
import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
    import jax

    from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel




class        UpperCAmelCase_			( unittest.TestCase     ):


  '''simple docstring'''







  def __init__( self			: Optional[int]			,							UpperCamelCase__			: Dict			,							UpperCamelCase__			: List[str]=13			,							UpperCamelCase__			: int=30			,							UpperCamelCase__			: Optional[int]=2			,							UpperCamelCase__			: List[Any]=3			,							UpperCamelCase__			: str=True			,							UpperCamelCase__			: Tuple=True			,							UpperCamelCase__			: Optional[Any]=32			,							UpperCamelCase__			: int=5			,							UpperCamelCase__			: List[Any]=4			,							UpperCamelCase__			: List[str]=37			,							UpperCamelCase__			: Any="gelu"			,							UpperCamelCase__			: List[Any]=0.1			,							UpperCamelCase__			: Union[str, Any]=0.1			,							UpperCamelCase__			: Dict=10			,							UpperCamelCase__			: List[Any]=0.02			,							)			->	Dict:







    """simple docstring"""






    __magic_name__				     =       parent
    __magic_name__				     =       batch_size
    __magic_name__				     =       image_size
    __magic_name__				     =       patch_size
    __magic_name__				     =       num_channels
    __magic_name__				     =       is_training
    __magic_name__				     =       use_labels
    __magic_name__				     =       hidden_size
    __magic_name__				     =       num_hidden_layers
    __magic_name__				     =       num_attention_heads
    __magic_name__				     =       intermediate_size
    __magic_name__				     =       hidden_act
    __magic_name__				     =       hidden_dropout_prob
    __magic_name__				     =       attention_probs_dropout_prob
    __magic_name__				     =       type_sequence_label_size
    __magic_name__				     =       initializer_range

    # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
    __magic_name__				     =       (image_size // patch_size) ** 2
    __magic_name__				     =       num_patches + 1
  def 							_lowercase							( self			: Any    )			->	Optional[int]:







    """simple docstring"""






    __magic_name__				     =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

    __magic_name__				     =       ViTConfig(
        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			,							is_decoder=UpperCamelCase__			,							initializer_range=self.initializer_range			,							)

    return config, pixel_values
  def 							_lowercase							( self			: List[Any]			,							UpperCamelCase__			: Union[str, Any]			,							UpperCamelCase__			: int    )			->	List[Any]:







    """simple docstring"""






    __magic_name__				     =       FlaxViTModel(config=UpperCamelCase__    )
    __magic_name__				     =       model(UpperCamelCase__    )
    # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
    __magic_name__				     =       (self.image_size, self.image_size)
    __magic_name__				     =       (self.patch_size, self.patch_size)
    __magic_name__				     =       (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
    self.parent.assertEqual(result.last_hidden_state.shape			,							(self.batch_size, num_patches + 1, self.hidden_size)    )
  def 							_lowercase							( self			: Optional[int]			,							UpperCamelCase__			: str			,							UpperCamelCase__			: List[Any]    )			->	Any:







    """simple docstring"""






    __magic_name__				     =       self.type_sequence_label_size
    __magic_name__				     =       FlaxViTForImageClassification(config=UpperCamelCase__    )
    __magic_name__				     =       model(UpperCamelCase__    )
    self.parent.assertEqual(result.logits.shape			,							(self.batch_size, self.type_sequence_label_size)    )

    # test greyscale images
    __magic_name__				     =       1
    __magic_name__				     =       FlaxViTForImageClassification(UpperCamelCase__    )

    __magic_name__				     =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
    __magic_name__				     =       model(UpperCamelCase__    )





  def 							_lowercase							( self			: int    )			->	int:







    """simple docstring"""






    __magic_name__				     =       self.prepare_config_and_inputs()
    (
        (
        __magic_name__
    )     ,    (
        __magic_name__
    )     ,    
    )				     =       config_and_inputs
    __magic_name__				     =       {"""pixel_values""": pixel_values}
    return config, inputs_dict







