

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class 							UpperCAmelCase_						(     __lowercase							,			unittest.TestCase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		Union[str, Any]             =       KandinskyInpaintPipeline
							UpperCAmelCase__			:		Optional[int]             =       ["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
							UpperCAmelCase__			:		Optional[Any]             =       [
							    "prompt",
							    "negative_prompt",
							    "image_embeds",
							    "negative_image_embeds",
							    "image",
							    "mask_image",
							]
							UpperCAmelCase__			:		Optional[int]             =       [
							    "generator",
							    "height",
							    "width",
							    "latents",
							    "guidance_scale",
							    "negative_prompt",
							    "num_inference_steps",
							    "return_dict",
							    "guidance_scale",
							    "num_images_per_prompt",
							    "output_type",
							    "return_dict",
							]
							UpperCAmelCase__			:		Any             =       False


							@property
							def 							__lowercase							(     self			)       ->       Optional[int]:
												return 3_2


							@property
							def 							__lowercase							(     self			)       ->       int:
												return 3_2


							@property
							def 							__lowercase							(     self			)       ->       List[str]:
												return self.time_input_dim


							@property
							def 							__lowercase							(     self			)       ->       List[str]:
												return self.time_input_dim * 4


							@property
							def 							__lowercase							(     self			)       ->       Optional[Any]:
												return 1_0_0


							@property
							def 							__lowercase							(     self			)       ->       Optional[Any]:
												_a      :       Any   =						XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''			)
												return tokenizer


							@property
							def 							__lowercase							(     self			)       ->       str:
												torch.manual_seed(0			)
												_a      :       List[Any]   =						MCLIPConfig(
												    numDims=self.cross_attention_dim			,				transformerDimensions=self.text_embedder_hidden_size			,				hidden_size=self.text_embedder_hidden_size			,				intermediate_size=3_7			,				num_attention_heads=4			,				num_hidden_layers=5			,				vocab_size=1_0_0_5			,				)

												_a      :       Optional[int]   =						MultilingualCLIP(_a			)
												_a      :       Tuple   =						text_encoder.eval()

												return text_encoder


							@property
							def 							__lowercase							(     self			)       ->       str:
												torch.manual_seed(0			)

												_a      :       List[str]   =						{
												    '''in_channels''': 9,
												    # Out channels is double in channels because predicts mean and variance
												    '''out_channels''': 8,
												    '''addition_embed_type''': '''text_image''',
												    '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
												    '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
												    '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
												    '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
												    '''layers_per_block''': 1,
												    '''encoder_hid_dim''': self.text_embedder_hidden_size,
												    '''encoder_hid_dim_type''': '''text_image_proj''',
												    '''cross_attention_dim''': self.cross_attention_dim,
												    '''attention_head_dim''': 4,
												    '''resnet_time_scale_shift''': '''scale_shift''',
												    '''class_embed_type''': None,
												}

												_a      :       Dict   =						UNetaDConditionModel(**_a			)
												return model


							@property
							def 							__lowercase							(     self			)       ->       Optional[int]:
												return {
												    "block_out_channels": [3_2, 6_4],
												    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
												    "in_channels": 3,
												    "latent_channels": 4,
												    "layers_per_block": 1,
												    "norm_num_groups": 8,
												    "norm_type": "spatial",
												    "num_vq_embeddings": 1_2,
												    "out_channels": 3,
												    "up_block_types": [
												        "AttnUpDecoderBlock2D",
												        "UpDecoderBlock2D",
												    ],
												    "vq_embed_dim": 4,
												}


							@property
							def 							__lowercase							(     self			)       ->       Tuple:
												torch.manual_seed(0			)
												_a      :       List[Any]   =						VQModel(**self.dummy_movq_kwargs			)
												return model


							def 							__lowercase							(     self			)       ->       Any:
												_a      :       List[Any]   =						self.dummy_text_encoder
												_a      :       Optional[Any]   =						self.dummy_tokenizer
												_a      :       Optional[Any]   =						self.dummy_unet
												_a      :       Union[str, Any]   =						self.dummy_movq

