





"""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


__magic_name__       :					int						=			logging.get_logger(__name__)

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

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		List[str]				   =			x.reshape(-1							,     3     )
						UpperCamelCase				:		List[str]				   =			squared_euclidean_distance(_snake_case							,     _snake_case     )
						return np.argmin(_snake_case							,     axis=1     )






class 							lowercase__			(   __lowerCAmelCase	):

			"""simple docstring"""
			__lowerCAmelCase     :				Optional[int]										=  ['''pixel_values''']







			def __init__(      self						,   _A = None						,   _A = True						,   _A = None						,   _A = PILImageResampling.BILINEAR						,   _A = True						,   _A = True						,   **_A						,   ):






									'''simple docstring'''







									super().__init__(**_UpperCAmelCase    )
									UpperCamelCase				:		Optional[Any]				   =			size if size is not None else {"""height""": 2_5_6, """width""": 2_5_6}
									UpperCamelCase				:		Optional[Any]				   =			get_size_dict(_UpperCAmelCase    )
									UpperCamelCase				:		Tuple				   =			np.array(_UpperCAmelCase    ) if clusters is not None else None
									UpperCamelCase				:		List[str]				   =			do_resize
									UpperCamelCase				:		Union[str, Any]				   =			size
									UpperCamelCase				:		str				   =			resample
									UpperCamelCase				:		Dict				   =			do_normalize
									UpperCamelCase				:		str				   =			do_color_quantize







			def   _a      (      self						,   _A						,   _A						,   _A = PILImageResampling.BILINEAR						,   _A = None						,   **_A						,   ):






									'''simple docstring'''







									UpperCamelCase				:		List[Any]				   =			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   _a      (      self						,   _A						,   _A = None						,   ):






									'''simple docstring'''







									UpperCamelCase				:		Dict				   =			rescale(image=_UpperCAmelCase						,   scale=1 / 1_27.5						,   data_format=_UpperCAmelCase    )
									UpperCamelCase				:		List[Any]				   =			image - 1
									return image







			def   _a      (      self						,   _A						,   _A = None						,   _A = None						,   _A = None						,   _A = None						,   _A = None						,   _A = None						,   _A = None						,   _A = ChannelDimension.FIRST						,   **_A						,   ):






									'''simple docstring'''







									UpperCamelCase				:		Tuple				   =			do_resize if do_resize is not None else self.do_resize
									UpperCamelCase				:		Any				   =			size if size is not None else self.size
									UpperCamelCase				:		Optional[int]				   =			get_size_dict(_UpperCAmelCase    )
									UpperCamelCase				:		List[str]				   =			resample if resample is not None else self.resample
									UpperCamelCase				:		List[str]				   =			do_normalize if do_normalize is not None else self.do_normalize
									UpperCamelCase				:		str				   =			do_color_quantize if do_color_quantize is not None else self.do_color_quantize
									UpperCamelCase				:		Optional[int]				   =			clusters if clusters is not None else self.clusters
									UpperCamelCase				:		List[str]				   =			np.array(_UpperCAmelCase    )

									UpperCamelCase				:		Tuple				   =			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.
									UpperCamelCase				:		List[str]				   =			[to_numpy_array(_UpperCAmelCase    ) for image in images]

									if do_resize:
															UpperCamelCase				:		Union[str, Any]				   =			[self.resize(image=_UpperCAmelCase						,   size=_UpperCAmelCase						,   resample=_UpperCAmelCase    ) for image in images]

									if do_normalize:
															UpperCamelCase				:		List[Any]				   =			[self.normalize(image=_UpperCAmelCase    ) for image in images]

									if do_color_quantize:
															UpperCamelCase				:		Optional[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)
															UpperCamelCase				:		Dict				   =			np.array(_UpperCAmelCase    )
															UpperCamelCase				:		Any				   =			color_quantize(_UpperCAmelCase						,   _UpperCAmelCase    ).reshape(images.shape[:-1]    )

															# flatten to (batch_size, height*width)
															UpperCamelCase				:		Dict				   =			images.shape[0]
															UpperCamelCase				:		Union[str, Any]				   =			images.reshape(_UpperCAmelCase						,   -1    )

