

import inspect
import unittest

import numpy as np

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

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


if is_flax_available():
       import jax

       from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel

if is_vision_available():
       from PIL import Image

       from transformers import BeitImageProcessor






class  UpperCAmelCase__	( unittest.TestCase ):






  """simple docstring"""


  def __init__(							self		:	Optional[Any]			,     __lowerCamelCase		:	List[str]			,     __lowerCamelCase		:	Any=100			,     __lowerCamelCase		:	Optional[Any]=13			,     __lowerCamelCase		:	Tuple=30			,     __lowerCamelCase		:	Optional[Any]=2			,     __lowerCamelCase		:	Dict=3			,     __lowerCamelCase		:	Optional[int]=True			,     __lowerCamelCase		:	str=True			,     __lowerCamelCase		:	int=32			,     __lowerCamelCase		:	List[Any]=5			,     __lowerCamelCase		:	Any=4			,     __lowerCamelCase		:	int=37			,     __lowerCamelCase		:	Dict="gelu"			,     __lowerCamelCase		:	str=0.1			,     __lowerCamelCase		:	Dict=0.1			,     __lowerCamelCase		:	Tuple=10			,     __lowerCamelCase		:	Optional[Any]=0.02			,     __lowerCamelCase		:	List[str]=3			,     )	->							str:
       SCREAMING_SNAKE_CASE__					=					parent
       SCREAMING_SNAKE_CASE__					=					vocab_size
       SCREAMING_SNAKE_CASE__					=					batch_size
       SCREAMING_SNAKE_CASE__					=					image_size
       SCREAMING_SNAKE_CASE__					=					patch_size
       SCREAMING_SNAKE_CASE__					=					num_channels
       SCREAMING_SNAKE_CASE__					=					is_training
       SCREAMING_SNAKE_CASE__					=					use_labels
       SCREAMING_SNAKE_CASE__					=					hidden_size
       SCREAMING_SNAKE_CASE__					=					num_hidden_layers
       SCREAMING_SNAKE_CASE__					=					num_attention_heads
       SCREAMING_SNAKE_CASE__					=					intermediate_size
       SCREAMING_SNAKE_CASE__					=					hidden_act
       SCREAMING_SNAKE_CASE__					=					hidden_dropout_prob
       SCREAMING_SNAKE_CASE__					=					attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__					=					type_sequence_label_size
       SCREAMING_SNAKE_CASE__					=					initializer_range

       # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
       SCREAMING_SNAKE_CASE__					=					(image_size // patch_size) ** 2
       SCREAMING_SNAKE_CASE__					=					num_patches + 1






  def    lowercase_	(							self		:	int      )	->							Union[str, Any]:
       SCREAMING_SNAKE_CASE__					=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

       SCREAMING_SNAKE_CASE__					=					None
       if self.use_labels:
            SCREAMING_SNAKE_CASE__					=					ids_tensor([self.batch_size]			,     self.type_sequence_label_size      )

       SCREAMING_SNAKE_CASE__					=					BeitConfig(
           vocab_size=self.vocab_size			,     image_size=self.image_size			,     patch_size=self.patch_size			,     num_channels=self.num_channels			,     hidden_size=self.hidden_size			,     num_hidden_layers=self.num_hidden_layers			,     num_attention_heads=self.num_attention_heads			,     intermediate_size=self.intermediate_size			,     hidden_act=self.hidden_act			,     hidden_dropout_prob=self.hidden_dropout_prob			,     attention_probs_dropout_prob=self.attention_probs_dropout_prob			,     is_decoder=__lowerCamelCase			,     initializer_range=self.initializer_range			,     )

       return config, pixel_values, labels






  def    lowercase_	(							self		:	Optional[Any]			,     __lowerCamelCase		:	Tuple			,     __lowerCamelCase		:	List[str]			,     __lowerCamelCase		:	List[Any]      )	->							Dict:
       SCREAMING_SNAKE_CASE__					=					FlaxBeitModel(config=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					model(__lowerCamelCase      )
       self.parent.assertEqual(result.last_hidden_state.shape			,     (self.batch_size, self.seq_length, self.hidden_size)      )






