
"""simple docstring"""





from math import cos, sin, sqrt, tau

from audio_filters.iir_filter import IIRFilter







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase = 1 / sqrt(2  )  ):
   UpperCAmelCase_					:					int			     =    tau * frequency / samplerate
   UpperCAmelCase_					:					List[str]			     =    sin(__lowerCamelCase  )
   UpperCAmelCase_					:					int			     =    cos(__lowerCamelCase  )
   UpperCAmelCase_					:					Optional[Any]			     =    _sin / (2 * q_factor)

   UpperCAmelCase_					:					int			     =    (1 - _cos) / 2
   UpperCAmelCase_					:					Optional[Any]			     =    1 - _cos

   UpperCAmelCase_					:					int			     =    1 + alpha
   UpperCAmelCase_					:					Dict			     =    -2 * _cos
   UpperCAmelCase_					:					Tuple			     =    1 - alpha

   UpperCAmelCase_					:					Dict			     =    IIRFilter(2  )
   filt.set_coefficients([aa, aa, aa],		[ba, ba, ba]  )
   return filt







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase = 1 / sqrt(2  )  ):
   UpperCAmelCase_					:					Dict			     =    tau * frequency / samplerate
   UpperCAmelCase_					:					Tuple			     =    sin(__lowerCamelCase  )
   UpperCAmelCase_					:					Any			     =    cos(__lowerCamelCase  )
   UpperCAmelCase_					:					List[str]			     =    _sin / (2 * q_factor)

   UpperCAmelCase_					:					List[Any]			     =    (1 + _cos) / 2
   UpperCAmelCase_					:					Optional[int]			     =    -1 - _cos

   UpperCAmelCase_					:					Union[str, Any]			     =    1 + alpha
   UpperCAmelCase_					:					Optional[int]			     =    -2 * _cos
   UpperCAmelCase_					:					Tuple			     =    1 - alpha

   UpperCAmelCase_					:					List[str]			     =    IIRFilter(2  )
   filt.set_coefficients([aa, aa, aa],		[ba, ba, ba]  )
   return filt







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase = 1 / sqrt(2  )  ):
   UpperCAmelCase_					:					Union[str, Any]			     =    tau * frequency / samplerate
   UpperCAmelCase_					:					str			     =    sin(__lowerCamelCase  )
   UpperCAmelCase_					:					Tuple			     =    cos(__lowerCamelCase  )
   UpperCAmelCase_					:					List[Any]			     =    _sin / (2 * q_factor)

   UpperCAmelCase_					:					Any			     =    _sin / 2
   UpperCAmelCase_					:					Union[str, Any]			     =    0
   UpperCAmelCase_					:					Tuple			     =    -ba

   UpperCAmelCase_					:					Optional[Any]			     =    1 + alpha
   UpperCAmelCase_					:					Dict			     =    -2 * _cos
   UpperCAmelCase_					:					Optional[int]			     =    1 - alpha

   UpperCAmelCase_					:					List[str]			     =    IIRFilter(2  )
   filt.set_coefficients([aa, aa, aa],		[ba, ba, ba]  )
   return filt







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase = 1 / sqrt(2  )  ):
   UpperCAmelCase_					:					Any			     =    tau * frequency / samplerate
   UpperCAmelCase_					:					Any			     =    sin(__lowerCamelCase  )
   UpperCAmelCase_					:					Optional[int]			     =    cos(__lowerCamelCase  )
   UpperCAmelCase_					:					str			     =    _sin / (2 * q_factor)

   UpperCAmelCase_					:					List[str]			     =    1 - alpha
   UpperCAmelCase_					:					str			     =    -2 * _cos
   UpperCAmelCase_					:					Any			     =    1 + alpha

   UpperCAmelCase_					:					Tuple			     =    IIRFilter(2  )
   filt.set_coefficients([ba, ba, ba],		[ba, ba, ba]  )
   return filt







