

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_(				_snake_case  :    Optional[int]      ):




			"""simple docstring"""






			__a     =384
			__a     =7
			if "tiny" in model_name:
						__a     =96
						__a     =(2, 2, 6, 2)
						__a     =(3, 6, 12, 24)
			elif "small" in model_name:
						__a     =96
						__a     =(2, 2, 18, 2)
						__a     =(3, 6, 12, 24)
			elif "base" in model_name:
						__a     =128
						__a     =(2, 2, 18, 2)
						__a     =(4, 8, 16, 32)
						__a     =12
						__a     =512
			elif "large" in model_name:
						__a     =192
						__a     =(2, 2, 18, 2)
						__a     =(6, 12, 24, 48)
						__a     =12
						__a     =768

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

			__a     =SwinConfig(
			    embed_dim=lowerCAmelCase__			,	depths=lowerCAmelCase__			,	num_heads=lowerCAmelCase__			,	window_size=lowerCAmelCase__			,	out_features=['stage1', 'stage2', 'stage3', 'stage4']			,	)
			__a     =UperNetConfig(
			    backbone_config=lowerCAmelCase__			,	auxiliary_in_channels=lowerCAmelCase__			,	num_labels=lowerCAmelCase__			,	idalabel=lowerCAmelCase__			,	labelaid=lowerCAmelCase__			,	)

			return config






def 			UpperCamelCase_(				_snake_case  :    Any      ):




			"""simple docstring"""






			__a     =[]

			# 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_(				_snake_case  :    Optional[int]			,	_snake_case  :    List[str]			,	_snake_case  :    Optional[int]      ):




			"""simple docstring"""






			__a     =dct.pop(lowerCAmelCase__      )
			__a     =val






def 			UpperCamelCase_(				_snake_case  :    Any			,	_snake_case  :    Union[str, Any]      ):




			"""simple docstring"""






			__a     =[int(backbone_config.embed_dim * 2**i      ) for i in range(len(backbone_config.depths      )      )]
			for i in range(len(backbone_config.depths      )      ):
						__a     =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)
									__a     =state_dict.pop(F'backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight'      )
									__a     =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
									__a     =in_proj_weight[:dim, :]
									__a     =in_proj_bias[: dim]
									__a     =in_proj_weight[
									    dim : dim * 2, :
									]
									__a     =in_proj_bias[
									    dim : dim * 2
									]
									__a     =in_proj_weight[
									    -dim :, :
									]
									__a     =in_proj_bias[-dim :]
									# fmt: on






def 			UpperCamelCase_(				_snake_case  :    Optional[int]      ):




			"""simple docstring"""






			__a ,       __a     =x.shape
			__a     =x.reshape(lowerCAmelCase__			,	4			,	in_channel // 4      )
			__a     =x[:, [0, 2, 1, 3], :].transpose(1			,	2      ).reshape(lowerCAmelCase__			,	lowerCAmelCase__      )
			return x






def 			UpperCamelCase_(				_snake_case  :    Optional[int]      ):




			"""simple docstring"""






			__a ,       __a     =x.shape
			__a     =x.reshape(lowerCAmelCase__			,	in_channel // 4			,	4      )
			__a     =x[:, :, [0, 2, 1, 3]].transpose(1			,	2      ).reshape(lowerCAmelCase__			,	lowerCAmelCase__      )

			return x






def 			UpperCamelCase_(				_snake_case  :    Any      ):




			"""simple docstring"""






			__a     =x.shape[0]
			__a     =x.reshape(4			,	in_channel // 4      )
			__a     =x[[0, 2, 1, 3], :].transpose(0			,	1      ).reshape(lowerCAmelCase__      )
			return x






def 			UpperCamelCase_(				_snake_case  :    Union[str, Any]      ):




			"""simple docstring"""






