

'''simple docstring'''

import os

import numpy
import onnx




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
	UpperCAmelCase	:     str						 =			a.name
	UpperCAmelCase	:     int						 =			b.name

	UpperCAmelCase	:     List[Any]						 =			''
	UpperCAmelCase	:     Dict						 =			''

	UpperCAmelCase	:     List[Any]						 =			a == b

	UpperCAmelCase	:     List[Any]						 =			name_a
	UpperCAmelCase	:     List[Any]						 =			name_b

	return res




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):


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

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




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):


	for n in graph_proto.node:
		_node_replace_input_with(_UpperCAmelCase  ,	_UpperCAmelCase  ,	_UpperCAmelCase     )




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
	UpperCAmelCase	:     Optional[int]						 =			list(model.graph.initializer     )
	UpperCAmelCase	:     Optional[Any]						 =			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

		UpperCAmelCase	:     Tuple						 =			inits[i].name
		UpperCAmelCase	:     Any						 =			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  ,	_UpperCAmelCase  ,	_UpperCAmelCase     )




def 				UpperCamelCase(					UpperCAmelCase_     ):
	UpperCAmelCase	:     Optional[int]						 =			os.path.dirname(_UpperCAmelCase     )
	UpperCAmelCase	:     Tuple						 =			os.path.basename(_UpperCAmelCase     )

	UpperCAmelCase	:     Tuple						 =			onnx.load(os.path.join(_UpperCAmelCase  ,	_UpperCAmelCase     )     )

	UpperCAmelCase	:     Tuple						 =			list(model.graph.initializer     )

	UpperCAmelCase	:     List[Any]						 =			set()
	UpperCAmelCase	:     Dict						 =			{}
	UpperCAmelCase	:     Optional[Any]						 =			[]

	UpperCAmelCase	:     int						 =			0

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

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

				UpperCAmelCase	:     Optional[Any]						 =			inits[j].data_type
				UpperCAmelCase	:     List[Any]						 =			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: '  ,	_UpperCAmelCase     )
				total_reduced_size += mem_size

				UpperCAmelCase	:     Optional[Any]						 =			inits[i].name
				UpperCAmelCase	:     List[Any]						 =			inits[j].name

				if name_i in dup_map:
					dup_map[name_i].append(_UpperCAmelCase     )
				else:
					UpperCAmelCase	:     Any						 =			[name_j]
				ind_to_replace.append((j, i)     )

	print('total reduced size: '  ,	total_reduced_size / 10_24 / 10_24 / 10_24  ,	'GB'     )

	UpperCAmelCase	:     Tuple						 =			sorted(_UpperCAmelCase     )
	_remove_dup_initializers_from_model(_UpperCAmelCase  ,	_UpperCAmelCase  ,	_UpperCAmelCase     )

	UpperCAmelCase	:     Dict						 =			'optimized_' + model_file_name
	UpperCAmelCase	:     int						 =			os.path.join(_UpperCAmelCase  ,	_UpperCAmelCase     )
	onnx.save(_UpperCAmelCase  ,	_UpperCAmelCase     )

	return new_model







'''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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


lowercase__         =      logging.get_logger(__name__)




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     Optional[Any]						 =			b.T
 UpperCAmelCase	:     Optional[int]						 =			np.sum(np.square(UpperCAmelCase_     )  ,	axis=1     )
 UpperCAmelCase	:     List[Any]						 =			np.sum(np.square(UpperCAmelCase_     )  ,	axis=0     )
 UpperCAmelCase	:     List[str]						 =			np.matmul(UpperCAmelCase_  ,	UpperCAmelCase_     )
 UpperCAmelCase	:     Union[str, Any]						 =			aa[:, None] - 2 * ab + ba[None, :]
 return d




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     int						 =			x.reshape(-1  ,	3     )
 UpperCAmelCase	:     Optional[int]						 =			squared_euclidean_distance(UpperCAmelCase_  ,	UpperCAmelCase_     )
 return np.argmin(UpperCAmelCase_  ,	axis=1     )




class 	A_			(    _snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:     List[Any]  		=  ["""pixel_values"""]


     def __init__(		self			:					List[Any]					,      lowercase_			:					Optional[Union[List[List[int]], np.ndarray]] = None					,      lowercase_			:					bool = True					,      lowercase_			:					Dict[str, int] = None					,      lowercase_			:					PILImageResampling = PILImageResampling.BILINEAR					,      lowercase_			:					bool = True					,      lowercase_			:					bool = True					,      **lowercase_			:					Optional[Any]					,      )						->     None:
      super().__init__(**lowercase_	)
      UpperCAmelCase	:     Any						 =			size if size is not None else {'height': 256, 'width': 256}
      UpperCAmelCase	:     List[Any]						 =			get_size_dict(lowercase_	)
      UpperCAmelCase	:     str						 =			np.array(lowercase_	) if clusters is not None else None
      UpperCAmelCase	:     Any						 =			do_resize
      UpperCAmelCase	:     List[Any]						 =			size
      UpperCAmelCase	:     Any						 =			resample
      UpperCAmelCase	:     Dict						 =			do_normalize
      UpperCAmelCase	:     List[Any]						 =			do_color_quantize