@require_flax
class        UpperCAmelCase_			( _A  ,       unittest.TestCase     ):


  '''simple docstring'''







  a__           =		(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()
  def 							_lowercase							( self			: Tuple    )			->	None:







    """simple docstring"""






    __magic_name__				     =       FlaxViTModelTester(self    )
    __magic_name__				     =       ConfigTester(self			,							config_class=UpperCamelCase__			,							has_text_modality=UpperCamelCase__			,							hidden_size=37    )
  def 							_lowercase							( self			: int    )			->	Dict:







    """simple docstring"""






    self.config_tester.run_common_tests()
  def 							_lowercase							( self			: Optional[Any]    )			->	str:







    """simple docstring"""






    __magic_name__				     =       self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*UpperCamelCase__    )
  def 							_lowercase							( self			: Dict    )			->	List[str]:







    """simple docstring"""






    __magic_name__				     =       self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__    )
  def 							_lowercase							( self			: str    )			->	Union[str, Any]:







    """simple docstring"""






    __magic_name__     ,    __magic_name__				     =       self.model_tester.prepare_config_and_inputs_for_common()

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

      __magic_name__				     =       ["""pixel_values"""]
      self.assertListEqual(arg_names[:1]			,							UpperCamelCase__    )
  def 							_lowercase							( self			: Dict    )			->	Optional[int]:







    """simple docstring"""






    __magic_name__     ,    __magic_name__				     =       self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      with self.subTest(model_class.__name__    ):
        __magic_name__				     =       self._prepare_for_class(UpperCamelCase__			,							UpperCamelCase__    )
        __magic_name__				     =       model_class(UpperCamelCase__    )

        @jax.jit
        def model_jitted(UpperCamelCase__			: Optional[Any]			,							**UpperCamelCase__			: Dict    ):
          return model(pixel_values=UpperCamelCase__			,							**UpperCamelCase__    )

        with self.subTest("""JIT Enabled"""    ):
          __magic_name__				     =       model_jitted(**UpperCamelCase__    ).to_tuple()

        with self.subTest("""JIT Disabled"""    ):
          with jax.disable_jit():
            __magic_name__				     =       model_jitted(**UpperCamelCase__    ).to_tuple()

        self.assertEqual(len(UpperCamelCase__    )			,							len(UpperCamelCase__    )    )
        for jitted_output, output in zip(UpperCamelCase__			,							UpperCamelCase__    ):
          self.assertEqual(jitted_output.shape			,							output.shape    )





  @slow
  def 							_lowercase							( self			: Tuple    )			->	Any:







    """simple docstring"""






    for model_class_name in self.all_model_classes:
      __magic_name__				     =       model_class_name.from_pretrained("""google/vit-base-patch16-224"""    )
      __magic_name__				     =       model(np.ones((1, 3, 224, 224)    )    )
      self.assertIsNotNone(UpperCamelCase__    )






'''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(				_lowerCAmelCase		)		-> List[Any]:
      snake_case__  :    Dict    						=      SwinConfig()
      snake_case__  :    Optional[Any]    						=      swin_name.split("""_"""		)

      snake_case__  :    Any    						=      name_split[1]
      snake_case__  :    List[Any]    						=      int(name_split[4]		)
      snake_case__  :    int    						=      int(name_split[3][-1]		)

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

      if "in22k" in swin_name:
            snake_case__  :    str    						=      21_841
      else:
            snake_case__  :    List[str]    						=      1_000
            snake_case__  :    int    						=      """huggingface/label-files"""
            snake_case__  :    Any    						=      """imagenet-1k-id2label.json"""
            snake_case__  :    List[Any]    						=      json.load(open(hf_hub_download(_lowerCAmelCase     ,   _lowerCAmelCase     ,   repo_type="""dataset"""		)     ,   """r"""		)		)
            snake_case__  :    Dict    						=      {int(_lowerCAmelCase		): v for k, v in idalabel.items()}
            snake_case__  :    Optional[int]    						=      idalabel
            snake_case__  :    List[Any]    						=      {v: k for k, v in idalabel.items()}

