




'''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      UpperCamelCase_(	snake_case							:       List[str]     ):

							'''simple docstring'''






							snake_case_						=      SwinvaConfig()
							snake_case_						=      swinva_name.split("_"     )

							snake_case_						=      name_split[1]
							if "to" in name_split[3]:
														snake_case_						=      int(name_split[3][-3:]     )
							else:
														snake_case_						=      int(name_split[3]     )
							if "to" in name_split[2]:
														snake_case_						=      int(name_split[2][-2:]     )
							else:
														snake_case_						=      int(name_split[2][6:]     )

							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 "to" in swinva_name:
														snake_case_						=      (1_2, 1_2, 1_2, 6)

							if ("22k" in swinva_name) and ("to" not in swinva_name):
														snake_case_						=      2_1_8_4_1
														snake_case_						=      "huggingface/label-files"
														snake_case_						=      "imagenet-22k-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()}

							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							:       Any     ):

							'''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 "q_bias" in name:
														snake_case_						=      name.replace("q_bias"							,				"query.bias"     )
							if "k_bias" in name:
														snake_case_						=      name.replace("k_bias"							,				"key.bias"     )
							if "v_bias" in name:
														snake_case_						=      name.replace("v_bias"							,				"value.bias"     )
							if "cpb_mlp" in name:
														snake_case_						=      name.replace("cpb_mlp"							,				"continuous_position_bias_mlp"     )
							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_						=      "swinv2." + name

							return name


def      UpperCamelCase_(	snake_case							:       int							,				snake_case							:       Tuple     ):

							'''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.swinva.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							:       Any							,				snake_case							:       List[Any]     ):

							'''simple docstring'''






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

							snake_case_						=      get_swinva_config(snake_case     )
							snake_case_						=      SwinvaForImageClassification(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(swinva_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 {swinva_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     )

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


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE							:   Optional[int]      		= 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."
		)

		_SCREAMING_SNAKE_CASE							:   str      		= parser.parse_args()
		convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)












"""simple docstring"""






import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 transformers import (
      MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
      MODEL_MAPPING,
      LevitForImageClassification,
      LevitForImageClassificationWithTeacher,
      LevitModel,
  )
  from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import LevitImageProcessor




class 							lowerCAmelCase__				(			UpperCAmelCase__ ):






      '''simple docstring'''

      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		self.config_class(**self.inputs_dict)
          self.parent.assertTrue(hasattr(lowercase_						,	'''hidden_sizes'''))
          self.parent.assertTrue(hasattr(lowercase_						,	'''num_attention_heads'''))




class 							lowerCAmelCase__				:






      '''simple docstring'''

      def __init__(						self     :  str						,	lowercase_     :  Union[str, Any]						,	lowercase_     :  List[Any]=13						,	lowercase_     :  Dict=64						,	lowercase_     :  Dict=3						,	lowercase_     :  Optional[Any]=3						,	lowercase_     :  List[Any]=2						,	lowercase_     :  Any=1						,	lowercase_     :  List[Any]=16						,	lowercase_     :  int=[128, 256, 384]						,	lowercase_     :  str=[4, 6, 8]						,	lowercase_     :  Optional[Any]=[2, 3, 4]						,	lowercase_     :  Union[str, Any]=[16, 16, 16]						,	lowercase_     :  Optional[Any]=0						,	lowercase_     :  Optional[int]=[2, 2, 2]						,	lowercase_     :  Any=[2, 2, 2]						,	lowercase_     :  List[str]=0.02						,	lowercase_     :  Any=True						,	lowercase_     :  Union[str, Any]=True						,	lowercase_     :  Optional[int]=2						,	):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[str]    						=		parent
          SCREAMING_SNAKE_CASE_      :  Any    						=		batch_size
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		image_size
          SCREAMING_SNAKE_CASE_      :  int    						=		num_channels
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		kernel_size
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		stride
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		padding
          SCREAMING_SNAKE_CASE_      :  int    						=		hidden_sizes
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		num_attention_heads
          SCREAMING_SNAKE_CASE_      :  int    						=		depths
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		key_dim
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		drop_path_rate
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		patch_size
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		attention_ratio
          SCREAMING_SNAKE_CASE_      :  str    						=		mlp_ratio
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		initializer_range
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		[
              ['''Subsample''', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
              ['''Subsample''', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
          ]
          SCREAMING_SNAKE_CASE_      :  Any    						=		is_training
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		use_labels
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		num_labels
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		initializer_range
      def _SCREAMING_SNAKE_CASE (						self     :  Union[str, Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  int    						=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

