

import inspect
import unittest

from huggingface_hub import hf_hub_download

from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import UperNetForSemanticSegmentation
					from transformers.models.upernet.modeling_upernet import UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor





class 	snake_case_   :







			'''simple docstring'''

			def __init__(     self :				Dict							,   _UpperCamelCase :				Optional[Any]							,   _UpperCamelCase :				Dict=1_3							,   _UpperCamelCase :				Optional[Any]=3_2							,   _UpperCamelCase :				List[Any]=3							,   _UpperCamelCase :				List[str]=4							,   _UpperCamelCase :				Optional[int]=[1_0, 2_0, 3_0, 4_0]							,   _UpperCamelCase :				Optional[int]=[2, 2, 3, 2]							,   _UpperCamelCase :				Union[str, Any]=True							,   _UpperCamelCase :				Optional[int]=True							,   _UpperCamelCase :				int=3_7							,   _UpperCamelCase :				Any="gelu"							,   _UpperCamelCase :				Tuple=1_0							,   _UpperCamelCase :				str=0.02							,   _UpperCamelCase :				Optional[int]=["stage2", "stage3", "stage4"]							,   _UpperCamelCase :				List[str]=3							,   _UpperCamelCase :				List[str]=None							,   )      ->str:
									snake_case_   =	parent
									snake_case_   =	batch_size
									snake_case_   =	image_size
									snake_case_   =	num_channels
									snake_case_   =	num_stages
									snake_case_   =	hidden_sizes
									snake_case_   =	depths
									snake_case_   =	is_training
									snake_case_   =	use_labels
									snake_case_   =	intermediate_size
									snake_case_   =	hidden_act
									snake_case_   =	type_sequence_label_size
									snake_case_   =	initializer_range
									snake_case_   =	out_features
									snake_case_   =	num_labels
									snake_case_   =	scope
									snake_case_   =	num_stages







			def 				snake_case__(     self :				Optional[int]							)      ->int:
									snake_case_   =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

									snake_case_   =	None
									if self.use_labels:
															snake_case_   =	ids_tensor([self.batch_size]							,   self.type_sequence_label_size							)

									snake_case_   =	self.get_config()

									return config, pixel_values, labels







			def 				snake_case__(     self :				int							)      ->List[str]:
									return ConvNextConfig(
									    num_channels=self.num_channels							,   num_stages=self.num_stages							,   hidden_sizes=self.hidden_sizes							,   depths=self.depths							,   is_training=self.is_training							,   intermediate_size=self.intermediate_size							,   hidden_act=self.hidden_act							,   out_features=self.out_features							,   )







			def 				snake_case__(     self :				Optional[int]							)      ->Optional[int]:
									return UperNetConfig(
									    backbone_config=self.get_backbone_config()							,   hidden_size=5_1_2							,   pool_scales=[1, 2, 3, 6]							,   use_auxiliary_head=_UpperCamelCase							,   auxiliary_loss_weight=0.4							,   auxiliary_in_channels=4_0							,   auxiliary_channels=2_5_6							,   auxiliary_num_convs=1							,   auxiliary_concat_input=_UpperCamelCase							,   loss_ignore_index=2_5_5							,   num_labels=self.num_labels							,   )







			def 				snake_case__(     self :				Optional[Any]							,   _UpperCamelCase :				Optional[int]							,   _UpperCamelCase :				Optional[int]							,   _UpperCamelCase :				List[Any]							)      ->int:
									snake_case_   =	UperNetForSemanticSegmentation(config=_UpperCamelCase							)
									model.to(_UpperCamelCase							)
									model.eval()
									snake_case_   =	model(_UpperCamelCase							)
									self.parent.assertEqual(
									    result.logits.shape							,   (self.batch_size, self.num_labels, self.image_size, self.image_size)							)







			def 				snake_case__(     self :				List[Any]							)      ->int:
									snake_case_   =	self.prepare_config_and_inputs()
									(
									    (
									    snake_case_
									),    (
									    snake_case_
									),    (
									    snake_case_
									),    
									)   =	config_and_inputs
									snake_case_   =	{'''pixel_values''': pixel_values}
									return config, inputs_dict






