
"""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       _snake_case				(		snake_case__   :     Optional[int]     ):
	A						   =   384
	if "tiny" in model_name:
		A						   =   [3, 3, 9, 3]
		A						   =   [96, 192, 384, 768]
	if "small" in model_name:
		A						   =   [3, 3, 27, 3]
		A						   =   [96, 192, 384, 768]
	if "base" in model_name:
		A						   =   [3, 3, 27, 3]
		A						   =   [128, 256, 512, 1024]
		A						   =   512
	if "large" in model_name:
		A						   =   [3, 3, 27, 3]
		A						   =   [192, 384, 768, 1536]
		A						   =   768
	if "xlarge" in model_name:
		A						   =   [3, 3, 27, 3]
		A						   =   [256, 512, 1024, 2048]
		A						   =   1024

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

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

	return config






def       _snake_case				(		snake_case__   :     Any     ):
	A						   =   []

	# 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       _snake_case				(		snake_case__   :     str	,      snake_case__   :     Optional[int]	,      snake_case__   :     Any     ):
	A						   =   dct.pop(snake_case__     )
	A						   =   val






def       _snake_case				(		snake_case__   :     Any	,      snake_case__   :     str	,      snake_case__   :     Tuple     ):
	A						   =   {
	    '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',
	}
	A						   =   model_name_to_url[model_name]
	A						   =   torch.hub.load_state_dict_from_url(snake_case__	,      map_location='cpu'     )['state_dict']

	A						   =   get_upernet_config(snake_case__     )
	A						   =   UperNetForSemanticSegmentation(snake_case__     )
	model.eval()

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

	# rename keys
	A						   =   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
	A						   =   'https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'
	A						   =   Image.open(requests.get(snake_case__	,      stream=snake_case__     ).raw     ).convert('RGB'     )

	A						   =   SegformerImageProcessor()
	A						   =   processor(snake_case__	,      return_tensors='pt'     ).pixel_values

	with torch.no_grad():
		A						   =   model(snake_case__     )

	if model_name == "upernet-convnext-tiny":
		A						   =   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":
		A						   =   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":
		A						   =   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":
		A						   =   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":
		A						   =   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							   =				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							   =				parser.parse_args()
		convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)

"""simple docstring"""


import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import ImageGPTImageProcessor







class 				lowerCAmelCase_				(      unittest.TestCase    ):



						'''simple docstring'''


						def __init__( self     :      int  ,A_     :      Tuple  ,A_     :      str=7  ,A_     :      Tuple=3  ,A_     :      List[Any]=18  ,A_     :      List[str]=30  ,A_     :      Optional[Any]=400  ,A_     :      Any=True  ,A_     :      Optional[Any]=None  ,A_     :      List[str]=True  ,)     ->   str:
							A						   =   size if size is not None else {'height': 18, 'width': 18}
							A						   =   parent
							A						   =   batch_size
							A						   =   num_channels
							A						   =   image_size
							A						   =   min_resolution
							A						   =   max_resolution
							A						   =   do_resize
							A						   =   size
							A						   =   do_normalize




						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   List[Any]:
							return {
							    # here we create 2 clusters for the sake of simplicity
							    "clusters": np.asarray(
							        [
							            [0.88_66_44_36_34_03_32_03, 0.66_18_82_93_69_54_49_83, 0.38_91_74_64_01_78_68_04],
							            [-0.60_42_55_91_46_88_11_04, -0.0_22_95_00_88_60_52_84_69, 0.54_23_79_73_69_00_32_96],
							        ]      ),
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_normalize": self.do_normalize,
							}







@require_torch
@require_vision
class 				lowerCAmelCase_				(      _lowercase		,					unittest.TestCase    ):



						'''simple docstring'''


						_lowerCamelCase:    List[Any]      =    ImageGPTImageProcessor if is_vision_available() else None


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   Any:
							A						   =   ImageGPTImageProcessingTester(self      )