			__a     =x.shape[0]
			__a     =x.reshape(in_channel // 4			,	4      )
			__a     =x[:, [0, 2, 1, 3]].transpose(0			,	1      ).reshape(lowerCAmelCase__      )
			return x






def 			UpperCamelCase_(				_snake_case  :    int			,	_snake_case  :    Any			,	_snake_case  :    Union[str, Any]      ):




			"""simple docstring"""






			__a     ={
			    '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',
			}
			__a     =model_name_to_url[model_name]
			__a     =torch.hub.load_state_dict_from_url(lowerCAmelCase__			,	map_location='cpu'			,	file_name=lowerCAmelCase__      )[
			    'state_dict'
			]

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

			__a     =get_upernet_config(lowerCAmelCase__      )
			__a     =UperNetForSemanticSegmentation(lowerCAmelCase__      )
			model.eval()

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

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

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

			model.load_state_dict(lowerCAmelCase__      )

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

			__a     =SegformerImageProcessor()
			__a     =processor(lowerCAmelCase__			,	return_tensors='pt'      ).pixel_values

			with torch.no_grad():
						__a     =model(lowerCAmelCase__      )
						__a     =outputs.logits

			print(logits.shape      )
			print('First values of logits:'			,	logits[0, 0, :3, :3]      )
			# assert values
			if model_name == "upernet-swin-tiny":
						__a     =torch.tensor(
						    [[-7.5_958, -7.5_958, -7.4_302], [-7.5_958, -7.5_958, -7.4_302], [-7.4_797, -7.4_797, -7.3_068]]      )
			elif model_name == "upernet-swin-small":
						__a     =torch.tensor(
						    [[-7.1_921, -7.1_921, -6.9_532], [-7.1_921, -7.1_921, -6.9_532], [-7.0_908, -7.0_908, -6.8_534]]      )
			elif model_name == "upernet-swin-base":
						__a     =torch.tensor(
						    [[-6.5_851, -6.5_851, -6.4_330], [-6.5_851, -6.5_851, -6.4_330], [-6.4_763, -6.4_763, -6.3_254]]      )
			elif model_name == "upernet-swin-large":
						__a     =torch.tensor(
						    [[-7.5_297, -7.5_297, -7.3_802], [-7.5_297, -7.5_297, -7.3_802], [-7.4_044, -7.4_044, -7.2_586]]      )
			print('Logits:'			,	outputs.logits[0, 0, :3, :3]      )
			assert torch.allclose(outputs.logits[0, 0, :3, :3]			,	lowerCAmelCase__			,	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(lowerCAmelCase__      )
						print(F'Saving processor to {pytorch_dump_folder_path}'      )
						processor.save_pretrained(lowerCAmelCase__      )

			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__":
						_lowerCAmelCase   :     str    							=     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."
						)

						_lowerCAmelCase   :     int    							=     parser.parse_args()
						convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)











import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
				import torch

				from transformers import AutoModelForCausalLM



@require_torch
class        lowerCAmelCase__					(  unittest.TestCase       ):


				def __UpperCamelCase						(							self      :  List[Any]       )		->     Optional[Any]:
											A      						=    AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'       )
											A      						=    AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'       ).to(__UpperCamelCase       )
											A      						=    -1

											A      						=    ids_tensor((1, 5)						, vocab_size=model.config.vocab_size       ).to(__UpperCamelCase       )
											A      						=    model.generate(__UpperCamelCase						, max_new_tokens=10						, do_sample=__UpperCamelCase       )
											A      						=    tokenizer.decode(greedy_ids[0]       )

											with CaptureStdout() as cs:
																		A      						=    TextStreamer(__UpperCamelCase       )
																		model.generate(__UpperCamelCase						, max_new_tokens=10						, do_sample=__UpperCamelCase						, streamer=__UpperCamelCase       )
											# The greedy text should be printed to stdout, except for the final "\n" in the streamer
											A      						=    cs.out[:-1]

											self.assertEqual(__UpperCamelCase						, __UpperCamelCase       )


				def __UpperCamelCase						(							self      :  int       )		->     List[Any]:
											A      						=    AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'       )
											A      						=    AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'       ).to(__UpperCamelCase       )
											A      						=    -1

