
'''simple docstring'''






import argparse
import json
from pathlib import Path

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

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification







def 		A_							(						_lowerCAmelCase :     str				):



      """simple docstring"""




      _lowerCamelCase					: Dict		    = SwinvaConfig()
      _lowerCamelCase					: List[Any]		    = swinva_name.split("_"				)

      _lowerCamelCase					: Optional[int]		    = name_split[1]
      if "to" in name_split[3]:
            _lowerCamelCase					: Optional[int]		    = int(name_split[3][-3:]				)
      else:
            _lowerCamelCase					: Tuple		    = int(name_split[3]				)
      if "to" in name_split[2]:
            _lowerCamelCase					: str		    = int(name_split[2][-2:]				)
      else:
            _lowerCamelCase					: str		    = int(name_split[2][6:]				)

      if model_size == "tiny":
            _lowerCamelCase					: Optional[Any]		    = 96
            _lowerCamelCase					: List[Any]		    = (2, 2, 6, 2)
            _lowerCamelCase					: int		    = (3, 6, 12, 24)
      elif model_size == "small":
            _lowerCamelCase					: Optional[int]		    = 96
            _lowerCamelCase					: int		    = (2, 2, 18, 2)
            _lowerCamelCase					: List[str]		    = (3, 6, 12, 24)
      elif model_size == "base":
            _lowerCamelCase					: Dict		    = 128
            _lowerCamelCase					: List[str]		    = (2, 2, 18, 2)
            _lowerCamelCase					: Dict		    = (4, 8, 16, 32)
      else:
            _lowerCamelCase					: List[Any]		    = 192
            _lowerCamelCase					: Any		    = (2, 2, 18, 2)
            _lowerCamelCase					: Optional[int]		    = (6, 12, 24, 48)

      if "to" in swinva_name:
            _lowerCamelCase					: Optional[int]		    = (12, 12, 12, 6)

      if ("22k" in swinva_name) and ("to" not in swinva_name):
            _lowerCamelCase					: Dict		    = 21841
            _lowerCamelCase					: List[Any]		    = "huggingface/label-files"
            _lowerCamelCase					: List[Any]		    = "imagenet-22k-id2label.json"
            _lowerCamelCase					: Union[str, Any]		    = json.load(open(hf_hub_download(_lowerCAmelCase					,						_lowerCAmelCase					,						repo_type="dataset"				)					,						"r"				)				)
            _lowerCamelCase					: int		    = {int(_lowerCAmelCase				): v for k, v in idalabel.items()}
            _lowerCamelCase					: Union[str, Any]		    = idalabel
            _lowerCamelCase					: Union[str, Any]		    = {v: k for k, v in idalabel.items()}

      else:
            _lowerCamelCase					: str		    = 1000
            _lowerCamelCase					: int		    = "huggingface/label-files"
            _lowerCamelCase					: Optional[Any]		    = "imagenet-1k-id2label.json"
            _lowerCamelCase					: Dict		    = json.load(open(hf_hub_download(_lowerCAmelCase					,						_lowerCAmelCase					,						repo_type="dataset"				)					,						"r"				)				)
            _lowerCamelCase					: Tuple		    = {int(_lowerCAmelCase				): v for k, v in idalabel.items()}
            _lowerCamelCase					: Union[str, Any]		    = idalabel
            _lowerCamelCase					: Union[str, Any]		    = {v: k for k, v in idalabel.items()}

      _lowerCamelCase					: Any		    = img_size
      _lowerCamelCase					: Tuple		    = num_classes
      _lowerCamelCase					: Any		    = embed_dim
      _lowerCamelCase					: str		    = depths
      _lowerCamelCase					: List[Any]		    = num_heads
      _lowerCamelCase					: List[Any]		    = window_size

      return config







def 		A_							(						_lowerCAmelCase :     int				):



