
'''simple docstring'''







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 			_a		(	_SCREAMING_SNAKE_CASE		:   int  ):
      _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				= 128
            _SCREAMING_SNAKE_CASE				= (2, 2, 18, 2)
            _SCREAMING_SNAKE_CASE				= (4, 8, 16, 32)
      else:
            _SCREAMING_SNAKE_CASE				= 192
            _SCREAMING_SNAKE_CASE				= (2, 2, 18, 2)
            _SCREAMING_SNAKE_CASE				= (6, 12, 24, 48)

      if "in22k" in swin_name:
            _SCREAMING_SNAKE_CASE				= 2_1841
      else:
            _SCREAMING_SNAKE_CASE				= 1000
            _SCREAMING_SNAKE_CASE				= "huggingface/label-files"
            _SCREAMING_SNAKE_CASE				= "imagenet-1k-id2label.json"
            _SCREAMING_SNAKE_CASE				= json.load(open(hf_hub_download(_lowerCAmelCase	,			_lowerCAmelCase	,			repo_type="dataset"  )	,			"r"  )  )
            _SCREAMING_SNAKE_CASE				= {int(_lowerCAmelCase  ): 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 			_a		(	_SCREAMING_SNAKE_CASE		:   List[Any]  ):
      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 			_a		(	_SCREAMING_SNAKE_CASE		:   List[Any]	,			_SCREAMING_SNAKE_CASE		:   Optional[Any]  ):
      for key in orig_state_dict.copy().keys():
            _SCREAMING_SNAKE_CASE				= orig_state_dict.pop(_lowerCAmelCase  )

            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 			_a		(	_SCREAMING_SNAKE_CASE		:   Any	,			_SCREAMING_SNAKE_CASE		:   List[str]  ):
      _SCREAMING_SNAKE_CASE				= timm.create_model(_lowerCAmelCase	,			pretrained=_lowerCAmelCase  )
      timm_model.eval()

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

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

      _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(_lowerCAmelCase	,			stream=_lowerCAmelCase  ).raw  )
      _SCREAMING_SNAKE_CASE				= image_processor(images=_lowerCAmelCase	,			return_tensors="pt"  )

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

      assert torch.allclose(_lowerCAmelCase	,			_lowerCAmelCase	,			atol=1e-3  )

      print(F'Saving model {swin_name} to {pytorch_dump_folder_path}'  )
      model.save_pretrained(_lowerCAmelCase  )

      print(F'Saving image processor to {pytorch_dump_folder_path}'  )
      image_processor.save_pretrained(_lowerCAmelCase  )


if __name__ == "__main__":
      _snake_case				:   Optional[Any]     =				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."""
      )

      _snake_case				:   int     =				parser.parse_args()
      convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)

'''simple docstring'''







from copy import deepcopy
from typing import Optional, Union

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available


if is_torch_available():
						import torch

if is_tf_available():
						import tensorflow as tf


class 			lowerCAmelCase (				__UpperCAmelCase			):
		a							:					Optional[int]       =					["""image_processor"""]
		a							:					Tuple       =					"""SamImageProcessor"""




		def __init__(				self					,						UpperCamelCase      ):
								super().__init__(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= self.image_processor
								_SCREAMING_SNAKE_CASE				= -10
								_SCREAMING_SNAKE_CASE				= self.image_processor.size["longest_edge"]




		def __call__(				self					,						UpperCamelCase=None					,						UpperCamelCase=None					,						UpperCamelCase=None					,						UpperCamelCase=None					,						UpperCamelCase = None					,						**UpperCamelCase					,						):
								_SCREAMING_SNAKE_CASE				= self.image_processor(
								    UpperCamelCase					,						return_tensors=UpperCamelCase					,						**UpperCamelCase					,						)

								# pop arguments that are not used in the foward but used nevertheless
								_SCREAMING_SNAKE_CASE				= encoding_image_processor["original_sizes"]

								if hasattr(UpperCamelCase					,						"numpy"      ):  # Checks if Torch or TF tensor
														_SCREAMING_SNAKE_CASE				= original_sizes.numpy()

								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= self._check_and_preprocess_points(
								    input_points=UpperCamelCase					,						input_labels=UpperCamelCase					,						input_boxes=UpperCamelCase					,						)

