



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_:List[Any]             =						logging.get_logger(__name__)

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




def 	__UpperCamelCase	(  )       ->  int:



    """simple docstring"""






    A :       str          =   """http://images.cocodataset.org/val2017/000000039769.jpg"""
    A :       str          =   Image.open(requests.get(_lowerCAmelCase      ,     stream=_lowerCAmelCase		).raw		)
    return im




def 	__UpperCamelCase	(  _lowerCAmelCase		)       ->  Optional[int]:



    """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		)       ->  Any:



    """simple docstring"""






    A :       List[str]          =   dct.pop(_lowerCAmelCase		)
    A :       Union[str, Any]          =   val




def 	__UpperCamelCase	(  _lowerCAmelCase		)       ->  int:



    """simple docstring"""






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



    """simple docstring"""






    A :       str          =   SwiftFormerConfig()

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

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

    elif swiftformer_name == "swiftformer_s":
        A :       List[Any]          =   [3, 3, 9, 6]
        A :       Optional[int]          =   [48, 64, 168, 224]

    elif swiftformer_name == "swiftformer_l1":
        A :       int          =   [4, 3, 10, 5]
        A :       Optional[int]          =   [48, 96, 192, 384]

    elif swiftformer_name == "swiftformer_l3":
        A :       Optional[int]          =   [4, 4, 12, 6]
        A :       str          =   [64, 128, 320, 512]

    # 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(_lowerCAmelCase      ,     map_location="""cpu"""      ,     check_hash=_lowerCAmelCase		)
        else:
            A :       List[str]          =   torch.load(_lowerCAmelCase      ,     map_location="""cpu"""		)
    A :       str          =   checkpoint

    A :       List[str]          =   create_rename_keys(_lowerCAmelCase		)
    for rename_key_src, rename_key_dest in rename_keys:
        rename_key(_lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase		)

    # load HuggingFace model
    A :       List[str]          =   SwiftFormerForImageClassification(_lowerCAmelCase		).eval()
    hf_model.load_state_dict(_lowerCAmelCase		)

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

    # compare outputs from both models
    A :       Tuple          =   get_expected_output(_lowerCAmelCase		)
    A :       str          =   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_:Union[str, Any]             =						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_:int             =						parser.parse_args()
     convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)










'''simple docstring'''







import unittest

import numpy as np

from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import DonutImageProcessor

class _lowercase	(				unittest.TestCase		):




							def __init__(							self:				str			,							UpperCamelCase__:				Optional[Any]			,							UpperCamelCase__:				Any=7			,							UpperCamelCase__:				Optional[int]=3			,							UpperCamelCase__:				List[str]=18			,							UpperCamelCase__:				Union[str, Any]=30			,							UpperCamelCase__:				List[str]=400			,							UpperCamelCase__:				Any=True			,							UpperCamelCase__:				Union[str, Any]=None			,							UpperCamelCase__:				List[Any]=True			,							UpperCamelCase__:				List[Any]=False			,							UpperCamelCase__:				Tuple=True			,							UpperCamelCase__:				Tuple=True			,							UpperCamelCase__:				Any=[0.5, 0.5, 0.5]			,							UpperCamelCase__:				Optional[Any]=[0.5, 0.5, 0.5]			,							):
														lowerCamelCase__					:	int         =       parent
														lowerCamelCase__					:	Any         =       batch_size
														lowerCamelCase__					:	Optional[int]         =       num_channels
														lowerCamelCase__					:	Union[str, Any]         =       image_size
														lowerCamelCase__					:	Optional[int]         =       min_resolution
														lowerCamelCase__					:	Optional[Any]         =       max_resolution
														lowerCamelCase__					:	Union[str, Any]         =       do_resize
														lowerCamelCase__					:	Optional[Any]         =       size if size is not None else {"""height""": 18, """width""": 20}
														lowerCamelCase__					:	Dict         =       do_thumbnail
														lowerCamelCase__					:	Optional[int]         =       do_align_axis
														lowerCamelCase__					:	Any         =       do_pad
														lowerCamelCase__					:	Optional[Any]         =       do_normalize
														lowerCamelCase__					:	Union[str, Any]         =       image_mean
														lowerCamelCase__					:	Union[str, Any]         =       image_std




							def 		lowerCamelCase_				(							self:				str     ):
														return {
														    "do_resize": self.do_resize,
														    "size": self.size,
														    "do_thumbnail": self.do_thumbnail,
														    "do_align_long_axis": self.do_align_axis,
														    "do_pad": self.do_pad,
														    "do_normalize": self.do_normalize,
														    "image_mean": self.image_mean,
														    "image_std": self.image_std,
														}



@require_torch
@require_vision
class _lowercase	(				_lowercase				,      unittest.TestCase		):
							a				      =       DonutImageProcessor if is_vision_available() else None




							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														lowerCamelCase__					:	Any         =       DonutImageProcessingTester(self     )




