



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


UpperCAmelCase_			      =  logging.get_logger(__name__)


if is_vision_available():
						import PIL





class 		__SCREAMING_SNAKE_CASE      (				lowercase__      ):






				"""simple docstring"""

				SCREAMING_SNAKE_CASE_ 				=  ['pixel_values']







				def __init__(				self						,	SCREAMING_SNAKE_CASE__ = True						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = PILImageResampling.BICUBIC						,	SCREAMING_SNAKE_CASE__ = True						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = True						,	SCREAMING_SNAKE_CASE__ = 1 / 2_55						,	SCREAMING_SNAKE_CASE__ = True						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = True						,	**SCREAMING_SNAKE_CASE__						,	):






					"""simple docstring"""

					super().__init__(**SCREAMING_SNAKE_CASE__      )
					_snake_case					:							List[Any]					     =   size if size is not None else {"""shortest_edge""": 2_24}
					_snake_case					:							List[str]					     =   get_size_dict(SCREAMING_SNAKE_CASE__						,	default_to_square=SCREAMING_SNAKE_CASE__      )
					_snake_case					:							int					     =   crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
					_snake_case					:							int					     =   get_size_dict(SCREAMING_SNAKE_CASE__      )

					_snake_case					:							List[str]					     =   do_resize
					_snake_case					:							List[str]					     =   size
					_snake_case					:							Dict					     =   resample
					_snake_case					:							Any					     =   do_center_crop
					_snake_case					:							Dict					     =   crop_size
					_snake_case					:							List[Any]					     =   do_rescale
					_snake_case					:							str					     =   rescale_factor
					_snake_case					:							Dict					     =   do_normalize
					_snake_case					:							List[str]					     =   image_mean if image_mean is not None else OPENAI_CLIP_MEAN
					_snake_case					:							List[str]					     =   image_std if image_std is not None else OPENAI_CLIP_STD
					_snake_case					:							Dict					     =   do_convert_rgb







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = PILImageResampling.BICUBIC						,	SCREAMING_SNAKE_CASE__ = None						,	**SCREAMING_SNAKE_CASE__						,	):






					"""simple docstring"""

					_snake_case					:							Dict					     =   get_size_dict(SCREAMING_SNAKE_CASE__						,	default_to_square=SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Tuple					     =   get_resize_output_image_size(
					    SCREAMING_SNAKE_CASE__						,	size=(size["""height"""], size["""width"""])						,	default_to_square=SCREAMING_SNAKE_CASE__      )
					return resize(SCREAMING_SNAKE_CASE__						,	size=SCREAMING_SNAKE_CASE__						,	resample=SCREAMING_SNAKE_CASE__						,	data_format=SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__      )







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = None						,	**SCREAMING_SNAKE_CASE__						,	):






					"""simple docstring"""

					_snake_case					:							Dict					     =   get_size_dict(SCREAMING_SNAKE_CASE__      )
					return center_crop(SCREAMING_SNAKE_CASE__						,	size=(size["""height"""], size["""width"""])						,	data_format=SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__      )







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = None						,	**SCREAMING_SNAKE_CASE__						,	):






					"""simple docstring"""

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







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = None						,	**SCREAMING_SNAKE_CASE__						,	):






					"""simple docstring"""

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







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = ChannelDimension.FIRST						,	**SCREAMING_SNAKE_CASE__						,	):






					"""simple docstring"""

					_snake_case					:							Optional[int]					     =   do_resize if do_resize is not None else self.do_resize
					_snake_case					:							Tuple					     =   size if size is not None else self.size
					_snake_case					:							Tuple					     =   get_size_dict(SCREAMING_SNAKE_CASE__						,	default_to_square=SCREAMING_SNAKE_CASE__      )
					_snake_case					:							List[Any]					     =   resample if resample is not None else self.resample
					_snake_case					:							Union[str, Any]					     =   do_center_crop if do_center_crop is not None else self.do_center_crop
					_snake_case					:							int					     =   crop_size if crop_size is not None else self.crop_size
					_snake_case					:							Optional[Any]					     =   get_size_dict(SCREAMING_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					:							int					     =   image_mean if image_mean is not None else self.image_mean
					_snake_case					:							Union[str, Any]					     =   image_std if image_std is not None else self.image_std
					_snake_case					:							Dict					     =   do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

					_snake_case					:							List[Any]					     =   make_list_of_images(SCREAMING_SNAKE_CASE__      )

					if not valid_images(SCREAMING_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					:							List[str]					     =   [convert_to_rgb(SCREAMING_SNAKE_CASE__      ) for image in images]

