






"""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 (
    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


SCREAMING_SNAKE_CASE						:						str					   =       logging.get_logger(__name__)


if is_vision_available():
					import PIL





class       snake_case_       (    _lowerCamelCase       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							List[str]   			=					["""pixel_values"""]


	def __init__(      self  ,					__a = True  ,					__a = None  ,					__a = PILImageResampling.BICUBIC  ,					__a = True  ,					__a = None  ,					__a = True  ,					__a = 1 / 255  ,					__a = True  ,					__a = None  ,					__a = None  ,					__a = True  ,					**__a  ,					):


					"""simple docstring"""

					super().__init__(**__a						)
					A__							 =       size if size is not None else {'shortest_edge': 224}
					A__							 =       get_size_dict(__a  ,					default_to_square=__a						)
					A__							 =       crop_size if crop_size is not None else {'height': 224, 'width': 224}
					A__							 =       get_size_dict(__a  ,					default_to_square=__a  ,					param_name='crop_size'						)

					A__							 =       do_resize
					A__							 =       size
					A__							 =       resample
					A__							 =       do_center_crop
					A__							 =       crop_size
					A__							 =       do_rescale
					A__							 =       rescale_factor
					A__							 =       do_normalize
					A__							 =       image_mean if image_mean is not None else OPENAI_CLIP_MEAN
					A__							 =       image_std if image_std is not None else OPENAI_CLIP_STD
					A__							 =       do_convert_rgb


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a  ,					__a = PILImageResampling.BICUBIC  ,					__a = None  ,					**__a  ,					):


					"""simple docstring"""

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


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a  ,					__a = None  ,					**__a  ,					):


					"""simple docstring"""

					A__							 =       get_size_dict(__a						)
					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(__a  ,					size=(size['height'], size['width'])  ,					data_format=__a  ,					**__a						)


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a  ,					__a = None  ,					**__a  ,					):


					"""simple docstring"""

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


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a  ,					__a  ,					__a = None  ,					**__a  ,					):


					"""simple docstring"""

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






	def       _UpperCAmelCase						(      self  ,					__a  ,					__a = None  ,					__a = None  ,					__a = None  ,					__a = None  ,					__a = None  ,					__a = None  ,					__a = None  ,					__a = None  ,					__a = None  ,					__a = None  ,					__a = None  ,					__a = None  ,					__a = ChannelDimension.FIRST  ,					**__a  ,					):


					"""simple docstring"""

					A__							 =       do_resize if do_resize is not None else self.do_resize
					A__							 =       size if size is not None else self.size
					A__							 =       get_size_dict(__a  ,					param_name='size'  ,					default_to_square=__a						)
					A__							 =       resample if resample is not None else self.resample
					A__							 =       do_center_crop if do_center_crop is not None else self.do_center_crop
					A__							 =       crop_size if crop_size is not None else self.crop_size
					A__							 =       get_size_dict(__a  ,					param_name='crop_size'  ,					default_to_square=__a						)
					A__							 =       do_rescale if do_rescale is not None else self.do_rescale
					A__							 =       rescale_factor if rescale_factor is not None else self.rescale_factor
					A__							 =       do_normalize if do_normalize is not None else self.do_normalize
					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__							 =       do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

					A__							 =       make_list_of_images(__a						)

					if not valid_images(__a						):
									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:
									A__							 =       [convert_to_rgb(__a						) for image in images]

					# All transformations expect numpy arrays.
					A__							 =       [to_numpy_array(__a						) for image in images]

					if do_resize:
									A__							 =       [self.resize(image=__a  ,					size=__a  ,					resample=__a						) for image in images]

					if do_center_crop:
									A__							 =       [self.center_crop(image=__a  ,					size=__a						) for image in images]

					if do_rescale:
									A__							 =       [self.rescale(image=__a  ,					scale=__a						) for image in images]

					if do_normalize:
									A__							 =       [self.normalize(image=__a  ,					mean=__a  ,					std=__a						) for image in images]

