





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()
_UpperCAmelCase       : Any			     =       logging.get_logger(__name__)

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


def  SCREAMING_SNAKE_CASE   ( )    ->  List[str]:
    lowerCamelCase__					:   Union[str, Any]      				=	'http://images.cocodataset.org/val2017/000000039769.jpg'
    lowerCamelCase__					:   int      				=	Image.open(requests.get(_UpperCAmelCase		,			stream=_UpperCAmelCase				).raw				)
    return im


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase				)    ->  int:
    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  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				)    ->  Union[str, Any]:
    lowerCamelCase__					:   List[str]      				=	dct.pop(_UpperCAmelCase				)
    lowerCamelCase__					:   Optional[Any]      				=	val


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


@torch.no_grad()
def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				)    ->  List[Any]:
    lowerCamelCase__					:   str      				=	SwiftFormerConfig()

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

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

    elif swiftformer_name == "swiftformer_s":
        lowerCamelCase__					:   Tuple      				=	[3, 3, 9, 6]
        lowerCamelCase__					:   Dict      				=	[48, 64, 168, 224]

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

    elif swiftformer_name == "swiftformer_l3":
        lowerCamelCase__					:   Optional[Any]      				=	[4, 4, 12, 6]
        lowerCamelCase__					:   Dict      				=	[64, 128, 320, 512]

    # load state_dict of original model, remove and rename some keys
    if original_ckpt:
        if original_ckpt.startswith('https'				):
            lowerCamelCase__					:   List[Any]      				=	torch.hub.load_state_dict_from_url(_UpperCAmelCase		,			map_location='cpu'		,			check_hash=_UpperCAmelCase				)
        else:
            lowerCamelCase__					:   Dict      				=	torch.load(_UpperCAmelCase		,			map_location='cpu'				)
    lowerCamelCase__					:   Optional[int]      				=	checkpoint

    lowerCamelCase__					:   int      				=	create_rename_keys(_UpperCAmelCase				)
    for rename_key_src, rename_key_dest in rename_keys:
        rename_key(_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				)

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

    # prepare test inputs
    lowerCamelCase__					:   List[Any]      				=	prepare_img()
    lowerCamelCase__					:   Dict      				=	ViTImageProcessor.from_pretrained('preprocessor_config'				)
    lowerCamelCase__					:   Any      				=	processor(images=_UpperCAmelCase		,			return_tensors='pt'				)

    # compare outputs from both models
    lowerCamelCase__					:   Optional[int]      				=	get_expected_output(_UpperCAmelCase				)
    lowerCamelCase__					:   Any      				=	hf_model(inputs['pixel_values']				).logits

    assert hf_logits.shape == torch.Size([1, 1000]				)
    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__":
       _UpperCAmelCase       : int			     =       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.""")

       _UpperCAmelCase       : str			     =       parser.parse_args()
       convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)












import inspect
import unittest

from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
    import jax
    import jax.numpy as jnp

    from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor

class      lowerCAmelCase							(      unittest.TestCase    ):
 def __init__(   self						:   List[Any]    ,  UpperCAmelCase						:   Optional[int]    ,  UpperCAmelCase						:   Optional[Any]=3    ,  UpperCAmelCase						:   str=32    ,  UpperCAmelCase						:   Union[str, Any]=3    ,  UpperCAmelCase						:   Dict=10    ,  UpperCAmelCase						:   List[str]=[10, 20, 30, 40]    ,  UpperCAmelCase						:   Any=[1, 1, 2, 1]    ,  UpperCAmelCase						:   List[str]=True    ,  UpperCAmelCase						:   List[str]=True    ,  UpperCAmelCase						:   Dict="relu"    ,  UpperCAmelCase						:   Tuple=3    ,  UpperCAmelCase						:   Dict=None    ,  )    ->			Optional[Any]:
     lowerCamelCase__					:   Union[str, Any]      				=	parent
     lowerCamelCase__					:   Optional[Any]      				=	batch_size
     lowerCamelCase__					:   Dict      				=	image_size
     lowerCamelCase__					:   int      				=	num_channels
     lowerCamelCase__					:   int      				=	embeddings_size
     lowerCamelCase__					:   str      				=	hidden_sizes
     lowerCamelCase__					:   Any      				=	depths
     lowerCamelCase__					:   str      				=	is_training
     lowerCamelCase__					:   List[Any]      				=	use_labels
     lowerCamelCase__					:   Union[str, Any]      				=	hidden_act
     lowerCamelCase__					:   Dict      				=	num_labels
     lowerCamelCase__					:   Dict      				=	scope
     lowerCamelCase__					:   List[str]      				=	len(UpperCAmelCase			)
 def   A_			(   self						:   Optional[int]			)    ->			Optional[int]:
     lowerCamelCase__					:   int      				=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

