

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


if is_vision_available():
 import PIL


__A      :   Union[str, Any]          =	logging.get_logger(__name__)
def 						SCREAMING_SNAKE_CASE__ (							_UpperCAmelCase )    ->       Optional[int]:



    '''simple docstring'''






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

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

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

    raise ValueError(f"Could not make batched video from {videos}" )






class       __A (							__a       ):
    lowerCAmelCase_							:						List[str]						 =							["pixel_values"]






    def __init__(		self	:			List[str]			, UpperCAmelCase_	:			Tuple = True			, UpperCAmelCase_	:			Dict = None			, UpperCAmelCase_	:			Union[str, Any] = PILImageResampling.BILINEAR			, UpperCAmelCase_	:			Optional[int] = True			, UpperCAmelCase_	:			Tuple = None			, UpperCAmelCase_	:			Union[str, Any] = True			, UpperCAmelCase_	:			int = 1 / 255			, UpperCAmelCase_	:			Any = True			, UpperCAmelCase_	:			Tuple = None			, UpperCAmelCase_	:			Optional[int] = None			, **UpperCAmelCase_	:			List[str]			, ):
        super().__init__(**_UpperCamelCase			)
        lowerCAmelCase :	Union[str, Any]				=					size if size is not None else {'shortest_edge': 224}
        lowerCAmelCase :	Optional[Any]				=					get_size_dict(_UpperCamelCase			, default_to_square=_UpperCamelCase			)
        lowerCAmelCase :	Dict				=					crop_size if crop_size is not None else {'height': 224, 'width': 224}
        lowerCAmelCase :	str				=					get_size_dict(_UpperCamelCase			, param_name='crop_size'			)

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






    def 		lowercase__       (		self	:			Dict			, UpperCAmelCase_	:			List[Any]			, UpperCAmelCase_	:			int			, UpperCAmelCase_	:			int = PILImageResampling.BILINEAR			, UpperCAmelCase_	:			List[Any] = None			, **UpperCAmelCase_	:			Union[str, Any]			, ):
        lowerCAmelCase :	Union[str, Any]				=					get_size_dict(_UpperCamelCase			, default_to_square=_UpperCamelCase			)
        if "shortest_edge" in size:
            lowerCAmelCase :	Union[str, Any]				=					get_resize_output_image_size(_UpperCamelCase			, size['shortest_edge']			, default_to_square=_UpperCamelCase			)
        elif "height" in size and "width" in size:
            lowerCAmelCase :	int				=					(size['height'], size['width'])
        else:
            raise ValueError(f"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"			)
        return resize(_UpperCamelCase			, size=_UpperCamelCase			, resample=_UpperCamelCase			, data_format=_UpperCamelCase			, **_UpperCamelCase			)






    def 		lowercase__       (		self	:			Optional[int]			, UpperCAmelCase_	:			str			, UpperCAmelCase_	:			Tuple			, UpperCAmelCase_	:			str = None			, **UpperCAmelCase_	:			Optional[int]			, ):
        lowerCAmelCase :	int				=					get_size_dict(_UpperCamelCase			)
        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(_UpperCamelCase			, size=(size['height'], size['width'])			, data_format=_UpperCamelCase			, **_UpperCamelCase			)






    def 		lowercase__       (		self	:			Union[str, Any]			, UpperCAmelCase_	:			Tuple			, UpperCAmelCase_	:			List[str]			, UpperCAmelCase_	:			Tuple = None			, **UpperCAmelCase_	:			Union[str, Any]			, ):
        return rescale(_UpperCamelCase			, scale=_UpperCamelCase			, data_format=_UpperCamelCase			, **_UpperCamelCase			)






    def 		lowercase__       (		self	:			Optional[int]			, UpperCAmelCase_	:			Optional[int]			, UpperCAmelCase_	:			Dict			, UpperCAmelCase_	:			Any			, UpperCAmelCase_	:			Any = None			, **UpperCAmelCase_	:			int			, ):
        return normalize(_UpperCamelCase			, mean=_UpperCamelCase			, std=_UpperCamelCase			, data_format=_UpperCamelCase			, **_UpperCamelCase			)






    def 		lowercase__       (		self	:			Any			, UpperCAmelCase_	:			int			, UpperCAmelCase_	:			List[Any] = None			, UpperCAmelCase_	:			str = None			, UpperCAmelCase_	:			int = None			, UpperCAmelCase_	:			Dict = None			, UpperCAmelCase_	:			Optional[Any] = None			, UpperCAmelCase_	:			Optional[Any] = None			, UpperCAmelCase_	:			Optional[Any] = None			, UpperCAmelCase_	:			List[str] = None			, UpperCAmelCase_	:			str = None			, UpperCAmelCase_	:			Any = None			, UpperCAmelCase_	:			str = ChannelDimension.FIRST			, ):
        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.'			)

