






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 (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    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


__lowerCamelCase :      List[str]										=				logging.get_logger(__name__)



class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					a_     =		["pixel_values"]



					def __init__( self					:  Any							,		__A					:  bool = True							,		__A					:  Dict[str, int] = None							,		__A					:  int = 0.9							,		__A					:  PILImageResampling = PILImageResampling.BICUBIC							,		__A					:  bool = True							,		__A					:  Dict[str, int] = None							,		__A					:  Union[int, float] = 1 / 2_5_5							,		__A					:  bool = True							,		__A					:  bool = True							,		__A					:  Optional[Union[float, List[float]]] = None							,		__A					:  Optional[Union[float, List[float]]] = None							,		**__A					:  Tuple							,		):
						super().__init__(**__A	)
						snake_case__			:				Union[str, Any]       =  size if size is not None else {"shortest_edge": 2_2_4}
						snake_case__			:				Tuple       =  get_size_dict(__A							,		default_to_square=__A	)
						snake_case__			:				Optional[Any]       =  crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
						snake_case__			:				Dict       =  get_size_dict(__A							,		param_name="crop_size"	)

						snake_case__			:				Optional[Any]       =  do_resize
						snake_case__			:				Optional[Any]       =  size
						snake_case__			:				Union[str, Any]       =  crop_pct
						snake_case__			:				Dict       =  resample
						snake_case__			:				Tuple       =  do_center_crop
						snake_case__			:				Any       =  crop_size
						snake_case__			:				int       =  do_rescale
						snake_case__			:				List[str]       =  rescale_factor
						snake_case__			:				Dict       =  do_normalize
						snake_case__			:				int       =  image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
						snake_case__			:				List[Any]       =  image_std if image_std is not None else IMAGENET_DEFAULT_STD



					def 	_lowercase					( self					:  Dict							,		__A					:  np.ndarray							,		__A					:  Dict[str, int]							,		__A					:  Optional[float] = None							,		__A					:  PILImageResampling = PILImageResampling.BICUBIC							,		__A					:  Optional[Union[str, ChannelDimension]] = None							,		**__A					:  List[Any]							,		):
						snake_case__			:				List[Any]       =  get_size_dict(__A							,		default_to_square=__A	)
						if "shortest_edge" not in size and ("height" not in size or "width" not in size):
							raise ValueError(f'''size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''	)
						if crop_pct is not None:
							if "shortest_edge" in size:
								snake_case__			:				Union[str, Any]       =  int(size["shortest_edge"] / crop_pct	)
							elif "height" in size and "width" in size:
								if size["height"] == size["width"]:
									snake_case__			:				int       =  int(size["height"] / crop_pct	)
								else:
									snake_case__			:				Optional[int]       =  (int(size["height"] / crop_pct	), int(size["width"] / crop_pct	))
							else:
								raise ValueError("Invalid size for resize: {}".format(__A	)	)

							snake_case__			:				Tuple       =  get_resize_output_image_size(__A							,		size=__A							,		default_to_square=__A	)
						else:
							if "shortest_edge" in size:
								snake_case__			:				int       =  get_resize_output_image_size(__A							,		size=size["shortest_edge"]							,		default_to_square=__A	)
							elif "height" in size and "width" in size:
								snake_case__			:				Dict       =  (size["height"], size["width"])
							else:
								raise ValueError("Invalid size for resize: {}".format(__A	)	)

						return resize(__A							,		size=__A							,		resample=__A							,		data_format=__A							,		**__A	)



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



					def 	_lowercase					( self					:  Tuple							,		__A					:  np.ndarray							,		__A					:  Union[int, float]							,		__A					:  Optional[Union[str, ChannelDimension]] = None							,		**__A					:  Any							,		):
						return rescale(__A							,		scale=__A							,		data_format=__A							,		**__A	)



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






					def 	_lowercase					( self					:  Dict							,		__A					:  ImageInput							,		__A					:  bool = None							,		__A					:  Dict[str, int] = None							,		__A					:  int = None							,		__A					:  PILImageResampling = None							,		__A					:  bool = None							,		__A					:  Dict[str, int] = None							,		__A					:  bool = None							,		__A					:  float = 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					:  List[Any]							,		):
						snake_case__			:				int       =  do_resize if do_resize is not None else self.do_resize
						snake_case__			:				int       =  crop_pct if crop_pct is not None else self.crop_pct
						snake_case__			:				Union[str, Any]       =  resample if resample is not None else self.resample
						snake_case__			:				Any       =  do_center_crop if do_center_crop is not None else self.do_center_crop
						snake_case__			:				Tuple       =  do_rescale if do_rescale is not None else self.do_rescale
						snake_case__			:				Union[str, Any]       =  rescale_factor if rescale_factor is not None else self.rescale_factor
						snake_case__			:				Optional[Any]       =  do_normalize if do_normalize is not None else self.do_normalize
						snake_case__			:				Dict       =  image_mean if image_mean is not None else self.image_mean
						snake_case__			:				Dict       =  image_std if image_std is not None else self.image_std

						snake_case__			:				int       =  size if size is not None else self.size
						snake_case__			:				List[Any]       =  get_size_dict(__A							,		default_to_square=__A	)
						snake_case__			:				List[str]       =  crop_size if crop_size is not None else self.crop_size
						snake_case__			:				Tuple       =  get_size_dict(__A							,		param_name="crop_size"	)

						snake_case__			:				Any       =  make_list_of_images(__A	)