												_a      :       Tuple   =						DDIMScheduler(
												    num_train_timesteps=1_0_0_0			,				beta_schedule='''linear'''			,				beta_start=0.0_0085			,				beta_end=0.012			,				clip_sample=_a			,				set_alpha_to_one=_a			,				steps_offset=1			,				prediction_type='''epsilon'''			,				thresholding=_a			,				)

												_a      :       str   =						{
												    '''text_encoder''': text_encoder,
												    '''tokenizer''': tokenizer,
												    '''unet''': unet,
												    '''scheduler''': scheduler,
												    '''movq''': movq,
												}

												return components


							def 							__lowercase							(     self			,				_a			,				_a=0			)       ->       int:
												_a      :       Union[str, Any]   =						floats_tensor((1, self.cross_attention_dim)			,				rng=random.Random(_a			)			).to(_a			)
												_a      :       List[str]   =						floats_tensor((1, self.cross_attention_dim)			,				rng=random.Random(seed + 1			)			).to(_a			)
												# create init_image
												_a      :       Tuple   =						floats_tensor((1, 3, 6_4, 6_4)			,				rng=random.Random(_a			)			).to(_a			)
												_a      :       Dict   =						image.cpu().permute(0			,				2			,				3			,				1			)[0]
												_a      :       Optional[int]   =						Image.fromarray(np.uinta(_a			)			).convert('''RGB'''			).resize((2_5_6, 2_5_6)			)
												# create mask
												_a      :       Union[str, Any]   =						np.ones((6_4, 6_4)			,				dtype=np.floataa			)
												_a      :       List[str]   =						0

												if str(_a			).startswith('''mps'''			):
																	_a      :       Tuple   =						torch.manual_seed(_a			)
												else:
																	_a      :       Any   =						torch.Generator(device=_a			).manual_seed(_a			)
												_a      :       Any   =						{
												    '''prompt''': '''horse''',
												    '''image''': init_image,
												    '''mask_image''': mask,
												    '''image_embeds''': image_embeds,
												    '''negative_image_embeds''': negative_image_embeds,
												    '''generator''': generator,
												    '''height''': 6_4,
												    '''width''': 6_4,
												    '''num_inference_steps''': 2,
												    '''guidance_scale''': 4.0,
												    '''output_type''': '''np''',
												}
												return inputs


							def 							__lowercase							(     self			)       ->       Optional[Any]:
												_a      :       Optional[Any]   =						'''cpu'''

												_a      :       List[Any]   =						self.get_dummy_components()

												_a      :       Tuple   =						self.pipeline_class(**_a			)
												_a      :       int   =						pipe.to(_a			)

												pipe.set_progress_bar_config(disable=_a			)

												_a      :       Any   =						pipe(**self.get_dummy_inputs(_a			)			)
												_a      :       str   =						output.images

												_a      :       Tuple   =						pipe(
												    **self.get_dummy_inputs(_a			)			,				return_dict=_a			,				)[0]

												_a      :       Union[str, Any]   =						image[0, -3:, -3:, -1]
												_a      :       Tuple   =						image_from_tuple[0, -3:, -3:, -1]

												print(F"""image.shape {image.shape}"""			)

												assert image.shape == (1, 6_4, 6_4, 3)

												_a      :       str   =						np.array(
												    [0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786]			)

												assert (
												    np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
												), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
												assert (
												    np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1e-2
												), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""




							def 							__lowercase							(     self			)       ->       Dict:
												super().test_inference_batch_single_identical(expected_max_diff=3e-3			)



@slow
@require_torch_gpu
class 							UpperCAmelCase_						(     unittest.TestCase  ):
							"""simple docstring"""







							def 							__lowercase							(     self			)       ->       str:
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()




							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												_a      :       Tuple   =						load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
												    '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'''			)

												_a      :       str   =						load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''			)
												_a      :       Tuple   =						np.ones((7_6_8, 7_6_8)			,				dtype=np.floataa			)
												_a      :       Any   =						0

