



"""simple docstring"""




from __future__ import annotations

import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import TFViTForImageClassification, TFViTModel


if is_vision_available():
 from PIL import Image

 from transformers import ViTImageProcessor






class       UpperCAmelCase_    :



      def __init__(	self	:      str	,      A	:      Union[str, Any]	,      A	:      str=1_3	,      A	:      List[Any]=3_0	,      A	:      Tuple=2	,      A	:      List[Any]=3	,      A	:      List[str]=True	,      A	:      Optional[int]=True	,      A	:      Union[str, Any]=3_2	,      A	:      Tuple=2	,      A	:      str=4	,      A	:      Any=3_7	,      A	:      Union[str, Any]="gelu"	,      A	:      Union[str, Any]=0.1	,      A	:      Tuple=0.1	,      A	:      Any=1_0	,      A	:      Optional[int]=0.02	,      A	:      Optional[int]=3	,      A	:      Union[str, Any]=None	,      ):
           _UpperCAmelCase     :      Optional[Any]														=       parent
           _UpperCAmelCase     :      Optional[Any]														=       batch_size
           _UpperCAmelCase     :      List[str]														=       image_size
           _UpperCAmelCase     :      List[Any]														=       patch_size
           _UpperCAmelCase     :      Dict														=       num_channels
           _UpperCAmelCase     :      Union[str, Any]														=       is_training
           _UpperCAmelCase     :      Tuple														=       use_labels
           _UpperCAmelCase     :      List[Any]														=       hidden_size
           _UpperCAmelCase     :      Union[str, Any]														=       num_hidden_layers
           _UpperCAmelCase     :      Tuple														=       num_attention_heads
           _UpperCAmelCase     :      List[str]														=       intermediate_size
           _UpperCAmelCase     :      Optional[Any]														=       hidden_act
           _UpperCAmelCase     :      Tuple														=       hidden_dropout_prob
           _UpperCAmelCase     :      Optional[int]														=       attention_probs_dropout_prob
           _UpperCAmelCase     :      Optional[Any]														=       type_sequence_label_size
           _UpperCAmelCase     :      Optional[Any]														=       initializer_range
           _UpperCAmelCase     :      int														=       scope

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



      def 		snake_case_   (	self	:      Union[str, Any]		):
           _UpperCAmelCase     :      List[Any]														=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

           _UpperCAmelCase     :      Dict														=       None
           if self.use_labels:
                _UpperCAmelCase     :      Optional[Any]														=       ids_tensor([self.batch_size]	,      self.type_sequence_label_size		)

           _UpperCAmelCase     :      Dict														=       self.get_config()

           return config, pixel_values, labels



      def 		snake_case_   (	self	:      Optional[Any]		):
           return 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=A	,      initializer_range=self.initializer_range	,      )



      def 		snake_case_   (	self	:      Optional[Any]	,      A	:      str	,      A	:      List[str]	,      A	:      Dict		):
           _UpperCAmelCase     :      Union[str, Any]														=       TFViTModel(config=A		)
           _UpperCAmelCase     :      Optional[Any]														=       model(A	,      training=A		)
           self.parent.assertEqual(result.last_hidden_state.shape	,      (self.batch_size, self.seq_length, self.hidden_size)		)

           # Test with an image with different size than the one specified in config.
           _UpperCAmelCase     :      int														=       self.image_size // 2
           _UpperCAmelCase     :      str														=       pixel_values[:, :, :image_size, :image_size]
           _UpperCAmelCase     :      List[Any]														=       model(A	,      interpolate_pos_encoding=A	,      training=A		)
           _UpperCAmelCase     :      str														=       (image_size // self.patch_size) ** 2 + 1
           self.parent.assertEqual(result.last_hidden_state.shape	,      (self.batch_size, seq_length, self.hidden_size)		)



      def 		snake_case_   (	self	:      int	,      A	:      Optional[int]	,      A	:      int	,      A	:      Any		):
           _UpperCAmelCase     :      str														=       self.type_sequence_label_size
           _UpperCAmelCase     :      int														=       TFViTForImageClassification(A		)
           _UpperCAmelCase     :      Union[str, Any]														=       model(A	,      labels=A	,      training=A		)
           self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.type_sequence_label_size)		)