  def    lowercase_	(							self		:	Optional[int]			,     __lowerCamelCase		:	Tuple			,     __lowerCamelCase		:	Tuple			,     __lowerCamelCase		:	Optional[Any]      )	->							List[str]:
       SCREAMING_SNAKE_CASE__					=					FlaxBeitForMaskedImageModeling(config=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					model(__lowerCamelCase      )
       self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.seq_length - 1, self.vocab_size)      )






  def    lowercase_	(							self		:	Optional[Any]			,     __lowerCamelCase		:	int			,     __lowerCamelCase		:	List[str]			,     __lowerCamelCase		:	Union[str, Any]      )	->							List[str]:
       SCREAMING_SNAKE_CASE__					=					self.type_sequence_label_size
       SCREAMING_SNAKE_CASE__					=					FlaxBeitForImageClassification(config=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					model(__lowerCamelCase      )
       self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.type_sequence_label_size)      )

       # test greyscale images
       SCREAMING_SNAKE_CASE__					=					1
       SCREAMING_SNAKE_CASE__					=					FlaxBeitForImageClassification(__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
       SCREAMING_SNAKE_CASE__					=					model(__lowerCamelCase      )







  def    lowercase_	(							self		:	Dict      )	->							List[Any]:
       SCREAMING_SNAKE_CASE__					=					self.prepare_config_and_inputs()
       (
           (
           SCREAMING_SNAKE_CASE__
       ),(
           SCREAMING_SNAKE_CASE__
       ),(
           SCREAMING_SNAKE_CASE__
       ),
       )					=					config_and_inputs
       SCREAMING_SNAKE_CASE__					=					{'''pixel_values''': pixel_values}
       return config, inputs_dict






@require_flax
class  UpperCAmelCase__	( A__		,  unittest.TestCase ):






  """simple docstring"""


  a     =      (
      (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
  )






  def    lowercase_	(							self		:	Dict      )	->							None:
       SCREAMING_SNAKE_CASE__					=					FlaxBeitModelTester(self      )
       SCREAMING_SNAKE_CASE__					=					ConfigTester(self			,     config_class=__lowerCamelCase			,     has_text_modality=__lowerCamelCase			,     hidden_size=37      )






  def    lowercase_	(							self		:	Any      )	->							List[str]:
       self.config_tester.run_common_tests()






  def    lowercase_	(							self		:	Optional[int]      )	->							Union[str, Any]:
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs_for_common()

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

            SCREAMING_SNAKE_CASE__					=					['''pixel_values''']
            self.assertListEqual(arg_names[:1]			,     __lowerCamelCase      )






  def    lowercase_	(							self		:	Dict      )	->							Union[str, Any]:
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					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__					=					self._prepare_for_class(__lowerCamelCase			,     __lowerCamelCase      )
                 SCREAMING_SNAKE_CASE__					=					model_class(__lowerCamelCase      )

                 @jax.jit
                 def model_jitted(__lowerCamelCase		:	str			,     **__lowerCamelCase		:	Dict      ):
                      return model(pixel_values=__lowerCamelCase			,     **__lowerCamelCase      )

                 with self.subTest('''JIT Enabled'''      ):
                      SCREAMING_SNAKE_CASE__					=					model_jitted(**__lowerCamelCase      ).to_tuple()

                 with self.subTest('''JIT Disabled'''      ):
                      with jax.disable_jit():
                           SCREAMING_SNAKE_CASE__					=					model_jitted(**__lowerCamelCase      ).to_tuple()

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






  def    lowercase_	(							self		:	Union[str, Any]      )	->							Union[str, Any]:
       SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__lowerCamelCase      )






  def    lowercase_	(							self		:	Optional[int]      )	->							Any:
       SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*__lowerCamelCase      )






  def    lowercase_	(							self		:	Any      )	->							Optional[Any]:
       SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase      )







