

"""simple docstring"""




import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation





def       lowercase__						(       snake_case_   :Optional[Any]    ):
	__UpperCAmelCase      		= 384
	if "tiny" in model_name:
		__UpperCAmelCase      		= [3, 3, 9, 3]
		__UpperCAmelCase      		= [96, 192, 384, 768]
	if "small" in model_name:
		__UpperCAmelCase      		= [3, 3, 27, 3]
		__UpperCAmelCase      		= [96, 192, 384, 768]
	if "base" in model_name:
		__UpperCAmelCase      		= [3, 3, 27, 3]
		__UpperCAmelCase      		= [128, 256, 512, 1_024]
		__UpperCAmelCase      		= 512
	if "large" in model_name:
		__UpperCAmelCase      		= [3, 3, 27, 3]
		__UpperCAmelCase      		= [192, 384, 768, 1_536]
		__UpperCAmelCase      		= 768
	if "xlarge" in model_name:
		__UpperCAmelCase      		= [3, 3, 27, 3]
		__UpperCAmelCase      		= [256, 512, 1_024, 2_048]
		__UpperCAmelCase      		= 1_024

	# set label information
	__UpperCAmelCase      		= 150
	__UpperCAmelCase      		= '''huggingface/label-files'''
	__UpperCAmelCase      		= '''ade20k-id2label.json'''
	__UpperCAmelCase      		= json.load(open(hf_hub_download(snake_case_			,		snake_case_			,		repo_type='''dataset'''    )			,		'''r'''    )    )
	__UpperCAmelCase      		= {int(snake_case_    ): v for k, v in idalabel.items()}
	__UpperCAmelCase      		= {v: k for k, v in idalabel.items()}

	__UpperCAmelCase      		= ConvNextConfig(
	    depths=snake_case_			,		hidden_sizes=snake_case_			,		out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']    )
	__UpperCAmelCase      		= UperNetConfig(
	    backbone_config=snake_case_			,		auxiliary_in_channels=snake_case_			,		num_labels=snake_case_			,		idalabel=snake_case_			,		labelaid=snake_case_			,		)

	return config





def       lowercase__						(       snake_case_   :Dict    ):
	__UpperCAmelCase      		= []

	# fmt: off
	# stem
	rename_keys.append(('''backbone.downsample_layers.0.0.weight''', '''backbone.embeddings.patch_embeddings.weight''')    )
	rename_keys.append(('''backbone.downsample_layers.0.0.bias''', '''backbone.embeddings.patch_embeddings.bias''')    )
	rename_keys.append(('''backbone.downsample_layers.0.1.weight''', '''backbone.embeddings.layernorm.weight''')    )
	rename_keys.append(('''backbone.downsample_layers.0.1.bias''', '''backbone.embeddings.layernorm.bias''')    )
	# stages
	for i in range(len(config.backbone_config.depths    )    ):
		for j in range(config.backbone_config.depths[i]    ):
			rename_keys.append((F'''backbone.stages.{i}.{j}.gamma''', F'''backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter''')    )
			rename_keys.append((F'''backbone.stages.{i}.{j}.depthwise_conv.weight''', F'''backbone.encoder.stages.{i}.layers.{j}.dwconv.weight''')    )
			rename_keys.append((F'''backbone.stages.{i}.{j}.depthwise_conv.bias''', F'''backbone.encoder.stages.{i}.layers.{j}.dwconv.bias''')    )
			rename_keys.append((F'''backbone.stages.{i}.{j}.norm.weight''', F'''backbone.encoder.stages.{i}.layers.{j}.layernorm.weight''')    )
			rename_keys.append((F'''backbone.stages.{i}.{j}.norm.bias''', F'''backbone.encoder.stages.{i}.layers.{j}.layernorm.bias''')    )
			rename_keys.append((F'''backbone.stages.{i}.{j}.pointwise_conv1.weight''', F'''backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight''')    )
			rename_keys.append((F'''backbone.stages.{i}.{j}.pointwise_conv1.bias''', F'''backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias''')    )
			rename_keys.append((F'''backbone.stages.{i}.{j}.pointwise_conv2.weight''', F'''backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight''')    )
			rename_keys.append((F'''backbone.stages.{i}.{j}.pointwise_conv2.bias''', F'''backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias''')    )
		if i > 0:
			rename_keys.append((F'''backbone.downsample_layers.{i}.0.weight''', F'''backbone.encoder.stages.{i}.downsampling_layer.0.weight''')    )
			rename_keys.append((F'''backbone.downsample_layers.{i}.0.bias''', F'''backbone.encoder.stages.{i}.downsampling_layer.0.bias''')    )
			rename_keys.append((F'''backbone.downsample_layers.{i}.1.weight''', F'''backbone.encoder.stages.{i}.downsampling_layer.1.weight''')    )
			rename_keys.append((F'''backbone.downsample_layers.{i}.1.bias''', F'''backbone.encoder.stages.{i}.downsampling_layer.1.bias''')    )

		rename_keys.append((F'''backbone.norm{i}.weight''', F'''backbone.hidden_states_norms.stage{i+1}.weight''')    )
		rename_keys.append((F'''backbone.norm{i}.bias''', F'''backbone.hidden_states_norms.stage{i+1}.bias''')    )

