





'''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	(	a    : List[str]     ) ->Tuple:
 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				=			96
  snake_case				=			(2, 2, 6, 2)
  snake_case				=			(3, 6, 12, 24)
 elif model_size == "small":
  snake_case				=			96
  snake_case				=			(2, 2, 18, 2)
  snake_case				=			(3, 6, 12, 24)
 elif model_size == "base":
  snake_case				=			128
  snake_case				=			(2, 2, 18, 2)
  snake_case				=			(4, 8, 16, 32)
 else:
  snake_case				=			192
  snake_case				=			(2, 2, 18, 2)
  snake_case				=			(6, 12, 24, 48)

 if "to" in swinva_name:
  snake_case				=			(12, 12, 12, 6)

 if ("22k" in swinva_name) and ("to" not in swinva_name):
  snake_case				=			2_1841
  snake_case				=			'''huggingface/label-files'''
  snake_case				=			'''imagenet-22k-id2label.json'''
  snake_case				=			json.load(open(hf_hub_download(a					,   a					,   repo_type='''dataset'''     )					,   '''r'''     )     )
  snake_case				=			{int(a     ): v for k, v in idalabel.items()}
  snake_case				=			idalabel
  snake_case				=			{v: k for k, v in idalabel.items()}

 else:
  snake_case				=			1000
  snake_case				=			'''huggingface/label-files'''
  snake_case				=			'''imagenet-1k-id2label.json'''
  snake_case				=			json.load(open(hf_hub_download(a					,   a					,   repo_type='''dataset'''     )					,   '''r'''     )     )
  snake_case				=			{int(a     ): 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	(	a    : Dict     ) ->int:
 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	(	a    : Any					,   a    : Union[str, Any]     ) ->Dict:
 for key in orig_state_dict.copy().keys():
  snake_case				=			orig_state_dict.pop(a     )

  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	(	a    : Optional[int]					,   a    : Union[str, Any]     ) ->str:
 snake_case				=			timm.create_model(a					,   pretrained=a     )
 timm_model.eval()

 snake_case				=			get_swinva_config(a     )
 snake_case				=			SwinvaForImageClassification(a     )
 model.eval()

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

 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(a					,   stream=a     ).raw     )
 snake_case				=			image_processor(images=a					,   return_tensors='''pt'''     )

 snake_case				=			timm_model(inputs['''pixel_values''']     )
 snake_case				=			model(**a     ).logits

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

 print(f"""Saving model {swinva_name} to {pytorch_dump_folder_path}"""     )
 model.save_pretrained(a     )

 print(f"""Saving image processor to {pytorch_dump_folder_path}"""     )
 image_processor.save_pretrained(a     )

 model.push_to_hub(
     repo_path_or_name=Path(a					,   a     )					,   organization='''nandwalritik'''					,   commit_message='''Add model'''					,   )


if __name__ == "__main__":
   _lowercase       						=			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.'
   )

   _lowercase       						=			parser.parse_args()
   convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)








'''simple docstring'''







import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image
else:
   class 			_lowercase						:







        @staticmethod
        def     UpperCamelCase			( *A__    ,   **A__    ) ->	List[Any]:
         pass

def    __UpperCamelCase	(	a    : Image     ) ->str:
 snake_case				=			hashlib.mda(image.tobytes()     )
 return m.hexdigest()






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 			_lowercase						(       unittest.TestCase					):
     _UpperCAmelCase										=					MODEL_FOR_DEPTH_ESTIMATION_MAPPING







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ) ->	Union[str, Any]:
      snake_case				=			DepthEstimationPipeline(model=A__    ,   image_processor=A__    )
      return depth_estimator, [
          "./tests/fixtures/tests_samples/COCO/000000039769.png",
          "./tests/fixtures/tests_samples/COCO/000000039769.png",
      ]







     def     UpperCamelCase			( self    ,   A__    ,   A__    ) ->	List[Any]:
      snake_case				=			depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    )
      self.assertEqual({'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )}    ,   A__    )
      import datasets

      snake_case				=			datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''    ,   '''image'''    ,   split='''test'''    )
      snake_case				=			depth_estimator(
          [
              Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    ),
              '''http://images.cocodataset.org/val2017/000000039769.jpg''',
              # RGBA
              dataset[0]['''file'''],
              # LA
              dataset[1]['''file'''],
              # L
              dataset[2]['''file'''],
          ]    )
      self.assertEqual(
          [
              {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
              {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
              {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
              {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
              {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
          ]    ,   A__    ,   )







     @require_tf
     @unittest.skip('''Depth estimation is not implemented in TF'''    )
     def     UpperCamelCase			( self    ) ->	Optional[Any]:
      pass







     @slow
     @require_torch
     def     UpperCamelCase			( self    ) ->	Dict:
      snake_case				=			'''Intel/dpt-large'''
      snake_case				=			pipeline('''depth-estimation'''    ,   model=A__    )
      snake_case				=			depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg'''    )
      snake_case				=			hashimage(outputs['''depth''']    )

      # This seems flaky.
      # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
      self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item()    )    ,   2_9.3_0_4    )
      self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item()    )    ,   2.6_6_2    )







     @require_torch
     def     UpperCamelCase			( self    ) ->	Any:
      # This is highly irregular to have no small tests.
      self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT'''    )

