code: 
  
  
  """simple docstring"""
  
  
  
  
  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 normalize, rescale, resize, to_channel_dimension_format
  from ...image_utils import (
      IMAGENET_STANDARD_MEAN,
      IMAGENET_STANDARD_STD,
      ChannelDimension,
      ImageInput,
      PILImageResampling,
      make_list_of_images,
      to_numpy_array,
      valid_images,
  )
  from ...utils import TensorType, logging
  
  
  __SCREAMING_SNAKE_CASE					=						logging.get_logger(__name__)
  
  
  
  
  
  class __snake_case		(					_SCREAMING_SNAKE_CASE      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     lowerCAmelCase_				:   Any     	=			['pixel_values']
  
  
  
  
     def __init__(		self							:List[Any]				,  UpperCamelCase__							:bool = True				,  UpperCamelCase__							:Optional[Dict[str, int]] = None				,  UpperCamelCase__							:PILImageResampling = PILImageResampling.BILINEAR				,  UpperCamelCase__							:bool = True				,  UpperCamelCase__							:Union[int, float] = 1 / 255				,  UpperCamelCase__							:bool = True				,  UpperCamelCase__							:Optional[Union[float, List[float]]] = None				,  UpperCamelCase__							:Optional[Union[float, List[float]]] = None				,  **UpperCamelCase__							:List[Any]				,  ):
            super().__init__(**UpperCamelCase__       )
            _a          =				size if size is not None else {"height": 224, "width": 224}
            _a          =				get_size_dict(UpperCamelCase__       )
            _a          =				do_resize
            _a          =				do_rescale
            _a          =				do_normalize
            _a          =				size
            _a          =				resample
            _a          =				rescale_factor
            _a          =				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
            _a          =				image_std if image_std is not None else IMAGENET_STANDARD_STD
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:str				,  UpperCamelCase__							:np.ndarray				,  UpperCamelCase__							:Dict[str, int]				,  UpperCamelCase__							:PILImageResampling = PILImageResampling.BILINEAR				,  UpperCamelCase__							:Optional[Union[str, ChannelDimension]] = None				,  **UpperCamelCase__							:List[str]				,  ):
            _a          =				get_size_dict(UpperCamelCase__       )
            if "height" not in size or "width" not in size:
                   raise ValueError(f'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'       )
            return resize(
                UpperCamelCase__				,  size=(size["height"], size["width"])				,  resample=UpperCamelCase__				,  data_format=UpperCamelCase__				,  **UpperCamelCase__       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Optional[Any]				,  UpperCamelCase__							:np.ndarray				,  UpperCamelCase__							:float				,  UpperCamelCase__							:Optional[Union[str, ChannelDimension]] = None				,  **UpperCamelCase__							:Dict       ):
            return rescale(UpperCamelCase__				,  scale=UpperCamelCase__				,  data_format=UpperCamelCase__				,  **UpperCamelCase__       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:List[str]				,  UpperCamelCase__							:np.ndarray				,  UpperCamelCase__							:Union[float, List[float]]				,  UpperCamelCase__							:Union[float, List[float]]				,  UpperCamelCase__							:Optional[Union[str, ChannelDimension]] = None				,  **UpperCamelCase__							:Dict				,  ):
            return normalize(UpperCamelCase__				,  mean=UpperCamelCase__				,  std=UpperCamelCase__				,  data_format=UpperCamelCase__				,  **UpperCamelCase__       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Optional[int]				,  UpperCamelCase__							:ImageInput				,  UpperCamelCase__							:Optional[bool] = None				,  UpperCamelCase__							:Dict[str, int] = None				,  UpperCamelCase__							:PILImageResampling = None				,  UpperCamelCase__							:Optional[bool] = None				,  UpperCamelCase__							:Optional[float] = None				,  UpperCamelCase__							:Optional[bool] = None				,  UpperCamelCase__							:Optional[Union[float, List[float]]] = None				,  UpperCamelCase__							:Optional[Union[float, List[float]]] = None				,  UpperCamelCase__							:Optional[Union[str, TensorType]] = None				,  UpperCamelCase__							:Union[str, ChannelDimension] = ChannelDimension.FIRST				,  **UpperCamelCase__							:Optional[int]				,  ):
            _a          =				do_resize if do_resize is not None else self.do_resize
            _a          =				do_rescale if do_rescale is not None else self.do_rescale
            _a          =				do_normalize if do_normalize is not None else self.do_normalize
            _a          =				resample if resample is not None else self.resample
            _a          =				rescale_factor if rescale_factor is not None else self.rescale_factor
            _a          =				image_mean if image_mean is not None else self.image_mean
            _a          =				image_std if image_std is not None else self.image_std
  
            _a          =				size if size is not None else self.size
            _a          =				get_size_dict(UpperCamelCase__       )
  
