


"""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      lowercase			(				A_       )->      Any:
  '''simple docstring'''







  a						:		int      							=			SwinvaConfig()
  a						:		Any      							=			swinva_name.split("_"       )

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

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

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

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

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

  a						:		str      							=			img_size
  a						:		Union[str, Any]      							=			num_classes
  a						:		Union[str, Any]      							=			embed_dim
  a						:		Optional[Any]      							=			depths
  a						:		Optional[int]      							=			num_heads
  a						:		int      							=			window_size

  return config


def      lowercase			(				A_       )->      Union[str, Any]:
  '''simple docstring'''







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

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

  return name


def      lowercase			(				A_  ,   A_       )->      Union[str, Any]:
  '''simple docstring'''







  for key in orig_state_dict.copy().keys():
    a						:		List[Any]      							=			orig_state_dict.pop(A_       )

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

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

  return orig_state_dict


def      lowercase			(				A_  ,   A_       )->      Optional[int]:
  '''simple docstring'''







  a						:		Union[str, Any]      							=			timm.create_model(A_  ,   pretrained=A_       )
  timm_model.eval()

  a						:		str      							=			get_swinva_config(A_       )
  a						:		Dict      							=			SwinvaForImageClassification(A_       )
  model.eval()

  a						:		Tuple      							=			convert_state_dict(timm_model.state_dict()  ,   A_       )
  model.load_state_dict(A_       )

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

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

  a						:		Tuple      							=			timm_model(inputs["pixel_values"]       )
  a						:		Optional[Any]      							=			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 os
import sys
import unittest


__lowercase    					=      os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
__lowercase    					=      os.path.join(git_repo_path, """src""", """diffusers""")






class 						_A  (  unittest.TestCase  ):







    """simple docstring"""




    def 	__snake_case							(					self				:    Any):
      a						:		List[Any]      							=			find_backend("    if not is_torch_available():")
      self.assertEqual(__UpperCAmelCase				, "torch")

      # backend_with_underscore = find_backend("    if not is_tensorflow_text_available():")
      # self.assertEqual(backend_with_underscore, "tensorflow_text")

      a						:		Dict      							=			find_backend("    if not (is_torch_available() and is_transformers_available()):")
      self.assertEqual(__UpperCAmelCase				, "torch_and_transformers")

      # double_backend_with_underscore = find_backend(
      #    "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
      # )
      # self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")

      a						:		int      							=			find_backend(
          "    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):")
      self.assertEqual(__UpperCAmelCase				, "torch_and_transformers_and_onnx")






    def 	__snake_case							(					self				:    Union[str, Any]):
      a						:		Dict      							=			read_init()
      # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
      self.assertIn("torch"				, __UpperCAmelCase)
      self.assertIn("torch_and_transformers"				, __UpperCAmelCase)
      self.assertIn("flax_and_transformers"				, __UpperCAmelCase)
      self.assertIn("torch_and_transformers_and_onnx"				, __UpperCAmelCase)

      # Likewise, we can't assert on the exact content of a key
      self.assertIn("UNet2DModel"				, objects["torch"])
      self.assertIn("FlaxUNet2DConditionModel"				, objects["flax"])
      self.assertIn("StableDiffusionPipeline"				, objects["torch_and_transformers"])
      self.assertIn("FlaxStableDiffusionPipeline"				, objects["flax_and_transformers"])
      self.assertIn("LMSDiscreteScheduler"				, objects["torch_and_scipy"])
      self.assertIn("OnnxStableDiffusionPipeline"				, objects["torch_and_transformers_and_onnx"])






    def 	__snake_case							(					self				:    Tuple):
      a						:		Optional[int]      							=			create_dummy_object("CONSTANT"				, "'torch'")
      self.assertEqual(__UpperCAmelCase				, "\nCONSTANT = None\n")

      a						:		Dict      							=			create_dummy_object("function"				, "'torch'")
      self.assertEqual(
          __UpperCAmelCase				, "\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n")

      a						:		Optional[Any]      							=			"\nclass FakeClass(metaclass=DummyObject):\n    _backends = 'torch'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, 'torch')\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, 'torch')\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, 'torch')\n"
      a						:		int      							=			create_dummy_object("FakeClass"				, "'torch'")
      self.assertEqual(__UpperCAmelCase				, __UpperCAmelCase)






    def 	__snake_case							(					self				:    List[str]):
      a						:		List[str]      							=			"# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, [\"torch\"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, [\"torch\"])\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, [\"torch\"])\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, [\"torch\"])\n"
      a						:		Tuple      							=			create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]})
      self.assertEqual(dummy_files["torch"]				, __UpperCAmelCase)