           # Test with an image with different size than the one specified in config.
           _UpperCAmelCase     :      Dict														=       self.image_size // 2
           _UpperCAmelCase     :      Any														=       pixel_values[:, :, :image_size, :image_size]
           _UpperCAmelCase     :      Optional[Any]														=       model(A	,      interpolate_pos_encoding=A	,      training=A		)
           self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.type_sequence_label_size)		)

           # test greyscale images
           _UpperCAmelCase     :      List[Any]														=       1
           _UpperCAmelCase     :      str														=       TFViTForImageClassification(A		)
           _UpperCAmelCase     :      Union[str, Any]														=       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
           _UpperCAmelCase     :      str														=       model(A		)
           self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.type_sequence_label_size)		)



      def 		snake_case_   (	self	:      int		):
           _UpperCAmelCase     :      List[str]														=       self.prepare_config_and_inputs()
           _UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase     :      Dict														=       config_and_inputs
           _UpperCAmelCase     :      str														=       {"pixel_values": pixel_values}
           return config, inputs_dict







@require_tf
class       UpperCAmelCase_    (							_UpperCamelCase			,     _UpperCamelCase			,     unittest.TestCase		):
      __SCREAMING_SNAKE_CASE		:  List[str]						=					(TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
      __SCREAMING_SNAKE_CASE		:  Tuple						=					(
          {'feature-extraction': TFViTModel, 'image-classification': TFViTForImageClassification}
          if is_tf_available()
          else {}
      )

      __SCREAMING_SNAKE_CASE		:  str						=					False
      __SCREAMING_SNAKE_CASE		:  Optional[int]						=					False
      __SCREAMING_SNAKE_CASE		:  Union[str, Any]						=					False



      def 		snake_case_   (	self	:      Tuple		):
           _UpperCAmelCase     :      int														=       TFViTModelTester(self		)
           _UpperCAmelCase     :      Union[str, Any]														=       ConfigTester(self	,      config_class=A	,      has_text_modality=A	,      hidden_size=3_7		)



      def 		snake_case_   (	self	:      List[Any]		):
           self.config_tester.run_common_tests()



      @unittest.skip(reason="ViT does not use inputs_embeds"		)
      def 		snake_case_   (	self	:      List[Any]		):
           pass



      @unittest.skip(reason="ViT does not use inputs_embeds"		)
      def 		snake_case_   (	self	:      Optional[Any]		):
           pass



      def 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase							,			_UpperCAmelCase     :      int														=       self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                _UpperCAmelCase     :      str														=       model_class(A		)
                self.assertIsInstance(model.get_input_embeddings()	,      (tf.keras.layers.Layer)		)
                _UpperCAmelCase     :      str														=       model.get_output_embeddings()
                self.assertTrue(x is None or isinstance(A	,      tf.keras.layers.Layer		)		)



      def 		snake_case_   (	self	:      List[Any]		):
           _UpperCAmelCase							,			_UpperCAmelCase     :      Dict														=       self.model_tester.prepare_config_and_inputs_for_common()

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

                _UpperCAmelCase     :      Union[str, Any]														=       ["pixel_values"]
                self.assertListEqual(arg_names[:1]	,      A		)



      def 		snake_case_   (	self	:      Any		):
           _UpperCAmelCase     :      Dict														=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*A		)



      def 		snake_case_   (	self	:      Any		):
           _UpperCAmelCase     :      Optional[Any]														=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*A		)



      @slow
      def 		snake_case_   (	self	:      Optional[int]		):
           _UpperCAmelCase     :      Optional[Any]														=       TFViTModel.from_pretrained("google/vit-base-patch16-224"		)
           self.assertIsNotNone(A		)







def 		__snake_case		( )    ->  int:





     '''simple docstring'''

     _UpperCAmelCase     :      Union[str, Any]														=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
     return image






@require_tf
@require_vision
class       UpperCAmelCase_    (							unittest.TestCase		):



      @cached_property
      def 		snake_case_   (	self	:      Union[str, Any]		):
           return ViTImageProcessor.from_pretrained("google/vit-base-patch16-224"		) if is_vision_available() else None



      @slow
      def 		snake_case_   (	self	:      List[str]		):
           _UpperCAmelCase     :      Any														=       TFViTForImageClassification.from_pretrained("google/vit-base-patch16-224"		)

           _UpperCAmelCase     :      Any														=       self.default_image_processor
           _UpperCAmelCase     :      Dict														=       prepare_img()
           _UpperCAmelCase     :      List[str]														=       image_processor(images=A	,      return_tensors="tf"		)

           # forward pass
           _UpperCAmelCase     :      Dict														=       model(**A		)

           # verify the logits
           _UpperCAmelCase     :      List[str]														=       tf.TensorShape((1, 1_0_0_0)		)
           self.assertEqual(outputs.logits.shape	,      A		)

           _UpperCAmelCase     :      Any														=       tf.constant([-0.2_744, 0.8_215, -0.0_836]		)

           tf.debugging.assert_near(outputs.logits[0, :3]	,      A	,      atol=1e-4		)





"""simple docstring"""




import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		List[Any]							)    ->  List[Any]:





     '''simple docstring'''

     _UpperCAmelCase     :      List[Any]														=       SwinConfig(image_size=192							)

     if "base" in model_name:
          _UpperCAmelCase     :      Tuple														=       6
          _UpperCAmelCase     :      Optional[Any]														=       128
          _UpperCAmelCase     :      Dict														=       (2, 2, 18, 2)
          _UpperCAmelCase     :      List[Any]														=       (4, 8, 16, 32)
     elif "large" in model_name:
          _UpperCAmelCase     :      int														=       12
          _UpperCAmelCase     :      Optional[int]														=       192
          _UpperCAmelCase     :      Optional[Any]														=       (2, 2, 18, 2)
          _UpperCAmelCase     :      str														=       (6, 12, 24, 48)
     else:
          raise ValueError("Model not supported, only supports base and large variants"							)

     _UpperCAmelCase     :      Optional[int]														=       window_size
     _UpperCAmelCase     :      Optional[int]														=       embed_dim
     _UpperCAmelCase     :      List[Any]														=       depths
     _UpperCAmelCase     :      Any														=       num_heads

     return config

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Dict							)    ->  str:





     '''simple docstring'''

     if "encoder.mask_token" in name:
          _UpperCAmelCase     :      Dict														=       name.replace("encoder.mask_token"		,    "embeddings.mask_token"							)
     if "encoder.patch_embed.proj" in name:
          _UpperCAmelCase     :      Optional[int]														=       name.replace("encoder.patch_embed.proj"		,    "embeddings.patch_embeddings.projection"							)
     if "encoder.patch_embed.norm" in name:
          _UpperCAmelCase     :      Any														=       name.replace("encoder.patch_embed.norm"		,    "embeddings.norm"							)
     if "attn.proj" in name:
          _UpperCAmelCase     :      int														=       name.replace("attn.proj"		,    "attention.output.dense"							)
     if "attn" in name:
          _UpperCAmelCase     :      Dict														=       name.replace("attn"		,    "attention.self"							)
     if "norm1" in name:
          _UpperCAmelCase     :      List[str]														=       name.replace("norm1"		,    "layernorm_before"							)
     if "norm2" in name:
          _UpperCAmelCase     :      int														=       name.replace("norm2"		,    "layernorm_after"							)
     if "mlp.fc1" in name:
          _UpperCAmelCase     :      List[str]														=       name.replace("mlp.fc1"		,    "intermediate.dense"							)
     if "mlp.fc2" in name:
          _UpperCAmelCase     :      Any														=       name.replace("mlp.fc2"		,    "output.dense"							)

     if name == "encoder.norm.weight":
          _UpperCAmelCase     :      Any														=       "layernorm.weight"
     if name == "encoder.norm.bias":
          _UpperCAmelCase     :      List[Any]														=       "layernorm.bias"

     if "decoder" in name:
          pass
     else:
          _UpperCAmelCase     :      Dict														=       "swin." + name

     return name

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Tuple		,    SCREAMING_SNAKE_CASE__      :		int							)    ->  Optional[Any]:





     '''simple docstring'''

     for key in orig_state_dict.copy().keys():
          _UpperCAmelCase     :      Dict														=       orig_state_dict.pop(SCREAMING_SNAKE_CASE__							)

          if "attn_mask" in key:
               pass
          elif "qkv" in key:
               _UpperCAmelCase     :      int														=       key.split("."							)
               _UpperCAmelCase     :      List[str]														=       int(key_split[2]							)
               _UpperCAmelCase     :      Dict														=       int(key_split[4]							)
               _UpperCAmelCase     :      str														=       model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

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

     return orig_state_dict

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Tuple		,    SCREAMING_SNAKE_CASE__      :		List[Any]		,    SCREAMING_SNAKE_CASE__      :		Any		,    SCREAMING_SNAKE_CASE__      :		int							)    ->  List[str]:





     '''simple docstring'''

     _UpperCAmelCase     :      Any														=       torch.load(SCREAMING_SNAKE_CASE__		,    map_location="cpu"							)["model"]

     _UpperCAmelCase     :      Union[str, Any]														=       get_swin_config(SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      str														=       SwinForMaskedImageModeling(SCREAMING_SNAKE_CASE__							)
     model.eval()

     _UpperCAmelCase     :      Optional[Any]														=       convert_state_dict(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)
     model.load_state_dict(SCREAMING_SNAKE_CASE__							)

     _UpperCAmelCase     :      Optional[int]														=       "http://images.cocodataset.org/val2017/000000039769.jpg"

     _UpperCAmelCase     :      List[Any]														=       ViTImageProcessor(size={"height": 192, "width": 192}							)
     _UpperCAmelCase     :      int														=       Image.open(requests.get(SCREAMING_SNAKE_CASE__		,    stream=SCREAMING_SNAKE_CASE__							).raw							)
     _UpperCAmelCase     :      str														=       image_processor(images=SCREAMING_SNAKE_CASE__		,    return_tensors="pt"							)

     with torch.no_grad():
          _UpperCAmelCase     :      List[str]														=       model(**SCREAMING_SNAKE_CASE__							).logits

     print(outputs.keys()							)
     print("Looks ok!"							)

     if pytorch_dump_folder_path is not None:
          print(f'Saving model {model_name} to {pytorch_dump_folder_path}'							)
          model.save_pretrained(SCREAMING_SNAKE_CASE__							)

          print(f'Saving image processor to {pytorch_dump_folder_path}'							)
          image_processor.save_pretrained(SCREAMING_SNAKE_CASE__							)

     if push_to_hub:
          print(f'Pushing model and image processor for {model_name} to hub'							)
          model.push_to_hub(f'microsoft/{model_name}'							)
          image_processor.push_to_hub(f'microsoft/{model_name}'							)


if __name__ == "__main__":
 _lowerCAmelCase							:   str				=				argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--model_name",
     default="swin-base-simmim-window6-192",
     type=str,
     choices=["swin-base-simmim-window6-192", "swin-large-simmim-window12-192"],
     help="Name of the Swin SimMIM model you'd like to convert.",
 )
 parser.add_argument(
     "--checkpoint_path",
     default="/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth",
     type=str,
     help="Path to the original PyTorch checkpoint (.pth file).",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
 )
 parser.add_argument(
     "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
 )

 _lowerCAmelCase							:   str				=				parser.parse_args()
 convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
