


import os

import numpy
import onnx






def 							__lowercase     (				__lowerCAmelCase							:       List[Any]      ,					__lowerCAmelCase							:       Optional[Any]     ):
							a__    =  a.name
							a__    =  b.name

							a__    =  ''
							a__    =  ''

							a__    =  a == b

							a__    =  name_a
							a__    =  name_b

							return res






def 							__lowercase     (				__lowerCAmelCase							:       Optional[Any]      ,					__lowerCAmelCase							:       Dict      ,					__lowerCAmelCase							:       Union[str, Any]     ):
							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 							__lowercase     (				__lowerCAmelCase							:       List[Any]      ,					__lowerCAmelCase							:       Any      ,					__lowerCAmelCase							:       List[Any]     ):
							for n in graph_proto.node:
														_node_replace_input_with(__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase     )






def 							__lowercase     (				__lowerCAmelCase							:       Any      ,					__lowerCAmelCase							:       Any      ,					__lowerCAmelCase							:       Dict     ):
							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 							__lowercase     (				__lowerCAmelCase							:       Optional[Any]     ):
							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 == 1_1:
																																			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 / 1_0_2_4 / 1_0_2_4 / 1_0_2_4      ,					'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








from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


snake_case							: Dict      =   logging.get_logger(__name__)







class  snake_case_     (lowerCamelCase_   ):
					UpperCAmelCase__      :					Dict					   =    ['''pixel_values''']
					def __init__(   self     :Optional[Any]    ,__snake_case     :bool = True    ,__snake_case     :int = 32    ,__snake_case     :Union[str, Any]=PILImageResampling.BILINEAR    ,__snake_case     :bool = True    ,**__snake_case     :Tuple    ,)      ->					None:
												a__    =  do_resize
												a__    =  do_rescale
												a__    =  size_divisor
												a__    =  resample
												super().__init__(**__snake_case   )
					def 				lowerCamelCase__(   self     :Union[str, Any]    ,__snake_case     :np.ndarray    ,__snake_case     :int    ,__snake_case     :Tuple    ,__snake_case     :Optional[ChannelDimension] = None    ,**__snake_case     :List[Any]   )      ->					np.ndarray:
												a__   ,      a__    =  get_image_size(__snake_case   )
												# Rounds the height and width down to the closest multiple of size_divisor
												a__    =  height // size_divisor * size_divisor
												a__    =  width // size_divisor * size_divisor
												a__    =  resize(__snake_case    ,(new_h, new_w)    ,resample=__snake_case    ,data_format=__snake_case    ,**__snake_case   )
												return image
					def 				lowerCamelCase__(   self     :List[str]    ,__snake_case     :np.ndarray    ,__snake_case     :float    ,__snake_case     :Optional[ChannelDimension] = None    ,**__snake_case     :str   )      ->					np.ndarray:
												return rescale(image=__snake_case    ,scale=__snake_case    ,data_format=__snake_case    ,**__snake_case   )



					def 				lowerCamelCase__(   self     :Tuple    ,__snake_case     :Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]    ,__snake_case     :Optional[bool] = None    ,__snake_case     :Optional[int] = None    ,__snake_case     :Union[str, Any]=None    ,__snake_case     :Optional[bool] = None    ,__snake_case     :Optional[Union[TensorType, str]] = None    ,__snake_case     :ChannelDimension = ChannelDimension.FIRST    ,**__snake_case     :List[Any]    ,)      ->					BatchFeature:
												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__    =  size_divisor if size_divisor is not None else self.size_divisor
												a__    =  resample if resample is not None else self.resample

												if do_resize and size_divisor is None:
																			raise ValueError('size_divisor is required for resizing'   )

												a__    =  make_list_of_images(__snake_case   )

												if not valid_images(__snake_case   ):
																			raise ValueError('Invalid image(s)'   )

												# All transformations expect numpy arrays.
												a__    =  [to_numpy_array(__snake_case   ) for img in images]

												if do_resize:
																			a__    =  [self.resize(__snake_case    ,size_divisor=__snake_case    ,resample=__snake_case   ) for image in images]

												if do_rescale:
																			a__    =  [self.rescale(__snake_case    ,scale=1 / 2_55   ) for image in images]

												a__    =  [to_channel_dimension_format(__snake_case    ,__snake_case   ) for image in images]

												a__    =  {'pixel_values': images}
												return BatchFeature(data=__snake_case    ,tensor_type=__snake_case   )




