
"""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 					_snake_case (						snake_case__      :   Optional[Any]   ):
	A        =      SwinConfig()
	A        =      swin_name.split('_'   )

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

	if model_size == "tiny":
		A        =      96
		A        =      (2, 2, 6, 2)
		A        =      (3, 6, 12, 24)
	elif model_size == "small":
		A        =      96
		A        =      (2, 2, 18, 2)
		A        =      (3, 6, 12, 24)
	elif model_size == "base":
		A        =      128
		A        =      (2, 2, 18, 2)
		A        =      (4, 8, 16, 32)
	else:
		A        =      192
		A        =      (2, 2, 18, 2)
		A        =      (6, 12, 24, 48)

	if "in22k" in swin_name:
		A        =      2_1841
	else:
		A        =      1000
		A        =      'huggingface/label-files'
		A        =      'imagenet-1k-id2label.json'
		A        =      json.load(open(hf_hub_download(snake_case__		,					snake_case__		,					repo_type='dataset'   )		,					'r'   )   )
		A        =      {int(snake_case__   ): v for k, v in idalabel.items()}
		A        =      idalabel
		A        =      {v: k for k, v in idalabel.items()}

	A        =      img_size
	A        =      num_classes
	A        =      embed_dim
	A        =      depths
	A        =      num_heads
	A        =      window_size

	return config






def 					_snake_case (						snake_case__      :   List[str]   ):
	if "patch_embed.proj" in name:
		A        =      name.replace('patch_embed.proj'		,					'embeddings.patch_embeddings.projection'   )
	if "patch_embed.norm" in name:
		A        =      name.replace('patch_embed.norm'		,					'embeddings.norm'   )
	if "layers" in name:
		A        =      'encoder.' + name
	if "attn.proj" in name:
		A        =      name.replace('attn.proj'		,					'attention.output.dense'   )
	if "attn" in name:
		A        =      name.replace('attn'		,					'attention.self'   )
	if "norm1" in name:
		A        =      name.replace('norm1'		,					'layernorm_before'   )
	if "norm2" in name:
		A        =      name.replace('norm2'		,					'layernorm_after'   )
	if "mlp.fc1" in name:
		A        =      name.replace('mlp.fc1'		,					'intermediate.dense'   )
	if "mlp.fc2" in name:
		A        =      name.replace('mlp.fc2'		,					'output.dense'   )

	if name == "norm.weight":
		A        =      'layernorm.weight'
	if name == "norm.bias":
		A        =      'layernorm.bias'

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

	return name






def 					_snake_case (						snake_case__      :   List[str]		,					snake_case__      :   Tuple   ):
	for key in orig_state_dict.copy().keys():
		A        =      orig_state_dict.pop(snake_case__   )

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

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

	return orig_state_dict






def 					_snake_case (						snake_case__      :   Optional[Any]		,					snake_case__      :   Union[str, Any]   ):
	A        =      timm.create_model(snake_case__		,					pretrained=snake_case__   )
	timm_model.eval()

	A        =      get_swin_config(snake_case__   )
	A        =      SwinForImageClassification(snake_case__   )
	model.eval()

	A        =      convert_state_dict(timm_model.state_dict()		,					snake_case__   )
	model.load_state_dict(snake_case__   )

	A        =      'http://images.cocodataset.org/val2017/000000039769.jpg'

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

	A        =      timm_model(inputs['pixel_values']   )
	A        =      model(**snake_case__   ).logits

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

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

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


if __name__ == "__main__":
		_lowercase							      = 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.'''
		)

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







"""simple docstring"""



from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch

@dataclass
class        a     :
    UpperCAmelCase_ :     torch.Tensor  # [batch_size x 3]
    UpperCAmelCase_ :     torch.Tensor  # [batch_size x 3]
    UpperCAmelCase_ :     torch.Tensor  # [batch_size x 3]
    UpperCAmelCase_ :     torch.Tensor  # [batch_size x 3]
    UpperCAmelCase_ :     int
    UpperCAmelCase_ :     int
    UpperCAmelCase_ :     float
    UpperCAmelCase_ :     float
    UpperCAmelCase_ :     Tuple[int]







    def 			UpperCamelCase_      (			self       ):
      assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
      assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
      assert len(self.x.shape       ) == len(self.y.shape       ) == len(self.z.shape       ) == len(self.origin.shape       ) == 2







    def 			UpperCamelCase_      (			self       ):
      return torch.from_numpy(np.array([self.width, self.height]							,		dtype=np.floataa       )       )







    def 			UpperCamelCase_      (			self       ):
      return torch.from_numpy(np.array([self.x_fov, self.y_fov]							,		dtype=np.floataa       )       )