	# decode head
	rename_keys.extend(
	    [
	        ('''decode_head.conv_seg.weight''', '''decode_head.classifier.weight'''),
	        ('''decode_head.conv_seg.bias''', '''decode_head.classifier.bias'''),
	        ('''auxiliary_head.conv_seg.weight''', '''auxiliary_head.classifier.weight'''),
	        ('''auxiliary_head.conv_seg.bias''', '''auxiliary_head.classifier.bias'''),
	    ]    )
	# fmt: on

	return rename_keys





def       lowercase__						(       snake_case_   :Tuple			,		snake_case_   :Optional[int]			,		snake_case_   :Any    ):
	__UpperCAmelCase      		= dct.pop(snake_case_    )
	__UpperCAmelCase      		= val





def       lowercase__						(       snake_case_   :Dict			,		snake_case_   :List[str]			,		snake_case_   :List[Any]    ):
	__UpperCAmelCase      		= {
	    '''upernet-convnext-tiny''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth''',
	    '''upernet-convnext-small''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth''',
	    '''upernet-convnext-base''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth''',
	    '''upernet-convnext-large''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth''',
	    '''upernet-convnext-xlarge''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth''',
	}
	__UpperCAmelCase      		= model_name_to_url[model_name]
	__UpperCAmelCase      		= torch.hub.load_state_dict_from_url(snake_case_			,		map_location='''cpu'''    )['''state_dict''']

	__UpperCAmelCase      		= get_upernet_config(snake_case_    )
	__UpperCAmelCase      		= UperNetForSemanticSegmentation(snake_case_    )
	model.eval()

	# replace "bn" => "batch_norm"
	for key in state_dict.copy().keys():
		__UpperCAmelCase      		= state_dict.pop(snake_case_    )
		if "bn" in key:
			__UpperCAmelCase      		= key.replace('''bn'''			,		'''batch_norm'''    )
		__UpperCAmelCase      		= val

	# rename keys
	__UpperCAmelCase      		= create_rename_keys(snake_case_    )
	for src, dest in rename_keys:
		rename_key(snake_case_			,		snake_case_			,		snake_case_    )

	model.load_state_dict(snake_case_    )

	# verify on image
	__UpperCAmelCase      		= '''https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'''
	__UpperCAmelCase      		= Image.open(requests.get(snake_case_			,		stream=snake_case_    ).raw    ).convert('''RGB'''    )

	__UpperCAmelCase      		= SegformerImageProcessor()
	__UpperCAmelCase      		= processor(snake_case_			,		return_tensors='''pt'''    ).pixel_values

	with torch.no_grad():
		__UpperCAmelCase      		= model(snake_case_    )

	if model_name == "upernet-convnext-tiny":
		__UpperCAmelCase      		= torch.tensor(
		    [[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]]    )
	elif model_name == "upernet-convnext-small":
		__UpperCAmelCase      		= torch.tensor(
		    [[-8.8236, -8.8236, -8.6771], [-8.8236, -8.8236, -8.6771], [-8.7638, -8.7638, -8.6240]]    )
	elif model_name == "upernet-convnext-base":
		__UpperCAmelCase      		= torch.tensor(
		    [[-8.8558, -8.8558, -8.6905], [-8.8558, -8.8558, -8.6905], [-8.7669, -8.7669, -8.6021]]    )
	elif model_name == "upernet-convnext-large":
		__UpperCAmelCase      		= torch.tensor(
		    [[-8.6660, -8.6660, -8.6210], [-8.6660, -8.6660, -8.6210], [-8.6310, -8.6310, -8.5964]]    )
	elif model_name == "upernet-convnext-xlarge":
		__UpperCAmelCase      		= torch.tensor(
		    [[-8.4980, -8.4980, -8.3977], [-8.4980, -8.4980, -8.3977], [-8.4379, -8.4379, -8.3412]]    )
	print('''Logits:'''			,		outputs.logits[0, 0, :3, :3]    )
	assert torch.allclose(outputs.logits[0, 0, :3, :3]			,		snake_case_			,		atol=1E-4    )
	print('''Looks ok!'''    )

	if pytorch_dump_folder_path is not None:
		print(F'''Saving model {model_name} to {pytorch_dump_folder_path}'''    )
		model.save_pretrained(snake_case_    )
		print(F'''Saving processor to {pytorch_dump_folder_path}'''    )
		processor.save_pretrained(snake_case_    )

	if push_to_hub:
		print(F'''Pushing model and processor for {model_name} to hub'''    )
		model.push_to_hub(F'''openmmlab/{model_name}'''    )
		processor.push_to_hub(F'''openmmlab/{model_name}'''    )


if __name__ == "__main__":
				_lowercase   :	Dict												=						argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--model_name',
				    default='upernet-convnext-tiny',
				    type=str,
				    choices=[f"""upernet-convnext-{size}""" for size in ['tiny', 'small', 'base', 'large', 'xlarge']],
				    help='Name of the ConvNext UperNet model you\'d like to convert.',
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
				)
				parser.add_argument(
				    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
				)