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

									UpperCamelCase				:		Union[str, Any]				   =			{"""input_ids""": images}
									return BatchFeature(data=_UpperCAmelCase						,   tensor_type=_UpperCAmelCase    )









"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a							      =     logging.get_logger(__name__)

a							      =     {
    '''facebook/xmod-base''': '''https://huggingface.co/facebook/xmod-base/resolve/main/config.json''',
    '''facebook/xmod-large-prenorm''': '''https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json''',
    '''facebook/xmod-base-13-125k''': '''https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json''',
    '''facebook/xmod-base-30-125k''': '''https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json''',
    '''facebook/xmod-base-30-195k''': '''https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json''',
    '''facebook/xmod-base-60-125k''': '''https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json''',
    '''facebook/xmod-base-60-265k''': '''https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json''',
    '''facebook/xmod-base-75-125k''': '''https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json''',
    '''facebook/xmod-base-75-269k''': '''https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json''',
}





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	Optional[Any]     =      '''xmod'''

 def __init__(					self			:			str       ,				_UpperCAmelCase			:			Optional[Any]=30_522       ,				_UpperCAmelCase			:			Any=768       ,				_UpperCAmelCase			:			int=12       ,				_UpperCAmelCase			:			List[str]=12       ,				_UpperCAmelCase			:			Dict=3_072       ,				_UpperCAmelCase			:			Union[str, Any]="gelu"       ,				_UpperCAmelCase			:			Optional[int]=0.1       ,				_UpperCAmelCase			:			Tuple=0.1       ,				_UpperCAmelCase			:			List[str]=512       ,				_UpperCAmelCase			:			Dict=2       ,				_UpperCAmelCase			:			Optional[int]=0.02       ,				_UpperCAmelCase			:			Any=1E-1_2       ,				_UpperCAmelCase			:			Tuple=1       ,				_UpperCAmelCase			:			int=0       ,				_UpperCAmelCase			:			List[Any]=2       ,				_UpperCAmelCase			:			List[str]="absolute"       ,				_UpperCAmelCase			:			Tuple=True       ,				_UpperCAmelCase			:			Tuple=None       ,				_UpperCAmelCase			:			int=False       ,				_UpperCAmelCase			:			Union[str, Any]=2       ,				_UpperCAmelCase			:			List[Any]=False       ,				_UpperCAmelCase			:			Dict=True       ,				_UpperCAmelCase			:			List[str]=True       ,				_UpperCAmelCase			:			Tuple=("en_XX",)       ,				_UpperCAmelCase			:			List[str]=None       ,				**_UpperCAmelCase			:			Optional[Any]       ,				):
       super().__init__(pad_token_id=_UpperCAmelCase       ,				bos_token_id=_UpperCAmelCase       ,				eos_token_id=_UpperCAmelCase       ,				**_UpperCAmelCase						)

       _A     =  vocab_size
       _A     =  hidden_size
       _A     =  num_hidden_layers
       _A     =  num_attention_heads
       _A     =  hidden_act
       _A     =  intermediate_size
       _A     =  hidden_dropout_prob
       _A     =  attention_probs_dropout_prob
       _A     =  max_position_embeddings
       _A     =  type_vocab_size
       _A     =  initializer_range
       _A     =  layer_norm_eps
       _A     =  position_embedding_type
       _A     =  use_cache
       _A     =  classifier_dropout
       _A     =  pre_norm
       _A     =  adapter_reduction_factor
       _A     =  adapter_layer_norm
       _A     =  adapter_reuse_layer_norm
       _A     =  ln_before_adapter
       _A     =  list(_UpperCAmelCase						)
       _A     =  default_language





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''

 @property
 def      lowerCAmelCase_				(					self			:			Dict						):
       if self.task == "multiple-choice":
             _A     =  {0: 'batch', 1: 'choice', 2: 'sequence'}
       else:
             _A     =  {0: 'batch', 1: 'sequence'}
       return OrderedDict(
           [
               ('input_ids', dynamic_axis),
               ('attention_mask', dynamic_axis),
           ]						)

