

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

     SCREAMING_SNAKE_CASE:str  					=    self.get_config()

     return config, pixel_values
   def 			__UpperCamelCase  (       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 			__UpperCamelCase  (       self	:					str   ,SCREAMING_SNAKE_CASE__	:					Dict   ,SCREAMING_SNAKE_CASE__	:					List[str]							):
     SCREAMING_SNAKE_CASE:Any  					=    FlaxRegNetModel(config=UpperCAmelCase__							)
     SCREAMING_SNAKE_CASE:str  					=    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 			__UpperCamelCase  (       self	:					Optional[Any]   ,SCREAMING_SNAKE_CASE__	:					List[str]   ,SCREAMING_SNAKE_CASE__	:					Optional[Any]							):
     SCREAMING_SNAKE_CASE:Union[str, Any]  					=    self.num_labels
     SCREAMING_SNAKE_CASE:Union[str, Any]  					=    FlaxRegNetForImageClassification(config=UpperCAmelCase__							)
     SCREAMING_SNAKE_CASE:Optional[int]  					=    model(UpperCAmelCase__							)
     self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.num_labels)							)







   def 			__UpperCamelCase  (       self	:					Optional[int]							):
     SCREAMING_SNAKE_CASE:int  					=    self.prepare_config_and_inputs()
     SCREAMING_SNAKE_CASE:Tuple  					=    config_and_inputs
     SCREAMING_SNAKE_CASE:Optional[Any]  					=    {"""pixel_values""": pixel_values}
     return config, inputs_dict





@require_flax
class 							_snake_case       (				snake_case_		,     unittest.TestCase  ):
   _A				:   str			      =    (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

   _A				:   Dict			      =    False
   _A				:   Optional[Any]			      =    False
   _A				:   int			      =    False
   def 			__UpperCamelCase  (       self	:					List[str]							):
     SCREAMING_SNAKE_CASE:str  					=    FlaxRegNetModelTester(self							)
     SCREAMING_SNAKE_CASE:Optional[int]  					=    ConfigTester(self   ,config_class=UpperCAmelCase__   ,has_text_modality=UpperCAmelCase__							)
   def 			__UpperCamelCase  (       self	:					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 			__UpperCamelCase  (       self	:					Optional[int]							):
     return
   def 			__UpperCamelCase  (       self	:					Union[str, Any]							):
     SCREAMING_SNAKE_CASE:List[Any]  					=    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*UpperCAmelCase__							)
   def 			__UpperCamelCase  (       self	:					Tuple							):
     SCREAMING_SNAKE_CASE:Tuple  					=    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 			__UpperCamelCase  (       self	:					Optional[Any]							):
     pass
   @unittest.skip(reason="RegNet does not support input and output embeddings"							)
   def 			__UpperCamelCase  (       self	:					Optional[Any]							):
     pass
   def 			__UpperCamelCase  (       self	:					Tuple							):
     SCREAMING_SNAKE_CASE:List[str]  					=    self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       SCREAMING_SNAKE_CASE:Tuple  					=    model_class(UpperCAmelCase__							)
       SCREAMING_SNAKE_CASE:Union[str, Any]  					=    inspect.signature(model.__call__							)
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       SCREAMING_SNAKE_CASE:Optional[Any]  					=    [*signature.parameters.keys()]

       SCREAMING_SNAKE_CASE:Tuple  					=    ["""pixel_values"""]
       self.assertListEqual(arg_names[:1]   ,UpperCAmelCase__							)
   def 			__UpperCamelCase  (       self	:					Tuple							):
     def check_hidden_states_output(SCREAMING_SNAKE_CASE__	:					Optional[int]   ,SCREAMING_SNAKE_CASE__	:					str   ,SCREAMING_SNAKE_CASE__	:					Optional[int]							):
       SCREAMING_SNAKE_CASE:List[str]  					=    model_class(UpperCAmelCase__							)

       SCREAMING_SNAKE_CASE:Any  					=    model(**self._prepare_for_class(UpperCAmelCase__   ,UpperCAmelCase__							)							)

       SCREAMING_SNAKE_CASE:Tuple  					=    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

       SCREAMING_SNAKE_CASE:List[Any]  					=    self.model_tester.num_stages
       self.assertEqual(len(UpperCAmelCase__							)   ,expected_num_stages + 1							)

     SCREAMING_SNAKE_CASE:Optional[int]  					=    self.model_tester.prepare_config_and_inputs_for_common()

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

       # check that output_hidden_states also work using config
       del inputs_dict["output_hidden_states"]
       SCREAMING_SNAKE_CASE:Union[str, Any]  					=    True

       check_hidden_states_output(UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__							)







   def 			__UpperCamelCase  (       self	:					Optional[Any]							):
     SCREAMING_SNAKE_CASE:str  					=    self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__							):
         SCREAMING_SNAKE_CASE:Optional[Any]  					=    self._prepare_for_class(UpperCAmelCase__   ,UpperCAmelCase__							)
         SCREAMING_SNAKE_CASE:Optional[int]  					=    model_class(UpperCAmelCase__							)

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

         with self.subTest("JIT Enabled"							):
           SCREAMING_SNAKE_CASE:str  					=    model_jitted(**UpperCAmelCase__							).to_tuple()

         with self.subTest("JIT Disabled"							):
           with jax.disable_jit():
             SCREAMING_SNAKE_CASE:Any  					=    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       A_     (       ):
  SCREAMING_SNAKE_CASE:Dict  					=    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
  return image





