

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


_lowerCAmelCase         =						logging.get_logger(__name__)


if is_vision_available():
      import PIL






class   _SCREAMING_SNAKE_CASE   (   __a     ):
 __SCREAMING_SNAKE_CASE					:str      	=					["""pixel_values"""]



 def __init__(   self		:   Any			,				a__		:   List[Any] = True			,				a__		:   Optional[int] = None			,				a__		:   List[str] = PILImageResampling.BICUBIC			,				a__		:   Union[str, Any] = True			,				a__		:   Tuple = None			,				a__		:   Dict = True			,				a__		:   str = 1 / 255			,				a__		:   Any = True			,				a__		:   Any = None			,				a__		:   Any = None			,				a__		:   Optional[int] = True			,				**a__		:   str			,				):
      super().__init__(**lowerCamelCase_      )
      __magic_name__						       =    size if size is not None else {'''shortest_edge''': 224}
      __magic_name__						       =    get_size_dict(lowerCamelCase_			,				default_to_square=lowerCamelCase_      )
      __magic_name__						       =    crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
      __magic_name__						       =    get_size_dict(lowerCamelCase_			,				default_to_square=lowerCamelCase_			,				param_name='''crop_size'''      )

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



 def 			snake_case__  (   self		:   int			,				a__		:   str			,				a__		:   Tuple			,				a__		:   Dict = PILImageResampling.BICUBIC			,				a__		:   str = None			,				**a__		:   Optional[int]			,				):
      __magic_name__						       =    get_size_dict(lowerCamelCase_			,				default_to_square=lowerCamelCase_      )
      if "shortest_edge" not in size:
           raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''      )
      __magic_name__						       =    get_resize_output_image_size(lowerCamelCase_			,				size=size['''shortest_edge''']			,				default_to_square=lowerCamelCase_      )
      return resize(lowerCamelCase_			,				size=lowerCamelCase_			,				resample=lowerCamelCase_			,				data_format=lowerCamelCase_			,				**lowerCamelCase_      )



 def 			snake_case__  (   self		:   Dict			,				a__		:   str			,				a__		:   Union[str, Any]			,				a__		:   Dict = None			,				**a__		:   Tuple			,				):
      __magic_name__						       =    get_size_dict(lowerCamelCase_      )
      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(lowerCamelCase_			,				size=(size['''height'''], size['''width'''])			,				data_format=lowerCamelCase_			,				**lowerCamelCase_      )



 def 			snake_case__  (   self		:   str			,				a__		:   List[str]			,				a__		:   Dict			,				a__		:   List[str] = None			,				**a__		:   List[str]			,				):
      return rescale(lowerCamelCase_			,				scale=lowerCamelCase_			,				data_format=lowerCamelCase_			,				**lowerCamelCase_      )



 def 			snake_case__  (   self		:   str			,				a__		:   int			,				a__		:   List[Any]			,				a__		:   Union[str, Any]			,				a__		:   Optional[Any] = None			,				**a__		:   Union[str, Any]			,				):
      return normalize(lowerCamelCase_			,				mean=lowerCamelCase_			,				std=lowerCamelCase_			,				data_format=lowerCamelCase_			,				**lowerCamelCase_      )



