





'''simple docstring'''






import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures

# Importing the dataset
__lowerCamelCase    :			List[Any]		= pd.read_csv(
    'https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/'
    'position_salaries.csv'
)
__lowerCamelCase    :			Any		= dataset.iloc[:, 1:2].values
__lowerCamelCase    :			Union[str, Any]		= dataset.iloc[:, 2].values


__lowerCamelCase		,			__lowerCamelCase		,			__lowerCamelCase		,			__lowerCamelCase    :			List[str]		= train_test_split(X, y, test_size=0.2, random_state=0)


__lowerCamelCase    :			Dict		= PolynomialFeatures(degree=4)
__lowerCamelCase    :			Dict		= poly_reg.fit_transform(X)
__lowerCamelCase    :			Optional[Any]		= LinearRegression()
pol_reg.fit(X_poly, y)


def        _a ():


  """simple docstring"""

  plt.scatter(__SCREAMING_SNAKE_CASE	,   __SCREAMING_SNAKE_CASE	,   color='''red''' )
  plt.plot(__SCREAMING_SNAKE_CASE	,   pol_reg.predict(poly_reg.fit_transform(__SCREAMING_SNAKE_CASE ) )	,   color='''blue''' )
  plt.title('''Truth or Bluff (Linear Regression)''' )
  plt.xlabel('''Position level''' )
  plt.ylabel('''Salary''' )
  plt.show()


if __name__ == "__main__":
      viz_polymonial()

      # Predicting a new result with Polymonial Regression
      pol_reg.predict(poly_reg.fit_transform([[5.5]]))
      # output should be 132148.43750003













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 				_UpperCAmelCase			(    A     ):




   '''simple docstring'''

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

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

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

   return config






def 				_UpperCAmelCase			(    A     ):




   '''simple docstring'''

   UpperCAmelCase__						=[]
   # 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				,							A				,							A     ):




   '''simple docstring'''

   UpperCAmelCase__						=dct.pop(A     )
   UpperCAmelCase__						=val






def 				_UpperCAmelCase			(    A				,							A     ):




   '''simple docstring'''

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






def 				_UpperCAmelCase			(    A				,							A     ):




   '''simple docstring'''

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






def 				_UpperCAmelCase			(    ):




   '''simple docstring'''

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






@torch.no_grad()
def 				_UpperCAmelCase			(    A				,							A				,							A				,							A = False     ):




   '''simple docstring'''

   UpperCAmelCase__						=get_maskformer_config(A     )

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

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

   # rename keys
   UpperCAmelCase__						=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():
      UpperCAmelCase__						=torch.from_numpy(A     )

   # load 🤗 model
   UpperCAmelCase__						=MaskFormerForInstanceSegmentation(A     )
   model.eval()

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

   UpperCAmelCase__			,							UpperCAmelCase__						=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
   UpperCAmelCase__						=prepare_img()
   if "vistas" in model_name:
      UpperCAmelCase__						=65
   elif "cityscapes" in model_name:
      UpperCAmelCase__						=65535
   else:
      UpperCAmelCase__						=255
   UpperCAmelCase__						=True if "ade" in model_name else False
   UpperCAmelCase__						=MaskFormerImageProcessor(ignore_index=A				,							reduce_labels=A     )

   UpperCAmelCase__						=image_processor(A				,							return_tensors="pt"     )

   UpperCAmelCase__						=model(**A     )

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

   if model_name == "maskformer-swin-tiny-ade":
      UpperCAmelCase__						=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__":
    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
    )