												_a      :       Optional[Any]   =						'''a hat'''

												_a      :       Optional[Any]   =						KandinskyPriorPipeline.from_pretrained(
												    '''kandinsky-community/kandinsky-2-1-prior'''			,				torch_dtype=torch.floataa			)
												pipe_prior.to(_a			)

												_a      :       Tuple   =						KandinskyInpaintPipeline.from_pretrained(
												    '''kandinsky-community/kandinsky-2-1-inpaint'''			,				torch_dtype=torch.floataa			)
												_a      :       Union[str, Any]   =						pipeline.to(_a			)
												pipeline.set_progress_bar_config(disable=_a			)

												_a      :       Union[str, Any]   =						torch.Generator(device='''cpu'''			).manual_seed(0			)
												_a ,   _a      :       Dict   =						pipe_prior(
												    _a			,				generator=_a			,				num_inference_steps=5			,				negative_prompt=''''''			,				).to_tuple()

												_a      :       Optional[int]   =						pipeline(
												    _a			,				image=_a			,				mask_image=_a			,				image_embeds=_a			,				negative_image_embeds=_a			,				generator=_a			,				num_inference_steps=1_0_0			,				height=7_6_8			,				width=7_6_8			,				output_type='''np'''			,				)

												_a      :       Optional[int]   =						output.images[0]

												assert image.shape == (7_6_8, 7_6_8, 3)

												assert_mean_pixel_difference(_a			,				_a			)






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()
a__												=						logging.get_logger(__name__)



def   __UpperCAmelCase					(      __a :     str      )     ->	List[Any]:

					"""simple docstring"""

					_a      :       Tuple   =						SwinConfig.from_pretrained(
					    '''microsoft/swin-tiny-patch4-window7-224'''						,out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']      )
					_a      :       Dict   =						MaskFormerConfig(backbone_config=__a      )

					_a      :       Optional[Any]   =						'''huggingface/label-files'''
					if "ade20k-full" in model_name:
										# this should be ok
										_a      :       Optional[Any]   =						847
										_a      :       List[Any]   =						'''maskformer-ade20k-full-id2label.json'''
					elif "ade" in model_name:
										# this should be ok
										_a      :       Union[str, Any]   =						150
										_a      :       Any   =						'''ade20k-id2label.json'''
					elif "coco-stuff" in model_name:
										# this should be ok
										_a      :       int   =						171
										_a      :       List[str]   =						'''maskformer-coco-stuff-id2label.json'''
					elif "coco" in model_name:
										# TODO
										_a      :       Dict   =						133
										_a      :       Optional[Any]   =						'''coco-panoptic-id2label.json'''
					elif "cityscapes" in model_name:
										# this should be ok
										_a      :       List[Any]   =						19
										_a      :       Optional[Any]   =						'''cityscapes-id2label.json'''
					elif "vistas" in model_name:
										# this should be ok
										_a      :       List[Any]   =						65
										_a      :       Dict   =						'''mapillary-vistas-id2label.json'''

					_a      :       Optional[int]   =						json.load(open(hf_hub_download(__a						,__a						,repo_type='''dataset'''      )						,'''r'''      )      )
					_a      :       Tuple   =						{int(__a      ): v for k, v in idalabel.items()}

					return config



def   __UpperCAmelCase					(      __a :     Optional[Any]      )     ->	Tuple:

					"""simple docstring"""

					_a      :       Optional[Any]   =						[]
					# 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   __UpperCAmelCase					(      __a :     List[str]						,__a :     List[Any]						,__a :     Optional[Any]      )     ->	Union[str, Any]:

					"""simple docstring"""

					_a      :       str   =						dct.pop(__a      )
					_a      :       str   =						val



def   __UpperCAmelCase					(      __a :     List[Any]						,__a :     Union[str, Any]      )     ->	Union[str, Any]:

					"""simple docstring"""