@require_torch
class 	snake_case_   (		__A	,       __A	,       unittest.TestCase					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   Dict									=				(UperNetForSemanticSegmentation,) if is_torch_available() else ()
			SCREAMING_SNAKE_CASE     :   Union[str, Any]									=				{"image-segmentation": UperNetForSemanticSegmentation} if is_torch_available() else {}
			SCREAMING_SNAKE_CASE     :   Optional[int]									=				False
			SCREAMING_SNAKE_CASE     :   Union[str, Any]									=				False
			SCREAMING_SNAKE_CASE     :   Any									=				False
			SCREAMING_SNAKE_CASE     :   List[Any]									=				False
			SCREAMING_SNAKE_CASE     :   List[str]									=				False
			SCREAMING_SNAKE_CASE     :   str									=				False







			def 				snake_case__(     self :				str							)      ->int:
									snake_case_   =	UperNetModelTester(self							)
									snake_case_   =	ConfigTester(self							,   config_class=_UpperCamelCase							,   has_text_modality=_UpperCamelCase							,   hidden_size=3_7							)







			def 				snake_case__(     self :				int							)      ->List[str]:
									self.create_and_test_config_common_properties()
									self.config_tester.create_and_test_config_to_json_string()
									self.config_tester.create_and_test_config_to_json_file()
									self.config_tester.create_and_test_config_from_and_save_pretrained()
									self.config_tester.create_and_test_config_with_num_labels()
									self.config_tester.check_config_can_be_init_without_params()
									self.config_tester.check_config_arguments_init()







			def 				snake_case__(     self :				List[Any]							)      ->Dict:
									return







			def 				snake_case__(     self :				Tuple							)      ->str:
									snake_case_,    snake_case_   =	self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															snake_case_   =	model_class(_UpperCamelCase							)
															snake_case_   =	inspect.signature(model.forward							)
															# signature.parameters is an OrderedDict => so arg_names order is deterministic
															snake_case_   =	[*signature.parameters.keys()]

															snake_case_   =	['''pixel_values''']
															self.assertListEqual(arg_names[:1]							,   _UpperCamelCase							)







			def 				snake_case__(     self :				List[str]							)      ->Optional[Any]:
									snake_case_   =	self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_semantic_segmentation(*_UpperCamelCase							)







			@unittest.skip(reason='''UperNet does not use inputs_embeds'''							)
			def 				snake_case__(     self :				List[str]							)      ->Tuple:
									pass







			@unittest.skip(reason='''UperNet does not support input and output embeddings'''							)
			def 				snake_case__(     self :				Any							)      ->Tuple:
									pass







			@unittest.skip(reason='''UperNet does not have a base model'''							)
			def 				snake_case__(     self :				Any							)      ->Optional[int]:
									pass







			@unittest.skip(reason='''UperNet does not have a base model'''							)
			def 				snake_case__(     self :				int							)      ->str:
									pass







			@require_torch_multi_gpu
			@unittest.skip(reason='''UperNet has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''							)
			def 				snake_case__(     self :				Dict							)      ->Tuple:
									pass







			@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''							)
			def 				snake_case__(     self :				Optional[int]							)      ->Dict:
									pass







			def 				snake_case__(     self :				Any							)      ->Any:
									def check_hidden_states_output(_UpperCamelCase :				List[str]							,   _UpperCamelCase :				Optional[int]							,   _UpperCamelCase :				List[str]							):
															snake_case_   =	model_class(_UpperCamelCase							)
															model.to(_UpperCamelCase							)
															model.eval()

															with torch.no_grad():
																					snake_case_   =	model(**self._prepare_for_class(_UpperCamelCase							,   _UpperCamelCase							)							)

															snake_case_   =	outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

															snake_case_   =	self.model_tester.num_stages
															self.assertEqual(len(_UpperCamelCase							)							,   expected_num_stages + 1							)

															# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
															self.assertListEqual(
															    list(hidden_states[0].shape[-2:]							)							,   [self.model_tester.image_size // 4, self.model_tester.image_size // 4]							,   )

									snake_case_,    snake_case_   =	self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															snake_case_   =	True
															check_hidden_states_output(_UpperCamelCase							,   _UpperCamelCase							,   _UpperCamelCase							)

