



'''simple docstring'''

import argparse
import json

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

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation







def 						__UpperCamelCase				(							_lowercase					) ->			Union[str, Any]:
	_lowercase					:       int       =		384
	_lowercase					:       Union[str, Any]       =		7
	if "tiny" in model_name:
		_lowercase					:       Optional[Any]       =		96
		_lowercase					:       Dict       =		(2, 2, 6, 2)
		_lowercase					:       Dict       =		(3, 6, 12, 24)
	elif "small" in model_name:
		_lowercase					:       Union[str, Any]       =		96
		_lowercase					:       Dict       =		(2, 2, 18, 2)
		_lowercase					:       Dict       =		(3, 6, 12, 24)
	elif "base" in model_name:
		_lowercase					:       Any       =		128
		_lowercase					:       Optional[Any]       =		(2, 2, 18, 2)
		_lowercase					:       str       =		(4, 8, 16, 32)
		_lowercase					:       List[Any]       =		12
		_lowercase					:       Any       =		512
	elif "large" in model_name:
		_lowercase					:       List[str]       =		192
		_lowercase					:       List[Any]       =		(2, 2, 18, 2)
		_lowercase					:       Union[str, Any]       =		(6, 12, 24, 48)
		_lowercase					:       int       =		12
		_lowercase					:       int       =		768

	# set label information
	_lowercase					:       List[Any]       =		150
	_lowercase					:       List[str]       =		'huggingface/label-files'
	_lowercase					:       str       =		'ade20k-id2label.json'
	_lowercase					:       List[Any]       =		json.load(open(hf_hub_download(_lowercase,				_lowercase,				repo_type='dataset'					),				'r'					)					)
	_lowercase					:       Optional[Any]       =		{int(_lowercase					): v for k, v in idalabel.items()}
	_lowercase					:       str       =		{v: k for k, v in idalabel.items()}

	_lowercase					:       Optional[Any]       =		SwinConfig(
	    embed_dim=_lowercase,				depths=_lowercase,				num_heads=_lowercase,				window_size=_lowercase,				out_features=['stage1', 'stage2', 'stage3', 'stage4'],				)
	_lowercase					:       Tuple       =		UperNetConfig(
	    backbone_config=_lowercase,				auxiliary_in_channels=_lowercase,				num_labels=_lowercase,				idalabel=_lowercase,				labelaid=_lowercase,				)

	return config







def 						__UpperCamelCase				(							_lowercase					) ->			int:
	_lowercase					:       Any       =		[]

	# fmt: off
	# stem
	rename_keys.append(('backbone.patch_embed.projection.weight', 'backbone.embeddings.patch_embeddings.projection.weight')					)
	rename_keys.append(('backbone.patch_embed.projection.bias', 'backbone.embeddings.patch_embeddings.projection.bias')					)
	rename_keys.append(('backbone.patch_embed.norm.weight', 'backbone.embeddings.norm.weight')					)
	rename_keys.append(('backbone.patch_embed.norm.bias', 'backbone.embeddings.norm.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}.blocks.{j}.norm1.weight''', f'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''')					)
			rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.norm1.bias''', f'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''')					)
			rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table''', f'''backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''')					)
			rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index''', f'''backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''')					)
			rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight''', f'''backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''')					)
			rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias''', f'''backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''')					)
			rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.norm2.weight''', f'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''')					)
			rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.norm2.bias''', f'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''')					)
			rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight''', f'''backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''')					)
			rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias''', f'''backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''')					)
			rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight''', f'''backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight''')					)
			rename_keys.append((f'''backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias''', f'''backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias''')					)

		if i < 3:
			rename_keys.append((f'''backbone.stages.{i}.downsample.reduction.weight''', f'''backbone.encoder.layers.{i}.downsample.reduction.weight''')					)
			rename_keys.append((f'''backbone.stages.{i}.downsample.norm.weight''', f'''backbone.encoder.layers.{i}.downsample.norm.weight''')					)
			rename_keys.append((f'''backbone.stages.{i}.downsample.norm.bias''', f'''backbone.encoder.layers.{i}.downsample.norm.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 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase					) ->			Optional[Any]:
	_lowercase					:       Optional[int]       =		dct.pop(_lowercase					)
	_lowercase					:       List[str]       =		val