					# All transformations expect numpy arrays.
					_snake_case					:							Tuple					     =   [to_numpy_array(SCREAMING_SNAKE_CASE__      ) for image in images]

					if do_resize:
						_snake_case					:							Optional[int]					     =   [self.resize(image=SCREAMING_SNAKE_CASE__						,	size=SCREAMING_SNAKE_CASE__						,	resample=SCREAMING_SNAKE_CASE__      ) for image in images]

					if do_center_crop:
						_snake_case					:							Any					     =   [self.center_crop(image=SCREAMING_SNAKE_CASE__						,	size=SCREAMING_SNAKE_CASE__      ) for image in images]

					if do_rescale:
						_snake_case					:							int					     =   [self.rescale(image=SCREAMING_SNAKE_CASE__						,	scale=SCREAMING_SNAKE_CASE__      ) for image in images]

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

					_snake_case					:							List[Any]					     =   [to_channel_dimension_format(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      ) for image in images]

					_snake_case					:							Dict					     =   {"""pixel_values""": images}
					return BatchFeature(data=SCREAMING_SNAKE_CASE__						,	tensor_type=SCREAMING_SNAKE_CASE__      )










# 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   UpperCAmelCase							(		A__				,  A__     )							->					Any:
	if name != "training_function" and name != "main":
		raise ValueError(f'''Incorrect function name passed: {name}, choose either \'main\' or \'training_function\''''     )
	_snake_case     , _snake_case					:							Union[str, Any]					     =   [], False
	for line in lines:
		if not found_start and f'''def {name}''' in line:
			_snake_case					:							Tuple					     =   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   UpperCAmelCase							(		A__     )							->					Dict:
	return [line for line in lines if not line.lstrip().startswith("""#"""     ) and line != "\n"]


def   UpperCAmelCase							(		A__				,  A__				,  A__				,  A__ = None     )							->					str:
	with open(A__				,  """r"""     ) as f:
		_snake_case					:							Tuple					     =   f.readlines()
	with open(os.path.abspath(os.path.join("""examples"""				,  """nlp_example.py"""     )     )				,  """r"""     ) as f:
		_snake_case					:							Optional[Any]					     =   f.readlines()
	with open(A__				,  """r"""     ) as f:
		_snake_case					:							Optional[Any]					     =   f.readlines()
	if secondary_filename is not None:
		with open(A__				,  """r"""     ) as f:
			_snake_case					:							List[str]					     =   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:
		_snake_case					:							Dict					     =   clean_lines(get_function_contents_by_name(A__				,  """main"""     )     )
		_snake_case					:							int					     =   clean_lines(get_function_contents_by_name(A__				,  """main"""     )     )
		_snake_case					:							Optional[Any]					     =   clean_lines(get_function_contents_by_name(A__				,  """main"""     )     )
		if secondary_filename is not None:
			_snake_case					:							Optional[Any]					     =   clean_lines(get_function_contents_by_name(A__				,  """main"""     )     )
	else:
		_snake_case					:							str					     =   clean_lines(get_function_contents_by_name(A__				,  """training_function"""     )     )
		_snake_case					:							List[Any]					     =   clean_lines(get_function_contents_by_name(A__				,  """training_function"""     )     )
		_snake_case					:							Union[str, Any]					     =   clean_lines(get_function_contents_by_name(A__				,  """training_function"""     )     )
		if secondary_filename is not None:
			_snake_case					:							List[Any]					     =   clean_lines(
			    get_function_contents_by_name(A__				,  """training_function"""     )     )

	_snake_case					:							str					     =   """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
	_snake_case					:							Dict					     =   []
	_snake_case					:							str					     =   []  # We keep track of the idxs just in case it's a repeated statement
	_snake_case					:							Optional[Any]					     =   iter(A__     )
	for i in range(len(A__     ) - 1     ):
		if i not in passed_idxs:
			_snake_case					:							Any					     =   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
					_snake_case					:							Optional[int]					     =   next(A__     )

    # Extract out just the new parts from the full_file_training_func
	_snake_case					:							Any					     =   []
	_snake_case					:							Tuple					     =   []  # 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
	_snake_case					:							Optional[int]					     =   [line for line in new_feature_code if line not in new_full_example_parts]
	if secondary_filename is not None:
		_snake_case					:							Optional[int]					     =   [line for line in full_file_contents if line not in secondary_file_func]
		_snake_case					:							int					     =   [line for line in diff_from_example if line not in diff_from_two]

	return diff_from_example