      """simple docstring"""




      if "patch_embed.proj" in name:
            _lowerCamelCase					: Union[str, Any]		    = name.replace("patch_embed.proj"					,						"embeddings.patch_embeddings.projection"				)
      if "patch_embed.norm" in name:
            _lowerCamelCase					: Optional[int]		    = name.replace("patch_embed.norm"					,						"embeddings.norm"				)
      if "layers" in name:
            _lowerCamelCase					: List[str]		    = "encoder." + name
      if "attn.proj" in name:
            _lowerCamelCase					: Optional[Any]		    = name.replace("attn.proj"					,						"attention.output.dense"				)
      if "attn" in name:
            _lowerCamelCase					: int		    = name.replace("attn"					,						"attention.self"				)
      if "norm1" in name:
            _lowerCamelCase					: str		    = name.replace("norm1"					,						"layernorm_before"				)
      if "norm2" in name:
            _lowerCamelCase					: List[str]		    = name.replace("norm2"					,						"layernorm_after"				)
      if "mlp.fc1" in name:
            _lowerCamelCase					: str		    = name.replace("mlp.fc1"					,						"intermediate.dense"				)
      if "mlp.fc2" in name:
            _lowerCamelCase					: int		    = name.replace("mlp.fc2"					,						"output.dense"				)
      if "q_bias" in name:
            _lowerCamelCase					: Union[str, Any]		    = name.replace("q_bias"					,						"query.bias"				)
      if "k_bias" in name:
            _lowerCamelCase					: List[Any]		    = name.replace("k_bias"					,						"key.bias"				)
      if "v_bias" in name:
            _lowerCamelCase					: Any		    = name.replace("v_bias"					,						"value.bias"				)
      if "cpb_mlp" in name:
            _lowerCamelCase					: Any		    = name.replace("cpb_mlp"					,						"continuous_position_bias_mlp"				)
      if name == "norm.weight":
            _lowerCamelCase					: List[str]		    = "layernorm.weight"
      if name == "norm.bias":
            _lowerCamelCase					: List[Any]		    = "layernorm.bias"

      if "head" in name:
            _lowerCamelCase					: Tuple		    = name.replace("head"					,						"classifier"				)
      else:
            _lowerCamelCase					: Any		    = "swinv2." + name

      return name







def 		A_							(						_lowerCAmelCase :     Any					,						_lowerCAmelCase :     Any				):



      """simple docstring"""




      for key in orig_state_dict.copy().keys():
            _lowerCamelCase					: Any		    = orig_state_dict.pop(_lowerCAmelCase				)

            if "mask" in key:
                  continue
            elif "qkv" in key:
                  _lowerCamelCase					: str		    = key.split("."				)
                  _lowerCamelCase					: Any		    = int(key_split[1]				)
                  _lowerCamelCase					: Optional[Any]		    = int(key_split[3]				)
                  _lowerCamelCase					: Tuple		    = model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

                  if "weight" in key:
                        _lowerCamelCase					: Optional[int]		    = val[:dim, :]
                        _lowerCamelCase					: Dict		    = val[dim : dim * 2, :]
                        _lowerCamelCase					: Union[str, Any]		    = val[-dim:, :]
                  else:
                        _lowerCamelCase					: int		    = val[:dim]
                        _lowerCamelCase					: str		    = val[
                            dim : dim * 2
                        ]
                        _lowerCamelCase					: str		    = val[-dim:]
            else:
                  _lowerCamelCase					: List[Any]		    = val

      return orig_state_dict







def 		A_							(						_lowerCAmelCase :     Dict					,						_lowerCAmelCase :     str				):



      """simple docstring"""




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

      _lowerCamelCase					: Dict		    = get_swinva_config(_lowerCAmelCase				)
      _lowerCamelCase					: Any		    = SwinvaForImageClassification(_lowerCAmelCase				)
      model.eval()

      _lowerCamelCase					: Optional[int]		    = convert_state_dict(timm_model.state_dict()					,						_lowerCAmelCase				)
      model.load_state_dict(_lowerCAmelCase				)

      _lowerCamelCase					: List[str]		    = "http://images.cocodataset.org/val2017/000000039769.jpg"

      _lowerCamelCase					: Tuple		    = AutoImageProcessor.from_pretrained("microsoft/{}".format(swinva_name.replace("_"					,						"-"				)				)				)
      _lowerCamelCase					: Tuple		    = Image.open(requests.get(_lowerCAmelCase					,						stream=_lowerCAmelCase				).raw				)
      _lowerCamelCase					: Any		    = image_processor(images=_lowerCAmelCase					,						return_tensors="pt"				)

      _lowerCamelCase					: Dict		    = timm_model(inputs["pixel_values"]				)
      _lowerCamelCase					: List[Any]		    = model(**_lowerCAmelCase				).logits