					_a      :       Union[str, Any]   =						[int(backbone_config.embed_dim * 2**i      ) for i in range(len(backbone_config.depths      )      )]
					for i in range(len(backbone_config.depths      )      ):
										_a      :       Optional[Any]   =						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      :       List[Any]   =						state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight"""      )
															_a      :       Optional[int]   =						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
															_a      :       Optional[int]   =						in_proj_weight[:dim, :]
															_a      :       List[Any]   =						in_proj_bias[: dim]
															_a      :       Optional[int]   =						in_proj_weight[
															    dim : dim * 2, :
															]
															_a      :       Tuple   =						in_proj_bias[
															    dim : dim * 2
															]
															_a      :       int   =						in_proj_weight[
															    -dim :, :
															]
															_a      :       Optional[int]   =						in_proj_bias[-dim :]
															# fmt: on



def   __UpperCAmelCase					(      __a :     List[str]						,__a :     List[Any]      )     ->	List[Any]:

					"""simple docstring"""

					_a      :       Optional[int]   =						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)
										_a      :       Union[str, Any]   =						state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"""      )
										_a      :       List[Any]   =						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
										_a      :       Union[str, Any]   =						in_proj_weight[: hidden_size, :]
										_a      :       List[Any]   =						in_proj_bias[:config.hidden_size]
										_a      :       Dict   =						in_proj_weight[hidden_size : hidden_size * 2, :]
										_a      :       Any   =						in_proj_bias[hidden_size : hidden_size * 2]
										_a      :       Tuple   =						in_proj_weight[-hidden_size :, :]
										_a      :       List[Any]   =						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)
										_a      :       List[Any]   =						state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"""      )
										_a      :       List[str]   =						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
										_a      :       Optional[Any]   =						in_proj_weight[: hidden_size, :]
										_a      :       Any   =						in_proj_bias[:config.hidden_size]
										_a      :       List[str]   =						in_proj_weight[hidden_size : hidden_size * 2, :]
										_a      :       Optional[Any]   =						in_proj_bias[hidden_size : hidden_size * 2]
										_a      :       List[str]   =						in_proj_weight[-hidden_size :, :]
										_a      :       int   =						in_proj_bias[-hidden_size :]
					# fmt: on



def   __UpperCAmelCase					(      )     ->	torch.Tensor:

					"""simple docstring"""

					_a      :       str   =						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
					_a      :       Dict   =						Image.open(requests.get(__a						,stream=__a      ).raw      )
					return im



@torch.no_grad()
def   __UpperCAmelCase					(      __a :     str						,__a :     str						,__a :     str						,__a :     bool = False      )     ->	Union[str, Any]:

					"""simple docstring"""

					_a      :       Optional[Any]   =						get_maskformer_config(__a      )

					# load original state_dict
					with open(__a						,'''rb'''      ) as f:
										_a      :       str   =						pickle.load(__a      )
					_a      :       Union[str, Any]   =						data['''model''']

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

					# rename keys
					_a      :       Any   =						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():
										_a      :       Optional[int]   =						torch.from_numpy(__a      )

					# load 🤗 model
					_a      :       Dict   =						MaskFormerForInstanceSegmentation(__a      )
					model.eval()

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

					_a ,   _a      :       Tuple   =						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
					_a      :       Union[str, Any]   =						prepare_img()
					if "vistas" in model_name:
										_a      :       int   =						65
					elif "cityscapes" in model_name:
										_a      :       Tuple   =						65_535
					else:
										_a      :       str   =						255
					_a      :       Dict   =						True if '''ade''' in model_name else False
					_a      :       Optional[Any]   =						MaskFormerImageProcessor(ignore_index=__a						,reduce_labels=__a      )

					_a      :       Optional[Any]   =						image_processor(__a						,return_tensors='''pt'''      )

					_a      :       int   =						model(**__a      )

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

					if model_name == "maskformer-swin-tiny-ade":
										_a      :       Union[str, Any]   =						torch.tensor(
										    [[3.63_53, -4.47_70, -2.60_65], [0.50_81, -4.23_94, -3.53_43], [2.19_09, -5.03_53, -1.93_23]]      )
					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__":
			a__												=						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.'''
			)

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