      snake_case__  :    List[Any]    						=      img_size
      snake_case__  :    Dict    						=      num_classes
      snake_case__  :    Dict    						=      embed_dim
      snake_case__  :    Optional[int]    						=      depths
      snake_case__  :    int    						=      num_heads
      snake_case__  :    Optional[int]    						=      window_size

      return config





def 						__snake_case(				_lowerCAmelCase		)		-> Dict:
      if "patch_embed.proj" in name:
            snake_case__  :    List[str]    						=      name.replace("""patch_embed.proj"""     ,   """embeddings.patch_embeddings.projection"""		)
      if "patch_embed.norm" in name:
            snake_case__  :    int    						=      name.replace("""patch_embed.norm"""     ,   """embeddings.norm"""		)
      if "layers" in name:
            snake_case__  :    str    						=      """encoder.""" + name
      if "attn.proj" in name:
            snake_case__  :    List[str]    						=      name.replace("""attn.proj"""     ,   """attention.output.dense"""		)
      if "attn" in name:
            snake_case__  :    Tuple    						=      name.replace("""attn"""     ,   """attention.self"""		)
      if "norm1" in name:
            snake_case__  :    List[str]    						=      name.replace("""norm1"""     ,   """layernorm_before"""		)
      if "norm2" in name:
            snake_case__  :    Optional[Any]    						=      name.replace("""norm2"""     ,   """layernorm_after"""		)
      if "mlp.fc1" in name:
            snake_case__  :    Union[str, Any]    						=      name.replace("""mlp.fc1"""     ,   """intermediate.dense"""		)
      if "mlp.fc2" in name:
            snake_case__  :    Dict    						=      name.replace("""mlp.fc2"""     ,   """output.dense"""		)

      if name == "norm.weight":
            snake_case__  :    Tuple    						=      """layernorm.weight"""
      if name == "norm.bias":
            snake_case__  :    Union[str, Any]    						=      """layernorm.bias"""

      if "head" in name:
            snake_case__  :    Optional[int]    						=      name.replace("""head"""     ,   """classifier"""		)
      else:
            snake_case__  :    List[str]    						=      """swin.""" + name

      return name





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> Optional[Any]:
      for key in orig_state_dict.copy().keys():
            snake_case__  :    Optional[int]    						=      orig_state_dict.pop(_lowerCAmelCase		)

            if "mask" in key:
                  continue
            elif "qkv" in key:
                  snake_case__  :    Dict    						=      key.split("""."""		)
                  snake_case__  :    Optional[int]    						=      int(key_split[1]		)
                  snake_case__  :    Union[str, Any]    						=      int(key_split[3]		)
                  snake_case__  :    List[Any]    						=      model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

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

      return orig_state_dict





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> int:
      snake_case__  :    Optional[int]    						=      timm.create_model(_lowerCAmelCase     ,   pretrained=_lowerCAmelCase		)
      timm_model.eval()

      snake_case__  :    Optional[int]    						=      get_swin_config(_lowerCAmelCase		)
      snake_case__  :    Optional[Any]    						=      SwinForImageClassification(_lowerCAmelCase		)
      model.eval()

      snake_case__  :    str    						=      convert_state_dict(timm_model.state_dict()     ,   _lowerCAmelCase		)
      model.load_state_dict(_lowerCAmelCase		)

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

      snake_case__  :    Dict    						=      AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swin_name.replace("""_"""     ,   """-"""		)		)		)
      snake_case__  :    Dict    						=      Image.open(requests.get(_lowerCAmelCase     ,   stream=_lowerCAmelCase		).raw		)
      snake_case__  :    Optional[int]    						=      image_processor(images=_lowerCAmelCase     ,   return_tensors="""pt"""		)

      snake_case__  :    Optional[Any]    						=      timm_model(inputs["""pixel_values"""]		)
      snake_case__  :    str    						=      model(**_lowerCAmelCase		).logits

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

      print(f"Saving model {swin_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		)


if __name__ == "__main__":
       __a          =		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."
       )

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