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

      print(F'Saving model {swinva_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				)

      model.push_to_hub(
          repo_path_or_name=Path(_lowerCAmelCase					,						_lowerCAmelCase				)					,						organization="nandwalritik"					,						commit_message="Add model"					,						)


if __name__ == "__main__":
 UpperCAmelCase_							: Union[str, Any] =							argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--swinv2_name',
     default='swinv2_tiny_patch4_window8_256',
     type=str,
     help='Name of the Swinv2 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.'
 )

 UpperCAmelCase_							: Tuple =							parser.parse_args()
 convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)
'''simple docstring'''


from random import randint, random





def    lowerCAmelCase       (		UpperCamelCase__					: int     ,       UpperCamelCase__					: int     ,       UpperCamelCase__					: int     ,       UpperCamelCase__					: bool = False     ,       UpperCamelCase__					: bool = False     ,       UpperCamelCase__					: int = 5     ,       ):


	"""simple docstring"""
	__UpperCAmelCase      					=      [[-1] * number_of_cells]  # Create a highway without any car
	__UpperCAmelCase      					=      0
	__UpperCAmelCase      					=      max(UpperCamelCase__     ,       0							)
	while i < number_of_cells:
		__UpperCAmelCase      					=      (
		    randint(0     ,       UpperCamelCase__							) if random_speed else initial_speed
		)  # Place the cars
		i += (
		    randint(1     ,       max_speed * 2							) if random_frequency else frequency
		)  # Arbitrary number, may need tuning
	return highway





def    lowerCAmelCase       (		UpperCamelCase__					: list     ,       UpperCamelCase__					: int							):


	"""simple docstring"""
	__UpperCAmelCase      					=      0
	__UpperCAmelCase      					=      highway_now[car_index + 1 :]
	for cell in range(len(UpperCamelCase__							)							):  # May need a better name for this
		if cells[cell] != -1:  # If the cell is not empty then
			return distance  # we have the distance we wanted
		distance += 1
	# Here if the car is near the end of the highway
	return distance + get_distance(UpperCamelCase__     ,       -1							)





def    lowerCAmelCase       (		UpperCamelCase__					: list     ,       UpperCamelCase__					: float     ,       UpperCamelCase__					: int							):


	"""simple docstring"""
	__UpperCAmelCase      					=      len(UpperCamelCase__							)
	# Beforce calculations, the highway is empty
	__UpperCAmelCase      					=      [-1] * number_of_cells

	for car_index in range(UpperCamelCase__							):
		if highway_now[car_index] != -1:
			# Add 1 to the current speed of the car and cap the speed
			__UpperCAmelCase      					=      min(highway_now[car_index] + 1     ,       UpperCamelCase__							)
			# Number of empty cell before the next car
			__UpperCAmelCase      					=      get_distance(UpperCamelCase__     ,       UpperCamelCase__							) - 1
			# We can't have the car causing an accident
			__UpperCAmelCase      					=      min(next_highway[car_index]     ,       UpperCamelCase__							)
			if random() < probability:
				# Randomly, a driver will slow down
				__UpperCAmelCase      					=      max(next_highway[car_index] - 1     ,       0							)
	return next_highway





def    lowerCAmelCase       (		UpperCamelCase__					: list     ,       UpperCamelCase__					: int     ,       UpperCamelCase__					: float     ,       UpperCamelCase__					: int							):


	"""simple docstring"""
	__UpperCAmelCase      					=      len(highway[0]							)

	for i in range(UpperCamelCase__							):
		__UpperCAmelCase      					=      update(highway[i]     ,       UpperCamelCase__     ,       UpperCamelCase__							)
		__UpperCAmelCase      					=      [-1] * number_of_cells

		for car_index in range(UpperCamelCase__							):
			__UpperCAmelCase      					=      next_speeds_calculated[car_index]
			if speed != -1:
				# Change the position based on the speed (with % to create the loop)
				__UpperCAmelCase      					=      (car_index + speed) % number_of_cells
				# Commit the change of position
				__UpperCAmelCase      					=      speed
		highway.append(UpperCamelCase__							)

	return highway


if __name__ == "__main__":
					import doctest

					doctest.testmod()
