




'''simple docstring'''





import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor





def       __a      (  _UpperCamelCase:       Union[str, Any]       )		->					Union[str, Any]:

     """simple docstring"""




     _snake_case				= SwinConfig(image_size=192       )

     if "base" in model_name:
          _snake_case				= 6
          _snake_case				= 128
          _snake_case				= (2, 2, 18, 2)
          _snake_case				= (4, 8, 16, 32)
     elif "large" in model_name:
          _snake_case				= 12
          _snake_case				= 192
          _snake_case				= (2, 2, 18, 2)
          _snake_case				= (6, 12, 24, 48)
     else:
          raise ValueError("Model not supported, only supports base and large variants"       )

     _snake_case				= window_size
     _snake_case				= embed_dim
     _snake_case				= depths
     _snake_case				= num_heads

     return config





def       __a      (  _UpperCamelCase:       Optional[Any]       )		->					Dict:

     """simple docstring"""




     if "encoder.mask_token" in name:
          _snake_case				= name.replace("encoder.mask_token"   ,		"embeddings.mask_token"       )
     if "encoder.patch_embed.proj" in name:
          _snake_case				= name.replace("encoder.patch_embed.proj"   ,		"embeddings.patch_embeddings.projection"       )
     if "encoder.patch_embed.norm" in name:
          _snake_case				= name.replace("encoder.patch_embed.norm"   ,		"embeddings.norm"       )
     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 name == "encoder.norm.weight":
          _snake_case				= "layernorm.weight"
     if name == "encoder.norm.bias":
          _snake_case				= "layernorm.bias"

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

     return name





def       __a      (  _UpperCamelCase:       Any   ,		_UpperCamelCase:       Union[str, Any]       )		->					Union[str, Any]:

     """simple docstring"""




     for key in orig_state_dict.copy().keys():
          _snake_case				= orig_state_dict.pop(_UpperCamelCase       )

          if "attn_mask" in key:
               pass
          elif "qkv" in key:
               _snake_case				= key.split("."       )
               _snake_case				= int(key_split[2]       )
               _snake_case				= int(key_split[4]       )
               _snake_case				= model.swin.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       __a      (  _UpperCamelCase:       Any   ,		_UpperCamelCase:       List[str]   ,		_UpperCamelCase:       Tuple   ,		_UpperCamelCase:       Optional[int]       )		->					Optional[int]:

     """simple docstring"""




     _snake_case				= torch.load(_UpperCamelCase   ,		map_location="cpu"       )["model"]

     _snake_case				= get_swin_config(_UpperCamelCase       )
     _snake_case				= SwinForMaskedImageModeling(_UpperCamelCase       )
     model.eval()

     _snake_case				= convert_state_dict(_UpperCamelCase   ,		_UpperCamelCase       )
     model.load_state_dict(_UpperCamelCase       )

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

     _snake_case				= ViTImageProcessor(size={"height": 192, "width": 192}       )
     _snake_case				= Image.open(requests.get(_UpperCamelCase   ,		stream=_UpperCamelCase       ).raw       )
     _snake_case				= image_processor(images=_UpperCamelCase   ,		return_tensors="pt"       )

     with torch.no_grad():
          _snake_case				= model(**_UpperCamelCase       ).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(_UpperCamelCase       )

          print(F"""Saving image processor to {pytorch_dump_folder_path}"""       )
          image_processor.save_pretrained(_UpperCamelCase       )

     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__":
   UpperCamelCase_		:     Any          =		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.'''
   )

   UpperCamelCase_		:     Union[str, Any]          =		parser.parse_args()
   convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)








'''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_		:     int          =		['''text''', '''image''', '''audio''']





def       __a      (  _UpperCamelCase:       List[str]       )		->					Dict:

     """simple docstring"""




     _snake_case				= []

     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(3_000       )       )
          elif isinstance(_UpperCamelCase   ,		_UpperCamelCase       ):
               inputs.append(create_inputs(_UpperCamelCase       )       )
          else:
               raise ValueError(F"""Invalid type requested: {input_type}"""       )

     return inputs





def       __a      (  _UpperCamelCase:       List       )		->					Dict:

     """simple docstring"""




     _snake_case				= []

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

     return output_types





@is_tool_test
class   _a							:






     def 							_lowercase      (					self       )				->     Any:
          self.assertTrue(hasattr(self.tool   ,"inputs"       )       )
          self.assertTrue(hasattr(self.tool   ,"outputs"       )       )

          _snake_case				= self.tool.inputs
          for _input in inputs:
               if isinstance(_input   ,_SCREAMING_SNAKE_CASE       ):
                    for __input in _input:
                         self.assertTrue(__input in authorized_types       )
               else:
                    self.assertTrue(_input in authorized_types       )

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






     def 							_lowercase      (					self       )				->     Any:
          _snake_case				= create_inputs(self.tool.inputs       )
          _snake_case				= self.tool(*_SCREAMING_SNAKE_CASE       )

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

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






     def 							_lowercase      (					self       )				->     str:
          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 							_lowercase      (					self       )				->     Tuple:
          _snake_case				= create_inputs(self.tool.inputs       )
          _snake_case				= self.tool(*_SCREAMING_SNAKE_CASE       )

          if not isinstance(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       ):
               _snake_case				= [outputs]

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

          for output, output_type in zip(_SCREAMING_SNAKE_CASE   ,self.tool.outputs       ):
               _snake_case				= AGENT_TYPE_MAPPING[output_type]
               self.assertTrue(isinstance(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )       )






     def 							_lowercase      (					self       )				->     Optional[Any]:
          _snake_case				= create_inputs(self.tool.inputs       )

          _snake_case				= []

          for _input, input_type in zip(_SCREAMING_SNAKE_CASE   ,self.tool.inputs       ):
               if isinstance(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       ):
                    _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
          _snake_case				= self.tool(*_SCREAMING_SNAKE_CASE       )

          if not isinstance(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       ):
               _snake_case				= [outputs]

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