					A__							 =       [to_channel_dimension_format(__a  ,					__a						) for image in images]

					A__							 =       {'pixel_values': images}
					return BatchFeature(data=__a  ,					tensor_type=__a						)










"""simple docstring"""






import os

import numpy
import onnx



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       a.name
				A__							 =       b.name

				A__							 =       ''
				A__							 =       ''

				A__							 =       a == b

				A__							 =       name_a
				A__							 =       name_b

				return res



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):
				for i, input_name in enumerate(node_proto.input				):
								if input_name == name:
												node_proto.input.insert(lowerCAmelCase__       ,lowerCAmelCase__				)
												node_proto.input.pop(i + 1				)

				if node_proto.op_type == "If":
								_graph_replace_input_with(node_proto.attribute[0].g       ,lowerCAmelCase__       ,lowerCAmelCase__				)
								_graph_replace_input_with(node_proto.attribute[1].g       ,lowerCAmelCase__       ,lowerCAmelCase__				)
				if node_proto.op_type == "Loop":
								_graph_replace_input_with(node_proto.attribute[0].g       ,lowerCAmelCase__       ,lowerCAmelCase__				)



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):
				for n in graph_proto.node:
								_node_replace_input_with(lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				)



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       list(model.graph.initializer				)
				A__							 =       list(model_without_ext.graph.initializer				)
				for i, ref_i in ind_to_replace:
								assert inits_with_data[i].name == inits[i].name
								assert inits_with_data[ref_i].name == inits[ref_i].name
								assert i > ref_i

								A__							 =       inits[i].name
								A__							 =       inits[ref_i].name

								model_without_ext.graph.initializer.remove(inits[i]				)

								# for n in model.graph.node:
								_graph_replace_input_with(model_without_ext.graph       ,lowerCAmelCase__       ,lowerCAmelCase__				)







def  __lowerCamelCase					(					lowerCAmelCase__				):
				A__							 =       os.path.dirname(lowerCAmelCase__				)
				A__							 =       os.path.basename(lowerCAmelCase__				)

				A__							 =       onnx.load(os.path.join(lowerCAmelCase__       ,lowerCAmelCase__				)				)

				A__							 =       list(model.graph.initializer				)

				A__							 =       set()
				A__							 =       {}
				A__							 =       []

				A__							 =       0

				for i in range(len(lowerCAmelCase__				)				):
								if i in dup_set:
												continue

								for j in range(i + 1       ,len(lowerCAmelCase__				)				):
												if j in dup_set:
																continue
												if _is_equal_tensor_proto(inits[i]       ,inits[j]				):
																dup_set.add(lowerCAmelCase__				)
																dup_set.add(lowerCAmelCase__				)

																A__							 =       inits[j].data_type
																A__							 =       numpy.prod(inits[j].dims				)
																if dtype == 1:
																				mem_size *= 4
																elif dtype == 6:
																				mem_size *= 4
																elif dtype == 7 or dtype == 11:
																				mem_size *= 8
																else:
																				print('unexpected data type: '       ,lowerCAmelCase__				)
																total_reduced_size += mem_size

																A__							 =       inits[i].name
																A__							 =       inits[j].name

																if name_i in dup_map:
																				dup_map[name_i].append(lowerCAmelCase__				)
																else:
																				A__							 =       [name_j]
																ind_to_replace.append((j, i)				)

				print('total reduced size: '       ,total_reduced_size / 1024 / 1024 / 1024       ,'GB'				)

				A__							 =       sorted(lowerCAmelCase__				)
				_remove_dup_initializers_from_model(lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				)

				A__							 =       'optimized_' + model_file_name
				A__							 =       os.path.join(lowerCAmelCase__       ,lowerCAmelCase__				)
				onnx.save(lowerCAmelCase__       ,lowerCAmelCase__				)

				return new_model


