


"""simple docstring"""







import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin






@skip_mps
class       __A				(       _SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE,    unittest.TestCase			):






 """simple docstring"""




 __lowerCAmelCase 					=  IFInpaintingPipeline
 __lowerCAmelCase 					=  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
 __lowerCAmelCase 					=  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 __lowerCAmelCase 					=  PipelineTesterMixin.required_optional_params - {"latents"}


 def       SCREAMING_SNAKE_CASE							(		self						)					->     str:
     return self._get_dummy_components()


 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A=0						)					->     List[str]:
     if str(__A						).startswith('''mps'''						):
         a 		=torch.manual_seed(__A						)
     else:
         a 		=torch.Generator(device=__A						).manual_seed(__A						)

     a 		=floats_tensor((1, 3, 32, 32) ,    rng=random.Random(__A						)						).to(__A						)
     a 		=floats_tensor((1, 3, 32, 32) ,    rng=random.Random(__A						)						).to(__A						)

     a 		={
         '''prompt''': '''A painting of a squirrel eating a burger''',
         '''image''': image,
         '''mask_image''': mask_image,
         '''generator''': generator,
         '''num_inference_steps''': 2,
         '''output_type''': '''numpy''',
     }

     return inputs


 @unittest.skipIf(
     torch_device != '''cuda''' or not is_xformers_available() ,    reason='''XFormers attention is only available with CUDA and `xformers` installed''' ,    )
 def       SCREAMING_SNAKE_CASE							(		self						)					->     str:
     self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3						)


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Union[str, Any]:
     self._test_save_load_optional_components()


 @unittest.skipIf(torch_device != '''cuda''' ,    reason='''float16 requires CUDA'''						)
 def       SCREAMING_SNAKE_CASE							(		self						)					->     str:
     # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
     super().test_save_load_floataa(expected_max_diff=1E-1						)


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Any:
     self._test_attention_slicing_forward_pass(expected_max_diff=1E-2						)


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Union[str, Any]:
     self._test_save_load_local()





 def       SCREAMING_SNAKE_CASE							(		self						)					->     int:
     self._test_inference_batch_single_identical(
         expected_max_diff=1E-2 ,    )

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
			)