  @slow
  def    lowercase_	(							self		:	List[Any]      )	->							Any:
       for model_class_name in self.all_model_classes:
            SCREAMING_SNAKE_CASE__					=					model_class_name.from_pretrained('''microsoft/beit-base-patch16-224'''      )
            SCREAMING_SNAKE_CASE__					=					model(np.ones((1, 3, 224, 224)      )      )
            self.assertIsNotNone(__lowerCamelCase      )



def        UpperCAmelCase_     (		):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
     return image







@require_vision
@require_flax
class  UpperCAmelCase__	( unittest.TestCase ):






  """simple docstring"""


  @cached_property
  def    lowercase_	(							self		:	List[str]      )	->							Dict:
       return BeitImageProcessor.from_pretrained('''microsoft/beit-base-patch16-224'''      ) if is_vision_available() else None






  @slow
  def    lowercase_	(							self		:	Dict      )	->							Dict:
       SCREAMING_SNAKE_CASE__					=					FlaxBeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k'''      )

       SCREAMING_SNAKE_CASE__					=					self.default_image_processor
       SCREAMING_SNAKE_CASE__					=					prepare_img()
       SCREAMING_SNAKE_CASE__					=					image_processor(images=__lowerCamelCase			,     return_tensors='''np'''      ).pixel_values

       # prepare bool_masked_pos
       SCREAMING_SNAKE_CASE__					=					np.ones((1, 196)			,     dtype=__lowerCamelCase      )

       # forward pass
       SCREAMING_SNAKE_CASE__					=					model(pixel_values=__lowerCamelCase			,     bool_masked_pos=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					outputs.logits

       # verify the logits
       SCREAMING_SNAKE_CASE__					=					(1, 196, 8192)
       self.assertEqual(logits.shape			,     __lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					np.array(
           [[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]]      )

       self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3]			,     __lowerCamelCase			,     atol=1e-2      )      )






  @slow
  def    lowercase_	(							self		:	List[str]      )	->							Any:
       SCREAMING_SNAKE_CASE__					=					FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224'''      )

       SCREAMING_SNAKE_CASE__					=					self.default_image_processor
       SCREAMING_SNAKE_CASE__					=					prepare_img()
       SCREAMING_SNAKE_CASE__					=					image_processor(images=__lowerCamelCase			,     return_tensors='''np'''      )

       # forward pass
       SCREAMING_SNAKE_CASE__					=					model(**__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					outputs.logits

       # verify the logits
       SCREAMING_SNAKE_CASE__					=					(1, 1000)
       self.assertEqual(logits.shape			,     __lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					np.array([-1.2385, -1.0987, -1.0108]      )

       self.assertTrue(np.allclose(logits[0, :3]			,     __lowerCamelCase			,     atol=1e-4      )      )

       SCREAMING_SNAKE_CASE__					=					281
       self.assertEqual(logits.argmax(-1      ).item()			,     __lowerCamelCase      )







  @slow
  def    lowercase_	(							self		:	Any      )	->							Union[str, Any]:
       SCREAMING_SNAKE_CASE__					=					FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k'''      )

       SCREAMING_SNAKE_CASE__					=					self.default_image_processor
       SCREAMING_SNAKE_CASE__					=					prepare_img()
       SCREAMING_SNAKE_CASE__					=					image_processor(images=__lowerCamelCase			,     return_tensors='''np'''      )

       # forward pass
       SCREAMING_SNAKE_CASE__					=					model(**__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					outputs.logits

       # verify the logits
       SCREAMING_SNAKE_CASE__					=					(1, 2_1841)
       self.assertEqual(logits.shape			,     __lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					np.array([1.6881, -0.2787, 0.5901]      )

       self.assertTrue(np.allclose(logits[0, :3]			,     __lowerCamelCase			,     atol=1e-4      )      )