								_SCREAMING_SNAKE_CASE				= self._normalize_and_convert(
								    UpperCamelCase					,						UpperCamelCase					,						input_points=UpperCamelCase					,						input_labels=UpperCamelCase					,						input_boxes=UpperCamelCase					,						return_tensors=UpperCamelCase					,						)

								return encoding_image_processor




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase=None					,						UpperCamelCase=None					,						UpperCamelCase=None					,						UpperCamelCase="pt"					,						):
								if input_points is not None:
														if len(UpperCamelCase      ) != len(UpperCamelCase      ):
																				_SCREAMING_SNAKE_CASE				= [
																				    self._normalize_coordinates(self.target_size					,						UpperCamelCase					,						original_sizes[0]      ) for point in input_points
																				]
														else:
																				_SCREAMING_SNAKE_CASE				= [
																				    self._normalize_coordinates(self.target_size					,						UpperCamelCase					,						UpperCamelCase      )
																				    for point, original_size in zip(UpperCamelCase					,						UpperCamelCase      )
																				]
														# check that all arrays have the same shape
														if not all(point.shape == input_points[0].shape for point in input_points      ):
																				if input_labels is not None:
																										_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= self._pad_points_and_labels(UpperCamelCase					,						UpperCamelCase      )

														_SCREAMING_SNAKE_CASE				= np.array(UpperCamelCase      )

								if input_labels is not None:
														_SCREAMING_SNAKE_CASE				= np.array(UpperCamelCase      )

								if input_boxes is not None:
														if len(UpperCamelCase      ) != len(UpperCamelCase      ):
																				_SCREAMING_SNAKE_CASE				= [
																				    self._normalize_coordinates(self.target_size					,						UpperCamelCase					,						original_sizes[0]					,						is_bounding_box=UpperCamelCase      )
																				    for box in input_boxes
																				]
														else:
																				_SCREAMING_SNAKE_CASE				= [
																				    self._normalize_coordinates(self.target_size					,						UpperCamelCase					,						UpperCamelCase					,						is_bounding_box=UpperCamelCase      )
																				    for box, original_size in zip(UpperCamelCase					,						UpperCamelCase      )
																				]
														_SCREAMING_SNAKE_CASE				= np.array(UpperCamelCase      )

								if input_boxes is not None:
														if return_tensors == "pt":
																				_SCREAMING_SNAKE_CASE				= torch.from_numpy(UpperCamelCase      )
																				# boxes batch size of 1 by default
																				_SCREAMING_SNAKE_CASE				= input_boxes.unsqueeze(1      ) if len(input_boxes.shape      ) != 3 else input_boxes
														elif return_tensors == "tf":
																				_SCREAMING_SNAKE_CASE				= tf.convert_to_tensor(UpperCamelCase      )
																				# boxes batch size of 1 by default
																				_SCREAMING_SNAKE_CASE				= tf.expand_dims(UpperCamelCase					,						1      ) if len(input_boxes.shape      ) != 3 else input_boxes
														encoding_image_processor.update({"input_boxes": input_boxes}      )
								if input_points is not None:
														if return_tensors == "pt":
																				_SCREAMING_SNAKE_CASE				= torch.from_numpy(UpperCamelCase      )
																				# point batch size of 1 by default
																				_SCREAMING_SNAKE_CASE				= input_points.unsqueeze(1      ) if len(input_points.shape      ) != 4 else input_points
														elif return_tensors == "tf":
																				_SCREAMING_SNAKE_CASE				= tf.convert_to_tensor(UpperCamelCase      )
																				# point batch size of 1 by default
																				_SCREAMING_SNAKE_CASE				= tf.expand_dims(UpperCamelCase					,						1      ) if len(input_points.shape      ) != 4 else input_points
														encoding_image_processor.update({"input_points": input_points}      )
								if input_labels is not None:
														if return_tensors == "pt":
																				_SCREAMING_SNAKE_CASE				= torch.from_numpy(UpperCamelCase      )
																				# point batch size of 1 by default
																				_SCREAMING_SNAKE_CASE				= input_labels.unsqueeze(1      ) if len(input_labels.shape      ) != 3 else input_labels
														elif return_tensors == "tf":
																				_SCREAMING_SNAKE_CASE				= tf.convert_to_tensor(UpperCamelCase      )
																				# point batch size of 1 by default
																				_SCREAMING_SNAKE_CASE				= tf.expand_dims(UpperCamelCase					,						1      ) if len(input_labels.shape      ) != 3 else input_labels
														encoding_image_processor.update({"input_labels": input_labels}      )