@require_flax
class 							_snake_case       (				unittest.TestCase  ):
   @cached_property
   def 			__UpperCamelCase  (       self	:					Any							):
     return AutoImageProcessor.from_pretrained("facebook/regnet-y-040"							) if is_vision_available() else None







   @slow
   def 			__UpperCamelCase  (       self	:					List[str]							):
     SCREAMING_SNAKE_CASE:Any  					=    FlaxRegNetForImageClassification.from_pretrained("facebook/regnet-y-040"							)

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

     SCREAMING_SNAKE_CASE:Optional[int]  					=    model(**UpperCAmelCase__							)

     # verify the logits
     SCREAMING_SNAKE_CASE:Optional[int]  					=    (1, 1_000)
     self.assertEqual(outputs.logits.shape   ,UpperCAmelCase__							)

     SCREAMING_SNAKE_CASE:Optional[Any]  					=    jnp.array([-0.4_180, -1.5_051, -3.4_836]							)

     self.assertTrue(jnp.allclose(outputs.logits[0, :3]   ,UpperCAmelCase__   ,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()
_snake_case          =   logging.get_logger(__name__)

_snake_case          =   torch.device('cpu')

def 			lowerCAmelCase__     (     ):



  '''simple docstring'''
  _a  :       int             =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
  _a  :       Dict             =	Image.open(requests.get(UpperCamelCase__     ,   stream=UpperCamelCase__				).raw				)
  return im

def 			lowerCAmelCase__     (     UpperCamelCase__				):



  '''simple docstring'''
  if swiftformer_name == "swiftformer_xs":
    return torch.tensor([-2.17_03e00, 2.11_07e00, -2.08_11e00, 8.86_85e-01, 2.43_60e-01]				)

  elif swiftformer_name == "swiftformer_s":
    return torch.tensor([3.96_36e-01, 2.34_78e-01, -1.69_63e00, -1.73_81e00, -8.63_37e-01]				)

  elif swiftformer_name == "swiftformer_l1":
    return torch.tensor([-4.27_68e-01, -4.74_29e-01, -1.08_97e00, -1.02_48e00, 3.55_23e-02]				)

  elif swiftformer_name == "swiftformer_l3":
    return torch.tensor([-2.53_30e-01, 2.42_11e-01, -6.01_85e-01, -8.27_89e-01, -6.04_46e-02]				)

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  _a  :       Any             =	dct.pop(UpperCamelCase__				)
  _a  :       Dict             =	val

def 			lowerCAmelCase__     (     UpperCamelCase__				):



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

@torch.no_grad()
def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  _a  :       Tuple             =	SwiftFormerConfig()

  # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
  _a  :       Optional[int]             =	1_0_0_0
  _a  :       Optional[Any]             =	"""huggingface/label-files"""
  _a  :       Optional[Any]             =	"""imagenet-1k-id2label.json"""
  _a  :       List[str]             =	json.load(open(hf_hub_download(UpperCamelCase__     ,   UpperCamelCase__     ,   repo_type="""dataset"""				)     ,   """r"""				)				)
  _a  :       Optional[Any]             =	{int(UpperCamelCase__				): v for k, v in idalabel.items()}
  _a  :       Dict             =	idalabel
  _a  :       Optional[int]             =	{v: k for k, v in idalabel.items()}

  # size of the architecture
  if swiftformer_name == "swiftformer_xs":
    _a  :       Any             =	[3, 3, 6, 4]
    _a  :       int             =	[4_8, 5_6, 1_1_2, 2_2_0]

  elif swiftformer_name == "swiftformer_s":
    _a  :       Any             =	[3, 3, 9, 6]
    _a  :       List[str]             =	[4_8, 6_4, 1_6_8, 2_2_4]

  elif swiftformer_name == "swiftformer_l1":
    _a  :       List[Any]             =	[4, 3, 1_0, 5]
    _a  :       Optional[int]             =	[4_8, 9_6, 1_9_2, 3_8_4]

  elif swiftformer_name == "swiftformer_l3":
    _a  :       List[Any]             =	[4, 4, 1_2, 6]
    _a  :       Optional[Any]             =	[6_4, 1_2_8, 3_2_0, 5_1_2]

  # 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(UpperCamelCase__     ,   map_location="""cpu"""     ,   check_hash=UpperCamelCase__				)
    else:
      _a  :       Dict             =	torch.load(UpperCamelCase__     ,   map_location="""cpu"""				)
  _a  :       int             =	checkpoint

  _a  :       Optional[Any]             =	create_rename_keys(UpperCamelCase__				)
  for rename_key_src, rename_key_dest in rename_keys:
    rename_key(UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				)

  # load HuggingFace model
  _a  :       Any             =	SwiftFormerForImageClassification(UpperCamelCase__				).eval()
  hf_model.load_state_dict(UpperCamelCase__				)

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

  # compare outputs from both models
  _a  :       Dict             =	get_expected_output(UpperCamelCase__				)
  _a  :       int             =	hf_model(inputs["""pixel_values"""]				).logits

  assert hf_logits.shape == torch.Size([1, 1_0_0_0]				)
  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__":
 _snake_case          =   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.')

 _snake_case          =   parser.parse_args()
 convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)

