'''simple docstring'''




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()
__lowercase        =    logging.get_logger(__name__)






def 							snake_case__				(     _A:   str					)       ->      List[str]:

		'''simple docstring'''


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

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

		lowerCAmelCase     =    json.load(open(hf_hub_download(_A ,  _A ,  repo_type="""dataset"""					) ,  """r"""					)					)
		lowerCAmelCase     =    {int(_A					): v for k, v in idalabel.items()}

		return config






def 							snake_case__				(     _A:   Any					)       ->      Dict:

		'''simple docstring'''


		lowerCAmelCase     =    []
		# 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 							snake_case__				(     _A:   int ,  _A:   str ,  _A:   List[Any]					)       ->      List[Any]:

		'''simple docstring'''


		lowerCAmelCase     =    dct.pop(_A					)
		lowerCAmelCase     =    val






def 							snake_case__				(     _A:   Optional[int] ,  _A:   Optional[Any]					)       ->      int:

		'''simple docstring'''


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






def 							snake_case__				(     _A:   Dict ,  _A:   Dict					)       ->      Dict:

		'''simple docstring'''


		lowerCAmelCase     =    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)
				lowerCAmelCase     =    state_dict.pop(f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"					)
				lowerCAmelCase     =    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
				lowerCAmelCase     =    in_proj_weight[: hidden_size, :]
				lowerCAmelCase     =    in_proj_bias[:config.hidden_size]
				lowerCAmelCase     =    in_proj_weight[hidden_size : hidden_size * 2, :]
				lowerCAmelCase     =    in_proj_bias[hidden_size : hidden_size * 2]
				lowerCAmelCase     =    in_proj_weight[-hidden_size :, :]
				lowerCAmelCase     =    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)
				lowerCAmelCase     =    state_dict.pop(f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"					)
				lowerCAmelCase     =    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
				lowerCAmelCase     =    in_proj_weight[: hidden_size, :]
				lowerCAmelCase     =    in_proj_bias[:config.hidden_size]
				lowerCAmelCase     =    in_proj_weight[hidden_size : hidden_size * 2, :]
				lowerCAmelCase     =    in_proj_bias[hidden_size : hidden_size * 2]
				lowerCAmelCase     =    in_proj_weight[-hidden_size :, :]
				lowerCAmelCase     =    in_proj_bias[-hidden_size :]
		# fmt: on






def 							snake_case__				(     )       ->      torch.Tensor:

		'''simple docstring'''


		lowerCAmelCase     =    """http://images.cocodataset.org/val2017/000000039769.jpg"""
		lowerCAmelCase     =    Image.open(requests.get(_A ,  stream=_A					).raw					)
		return im






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

		'''simple docstring'''


		lowerCAmelCase     =    get_maskformer_config(_A					)

		# load original state_dict
		with open(_A ,  """rb"""					) as f:
				lowerCAmelCase     =    pickle.load(_A					)
		lowerCAmelCase     =    data["""model"""]

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

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

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

		# load 🤗 model
		lowerCAmelCase     =    MaskFormerForInstanceSegmentation(_A					)
		model.eval()

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

		lowerCAmelCase   ,	lowerCAmelCase     =    model.load_state_dict(_A ,  strict=_A					)
		assert missing_keys == [
		    "model.pixel_level_module.encoder.model.layernorm.weight",
		    "model.pixel_level_module.encoder.model.layernorm.bias",
		]
		assert len(_A					) == 0, f"Unexpected keys: {unexpected_keys}"

		# verify results
		lowerCAmelCase     =    prepare_img()
		if "vistas" in model_name:
				lowerCAmelCase     =    65
		elif "cityscapes" in model_name:
				lowerCAmelCase     =    65535
		else:
				lowerCAmelCase     =    255
		lowerCAmelCase     =    True if """ade""" in model_name else False
		lowerCAmelCase     =    MaskFormerImageProcessor(ignore_index=_A ,  reduce_labels=_A					)

		lowerCAmelCase     =    image_processor(_A ,  return_tensors="""pt"""					)

		lowerCAmelCase     =    model(**_A					)

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

		if model_name == "maskformer-swin-tiny-ade":
				lowerCAmelCase     =    torch.tensor(
				    [[3.6_353, -4.4_770, -2.6_065], [0.5_081, -4.2_394, -3.5_343], [2.1_909, -5.0_353, -1.9_323]]					)
		assert torch.allclose(outputs.class_queries_logits[0, :3, :3] ,  _A ,  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(_A					).mkdir(exist_ok=_A					)
				model.save_pretrained(_A					)
				image_processor.save_pretrained(_A					)

		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__":
						__lowercase        =    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.'''
						)

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





'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowercase        =    logging.get_logger(__name__)

__lowercase        =    {
    '''google/mobilenet_v1_1.0_224''': '''https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json''',
    '''google/mobilenet_v1_0.75_192''': '''https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json''',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}






class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	List[str]						=			'''mobilenet_v1'''



			def __init__(  self			,       __lowerCAmelCase=3			,       __lowerCAmelCase=224			,       __lowerCAmelCase=1.0			,       __lowerCAmelCase=8			,       __lowerCAmelCase="relu6"			,       __lowerCAmelCase=True			,       __lowerCAmelCase=0.999			,       __lowerCAmelCase=0.02			,       __lowerCAmelCase=0.001			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					super().__init__(**__lowerCAmelCase)

					if depth_multiplier <= 0:
							raise ValueError("""depth_multiplier must be greater than zero.""")

					lowerCAmelCase     =    num_channels
					lowerCAmelCase     =    image_size
					lowerCAmelCase     =    depth_multiplier
					lowerCAmelCase     =    min_depth
					lowerCAmelCase     =    hidden_act
					lowerCAmelCase     =    tf_padding
					lowerCAmelCase     =    classifier_dropout_prob
					lowerCAmelCase     =    initializer_range
					lowerCAmelCase     =    layer_norm_eps







class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	Dict						=			version.parse('''1.11'''      )



			@property
			def 				a_			(  self):




					"""simple docstring"""


					return OrderedDict([("""pixel_values""", {0: """batch"""})])



			@property
			def 				a_			(  self):




					"""simple docstring"""


					if self.task == "image-classification":
							return OrderedDict([("""logits""", {0: """batch"""})])
					else:
							return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})])







			@property
			def 				a_			(  self):




					"""simple docstring"""


					return 1E-4