						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."	)

						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_pct is None:
							raise ValueError("Crop_pct 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."	)

						# All transformations expect numpy arrays.
						snake_case__			:				Tuple       =  [to_numpy_array(__A	) for image in images]

						if do_resize:
							snake_case__			:				Optional[Any]       =  [self.resize(image=__A							,		size=__A							,		crop_pct=__A							,		resample=__A	) for image in images]

						if do_center_crop:
							snake_case__			:				str       =  [self.center_crop(image=__A							,		size=__A	) for image in images]

						if do_rescale:
							snake_case__			:				List[str]       =  [self.rescale(image=__A							,		scale=__A	) for image in images]

						if do_normalize:
							snake_case__			:				Dict       =  [self.normalize(image=__A							,		mean=__A							,		std=__A	) for image in images]

						snake_case__			:				Optional[Any]       =  [to_channel_dimension_format(__A							,		__A	) for image in images]

						snake_case__			:				Optional[int]       =  {"pixel_values": images}
						return BatchFeature(data=__A							,		tensor_type=__A	)











from collections import OrderedDict
from typing import Mapping

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


__lowerCamelCase :      List[str]										=				logging.get_logger(__name__)

__lowerCamelCase :      List[Any]										=				{
    """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 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					a_     =		"xmod"



					def __init__( self					:  List[str]							,		__A					:  List[str]=3_0_5_2_2							,		__A					:  Tuple=7_6_8							,		__A					:  str=1_2							,		__A					:  List[Any]=1_2							,		__A					:  List[str]=3_0_7_2							,		__A					:  List[str]="gelu"							,		__A					:  List[Any]=0.1							,		__A					:  Tuple=0.1							,		__A					:  str=5_1_2							,		__A					:  Union[str, Any]=2							,		__A					:  List[Any]=0.0_2							,		__A					:  List[str]=1e-1_2							,		__A					:  Tuple=1							,		__A					:  List[Any]=0							,		__A					:  Optional[Any]=2							,		__A					:  Optional[int]="absolute"							,		__A					:  Optional[int]=True							,		__A					:  Dict=None							,		__A					:  Optional[int]=False							,		__A					:  Dict=2							,		__A					:  List[str]=False							,		__A					:  Dict=True							,		__A					:  Union[str, Any]=True							,		__A					:  Tuple=("en_XX",)							,		__A					:  Optional[Any]=None							,		**__A					:  Tuple							,		):
						super().__init__(pad_token_id=__A							,		bos_token_id=__A							,		eos_token_id=__A							,		**__A	)

						snake_case__			:				Tuple       =  vocab_size
						snake_case__			:				Union[str, Any]       =  hidden_size
						snake_case__			:				int       =  num_hidden_layers
						snake_case__			:				str       =  num_attention_heads
						snake_case__			:				Tuple       =  hidden_act
						snake_case__			:				Optional[Any]       =  intermediate_size
						snake_case__			:				List[str]       =  hidden_dropout_prob
						snake_case__			:				Optional[int]       =  attention_probs_dropout_prob
						snake_case__			:				Union[str, Any]       =  max_position_embeddings
						snake_case__			:				str       =  type_vocab_size
						snake_case__			:				List[str]       =  initializer_range
						snake_case__			:				Dict       =  layer_norm_eps
						snake_case__			:				str       =  position_embedding_type
						snake_case__			:				List[str]       =  use_cache
						snake_case__			:				Tuple       =  classifier_dropout
						snake_case__			:				Any       =  pre_norm
						snake_case__			:				List[str]       =  adapter_reduction_factor
						snake_case__			:				List[Any]       =  adapter_layer_norm
						snake_case__			:				str       =  adapter_reuse_layer_norm
						snake_case__			:				Union[str, Any]       =  ln_before_adapter
						snake_case__			:				Tuple       =  list(__A	)
						snake_case__			:				int       =  default_language



class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					@property
					def 	_lowercase					( self					:  int	):
						if self.task == "multiple-choice":
							snake_case__			:				Any       =  {0: "batch", 1: "choice", 2: "sequence"}
						else:
							snake_case__			:				Any       =  {0: "batch", 1: "sequence"}
						return OrderedDict(
						    [
						        ("input_ids", dynamic_axis),
						        ("attention_mask", dynamic_axis),
						    ]	)



