



from collections import OrderedDict
from typing import Mapping

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


UpperCAmelCase_      							=     logging.get_logger(__name__)

UpperCAmelCase_      							=     {
    """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 			UpperCamelCase_		(  _a					):
					lowerCAmelCase_							=	'''xmod'''




					def __init__(		self				,						lowerCAmelCase_=3_0522				,						lowerCAmelCase_=768				,						lowerCAmelCase_=12				,						lowerCAmelCase_=12				,						lowerCAmelCase_=3072				,						lowerCAmelCase_="gelu"				,						lowerCAmelCase_=0.1				,						lowerCAmelCase_=0.1				,						lowerCAmelCase_=512				,						lowerCAmelCase_=2				,						lowerCAmelCase_=0.02				,						lowerCAmelCase_=1E-12				,						lowerCAmelCase_=1				,						lowerCAmelCase_=0				,						lowerCAmelCase_=2				,						lowerCAmelCase_="absolute"				,						lowerCAmelCase_=True				,						lowerCAmelCase_=None				,						lowerCAmelCase_=False				,						lowerCAmelCase_=2				,						lowerCAmelCase_=False				,						lowerCAmelCase_=True				,						lowerCAmelCase_=True				,						lowerCAmelCase_=("en_XX",)				,						lowerCAmelCase_=None				,						**lowerCAmelCase_				,						)		->							List[str]:
										super().__init__(pad_token_id=_A				,						bos_token_id=_A				,						eos_token_id=_A				,						**_A  )

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





class 			UpperCamelCase_		(  _a					):




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










from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
					import PIL

UpperCAmelCase_      							=     logging.get_logger(__name__)





def 						lowerCamelCase__     (     UpperCamelCase__			:       Tuple						)			-> List[List[ImageInput]]:



					'''simple docstring'''




					if isinstance(UpperCamelCase__       ,    (list, tuple)						) and isinstance(videos[0]       ,    (list, tuple)						) and is_valid_image(videos[0][0]						):
										return videos

					elif isinstance(UpperCamelCase__       ,    (list, tuple)						) and is_valid_image(videos[0]						):
										return [videos]

					elif is_valid_image(UpperCamelCase__						):
										return [[videos]]

					raise ValueError(F'''Could not make batched video from {videos}'''						)




class 			UpperCamelCase_		(  _lowerCamelCase					):
					lowerCAmelCase_							=	['''pixel_values''']




					def __init__(		self				,						lowerCAmelCase_ = True				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = PILImageResampling.BILINEAR				,						lowerCAmelCase_ = True				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = True				,						lowerCAmelCase_ = 1 / 255				,						lowerCAmelCase_ = True				,						lowerCAmelCase_ = True				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						**lowerCAmelCase_				,						)		->							None:
										super().__init__(**lowerCAmelCase_  )
										_snake_case									=				size if size is not None else {'shortest_edge': 256}
										_snake_case									=				get_size_dict(lowerCAmelCase_				,						default_to_square=lowerCAmelCase_  )
										_snake_case									=				crop_size if crop_size is not None else {'height': 224, 'width': 224}
										_snake_case									=				get_size_dict(lowerCAmelCase_				,						param_name='crop_size'  )

										_snake_case									=				do_resize
										_snake_case									=				size
										_snake_case									=				do_center_crop
										_snake_case									=				crop_size
										_snake_case									=				resample
										_snake_case									=				do_rescale
										_snake_case									=				rescale_factor
										_snake_case									=				offset
										_snake_case									=				do_normalize
										_snake_case									=				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										_snake_case									=				image_std if image_std is not None else IMAGENET_STANDARD_STD




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_ = PILImageResampling.BILINEAR				,						lowerCAmelCase_ = None				,						**lowerCAmelCase_				,						)		->							np.ndarray:
										_snake_case									=				get_size_dict(lowerCAmelCase_				,						default_to_square=lowerCAmelCase_  )
										if "shortest_edge" in size:
															_snake_case									=				get_resize_output_image_size(lowerCAmelCase_				,						size['shortest_edge']				,						default_to_square=lowerCAmelCase_  )
										elif "height" in size and "width" in size:
															_snake_case									=				(size['height'], size['width'])
										else:
															raise ValueError(F'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''  )
										return resize(lowerCAmelCase_				,						size=lowerCAmelCase_				,						resample=lowerCAmelCase_				,						data_format=lowerCAmelCase_				,						**lowerCAmelCase_  )




