



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()
SCREAMING_SNAKE_CASE_:int					=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:Optional[Any]					=						torch.device("""cpu""")




def      __UpperCamelCase     (  )	->  Optional[int]:



    """simple docstring"""






    __UpperCamelCase			:	int										=       """http://images.cocodataset.org/val2017/000000039769.jpg"""
    __UpperCamelCase			:	Optional[int]										=       Image.open(requests.get(_lowerCAmelCase				,					stream=_lowerCAmelCase		).raw		)
    return im




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  Dict:



    """simple docstring"""






    if swiftformer_name == "swiftformer_xs":
        return torch.tensor([-2.1_703e00, 2.1_107e00, -2.0_811e00, 8.8_685e-01, 2.4_360e-01]		)

    elif swiftformer_name == "swiftformer_s":
        return torch.tensor([3.9_636e-01, 2.3_478e-01, -1.6_963e00, -1.7_381e00, -8.6_337e-01]		)

    elif swiftformer_name == "swiftformer_l1":
        return torch.tensor([-4.2_768e-01, -4.7_429e-01, -1.0_897e00, -1.0_248e00, 3.5_523e-02]		)

    elif swiftformer_name == "swiftformer_l3":
        return torch.tensor([-2.5_330e-01, 2.4_211e-01, -6.0_185e-01, -8.2_789e-01, -6.0_446e-02]		)




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase		)	->  str:



    """simple docstring"""






    __UpperCamelCase			:	Optional[Any]										=       dct.pop(_lowerCAmelCase		)
    __UpperCamelCase			:	Optional[int]										=       val




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  Union[str, Any]:



    """simple docstring"""






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




@torch.no_grad()
def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase		)	->  Dict:



    """simple docstring"""






    __UpperCamelCase			:	Tuple										=       SwiftFormerConfig()

    # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
    __UpperCamelCase			:	List[str]										=       1000
    __UpperCamelCase			:	Any										=       """huggingface/label-files"""
    __UpperCamelCase			:	Dict										=       """imagenet-1k-id2label.json"""
    __UpperCamelCase			:	int										=       json.load(open(hf_hub_download(_lowerCAmelCase				,					_lowerCAmelCase				,					repo_type="""dataset"""		)				,					"""r"""		)		)
    __UpperCamelCase			:	List[Any]										=       {int(_lowerCAmelCase		): v for k, v in idalabel.items()}
    __UpperCamelCase			:	Union[str, Any]										=       idalabel
    __UpperCamelCase			:	List[Any]										=       {v: k for k, v in idalabel.items()}

    # size of the architecture
    if swiftformer_name == "swiftformer_xs":
        __UpperCamelCase			:	str										=       [3, 3, 6, 4]
        __UpperCamelCase			:	Any										=       [48, 56, 112, 220]

    elif swiftformer_name == "swiftformer_s":
        __UpperCamelCase			:	Optional[int]										=       [3, 3, 9, 6]
        __UpperCamelCase			:	Tuple										=       [48, 64, 168, 224]

    elif swiftformer_name == "swiftformer_l1":
        __UpperCamelCase			:	Union[str, Any]										=       [4, 3, 10, 5]
        __UpperCamelCase			:	Optional[int]										=       [48, 96, 192, 384]

    elif swiftformer_name == "swiftformer_l3":
        __UpperCamelCase			:	int										=       [4, 4, 12, 6]
        __UpperCamelCase			:	Union[str, Any]										=       [64, 128, 320, 512]

    # load state_dict of original model, remove and rename some keys
    if original_ckpt:
        if original_ckpt.startswith("""https"""		):
            __UpperCamelCase			:	Optional[int]										=       torch.hub.load_state_dict_from_url(_lowerCAmelCase				,					map_location="""cpu"""				,					check_hash=_lowerCAmelCase		)
        else:
            __UpperCamelCase			:	Dict										=       torch.load(_lowerCAmelCase				,					map_location="""cpu"""		)
    __UpperCamelCase			:	Union[str, Any]										=       checkpoint

    __UpperCamelCase			:	int										=       create_rename_keys(_lowerCAmelCase		)
    for rename_key_src, rename_key_dest in rename_keys:
        rename_key(_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase		)

    # load HuggingFace model
    __UpperCamelCase			:	Optional[Any]										=       SwiftFormerForImageClassification(_lowerCAmelCase		).eval()
    hf_model.load_state_dict(_lowerCAmelCase		)

    # prepare test inputs
    __UpperCamelCase			:	List[Any]										=       prepare_img()
    __UpperCamelCase			:	int										=       ViTImageProcessor.from_pretrained("""preprocessor_config"""		)
    __UpperCamelCase			:	List[Any]										=       processor(images=_lowerCAmelCase				,					return_tensors="""pt"""		)

    # compare outputs from both models
    __UpperCamelCase			:	Optional[Any]										=       get_expected_output(_lowerCAmelCase		)
    __UpperCamelCase			:	int										=       hf_model(inputs["""pixel_values"""]		).logits

    assert hf_logits.shape == torch.Size([1, 1000]		)
    assert torch.allclose(hf_logits[0, 0:5]				,					_lowerCAmelCase				,					atol=1e-3		)

    Path(_lowerCAmelCase		).mkdir(exist_ok=_lowerCAmelCase		)
    print(f'''Saving model {swiftformer_name} to {pytorch_dump_folder_path}'''		)
    hf_model.save_pretrained(_lowerCAmelCase		)


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_:str					=						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.""")