								return encoding_image_processor




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= max([point.shape[0] for point in input_points]      )
								_SCREAMING_SNAKE_CASE				= []
								for i, point in enumerate(UpperCamelCase      ):
														if point.shape[0] != expected_nb_points:
																				_SCREAMING_SNAKE_CASE				= np.concatenate(
																				    [point, np.zeros((expected_nb_points - point.shape[0], 2)      ) + self.point_pad_value]					,						axis=0      )
																				_SCREAMING_SNAKE_CASE				= np.append(input_labels[i]					,						[self.point_pad_value]      )
														processed_input_points.append(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= processed_input_points
								return input_points, input_labels




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase=False      ):
								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= original_size
								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= self.image_processor._get_preprocess_shape(UpperCamelCase					,						longest_edge=UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= deepcopy(UpperCamelCase      ).astype(UpperCamelCase      )

								if is_bounding_box:
														_SCREAMING_SNAKE_CASE				= coords.reshape(-1					,						2					,						2      )

								_SCREAMING_SNAKE_CASE				= coords[..., 0] * (new_w / old_w)
								_SCREAMING_SNAKE_CASE				= coords[..., 1] * (new_h / old_h)

								if is_bounding_box:
														_SCREAMING_SNAKE_CASE				= coords.reshape(-1					,						4      )

								return coords




		def     lowercase       (				self					,						UpperCamelCase=None					,						UpperCamelCase=None					,						UpperCamelCase=None					,						):
								if input_points is not None:
														if hasattr(UpperCamelCase					,						"numpy"      ):  # Checks for TF or Torch tensor
																				_SCREAMING_SNAKE_CASE				= input_points.numpy().tolist()

														if not isinstance(UpperCamelCase					,						UpperCamelCase      ) or not isinstance(input_points[0]					,						UpperCamelCase      ):
																				raise ValueError("Input points must be a list of list of floating points."      )
														_SCREAMING_SNAKE_CASE				= [np.array(UpperCamelCase      ) for input_point in input_points]
								else:
														_SCREAMING_SNAKE_CASE				= None

								if input_labels is not None:
														if hasattr(UpperCamelCase					,						"numpy"      ):
																				_SCREAMING_SNAKE_CASE				= input_labels.numpy().tolist()

														if not isinstance(UpperCamelCase					,						UpperCamelCase      ) or not isinstance(input_labels[0]					,						UpperCamelCase      ):
																				raise ValueError("Input labels must be a list of list integers."      )
														_SCREAMING_SNAKE_CASE				= [np.array(UpperCamelCase      ) for label in input_labels]
								else:
														_SCREAMING_SNAKE_CASE				= None

								if input_boxes is not None:
														if hasattr(UpperCamelCase					,						"numpy"      ):
																				_SCREAMING_SNAKE_CASE				= input_boxes.numpy().tolist()

														if (
														    not isinstance(UpperCamelCase					,						UpperCamelCase      )
														    or not isinstance(input_boxes[0]					,						UpperCamelCase      )
														    or not isinstance(input_boxes[0][0]					,						UpperCamelCase      )
														):
																				raise ValueError("Input boxes must be a list of list of list of floating points."      )
														_SCREAMING_SNAKE_CASE				= [np.array(UpperCamelCase      ).astype(np.floataa      ) for box in input_boxes]
								else:
														_SCREAMING_SNAKE_CASE				= None

								return input_points, input_labels, input_boxes




		@property
		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.image_processor.model_input_names
								return list(dict.fromkeys(UpperCamelCase      )      )




		def     lowercase       (				self					,						*UpperCamelCase					,						**UpperCamelCase      ):
								return self.image_processor.post_process_masks(*UpperCamelCase					,						**UpperCamelCase      )