

'''simple docstring'''

import os
import sys
import unittest


A_				  =							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
A_				  =							os.path.join(git_repo_path, "src", "diffusers")





class 							_snake_case       (				unittest.TestCase  ):
   def 			__UpperCamelCase  (       self	:					Tuple							):
     SCREAMING_SNAKE_CASE: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")

     SCREAMING_SNAKE_CASE:Any  					=    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")

     SCREAMING_SNAKE_CASE: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 			__UpperCamelCase  (       self	:					str							):
     SCREAMING_SNAKE_CASE:str  					=    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 			__UpperCamelCase  (       self	:					Any							):
     SCREAMING_SNAKE_CASE:List[str]  					=    create_dummy_object("CONSTANT"   ,"'torch'"							)
     self.assertEqual(UpperCAmelCase__   ,"\nCONSTANT = None\n"							)

     SCREAMING_SNAKE_CASE:Tuple  					=    create_dummy_object("function"   ,"'torch'"							)
     self.assertEqual(
         UpperCAmelCase__   ,"\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"							)

     SCREAMING_SNAKE_CASE:List[Any]  					=    """
class FakeClass(metaclass=DummyObject):
    _backends = 'torch'

    def __init__(self, *args, **kwargs):
        requires_backends(self, 'torch')

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, 'torch')

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, 'torch')
"""
     SCREAMING_SNAKE_CASE:List[Any]  					=    create_dummy_object("FakeClass"   ,"'torch'"							)
     self.assertEqual(UpperCAmelCase__   ,UpperCAmelCase__							)







   def 			__UpperCamelCase  (       self	:					Optional[int]							):
     SCREAMING_SNAKE_CASE:List[Any]  					=    """# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, [\"torch\"])


class FakeClass(metaclass=DummyObject):
    _backends = [\"torch\"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, [\"torch\"])

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, [\"torch\"])

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, [\"torch\"])
"""
     SCREAMING_SNAKE_CASE:Optional[int]  					=    create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]}							)
     self.assertEqual(dummy_files["torch"]   ,UpperCAmelCase__							)














"""simple docstring"""




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_snake_case          =   logging.get_logger(__name__)

_snake_case          =   torch.device('cpu')

def 			lowerCAmelCase__     (     ):



  '''simple docstring'''
  _a  :       int             =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
  _a  :       Dict             =	Image.open(requests.get(UpperCamelCase__     ,   stream=UpperCamelCase__				).raw				)
  return im

def 			lowerCAmelCase__     (     UpperCamelCase__				):



  '''simple docstring'''
  if swiftformer_name == "swiftformer_xs":
    return torch.tensor([-2.17_03e00, 2.11_07e00, -2.08_11e00, 8.86_85e-01, 2.43_60e-01]				)

  elif swiftformer_name == "swiftformer_s":
    return torch.tensor([3.96_36e-01, 2.34_78e-01, -1.69_63e00, -1.73_81e00, -8.63_37e-01]				)

  elif swiftformer_name == "swiftformer_l1":
    return torch.tensor([-4.27_68e-01, -4.74_29e-01, -1.08_97e00, -1.02_48e00, 3.55_23e-02]				)

  elif swiftformer_name == "swiftformer_l3":
    return torch.tensor([-2.53_30e-01, 2.42_11e-01, -6.01_85e-01, -8.27_89e-01, -6.04_46e-02]				)

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  _a  :       Any             =	dct.pop(UpperCamelCase__				)
  _a  :       Dict             =	val

def 			lowerCAmelCase__     (     UpperCamelCase__				):



  '''simple docstring'''
  _a  :       Tuple             =	[]
  for k in state_dict.keys():
    _a  :       Any             =	k
    if ".pwconv" in k:
      _a  :       int             =	k_new.replace(""".pwconv"""     ,   """.point_wise_conv"""				)
    if ".dwconv" in k:
      _a  :       List[str]             =	k_new.replace(""".dwconv"""     ,   """.depth_wise_conv"""				)
    if ".Proj." in k:
      _a  :       Optional[int]             =	k_new.replace(""".Proj."""     ,   """.proj."""				)
    if "patch_embed" in k_new:
      _a  :       Tuple             =	k_new.replace("""patch_embed"""     ,   """swiftformer.patch_embed.patch_embedding"""				)
    if "network" in k_new:
      _a  :       int             =	k_new.split("""."""				)
      if ls[2].isdigit():
        _a  :       Union[str, Any]             =	"""swiftformer.encoder.network.""" + ls[1] + """.blocks.""" + ls[2] + """.""" + """.""".join(ls[3:]				)
      else:
        _a  :       Tuple             =	k_new.replace("""network"""     ,   """swiftformer.encoder.network"""				)
    rename_keys.append((k, k_new)				)
  return rename_keys

