



import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	List[str]				,       SCREAMING_SNAKE_CASE__       :	Dict				)  ->     Optional[int]:
 _snake_case    :  Optional[Any]					=					old_name

 if "patch_embed" in old_name:
  _snake_case	,    _snake_case	,    _snake_case    :  int					=					old_name.split("""."""				)

  if layer == "0":
   _snake_case    :  Optional[int]					=					old_name.replace("""0"""				,       """convolution1"""				)
  elif layer == "1":
   _snake_case    :  Tuple					=					old_name.replace("""1"""				,       """batchnorm_before"""				)
  elif layer == "3":
   _snake_case    :  List[Any]					=					old_name.replace("""3"""				,       """convolution2"""				)
  else:
   _snake_case    :  List[str]					=					old_name.replace("""4"""				,       """batchnorm_after"""				)

 if "network" in old_name and re.search(R"""\d\.\d"""				,       SCREAMING_SNAKE_CASE__				):
  _snake_case    :  Union[str, Any]					=					R"""\b\d{2}\b"""
  if bool(re.search(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)				):
   _snake_case    :  Union[str, Any]					=					re.search(R"""\d\.\d\d."""				,       SCREAMING_SNAKE_CASE__				).group()
  else:
   _snake_case    :  List[str]					=					re.search(R"""\d\.\d."""				,       SCREAMING_SNAKE_CASE__				).group()
  if int(match[0]				) < 6:
   _snake_case    :  Optional[int]					=					old_name.replace(SCREAMING_SNAKE_CASE__				,       """"""				)
   _snake_case    :  List[Any]					=					trimmed_name.replace("""network"""				,       match[0] + """.meta4D_layers.blocks.""" + match[2:-1]				)
   _snake_case    :  Optional[int]					=					"""intermediate_stages.""" + trimmed_name
  else:
   _snake_case    :  Any					=					old_name.replace(SCREAMING_SNAKE_CASE__				,       """"""				)
   if int(match[2]				) < num_meta4D_last_stage:
    _snake_case    :  Optional[Any]					=					trimmed_name.replace("""network"""				,       """meta4D_layers.blocks.""" + match[2]				)
   else:
    _snake_case    :  Any					=					str(int(match[2]				) - num_meta4D_last_stage				)
    _snake_case    :  List[Any]					=					trimmed_name.replace("""network"""				,       """meta3D_layers.blocks.""" + layer_index				)
    if "norm1" in old_name:
     _snake_case    :  Optional[Any]					=					trimmed_name.replace("""norm1"""				,       """layernorm1"""				)
    elif "norm2" in old_name:
     _snake_case    :  int					=					trimmed_name.replace("""norm2"""				,       """layernorm2"""				)
    elif "fc1" in old_name:
     _snake_case    :  Optional[Any]					=					trimmed_name.replace("""fc1"""				,       """linear_in"""				)
    elif "fc2" in old_name:
     _snake_case    :  Union[str, Any]					=					trimmed_name.replace("""fc2"""				,       """linear_out"""				)

   _snake_case    :  Tuple					=					"""last_stage.""" + trimmed_name

 elif "network" in old_name and re.search(R""".\d."""				,       SCREAMING_SNAKE_CASE__				):
  _snake_case    :  List[Any]					=					old_name.replace("""network"""				,       """intermediate_stages"""				)

 if "fc" in new_name:
  _snake_case    :  Optional[int]					=					new_name.replace("""fc"""				,       """convolution"""				)
 elif ("norm1" in new_name) and ("layernorm1" not in new_name):
  _snake_case    :  str					=					new_name.replace("""norm1"""				,       """batchnorm_before"""				)
 elif ("norm2" in new_name) and ("layernorm2" not in new_name):
  _snake_case    :  List[str]					=					new_name.replace("""norm2"""				,       """batchnorm_after"""				)
 if "proj" in new_name:
  _snake_case    :  Optional[Any]					=					new_name.replace("""proj"""				,       """projection"""				)
 if "dist_head" in new_name:
  _snake_case    :  int					=					new_name.replace("""dist_head"""				,       """distillation_classifier"""				)
 elif "head" in new_name:
  _snake_case    :  List[Any]					=					new_name.replace("""head"""				,       """classifier"""				)
 elif "patch_embed" in new_name:
  _snake_case    :  Union[str, Any]					=					"""efficientformer.""" + new_name
 elif new_name == "norm.weight" or new_name == "norm.bias":
  _snake_case    :  Tuple					=					new_name.replace("""norm"""				,       """layernorm"""				)
  _snake_case    :  Tuple					=					"""efficientformer.""" + new_name
 else:
  _snake_case    :  Any					=					"""efficientformer.encoder.""" + new_name