				_lowercase   :	Dict												=						parser.parse_args()
				convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





import unittest

from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
							from PIL import Image
else:


							class   __lowerCAmelCase  :
												"""simple docstring"""






												@staticmethod
												def      _a				(  *_snake_case       :							Any					,       **_snake_case       :							Optional[int]       ):






															"""simple docstring"""


															pass


@is_pipeline_test
@require_torch
@require_vision
class   __lowerCAmelCase  (		unittest.TestCase   ):
					"""simple docstring"""






					A__					:		Union[str, Any]      							=     MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING



					def      _a				(  self       :							List[Any]					,       _snake_case       :							Union[str, Any]					,       _snake_case       :							Tuple					,       _snake_case       :							Union[str, Any]       ):






								"""simple docstring"""


								A__	  =     pipeline('visual-question-answering'					,       model='hf-internal-testing/tiny-vilt-random-vqa'       )
								A__	  =     [
								    {
								        'image': Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       ),
								        'question': 'How many cats are there?',
								    },
								    {
								        'image': './tests/fixtures/tests_samples/COCO/000000039769.png',
								        'question': 'How many cats are there?',
								    },
								]
								return vqa_pipeline, examples



					def      _a				(  self       :							Optional[Any]					,       _snake_case       :							Union[str, Any]					,       _snake_case       :							List[str]       ):






								"""simple docstring"""


								A__	  =     vqa_pipeline(_snake_case					,       top_k=1       )
								self.assertEqual(
								    _snake_case					,       [
								        [{'score': ANY(_snake_case       ), 'answer': ANY(_snake_case       )}],
								        [{'score': ANY(_snake_case       ), 'answer': ANY(_snake_case       )}],
								    ]					,       )



					@require_torch
					def      _a				(  self       :							Any       ):






								"""simple docstring"""


								A__	  =     pipeline('visual-question-answering'					,       model='hf-internal-testing/tiny-vilt-random-vqa'       )
								A__	  =     './tests/fixtures/tests_samples/COCO/000000039769.png'
								A__	  =     'How many cats are there?'

								A__	  =     vqa_pipeline(image=_snake_case					,       question='How many cats are there?'					,       top_k=2       )
								self.assertEqual(
								    _snake_case					,       [{'score': ANY(_snake_case       ), 'answer': ANY(_snake_case       )}, {'score': ANY(_snake_case       ), 'answer': ANY(_snake_case       )}]       )

								A__	  =     vqa_pipeline({'image': image, 'question': question}					,       top_k=2       )
								self.assertEqual(
								    _snake_case					,       [{'score': ANY(_snake_case       ), 'answer': ANY(_snake_case       )}, {'score': ANY(_snake_case       ), 'answer': ANY(_snake_case       )}]       )



					@slow
					@require_torch
					def      _a				(  self       :							Any       ):






								"""simple docstring"""


								A__	  =     pipeline('visual-question-answering'					,       model='dandelin/vilt-b32-finetuned-vqa'       )
								A__	  =     './tests/fixtures/tests_samples/COCO/000000039769.png'
								A__	  =     'How many cats are there?'

								A__	  =     vqa_pipeline(image=_snake_case					,       question=_snake_case					,       top_k=2       )
								self.assertEqual(
								    nested_simplify(_snake_case					,       decimals=4       )					,       [{'score': 0.8799, 'answer': '2'}, {'score': 0.296, 'answer': '1'}]       )

								A__	  =     vqa_pipeline({'image': image, 'question': question}					,       top_k=2       )
								self.assertEqual(
								    nested_simplify(_snake_case					,       decimals=4       )					,       [{'score': 0.8799, 'answer': '2'}, {'score': 0.296, 'answer': '1'}]       )

								A__	  =     vqa_pipeline(
								    [{'image': image, 'question': question}, {'image': image, 'question': question}]					,       top_k=2       )
								self.assertEqual(
								    nested_simplify(_snake_case					,       decimals=4       )					,       [[{'score': 0.8799, 'answer': '2'}, {'score': 0.296, 'answer': '1'}]] * 2					,       )



					@require_tf
					@unittest.skip('Visual question answering not implemented in TF'       )
					def      _a				(  self       :							Dict       ):






								"""simple docstring"""


								pass




