





def    __lowercase   (      _SCREAMING_SNAKE_CASE	) ->   list[list]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						current_set.copy()
      for row_index, row in enumerate(_SCREAMING_SNAKE_CASE	):
            SCREAMING_SNAKE_CASE		=						row[0]
            for column_index, column in enumerate(_SCREAMING_SNAKE_CASE	):
                  if magnitude == 0:
                        SCREAMING_SNAKE_CASE		=						column
                        continue
                  SCREAMING_SNAKE_CASE		=						column / magnitude
    # Subtract to cancel term
      SCREAMING_SNAKE_CASE		=						current_set[0]
      SCREAMING_SNAKE_CASE		=						[first_row]
      SCREAMING_SNAKE_CASE		=						current_set[1::]
      for row in current_set:
            SCREAMING_SNAKE_CASE		=						[]
            # If first term is 0, it is already in form we want, so we preserve it
            if row[0] == 0:
                  final_set.append(_SCREAMING_SNAKE_CASE	)
                  continue
            for column_index in range(len(_SCREAMING_SNAKE_CASE	)	):
                  temp_row.append(first_row[column_index] - row[column_index]	)
            final_set.append(_SCREAMING_SNAKE_CASE	)
      # Create next recursion iteration set
      if len(final_set[0]	) != 3:
            SCREAMING_SNAKE_CASE		=						final_set[0]
            SCREAMING_SNAKE_CASE		=						[]
            SCREAMING_SNAKE_CASE		=						[]
            for row in final_set[1::]:
                  current_first_column.append(row[0]	)
                  next_iteration.append(row[1::]	)
            SCREAMING_SNAKE_CASE		=						simplify(_SCREAMING_SNAKE_CASE	)
            for i in range(len(_SCREAMING_SNAKE_CASE	)	):
                  resultant[i].insert(0 ,						current_first_column[i]	)
            resultant.insert(0 ,						_SCREAMING_SNAKE_CASE	)
            SCREAMING_SNAKE_CASE		=						resultant
      return final_set



def    __lowercase   (      _SCREAMING_SNAKE_CASE	) ->   list:


      '''simple docstring'''


      if len(_SCREAMING_SNAKE_CASE	) == 0:
            raise IndexError("""solve_simultaneous() requires n lists of length n+1"""	)
      SCREAMING_SNAKE_CASE		=						len(_SCREAMING_SNAKE_CASE	) + 1
      if any(len(_SCREAMING_SNAKE_CASE	) != _length for item in equations	):
            raise IndexError("""solve_simultaneous() requires n lists of length n+1"""	)
      for row in equations:
            if any(not isinstance(_SCREAMING_SNAKE_CASE ,						(int, float)	) for column in row	):
                  raise ValueError("""solve_simultaneous() requires lists of integers"""	)
      if len(_SCREAMING_SNAKE_CASE	) == 1:
            return [equations[0][-1] / equations[0][0]]
      SCREAMING_SNAKE_CASE		=						equations.copy()
      if any(0 in row for row in data_set	):
            SCREAMING_SNAKE_CASE		=						data_set.copy()
            SCREAMING_SNAKE_CASE		=						[]
            for row_index, row in enumerate(_SCREAMING_SNAKE_CASE	):
                  if 0 not in row:
                        SCREAMING_SNAKE_CASE		=						data_set.pop(_SCREAMING_SNAKE_CASE	)
                        break
            if not full_row:
                  raise ValueError("""solve_simultaneous() requires at least 1 full equation"""	)
            data_set.insert(0 ,						_SCREAMING_SNAKE_CASE	)
      SCREAMING_SNAKE_CASE		=						data_set.copy()
      SCREAMING_SNAKE_CASE		=						simplify(_SCREAMING_SNAKE_CASE	)
      SCREAMING_SNAKE_CASE		=						simplified[::-1]
      SCREAMING_SNAKE_CASE		=						[]
      for row in simplified:
            SCREAMING_SNAKE_CASE		=						row[-1]
            if not solutions:
                  if row[-2] == 0:
                        solutions.append(0	)
                        continue
                  solutions.append(current_solution / row[-2]	)
                  continue
            SCREAMING_SNAKE_CASE		=						row.copy()[: len(_SCREAMING_SNAKE_CASE	) - 1 :]
            while temp_row[0] == 0:
                  temp_row.pop(0	)
            if len(_SCREAMING_SNAKE_CASE	) == 0:
                  solutions.append(0	)
                  continue
            SCREAMING_SNAKE_CASE		=						temp_row[1::]
            SCREAMING_SNAKE_CASE		=						temp_row[::-1]
            for column_index, column in enumerate(_SCREAMING_SNAKE_CASE	):
                  current_solution -= column * solutions[column_index]
            solutions.append(_SCREAMING_SNAKE_CASE	)
      SCREAMING_SNAKE_CASE		=						[]
      for item in solutions:
            final.append(float(round(_SCREAMING_SNAKE_CASE ,						5	)	)	)
      return final[::-1]


