
import os

import numpy
import onnx






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

       lowerCAmelCase__					     = ''
       lowerCAmelCase__					     = ''

       lowerCAmelCase__					     = a == b

       lowerCAmelCase__					     = name_a
       lowerCAmelCase__					     = 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__     ):
       lowerCAmelCase__					     = list(model.graph.initializer     )
       lowerCAmelCase__					     = 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

              lowerCAmelCase__					     = inits[i].name
              lowerCAmelCase__					     = 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__     ):
       lowerCAmelCase__					     = os.path.dirname(lowerCAmelCase__     )
       lowerCAmelCase__					     = os.path.basename(lowerCAmelCase__     )

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

       lowerCAmelCase__					     = list(model.graph.initializer     )

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

       lowerCAmelCase__					     = 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__     )

                            lowerCAmelCase__					     = inits[j].data_type
                            lowerCAmelCase__					     = 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

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

                            if name_i in dup_map:
                                   dup_map[name_i].append(lowerCAmelCase__     )
                            else:
                                   lowerCAmelCase__					     = [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'     )

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

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

       return new_model

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 convert_to_rgb, 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


if is_vision_available():
    import PIL


lowerCAmelCase__     						=			logging.get_logger(__name__)
class     a_			(						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_     			= ['pixel_values']




 def __init__(      self :  Tuple      ,   lowercase__ :  bool = True      ,   lowercase__ :  Dict[str, int] = None      ,   lowercase__ :  PILImageResampling = PILImageResampling.BICUBIC      ,   lowercase__ :  bool = True      ,   lowercase__ :  Union[int, float] = 1 / 255      ,   lowercase__ :  bool = True      ,   lowercase__ :  Optional[Union[float, List[float]]] = None      ,   lowercase__ :  Optional[Union[float, List[float]]] = None      ,   lowercase__ :  bool = True      ,   **lowercase__ :  List[Any]      ,   ):







        '''simple docstring'''



        super().__init__(**lowercase__)
        lowerCAmelCase__					     = size if size is not None else {'height': 384, 'width': 384}
        lowerCAmelCase__					     = get_size_dict(lowercase__      ,   default_to_square=lowercase__)

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




 def 		__snake_case						(      self :  List[str]      ,   lowercase__ :  np.ndarray      ,   lowercase__ :  Dict[str, int]      ,   lowercase__ :  PILImageResampling = PILImageResampling.BICUBIC      ,   lowercase__ :  Optional[Union[str, ChannelDimension]] = None      ,   **lowercase__ :  Dict      ,   ):







        '''simple docstring'''



        lowerCAmelCase__					     = get_size_dict(lowercase__      ,   default_to_square=lowercase__)
        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()}""")
        lowerCAmelCase__					     = (size['height'], size['width'])
        return resize(lowercase__      ,   size=lowercase__      ,   resample=lowercase__      ,   data_format=lowercase__      ,   **lowercase__)




 def 		__snake_case						(      self :  List[str]      ,   lowercase__ :  np.ndarray      ,   lowercase__ :  Union[int, float]      ,   lowercase__ :  Optional[Union[str, ChannelDimension]] = None      ,   **lowercase__ :  Union[str, Any]      ,   ):







        '''simple docstring'''



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




 def 		__snake_case						(      self :  Optional[Any]      ,   lowercase__ :  np.ndarray      ,   lowercase__ :  Union[float, List[float]]      ,   lowercase__ :  Union[float, List[float]]      ,   lowercase__ :  Optional[Union[str, ChannelDimension]] = None      ,   **lowercase__ :  Any      ,   ):







        '''simple docstring'''



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




 def 		__snake_case						(      self :  Any      ,   lowercase__ :  ImageInput      ,   lowercase__ :  Optional[bool] = None      ,   lowercase__ :  Optional[Dict[str, int]] = None      ,   lowercase__ :  PILImageResampling = None      ,   lowercase__ :  Optional[bool] = None      ,   lowercase__ :  Optional[float] = None      ,   lowercase__ :  Optional[bool] = None      ,   lowercase__ :  Optional[Union[float, List[float]]] = None      ,   lowercase__ :  Optional[Union[float, List[float]]] = None      ,   lowercase__ :  Optional[Union[str, TensorType]] = None      ,   lowercase__ :  bool = None      ,   lowercase__ :  ChannelDimension = ChannelDimension.FIRST      ,   **lowercase__ :  Dict      ,   ):







        '''simple docstring'''



        lowerCAmelCase__					     = do_resize if do_resize is not None else self.do_resize
        lowerCAmelCase__					     = resample if resample is not None else self.resample
        lowerCAmelCase__					     = do_rescale if do_rescale is not None else self.do_rescale
        lowerCAmelCase__					     = rescale_factor if rescale_factor is not None else self.rescale_factor
        lowerCAmelCase__					     = do_normalize if do_normalize is not None else self.do_normalize
        lowerCAmelCase__					     = image_mean if image_mean is not None else self.image_mean
        lowerCAmelCase__					     = image_std if image_std is not None else self.image_std
        lowerCAmelCase__					     = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        lowerCAmelCase__					     = size if size is not None else self.size
        lowerCAmelCase__					     = get_size_dict(lowercase__      ,   default_to_square=lowercase__)

        lowerCAmelCase__					     = 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_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:
               lowerCAmelCase__					     = [convert_to_rgb(lowercase__) for image in images]

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

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

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

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

        lowerCAmelCase__					     = [to_channel_dimension_format(lowercase__      ,   lowercase__) for image in images]

        lowerCAmelCase__					     = BatchFeature(data={'pixel_values': images}      ,   tensor_type=lowercase__)

        return encoded_outputs
