




'''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


_SCREAMING_SNAKE_CASE			=						logging.get_logger(__name__)





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )		->		Any:
			A_									= b.T
			A_									= np.sum(np.square(SCREAMING_SNAKE_CASE_     )       ,axis=1     )
			A_									= np.sum(np.square(SCREAMING_SNAKE_CASE_     )       ,axis=0     )
			A_									= np.matmul(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )
			A_									= aa[:, None] - 2 * ab + ba[None, :]
			return d





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )		->		Any:
			A_									= x.reshape(-1       ,3     )
			A_									= squared_euclidean_distance(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )
			return np.argmin(SCREAMING_SNAKE_CASE_       ,axis=1     )






class   __UpperCAmelCase				(     lowerCAmelCase				):



					'''simple docstring'''





					_UpperCamelCase							=		["""pixel_values"""]



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



					def     __snake_case						(	self     :						Tuple      ,       _lowercase     :						np.ndarray      ,       _lowercase     :						Dict[str, int]      ,       _lowercase     :						PILImageResampling = PILImageResampling.BILINEAR      ,       _lowercase     :						Optional[Union[str, ChannelDimension]] = None      ,       **_lowercase     :						str      ,       )    ->							np.ndarray:
								A_									= get_size_dict(_lowercase)
								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(
								    _lowercase      ,       size=(size['height'], size['width'])      ,       resample=_lowercase      ,       data_format=_lowercase      ,       **_lowercase)



					def     __snake_case						(	self     :						List[Any]      ,       _lowercase     :						np.ndarray      ,       _lowercase     :						Optional[Union[str, ChannelDimension]] = None      ,       )    ->							np.ndarray:
								A_									= rescale(image=_lowercase      ,       scale=1 / 1_27.5      ,       data_format=_lowercase)
								A_									= image - 1
								return image



					def     __snake_case						(	self     :						Any      ,       _lowercase     :						ImageInput      ,       _lowercase     :						bool = None      ,       _lowercase     :						Dict[str, int] = None      ,       _lowercase     :						PILImageResampling = None      ,       _lowercase     :						bool = None      ,       _lowercase     :						Optional[bool] = None      ,       _lowercase     :						Optional[Union[List[List[int]], np.ndarray]] = None      ,       _lowercase     :						Optional[Union[str, TensorType]] = None      ,       _lowercase     :						Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST      ,       **_lowercase     :						Dict      ,       )    ->							PIL.Image.Image:
								A_									= do_resize if do_resize is not None else self.do_resize
								A_									= size if size is not None else self.size
								A_									= get_size_dict(_lowercase)
								A_									= resample if resample is not None else self.resample
								A_									= do_normalize if do_normalize is not None else self.do_normalize
								A_									= do_color_quantize if do_color_quantize is not None else self.do_color_quantize
								A_									= clusters if clusters is not None else self.clusters
								A_									= np.array(_lowercase)

								A_									= make_list_of_images(_lowercase)

								if not valid_images(_lowercase):
											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_									= [to_numpy_array(_lowercase) for image in images]

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

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

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

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

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

								A_									= {'input_ids': images}
								return BatchFeature(data=_lowercase      ,       tensor_type=_lowercase)










'''simple docstring'''
import warnings

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


_SCREAMING_SNAKE_CASE			=						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE			=						{
    "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json",
}
class   __UpperCAmelCase				(     lowerCAmelCase				):



					'''simple docstring'''





					_UpperCamelCase							=		"""mvp"""
					_UpperCamelCase							=		["""past_key_values"""]
					_UpperCamelCase							=		{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



					def __init__(	self     :						List[str]      ,       _lowercase     :						int=50_267      ,       _lowercase     :						str=1_024      ,       _lowercase     :						int=12      ,       _lowercase     :						int=4_096      ,       _lowercase     :						Tuple=16      ,       _lowercase     :						Optional[Any]=12      ,       _lowercase     :						Union[str, Any]=4_096      ,       _lowercase     :						List[Any]=16      ,       _lowercase     :						str=0.0      ,       _lowercase     :						List[str]=0.0      ,       _lowercase     :						Dict="gelu"      ,       _lowercase     :						Union[str, Any]=1_024      ,       _lowercase     :						str=0.1      ,       _lowercase     :						List[Any]=0.0      ,       _lowercase     :						Any=0.0      ,       _lowercase     :						List[str]=0.02      ,       _lowercase     :						List[Any]=0.0      ,       _lowercase     :						List[str]=False      ,       _lowercase     :						Tuple=True      ,       _lowercase     :						Any=1      ,       _lowercase     :						Optional[Any]=0      ,       _lowercase     :						Dict=2      ,       _lowercase     :						int=True      ,       _lowercase     :						Any=2      ,       _lowercase     :						Tuple=2      ,       _lowercase     :						Optional[Any]=False      ,       _lowercase     :						List[str]=100      ,       _lowercase     :						List[str]=800      ,       **_lowercase     :						Dict      ,       )    ->							Dict:
								A_									= vocab_size
								A_									= max_position_embeddings
								A_									= d_model
								A_									= encoder_ffn_dim
								A_									= encoder_layers
								A_									= encoder_attention_heads
								A_									= decoder_ffn_dim
								A_									= decoder_layers
								A_									= decoder_attention_heads
								A_									= dropout
								A_									= attention_dropout
								A_									= activation_dropout
								A_									= activation_function
								A_									= init_std
								A_									= encoder_layerdrop
								A_									= decoder_layerdrop
								A_									= classifier_dropout
								A_									= use_cache
								A_									= encoder_layers
								A_									= scale_embedding  # scale factor will be sqrt(d_model) if True
								A_									= use_prompt
								A_									= prompt_length
								A_									= prompt_mid_dim

								super().__init__(
								    pad_token_id=_lowercase      ,       bos_token_id=_lowercase      ,       eos_token_id=_lowercase      ,       is_encoder_decoder=_lowercase      ,       decoder_start_token_id=_lowercase      ,       forced_eos_token_id=_lowercase      ,       **_lowercase      ,       )

								if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated'      ,       _lowercase):
											A_									= self.bos_token_id
											warnings.warn(
											    F'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
											    'The config can simply be saved and uploaded again to be fixed.')




