





"""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


lowerCamelCase_     :						Optional[int]						=					logging.get_logger(__name__)





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





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





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  A_  :       Dict										=  x.reshape(-1 ,					3					)
  A_  :       Any										=  squared_euclidean_distance(_UpperCAmelCase ,					_UpperCAmelCase					)
  return np.argmin(_UpperCAmelCase ,					axis=1					)





class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''
      lowercase_      : int	    =   ["""pixel_values"""]

      def __init__(     self					,      snake_case_ = None					,      snake_case_ = True					,      snake_case_ = None					,      snake_case_ = PILImageResampling.BILINEAR					,      snake_case_ = True					,      snake_case_ = True					,      **snake_case_					,      ):

        """simple docstring"""

        super().__init__(**snake_case_  )
        A_  :       Dict										=  size if size is not None else {'height': 2_5_6, 'width': 2_5_6}
        A_  :       Tuple										=  get_size_dict(snake_case_  )
        A_  :       Dict										=  np.array(snake_case_  ) if clusters is not None else None
        A_  :       Any										=  do_resize
        A_  :       Tuple										=  size
        A_  :       Optional[int]										=  resample
        A_  :       Optional[Any]										=  do_normalize
        A_  :       Tuple										=  do_color_quantize

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_					,      snake_case_ = PILImageResampling.BILINEAR					,      snake_case_ = None					,      **snake_case_					,      ):

        """simple docstring"""

        A_  :       Any										=  get_size_dict(snake_case_  )
        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(
            snake_case_					,      size=(size['height'], size['width'])					,      resample=snake_case_					,      data_format=snake_case_					,      **snake_case_  )

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_ = None					,      ):

        """simple docstring"""

        A_  :       Any										=  rescale(image=snake_case_					,      scale=1 / 1_27.5					,      data_format=snake_case_  )
        A_  :       str										=  image - 1
        return image

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_ = None					,      snake_case_ = None					,      snake_case_ = None					,      snake_case_ = None					,      snake_case_ = None					,      snake_case_ = None					,      snake_case_ = None					,      snake_case_ = ChannelDimension.FIRST					,      **snake_case_					,      ):

        """simple docstring"""

        A_  :       Union[str, Any]										=  do_resize if do_resize is not None else self.do_resize
        A_  :       List[str]										=  size if size is not None else self.size
        A_  :       Tuple										=  get_size_dict(snake_case_  )
        A_  :       Optional[Any]										=  resample if resample is not None else self.resample
        A_  :       Optional[Any]										=  do_normalize if do_normalize is not None else self.do_normalize
        A_  :       List[str]										=  do_color_quantize if do_color_quantize is not None else self.do_color_quantize
        A_  :       Union[str, Any]										=  clusters if clusters is not None else self.clusters
        A_  :       Tuple										=  np.array(snake_case_  )

        A_  :       Optional[int]										=  make_list_of_images(snake_case_  )

        if not valid_images(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 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.
        A_  :       List[Any]										=  [to_numpy_array(snake_case_  ) for image in images]

        if do_resize:
          A_  :       Dict										=  [self.resize(image=snake_case_					,      size=snake_case_					,      resample=snake_case_  ) for image in images]

        if do_normalize:
          A_  :       int										=  [self.normalize(image=snake_case_  ) for image in images]

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

          # flatten to (batch_size, height*width)
          A_  :       Dict										=  images.shape[0]
          A_  :       str										=  images.reshape(snake_case_					,      -1  )

          # We need to convert back to a list of images to keep consistent behaviour across processors.
          A_  :       Optional[int]										=  list(snake_case_  )
        else:
          A_  :       Union[str, Any]										=  [to_channel_dimension_format(snake_case_					,      snake_case_  ) for image in images]

        A_  :       List[str]										=  {'input_ids': images}
        return BatchFeature(data=snake_case_					,      tensor_type=snake_case_  )






"""simple docstring"""




import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


lowerCamelCase_     :						Tuple						=					logging.get_logger(__name__)





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  A_  :       Optional[Any]										=  R'\w+[.]\d+'
  A_  :       int										=  re.findall(_UpperCAmelCase ,					_UpperCAmelCase					)
  for pat in pats:
    A_  :       Optional[int]										=  key.replace(_UpperCAmelCase ,					'_'.join(pat.split('.'					)					)					)
  return key





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  A_  :       List[Any]										=  pt_tuple_key[:-1] + ('scale',)
  if (
      any('norm' in str_ for str_ in pt_tuple_key					)
      and (pt_tuple_key[-1] == "bias")
      and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
      and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
  ):
    A_  :       Union[str, Any]										=  pt_tuple_key[:-1] + ('scale',)
    return renamed_pt_tuple_key, pt_tensor
  elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
    A_  :       List[str]										=  pt_tuple_key[:-1] + ('scale',)
    return renamed_pt_tuple_key, pt_tensor

  # embedding
  if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
    A_  :       Optional[Any]										=  pt_tuple_key[:-1] + ('embedding',)
    return renamed_pt_tuple_key, pt_tensor

  # conv layer
  A_  :       int										=  pt_tuple_key[:-1] + ('kernel',)
  if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
    A_  :       str										=  pt_tensor.transpose(2 ,					3 ,					1 ,					0					)
    return renamed_pt_tuple_key, pt_tensor

  # linear layer
  A_  :       Optional[Any]										=  pt_tuple_key[:-1] + ('kernel',)
  if pt_tuple_key[-1] == "weight":
    A_  :       Optional[Any]										=  pt_tensor.T
    return renamed_pt_tuple_key, pt_tensor

  # old PyTorch layer norm weight
  A_  :       Tuple										=  pt_tuple_key[:-1] + ('weight',)
  if pt_tuple_key[-1] == "gamma":
    return renamed_pt_tuple_key, pt_tensor

  # old PyTorch layer norm bias
  A_  :       Optional[int]										=  pt_tuple_key[:-1] + ('bias',)
  if pt_tuple_key[-1] == "beta":
    return renamed_pt_tuple_key, pt_tensor

  return pt_tuple_key, pt_tensor





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase=42					):






  """simple docstring"""





  A_  :       int										=  {k: v.numpy() for k, v in pt_state_dict.items()}

  # Step 2: Since the model is stateless, get random Flax params
  A_  :       Union[str, Any]										=  flax_model.init_weights(PRNGKey(_UpperCAmelCase					)					)

  A_  :       Optional[Any]										=  flatten_dict(_UpperCAmelCase					)
  A_  :       Tuple										=  {}

  # Need to change some parameters name to match Flax names
  for pt_key, pt_tensor in pt_state_dict.items():
    A_  :       Any										=  rename_key(_UpperCAmelCase					)
    A_  :       List[str]										=  tuple(renamed_pt_key.split('.'					)					)

    # Correctly rename weight parameters
    A_						,       A_  :       Union[str, Any]										=  rename_key_and_reshape_tensor(_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					)

    if flax_key in random_flax_state_dict:
      if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
        raise ValueError(
            f"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
            f"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""					)

        # also add unexpected weight so that warning is thrown
    A_  :       str										=  jnp.asarray(_UpperCAmelCase					)

  return unflatten_dict(_UpperCAmelCase					)