





"""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__				(			UpperCamelCase       ):
							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		=  21_841
							else:
														A		=  1_000
														A		=  "huggingface/label-files"
														A		=  "imagenet-1k-id2label.json"
														A		=  json.load(open(hf_hub_download(UpperCamelCase					,  UpperCamelCase					,  repo_type="dataset"       )					,  "r"       )       )
														A		=  {int(UpperCamelCase       ): 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 					A__				(			UpperCamelCase       ):
							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 					A__				(			UpperCamelCase					,  UpperCamelCase       ):
							for key in orig_state_dict.copy().keys():
														A		=  orig_state_dict.pop(UpperCamelCase       )

														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 					A__				(			UpperCamelCase					,  UpperCamelCase       ):
							A		=  timm.create_model(UpperCamelCase					,  pretrained=UpperCamelCase       )
							timm_model.eval()

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

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

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

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

							A		=  timm_model(inputs["pixel_values"]       )
							A		=  model(**UpperCamelCase       ).logits

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

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

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


if __name__ == "__main__":
		_snake_case     :		Dict       						= 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     :		Optional[int]       						= parser.parse_args()
		convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)









"""simple docstring"""
import inspect
import unittest

from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch
		from torch import nn

		from transformers import ViTMSNForImageClassification, ViTMSNModel
		from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import ViTImageProcessor






class 					_UpperCAmelCase   :


					def __init__(					self			:List[Any]     ,       __UpperCamelCase			:Tuple     ,       __UpperCamelCase			:List[str]=13     ,       __UpperCamelCase			:Any=30     ,       __UpperCamelCase			:int=2     ,       __UpperCamelCase			:Union[str, Any]=3     ,       __UpperCamelCase			:Union[str, Any]=True     ,       __UpperCamelCase			:Optional[int]=True     ,       __UpperCamelCase			:List[str]=32     ,       __UpperCamelCase			:List[Any]=5     ,       __UpperCamelCase			:Dict=4     ,       __UpperCamelCase			:List[str]=37     ,       __UpperCamelCase			:str="gelu"     ,       __UpperCamelCase			:Union[str, Any]=0.1     ,       __UpperCamelCase			:List[Any]=0.1     ,       __UpperCamelCase			:Tuple=10     ,       __UpperCamelCase			:Tuple=0.02     ,       __UpperCamelCase			:int=None     ,       ):
												A		=  parent
												A		=  batch_size
												A		=  image_size
												A		=  patch_size
												A		=  num_channels
												A		=  is_training
												A		=  use_labels
												A		=  hidden_size
												A		=  num_hidden_layers
												A		=  num_attention_heads
												A		=  intermediate_size
												A		=  hidden_act
												A		=  hidden_dropout_prob
												A		=  attention_probs_dropout_prob
												A		=  type_sequence_label_size
												A		=  initializer_range
												A		=  scope

												# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
												A		=  (image_size // patch_size) ** 2
												A		=  num_patches + 1


					def 	lowerCamelCase       (					self			:Any		):
												A		=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

												A		=  None
												if self.use_labels:
																			A		=  ids_tensor([self.batch_size]     ,       self.type_sequence_label_size		)

												A		=  self.get_config()

												return config, pixel_values, labels


					def 	lowerCamelCase       (					self			:Union[str, Any]		):
												return ViTMSNConfig(
												    image_size=self.image_size     ,       patch_size=self.patch_size     ,       num_channels=self.num_channels     ,       hidden_size=self.hidden_size     ,       num_hidden_layers=self.num_hidden_layers     ,       num_attention_heads=self.num_attention_heads     ,       intermediate_size=self.intermediate_size     ,       hidden_act=self.hidden_act     ,       hidden_dropout_prob=self.hidden_dropout_prob     ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,       initializer_range=self.initializer_range     ,       )


					def 	lowerCamelCase       (					self			:Dict     ,       __UpperCamelCase			:Dict     ,       __UpperCamelCase			:Any     ,       __UpperCamelCase			:Any		):
												A		=  ViTMSNModel(config=__UpperCamelCase		)
												model.to(__UpperCamelCase		)
												model.eval()
												A		=  model(__UpperCamelCase		)
												self.parent.assertEqual(result.last_hidden_state.shape     ,       (self.batch_size, self.seq_length, self.hidden_size)		)


					def 	lowerCamelCase       (					self			:Optional[int]     ,       __UpperCamelCase			:List[str]     ,       __UpperCamelCase			:Union[str, Any]     ,       __UpperCamelCase			:Optional[Any]		):
												A		=  self.type_sequence_label_size
												A		=  ViTMSNForImageClassification(__UpperCamelCase		)
												model.to(__UpperCamelCase		)
												model.eval()
												A		=  model(__UpperCamelCase     ,       labels=__UpperCamelCase		)
												print("Pixel and labels shape: {pixel_values.shape}, {labels.shape}"		)
												print("Labels: {labels}"		)
												self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.type_sequence_label_size)		)

