





import argparse
import json
import pickle
from pathlib import Path

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

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_      							=     logging.get_logger(__name__)





def 						lowerCamelCase__     (     UpperCamelCase__			:       str						)			-> int:



					'''simple docstring'''




					_snake_case									=				SwinConfig.from_pretrained(
					    'microsoft/swin-tiny-patch4-window7-224'       ,    out_features=['stage1', 'stage2', 'stage3', 'stage4']						)
					_snake_case									=				MaskFormerConfig(backbone_config=UpperCamelCase__						)

					_snake_case									=				'huggingface/label-files'
					if "ade20k-full" in model_name:
										# this should be ok
										_snake_case									=				847
										_snake_case									=				'maskformer-ade20k-full-id2label.json'
					elif "ade" in model_name:
										# this should be ok
										_snake_case									=				150
										_snake_case									=				'ade20k-id2label.json'
					elif "coco-stuff" in model_name:
										# this should be ok
										_snake_case									=				171
										_snake_case									=				'maskformer-coco-stuff-id2label.json'
					elif "coco" in model_name:
										# TODO
										_snake_case									=				133
										_snake_case									=				'coco-panoptic-id2label.json'
					elif "cityscapes" in model_name:
										# this should be ok
										_snake_case									=				19
										_snake_case									=				'cityscapes-id2label.json'
					elif "vistas" in model_name:
										# this should be ok
										_snake_case									=				65
										_snake_case									=				'mapillary-vistas-id2label.json'

					_snake_case									=				json.load(open(hf_hub_download(UpperCamelCase__       ,    UpperCamelCase__       ,    repo_type='dataset'						)       ,    'r'						)						)
					_snake_case									=				{int(UpperCamelCase__						): v for k, v in idalabel.items()}

					return config





def 						lowerCamelCase__     (     UpperCamelCase__			:       List[str]						)			-> str:



					'''simple docstring'''




					_snake_case									=				[]
					# stem
					# fmt: off
					rename_keys.append(('backbone.patch_embed.proj.weight', 'model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight')						)
					rename_keys.append(('backbone.patch_embed.proj.bias', 'model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias')						)
					rename_keys.append(('backbone.patch_embed.norm.weight', 'model.pixel_level_module.encoder.model.embeddings.norm.weight')						)
					rename_keys.append(('backbone.patch_embed.norm.bias', 'model.pixel_level_module.encoder.model.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.layers.{i}.blocks.{j}.norm1.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''')						)
															rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm1.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''')						)
															rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''')						)
															rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.relative_position_index''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''')						)
															rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.proj.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''')						)
															rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.proj.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''')						)
															rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm2.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''')						)
															rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm2.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''')						)
															rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc1.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''')						)
															rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc1.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''')						)
															rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc2.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight''')						)
															rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc2.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias''')						)

										if i < 3:
															rename_keys.append((F'''backbone.layers.{i}.downsample.reduction.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight''')						)
															rename_keys.append((F'''backbone.layers.{i}.downsample.norm.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight''')						)
															rename_keys.append((F'''backbone.layers.{i}.downsample.norm.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias''')						)
										rename_keys.append((F'''backbone.norm{i}.weight''', F'''model.pixel_level_module.encoder.hidden_states_norms.{i}.weight''')						)
										rename_keys.append((F'''backbone.norm{i}.bias''', F'''model.pixel_level_module.encoder.hidden_states_norms.{i}.bias''')						)

					# FPN
					rename_keys.append(('sem_seg_head.layer_4.weight', 'model.pixel_level_module.decoder.fpn.stem.0.weight')						)
					rename_keys.append(('sem_seg_head.layer_4.norm.weight', 'model.pixel_level_module.decoder.fpn.stem.1.weight')						)
					rename_keys.append(('sem_seg_head.layer_4.norm.bias', 'model.pixel_level_module.decoder.fpn.stem.1.bias')						)
					for source_index, target_index in zip(range(3       ,    0       ,    -1						)       ,    range(0       ,    3						)						):
										rename_keys.append((F'''sem_seg_head.adapter_{source_index}.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight''')						)
										rename_keys.append((F'''sem_seg_head.adapter_{source_index}.norm.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight''')						)
										rename_keys.append((F'''sem_seg_head.adapter_{source_index}.norm.bias''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias''')						)
										rename_keys.append((F'''sem_seg_head.layer_{source_index}.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight''')						)
										rename_keys.append((F'''sem_seg_head.layer_{source_index}.norm.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight''')						)
										rename_keys.append((F'''sem_seg_head.layer_{source_index}.norm.bias''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias''')						)
					rename_keys.append(('sem_seg_head.mask_features.weight', 'model.pixel_level_module.decoder.mask_projection.weight')						)
					rename_keys.append(('sem_seg_head.mask_features.bias', 'model.pixel_level_module.decoder.mask_projection.bias')						)