          SCREAMING_SNAKE_CASE_      :  Dict    						=		None
          if self.use_labels:
              SCREAMING_SNAKE_CASE_      :  str    						=		ids_tensor([self.batch_size]						,	self.num_labels)

          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		self.get_config()
          return config, pixel_values, labels
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]):




          '''simple docstring'''


          return LevitConfig(
              image_size=self.image_size						,	num_channels=self.num_channels						,	kernel_size=self.kernel_size						,	stride=self.stride						,	padding=self.padding						,	patch_size=self.patch_size						,	hidden_sizes=self.hidden_sizes						,	num_attention_heads=self.num_attention_heads						,	depths=self.depths						,	key_dim=self.key_dim						,	drop_path_rate=self.drop_path_rate						,	mlp_ratio=self.mlp_ratio						,	attention_ratio=self.attention_ratio						,	initializer_range=self.initializer_range						,	down_ops=self.down_ops						,	)
      def _SCREAMING_SNAKE_CASE (						self     :  Union[str, Any]						,	lowercase_     :  Any						,	lowercase_     :  int						,	lowercase_     :  Optional[int]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		LevitModel(config=lowercase_)
          model.to(lowercase_)
          model.eval()
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		model(lowercase_)
          SCREAMING_SNAKE_CASE_      :  Any    						=		(self.image_size, self.image_size)
          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Tuple    						=		image_size[0], image_size[1]
          for _ in range(4):
              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1)
              SCREAMING_SNAKE_CASE_      :  Dict    						=		floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1)
          self.parent.assertEqual(
              result.last_hidden_state.shape						,	(self.batch_size, ceil(height / 4) * ceil(width / 4), self.hidden_sizes[-1])						,	)
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]						,	lowercase_     :  int						,	lowercase_     :  Union[str, Any]						,	lowercase_     :  Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Dict    						=		self.num_labels
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		LevitForImageClassification(lowercase_)
          model.to(lowercase_)
          model.eval()
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		model(lowercase_						,	labels=lowercase_)
          self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_labels))



      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		self.prepare_config_and_inputs()
          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		config_and_inputs
          SCREAMING_SNAKE_CASE_      :  int    						=		{'''pixel_values''': pixel_values}
          return config, inputs_dict




@require_torch
class 							lowerCAmelCase__				(			UpperCAmelCase__				,		UpperCAmelCase__				,		unittest.TestCase ):






      '''simple docstring'''

      __UpperCamelCase	 =  (
          (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
          if is_torch_available()
          else ()
      )
      __UpperCamelCase	 =  (
          {
              "feature-extraction": LevitModel,
              "image-classification": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
          }
          if is_torch_available()
          else {}
      )

      __UpperCamelCase	 =  False
      __UpperCamelCase	 =  False
      __UpperCamelCase	 =  False
      __UpperCamelCase	 =  False
      __UpperCamelCase	 =  False
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		LevitModelTester(self)
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		ConfigTester(self						,	config_class=lowercase_						,	has_text_modality=lowercase_						,	hidden_size=37)
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]):




          '''simple docstring'''


          self.create_and_test_config_common_properties()
          self.config_tester.create_and_test_config_to_json_string()
          self.config_tester.create_and_test_config_to_json_file()
          self.config_tester.create_and_test_config_from_and_save_pretrained()
          self.config_tester.create_and_test_config_with_num_labels()
          self.config_tester.check_config_can_be_init_without_params()
          self.config_tester.check_config_arguments_init()
      def _SCREAMING_SNAKE_CASE (						self     :  Union[str, Any]):




