



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      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: Optional[int]     			=SwinvaConfig()
   SCREAMING_SNAKE_CASE_: Union[str, Any]     			=swinva_name.split("""_""" )

   SCREAMING_SNAKE_CASE_: Tuple     			=name_split[1]
   if "to" in name_split[3]:
      SCREAMING_SNAKE_CASE_: List[str]     			=int(name_split[3][-3:] )
   else:
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=int(name_split[3] )
   if "to" in name_split[2]:
      SCREAMING_SNAKE_CASE_: List[Any]     			=int(name_split[2][-2:] )
   else:
      SCREAMING_SNAKE_CASE_: int     			=int(name_split[2][6:] )

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

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

   if ("22k" in swinva_name) and ("to" not in swinva_name):
      SCREAMING_SNAKE_CASE_: List[str]     			=2_1841
      SCREAMING_SNAKE_CASE_: List[Any]     			="huggingface/label-files"
      SCREAMING_SNAKE_CASE_: List[Any]     			="imagenet-22k-id2label.json"
      SCREAMING_SNAKE_CASE_: Dict     			=json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			repo_type="""dataset""" )							,			"""r""" ) )
      SCREAMING_SNAKE_CASE_: List[str]     			={int(__SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
      SCREAMING_SNAKE_CASE_: Tuple     			=idalabel
      SCREAMING_SNAKE_CASE_: Any     			={v: k for k, v in idalabel.items()}

   else:
      SCREAMING_SNAKE_CASE_: Dict     			=1000
      SCREAMING_SNAKE_CASE_: str     			="huggingface/label-files"
      SCREAMING_SNAKE_CASE_: str     			="imagenet-1k-id2label.json"
      SCREAMING_SNAKE_CASE_: int     			=json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			repo_type="""dataset""" )							,			"""r""" ) )
      SCREAMING_SNAKE_CASE_: int     			={int(__SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
      SCREAMING_SNAKE_CASE_: List[Any]     			=idalabel
      SCREAMING_SNAKE_CASE_: List[str]     			={v: k for k, v in idalabel.items()}

   SCREAMING_SNAKE_CASE_: int     			=img_size
   SCREAMING_SNAKE_CASE_: Tuple     			=num_classes
   SCREAMING_SNAKE_CASE_: Dict     			=embed_dim
   SCREAMING_SNAKE_CASE_: str     			=depths
   SCREAMING_SNAKE_CASE_: Any     			=num_heads
   SCREAMING_SNAKE_CASE_: int     			=window_size

   return config





def      __magic_name__				(							lowercase ):






   if "patch_embed.proj" in name:
      SCREAMING_SNAKE_CASE_: str     			=name.replace("""patch_embed.proj"""							,			"""embeddings.patch_embeddings.projection""" )
   if "patch_embed.norm" in name:
      SCREAMING_SNAKE_CASE_: Any     			=name.replace("""patch_embed.norm"""							,			"""embeddings.norm""" )
   if "layers" in name:
      SCREAMING_SNAKE_CASE_: Tuple     			="encoder." + name
   if "attn.proj" in name:
      SCREAMING_SNAKE_CASE_: int     			=name.replace("""attn.proj"""							,			"""attention.output.dense""" )
   if "attn" in name:
      SCREAMING_SNAKE_CASE_: str     			=name.replace("""attn"""							,			"""attention.self""" )
   if "norm1" in name:
      SCREAMING_SNAKE_CASE_: Tuple     			=name.replace("""norm1"""							,			"""layernorm_before""" )
   if "norm2" in name:
      SCREAMING_SNAKE_CASE_: Union[str, Any]     			=name.replace("""norm2"""							,			"""layernorm_after""" )
   if "mlp.fc1" in name:
      SCREAMING_SNAKE_CASE_: str     			=name.replace("""mlp.fc1"""							,			"""intermediate.dense""" )
   if "mlp.fc2" in name:
      SCREAMING_SNAKE_CASE_: int     			=name.replace("""mlp.fc2"""							,			"""output.dense""" )
   if "q_bias" in name:
      SCREAMING_SNAKE_CASE_: Any     			=name.replace("""q_bias"""							,			"""query.bias""" )
   if "k_bias" in name:
      SCREAMING_SNAKE_CASE_: int     			=name.replace("""k_bias"""							,			"""key.bias""" )
   if "v_bias" in name:
      SCREAMING_SNAKE_CASE_: Tuple     			=name.replace("""v_bias"""							,			"""value.bias""" )
   if "cpb_mlp" in name:
      SCREAMING_SNAKE_CASE_: Union[str, Any]     			=name.replace("""cpb_mlp"""							,			"""continuous_position_bias_mlp""" )
   if name == "norm.weight":
      SCREAMING_SNAKE_CASE_: List[Any]     			="layernorm.weight"
   if name == "norm.bias":
      SCREAMING_SNAKE_CASE_: List[Any]     			="layernorm.bias"

   if "head" in name:
      SCREAMING_SNAKE_CASE_: List[str]     			=name.replace("""head"""							,			"""classifier""" )
   else:
      SCREAMING_SNAKE_CASE_: List[Any]     			="swinv2." + name

   return name





def      __magic_name__				(							lowercase							,			lowercase ):






   for key in orig_state_dict.copy().keys():
      SCREAMING_SNAKE_CASE_: List[str]     			=orig_state_dict.pop(__SCREAMING_SNAKE_CASE )

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

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

   return orig_state_dict





def      __magic_name__				(							lowercase							,			lowercase ):
   SCREAMING_SNAKE_CASE_: Optional[int]     			=timm.create_model(__SCREAMING_SNAKE_CASE							,			pretrained=__SCREAMING_SNAKE_CASE )
   timm_model.eval()