def 						__UpperCamelCase				(							_lowercase,				_lowercase					) ->			str:
	_lowercase					:       Optional[int]       =		[int(backbone_config.embed_dim * 2**i					) for i in range(len(backbone_config.depths					)					)]
	for i in range(len(backbone_config.depths					)					):
		_lowercase					:       Dict       =		num_features[i]
		for j in range(backbone_config.depths[i]					):
			# fmt: off
			# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
			_lowercase					:       Optional[int]       =		state_dict.pop(f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight'''					)
			_lowercase					:       str       =		state_dict.pop(f'''backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias'''					)
			# next, add query, keys and values (in that order) to the state dict
			_lowercase					:       Dict       =		in_proj_weight[:dim, :]
			_lowercase					:       List[str]       =		in_proj_bias[: dim]
			_lowercase					:       Dict       =		in_proj_weight[
			    dim : dim * 2, :
			]
			_lowercase					:       List[Any]       =		in_proj_bias[
			    dim : dim * 2
			]
			_lowercase					:       Optional[int]       =		in_proj_weight[
			    -dim :, :
			]
			_lowercase					:       int       =		in_proj_bias[-dim :]
			# fmt: on







def 						__UpperCamelCase				(							_lowercase					) ->			List[str]:
	_lowercase					:       Dict       =		x.shape
	_lowercase					:       Dict       =		x.reshape(_lowercase,				4,				in_channel // 4					)
	_lowercase					:       Optional[Any]       =		x[:, [0, 2, 1, 3], :].transpose(1,				2					).reshape(_lowercase,				_lowercase					)
	return x







def 						__UpperCamelCase				(							_lowercase					) ->			List[Any]:
	_lowercase					:       Optional[int]       =		x.shape
	_lowercase					:       Optional[Any]       =		x.reshape(_lowercase,				in_channel // 4,				4					)
	_lowercase					:       str       =		x[:, :, [0, 2, 1, 3]].transpose(1,				2					).reshape(_lowercase,				_lowercase					)

	return x







def 						__UpperCamelCase				(							_lowercase					) ->			Tuple:
	_lowercase					:       str       =		x.shape[0]
	_lowercase					:       List[Any]       =		x.reshape(4,				in_channel // 4					)
	_lowercase					:       Union[str, Any]       =		x[[0, 2, 1, 3], :].transpose(0,				1					).reshape(_lowercase					)
	return x







def 						__UpperCamelCase				(							_lowercase					) ->			List[Any]:
	_lowercase					:       Dict       =		x.shape[0]
	_lowercase					:       Any       =		x.reshape(in_channel // 4,				4					)
	_lowercase					:       List[str]       =		x[:, [0, 2, 1, 3]].transpose(0,				1					).reshape(_lowercase					)
	return x







def 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase					) ->			str:
	_lowercase					:       Dict       =		{
	    'upernet-swin-tiny': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth',
	    'upernet-swin-small': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth',
	    'upernet-swin-base': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth',
	    'upernet-swin-large': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth',
	}
	_lowercase					:       Dict       =		model_name_to_url[model_name]
	_lowercase					:       Any       =		torch.hub.load_state_dict_from_url(_lowercase,				map_location='cpu',				file_name=_lowercase					)[
	    'state_dict'
	]

	for name, param in state_dict.items():
		print(_lowercase,				param.shape					)

	_lowercase					:       str       =		get_upernet_config(_lowercase					)
	_lowercase					:       List[Any]       =		UperNetForSemanticSegmentation(_lowercase					)
	model.eval()

	# replace "bn" => "batch_norm"
	for key in state_dict.copy().keys():
		_lowercase					:       Tuple       =		state_dict.pop(_lowercase					)
		if "bn" in key:
			_lowercase					:       str       =		key.replace('bn',				'batch_norm'					)
		_lowercase					:       Union[str, Any]       =		val

	# rename keys
	_lowercase					:       str       =		create_rename_keys(_lowercase					)
	for src, dest in rename_keys:
		rename_key(_lowercase,				_lowercase,				_lowercase					)
	read_in_q_k_v(_lowercase,				config.backbone_config					)

	# fix downsample parameters
	for key, value in state_dict.items():
		if "downsample" in key:
			if "reduction" in key:
				_lowercase					:       Any       =		reverse_correct_unfold_reduction_order(_lowercase					)
			if "norm" in key:
				_lowercase					:       Tuple       =		reverse_correct_unfold_norm_order(_lowercase					)

	model.load_state_dict(_lowercase					)