							@property
							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														return self.image_processor_tester.prepare_image_processor_dict()




							def 		lowerCamelCase_				(							self:				Tuple     ):
														lowerCamelCase__					:	Dict         =       self.image_processing_class(**self.image_processor_dict     )
														self.assertTrue(hasattr(UpperCamelCase__			,							"""do_resize"""     )     )
														self.assertTrue(hasattr(UpperCamelCase__			,							"""size"""     )     )
														self.assertTrue(hasattr(UpperCamelCase__			,							"""do_thumbnail"""     )     )
														self.assertTrue(hasattr(UpperCamelCase__			,							"""do_align_long_axis"""     )     )
														self.assertTrue(hasattr(UpperCamelCase__			,							"""do_pad"""     )     )
														self.assertTrue(hasattr(UpperCamelCase__			,							"""do_normalize"""     )     )
														self.assertTrue(hasattr(UpperCamelCase__			,							"""image_mean"""     )     )
														self.assertTrue(hasattr(UpperCamelCase__			,							"""image_std"""     )     )




							def 		lowerCamelCase_				(							self:				Optional[Any]     ):
														lowerCamelCase__					:	Optional[int]         =       self.image_processing_class.from_dict(self.image_processor_dict     )
														self.assertEqual(image_processor.size			,							{"""height""": 18, """width""": 20}     )

														lowerCamelCase__					:	Optional[int]         =       self.image_processing_class.from_dict(self.image_processor_dict			,							size=42     )
														self.assertEqual(image_processor.size			,							{"""height""": 42, """width""": 42}     )

														# Previous config had dimensions in (width, height) order
														lowerCamelCase__					:	List[str]         =       self.image_processing_class.from_dict(self.image_processor_dict			,							size=(42, 84)     )
														self.assertEqual(image_processor.size			,							{"""height""": 84, """width""": 42}     )




							def 		lowerCamelCase_				(							self:				List[str]     ):
														pass




							@is_flaky()
							def 		lowerCamelCase_				(							self:				Union[str, Any]     ):
														# Initialize image_processing
														lowerCamelCase__					:	str         =       self.image_processing_class(**self.image_processor_dict     )
														# create random PIL images
														lowerCamelCase__					:	Dict         =       prepare_image_inputs(self.image_processor_tester			,							equal_resolution=UpperCamelCase__     )
														for image in image_inputs:
																					self.assertIsInstance(UpperCamelCase__			,							Image.Image     )

														# Test not batched input
														lowerCamelCase__					:	Union[str, Any]         =       image_processing(image_inputs[0]			,							return_tensors="""pt"""     ).pixel_values
														self.assertEqual(
														    encoded_images.shape			,							(
														        1,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.size["""height"""],
														        self.image_processor_tester.size["""width"""],
														    )			,							)

														# Test batched
														lowerCamelCase__					:	List[str]         =       image_processing(UpperCamelCase__			,							return_tensors="""pt"""     ).pixel_values
														self.assertEqual(
														    encoded_images.shape			,							(
														        self.image_processor_tester.batch_size,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.size["""height"""],
														        self.image_processor_tester.size["""width"""],
														    )			,							)




							@is_flaky()
							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														# Initialize image_processing
														lowerCamelCase__					:	Dict         =       self.image_processing_class(**self.image_processor_dict     )
														# create random numpy tensors
														lowerCamelCase__					:	Dict         =       prepare_image_inputs(self.image_processor_tester			,							equal_resolution=UpperCamelCase__			,							numpify=UpperCamelCase__     )
														for image in image_inputs:
																					self.assertIsInstance(UpperCamelCase__			,							np.ndarray     )

														# Test not batched input
														lowerCamelCase__					:	List[Any]         =       image_processing(image_inputs[0]			,							return_tensors="""pt"""     ).pixel_values
														self.assertEqual(
														    encoded_images.shape			,							(
														        1,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.size["""height"""],
														        self.image_processor_tester.size["""width"""],
														    )			,							)

														# Test batched
														lowerCamelCase__					:	Optional[Any]         =       image_processing(UpperCamelCase__			,							return_tensors="""pt"""     ).pixel_values
														self.assertEqual(
														    encoded_images.shape			,							(
														        self.image_processor_tester.batch_size,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.size["""height"""],
														        self.image_processor_tester.size["""width"""],
														    )			,							)




							@is_flaky()
							def 		lowerCamelCase_				(							self:				Dict     ):
														# Initialize image_processing
														lowerCamelCase__					:	int         =       self.image_processing_class(**self.image_processor_dict     )
														# create random PyTorch tensors
														lowerCamelCase__					:	Optional[int]         =       prepare_image_inputs(self.image_processor_tester			,							equal_resolution=UpperCamelCase__			,							torchify=UpperCamelCase__     )
														for image in image_inputs:
																					self.assertIsInstance(UpperCamelCase__			,							torch.Tensor     )

														# Test not batched input
														lowerCamelCase__					:	Union[str, Any]         =       image_processing(image_inputs[0]			,							return_tensors="""pt"""     ).pixel_values
														self.assertEqual(
														    encoded_images.shape			,							(
														        1,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.size["""height"""],
														        self.image_processor_tester.size["""width"""],
														    )			,							)

														# Test batched
														lowerCamelCase__					:	Tuple         =       image_processing(UpperCamelCase__			,							return_tensors="""pt"""     ).pixel_values
														self.assertEqual(
														    encoded_images.shape			,							(
														        self.image_processor_tester.batch_size,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.size["""height"""],
														        self.image_processor_tester.size["""width"""],
														    )			,							)

