






'''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 				snake_case       (  a_				:			Optional[int]						)							->					List[Any]:





      """simple docstring"""

      UpperCamelCase_					: List[str]            =				SwinvaConfig()
      UpperCamelCase_					: Tuple            =				swinva_name.split("""_"""						)

      UpperCamelCase_					: Dict            =				name_split[1]
      if "to" in name_split[3]:
            UpperCamelCase_					: Dict            =				int(name_split[3][-3:]						)
      else:
            UpperCamelCase_					: Optional[Any]            =				int(name_split[3]						)
      if "to" in name_split[2]:
            UpperCamelCase_					: int            =				int(name_split[2][-2:]						)
      else:
            UpperCamelCase_					: Union[str, Any]            =				int(name_split[2][6:]						)

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

      if "to" in swinva_name:
            UpperCamelCase_					: Union[str, Any]            =				(12, 12, 12, 6)

      if ("22k" in swinva_name) and ("to" not in swinva_name):
            UpperCamelCase_					: Any            =				21_841
            UpperCamelCase_					: Any            =				"""huggingface/label-files"""
            UpperCamelCase_					: Dict            =				"""imagenet-22k-id2label.json"""
            UpperCamelCase_					: Optional[Any]            =				json.load(open(hf_hub_download(a_     , a_     , repo_type="""dataset"""						)     , """r"""						)						)
            UpperCamelCase_					: int            =				{int(a_						): v for k, v in idalabel.items()}
            UpperCamelCase_					: Optional[Any]            =				idalabel
            UpperCamelCase_					: List[Any]            =				{v: k for k, v in idalabel.items()}

      else:
            UpperCamelCase_					: Tuple            =				1_000
            UpperCamelCase_					: Optional[int]            =				"""huggingface/label-files"""
            UpperCamelCase_					: Optional[Any]            =				"""imagenet-1k-id2label.json"""
            UpperCamelCase_					: Optional[Any]            =				json.load(open(hf_hub_download(a_     , a_     , repo_type="""dataset"""						)     , """r"""						)						)
            UpperCamelCase_					: str            =				{int(a_						): v for k, v in idalabel.items()}
            UpperCamelCase_					: int            =				idalabel
            UpperCamelCase_					: Dict            =				{v: k for k, v in idalabel.items()}

      UpperCamelCase_					: Dict            =				img_size
      UpperCamelCase_					: Union[str, Any]            =				num_classes
      UpperCamelCase_					: List[str]            =				embed_dim
      UpperCamelCase_					: List[str]            =				depths
      UpperCamelCase_					: List[Any]            =				num_heads
      UpperCamelCase_					: Tuple            =				window_size

      return config



def 				snake_case       (  a_				:			Tuple						)							->					List[Any]:





      """simple docstring"""

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

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

      return name



def 				snake_case       (  a_				:			Optional[Any]     , a_				:			Optional[int]						)							->					Dict:





      """simple docstring"""

      for key in orig_state_dict.copy().keys():
            UpperCamelCase_					: str            =				orig_state_dict.pop(a_						)

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

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

      return orig_state_dict



def 				snake_case       (  a_				:			Optional[int]     , a_				:			str						)							->					Tuple:





      """simple docstring"""

      UpperCamelCase_					: List[Any]            =				timm.create_model(a_     , pretrained=a_						)
      timm_model.eval()

      UpperCamelCase_					: int            =				get_swinva_config(a_						)
      UpperCamelCase_					: Dict            =				SwinvaForImageClassification(a_						)
      model.eval()

      UpperCamelCase_					: str            =				convert_state_dict(timm_model.state_dict()     , a_						)
      model.load_state_dict(a_						)

      UpperCamelCase_					: Optional[Any]            =				"""http://images.cocodataset.org/val2017/000000039769.jpg"""

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

      UpperCamelCase_					: Dict            =				timm_model(inputs["""pixel_values"""]						)
      UpperCamelCase_					: Dict            =				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__":
      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'''

import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


UpperCamelCase      					="\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.\n\nIn March 2021, Hugging Face raised $40 million in a Series B funding round.[3]\n\nOn April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]\n"




class      A     (   unittest.TestCase, SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Optional[Any]            =				load_tool("""text-question-answering"""			)
       self.tool.setup()
       UpperCamelCase_					: List[str]            =				load_tool("""text-question-answering"""      ,  remote=__lowerCAmelCase			)
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Union[str, Any]            =				self.tool(__lowerCAmelCase      ,  """What did Hugging Face do in April 2021?"""			)
       self.assertEqual(__lowerCAmelCase      ,  """launched the BigScience Research Workshop"""			)
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Optional[Any]            =				self.remote_tool(__lowerCAmelCase      ,  """What did Hugging Face do in April 2021?"""			)
       self.assertEqual(__lowerCAmelCase      ,  """launched the BigScience Research Workshop"""			)
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: List[Any]            =				self.tool(text=__lowerCAmelCase      ,  question="""What did Hugging Face do in April 2021?"""			)
       self.assertEqual(__lowerCAmelCase      ,  """launched the BigScience Research Workshop"""			)





 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Dict            =				self.remote_tool(text=__lowerCAmelCase      ,  question="""What did Hugging Face do in April 2021?"""			)
       self.assertEqual(__lowerCAmelCase      ,  """launched the BigScience Research Workshop"""			)