 return new_name



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	int				,       SCREAMING_SNAKE_CASE__       :	Optional[int]				)  ->     Any:
 for key in checkpoint.copy().keys():
  _snake_case    :  List[str]					=					checkpoint.pop(SCREAMING_SNAKE_CASE__				)
  _snake_case    :  List[Any]					=					val

 return checkpoint



def       lowercase       (   )  ->     Any:
 _snake_case    :  List[Any]					=					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
 _snake_case    :  Any					=					Image.open(requests.get(SCREAMING_SNAKE_CASE__				,       stream=SCREAMING_SNAKE_CASE__				).raw				)

 return image



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Path				,       SCREAMING_SNAKE_CASE__       :	Path				,       SCREAMING_SNAKE_CASE__       :	Path				,       SCREAMING_SNAKE_CASE__       :	bool				)  ->     Optional[Any]:
 _snake_case    :  Dict					=					torch.load(SCREAMING_SNAKE_CASE__				,       map_location="""cpu"""				)["""model"""]
 _snake_case    :  Optional[Any]					=					EfficientFormerConfig.from_json_file(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  List[Any]					=					EfficientFormerForImageClassificationWithTeacher(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  Tuple					=					"""_""".join(checkpoint_path.split("""/"""				)[-1].split("""."""				)[0].split("""_"""				)[:-1]				)

 _snake_case    :  Tuple					=					config.depths[-1] - config.num_metaad_blocks + 1
 _snake_case    :  Union[str, Any]					=					convert_torch_checkpoint(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)

 model.load_state_dict(SCREAMING_SNAKE_CASE__				)
 model.eval()

 _snake_case    :  List[str]					=					{
     """bilinear""": PILImageResampling.BILINEAR,
     """bicubic""": PILImageResampling.BICUBIC,
     """nearest""": PILImageResampling.NEAREST,
 }

 # prepare image
 _snake_case    :  List[Any]					=					prepare_img()
 _snake_case    :  Union[str, Any]					=					256
 _snake_case    :  List[Any]					=					224
 _snake_case    :  int					=					EfficientFormerImageProcessor(
     size={"""shortest_edge""": image_size}				,       crop_size={"""height""": crop_size, """width""": crop_size}				,       resample=pillow_resamplings["""bicubic"""]				,       )
 _snake_case    :  Optional[Any]					=					processor(images=SCREAMING_SNAKE_CASE__				,       return_tensors="""pt"""				).pixel_values

 # original processing pipeline
 _snake_case    :  int					=					Compose(
     [
         Resize(SCREAMING_SNAKE_CASE__				,       interpolation=pillow_resamplings["""bicubic"""]				),
         CenterCrop(SCREAMING_SNAKE_CASE__				),
         ToTensor(),
         Normalize(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				),
     ]				)
 _snake_case    :  List[str]					=					image_transforms(SCREAMING_SNAKE_CASE__				).unsqueeze(0				)

 assert torch.allclose(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)

 _snake_case    :  Optional[Any]					=					model(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  Union[str, Any]					=					outputs.logits