     def        UpperCAmelCase_	(		self			:					int					,      lowercase_			:					np.ndarray					,      lowercase_			:					Dict[str, int]					,      lowercase_			:					PILImageResampling = PILImageResampling.BILINEAR					,      lowercase_			:					Optional[Union[str, ChannelDimension]] = None					,      **lowercase_			:					Any					,      )						->     np.ndarray:
      UpperCAmelCase	:     Dict						 =			get_size_dict(lowercase_	)
      if "height" not in size or "width" not in size:
       raise ValueError(f"""Size dictionary must contain both height and width keys. Got {size.keys()}"""	)
      return resize(
          lowercase_					,      size=(size['height'], size['width'])					,      resample=lowercase_					,      data_format=lowercase_					,      **lowercase_	)


     def        UpperCAmelCase_	(		self			:					Optional[int]					,      lowercase_			:					np.ndarray					,      lowercase_			:					Optional[Union[str, ChannelDimension]] = None					,      )						->     np.ndarray:
      UpperCAmelCase	:     int						 =			rescale(image=lowercase_					,      scale=1 / 127.5					,      data_format=lowercase_	)
      UpperCAmelCase	:     Dict						 =			image - 1
      return image




     def        UpperCAmelCase_	(		self			:					str					,      lowercase_			:					ImageInput					,      lowercase_			:					bool = None					,      lowercase_			:					Dict[str, int] = None					,      lowercase_			:					PILImageResampling = None					,      lowercase_			:					bool = None					,      lowercase_			:					Optional[bool] = None					,      lowercase_			:					Optional[Union[List[List[int]], np.ndarray]] = None					,      lowercase_			:					Optional[Union[str, TensorType]] = None					,      lowercase_			:					Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST					,      **lowercase_			:					List[str]					,      )						->     PIL.Image.Image:
      UpperCAmelCase	:     Optional[int]						 =			do_resize if do_resize is not None else self.do_resize
      UpperCAmelCase	:     Optional[Any]						 =			size if size is not None else self.size
      UpperCAmelCase	:     Optional[int]						 =			get_size_dict(lowercase_	)
      UpperCAmelCase	:     Any						 =			resample if resample is not None else self.resample
      UpperCAmelCase	:     Optional[Any]						 =			do_normalize if do_normalize is not None else self.do_normalize
      UpperCAmelCase	:     str						 =			do_color_quantize if do_color_quantize is not None else self.do_color_quantize
      UpperCAmelCase	:     Optional[int]						 =			clusters if clusters is not None else self.clusters
      UpperCAmelCase	:     List[str]						 =			np.array(lowercase_	)

      UpperCAmelCase	:     int						 =			make_list_of_images(lowercase_	)

      if not valid_images(lowercase_	):
       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 or resample is None:
       raise ValueError('Size and resample must be specified if do_resize is True.'	)

      if do_color_quantize and clusters is None:
       raise ValueError('Clusters must be specified if do_color_quantize is True.'	)

      # All transformations expect numpy arrays.
      UpperCAmelCase	:     Dict						 =			[to_numpy_array(lowercase_	) for image in images]

      if do_resize:
       UpperCAmelCase	:     List[Any]						 =			[self.resize(image=lowercase_					,      size=lowercase_					,      resample=lowercase_	) for image in images]

      if do_normalize:
       UpperCAmelCase	:     Tuple						 =			[self.normalize(image=lowercase_	) for image in images]

      if do_color_quantize:
       UpperCAmelCase	:     List[str]						 =			[to_channel_dimension_format(lowercase_					,      ChannelDimension.LAST	) for image in images]
       # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
       UpperCAmelCase	:     int						 =			np.array(lowercase_	)
       UpperCAmelCase	:     str						 =			color_quantize(lowercase_					,      lowercase_	).reshape(images.shape[:-1]	)

       # flatten to (batch_size, height*width)
       UpperCAmelCase	:     Optional[int]						 =			images.shape[0]
       UpperCAmelCase	:     Union[str, Any]						 =			images.reshape(lowercase_					,      -1	)

       # We need to convert back to a list of images to keep consistent behaviour across processors.
       UpperCAmelCase	:     int						 =			list(lowercase_	)
      else:
       UpperCAmelCase	:     Dict						 =			[to_channel_dimension_format(lowercase_					,      lowercase_	) for image in images]

      UpperCAmelCase	:     Any						 =			{'input_ids': images}
      return BatchFeature(data=lowercase_					,      tensor_type=lowercase_	)