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase = 1 / sqrt(2  ),		):
   UpperCAmelCase_					:					Dict			     =    tau * frequency / samplerate
   UpperCAmelCase_					:					Union[str, Any]			     =    sin(__lowerCamelCase  )
   UpperCAmelCase_					:					int			     =    cos(__lowerCamelCase  )
   UpperCAmelCase_					:					Optional[int]			     =    _sin / (2 * q_factor)
   UpperCAmelCase_					:					List[str]			     =    10 ** (gain_db / 40)

   UpperCAmelCase_					:					List[Any]			     =    1 + alpha * big_a
   UpperCAmelCase_					:					Tuple			     =    -2 * _cos
   UpperCAmelCase_					:					Tuple			     =    1 - alpha * big_a
   UpperCAmelCase_					:					str			     =    1 + alpha / big_a
   UpperCAmelCase_					:					List[str]			     =    -2 * _cos
   UpperCAmelCase_					:					List[str]			     =    1 - alpha / big_a

   UpperCAmelCase_					:					Tuple			     =    IIRFilter(2  )
   filt.set_coefficients([aa, aa, aa],		[ba, ba, ba]  )
   return filt







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase = 1 / sqrt(2  ),		):
   UpperCAmelCase_					:					str			     =    tau * frequency / samplerate
   UpperCAmelCase_					:					int			     =    sin(__lowerCamelCase  )
   UpperCAmelCase_					:					Optional[Any]			     =    cos(__lowerCamelCase  )
   UpperCAmelCase_					:					Tuple			     =    _sin / (2 * q_factor)
   UpperCAmelCase_					:					List[Any]			     =    10 ** (gain_db / 40)
   UpperCAmelCase_					:					Tuple			     =    (big_a + 1) - (big_a - 1) * _cos
   UpperCAmelCase_					:					int			     =    (big_a + 1) + (big_a - 1) * _cos
   UpperCAmelCase_					:					Optional[Any]			     =    (big_a - 1) - (big_a + 1) * _cos
   UpperCAmelCase_					:					Optional[int]			     =    (big_a - 1) + (big_a + 1) * _cos
   UpperCAmelCase_					:					Dict			     =    2 * sqrt(__lowerCamelCase  ) * alpha

   UpperCAmelCase_					:					List[str]			     =    big_a * (pmc + aaa)
   UpperCAmelCase_					:					int			     =    2 * big_a * mpc
   UpperCAmelCase_					:					int			     =    big_a * (pmc - aaa)
   UpperCAmelCase_					:					Dict			     =    ppmc + aaa
   UpperCAmelCase_					:					Any			     =    -2 * pmpc
   UpperCAmelCase_					:					List[str]			     =    ppmc - aaa

   UpperCAmelCase_					:					List[Any]			     =    IIRFilter(2  )
   filt.set_coefficients([aa, aa, aa],		[ba, ba, ba]  )
   return filt







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase = 1 / sqrt(2  ),		):
   UpperCAmelCase_					:					int			     =    tau * frequency / samplerate
   UpperCAmelCase_					:					Optional[Any]			     =    sin(__lowerCamelCase  )
   UpperCAmelCase_					:					Optional[Any]			     =    cos(__lowerCamelCase  )
   UpperCAmelCase_					:					Optional[Any]			     =    _sin / (2 * q_factor)
   UpperCAmelCase_					:					Tuple			     =    10 ** (gain_db / 40)
   UpperCAmelCase_					:					Tuple			     =    (big_a + 1) - (big_a - 1) * _cos
   UpperCAmelCase_					:					Optional[Any]			     =    (big_a + 1) + (big_a - 1) * _cos
   UpperCAmelCase_					:					List[Any]			     =    (big_a - 1) - (big_a + 1) * _cos
   UpperCAmelCase_					:					Any			     =    (big_a - 1) + (big_a + 1) * _cos
   UpperCAmelCase_					:					Dict			     =    2 * sqrt(__lowerCamelCase  ) * alpha

