




'''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      UpperCamelCase_(	snake_case							:       Optional[int]     ):

							'''simple docstring'''






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

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

							if model_size == "tiny":
														snake_case_						=      9_6
														snake_case_						=      (2, 2, 6, 2)
														snake_case_						=      (3, 6, 1_2, 2_4)
							elif model_size == "small":
														snake_case_						=      9_6
														snake_case_						=      (2, 2, 1_8, 2)
														snake_case_						=      (3, 6, 1_2, 2_4)
							elif model_size == "base":
														snake_case_						=      1_2_8
														snake_case_						=      (2, 2, 1_8, 2)
														snake_case_						=      (4, 8, 1_6, 3_2)
							else:
														snake_case_						=      1_9_2
														snake_case_						=      (2, 2, 1_8, 2)
														snake_case_						=      (6, 1_2, 2_4, 4_8)

							if "in22k" in swin_name:
														snake_case_						=      2_1_8_4_1
							else:
														snake_case_						=      1_0_0_0
														snake_case_						=      "huggingface/label-files"
														snake_case_						=      "imagenet-1k-id2label.json"
														snake_case_						=      json.load(open(hf_hub_download(snake_case							,				snake_case							,				repo_type="dataset"     )							,				"r"     )     )
														snake_case_						=      {int(snake_case     ): v for k, v in idalabel.items()}
														snake_case_						=      idalabel
														snake_case_						=      {v: k for k, v in idalabel.items()}

							snake_case_						=      img_size
							snake_case_						=      num_classes
							snake_case_						=      embed_dim
							snake_case_						=      depths
							snake_case_						=      num_heads
							snake_case_						=      window_size

							return config


def      UpperCamelCase_(	snake_case							:       Optional[int]     ):

							'''simple docstring'''






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

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

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

							return name


def      UpperCamelCase_(	snake_case							:       Optional[Any]							,				snake_case							:       Any     ):

							'''simple docstring'''






							for key in orig_state_dict.copy().keys():
														snake_case_						=      orig_state_dict.pop(snake_case     )

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

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

							return orig_state_dict


def      UpperCamelCase_(	snake_case							:       str							,				snake_case							:       str     ):

							'''simple docstring'''






							snake_case_						=      timm.create_model(snake_case							,				pretrained=snake_case     )
							timm_model.eval()

							snake_case_						=      get_swin_config(snake_case     )
							snake_case_						=      SwinForImageClassification(snake_case     )
							model.eval()

							snake_case_						=      convert_state_dict(timm_model.state_dict()							,				snake_case     )
							model.load_state_dict(snake_case     )

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

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

							snake_case_						=      timm_model(inputs["pixel_values"]     )
							snake_case_						=      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__":
		_SCREAMING_SNAKE_CASE							:   Union[str, 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."
		)

		_SCREAMING_SNAKE_CASE							:   Dict      		= parser.parse_args()
		convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)






import tempfile
import unittest

from make_student import create_student_by_copying_alternating_layers

from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch


__a  			=					'sshleifer/bart-tiny-random'
__a  			=					'patrickvonplaten/t5-tiny-random'
@require_torch
class  lowercase__(				unittest.TestCase							):



	"""simple docstring"""




	@cached_property
	def 						_lowercase  (       self  :			Any       )       ->     Tuple:
					return AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			int       )       ->     List[Any]:
					lowercase_  ,	*lowercase_       =					create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_       ,							tempfile.mkdtemp()       ,							e=1       ,							d=1       )
					self.assertEqual(student.config.num_hidden_layers       ,							1       )
	def 						_lowercase  (       self  :			Union[str, Any]       )       ->     Optional[int]:
					lowercase_  ,	*lowercase_       =					create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_       ,							tempfile.mkdtemp()       ,							e=1       ,							d=SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			List[str]       )       ->     List[Any]:
					lowercase_  ,	*lowercase_       =					create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_       ,							tempfile.mkdtemp()       ,							e=1       ,							d=SCREAMING_SNAKE_CASE_       )
					self.assertEqual(student.config.encoder_layers       ,							1       )
					self.assertEqual(student.config.decoder_layers       ,							self.teacher_config.encoder_layers       )
	def 						_lowercase  (       self  :			List[Any]       )       ->     List[Any]:
					lowercase_  ,	*lowercase_       =					create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_       ,							tempfile.mkdtemp()       ,							e=1       ,							d=1       )
					self.assertEqual(student.config.encoder_layers       ,							1       )
					self.assertEqual(student.config.decoder_layers       ,							1       )




	def 						_lowercase  (       self  :			Union[str, Any]       )       ->     Optional[int]:
					with self.assertRaises(SCREAMING_SNAKE_CASE_       ):
									create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_       ,							tempfile.mkdtemp()       ,							e=SCREAMING_SNAKE_CASE_       ,							d=SCREAMING_SNAKE_CASE_       )