 def 			snake_case__  (   self		:   Any			,				a__		:   int			,				a__		:   Dict = None			,				a__		:   List[Any] = None			,				a__		:   List[str] = None			,				a__		:   Union[str, Any] = None			,				a__		:   Any = None			,				a__		:   Tuple = None			,				a__		:   int = None			,				a__		:   int = None			,				a__		:   Optional[Any] = None			,				a__		:   Tuple = None			,				a__		:   Tuple = None			,				a__		:   Dict = None			,				a__		:   Tuple = ChannelDimension.FIRST			,				**a__		:   int			,				):
      __magic_name__						       =    do_resize if do_resize is not None else self.do_resize
      __magic_name__						       =    size if size is not None else self.size
      __magic_name__						       =    get_size_dict(lowerCamelCase_			,				param_name='''size'''			,				default_to_square=lowerCamelCase_      )
      __magic_name__						       =    resample if resample is not None else self.resample
      __magic_name__						       =    do_center_crop if do_center_crop is not None else self.do_center_crop
      __magic_name__						       =    crop_size if crop_size is not None else self.crop_size
      __magic_name__						       =    get_size_dict(lowerCamelCase_			,				param_name='''crop_size'''			,				default_to_square=lowerCamelCase_      )
      __magic_name__						       =    do_rescale if do_rescale is not None else self.do_rescale
      __magic_name__						       =    rescale_factor if rescale_factor is not None else self.rescale_factor
      __magic_name__						       =    do_normalize if do_normalize is not None else self.do_normalize
      __magic_name__						       =    image_mean if image_mean is not None else self.image_mean
      __magic_name__						       =    image_std if image_std is not None else self.image_std
      __magic_name__						       =    do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

      __magic_name__						       =    make_list_of_images(lowerCamelCase_      )

      if not valid_images(lowerCamelCase_      ):
           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:
           __magic_name__						       =    [convert_to_rgb(lowerCamelCase_      ) for image in images]

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

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

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

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

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

      __magic_name__						       =    [to_channel_dimension_format(lowerCamelCase_			,				lowerCamelCase_      ) for image in images]

      __magic_name__						       =    {'''pixel_values''': images}
      return BatchFeature(data=lowerCamelCase_			,				tensor_type=lowerCamelCase_      )









'''simple docstring'''




import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
_lowerCAmelCase         =						object()

# For specifying empty leaf dict `{}`
_lowerCAmelCase         =						object()




def 						UpperCamelCase	(			a		,						a			)				->     Union[str, Any]:






     '''simple docstring'''
     __magic_name__						       =    tuple((re.compile(x + '''$'''			) for x in qs)			)
     for i in range(len(a			) - len(a			) + 1			):
          __magic_name__						       =    [x.match(a			) for x, y in zip(a		,						ks[i:]			)]
          if matches and all(a			):
               return True
     return False




def 						UpperCamelCase	(			a			)				->     Tuple:






     '''simple docstring'''
     def replace(a		,						a			):
          for rule, replacement in rules:
               if _match(a		,						a			):
                    return replacement
          return val

     return replace




def 						UpperCamelCase	(			)				->     Union[str, Any]:






     '''simple docstring'''
     return [
         # embeddings
         (("transformer", "wpe", "embedding"), P('''mp'''		,						a			)),
         (("transformer", "wte", "embedding"), P('''mp'''		,						a			)),
         # atention
         (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(a		,						'''mp'''			)),
         (("attention", "out_proj", "kernel"), P('''mp'''		,						a			)),
         (("attention", "out_proj", "bias"), None),
         # mlp
         (("mlp", "c_fc", "kernel"), P(a		,						'''mp'''			)),
         (("mlp", "c_fc", "bias"), P('''mp'''			)),
         (("mlp", "c_proj", "kernel"), P('''mp'''		,						a			)),
         (("mlp", "c_proj", "bias"), None),
         # layer norms
         ((r"ln_\d+", "bias"), None),
         ((r"\d+", r"ln_\d+", "scale"), None),
         (("ln_f", "bias"), None),
         (("ln_f", "scale"), None),
     ]




def 						UpperCamelCase	(			a			)				->     str:






     '''simple docstring'''
     __magic_name__						       =    _get_partition_rules()
     __magic_name__						       =    _replacement_rules(a			)
     __magic_name__						       =    {k: _unmatched for k in flatten_dict(a			)}
     __magic_name__						       =    {k: replace(a		,						a			) for k, v in initd.items()}
     assert _unmatched not in result.values(), "Incomplete partition spec."
     return freeze(unflatten_dict(a			)			)