          '''simple docstring'''


          return
      @unittest.skip(reason='''Levit does not use inputs_embeds''')
      def _SCREAMING_SNAKE_CASE (						self     :  Union[str, Any]):




          '''simple docstring'''


          pass
      @unittest.skip(reason='''Levit does not support input and output embeddings''')
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]):




          '''simple docstring'''


          pass
      @unittest.skip(reason='''Levit does not output attentions''')
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          pass
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE_      :  Any    						=		model_class(lowercase_)
              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		inspect.signature(model.forward)
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              SCREAMING_SNAKE_CASE_      :  Dict    						=		[*signature.parameters.keys()]

              SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		['''pixel_values''']
              self.assertListEqual(arg_names[:1]						,	lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  str):




          '''simple docstring'''


          def check_hidden_states_output(lowercase_     :  List[str]						,	lowercase_     :  Optional[Any]						,	lowercase_     :  str):
              SCREAMING_SNAKE_CASE_      :  str    						=		model_class(lowercase_)
              model.to(lowercase_)
              model.eval()

              with torch.no_grad():
                  SCREAMING_SNAKE_CASE_      :  Tuple    						=		model(**self._prepare_for_class(lowercase_						,	lowercase_))

              SCREAMING_SNAKE_CASE_      :  str    						=		outputs.hidden_states

              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		len(self.model_tester.depths) + 1
              self.assertEqual(len(lowercase_)						,	lowercase_)

              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		(self.model_tester.image_size, self.model_tester.image_size)
              SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		image_size[0], image_size[1]
              for _ in range(4):
                  SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		floor(
                      (
                          (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                          / self.model_tester.stride
                      )
                      + 1)
                  SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		floor(
                      (
                          (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                          / self.model_tester.stride
                      )
                      + 1)
              # verify the first hidden states (first block)
              self.assertListEqual(
                  list(hidden_states[0].shape[-2:])						,	[
                      height * width,
                      self.model_tester.hidden_sizes[0],
                  ]						,	)

          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  List[str]    						=		self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		True
              check_hidden_states_output(lowercase_						,	lowercase_						,	lowercase_)

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              SCREAMING_SNAKE_CASE_      :  Tuple    						=		True

              check_hidden_states_output(lowercase_						,	lowercase_						,	lowercase_)
      @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''')
      def _SCREAMING_SNAKE_CASE (						self     :  int):




          '''simple docstring'''


          pass
      def _SCREAMING_SNAKE_CASE (						self     :  int						,	lowercase_     :  Optional[Any]						,	lowercase_     :  Optional[int]						,	lowercase_     :  Tuple=False):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  int    						=		super()._prepare_for_class(lowercase_						,	lowercase_						,	return_labels=lowercase_)

          if return_labels:
              if model_class.__name__ == "LevitForImageClassificationWithTeacher":
                  del inputs_dict["labels"]

          return inputs_dict
      def _SCREAMING_SNAKE_CASE (						self     :  List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  int    						=		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  List[str]):




          '''simple docstring'''


          if not self.model_tester.is_training:
              return

          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  List[str]    						=		self.model_tester.prepare_config_and_inputs_for_common()
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		True

          for model_class in self.all_model_classes:
              # LevitForImageClassificationWithTeacher supports inference-only
              if (
                  model_class in get_values(lowercase_)
                  or model_class.__name__ == "LevitForImageClassificationWithTeacher"
              ):
                  continue
              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		model_class(lowercase_)
              model.to(lowercase_)
              model.train()
              SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		self._prepare_for_class(lowercase_						,	lowercase_						,	return_labels=lowercase_)
              SCREAMING_SNAKE_CASE_      :  Dict    						=		model(**lowercase_).loss
              loss.backward()
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  int    						=		self.model_tester.prepare_config_and_inputs_for_common()
          if not self.model_tester.is_training:
              return

