

'''simple docstring'''




from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
      import PIL

_lowerCAmelCase         =						logging.get_logger(__name__)




def 						UpperCamelCase	(			a			)				->     List[List[ImageInput]]:






     '''simple docstring'''
     if isinstance(a		,						(list, tuple)			) and isinstance(videos[0]		,						(list, tuple)			) and is_valid_image(videos[0][0]			):
          return videos

     elif isinstance(a		,						(list, tuple)			) and is_valid_image(videos[0]			):
          return [videos]

     elif is_valid_image(a			):
          return [[videos]]

     raise ValueError(F'''Could not make batched video from {videos}'''			)






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



 def __init__(   self		:   Any			,				a__		:   bool = True			,				a__		:   Dict[str, int] = None			,				a__		:   PILImageResampling = PILImageResampling.BILINEAR			,				a__		:   bool = True			,				a__		:   Dict[str, int] = None			,				a__		:   bool = True			,				a__		:   Union[int, float] = 1 / 255			,				a__		:   bool = True			,				a__		:   bool = True			,				a__		:   Optional[Union[float, List[float]]] = None			,				a__		:   Optional[Union[float, List[float]]] = None			,				**a__		:   Any			,				):
      super().__init__(**a__      )
      __magic_name__						       =    size if size is not None else {'''shortest_edge''': 256}
      __magic_name__						       =    get_size_dict(a__			,				default_to_square=a__      )
      __magic_name__						       =    crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
      __magic_name__						       =    get_size_dict(a__			,				param_name='''crop_size'''      )

      __magic_name__						       =    do_resize
      __magic_name__						       =    size
      __magic_name__						       =    do_center_crop
      __magic_name__						       =    crop_size
      __magic_name__						       =    resample
      __magic_name__						       =    do_rescale
      __magic_name__						       =    rescale_factor
      __magic_name__						       =    offset
      __magic_name__						       =    do_normalize
      __magic_name__						       =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      __magic_name__						       =    image_std if image_std is not None else IMAGENET_STANDARD_STD



 def 			snake_case__  (   self		:   Any			,				a__		:   np.ndarray			,				a__		:   Dict[str, int]			,				a__		:   PILImageResampling = PILImageResampling.BILINEAR			,				a__		:   Optional[Union[str, ChannelDimension]] = None			,				**a__		:   int			,				):
      __magic_name__						       =    get_size_dict(a__			,				default_to_square=a__      )
      if "shortest_edge" in size:
           __magic_name__						       =    get_resize_output_image_size(a__			,				size['''shortest_edge''']			,				default_to_square=a__      )
      elif "height" in size and "width" in size:
           __magic_name__						       =    (size['''height'''], size['''width'''])
      else:
           raise ValueError(F'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''      )
      return resize(a__			,				size=a__			,				resample=a__			,				data_format=a__			,				**a__      )



 def 			snake_case__  (   self		:   Tuple			,				a__		:   np.ndarray			,				a__		:   Dict[str, int]			,				a__		:   Optional[Union[str, ChannelDimension]] = None			,				**a__		:   Dict			,				):
      __magic_name__						       =    get_size_dict(a__      )
      if "height" not in size or "width" not in size:
           raise ValueError(F'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}'''      )
      return center_crop(a__			,				size=(size['''height'''], size['''width'''])			,				data_format=a__			,				**a__      )



 def 			snake_case__  (   self		:   str			,				a__		:   np.ndarray			,				a__		:   Union[int, float]			,				a__		:   bool = True			,				a__		:   Optional[Union[str, ChannelDimension]] = None			,				**a__		:   Optional[Any]			,				):
      __magic_name__						       =    image.astype(np.floataa      )
      if offset:
           __magic_name__						       =    image - (scale / 2)
      return rescale(a__			,				scale=a__			,				data_format=a__			,				**a__      )



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



 def 			snake_case__  (   self		:   List[Any]			,				a__		:   ImageInput			,				a__		:   bool = None			,				a__		:   Dict[str, int] = None			,				a__		:   PILImageResampling = None			,				a__		:   bool = None			,				a__		:   Dict[str, int] = None			,				a__		:   bool = None			,				a__		:   float = None			,				a__		:   bool = None			,				a__		:   bool = None			,				a__		:   Optional[Union[float, List[float]]] = None			,				a__		:   Optional[Union[float, List[float]]] = None			,				a__		:   Optional[ChannelDimension] = ChannelDimension.FIRST			,				):
      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_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.'''      )

      if offset and not do_rescale:
           raise ValueError('''For offset, do_rescale must also be set to True.'''      )

      # All transformations expect numpy arrays.
      __magic_name__						       =    to_numpy_array(a__      )

      if do_resize:
           __magic_name__						       =    self.resize(image=a__			,				size=a__			,				resample=a__      )

      if do_center_crop:
           __magic_name__						       =    self.center_crop(a__			,				size=a__      )

      if do_rescale:
           __magic_name__						       =    self.rescale(image=a__			,				scale=a__			,				offset=a__      )

      if do_normalize:
           __magic_name__						       =    self.normalize(image=a__			,				mean=a__			,				std=a__      )

      __magic_name__						       =    to_channel_dimension_format(a__			,				a__      )
      return image



 def 			snake_case__  (   self		:   Optional[Any]			,				a__		:   ImageInput			,				a__		:   bool = None			,				a__		:   Dict[str, int] = None			,				a__		:   PILImageResampling = None			,				a__		:   bool = None			,				a__		:   Dict[str, int] = None			,				a__		:   bool = None			,				a__		:   float = None			,				a__		:   bool = None			,				a__		:   bool = None			,				a__		:   Optional[Union[float, List[float]]] = None			,				a__		:   Optional[Union[float, List[float]]] = None			,				a__		:   Optional[Union[str, TensorType]] = None			,				a__		:   ChannelDimension = ChannelDimension.FIRST			,				**a__		:   str			,				):
      __magic_name__						       =    do_resize if do_resize is not None else self.do_resize
      __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__						       =    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__						       =    offset if offset is not None else self.offset
      __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__						       =    size if size is not None else self.size
      __magic_name__						       =    get_size_dict(a__			,				default_to_square=a__      )
      __magic_name__						       =    crop_size if crop_size is not None else self.crop_size
      __magic_name__						       =    get_size_dict(a__			,				param_name='''crop_size'''      )

      if not valid_images(a__      ):
           raise ValueError(
               '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
               '''torch.Tensor, tf.Tensor or jax.ndarray.'''      )

      __magic_name__						       =    make_batched(a__      )

      __magic_name__						       =    [
          [
              self._preprocess_image(
                  image=a__			,				do_resize=a__			,				size=a__			,				resample=a__			,				do_center_crop=a__			,				crop_size=a__			,				do_rescale=a__			,				rescale_factor=a__			,				offset=a__			,				do_normalize=a__			,				image_mean=a__			,				image_std=a__			,				data_format=a__			,				)
              for img in video
          ]
          for video in videos
      ]

      __magic_name__						       =    {'''pixel_values''': videos}
      return BatchFeature(data=a__			,				tensor_type=a__      )









'''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			)			)