       SCREAMING_SNAKE_CASE__					=					2396
       self.assertEqual(logits.argmax(-1      ).item()			,     __lowerCamelCase      )





import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE  :						Dict         =    logging.get_logger(__name__)
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					original_name.split('''.'''       )[0]
     SCREAMING_SNAKE_CASE__					=					key.split('''.'''       )
     SCREAMING_SNAKE_CASE__					=					int(key_list[key_list.index(_A       ) - 2]       )
     SCREAMING_SNAKE_CASE__					=					int(key_list[key_list.index(_A       ) - 1]       )
     SCREAMING_SNAKE_CASE__					=					orig_block_num - offset

     SCREAMING_SNAKE_CASE__					=					key.replace(F'''{orig_block_num}.{layer_num}.{original_name}'''   ,	F'''block.{new_block_num}.{layer_num}.{new_name}'''       )
     return key
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					OrderedDict()
     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					0, 0
     for key, value in state_dict.items():
          if key.startswith('''network'''       ):
               SCREAMING_SNAKE_CASE__					=					key.replace('''network'''   ,	'''poolformer.encoder'''       )
          if "proj" in key:
               # Works for the first embedding as well as the internal embedding layers
               if key.endswith('''bias'''       ) and "patch_embed" not in key:
                    patch_emb_offset += 1
               SCREAMING_SNAKE_CASE__					=					key[: key.find('''proj'''       )]
               SCREAMING_SNAKE_CASE__					=					key.replace(_A   ,	F'''patch_embeddings.{total_embed_found}.'''       )
               SCREAMING_SNAKE_CASE__					=					key.replace('''proj'''   ,	'''projection'''       )
               if key.endswith('''bias'''       ):
                    total_embed_found += 1
          if "patch_embeddings" in key:
               SCREAMING_SNAKE_CASE__					=					'''poolformer.encoder.''' + key
          if "mlp.fc1" in key:
               SCREAMING_SNAKE_CASE__					=					replace_key_with_offset(_A   ,	_A   ,	'''mlp.fc1'''   ,	'''output.conv1'''       )
          if "mlp.fc2" in key:
               SCREAMING_SNAKE_CASE__					=					replace_key_with_offset(_A   ,	_A   ,	'''mlp.fc2'''   ,	'''output.conv2'''       )
          if "norm1" in key:
               SCREAMING_SNAKE_CASE__					=					replace_key_with_offset(_A   ,	_A   ,	'''norm1'''   ,	'''before_norm'''       )
          if "norm2" in key:
               SCREAMING_SNAKE_CASE__					=					replace_key_with_offset(_A   ,	_A   ,	'''norm2'''   ,	'''after_norm'''       )
          if "layer_scale_1" in key:
               SCREAMING_SNAKE_CASE__					=					replace_key_with_offset(_A   ,	_A   ,	'''layer_scale_1'''   ,	'''layer_scale_1'''       )
          if "layer_scale_2" in key:
               SCREAMING_SNAKE_CASE__					=					replace_key_with_offset(_A   ,	_A   ,	'''layer_scale_2'''   ,	'''layer_scale_2'''       )
          if "head" in key:
               SCREAMING_SNAKE_CASE__					=					key.replace('''head'''   ,	'''classifier'''       )
          SCREAMING_SNAKE_CASE__					=					value
     return new_state_dict
def        UpperCAmelCase_     (		):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
     SCREAMING_SNAKE_CASE__					=					Image.open(requests.get(_A   ,	stream=_A       ).raw       )

     return image
@torch.no_grad()
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					PoolFormerConfig()

     # set attributes based on model_name
     SCREAMING_SNAKE_CASE__					=					'''huggingface/label-files'''
     SCREAMING_SNAKE_CASE__					=					model_name[-3:]
     SCREAMING_SNAKE_CASE__					=					10_00
     SCREAMING_SNAKE_CASE__					=					'''imagenet-1k-id2label.json'''
     SCREAMING_SNAKE_CASE__					=					(1, 10_00)

