']			):
    UpperCamelCase_					:						Optional[Any] =					model_result['result'][batch_size][sequence_length]
    self.assertIsNotNone(snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							int			)   ->						Tuple:

  """simple docstring"""





  UpperCamelCase_					:						Optional[Any] =					'sshleifer/tiny-gpt2'
  UpperCamelCase_					:						Dict =					TensorFlowBenchmarkArguments(
      models=[MODEL_ID]   ,      training=snake_case   ,      inference=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      eager_mode=snake_case   ,      multi_process=snake_case   ,      )
  UpperCamelCase_					:						Tuple =					TensorFlowBenchmark(snake_case			)
  UpperCamelCase_					:						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 						SCREAMING_SNAKE_CASE__   ( self	:							str			)   ->						Optional[Any]:

  """simple docstring"""





  UpperCamelCase_					:						Dict =					'sgugger/tiny-distilbert-classification'
  UpperCamelCase_					:						int =					TensorFlowBenchmarkArguments(
      models=[MODEL_ID]   ,      training=snake_case   ,      inference=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      multi_process=snake_case   ,      only_pretrain_model=snake_case   ,      )
  UpperCamelCase_					:						int =					TensorFlowBenchmark(snake_case			)
  UpperCamelCase_					:						Tuple =					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	:							Tuple			)   ->						List[str]:

  """simple docstring"""





  UpperCamelCase_					:						str =					'sshleifer/tiny-gpt2'
  UpperCamelCase_					:						Any =					TensorFlowBenchmarkArguments(
      models=[MODEL_ID]   ,      training=snake_case   ,      inference=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      multi_process=snake_case   ,      )
  UpperCamelCase_					:						List[str] =					TensorFlowBenchmark(snake_case			)
  UpperCamelCase_					:						Optional[int] =					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	:							Tuple			)   ->						Dict:

  """simple docstring"""





  UpperCamelCase_					:						List[str] =					'sshleifer/tiny-gpt2'
  UpperCamelCase_					:						str =					AutoConfig.from_pretrained(snake_case			)
  UpperCamelCase_					:						List[str] =					TensorFlowBenchmarkArguments(
      models=[MODEL_ID]   ,      training=snake_case   ,      inference=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      eager_mode=snake_case   ,      multi_process=snake_case   ,      )
  UpperCamelCase_					:						int =					TensorFlowBenchmark(snake_case   ,      [config]			)
  UpperCamelCase_					:						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 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]			)   ->						List[str]:

  """simple docstring"""





  UpperCamelCase_					:						List[str] =					'sshleifer/tiny-gpt2'
  UpperCamelCase_					:						int =					AutoConfig.from_pretrained(snake_case			)
  UpperCamelCase_					:						Any =					TensorFlowBenchmarkArguments(
      models=[MODEL_ID]   ,      training=snake_case   ,      inference=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      multi_process=snake_case   ,      )
  UpperCamelCase_					:						Tuple =					TensorFlowBenchmark(snake_case   ,      [config]			)
  UpperCamelCase_					:						Tuple =					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	:							Tuple			)   ->						int:

  """simple docstring"""





  UpperCamelCase_					:						Optional[int] =					'sshleifer/tiny-gpt2'
  UpperCamelCase_					:						Tuple =					TensorFlowBenchmarkArguments(
      models=[MODEL_ID]   ,      training=snake_case   ,      inference=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      multi_process=snake_case   ,      )
  UpperCamelCase_					:						Optional[int] =					TensorFlowBenchmark(snake_case			)
  UpperCamelCase_					:						str =					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	:							Any			)   ->						int:

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					'sshleifer/tiny-gpt2'
  UpperCamelCase_					:						List[str] =					AutoConfig.from_pretrained(snake_case			)
  UpperCamelCase_					:						int =					TensorFlowBenchmarkArguments(
      models=[MODEL_ID]   ,      training=snake_case   ,      inference=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      multi_process=snake_case   ,      )
  UpperCamelCase_					:						List[Any] =					TensorFlowBenchmark(snake_case   ,      [config]			)
  UpperCamelCase_					:						int =					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	:							Union[str, Any]			)   ->						Dict:

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					'patrickvonplaten/t5-tiny-random'
  UpperCamelCase_					:						List[str] =					AutoConfig.from_pretrained(snake_case			)
  UpperCamelCase_					:						List[str] =					TensorFlowBenchmarkArguments(
      models=[MODEL_ID]   ,      training=snake_case   ,      inference=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      multi_process=snake_case   ,      )
  UpperCamelCase_					:						int =					TensorFlowBenchmark(snake_case   ,      configs=[config]			)
  UpperCamelCase_					:						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(is_tf_available() and len(tf.config.list_physical_devices('GPU'			)			) == 0   ,      'Cannot do xla on CPU.'			)
 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[int]			)   ->						Dict:

  """simple docstring"""





  UpperCamelCase_					:						int =					'sshleifer/tiny-gpt2'
  UpperCamelCase_					:						Union[str, Any] =					TensorFlowBenchmarkArguments(
      models=[MODEL_ID]   ,      training=snake_case   ,      inference=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      use_xla=snake_case   ,      multi_process=snake_case   ,      )
  UpperCamelCase_					:						Union[str, Any] =					TensorFlowBenchmark(snake_case			)
  UpperCamelCase_					:						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 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]			)   ->						Optional[int]:

  """simple docstring"""





  UpperCamelCase_					:						List[Any] =					'sshleifer/tiny-gpt2'
  with tempfile.TemporaryDirectory() as tmp_dir:
   UpperCamelCase_					:						List[Any] =					TensorFlowBenchmarkArguments(
       models=[MODEL_ID]   ,      inference=snake_case   ,      save_to_csv=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      inference_time_csv_file=os.path.join(snake_case   ,      'inf_time.csv'			)   ,      inference_memory_csv_file=os.path.join(snake_case   ,      'inf_mem.csv'			)   ,      env_info_csv_file=os.path.join(snake_case   ,      'env.csv'			)   ,      multi_process=snake_case   ,      )
   UpperCamelCase_					:						List[str] =					TensorFlowBenchmark(snake_case			)
   benchmark.run()
   self.assertTrue(Path(os.path.join(snake_case   ,      'inf_time.csv'			)			).exists()			)
   self.assertTrue(Path(os.path.join(snake_case   ,      'inf_mem.csv'			)			).exists()			)
   self.assertTrue(Path(os.path.join(snake_case   ,      'env.csv'			)			).exists()			)


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

  """simple docstring"""





  UpperCamelCase_					:						Optional[Any] =					'sshleifer/tiny-gpt2'

  def _check_summary_is_not_empty(snake_case	:							Union[str, Any]			):
   self.assertTrue(hasattr(snake_case   ,      'sequential'			)			)
   self.assertTrue(hasattr(snake_case   ,      'cumulative'			)			)
   self.assertTrue(hasattr(snake_case   ,      'current'			)			)
   self.assertTrue(hasattr(snake_case   ,      'total'			)			)

  with tempfile.TemporaryDirectory() as tmp_dir:
   UpperCamelCase_					:						str =					TensorFlowBenchmarkArguments(
       models=[MODEL_ID]   ,      inference=snake_case   ,      sequence_lengths=[8]   ,      batch_sizes=[1]   ,      log_filename=os.path.join(snake_case   ,      'log.txt'			)   ,      log_print=snake_case   ,      trace_memory_line_by_line=snake_case   ,      eager_mode=snake_case   ,      multi_process=snake_case   ,      )
   UpperCamelCase_					:						Tuple =					TensorFlowBenchmark(snake_case			)
   UpperCamelCase_					:						Union[str, Any] =					benchmark.run()
   _check_summary_is_not_empty(result.inference_summary			)
   self.assertTrue(Path(os.path.join(snake_case   ,      'log.txt'			)			).exists()			)