     SCREAMING_SNAKE_CASE_:List[str]					=						parser.parse_args()
     convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)







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
SCREAMING_SNAKE_CASE_:Any					=						"""src/diffusers"""

# Matches is_xxx_available()
SCREAMING_SNAKE_CASE_:Optional[Any]					=						re.compile(R"""is\_([a-z_]*)_available\(\)""")
# Matches from xxx import bla
SCREAMING_SNAKE_CASE_:Union[str, Any]					=						re.compile(R"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")


SCREAMING_SNAKE_CASE_:List[Any]					=						"""
{0} = None
"""

SCREAMING_SNAKE_CASE_:Optional[Any]					=						"""
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})
"""


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




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  Tuple:



    """simple docstring"""






    A			:	Union[str, Any]										=       _re_backend.findall(_lowerCAmelCase		)
    if len(_lowerCAmelCase		) == 0:
        return None

    return "_and_".join(_lowerCAmelCase		)




def      __UpperCamelCase     (  )	->  str:



    """simple docstring"""






    with open(os.path.join(_lowerCAmelCase				,					"""__init__.py"""		)				,					"""r"""				,					encoding="""utf-8"""				,					newline="""\n"""		) as f:
        A			:	Dict										=       f.readlines()

    # Get to the point we do the actual imports for type checking
    A			:	Dict										=       0
    A			:	List[Any]										=       {}
    # Go through the end of the file
    while line_index < len(_lowerCAmelCase		):
        # If the line contains is_backend_available, we grab all objects associated with the `else` block
        A			:	Optional[int]										=       find_backend(lines[line_index]		)
        if backend is not None:
            while not lines[line_index].startswith("""else:"""		):
                line_index += 1
            line_index += 1
            A			:	str										=       []
            # Until we unindent, add backend objects to the list
            while line_index < len(_lowerCAmelCase		) and len(lines[line_index]		) > 1:
                A			:	Tuple										=       lines[line_index]
                A			:	List[str]										=       _re_single_line_import.search(_lowerCAmelCase		)
                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(_lowerCAmelCase		) > 0:
                A			:	List[str]										=       objects
        else:
            line_index += 1

    return backend_specific_objects




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase		)	->  str:



    """simple docstring"""






    if name.isupper():
        return DUMMY_CONSTANT.format(_lowerCAmelCase		)
    elif name.islower():
        return DUMMY_FUNCTION.format(_lowerCAmelCase				,					_lowerCAmelCase		)
    else:
        return DUMMY_CLASS.format(_lowerCAmelCase				,					_lowerCAmelCase		)




def      __UpperCamelCase     (  _lowerCAmelCase=None		)	->  Tuple:



    """simple docstring"""






    if backend_specific_objects is None:
        A			:	Union[str, Any]										=       read_init()
    # For special correspondence backend to module name as used in the function requires_modulename
    A			:	Any										=       {}

    for backend, objects in backend_specific_objects.items():
        A			:	str										=       """[""" + """, """.join(f'''"{b}"''' for b in backend.split("""_and_"""		)		) + """]"""
        A			:	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(_lowerCAmelCase				,					_lowerCAmelCase		) for o in objects]		)
        A			:	Optional[Any]										=       dummy_file

    return dummy_files




def      __UpperCamelCase     (  _lowerCAmelCase=False		)	->  str:



    """simple docstring"""






    A			:	str										=       create_dummy_files()
    # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
    A			:	List[str]										=       {"""torch""": """pt"""}

    # Locate actual dummy modules and read their content.
    A			:	Union[str, Any]										=       os.path.join(_lowerCAmelCase				,					"""utils"""		)
    A			:	Any										=       {
        backend: os.path.join(_lowerCAmelCase				,					f'''dummy_{short_names.get(_lowerCAmelCase				,					_lowerCAmelCase		)}_objects.py'''		)
        for backend in dummy_files.keys()
    }

    A			:	List[Any]										=       {}
    for backend, file_path in dummy_file_paths.items():
        if os.path.isfile(_lowerCAmelCase		):
            with open(_lowerCAmelCase				,					"""r"""				,					encoding="""utf-8"""				,					newline="""\n"""		) as f:
                A			:	Dict										=       f.read()
        else:
            A			:	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(_lowerCAmelCase				,					_lowerCAmelCase		)}_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(_lowerCAmelCase				,					_lowerCAmelCase		)}_objects.py. Run `make fix-copies` '''
                    """to fix this."""		)


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

     check_dummies(args.fix_and_overwrite)