   SCREAMING_SNAKE_CASE_: int     			=get_swinva_config(__SCREAMING_SNAKE_CASE )
   SCREAMING_SNAKE_CASE_: int     			=SwinvaForImageClassification(__SCREAMING_SNAKE_CASE )
   model.eval()

   SCREAMING_SNAKE_CASE_: List[Any]     			=convert_state_dict(timm_model.state_dict()							,			__SCREAMING_SNAKE_CASE )
   model.load_state_dict(__SCREAMING_SNAKE_CASE )

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

   SCREAMING_SNAKE_CASE_: List[Any]     			=AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swinva_name.replace("""_"""							,			"""-""" ) ) )
   SCREAMING_SNAKE_CASE_: str     			=Image.open(requests.get(__SCREAMING_SNAKE_CASE							,			stream=__SCREAMING_SNAKE_CASE ).raw )
   SCREAMING_SNAKE_CASE_: Union[str, Any]     			=image_processor(images=__SCREAMING_SNAKE_CASE							,			return_tensors="""pt""" )

   SCREAMING_SNAKE_CASE_: int     			=timm_model(inputs["""pixel_values"""] )
   SCREAMING_SNAKE_CASE_: Tuple     			=model(**__SCREAMING_SNAKE_CASE ).logits

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

   print(f'''Saving model {swinva_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 )

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


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

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








"""simple docstring"""
from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image


_UpperCAmelCase     =			["""text""", """image""", """audio"""]





def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: str     			=[]

   for input_type in input_types:
      if input_type == "text":
         inputs.append("""Text input""" )
      elif input_type == "image":
         inputs.append(
             Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO""" ) ) / """000000039769.png""" ).resize((512, 512) ) )
      elif input_type == "audio":
         inputs.append(torch.ones(3000 ) )
      elif isinstance(lowercase							,			lowercase ):
         inputs.append(create_inputs(lowercase ) )
      else:
         raise ValueError(f'''Invalid type requested: {input_type}''' )

   return inputs





def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: int     			=[]

   for output in outputs:
      if isinstance(lowercase							,			(str, AgentText) ):
         output_types.append("""text""" )
      elif isinstance(lowercase							,			(Image.Image, AgentImage) ):
         output_types.append("""image""" )
      elif isinstance(lowercase							,			(torch.Tensor, AgentAudio) ):
         output_types.append("""audio""" )
      else:
         raise ValueError(f'''Invalid output: {output}''' )

   return output_types
@is_tool_test
class 					a	:



  def 	lowerCamelCase__       (						self :					Tuple		)     ->					List[Any]:


     '''simple docstring'''




     self.assertTrue(hasattr(self.tool       ,		"""inputs"""		)		)
     self.assertTrue(hasattr(self.tool       ,		"""outputs"""		)		)

     SCREAMING_SNAKE_CASE_: Optional[int]     			=self.tool.inputs
     for _input in inputs:
        if isinstance(_input       ,		lowerCAmelCase		):
           for __input in _input:
              self.assertTrue(__input in authorized_types		)
        else:
           self.assertTrue(_input in authorized_types		)

     SCREAMING_SNAKE_CASE_: Any     			=self.tool.outputs
     for _output in outputs:
        self.assertTrue(_output in authorized_types		)



  def 	lowerCamelCase__       (						self :					str		)     ->					List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Dict     			=create_inputs(self.tool.inputs		)
     SCREAMING_SNAKE_CASE_: List[Any]     			=self.tool(*lowerCAmelCase		)

     # There is a single output
     if len(self.tool.outputs		) == 1:
        SCREAMING_SNAKE_CASE_: str     			=[outputs]

     self.assertListEqual(output_types(lowerCAmelCase		)       ,		self.tool.outputs		)



  def 	lowerCamelCase__       (						self :					str		)     ->					Optional[Any]:


     '''simple docstring'''




     self.assertTrue(hasattr(self.tool       ,		"""description"""		)		)
     self.assertTrue(hasattr(self.tool       ,		"""default_checkpoint"""		)		)
     self.assertTrue(self.tool.description.startswith("""This is a tool that"""		)		)



  def 	lowerCamelCase__       (						self :					Optional[Any]		)     ->					List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: str     			=create_inputs(self.tool.inputs		)
     SCREAMING_SNAKE_CASE_: Tuple     			=self.tool(*lowerCAmelCase		)

     if not isinstance(lowerCAmelCase       ,		lowerCAmelCase		):
        SCREAMING_SNAKE_CASE_: int     			=[outputs]

     self.assertEqual(len(lowerCAmelCase		)       ,		len(self.tool.outputs		)		)

     for output, output_type in zip(lowerCAmelCase       ,		self.tool.outputs		):
        SCREAMING_SNAKE_CASE_: int     			=AGENT_TYPE_MAPPING[output_type]
        self.assertTrue(isinstance(lowerCAmelCase       ,		lowerCAmelCase		)		)





  def 	lowerCamelCase__       (						self :					List[Any]		)     ->					List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Tuple     			=create_inputs(self.tool.inputs		)

     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=[]

     for _input, input_type in zip(lowerCAmelCase       ,		self.tool.inputs		):
        if isinstance(lowerCAmelCase       ,		lowerCAmelCase		):
           _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input		) for _input_type in input_type]		)
        else:
           _inputs.append(AGENT_TYPE_MAPPING[input_type](_input		)		)

        # Should not raise an error
     SCREAMING_SNAKE_CASE_: Dict     			=self.tool(*lowerCAmelCase		)

     if not isinstance(lowerCAmelCase       ,		lowerCAmelCase		):
        SCREAMING_SNAKE_CASE_: List[str]     			=[outputs]

     self.assertEqual(len(lowerCAmelCase		)       ,		len(self.tool.outputs		)		)