     lowerCamelCase__					:   str      				=	self.get_config()

     return config, pixel_values
 def   A_			(   self						:   Optional[int]			)    ->			Dict:
     return RegNetConfig(
         num_channels=self.num_channels    ,  embeddings_size=self.embeddings_size    ,  hidden_sizes=self.hidden_sizes    ,  depths=self.depths    ,  hidden_act=self.hidden_act    ,  num_labels=self.num_labels    ,  image_size=self.image_size    ,  )
 def   A_			(   self						:   str    ,  UpperCAmelCase						:   Optional[int]    ,  UpperCAmelCase						:   Any			)    ->			Optional[Any]:
     lowerCamelCase__					:   Optional[Any]      				=	FlaxRegNetModel(config=UpperCAmelCase			)
     lowerCamelCase__					:   Optional[Any]      				=	model(UpperCAmelCase			)

     # Output shape (b, c, h, w)
     self.parent.assertEqual(
         result.last_hidden_state.shape    ,  (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)    ,  )
 def   A_			(   self						:   str    ,  UpperCAmelCase						:   int    ,  UpperCAmelCase						:   Tuple			)    ->			Tuple:
     lowerCamelCase__					:   List[str]      				=	self.num_labels
     lowerCamelCase__					:   str      				=	FlaxRegNetForImageClassification(config=UpperCAmelCase			)
     lowerCamelCase__					:   Union[str, Any]      				=	model(UpperCAmelCase			)
     self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.num_labels)			)







 def   A_			(   self						:   Dict			)    ->			str:
     lowerCamelCase__					:   Optional[int]      				=	self.prepare_config_and_inputs()
     lowerCamelCase__				,							lowerCamelCase__					:   Any      				=	config_and_inputs
     lowerCamelCase__					:   Dict      				=	{'pixel_values': pixel_values}
     return config, inputs_dict


@require_flax
class      lowerCAmelCase							(      __UpperCamelCase,						unittest.TestCase    ):
 UpperCAmelCase__									=	(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

 UpperCAmelCase__									=	False
 UpperCAmelCase__									=	False
 UpperCAmelCase__									=	False
 def   A_			(   self						:   List[Any]			)    ->			None:
     lowerCamelCase__					:   List[Any]      				=	FlaxRegNetModelTester(self			)
     lowerCamelCase__					:   Union[str, Any]      				=	ConfigTester(self    ,  config_class=UpperCAmelCase    ,  has_text_modality=UpperCAmelCase			)
 def   A_			(   self						:   int			)    ->			Tuple:
     self.create_and_test_config_common_properties()
     self.config_tester.create_and_test_config_to_json_string()
     self.config_tester.create_and_test_config_to_json_file()
     self.config_tester.create_and_test_config_from_and_save_pretrained()
     self.config_tester.create_and_test_config_with_num_labels()
     self.config_tester.check_config_can_be_init_without_params()
     self.config_tester.check_config_arguments_init()
 def   A_			(   self						:   Any			)    ->			str:
     return
 def   A_			(   self						:   Optional[Any]			)    ->			Optional[Any]:
     lowerCamelCase__					:   Union[str, Any]      				=	self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*UpperCAmelCase			)
 def   A_			(   self						:   Dict			)    ->			Optional[Any]:
     lowerCamelCase__					:   Any      				=	self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase			)
 @unittest.skip(reason='RegNet does not use inputs_embeds'			)
 def   A_			(   self						:   Dict			)    ->			Dict:
     pass
 @unittest.skip(reason='RegNet does not support input and output embeddings'			)
 def   A_			(   self						:   Any			)    ->			Tuple:
     pass
 def   A_			(   self						:   Union[str, Any]			)    ->			Union[str, Any]:
     lowerCamelCase__				,							lowerCamelCase__					:   Tuple      				=	self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         lowerCamelCase__					:   Optional[int]      				=	model_class(UpperCAmelCase			)
         lowerCamelCase__					:   Optional[Any]      				=	inspect.signature(model.__call__			)
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         lowerCamelCase__					:   List[Any]      				=	[*signature.parameters.keys()]