												# test greyscale images
												A		=  1
												A		=  ViTMSNForImageClassification(__UpperCamelCase		)
												model.to(__UpperCamelCase		)
												model.eval()

												A		=  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
												A		=  model(__UpperCamelCase		)
												self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.type_sequence_label_size)		)


					def 	lowerCamelCase       (					self			:Optional[Any]		):
												A		=  self.prepare_config_and_inputs()
												A,	A,	A		=  config_and_inputs
												A		=  {"pixel_values": pixel_values}
												return config, inputs_dict







@require_torch
class 					_UpperCAmelCase   (	lowercase_     ,							lowercase_     ,							unittest.TestCase ):
					UpperCamelCase      =							(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
					UpperCamelCase      =							(
					    {'''feature-extraction''': ViTMSNModel, '''image-classification''': ViTMSNForImageClassification}
					    if is_torch_available()
					    else {}
					)

					UpperCamelCase      =							False
					UpperCamelCase      =							False
					UpperCamelCase      =							False
					UpperCamelCase      =							False


					def 	lowerCamelCase       (					self			:Optional[int]		):
												A		=  ViTMSNModelTester(self		)
												A		=  ConfigTester(self     ,       config_class=__UpperCamelCase     ,       has_text_modality=__UpperCamelCase     ,       hidden_size=37		)


					def 	lowerCamelCase       (					self			:Any		):
												self.config_tester.run_common_tests()


					@unittest.skip(reason="ViTMSN does not use inputs_embeds"		)
					def 	lowerCamelCase       (					self			:Union[str, Any]		):
												pass


					def 	lowerCamelCase       (					self			:int		):
												A,	A		=  self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			A		=  model_class(__UpperCamelCase		)
																			self.assertIsInstance(model.get_input_embeddings()     ,       (nn.Module)		)
																			A		=  model.get_output_embeddings()
																			self.assertTrue(x is None or isinstance(__UpperCamelCase     ,       nn.Linear		)		)


					def 	lowerCamelCase       (					self			:Tuple		):
												A,	A		=  self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			A		=  model_class(__UpperCamelCase		)
																			A		=  inspect.signature(model.forward		)
																			# signature.parameters is an OrderedDict => so arg_names order is deterministic
																			A		=  [*signature.parameters.keys()]

																			A		=  ["pixel_values"]
																			self.assertListEqual(arg_names[:1]     ,       __UpperCamelCase		)


					def 	lowerCamelCase       (					self			:List[str]		):
												A		=  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Dict		):
												A		=  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase		)


					@slow
					def 	lowerCamelCase       (					self			:List[Any]		):
												for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			A		=  ViTMSNModel.from_pretrained(__UpperCamelCase		)
																			self.assertIsNotNone(__UpperCamelCase		)







def 					A__				(			):
							A		=  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
							return image






@require_torch
@require_vision
class 					_UpperCAmelCase   (	unittest.TestCase ):


					@cached_property
					def 	lowerCamelCase       (					self			:Union[str, Any]		):
												return ViTImageProcessor.from_pretrained("facebook/vit-msn-small"		) if is_vision_available() else None


					@slow
					def 	lowerCamelCase       (					self			:Any		):
												torch.manual_seed(2		)
												A		=  ViTMSNForImageClassification.from_pretrained("facebook/vit-msn-small"		).to(__UpperCamelCase		)

												A		=  self.default_image_processor
												A		=  prepare_img()
												A		=  image_processor(images=__UpperCamelCase     ,       return_tensors="pt"		).to(__UpperCamelCase		)

												# forward pass
												with torch.no_grad():
																			A		=  model(**__UpperCamelCase		)

												# verify the logits
												A		=  torch.Size((1, 10_00)		)
												self.assertEqual(outputs.logits.shape     ,       __UpperCamelCase		)

												A		=  torch.tensor([-0.0_803, -0.4_454, -0.2_375]		).to(__UpperCamelCase		)

												self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,       __UpperCamelCase     ,       atol=1e-4		)		)