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      SCREAMING_SNAKE_CASE_						   =       [
          [2, 1, 1, 1, 1, 4],
          [1, 2, 1, 1, 1, 5],
          [1, 1, 2, 1, 1, 6],
          [1, 1, 1, 2, 1, 7],
          [1, 1, 1, 1, 2, 8],
      ]
      print(solve_simultaneous(eq))
      print(solve_simultaneous([[4, 2]]))









import argparse
import json

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

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification



def    __lowercase   (      _SCREAMING_SNAKE_CASE	) ->   Union[str, Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						SwinConfig()
      SCREAMING_SNAKE_CASE		=						swin_name.split("""_"""	)

      SCREAMING_SNAKE_CASE		=						name_split[1]
      SCREAMING_SNAKE_CASE		=						int(name_split[4]	)
      SCREAMING_SNAKE_CASE		=						int(name_split[3][-1]	)

      if model_size == "tiny":
            SCREAMING_SNAKE_CASE		=						96
            SCREAMING_SNAKE_CASE		=						(2, 2, 6, 2)
            SCREAMING_SNAKE_CASE		=						(3, 6, 12, 24)
      elif model_size == "small":
            SCREAMING_SNAKE_CASE		=						96
            SCREAMING_SNAKE_CASE		=						(2, 2, 18, 2)
            SCREAMING_SNAKE_CASE		=						(3, 6, 12, 24)
      elif model_size == "base":
            SCREAMING_SNAKE_CASE		=						1_28
            SCREAMING_SNAKE_CASE		=						(2, 2, 18, 2)
            SCREAMING_SNAKE_CASE		=						(4, 8, 16, 32)
      else:
            SCREAMING_SNAKE_CASE		=						1_92
            SCREAMING_SNAKE_CASE		=						(2, 2, 18, 2)
            SCREAMING_SNAKE_CASE		=						(6, 12, 24, 48)

      if "in22k" in swin_name:
            SCREAMING_SNAKE_CASE		=						2_18_41
      else:
            SCREAMING_SNAKE_CASE		=						10_00
            SCREAMING_SNAKE_CASE		=						"""huggingface/label-files"""
            SCREAMING_SNAKE_CASE		=						"""imagenet-1k-id2label.json"""
            SCREAMING_SNAKE_CASE		=						json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						repo_type="""dataset"""	) ,						"""r"""	)	)
            SCREAMING_SNAKE_CASE		=						{int(_SCREAMING_SNAKE_CASE	): v for k, v in idalabel.items()}
            SCREAMING_SNAKE_CASE		=						idalabel
            SCREAMING_SNAKE_CASE		=						{v: k for k, v in idalabel.items()}

      SCREAMING_SNAKE_CASE		=						img_size
      SCREAMING_SNAKE_CASE		=						num_classes
      SCREAMING_SNAKE_CASE		=						embed_dim
      SCREAMING_SNAKE_CASE		=						depths
      SCREAMING_SNAKE_CASE		=						num_heads
      SCREAMING_SNAKE_CASE		=						window_size

      return config



def    __lowercase   (      _SCREAMING_SNAKE_CASE	) ->   Dict:


      '''simple docstring'''


      if "patch_embed.proj" in name:
            SCREAMING_SNAKE_CASE		=						name.replace("""patch_embed.proj""" ,						"""embeddings.patch_embeddings.projection"""	)
      if "patch_embed.norm" in name:
            SCREAMING_SNAKE_CASE		=						name.replace("""patch_embed.norm""" ,						"""embeddings.norm"""	)
      if "layers" in name:
            SCREAMING_SNAKE_CASE		=						"""encoder.""" + name
      if "attn.proj" in name:
            SCREAMING_SNAKE_CASE		=						name.replace("""attn.proj""" ,						"""attention.output.dense"""	)
      if "attn" in name:
            SCREAMING_SNAKE_CASE		=						name.replace("""attn""" ,						"""attention.self"""	)
      if "norm1" in name:
            SCREAMING_SNAKE_CASE		=						name.replace("""norm1""" ,						"""layernorm_before"""	)
      if "norm2" in name:
            SCREAMING_SNAKE_CASE		=						name.replace("""norm2""" ,						"""layernorm_after"""	)
      if "mlp.fc1" in name:
            SCREAMING_SNAKE_CASE		=						name.replace("""mlp.fc1""" ,						"""intermediate.dense"""	)
      if "mlp.fc2" in name:
            SCREAMING_SNAKE_CASE		=						name.replace("""mlp.fc2""" ,						"""output.dense"""	)