											A      						=    ids_tensor((1, 5)						, vocab_size=model.config.vocab_size       ).to(__UpperCamelCase       )
											A      						=    model.generate(__UpperCamelCase						, max_new_tokens=10						, do_sample=__UpperCamelCase       )
											A      						=    tokenizer.decode(greedy_ids[0]       )

											A      						=    TextIteratorStreamer(__UpperCamelCase       )
											A      						=    {'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
											A      						=    Thread(target=model.generate						, kwargs=__UpperCamelCase       )
											thread.start()
											A      						=    ''
											for new_text in streamer:
																		streamer_text += new_text

											self.assertEqual(__UpperCamelCase						, __UpperCamelCase       )


				def __UpperCamelCase						(							self      :  int       )		->     Tuple:
											A      						=    AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'       )
											A      						=    AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'       ).to(__UpperCamelCase       )
											A      						=    -1

											A      						=    ids_tensor((1, 5)						, vocab_size=model.config.vocab_size       ).to(__UpperCamelCase       )
											A      						=    model.generate(__UpperCamelCase						, max_new_tokens=10						, do_sample=__UpperCamelCase       )
											A      						=    greedy_ids[:, input_ids.shape[1] :]
											A      						=    tokenizer.decode(new_greedy_ids[0]       )

											with CaptureStdout() as cs:
																		A      						=    TextStreamer(__UpperCamelCase						, skip_prompt=__UpperCamelCase       )
																		model.generate(__UpperCamelCase						, max_new_tokens=10						, do_sample=__UpperCamelCase						, streamer=__UpperCamelCase       )
											# The greedy text should be printed to stdout, except for the final "\n" in the streamer
											A      						=    cs.out[:-1]

											self.assertEqual(__UpperCamelCase						, __UpperCamelCase       )


				def __UpperCamelCase						(							self      :  List[Any]       )		->     Tuple:
											# Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
											# with actual models -- the dummy models' tokenizers are not aligned with their models, and
											# `skip_special_tokens=True` has no effect on them
											A      						=    AutoTokenizer.from_pretrained('distilgpt2'       )
											A      						=    AutoModelForCausalLM.from_pretrained('distilgpt2'       ).to(__UpperCamelCase       )
											A      						=    -1

											A      						=    torch.ones((1, 5)						, device=__UpperCamelCase       ).long() * model.config.bos_token_id
											with CaptureStdout() as cs:
																		A      						=    TextStreamer(__UpperCamelCase						, skip_special_tokens=__UpperCamelCase       )
																		model.generate(__UpperCamelCase						, max_new_tokens=1						, do_sample=__UpperCamelCase						, streamer=__UpperCamelCase       )

											# The prompt contains a special token, so the streamer should not print it. As such, the output text, when
											# re-tokenized, must only contain one token
											A      						=    cs.out[:-1]  # Remove the final "\n"
											A      						=    tokenizer(__UpperCamelCase						, return_tensors='pt'       )
											self.assertEqual(streamer_text_tokenized.input_ids.shape						, (1, 1)       )


				def __UpperCamelCase						(							self      :  Any       )		->     Dict:
											A      						=    AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'       )
											A      						=    AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'       ).to(__UpperCamelCase       )
											A      						=    -1

											A      						=    ids_tensor((1, 5)						, vocab_size=model.config.vocab_size       ).to(__UpperCamelCase       )
											A      						=    TextIteratorStreamer(__UpperCamelCase						, timeout=0.0_0_1       )
											A      						=    {'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
											A      						=    Thread(target=model.generate						, kwargs=__UpperCamelCase       )
											thread.start()

											# The streamer will timeout after 0.001 seconds, so an exception will be raised
											with self.assertRaises(__UpperCamelCase       ):
																		A      						=    ''
																		for new_text in streamer:
																									streamer_text += new_text