					# Transformer decoder
					for idx in range(config.decoder_config.decoder_layers						):
										# self-attention out projection
										rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight''', F'''model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight''')						)
										rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias''', F'''model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias''')						)
										# cross-attention out projection
										rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight''')						)
										rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias''')						)
										# MLP 1
										rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight''', F'''model.transformer_module.decoder.layers.{idx}.fc1.weight''')						)
										rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias''', F'''model.transformer_module.decoder.layers.{idx}.fc1.bias''')						)
										# MLP 2
										rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight''', F'''model.transformer_module.decoder.layers.{idx}.fc2.weight''')						)
										rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias''', F'''model.transformer_module.decoder.layers.{idx}.fc2.bias''')						)
										# layernorm 1 (self-attention layernorm)
										rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight''', F'''model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight''')						)
										rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias''', F'''model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias''')						)
										# layernorm 2 (cross-attention layernorm)
										rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight''')						)
										rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias''')						)
										# layernorm 3 (final layernorm)
										rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight''', F'''model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight''')						)
										rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias''', F'''model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias''')						)

					rename_keys.append(('sem_seg_head.predictor.transformer.decoder.norm.weight', 'model.transformer_module.decoder.layernorm.weight')						)
					rename_keys.append(('sem_seg_head.predictor.transformer.decoder.norm.bias', 'model.transformer_module.decoder.layernorm.bias')						)

					# heads on top
					rename_keys.append(('sem_seg_head.predictor.query_embed.weight', 'model.transformer_module.queries_embedder.weight')						)

					rename_keys.append(('sem_seg_head.predictor.input_proj.weight', 'model.transformer_module.input_projection.weight')						)
					rename_keys.append(('sem_seg_head.predictor.input_proj.bias', 'model.transformer_module.input_projection.bias')						)

					rename_keys.append(('sem_seg_head.predictor.class_embed.weight', 'class_predictor.weight')						)
					rename_keys.append(('sem_seg_head.predictor.class_embed.bias', 'class_predictor.bias')						)

					for i in range(3						):
										rename_keys.append((F'''sem_seg_head.predictor.mask_embed.layers.{i}.weight''', F'''mask_embedder.{i}.0.weight''')						)
										rename_keys.append((F'''sem_seg_head.predictor.mask_embed.layers.{i}.bias''', F'''mask_embedder.{i}.0.bias''')						)
					# fmt: on

					return rename_keys





def 						lowerCamelCase__     (     UpperCamelCase__			:       Any       ,    UpperCamelCase__			:       Optional[Any]       ,    UpperCamelCase__			:       Union[str, Any]						)			-> Optional[Any]:



					'''simple docstring'''




					_snake_case									=				dct.pop(UpperCamelCase__						)
					_snake_case									=				val





def 						lowerCamelCase__     (     UpperCamelCase__			:       Union[str, Any]       ,    UpperCamelCase__			:       Optional[int]						)			-> Optional[int]:



					'''simple docstring'''




					_snake_case									=				[int(backbone_config.embed_dim * 2**i						) for i in range(len(backbone_config.depths						)						)]
					for i in range(len(backbone_config.depths						)						):
										_snake_case									=				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)
															_snake_case									=				state_dict.pop(F'''backbone.layers.{i}.blocks.{j}.attn.qkv.weight'''						)
															_snake_case									=				state_dict.pop(F'''backbone.layers.{i}.blocks.{j}.attn.qkv.bias'''						)
															# next, add query, keys and values (in that order) to the state dict
															_snake_case									=				in_proj_weight[:dim, :]
															_snake_case									=				in_proj_bias[: dim]
															_snake_case									=				in_proj_weight[
															    dim : dim * 2, :
															]
															_snake_case									=				in_proj_bias[
															    dim : dim * 2
															]
															_snake_case									=				in_proj_weight[
															    -dim :, :
															]
															_snake_case									=				in_proj_bias[-dim :]
															# fmt: on





def 						lowerCamelCase__     (     UpperCamelCase__			:       Any       ,    UpperCamelCase__			:       int						)			-> List[Any]:



					'''simple docstring'''




					_snake_case									=				config.decoder_config.hidden_size
					for idx in range(config.decoder_config.decoder_layers						):
										# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
										_snake_case									=				state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'''						)
										_snake_case									=				state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias'''						)
										# next, add query, keys and values (in that order) to the state dict
										_snake_case									=				in_proj_weight[: hidden_size, :]
										_snake_case									=				in_proj_bias[:config.hidden_size]
										_snake_case									=				in_proj_weight[hidden_size : hidden_size * 2, :]
										_snake_case									=				in_proj_bias[hidden_size : hidden_size * 2]
										_snake_case									=				in_proj_weight[-hidden_size :, :]
										_snake_case									=				in_proj_bias[-hidden_size :]
										# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
										_snake_case									=				state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'''						)
										_snake_case									=				state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias'''						)
										# next, add query, keys and values (in that order) to the state dict
										_snake_case									=				in_proj_weight[: hidden_size, :]
										_snake_case									=				in_proj_bias[:config.hidden_size]
										_snake_case									=				in_proj_weight[hidden_size : hidden_size * 2, :]
										_snake_case									=				in_proj_bias[hidden_size : hidden_size * 2]
										_snake_case									=				in_proj_weight[-hidden_size :, :]
										_snake_case									=				in_proj_bias[-hidden_size :]
					# fmt: on





def 						lowerCamelCase__     (     )			-> torch.Tensor:



					'''simple docstring'''




					_snake_case									=				'http://images.cocodataset.org/val2017/000000039769.jpg'
					_snake_case									=				Image.open(requests.get(UpperCamelCase__       ,    stream=UpperCamelCase__						).raw						)
					return im





@torch.no_grad()
def 						lowerCamelCase__     (     UpperCamelCase__			:       str       ,    UpperCamelCase__			:       str       ,    UpperCamelCase__			:       str       ,    UpperCamelCase__			:       bool = False						)			-> Any:



					'''simple docstring'''




					_snake_case									=				get_maskformer_config(UpperCamelCase__						)

					# load original state_dict
					with open(UpperCamelCase__       ,    'rb'						) as f:
										_snake_case									=				pickle.load(UpperCamelCase__						)
					_snake_case									=				data['model']

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

					# rename keys
					_snake_case									=				create_rename_keys(UpperCamelCase__						)
					for src, dest in rename_keys:
										rename_key(UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__						)
					read_in_swin_q_k_v(UpperCamelCase__       ,    config.backbone_config						)
					read_in_decoder_q_k_v(UpperCamelCase__       ,    UpperCamelCase__						)

					# update to torch tensors
					for key, value in state_dict.items():
										_snake_case									=				torch.from_numpy(UpperCamelCase__						)

					# load 🤗 model
					_snake_case									=				MaskFormerForInstanceSegmentation(UpperCamelCase__						)
					model.eval()

					for name, param in model.named_parameters():
										print(UpperCamelCase__       ,    param.shape						)

					_snake_case							,     _snake_case									=				model.load_state_dict(UpperCamelCase__       ,    strict=UpperCamelCase__						)
					assert missing_keys == [
					    "model.pixel_level_module.encoder.model.layernorm.weight",
					    "model.pixel_level_module.encoder.model.layernorm.bias",
					]
					assert len(UpperCamelCase__						) == 0, F'''Unexpected keys: {unexpected_keys}'''

					# verify results
					_snake_case									=				prepare_img()
					if "vistas" in model_name:
										_snake_case									=				65
					elif "cityscapes" in model_name:
										_snake_case									=				65_535
					else:
										_snake_case									=				255
					_snake_case									=				True if 'ade' in model_name else False
					_snake_case									=				MaskFormerImageProcessor(ignore_index=UpperCamelCase__       ,    reduce_labels=UpperCamelCase__						)

					_snake_case									=				image_processor(UpperCamelCase__       ,    return_tensors='pt'						)

					_snake_case									=				model(**UpperCamelCase__						)

					print('Logits:'       ,    outputs.class_queries_logits[0, :3, :3]						)

					if model_name == "maskformer-swin-tiny-ade":
										_snake_case									=				torch.tensor(
										    [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]]						)
					assert torch.allclose(outputs.class_queries_logits[0, :3, :3]       ,    UpperCamelCase__       ,    atol=1e-4						)
					print('Looks ok!'						)

					if pytorch_dump_folder_path is not None:
										print(F'''Saving model and image processor to {pytorch_dump_folder_path}'''						)
										Path(UpperCamelCase__						).mkdir(exist_ok=UpperCamelCase__						)
										model.save_pretrained(UpperCamelCase__						)
										image_processor.save_pretrained(UpperCamelCase__						)

					if push_to_hub:
										print('Pushing model and image processor to the hub...'						)
										model.push_to_hub(F'''nielsr/{model_name}'''						)
										image_processor.push_to_hub(F'''nielsr/{model_name}'''						)


if __name__ == "__main__":
					UpperCAmelCase_      							=     argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--model_name""",
					    default="""maskformer-swin-tiny-ade""",
					    type=str,
					    help=("""Name of the MaskFormer model you'd like to convert""",),
					)
					parser.add_argument(
					    """--checkpoint_path""",
					    default="""/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl""",
					    type=str,
					    help="""Path to the original state dict (.pth file).""",
					)
					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."""
					)

					UpperCAmelCase_      							=     parser.parse_args()
					convert_maskformer_checkpoint(
					    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
					)










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_      							=     {
    """configuration_bigbird_pegasus""": [
        """BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BigBirdPegasusConfig""",
        """BigBirdPegasusOnnxConfig""",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase_      							=     [
					    """BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """BigBirdPegasusForCausalLM""",
					    """BigBirdPegasusForConditionalGeneration""",
					    """BigBirdPegasusForQuestionAnswering""",
					    """BigBirdPegasusForSequenceClassification""",
					    """BigBirdPegasusModel""",
					    """BigBirdPegasusPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_bigbird_pegasus import (
					    BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    BigBirdPegasusConfig,
					    BigBirdPegasusOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_bigbird_pegasus import (
										    BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
										    BigBirdPegasusForCausalLM,
										    BigBirdPegasusForConditionalGeneration,
										    BigBirdPegasusForQuestionAnswering,
										    BigBirdPegasusForSequenceClassification,
										    BigBirdPegasusModel,
										    BigBirdPegasusPreTrainedModel,
										)


else:
					import sys

					UpperCAmelCase_      							=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