            _a          =				make_list_of_images(UpperCamelCase__       )
  
            if not valid_images(UpperCamelCase__       ):
                   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_rescale and rescale_factor is None:
                   raise ValueError("Rescale factor must be specified if do_rescale is True."       )
  
            # All transformations expect numpy arrays.
            _a          =				[to_numpy_array(UpperCamelCase__       ) for image in images]
  
            if do_resize:
                   _a          =				[self.resize(image=UpperCamelCase__				,  size=UpperCamelCase__				,  resample=UpperCamelCase__       ) for image in images]
  
            if do_rescale:
                   _a          =				[self.rescale(image=UpperCamelCase__				,  scale=UpperCamelCase__       ) for image in images]
  
            if do_normalize:
                   _a          =				[self.normalize(image=UpperCamelCase__				,  mean=UpperCamelCase__				,  std=UpperCamelCase__       ) for image in images]
  
            _a          =				[to_channel_dimension_format(UpperCamelCase__				,  UpperCamelCase__       ) for image in images]
  
            _a          =				{"pixel_values": images}
            return BatchFeature(data=UpperCamelCase__				,  tensor_type=UpperCamelCase__       )
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  # Copyright 2022 The HuggingFace Team. All rights reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  import os
  from typing import List
  
  
  
  def 						__a      (  a,		a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         if name != "training_function" and name != "main":
                raise ValueError(F'Incorrect function name passed: {name}, choose either \'main\' or \'training_function\''					)
         _a   , _a          =				[], False
         for line in lines:
                if not found_start and F'def {name}' in line:
                       _a          =				True
                       good_lines.append(a					)
                       continue
                if found_start:
                       if name == "training_function" and "def main" in line:
                              return good_lines
                       if name == "main" and "if __name__" in line:
                              return good_lines
                       good_lines.append(a					)
  
  
  
  def 						__a      (  a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         return [line for line in lines if not line.lstrip().startswith("#"					) and line != "\n"]
  
  
  
  def 						__a      (  a,		a,		a,		a = None					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         with open(a,		"r"					) as f:
                _a          =				f.readlines()
         with open(os.path.abspath(os.path.join("examples",		"nlp_example.py"					)					),		"r"					) as f:
                _a          =				f.readlines()
         with open(a,		"r"					) as f:
                _a          =				f.readlines()
         if secondary_filename is not None:
                with open(a,		"r"					) as f:
                       _a          =				f.readlines()
  
      # This is our base, we remove all the code from here in our `full_filename` and `feature_filename` to find the new content
         if parser_only:
                _a          =				clean_lines(get_function_contents_by_name(a,		"main"					)					)
                _a          =				clean_lines(get_function_contents_by_name(a,		"main"					)					)
                _a          =				clean_lines(get_function_contents_by_name(a,		"main"					)					)
                if secondary_filename is not None:
                       _a          =				clean_lines(get_function_contents_by_name(a,		"main"					)					)
         else:
                _a          =				clean_lines(get_function_contents_by_name(a,		"training_function"					)					)
                _a          =				clean_lines(get_function_contents_by_name(a,		"training_function"					)					)
                _a          =				clean_lines(get_function_contents_by_name(a,		"training_function"					)					)
                if secondary_filename is not None:
                       _a          =				clean_lines(
                           get_function_contents_by_name(a,		"training_function"					)					)
  
         _a          =				"train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)\n"
  
         # Specific code in our script that differs from the full version, aka what is new
         _a          =				[]
         _a          =				[]  # We keep track of the idxs just in case it's a repeated statement
         _a          =				iter(a					)
         for i in range(len(a					) - 1					):
                if i not in passed_idxs:
                       _a          =				next(a					)
                       if (line not in full_file_func) and (line.lstrip() != _dl_line):
                              if "TESTING_MOCKED_DATALOADERS" not in line:
                                     new_feature_code.append(a					)
                                     passed_idxs.append(a					)
                              else:
                                     # Skip over the `config['num_epochs'] = 2` statement
                                     _a          =				next(a					)
  
      # Extract out just the new parts from the full_file_training_func
         _a          =				[]
         _a          =				[]  # We keep track of the idxs just in case it's a repeated statement
         for i, line in enumerate(a					):
                if i not in passed_idxs:
                       if (line not in full_file_func) and (line.lstrip() != _dl_line):
                              if "TESTING_MOCKED_DATALOADERS" not in line:
                                     new_full_example_parts.append(a					)
                                     passed_idxs.append(a					)
  
      # Finally, get the overall diff
         _a          =				[line for line in new_feature_code if line not in new_full_example_parts]
         if secondary_filename is not None:
                _a          =				[line for line in full_file_contents if line not in secondary_file_func]
                _a          =				[line for line in diff_from_example if line not in diff_from_two]
  
         return diff_from_example
  