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

	_lowercase					:       str       =		SegformerImageProcessor()
	_lowercase					:       Optional[int]       =		processor(_lowercase,				return_tensors='pt'					).pixel_values

	with torch.no_grad():
		_lowercase					:       str       =		model(_lowercase					)
		_lowercase					:       Tuple       =		outputs.logits

	print(logits.shape					)
	print('First values of logits:',				logits[0, 0, :3, :3]					)
	# assert values
	if model_name == "upernet-swin-tiny":
		_lowercase					:       Dict       =		torch.tensor(
		    [[-7.5_9_5_8, -7.5_9_5_8, -7.4_3_0_2], [-7.5_9_5_8, -7.5_9_5_8, -7.4_3_0_2], [-7.4_7_9_7, -7.4_7_9_7, -7.3_0_6_8]]					)
	elif model_name == "upernet-swin-small":
		_lowercase					:       Union[str, Any]       =		torch.tensor(
		    [[-7.1_9_2_1, -7.1_9_2_1, -6.9_5_3_2], [-7.1_9_2_1, -7.1_9_2_1, -6.9_5_3_2], [-7.0_9_0_8, -7.0_9_0_8, -6.8_5_3_4]]					)
	elif model_name == "upernet-swin-base":
		_lowercase					:       int       =		torch.tensor(
		    [[-6.5_8_5_1, -6.5_8_5_1, -6.4_3_3_0], [-6.5_8_5_1, -6.5_8_5_1, -6.4_3_3_0], [-6.4_7_6_3, -6.4_7_6_3, -6.3_2_5_4]]					)
	elif model_name == "upernet-swin-large":
		_lowercase					:       Any       =		torch.tensor(
		    [[-7.5_2_9_7, -7.5_2_9_7, -7.3_8_0_2], [-7.5_2_9_7, -7.5_2_9_7, -7.3_8_0_2], [-7.4_0_4_4, -7.4_0_4_4, -7.2_5_8_6]]					)
	print('Logits:',				outputs.logits[0, 0, :3, :3]					)
	assert torch.allclose(outputs.logits[0, 0, :3, :3],				_lowercase,				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(_lowercase					)
		print(f'''Saving processor to {pytorch_dump_folder_path}'''					)
		processor.save_pretrained(_lowercase					)

	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__":
		_A				:    Optional[Any]     						=				argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--model_name''',
		    default='''upernet-swin-tiny''',
		    type=str,
		    choices=[F'''upernet-swin-{size}''' for size in ['''tiny''', '''small''', '''base''', '''large''']],
		    help='''Name of the Swin + 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.'''
		)

		_A				:    Optional[Any]     						=				parser.parse_args()
		convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





'''simple docstring'''

import unittest

from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow


if is_flax_available():
		import jax.numpy as jnp

		from transformers import FlaxXLMRobertaModel



@require_sentencepiece
@require_tokenizers
@require_flax
class 		lowerCamelCase__      (						unittest.TestCase     ):




					'''simple docstring'''



					@slow
					def 						__UpperCAmelCase     (  self				:	int					)  ->       Union[str, Any]:



						'''simple docstring'''





						_lowercase					:       List[Any]       =		FlaxXLMRobertaModel.from_pretrained('xlm-roberta-base'					)
						_lowercase					:       str       =		AutoTokenizer.from_pretrained('xlm-roberta-base'					)
						_lowercase					:       List[Any]       =		'The dog is cute and lives in the garden house'
						_lowercase					:       Optional[int]       =		jnp.array([tokenizer.encode(UpperCamelCase_					)]					)

						_lowercase					:       int       =		(1, 12, 768)  # batch_size, sequence_length, embedding_vector_dim
						_lowercase					:       Tuple       =		jnp.array(
						    [[-0.01_01, 0.12_18, -0.08_03, 0.08_01, 0.13_27, 0.07_76, -0.12_15, 0.23_83, 0.33_38, 0.31_06, 0.03_00, 0.02_52]]					)

						_lowercase					:       List[str]       =		model(UpperCamelCase_					)['last_hidden_state']
						self.assertEqual(output.shape     ,  UpperCamelCase_					)
						# compare the actual values for a slice of last dim
						self.assertTrue(jnp.allclose(output[:, :, -1]     ,  UpperCamelCase_     ,  atol=1E-3					)					)