          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		False
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		True

          for model_class in self.all_model_classes:
              if model_class in get_values(lowercase_) or not model_class.supports_gradient_checkpointing:
                  continue
              # LevitForImageClassificationWithTeacher supports inference-only
              if model_class.__name__ == "LevitForImageClassificationWithTeacher":
                  continue
              SCREAMING_SNAKE_CASE_      :  List[str]    						=		model_class(lowercase_)
              model.gradient_checkpointing_enable()
              model.to(lowercase_)
              model.train()
              SCREAMING_SNAKE_CASE_      :  Dict    						=		self._prepare_for_class(lowercase_						,	lowercase_						,	return_labels=lowercase_)
              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		model(**lowercase_).loss
              loss.backward()
      def _SCREAMING_SNAKE_CASE (						self     :  Union[str, Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  str    						=		self.model_tester.prepare_config_and_inputs_for_common()

          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		[
              {'''title''': '''multi_label_classification''', '''num_labels''': 2, '''dtype''': torch.float},
              {'''title''': '''single_label_classification''', '''num_labels''': 1, '''dtype''': torch.long},
              {'''title''': '''regression''', '''num_labels''': 1, '''dtype''': torch.float},
          ]

          for model_class in self.all_model_classes:
              if (
                  model_class
                  not in [
                      *get_values(lowercase_),
                  ]
                  or model_class.__name__ == "LevitForImageClassificationWithTeacher"
              ):
                  continue

              for problem_type in problem_types:
                  with self.subTest(msg=F'Testing {model_class} with {problem_type["title"]}'):
                      SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		problem_type['''title''']
                      SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		problem_type['''num_labels''']

                      SCREAMING_SNAKE_CASE_      :  str    						=		model_class(lowercase_)
                      model.to(lowercase_)
                      model.train()

                      SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		self._prepare_for_class(lowercase_						,	lowercase_						,	return_labels=lowercase_)

                      if problem_type["num_labels"] > 1:
                          SCREAMING_SNAKE_CASE_      :  str    						=		inputs['''labels'''].unsqueeze(1).repeat(1						,	problem_type['''num_labels'''])

                      SCREAMING_SNAKE_CASE_      :  Any    						=		inputs['''labels'''].to(problem_type['''dtype'''])

                      # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
                      # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
                      # they have the same size." which is a symptom something in wrong for the regression problem.
                      # See https://github.com/huggingface/transformers/issues/11780
                      with warnings.catch_warnings(record=lowercase_) as warning_list:
                          SCREAMING_SNAKE_CASE_      :  int    						=		model(**lowercase_).loss
                      for w in warning_list:
                          if "Using a target size that is different to the input size" in str(w.message):
                              raise ValueError(
                                  F'Something is going wrong in the regression problem: intercepted {w.message}')

                      loss.backward()



      @slow
      def _SCREAMING_SNAKE_CASE (						self     :  Any):




          '''simple docstring'''


          for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		LevitModel.from_pretrained(lowercase_)
              self.assertIsNotNone(lowercase_)



def 					_A			() ->       Union[str, Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Any    						=		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
    return image





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






      '''simple docstring'''

      @cached_property
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0])



      @slow
      def _SCREAMING_SNAKE_CASE (						self     :  Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(
              lowercase_)

          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		self.default_image_processor
          SCREAMING_SNAKE_CASE_      :  str    						=		prepare_img()
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		image_processor(images=lowercase_						,	return_tensors='''pt''').to(lowercase_)

          # forward pass
          with torch.no_grad():
              SCREAMING_SNAKE_CASE_      :  Any    						=		model(**lowercase_)

          # verify the logits
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		torch.Size((1, 1000))
          self.assertEqual(outputs.logits.shape						,	lowercase_)

          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		torch.tensor([1.04_48, -0.37_45, -1.83_17]).to(lowercase_)

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