 _snake_case    :  List[str]					=					(1, 1_000)

 if "l1" in model_name:
  _snake_case    :  Tuple					=					torch.Tensor(
      [-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9, -0.5_1_2_4, 0.4_1_8_3, -0.6_7_9_3, -1.3_7_7_7, -0.0_8_9_3, -0.7_3_5_8, -2.4_3_2_8]				)
  assert torch.allclose(logits[0, :10]				,       SCREAMING_SNAKE_CASE__				,       atol=1e-3				)
  assert logits.shape == expected_shape
 elif "l3" in model_name:
  _snake_case    :  List[str]					=					torch.Tensor(
      [-1.3_1_5_0, -1.5_4_5_6, -1.2_5_5_6, -0.8_4_9_6, -0.7_1_2_7, -0.7_8_9_7, -0.9_7_2_8, -0.3_0_5_2, 0.3_7_5_1, -0.3_1_2_7]				)
  assert torch.allclose(logits[0, :10]				,       SCREAMING_SNAKE_CASE__				,       atol=1e-3				)
  assert logits.shape == expected_shape
 elif "l7" in model_name:
  _snake_case    :  List[Any]					=					torch.Tensor(
      [-1.0_2_8_3, -1.4_1_3_1, -0.5_6_4_4, -1.3_1_1_5, -0.5_7_8_5, -1.2_0_4_9, -0.7_5_2_8, 0.1_9_9_2, -0.3_8_2_2, -0.0_8_7_8]				)
  assert logits.shape == expected_shape
 else:
  raise ValueError(
      F'''Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7'''				)

 # Save Checkpoints
 Path(SCREAMING_SNAKE_CASE__				).mkdir(exist_ok=SCREAMING_SNAKE_CASE__				)
 model.save_pretrained(SCREAMING_SNAKE_CASE__				)
 print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''				)
 processor.save_pretrained(SCREAMING_SNAKE_CASE__				)
 print(F'''Processor successfuly saved at {pytorch_dump_path}'''				)

 if push_to_hub:
  print("""Pushing model to the hub..."""				)

  model.push_to_hub(
      repo_id=F'''Bearnardd/{pytorch_dump_path}'''				,       commit_message="""Add model"""				,       use_temp_dir=SCREAMING_SNAKE_CASE__				,       )
  processor.push_to_hub(
      repo_id=F'''Bearnardd/{pytorch_dump_path}'''				,       commit_message="""Add image processor"""				,       use_temp_dir=SCREAMING_SNAKE_CASE__				,       )


if __name__ == "__main__":
  a__    =	argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--pytorch_model_path""",
      default=None,
      type=str,
      required=True,
      help="""Path to EfficientFormer pytorch checkpoint.""",
  )
  parser.add_argument(
      """--config_file""",
      default=None,
      type=str,
      required=True,
      help="""The json file for EfficientFormer model config.""",
  )
  parser.add_argument(
      """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )

  parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")
  parser.add_argument(
      """--no-push_to_hub""",
      dest="""push_to_hub""",
      action="""store_false""",
      help="""Do not push model and image processor to the hub""",
  )
  parser.set_defaults(push_to_hub=True)

  a__    =	parser.parse_args()
  convert_efficientformer_checkpoint(
      checkpoint_path=args.pytorch_model_path,
      efficientformer_config_file=args.config_file,
      pytorch_dump_path=args.pytorch_dump_path,
      push_to_hub=args.push_to_hub,
  )





import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
a__    =	"""src/diffusers"""

# Matches is_xxx_available()
a__    =	re.compile(R"""is\_([a-z_]*)_available\(\)""")
# Matches from xxx import bla
a__    =	re.compile(R"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")


a__    =	"""
{0} = None
"""

a__    =	"""
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, {1})

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, {1})
"""


a__    =	"""
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Tuple				)  ->     Optional[Any]:
 _snake_case    :  str					=					_re_backend.findall(SCREAMING_SNAKE_CASE__				)
 if len(SCREAMING_SNAKE_CASE__				) == 0:
  return None

 return "_and_".join(SCREAMING_SNAKE_CASE__				)



def       lowercase       (   )  ->     Optional[Any]:
 with open(os.path.join(SCREAMING_SNAKE_CASE__				,       """__init__.py"""				)				,       """r"""				,       encoding="""utf-8"""				,       newline="""\n"""				) as f:
  _snake_case    :  Dict					=					f.readlines()

