



"""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   lowerCAmelCase__			(     lowerCamelCase__			) ->							Union[str, Any]:
							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   lowerCAmelCase__			(     lowerCamelCase__			) ->							Any:
							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   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__			) ->							int:
							A         =					dct.pop(lowerCamelCase__			)
							A         =					val

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__			) ->							Any:
							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   lowerCAmelCase__			(     lowerCamelCase__			) ->							Any:
							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   lowerCAmelCase__			(     lowerCamelCase__			) ->							Union[str, Any]:
							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   lowerCAmelCase__			(     lowerCamelCase__			) ->							str:
							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   lowerCAmelCase__			(     lowerCamelCase__			) ->							List[str]:
							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   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__			) ->							Optional[Any]:
							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(
														    [[-7.59_58, -7.59_58, -7.43_02], [-7.59_58, -7.59_58, -7.43_02], [-7.47_97, -7.47_97, -7.30_68]]			)
							elif model_name == "upernet-swin-small":
														A         =					torch.tensor(
														    [[-7.19_21, -7.19_21, -6.95_32], [-7.19_21, -7.19_21, -6.95_32], [-7.09_08, -7.09_08, -6.85_34]]			)
							elif model_name == "upernet-swin-base":
														A         =					torch.tensor(
														    [[-6.58_51, -6.58_51, -6.43_30], [-6.58_51, -6.58_51, -6.43_30], [-6.47_63, -6.47_63, -6.32_54]]			)
							elif model_name == "upernet-swin-large":
														A         =					torch.tensor(
														    [[-7.52_97, -7.52_97, -7.38_02], [-7.52_97, -7.52_97, -7.38_02], [-7.40_44, -7.40_44, -7.25_86]]			)
							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__":
							A    =			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    =			parser.parse_args()
							convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


A    =			logging.get_logger(__name__)

A    =			{
    'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/config.json',
    'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/config.json'
    # See all FNet models at https://huggingface.co/models?filter=fnet
}






class      UpperCAmelCase__		(      UpperCamelCase     ):
		lowerCAmelCase_       :	List[Any] 							=     """fnet"""



		def __init__( self  :    Tuple      ,							snake_case  :    Optional[int]=32_000      ,							snake_case  :    Optional[Any]=768      ,							snake_case  :    Union[str, Any]=12      ,							snake_case  :    List[str]=3_072      ,							snake_case  :    str="gelu_new"      ,							snake_case  :    Any=0.1      ,							snake_case  :    List[str]=512      ,							snake_case  :    Any=4      ,							snake_case  :    str=0.02      ,							snake_case  :    Union[str, Any]=1E-1_2      ,							snake_case  :    Optional[Any]=False      ,							snake_case  :    List[Any]=512      ,							snake_case  :    int=3      ,							snake_case  :    Any=1      ,							snake_case  :    Union[str, Any]=2      ,							**snake_case  :    Union[str, Any]      ,							)			->						Dict:






									'''simple docstring'''

									super().__init__(pad_token_id=snake_case      ,							bos_token_id=snake_case      ,							eos_token_id=snake_case      ,							**snake_case				)

									A         =					vocab_size
									A         =					max_position_embeddings
									A         =					hidden_size
									A         =					num_hidden_layers
									A         =					intermediate_size
									A         =					hidden_act
									A         =					hidden_dropout_prob
									A         =					initializer_range
									A         =					type_vocab_size
									A         =					layer_norm_eps
									A         =					use_tpu_fourier_optimizations
									A         =					tpu_short_seq_length



