






'''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


_A			:    List[Any]					  =logging.get_logger(__name__)





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





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase   )   -> Optional[int]:
							lowerCamelCase__      :			str 						= x.reshape(-1       ,    3   )
							lowerCamelCase__      :			int 						= squared_euclidean_distance(UpperCamelCase       ,    UpperCamelCase   )
							return np.argmin(UpperCamelCase       ,    axis=1   )



class _lowercase			(       _lowercase	):

							'''simple docstring'''





							a          = ["""pixel_values"""]




							def __init__(							self:       List[str]    ,							UpperCamelCase__:       Optional[Union[List[List[int]], np.ndarray]] = None    ,							UpperCamelCase__:       bool = True    ,							UpperCamelCase__:       Dict[str, int] = None    ,							UpperCamelCase__:       PILImageResampling = PILImageResampling.BILINEAR    ,							UpperCamelCase__:       bool = True    ,							UpperCamelCase__:       bool = True    ,							**UpperCamelCase__:       int    ,							):
														super().__init__(**UpperCamelCase__  )
														lowerCamelCase__      :			Any 						= size if size is not None else {"""height""": 256, """width""": 256}
														lowerCamelCase__      :			List[str] 						= get_size_dict(UpperCamelCase__  )
														lowerCamelCase__      :			Tuple 						= np.array(UpperCamelCase__  ) if clusters is not None else None
														lowerCamelCase__      :			Optional[Any] 						= do_resize
														lowerCamelCase__      :			List[str] 						= size
														lowerCamelCase__      :			Tuple 						= resample
														lowerCamelCase__      :			str 						= do_normalize
														lowerCamelCase__      :			Tuple 						= do_color_quantize




							def 	lowerCamelCase_       (							self:       Union[str, Any]    ,							UpperCamelCase__:       np.ndarray    ,							UpperCamelCase__:       Dict[str, int]    ,							UpperCamelCase__:       PILImageResampling = PILImageResampling.BILINEAR    ,							UpperCamelCase__:       Optional[Union[str, ChannelDimension]] = None    ,							**UpperCamelCase__:       List[Any]    ,							):
														lowerCamelCase__      :			Tuple 						= get_size_dict(UpperCamelCase__  )
														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(
														    UpperCamelCase__    ,							size=(size["""height"""], size["""width"""])    ,							resample=UpperCamelCase__    ,							data_format=UpperCamelCase__    ,							**UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       List[Any]    ,							UpperCamelCase__:       np.ndarray    ,							UpperCamelCase__:       Optional[Union[str, ChannelDimension]] = None    ,							):
														lowerCamelCase__      :			Tuple 						= rescale(image=UpperCamelCase__    ,							scale=1 / 127.5    ,							data_format=UpperCamelCase__  )
														lowerCamelCase__      :			Optional[Any] 						= image - 1
														return image




							def 	lowerCamelCase_       (							self:       List[str]    ,							UpperCamelCase__:       ImageInput    ,							UpperCamelCase__:       bool = None    ,							UpperCamelCase__:       Dict[str, int] = None    ,							UpperCamelCase__:       PILImageResampling = None    ,							UpperCamelCase__:       bool = None    ,							UpperCamelCase__:       Optional[bool] = None    ,							UpperCamelCase__:       Optional[Union[List[List[int]], np.ndarray]] = None    ,							UpperCamelCase__:       Optional[Union[str, TensorType]] = None    ,							UpperCamelCase__:       Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST    ,							**UpperCamelCase__:       Any    ,							):
														lowerCamelCase__      :			Tuple 						= do_resize if do_resize is not None else self.do_resize
														lowerCamelCase__      :			str 						= size if size is not None else self.size
														lowerCamelCase__      :			Any 						= get_size_dict(UpperCamelCase__  )
														lowerCamelCase__      :			List[str] 						= resample if resample is not None else self.resample
														lowerCamelCase__      :			Optional[int] 						= do_normalize if do_normalize is not None else self.do_normalize
														lowerCamelCase__      :			Optional[Any] 						= do_color_quantize if do_color_quantize is not None else self.do_color_quantize
														lowerCamelCase__      :			Optional[Any] 						= clusters if clusters is not None else self.clusters
														lowerCamelCase__      :			Any 						= np.array(UpperCamelCase__  )