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															snake_case_   =	True

															check_hidden_states_output(_UpperCamelCase							,   _UpperCamelCase							,   _UpperCamelCase							)







			def 				snake_case__(     self :				Tuple							)      ->Union[str, Any]:
									snake_case_,    snake_case_   =	self.model_tester.prepare_config_and_inputs_for_common()

									snake_case_   =	_config_zero_init(_UpperCamelCase							)
									snake_case_   =	_config_zero_init(configs_no_init.backbone_config							)
									for model_class in self.all_model_classes:
															snake_case_   =	model_class(config=_UpperCamelCase							)
															for name, param in model.named_parameters():
																					if param.requires_grad:
																											self.assertIn(
																											    ((param.data.mean() * 1e9).round() / 1e9).item()							,   [0.0, 1.0]							,   msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''							,   )







			@unittest.skip(reason='''UperNet does not have tied weights'''							)
			def 				snake_case__(     self :				List[Any]							)      ->Tuple:
									pass







			@slow
			def 				snake_case__(     self :				List[str]							)      ->List[Any]:
									for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															snake_case_   =	UperNetForSemanticSegmentation.from_pretrained(_UpperCamelCase							)
															self.assertIsNotNone(_UpperCamelCase							)






def   __SCREAMING_SNAKE_CASE					():
						snake_case_   =	hf_hub_download(
						    repo_id='''hf-internal-testing/fixtures_ade20k'''  ,		repo_type='''dataset'''  ,		filename='''ADE_val_00000001.jpg'''       )
						snake_case_   =	Image.open(SCREAMING_SNAKE_CASE__       ).convert('''RGB'''       )
						return image





@require_torch
@require_vision
@slow
class 	snake_case_   (		unittest.TestCase					):







			'''simple docstring'''

			def 				snake_case__(     self :				Optional[Any]							)      ->Dict:
									snake_case_   =	AutoImageProcessor.from_pretrained('''openmmlab/upernet-swin-tiny'''							)
									snake_case_   =	UperNetForSemanticSegmentation.from_pretrained('''openmmlab/upernet-swin-tiny'''							).to(_UpperCamelCase							)

									snake_case_   =	prepare_img()
									snake_case_   =	processor(images=_UpperCamelCase							,   return_tensors='''pt'''							).to(_UpperCamelCase							)

									with torch.no_grad():
															snake_case_   =	model(**_UpperCamelCase							)

									snake_case_   =	torch.Size((1, model.config.num_labels, 5_1_2, 5_1_2)							)
									self.assertEqual(outputs.logits.shape							,   _UpperCamelCase							)

									snake_case_   =	torch.tensor(
									    [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]]							).to(_UpperCamelCase							)
									self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]							,   _UpperCamelCase							,   atol=1e-4							)							)







			def 				snake_case__(     self :				int							)      ->Optional[Any]:
									snake_case_   =	AutoImageProcessor.from_pretrained('''openmmlab/upernet-convnext-tiny'''							)
									snake_case_   =	UperNetForSemanticSegmentation.from_pretrained('''openmmlab/upernet-convnext-tiny'''							).to(_UpperCamelCase							)

									snake_case_   =	prepare_img()
									snake_case_   =	processor(images=_UpperCamelCase							,   return_tensors='''pt'''							).to(_UpperCamelCase							)

									with torch.no_grad():
															snake_case_   =	model(**_UpperCamelCase							)

									snake_case_   =	torch.Size((1, model.config.num_labels, 5_1_2, 5_1_2)							)
									self.assertEqual(outputs.logits.shape							,   _UpperCamelCase							)

									snake_case_   =	torch.tensor(
									    [[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]]							).to(_UpperCamelCase							)
									self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]							,   _UpperCamelCase							,   atol=1e-4							)							)
'''simple docstring'''

import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel



class 			UpperCAmelCase  (						pl.LightningModule					):





       '''simple docstring'''



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


       def _lowerCAmelCase(						self )  ->  str:
        pass


def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):
 # load longformer model from model identifier
 lowercase__				:    Dict  =					LongformerModel.from_pretrained(UpperCAmelCase     )
 lowercase__				:    List[str]  =					LightningModel(UpperCAmelCase     )

 lowercase__				:    List[Any]  =					torch.load(UpperCAmelCase		,     map_location=torch.device('''cpu'''     )     )
 lightning_model.load_state_dict(ckpt['''state_dict''']     )

 # init longformer question answering model
 lowercase__				:    Optional[int]  =					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__":
  __a: List[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."""
  )
  __a: Tuple          =   parser.parse_args()
  convert_longformer_qa_checkpoint_to_pytorch(
      args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
  )