      if name == "norm.weight":
            SCREAMING_SNAKE_CASE		=						"""layernorm.weight"""
      if name == "norm.bias":
            SCREAMING_SNAKE_CASE		=						"""layernorm.bias"""

      if "head" in name:
            SCREAMING_SNAKE_CASE		=						name.replace("""head""" ,						"""classifier"""	)
      else:
            SCREAMING_SNAKE_CASE		=						"""swin.""" + name

      return name



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ->   Any:


      '''simple docstring'''


      for key in orig_state_dict.copy().keys():
            SCREAMING_SNAKE_CASE		=						orig_state_dict.pop(_SCREAMING_SNAKE_CASE	)

            if "mask" in key:
                  continue
            elif "qkv" in key:
                  SCREAMING_SNAKE_CASE		=						key.split("""."""	)
                  SCREAMING_SNAKE_CASE		=						int(key_split[1]	)
                  SCREAMING_SNAKE_CASE		=						int(key_split[3]	)
                  SCREAMING_SNAKE_CASE		=						model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

                  if "weight" in key:
                        SCREAMING_SNAKE_CASE		=						val[:dim, :]
                        SCREAMING_SNAKE_CASE		=						val[
                            dim : dim * 2, :
                        ]
                        SCREAMING_SNAKE_CASE		=						val[-dim:, :]
                  else:
                        SCREAMING_SNAKE_CASE		=						val[
                            :dim
                        ]
                        SCREAMING_SNAKE_CASE		=						val[
                            dim : dim * 2
                        ]
                        SCREAMING_SNAKE_CASE		=						val[
                            -dim:
                        ]
            else:
                  SCREAMING_SNAKE_CASE		=						val

      return orig_state_dict



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ->   List[str]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						timm.create_model(_SCREAMING_SNAKE_CASE ,						pretrained=_SCREAMING_SNAKE_CASE	)
      timm_model.eval()

      SCREAMING_SNAKE_CASE		=						get_swin_config(_SCREAMING_SNAKE_CASE	)
      SCREAMING_SNAKE_CASE		=						SwinForImageClassification(_SCREAMING_SNAKE_CASE	)
      model.eval()

      SCREAMING_SNAKE_CASE		=						convert_state_dict(timm_model.state_dict() ,						_SCREAMING_SNAKE_CASE	)
      model.load_state_dict(_SCREAMING_SNAKE_CASE	)

      SCREAMING_SNAKE_CASE		=						"""http://images.cocodataset.org/val2017/000000039769.jpg"""

      SCREAMING_SNAKE_CASE		=						AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swin_name.replace("""_""" ,						"""-"""	)	)	)
      SCREAMING_SNAKE_CASE		=						Image.open(requests.get(_SCREAMING_SNAKE_CASE ,						stream=_SCREAMING_SNAKE_CASE	).raw	)
      SCREAMING_SNAKE_CASE		=						image_processor(images=_SCREAMING_SNAKE_CASE ,						return_tensors="""pt"""	)

      SCREAMING_SNAKE_CASE		=						timm_model(inputs["""pixel_values"""]	)
      SCREAMING_SNAKE_CASE		=						model(**_SCREAMING_SNAKE_CASE	).logits

      assert torch.allclose(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						atol=1E-3	)

      print(F"""Saving model {swin_name} to {pytorch_dump_folder_path}"""	)
      model.save_pretrained(_SCREAMING_SNAKE_CASE	)

      print(F"""Saving image processor to {pytorch_dump_folder_path}"""	)
      image_processor.save_pretrained(_SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE_						   =       argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--swin_name""",
          default="""swin_tiny_patch4_window7_224""",
          type=str,
          help="""Name of the Swin timm model you'd like to convert.""",
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
      )

      SCREAMING_SNAKE_CASE_						   =       parser.parse_args()
      convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)