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       torch.arange(self.height * self.width       )
      lowercase							    =       torch.stack(
          [
              pixel_indices % self.width,
              torch.div(_lowerCamelCase							,		self.width							,		rounding_mode='trunc'       ),
          ]							,		axis=1							,		)
      return coords







    @property
    def 			UpperCamelCase_      (			self       ):
      lowercase    ,     *lowercase							    =       self.shape
      lowercase							    =       int(np.prod(_lowerCamelCase       )       )

      lowercase							    =       self.get_image_coords()
      lowercase							    =       torch.broadcast_to(coords.unsqueeze(0       )							,		[batch_size * inner_batch_size, *coords.shape]       )
      lowercase							    =       self.get_camera_rays(_lowerCamelCase       )

      lowercase							    =       rays.view(_lowerCamelCase							,		inner_batch_size * self.height * self.width							,		2							,		3       )

      return rays







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      lowercase    ,     *lowercase    ,     lowercase							    =       coords.shape
      assert n_coords == 2
      assert batch_size == self.origin.shape[0]

      lowercase							    =       coords.view(_lowerCamelCase							,		-1							,		2       )

      lowercase							    =       self.resolution()
      lowercase							    =       self.fov()

      lowercase							    =       (flat.float() / (res - 1)) * 2 - 1
      lowercase							    =       fracs * torch.tan(fov / 2       )

      lowercase							    =       fracs.view(_lowerCamelCase							,		-1							,		2       )
      lowercase							    =       (
          self.z.view(_lowerCamelCase							,		1							,		3       )
          + self.x.view(_lowerCamelCase							,		1							,		3       ) * fracs[:, :, :1]
          + self.y.view(_lowerCamelCase							,		1							,		3       ) * fracs[:, :, 1:]
      )
      lowercase							    =       directions / directions.norm(dim=-1							,		keepdim=_lowerCamelCase       )
      lowercase							    =       torch.stack(
          [
              torch.broadcast_to(self.origin.view(_lowerCamelCase							,		1							,		3       )							,		[batch_size, directions.shape[1], 3]       ),
              directions,
          ]							,		dim=2							,		)
      return rays.view(_lowerCamelCase							,		*_lowerCamelCase							,		2							,		3       )







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase       ):
      assert width * self.height == height * self.width, "The aspect ratio should not change."
      return DifferentiableProjectiveCamera(
          origin=self.origin							,		x=self.x							,		y=self.y							,		z=self.z							,		width=_lowerCamelCase							,		height=_lowerCamelCase							,		x_fov=self.x_fov							,		y_fov=self.y_fov							,		)


def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  int    ):





  '''simple docstring'''





  lowercase							    =       []
  lowercase							    =       []
  lowercase							    =       []
  lowercase							    =       []
  for theta in np.linspace(0		,					2 * np.pi		,					num=20    ):
    lowercase							    =       np.array([np.sin(__snake_case    ), np.cos(__snake_case    ), -0.5]    )
    z /= np.sqrt(np.sum(z**2    )    )
    lowercase							    =       -z * 4
    lowercase							    =       np.array([np.cos(__snake_case    ), -np.sin(__snake_case    ), 0.0]    )
    lowercase							    =       np.cross(__snake_case		,					__snake_case    )
    origins.append(__snake_case    )
    xs.append(__snake_case    )
    ys.append(__snake_case    )
    zs.append(__snake_case    )
  return DifferentiableProjectiveCamera(
      origin=torch.from_numpy(np.stack(__snake_case		,					axis=0    )    ).float()		,					x=torch.from_numpy(np.stack(__snake_case		,					axis=0    )    ).float()		,					y=torch.from_numpy(np.stack(__snake_case		,					axis=0    )    ).float()		,					z=torch.from_numpy(np.stack(__snake_case		,					axis=0    )    ).float()		,					width=__snake_case		,					height=__snake_case		,					x_fov=0.7		,					y_fov=0.7		,					shape=(1, len(__snake_case    ))		,					)


