


'''simple docstring'''







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	):
    """simple docstring"""


    def __init__(  self      :							Any				,							SCREAMING_SNAKE_CASE      :							List[str]				,							SCREAMING_SNAKE_CASE      :							Optional[int]=3				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]=32				,							SCREAMING_SNAKE_CASE      :							int=3				,							SCREAMING_SNAKE_CASE      :							Tuple=10				,							SCREAMING_SNAKE_CASE      :							Optional[Any]=[10, 20, 30, 40]				,							SCREAMING_SNAKE_CASE      :							Any=[1, 1, 2, 1]				,							SCREAMING_SNAKE_CASE      :							int=True				,							SCREAMING_SNAKE_CASE      :							str=True				,							SCREAMING_SNAKE_CASE      :							Dict="relu"				,							SCREAMING_SNAKE_CASE      :							List[Any]=3				,							SCREAMING_SNAKE_CASE      :							Optional[int]=None				,							):
           _A					:     Any     = parent
           _A					:     Union[str, Any]     = batch_size
           _A					:     str     = image_size
           _A					:     Tuple     = num_channels
           _A					:     Tuple     = embeddings_size
           _A					:     Union[str, Any]     = hidden_sizes
           _A					:     Optional[Any]     = depths
           _A					:     Union[str, Any]     = is_training
           _A					:     int     = use_labels
           _A					:     List[str]     = hidden_act
           _A					:     Optional[Any]     = num_labels
           _A					:     Tuple     = scope
           _A					:     List[Any]     = len(SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Optional[int]):
           _A					:     int     = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

           _A					:     Optional[int]     = self.get_config()

           return config, pixel_values


    def        A   (  self      :							Tuple):
           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      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							List[str]				,							SCREAMING_SNAKE_CASE      :							List[str]):
           _A					:     Optional[int]     = FlaxRegNetModel(config=SCREAMING_SNAKE_CASE)
           _A					:     int     = model(SCREAMING_SNAKE_CASE)

           # 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      :							Tuple				,							SCREAMING_SNAKE_CASE      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							List[Any]):
           _A					:     List[Any]     = self.num_labels
           _A					:     Tuple     = FlaxRegNetForImageClassification(config=SCREAMING_SNAKE_CASE)
           _A					:     Optional[Any]     = model(SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.num_labels))


    def        A   (  self      :							Union[str, Any]):
           _A					:     List[Any]     = self.prepare_config_and_inputs()
           _A  ,			_A					:     Optional[Any]     = config_and_inputs
           _A					:     Optional[int]     = {'pixel_values': pixel_values}
           return config, inputs_dict




@require_flax
class 			__lowerCamelCase			(	a_    , unittest.TestCase	):
    """simple docstring"""
    a													=   (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

    a													=   False
    a													=   False
    a													=   False


    def        A   (  self      :							Union[str, Any]):
           _A					:     Optional[Any]     = FlaxRegNetModelTester(self)
           _A					:     Dict     = ConfigTester(self				,							config_class=SCREAMING_SNAKE_CASE				,							has_text_modality=SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Any):
           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      :							Union[str, Any]):
           return


    def        A   (  self      :							str):
           _A					:     Any     = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Optional[int]):
           _A					:     List[Any]     = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE)


    @unittest.skip(reason='RegNet does not use inputs_embeds')
    def        A   (  self      :							List[str]):
           pass


    @unittest.skip(reason='RegNet does not support input and output embeddings')
    def        A   (  self      :							Optional[Any]):
           pass


    def        A   (  self      :							Any):
           _A  ,			_A					:     List[str]     = self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  _A					:     Dict     = model_class(SCREAMING_SNAKE_CASE)
                  _A					:     str     = inspect.signature(model.__call__)
                  # signature.parameters is an OrderedDict => so arg_names order is deterministic
                  _A					:     List[str]     = [*signature.parameters.keys()]

                  _A					:     Optional[Any]     = ['pixel_values']
                  self.assertListEqual(arg_names[:1]				,							SCREAMING_SNAKE_CASE)


    def        A   (  self      :							List[Any]):
           def check_hidden_states_output(SCREAMING_SNAKE_CASE      :							Dict				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							Optional[int]):
                  _A					:     int     = model_class(SCREAMING_SNAKE_CASE)

                  _A					:     Optional[int]     = model(**self._prepare_for_class(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE))

                  _A					:     str     = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                  _A					:     Dict     = self.model_tester.num_stages
                  self.assertEqual(len(SCREAMING_SNAKE_CASE)				,							expected_num_stages + 1)