        # All transformations expect numpy arrays.
        lowerCAmelCase :	Optional[Any]				=					to_numpy_array(_UpperCamelCase			)

        if do_resize:
            lowerCAmelCase :	Any				=					self.resize(image=_UpperCamelCase			, size=_UpperCamelCase			, resample=_UpperCamelCase			)

        if do_center_crop:
            lowerCAmelCase :	List[str]				=					self.center_crop(_UpperCamelCase			, size=_UpperCamelCase			)

        if do_rescale:
            lowerCAmelCase :	Any				=					self.rescale(image=_UpperCamelCase			, scale=_UpperCamelCase			)

        if do_normalize:
            lowerCAmelCase :	Union[str, Any]				=					self.normalize(image=_UpperCamelCase			, mean=_UpperCamelCase			, std=_UpperCamelCase			)

        lowerCAmelCase :	Optional[int]				=					to_channel_dimension_format(_UpperCamelCase			, _UpperCamelCase			)
        return image






    def 		lowercase__       (		self	:			str			, UpperCAmelCase_	:			Dict			, UpperCAmelCase_	:			List[str] = None			, UpperCAmelCase_	:			List[Any] = None			, UpperCAmelCase_	:			List[Any] = None			, UpperCAmelCase_	:			Any = None			, UpperCAmelCase_	:			Optional[int] = None			, UpperCAmelCase_	:			Optional[Any] = None			, UpperCAmelCase_	:			Tuple = None			, UpperCAmelCase_	:			int = None			, UpperCAmelCase_	:			int = None			, UpperCAmelCase_	:			str = None			, UpperCAmelCase_	:			Union[str, Any] = None			, UpperCAmelCase_	:			List[str] = ChannelDimension.FIRST			, **UpperCAmelCase_	:			Union[str, Any]			, ):
        lowerCAmelCase :	List[str]				=					do_resize if do_resize is not None else self.do_resize
        lowerCAmelCase :	Optional[Any]				=					resample if resample is not None else self.resample
        lowerCAmelCase :	List[str]				=					do_center_crop if do_center_crop is not None else self.do_center_crop
        lowerCAmelCase :	Any				=					do_rescale if do_rescale is not None else self.do_rescale
        lowerCAmelCase :	Optional[Any]				=					rescale_factor if rescale_factor is not None else self.rescale_factor
        lowerCAmelCase :	Dict				=					do_normalize if do_normalize is not None else self.do_normalize
        lowerCAmelCase :	List[str]				=					image_mean if image_mean is not None else self.image_mean
        lowerCAmelCase :	Dict				=					image_std if image_std is not None else self.image_std

        lowerCAmelCase :	int				=					size if size is not None else self.size
        lowerCAmelCase :	Union[str, Any]				=					get_size_dict(_UpperCamelCase			, default_to_square=_UpperCamelCase			)
        lowerCAmelCase :	Tuple				=					crop_size if crop_size is not None else self.crop_size
        lowerCAmelCase :	Dict				=					get_size_dict(_UpperCamelCase			, param_name='crop_size'			)

        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.'			)

        lowerCAmelCase :	List[Any]				=					make_batched(_UpperCamelCase			)

        lowerCAmelCase :	Union[str, Any]				=					[
            [
                self._preprocess_image(
                    image=_UpperCamelCase			, do_resize=_UpperCamelCase			, size=_UpperCamelCase			, resample=_UpperCamelCase			, do_center_crop=_UpperCamelCase			, crop_size=_UpperCamelCase			, do_rescale=_UpperCamelCase			, rescale_factor=_UpperCamelCase			, do_normalize=_UpperCamelCase			, image_mean=_UpperCamelCase			, image_std=_UpperCamelCase			, data_format=_UpperCamelCase			, )
                for img in video
            ]
            for video in videos
        ]

        lowerCAmelCase :	List[str]				=					{'pixel_values': videos}
        return BatchFeature(data=_UpperCamelCase			, tensor_type=_UpperCamelCase			)







import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel





class 	_lowerCAmelCase					(	pl.LightningModule       ):




				def __init__(					self				,				_UpperCamelCase     )				->      List[str]:
							super().__init__()
							lowerCAmelCase_						=	model
							lowerCAmelCase_						=	2
							lowerCAmelCase_						=	nn.Linear(self.model.config.hidden_size				,				self.num_labels     )




				def      __a						(					self     )				->      Tuple:
							pass






def    lowerCamelCase__				(				__lowerCAmelCase  :  str    ,							__lowerCAmelCase  :  str    ,							__lowerCAmelCase  :  str				):




			"""simple docstring"""


			lowerCAmelCase_						=	LongformerModel.from_pretrained(__lowerCAmelCase				)
			lowerCAmelCase_						=	LightningModel(__lowerCAmelCase				)

			lowerCAmelCase_						=	torch.load(__lowerCAmelCase    ,							map_location=torch.device("cpu"				)				)
			lightning_model.load_state_dict(ckpt["state_dict"]				)

			# init longformer question answering model
			lowerCAmelCase_						=	LongformerForQuestionAnswering.from_pretrained(__lowerCAmelCase				)

			# transfer weights
			longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict()				)
			longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict()				)
			longformer_for_qa.eval()

			# save model
			longformer_for_qa.save_pretrained(__lowerCAmelCase				)

			print(F"""Conversion successful. Model saved under {pytorch_dump_folder_path}"""				)


if __name__ == "__main__":
			_A   =     argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--longformer_model",
			    default=None,
			    type=str,
			    required=True,
			    help="model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.",
			)
			parser.add_argument(
			    "--longformer_question_answering_ckpt_path",
			    default=None,
			    type=str,
			    required=True,
			    help="Path the official PyTorch Lightning Checkpoint.",
			)
			parser.add_argument(
			    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
			)
			_A   =     parser.parse_args()
			convert_longformer_qa_checkpoint_to_pytorch(
			    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
			)