         lowerCamelCase__					:   int      				=	['pixel_values']
         self.assertListEqual(arg_names[:1]    ,  UpperCAmelCase			)
 def   A_			(   self						:   int			)    ->			List[str]:
     def check_hidden_states_output(UpperCAmelCase						:   Union[str, Any]    ,  UpperCAmelCase						:   List[str]    ,  UpperCAmelCase						:   Optional[Any]			):
         lowerCamelCase__					:   Any      				=	model_class(UpperCAmelCase			)

         lowerCamelCase__					:   Optional[Any]      				=	model(**self._prepare_for_class(UpperCAmelCase    ,  UpperCAmelCase			)			)

         lowerCamelCase__					:   Union[str, Any]      				=	outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

         lowerCamelCase__					:   Optional[int]      				=	self.model_tester.num_stages
         self.assertEqual(len(UpperCAmelCase			)    ,  expected_num_stages + 1			)

     lowerCamelCase__				,							lowerCamelCase__					:   Optional[Any]      				=	self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         lowerCamelCase__					:   List[str]      				=	True
         check_hidden_states_output(UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase			)

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         lowerCamelCase__					:   List[Any]      				=	True

         check_hidden_states_output(UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase			)







 def   A_			(   self						:   Dict			)    ->			int:
     lowerCamelCase__				,							lowerCamelCase__					:   int      				=	self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__			):
             lowerCamelCase__					:   int      				=	self._prepare_for_class(UpperCAmelCase    ,  UpperCAmelCase			)
             lowerCamelCase__					:   Tuple      				=	model_class(UpperCAmelCase			)

             @jax.jit
             def model_jitted(UpperCAmelCase						:   Tuple    ,  **UpperCAmelCase						:   Tuple			):
                 return model(pixel_values=UpperCAmelCase    ,  **UpperCAmelCase			)

             with self.subTest('JIT Enabled'			):
                 lowerCamelCase__					:   Dict      				=	model_jitted(**UpperCAmelCase			).to_tuple()

             with self.subTest('JIT Disabled'			):
                 with jax.disable_jit():
                     lowerCamelCase__					:   Optional[int]      				=	model_jitted(**UpperCAmelCase			).to_tuple()

             self.assertEqual(len(UpperCAmelCase			)    ,  len(UpperCAmelCase			)			)
             for jitted_output, output in zip(UpperCAmelCase    ,  UpperCAmelCase			):
                 self.assertEqual(jitted_output.shape    ,  output.shape			)


def  SCREAMING_SNAKE_CASE   ( )    ->  Optional[Any]:
    lowerCamelCase__					:   Optional[int]      				=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
    return image


@require_flax
class      lowerCAmelCase							(      unittest.TestCase    ):
 @cached_property
 def   A_			(   self						:   Any			)    ->			List[Any]:
     return AutoImageProcessor.from_pretrained('facebook/regnet-y-040'			) if is_vision_available() else None







 @slow
 def   A_			(   self						:   Dict			)    ->			Tuple:
     lowerCamelCase__					:   Union[str, Any]      				=	FlaxRegNetForImageClassification.from_pretrained('facebook/regnet-y-040'			)

     lowerCamelCase__					:   Dict      				=	self.default_image_processor
     lowerCamelCase__					:   List[Any]      				=	prepare_img()
     lowerCamelCase__					:   List[str]      				=	image_processor(images=UpperCAmelCase    ,  return_tensors='np'			)

     lowerCamelCase__					:   List[Any]      				=	model(**UpperCAmelCase			)

     # verify the logits
     lowerCamelCase__					:   Union[str, Any]      				=	(1, 1000)
     self.assertEqual(outputs.logits.shape    ,  UpperCAmelCase			)

     lowerCamelCase__					:   Tuple      				=	jnp.array([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6]			)

     self.assertTrue(jnp.allclose(outputs.logits[0, :3]    ,  UpperCAmelCase    ,  atol=1e-4			)			)





