


from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
   from PIL import Image

   from ..image_utils import load_image

if is_torch_available():
   import torch

   from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

_SCREAMING_SNAKE_CASE			:       Optional[Any]      =						logging.get_logger(__name__)

@add_end_docstrings(__lowerCamelCase      )
class 			UpperCamelCase__			(			__lowerCamelCase      ):






      def __init__(  self       : Optional[Any],		*__lowerCamelCase       : Union[str, Any],		**__lowerCamelCase       : Union[str, Any] )	->			List[Any]:
           super().__init__(*__a,		**__a )
           requires_backends(self,		'''vision''' )
           self.check_model_type(__a )






      def __call__(  self       : Dict,		__lowerCamelCase       : Union[str, List[str], "Image.Image", List["Image.Image"]],		**__lowerCamelCase       : str )	->			Optional[int]:
           return super().__call__(__a,		**__a )






      def 						__lowercase(  self       : Any,		**__lowerCamelCase       : Union[str, Any] )	->			Dict:
           return {}, {}, {}






      def 						__lowercase(  self       : List[str],		__lowerCamelCase       : Union[str, Any] )	->			List[str]:
           UpperCamelCase__				:	List[str]										=  load_image(__a )
           UpperCamelCase__				:	Tuple										=  image.size
           UpperCamelCase__				:	List[str]										=  self.image_processor(images=__a,		return_tensors=self.framework )
           return model_inputs






      def 						__lowercase(  self       : Optional[int],		__lowerCamelCase       : Optional[int] )	->			Optional[int]:
           UpperCamelCase__				:	Optional[Any]										=  self.model(**__a )
           return model_outputs






      def 						__lowercase(  self       : List[str],		__lowerCamelCase       : int )	->			List[Any]:
           UpperCamelCase__				:	Dict										=  model_outputs.predicted_depth
           UpperCamelCase__				:	Dict										=  torch.nn.functional.interpolate(
               predicted_depth.unsqueeze(1 ),		size=self.image_size[::-1],		mode='''bicubic''',		align_corners=__a )
           UpperCamelCase__				:	Tuple										=  prediction.squeeze().cpu().numpy()
           UpperCamelCase__				:	Tuple										=  (output * 2_55 / np.max(__a )).astype('''uint8''' )
           UpperCamelCase__				:	Optional[Any]										=  Image.fromarray(__a )
           UpperCamelCase__				:	List[str]										=  {}
           UpperCamelCase__				:	int										=  predicted_depth
           UpperCamelCase__				:	List[str]										=  depth
           return output_dict





'''simple docstring'''


import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel






class     A ( pl.LightningModule ):





							def __init__(   self					:				Dict  ,		__a					:				List[str]						)				->						Tuple:
								super().__init__()
								__UpperCAmelCase      					=      model
								__UpperCAmelCase      					=      2
								__UpperCAmelCase      					=      nn.Linear(self.model.config.hidden_size  ,		self.num_labels						)






							def    snake_case__    (   self					:				int						)				->						int:
								pass





def    lowerCAmelCase       (		UpperCamelCase__					: str     ,       UpperCamelCase__					: str     ,       UpperCamelCase__					: str							):


	"""simple docstring"""
	# load longformer model from model identifier
	__UpperCAmelCase      					=      LongformerModel.from_pretrained(UpperCamelCase__							)
	__UpperCAmelCase      					=      LightningModel(UpperCamelCase__							)

	__UpperCAmelCase      					=      torch.load(UpperCamelCase__     ,       map_location=torch.device('''cpu'''							)							)
	lightning_model.load_state_dict(ckpt['''state_dict''']							)

	# init longformer question answering model
	__UpperCAmelCase      					=      LongformerForQuestionAnswering.from_pretrained(UpperCamelCase__							)

	# 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(UpperCamelCase__							)

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


if __name__ == "__main__":
					__lowerCAmelCase						:							Optional[Any]    							=						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."
					)
					__lowerCAmelCase						:							List[str]    							=						parser.parse_args()
					convert_longformer_qa_checkpoint_to_pytorch(
					    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
					)