   UpperCAmelCase_					:					Any			     =    big_a * (ppmc + aaa)
   UpperCAmelCase_					:					Union[str, Any]			     =    -2 * big_a * pmpc
   UpperCAmelCase_					:					Dict			     =    big_a * (ppmc - aaa)
   UpperCAmelCase_					:					Optional[int]			     =    pmc + aaa
   UpperCAmelCase_					:					Union[str, Any]			     =    2 * mpc
   UpperCAmelCase_					:					int			     =    pmc - aaa

   UpperCAmelCase_					:					Union[str, Any]			     =    IIRFilter(2  )
   filt.set_coefficients([aa, aa, aa],		[ba, ba, ba]  )
   return filt




"""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()
_a		       =							logging.get_logger(__name__)







def 	__a     (		__lowerCamelCase  ):
   UpperCAmelCase_					:					Optional[Any]			     =    SwinConfig.from_pretrained(
       "microsoft/swin-tiny-patch4-window7-224",		out_features=["stage1", "stage2", "stage3", "stage4"]  )
   UpperCAmelCase_					:					Dict			     =    MaskFormerConfig(backbone_config=__lowerCamelCase  )

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

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

   return config







def 	__a     (		__lowerCamelCase  ):
   UpperCAmelCase_					:					Dict			     =    []
   # 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 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  ):
   UpperCAmelCase_					:					Union[str, Any]			     =    dct.pop(__lowerCamelCase  )
   UpperCAmelCase_					:					str			     =    val







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







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







def 	__a     (		):
   UpperCAmelCase_					:					List[Any]			     =    "http://images.cocodataset.org/val2017/000000039769.jpg"
   UpperCAmelCase_					:					Tuple			     =    Image.open(requests.get(__lowerCamelCase,		stream=__lowerCamelCase  ).raw  )
   return im







@torch.no_grad()
def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase = False  ):
   UpperCAmelCase_					:					List[str]			     =    get_maskformer_config(__lowerCamelCase  )

   # load original state_dict
   with open(__lowerCamelCase,		"rb"  ) as f:
      UpperCAmelCase_					:					Union[str, Any]			     =    pickle.load(__lowerCamelCase  )
   UpperCAmelCase_					:					str			     =    data["model"]

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

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

   # update to torch tensors
   for key, value in state_dict.items():
      UpperCAmelCase_					:					Optional[int]			     =    torch.from_numpy(__lowerCamelCase  )

   # load 🤗 model
   UpperCAmelCase_					:					Dict			     =    MaskFormerForInstanceSegmentation(__lowerCamelCase  )
   model.eval()

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

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

   # verify results
   UpperCAmelCase_					:					Optional[int]			     =    prepare_img()
   if "vistas" in model_name:
      UpperCAmelCase_					:					List[str]			     =    65
   elif "cityscapes" in model_name:
      UpperCAmelCase_					:					Tuple			     =    6_5535
   else:
      UpperCAmelCase_					:					Dict			     =    255
   UpperCAmelCase_					:					Optional[Any]			     =    True if "ade" in model_name else False
   UpperCAmelCase_					:					Dict			     =    MaskFormerImageProcessor(ignore_index=__lowerCamelCase,		reduce_labels=__lowerCamelCase  )

   UpperCAmelCase_					:					Union[str, Any]			     =    image_processor(__lowerCamelCase,		return_tensors="pt"  )

   UpperCAmelCase_					:					Dict			     =    model(**__lowerCamelCase  )

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

   if model_name == "maskformer-swin-tiny-ade":
      UpperCAmelCase_					:					Any			     =    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],		__lowerCamelCase,		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(__lowerCamelCase  ).mkdir(exist_ok=__lowerCamelCase  )
      model.save_pretrained(__lowerCamelCase  )
      image_processor.save_pretrained(__lowerCamelCase  )

   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
    )