 # Get to the point we do the actual imports for type checking
 _snake_case    :  Optional[int]					=					0
 _snake_case    :  Union[str, Any]					=					{}
 # Go through the end of the file
 while line_index < len(SCREAMING_SNAKE_CASE__				):
  # If the line contains is_backend_available, we grab all objects associated with the `else` block
  _snake_case    :  int					=					find_backend(lines[line_index]				)
  if backend is not None:
   while not lines[line_index].startswith("""else:"""				):
    line_index += 1
   line_index += 1
   _snake_case    :  str					=					[]
   # Until we unindent, add backend objects to the list
   while line_index < len(SCREAMING_SNAKE_CASE__				) and len(lines[line_index]				) > 1:
    _snake_case    :  int					=					lines[line_index]
    _snake_case    :  str					=					_re_single_line_import.search(SCREAMING_SNAKE_CASE__				)
    if single_line_import_search is not None:
     objects.extend(single_line_import_search.groups()[0].split(""", """				)				)
    elif line.startswith(""" """ * 8				):
     objects.append(line[8:-2]				)
    line_index += 1

   if len(SCREAMING_SNAKE_CASE__				) > 0:
    _snake_case    :  Optional[int]					=					objects
  else:
   line_index += 1

 return backend_specific_objects



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Union[str, Any]				,       SCREAMING_SNAKE_CASE__       :	Tuple				)  ->     Dict:
 if name.isupper():
  return DUMMY_CONSTANT.format(SCREAMING_SNAKE_CASE__				)
 elif name.islower():
  return DUMMY_FUNCTION.format(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
 else:
  return DUMMY_CLASS.format(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Dict=None				)  ->     List[Any]:
 if backend_specific_objects is None:
  _snake_case    :  Dict					=					read_init()
 # For special correspondence backend to module name as used in the function requires_modulename
 _snake_case    :  Union[str, Any]					=					{}

 for backend, objects in backend_specific_objects.items():
  _snake_case    :  List[Any]					=					"""[""" + """, """.join(F'''"{b}"''' for b in backend.split("""_and_"""				)				) + """]"""
  _snake_case    :  Any					=					"""# This file is autogenerated by the command `make fix-copies`, do not edit.\n"""
  dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
  dummy_file += "\n".join([create_dummy_object(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				) for o in objects]				)
  _snake_case    :  Any					=					dummy_file

 return dummy_files



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Tuple=False				)  ->     Optional[int]:
 _snake_case    :  str					=					create_dummy_files()
 # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
 _snake_case    :  List[Any]					=					{"""torch""": """pt"""}

 # Locate actual dummy modules and read their content.
 _snake_case    :  Dict					=					os.path.join(SCREAMING_SNAKE_CASE__				,       """utils"""				)
 _snake_case    :  int					=					{
     backend: os.path.join(SCREAMING_SNAKE_CASE__				,       F'''dummy_{short_names.get(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)}_objects.py'''				)
     for backend in dummy_files.keys()
 }

 _snake_case    :  int					=					{}
 for backend, file_path in dummy_file_paths.items():
  if os.path.isfile(SCREAMING_SNAKE_CASE__				):
   with open(SCREAMING_SNAKE_CASE__				,       """r"""				,       encoding="""utf-8"""				,       newline="""\n"""				) as f:
    _snake_case    :  Union[str, Any]					=					f.read()
  else:
   _snake_case    :  str					=					""""""

 for backend in dummy_files.keys():
  if dummy_files[backend] != actual_dummies[backend]:
   if overwrite:
    print(
        F'''Updating diffusers.utils.dummy_{short_names.get(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)}_objects.py as the main '''
        """__init__ has new objects."""				)
    with open(dummy_file_paths[backend]				,       """w"""				,       encoding="""utf-8"""				,       newline="""\n"""				) as f:
     f.write(dummy_files[backend]				)
   else:
    raise ValueError(
        """The main __init__ has objects that are not present in """
        F'''diffusers.utils.dummy_{short_names.get(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)}_objects.py. Run `make fix-copies` '''
        """to fix this."""				)


if __name__ == "__main__":
  a__    =	argparse.ArgumentParser()
  parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
  a__    =	parser.parse_args()

  check_dummies(args.fix_and_overwrite)