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_ = None				,						**lowerCAmelCase_				,						)		->							np.ndarray:
										_snake_case									=				get_size_dict(lowerCAmelCase_  )
										if "height" not in size or "width" not in size:
															raise ValueError(F'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}'''  )
										return center_crop(lowerCAmelCase_				,						size=(size['height'], size['width'])				,						data_format=lowerCAmelCase_				,						**lowerCAmelCase_  )




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_ = True				,						lowerCAmelCase_ = None				,						**lowerCAmelCase_				,						)		->							int:
										_snake_case									=				image.astype(np.floataa  )
										if offset:
															_snake_case									=				image - (scale / 2)
										return rescale(lowerCAmelCase_				,						scale=lowerCAmelCase_				,						data_format=lowerCAmelCase_				,						**lowerCAmelCase_  )




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_ = None				,						**lowerCAmelCase_				,						)		->							np.ndarray:
										return normalize(lowerCAmelCase_				,						mean=lowerCAmelCase_				,						std=lowerCAmelCase_				,						data_format=lowerCAmelCase_				,						**lowerCAmelCase_  )




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = ChannelDimension.FIRST				,						)		->							np.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_size is None:
															raise ValueError('Crop size 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.'  )

										if offset and not do_rescale:
															raise ValueError('For offset, do_rescale must also be set to True.'  )

										# All transformations expect numpy arrays.
										_snake_case									=				to_numpy_array(lowerCAmelCase_  )

										if do_resize:
															_snake_case									=				self.resize(image=lowerCAmelCase_				,						size=lowerCAmelCase_				,						resample=lowerCAmelCase_  )

										if do_center_crop:
															_snake_case									=				self.center_crop(lowerCAmelCase_				,						size=lowerCAmelCase_  )

										if do_rescale:
															_snake_case									=				self.rescale(image=lowerCAmelCase_				,						scale=lowerCAmelCase_				,						offset=lowerCAmelCase_  )

										if do_normalize:
															_snake_case									=				self.normalize(image=lowerCAmelCase_				,						mean=lowerCAmelCase_				,						std=lowerCAmelCase_  )

										_snake_case									=				to_channel_dimension_format(lowerCAmelCase_				,						lowerCAmelCase_  )
										return image






					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = ChannelDimension.FIRST				,						**lowerCAmelCase_				,						)		->							PIL.Image.Image:
										_snake_case									=				do_resize if do_resize is not None else self.do_resize
										_snake_case									=				resample if resample is not None else self.resample
										_snake_case									=				do_center_crop if do_center_crop is not None else self.do_center_crop
										_snake_case									=				do_rescale if do_rescale is not None else self.do_rescale
										_snake_case									=				rescale_factor if rescale_factor is not None else self.rescale_factor
										_snake_case									=				offset if offset is not None else self.offset
										_snake_case									=				do_normalize if do_normalize is not None else self.do_normalize
										_snake_case									=				image_mean if image_mean is not None else self.image_mean
										_snake_case									=				image_std if image_std is not None else self.image_std

										_snake_case									=				size if size is not None else self.size
										_snake_case									=				get_size_dict(lowerCAmelCase_				,						default_to_square=lowerCAmelCase_  )
										_snake_case									=				crop_size if crop_size is not None else self.crop_size
										_snake_case									=				get_size_dict(lowerCAmelCase_				,						param_name='crop_size'  )

										if not valid_images(lowerCAmelCase_  ):
															raise ValueError(
															    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
															    'torch.Tensor, tf.Tensor or jax.ndarray.'  )

										_snake_case									=				make_batched(lowerCAmelCase_  )

										_snake_case									=				[
										    [
										        self._preprocess_image(
										            image=lowerCAmelCase_				,						do_resize=lowerCAmelCase_				,						size=lowerCAmelCase_				,						resample=lowerCAmelCase_				,						do_center_crop=lowerCAmelCase_				,						crop_size=lowerCAmelCase_				,						do_rescale=lowerCAmelCase_				,						rescale_factor=lowerCAmelCase_				,						offset=lowerCAmelCase_				,						do_normalize=lowerCAmelCase_				,						image_mean=lowerCAmelCase_				,						image_std=lowerCAmelCase_				,						data_format=lowerCAmelCase_				,						)
										        for img in video
										    ]
										    for video in videos
										]

										_snake_case									=				{'pixel_values': videos}
										return BatchFeature(data=lowerCAmelCase_				,						tensor_type=lowerCAmelCase_  )



