






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	:		Any										=   object()

# For specifying empty leaf dict `{}`
__lowerCamelCase	:		Any										=   object()







def 			A__							(      _a  :					List[str]		,  _a  :					Any	):





 '''simple docstring'''







 snake_case__  :    int							       =tuple((re.compile(x + """$"""	) for x in qs)	)
 for i in range(len(_a	) - len(_a	) + 1	):
  snake_case__  :    str							       =[x.match(_a	) for x, y in zip(_a		,  ks[i:]	)]
  if matches and all(_a	):
   return True
 return False







def 			A__							(      _a  :					Optional[Any]	):





 '''simple docstring'''







 def replace(_a  :					List[Any]		,  _a  :					Any	):
  for rule, replacement in rules:
   if _match(_a		,  _a	):
    return replacement
  return val

 return replace







def 			A__							(      ):





 '''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 			A__							(      _a  :					Tuple	):





 '''simple docstring'''







 snake_case__  :    List[Any]							       =_get_partition_rules()
 snake_case__  :    Optional[Any]							       =_replacement_rules(_a	)
 snake_case__  :    int							       ={k: _unmatched for k in flatten_dict(_a	)}
 snake_case__  :    List[Any]							       ={k: replace(_a		,  _a	) for k, v in initd.items()}
 assert _unmatched not in result.values(), "Incomplete partition spec."
 return freeze(unflatten_dict(_a	)	)













from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowerCamelCase	:		Tuple										=   logging.get_logger(__name__)







class  _lowercase   (   _A						):
      _a							:						Optional[int]										=							['pixel_values']
      def __init__(     self				,			a = True				,			a = 3_2				,			a=PILImageResampling.BILINEAR				,			a = True				,			**a				,			):
       snake_case__  :    Any							       =do_resize
       snake_case__  :    Optional[int]							       =do_rescale
       snake_case__  :    Optional[Any]							       =size_divisor
       snake_case__  :    Optional[int]							       =resample
       super().__init__(**a   )
      def 						lowercase__					(     self				,			a				,			a				,			a				,			a = None				,			**a   ):
       snake_case__       ,   snake_case__  :    List[str]							       =get_image_size(a   )
       # Rounds the height and width down to the closest multiple of size_divisor
       snake_case__  :    str							       =height // size_divisor * size_divisor
       snake_case__  :    List[str]							       =width // size_divisor * size_divisor
       snake_case__  :    int							       =resize(a				,			(new_h, new_w)				,			resample=a				,			data_format=a				,			**a   )
       return image
      def 						lowercase__					(     self				,			a				,			a				,			a = None				,			**a   ):
       return rescale(image=a				,			scale=a				,			data_format=a				,			**a   )



      def 						lowercase__					(     self				,			a				,			a = None				,			a = None				,			a=None				,			a = None				,			a = None				,			a = ChannelDimension.FIRST				,			**a				,			):
       snake_case__  :    Optional[Any]							       =do_resize if do_resize is not None else self.do_resize
       snake_case__  :    str							       =do_rescale if do_rescale is not None else self.do_rescale
       snake_case__  :    List[Any]							       =size_divisor if size_divisor is not None else self.size_divisor
       snake_case__  :    Optional[int]							       =resample if resample is not None else self.resample

       if do_resize and size_divisor is None:
        raise ValueError("""size_divisor is required for resizing"""   )

       snake_case__  :    Optional[int]							       =make_list_of_images(a   )

       if not valid_images(a   ):
        raise ValueError("""Invalid image(s)"""   )

       # All transformations expect numpy arrays.
       snake_case__  :    str							       =[to_numpy_array(a   ) for img in images]

       if do_resize:
        snake_case__  :    str							       =[self.resize(a				,			size_divisor=a				,			resample=a   ) for image in images]

       if do_rescale:
        snake_case__  :    Union[str, Any]							       =[self.rescale(a				,			scale=1 / 2_5_5   ) for image in images]

       snake_case__  :    int							       =[to_channel_dimension_format(a				,			a   ) for image in images]

       snake_case__  :    Tuple							       ={"""pixel_values""": images}
       return BatchFeature(data=a				,			tensor_type=a   )