@torch.no_grad()
def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  _a  :       Tuple             =	SwiftFormerConfig()

  # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
  _a  :       Optional[int]             =	1_0_0_0
  _a  :       Optional[Any]             =	"""huggingface/label-files"""
  _a  :       Optional[Any]             =	"""imagenet-1k-id2label.json"""
  _a  :       List[str]             =	json.load(open(hf_hub_download(UpperCamelCase__     ,   UpperCamelCase__     ,   repo_type="""dataset"""				)     ,   """r"""				)				)
  _a  :       Optional[Any]             =	{int(UpperCamelCase__				): v for k, v in idalabel.items()}
  _a  :       Dict             =	idalabel
  _a  :       Optional[int]             =	{v: k for k, v in idalabel.items()}

  # size of the architecture
  if swiftformer_name == "swiftformer_xs":
    _a  :       Any             =	[3, 3, 6, 4]
    _a  :       int             =	[4_8, 5_6, 1_1_2, 2_2_0]

  elif swiftformer_name == "swiftformer_s":
    _a  :       Any             =	[3, 3, 9, 6]
    _a  :       List[str]             =	[4_8, 6_4, 1_6_8, 2_2_4]

  elif swiftformer_name == "swiftformer_l1":
    _a  :       List[Any]             =	[4, 3, 1_0, 5]
    _a  :       Optional[int]             =	[4_8, 9_6, 1_9_2, 3_8_4]

  elif swiftformer_name == "swiftformer_l3":
    _a  :       List[Any]             =	[4, 4, 1_2, 6]
    _a  :       Optional[Any]             =	[6_4, 1_2_8, 3_2_0, 5_1_2]

  # load state_dict of original model, remove and rename some keys
  if original_ckpt:
    if original_ckpt.startswith("""https"""				):
      _a  :       Tuple             =	torch.hub.load_state_dict_from_url(UpperCamelCase__     ,   map_location="""cpu"""     ,   check_hash=UpperCamelCase__				)
    else:
      _a  :       Dict             =	torch.load(UpperCamelCase__     ,   map_location="""cpu"""				)
  _a  :       int             =	checkpoint

  _a  :       Optional[Any]             =	create_rename_keys(UpperCamelCase__				)
  for rename_key_src, rename_key_dest in rename_keys:
    rename_key(UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				)

  # load HuggingFace model
  _a  :       Any             =	SwiftFormerForImageClassification(UpperCamelCase__				).eval()
  hf_model.load_state_dict(UpperCamelCase__				)

  # prepare test inputs
  _a  :       Any             =	prepare_img()
  _a  :       Union[str, Any]             =	ViTImageProcessor.from_pretrained("""preprocessor_config"""				)
  _a  :       Optional[int]             =	processor(images=UpperCamelCase__     ,   return_tensors="""pt"""				)

  # compare outputs from both models
  _a  :       Dict             =	get_expected_output(UpperCamelCase__				)
  _a  :       int             =	hf_model(inputs["""pixel_values"""]				).logits

  assert hf_logits.shape == torch.Size([1, 1_0_0_0]				)
  assert torch.allclose(hf_logits[0, 0:5]     ,   UpperCamelCase__     ,   atol=1e-3				)

  Path(UpperCamelCase__				).mkdir(exist_ok=UpperCamelCase__				)
  print(F"""Saving model {swiftformer_name} to {pytorch_dump_folder_path}"""				)
  hf_model.save_pretrained(UpperCamelCase__				)


if __name__ == "__main__":
 _snake_case          =   argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--swiftformer_name',
     default='swiftformer_xs',
     choices=['swiftformer_xs', 'swiftformer_s', 'swiftformer_l1', 'swiftformer_l3'],
     type=str,
     help='Name of the SwiftFormer model you\'d like to convert.',
 )
 parser.add_argument(
     '--pytorch_dump_folder_path',
     default='./converted_outputs/',
     type=str,
     help='Path to the output PyTorch model directory.',
 )
 parser.add_argument('--original_ckpt', default=None, type=str, help='Path to the original model checkpoint.')

 _snake_case          =   parser.parse_args()
 convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)