           _A  ,			_A					:     str     = self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  _A					:     Dict     = True
                  check_hidden_states_output(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)

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

                  check_hidden_states_output(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Any):
           _A  ,			_A					:     str     = self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  with self.subTest(model_class.__name__):
                         _A					:     List[str]     = self._prepare_for_class(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)
                         _A					:     Union[str, Any]     = model_class(SCREAMING_SNAKE_CASE)

                         @jax.jit
                         def model_jitted(SCREAMING_SNAKE_CASE      :							List[str]				,							**SCREAMING_SNAKE_CASE      :							Union[str, Any]):
                                return model(pixel_values=SCREAMING_SNAKE_CASE				,							**SCREAMING_SNAKE_CASE)

                         with self.subTest('JIT Enabled'):
                                _A					:     Union[str, Any]     = model_jitted(**SCREAMING_SNAKE_CASE).to_tuple()

                         with self.subTest('JIT Disabled'):
                                with jax.disable_jit():
                                       _A					:     List[Any]     = model_jitted(**SCREAMING_SNAKE_CASE).to_tuple()

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






def 				lowerCAmelCase__	(    ):
       _A					:     Dict     = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
       return image


@require_flax
class 			__lowerCamelCase			(	unittest.TestCase	):
    """simple docstring"""


    @cached_property
    def        A   (  self      :							Any):
           return AutoImageProcessor.from_pretrained('facebook/regnet-y-040') if is_vision_available() else None


    @slow
    def        A   (  self      :							List[str]):
           _A					:     List[str]     = FlaxRegNetForImageClassification.from_pretrained('facebook/regnet-y-040')

           _A					:     Optional[int]     = self.default_image_processor
           _A					:     Any     = prepare_img()
           _A					:     Dict     = image_processor(images=SCREAMING_SNAKE_CASE				,							return_tensors='np')

           _A					:     List[Any]     = model(**SCREAMING_SNAKE_CASE)

           # verify the logits
           _A					:     List[Any]     = (1, 1000)
           self.assertEqual(outputs.logits.shape				,							SCREAMING_SNAKE_CASE)

           _A					:     Tuple     = jnp.array([-0.4180, -1.5051, -3.4836])

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










'''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()
A				:					Optional[int]    						=			logging.get_logger(__name__)

A				:					Union[str, Any]    						=			torch.device('''cpu''')






def 				lowerCAmelCase__	(    ):
       _A					:     Tuple     = 'http://images.cocodataset.org/val2017/000000039769.jpg'
       _A					:     Dict     = Image.open(requests.get(lowerCamelCase  ,stream=lowerCamelCase       ).raw       )
       return im






def 				lowerCAmelCase__	(    lowerCamelCase					:      List[Any]       ):
       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 				lowerCAmelCase__	(    lowerCamelCase					:      Union[str, Any]  ,lowerCamelCase					:      List[Any]  ,lowerCamelCase					:      Dict       ):
       _A					:     Union[str, Any]     = dct.pop(lowerCamelCase       )
       _A					:     List[str]     = val






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






@torch.no_grad()
def 				lowerCAmelCase__	(    lowerCamelCase					:      int  ,lowerCamelCase					:      str  ,lowerCamelCase					:      List[str]       ):
       _A					:     Dict     = SwiftFormerConfig()

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

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

       elif swiftformer_name == "swiftformer_s":
              _A					:     List[Any]     = [3, 3, 9, 6]
              _A					:     Tuple     = [48, 64, 168, 224]

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

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

       # load state_dict of original model, remove and rename some keys
       if original_ckpt:
              if original_ckpt.startswith('https'       ):
                     _A					:     Optional[Any]     = torch.hub.load_state_dict_from_url(lowerCamelCase  ,map_location='cpu'  ,check_hash=lowerCamelCase       )
              else:
                     _A					:     Union[str, Any]     = torch.load(lowerCamelCase  ,map_location='cpu'       )
       _A					:     Union[str, Any]     = 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					:     str     = SwiftFormerForImageClassification(lowerCamelCase       ).eval()
       hf_model.load_state_dict(lowerCamelCase       )

       # prepare test inputs
       _A					:     Any     = prepare_img()
       _A					:     Optional[int]     = ViTImageProcessor.from_pretrained('preprocessor_config'       )
       _A					:     Any     = processor(images=lowerCamelCase  ,return_tensors='pt'       )

       # compare outputs from both models
       _A					:     int     = get_expected_output(lowerCamelCase       )
       _A					:     Optional[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__":
   A				:					Dict    						=			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.''')

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