						@property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   Union[str, Any]:
							return self.image_processor_tester.prepare_image_processor_dict()


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   List[Any]:
							A						   =   self.image_processing_class(**self.image_processor_dict      )
							self.assertTrue(hasattr(A_  ,'clusters'      )      )
							self.assertTrue(hasattr(A_  ,'do_resize'      )      )
							self.assertTrue(hasattr(A_  ,'size'      )      )
							self.assertTrue(hasattr(A_  ,'do_normalize'      )      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   Tuple:
							A						   =   self.image_processing_class.from_dict(self.image_processor_dict      )
							self.assertEqual(image_processor.size  ,{'height': 18, 'width': 18}      )

							A						   =   self.image_processing_class.from_dict(self.image_processor_dict  ,size=42      )
							self.assertEqual(image_processor.size  ,{'height': 42, 'width': 42}      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   str:
							A						   =   self.image_processing_class(**self.image_processor_dict      )
							A						   =   json.loads(image_processor.to_json_string()      )
							for key, value in self.image_processor_dict.items():
								if key == "clusters":
									self.assertTrue(np.array_equal(A_  ,obj[key]      )      )
								else:
									self.assertEqual(obj[key]  ,A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   Union[str, Any]:
							A						   =   self.image_processing_class(**self.image_processor_dict      )

							with tempfile.TemporaryDirectory() as tmpdirname:
								A						   =   os.path.join(A_  ,'image_processor.json'      )
								image_processor_first.to_json_file(A_      )
								A						   =   self.image_processing_class.from_json_file(A_      ).to_dict()

							A						   =   image_processor_first.to_dict()
							for key, value in image_processor_first.items():
								if key == "clusters":
									self.assertTrue(np.array_equal(A_  ,image_processor_second[key]      )      )
								else:
									self.assertEqual(image_processor_first[key]  ,A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   int:
							A						   =   self.image_processing_class(**self.image_processor_dict      )

							with tempfile.TemporaryDirectory() as tmpdirname:
								image_processor_first.save_pretrained(A_      )
								A						   =   self.image_processing_class.from_pretrained(A_      ).to_dict()

							A						   =   image_processor_first.to_dict()
							for key, value in image_processor_first.items():
								if key == "clusters":
									self.assertTrue(np.array_equal(A_  ,image_processor_second[key]      )      )
								else:
									self.assertEqual(image_processor_first[key]  ,A_      )




						@unittest.skip('ImageGPT requires clusters at initialization'      )
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   Union[str, Any]:
							pass






def       _snake_case				(		):
	A						   =   load_dataset('hf-internal-testing/fixtures_image_utils'	,      split='test'     )

	A						   =   Image.open(dataset[4]['file']     )
	A						   =   Image.open(dataset[5]['file']     )

	A						   =   [imagea, imagea]

	return images







@require_vision
@require_torch
class 				lowerCAmelCase_				(      unittest.TestCase    ):



						'''simple docstring'''


						@slow
						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   int:
							A						   =   ImageGPTImageProcessor.from_pretrained('openai/imagegpt-small'      )

							A						   =   prepare_images()

							# test non-batched
							A						   =   image_processing(images[0]  ,return_tensors='pt'      )

							self.assertIsInstance(encoding.input_ids  ,torch.LongTensor      )
							self.assertEqual(encoding.input_ids.shape  ,(1, 1024)      )

							A						   =   [306, 191, 191]
							self.assertEqual(encoding.input_ids[0, :3].tolist()  ,A_      )

							# test batched
							A						   =   image_processing(A_  ,return_tensors='pt'      )

							self.assertIsInstance(encoding.input_ids  ,torch.LongTensor      )
							self.assertEqual(encoding.input_ids.shape  ,(2, 1024)      )

							A						   =   [303, 13, 13]
							self.assertEqual(encoding.input_ids[1, -3:].tolist()  ,A_      )