														lowerCamelCase__      :			str 						= make_list_of_images(UpperCamelCase__  )

														if not valid_images(UpperCamelCase__  ):
																					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.
														lowerCamelCase__      :			Optional[Any] 						= [to_numpy_array(UpperCamelCase__  ) for image in images]

														if do_resize:
																					lowerCamelCase__      :			Tuple 						= [self.resize(image=UpperCamelCase__    ,							size=UpperCamelCase__    ,							resample=UpperCamelCase__  ) for image in images]

														if do_normalize:
																					lowerCamelCase__      :			Dict 						= [self.normalize(image=UpperCamelCase__  ) for image in images]

														if do_color_quantize:
																					lowerCamelCase__      :			Any 						= [to_channel_dimension_format(UpperCamelCase__    ,							ChannelDimension.LAST  ) for image in images]
																					# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
																					lowerCamelCase__      :			Any 						= np.array(UpperCamelCase__  )
																					lowerCamelCase__      :			Optional[Any] 						= color_quantize(UpperCamelCase__    ,							UpperCamelCase__  ).reshape(images.shape[:-1]  )

																					# flatten to (batch_size, height*width)
																					lowerCamelCase__      :			Dict 						= images.shape[0]
																					lowerCamelCase__      :			Optional[int] 						= images.reshape(UpperCamelCase__    ,							-1  )

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

														lowerCamelCase__      :			int 						= {"""input_ids""": images}
														return BatchFeature(data=UpperCamelCase__    ,							tensor_type=UpperCamelCase__  )









'''simple docstring'''







from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A			:    Optional[Any]					  =logging.get_logger(__name__)

_A			:    Optional[int]					  ={
    '''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-430m-pile''': '''https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-1b5-pile''': '''https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-3b-pile''': '''https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-7b-pile''': '''https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-14b-pile''': '''https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-raven-1b5''': '''https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json''',
    '''RWKV/rwkv-raven-3b''': '''https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json''',
    '''RWKV/rwkv-raven-7b''': '''https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json''',
    '''RWKV/rwkv-raven-14b''': '''https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json''',
}

class _lowercase			(       _lowercase	):
							a          = """rwkv"""
							a          = {"""max_position_embeddings""": """context_length"""}




							def __init__(							self:       Tuple    ,							UpperCamelCase__:       Optional[Any]=50_277    ,							UpperCamelCase__:       Union[str, Any]=1_024    ,							UpperCamelCase__:       Tuple=4_096    ,							UpperCamelCase__:       List[Any]=32    ,							UpperCamelCase__:       Dict=None    ,							UpperCamelCase__:       Dict=None    ,							UpperCamelCase__:       int=1e-5    ,							UpperCamelCase__:       Any=0    ,							UpperCamelCase__:       str=0    ,							UpperCamelCase__:       Union[str, Any]=6    ,							UpperCamelCase__:       Optional[int]=False    ,							UpperCamelCase__:       Dict=True    ,							**UpperCamelCase__:       Dict    ,							):
														lowerCamelCase__      :			Dict 						= vocab_size
														lowerCamelCase__      :			Optional[Any] 						= context_length
														lowerCamelCase__      :			Optional[Any] 						= hidden_size
														lowerCamelCase__      :			Any 						= num_hidden_layers
														lowerCamelCase__      :			int 						= attention_hidden_size if attention_hidden_size is not None else hidden_size
														lowerCamelCase__      :			Union[str, Any] 						= intermediate_size if intermediate_size is not None else 4 * hidden_size
														lowerCamelCase__      :			List[str] 						= layer_norm_epsilon
														lowerCamelCase__      :			int 						= rescale_every
														lowerCamelCase__      :			Optional[int] 						= use_cache

														lowerCamelCase__      :			Dict 						= bos_token_id
														lowerCamelCase__      :			Any 						= eos_token_id

														super().__init__(
														    tie_word_embeddings=UpperCamelCase__    ,							bos_token_id=UpperCamelCase__    ,							eos_token_id=UpperCamelCase__    ,							**UpperCamelCase__  )

