



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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


SCREAMING_SNAKE_CASE_:str             =						logging.get_logger(__name__)


if is_vision_available():
     import PIL






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Any					=      ["pixel_values"]






    def __init__(       self,		lowerCamelCase__ = True,		lowerCamelCase__ = None,		lowerCamelCase__ = PILImageResampling.BICUBIC,		lowerCamelCase__ = True,		lowerCamelCase__ = None,		lowerCamelCase__ = True,		lowerCamelCase__ = 1 / 255,		lowerCamelCase__ = True,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = True,		**lowerCamelCase__,		):
        super().__init__(**lowerCamelCase__		)
        A :       List[Any]          =   size if size is not None else {"""shortest_edge""": 224}
        A :       Any          =   get_size_dict(lowerCamelCase__,		default_to_square=lowerCamelCase__		)
        A :       Optional[Any]          =   crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
        A :       Optional[Any]          =   get_size_dict(lowerCamelCase__,		default_to_square=lowerCamelCase__,		param_name="""crop_size"""		)

        A :       Any          =   do_resize
        A :       Optional[Any]          =   size
        A :       Dict          =   resample
        A :       Optional[Any]          =   do_center_crop
        A :       List[str]          =   crop_size
        A :       Optional[int]          =   do_rescale
        A :       Any          =   rescale_factor
        A :       int          =   do_normalize
        A :       Optional[int]          =   image_mean if image_mean is not None else OPENAI_CLIP_MEAN
        A :       int          =   image_std if image_std is not None else OPENAI_CLIP_STD
        A :       Optional[Any]          =   do_convert_rgb






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__ = PILImageResampling.BICUBIC,		lowerCamelCase__ = None,		**lowerCamelCase__,		):
        A :       Tuple          =   get_size_dict(lowerCamelCase__,		default_to_square=lowerCamelCase__		)
        if "shortest_edge" not in size:
            raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''		)
        A :       Dict          =   get_resize_output_image_size(lowerCamelCase__,		size=size["""shortest_edge"""],		default_to_square=lowerCamelCase__		)
        return resize(lowerCamelCase__,		size=lowerCamelCase__,		resample=lowerCamelCase__,		data_format=lowerCamelCase__,		**lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__ = None,		**lowerCamelCase__,		):
        A :       Optional[int]          =   get_size_dict(lowerCamelCase__		)
        if "height" not in size or "width" not in size:
            raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''		)
        return center_crop(lowerCamelCase__,		size=(size["""height"""], size["""width"""]),		data_format=lowerCamelCase__,		**lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__ = None,		**lowerCamelCase__,		):
        return rescale(lowerCamelCase__,		scale=lowerCamelCase__,		data_format=lowerCamelCase__,		**lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__ = None,		**lowerCamelCase__,		):
        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__ = None,		lowerCamelCase__ = ChannelDimension.FIRST,		**lowerCamelCase__,		):
        A :       Optional[Any]          =   do_resize if do_resize is not None else self.do_resize
        A :       Dict          =   size if size is not None else self.size
        A :       Dict          =   get_size_dict(lowerCamelCase__,		param_name="""size""",		default_to_square=lowerCamelCase__		)
        A :       Tuple          =   resample if resample is not None else self.resample
        A :       Tuple          =   do_center_crop if do_center_crop is not None else self.do_center_crop
        A :       str          =   crop_size if crop_size is not None else self.crop_size
        A :       int          =   get_size_dict(lowerCamelCase__,		param_name="""crop_size""",		default_to_square=lowerCamelCase__		)
        A :       str          =   do_rescale if do_rescale is not None else self.do_rescale
        A :       Dict          =   rescale_factor if rescale_factor is not None else self.rescale_factor
        A :       List[Any]          =   do_normalize if do_normalize is not None else self.do_normalize
        A :       Dict          =   image_mean if image_mean is not None else self.image_mean
        A :       Dict          =   image_std if image_std is not None else self.image_std
        A :       List[str]          =   do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        A :       List[str]          =   make_list_of_images(lowerCamelCase__		)

        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."""		)

        if do_resize and size is None:
            raise ValueError("""Size 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."""		)

        # PIL RGBA images are converted to RGB
        if do_convert_rgb:
            A :       Union[str, Any]          =   [convert_to_rgb(lowerCamelCase__		) for image in images]

        # All transformations expect numpy arrays.
        A :       int          =   [to_numpy_array(lowerCamelCase__		) for image in images]

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

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

        if do_rescale:
            A :       int          =   [self.rescale(image=lowerCamelCase__,		scale=lowerCamelCase__		) for image in images]

        if do_normalize:
            A :       Tuple          =   [self.normalize(image=lowerCamelCase__,		mean=lowerCamelCase__,		std=lowerCamelCase__		) for image in images]

        A :       List[Any]          =   [to_channel_dimension_format(lowerCamelCase__,		lowerCamelCase__		) for image in images]

        A :       int          =   {"""pixel_values""": images}
        return BatchFeature(data=lowerCamelCase__,		tensor_type=lowerCamelCase__		)







from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE_:Any             =						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:Dict             =						{
    """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__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Dict					=      "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__,		):
        super().__init__(pad_token_id=lowerCamelCase__,		bos_token_id=lowerCamelCase__,		eos_token_id=lowerCamelCase__,		**lowerCamelCase__		)

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






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






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