     # set config attributes
     SCREAMING_SNAKE_CASE__					=					json.load(open(hf_hub_download(_A   ,	_A   ,	repo_type='''dataset'''       )   ,	'''r'''       )       )
     SCREAMING_SNAKE_CASE__					=					{int(_A       ): v for k, v in idalabel.items()}
     SCREAMING_SNAKE_CASE__					=					idalabel
     SCREAMING_SNAKE_CASE__					=					{v: k for k, v in idalabel.items()}
     if size == "s12":
          SCREAMING_SNAKE_CASE__					=					[2, 2, 6, 2]
          SCREAMING_SNAKE_CASE__					=					[64, 1_28, 3_20, 5_12]
          SCREAMING_SNAKE_CASE__					=					4.0
          SCREAMING_SNAKE_CASE__					=					0.9
     elif size == "s24":
          SCREAMING_SNAKE_CASE__					=					[4, 4, 12, 4]
          SCREAMING_SNAKE_CASE__					=					[64, 1_28, 3_20, 5_12]
          SCREAMING_SNAKE_CASE__					=					4.0
          SCREAMING_SNAKE_CASE__					=					0.9
     elif size == "s36":
          SCREAMING_SNAKE_CASE__					=					[6, 6, 18, 6]
          SCREAMING_SNAKE_CASE__					=					[64, 1_28, 3_20, 5_12]
          SCREAMING_SNAKE_CASE__					=					4.0
          SCREAMING_SNAKE_CASE__					=					1e-6
          SCREAMING_SNAKE_CASE__					=					0.9
     elif size == "m36":
          SCREAMING_SNAKE_CASE__					=					[6, 6, 18, 6]
          SCREAMING_SNAKE_CASE__					=					[96, 1_92, 3_84, 7_68]
          SCREAMING_SNAKE_CASE__					=					4.0
          SCREAMING_SNAKE_CASE__					=					1e-6
          SCREAMING_SNAKE_CASE__					=					0.9_5
     elif size == "m48":
          SCREAMING_SNAKE_CASE__					=					[8, 8, 24, 8]
          SCREAMING_SNAKE_CASE__					=					[96, 1_92, 3_84, 7_68]
          SCREAMING_SNAKE_CASE__					=					4.0
          SCREAMING_SNAKE_CASE__					=					1e-6
          SCREAMING_SNAKE_CASE__					=					0.9_5
     else:
          raise ValueError(F'''Size {size} not supported'''       )

     # load image processor
     SCREAMING_SNAKE_CASE__					=					PoolFormerImageProcessor(crop_pct=_A       )

     # Prepare image
     SCREAMING_SNAKE_CASE__					=					prepare_img()
     SCREAMING_SNAKE_CASE__					=					image_processor(images=_A   ,	return_tensors='''pt'''       ).pixel_values

     logger.info(F'''Converting model {model_name}...'''       )

     # load original state dict
     SCREAMING_SNAKE_CASE__					=					torch.load(_A   ,	map_location=torch.device('''cpu'''       )       )

     # rename keys
     SCREAMING_SNAKE_CASE__					=					rename_keys(_A       )

     # create HuggingFace model and load state dict
     SCREAMING_SNAKE_CASE__					=					PoolFormerForImageClassification(_A       )
     model.load_state_dict(_A       )
     model.eval()

     # Define image processor
     SCREAMING_SNAKE_CASE__					=					PoolFormerImageProcessor(crop_pct=_A       )
     SCREAMING_SNAKE_CASE__					=					image_processor(images=prepare_img()   ,	return_tensors='''pt'''       ).pixel_values

     # forward pass
     SCREAMING_SNAKE_CASE__					=					model(_A       )
     SCREAMING_SNAKE_CASE__					=					outputs.logits

     # define expected logit slices for different models
     if size == "s12":
          SCREAMING_SNAKE_CASE__					=					torch.tensor([-0.3_0_4_5, -0.6_7_5_8, -0.4_8_6_9]       )
     elif size == "s24":
          SCREAMING_SNAKE_CASE__					=					torch.tensor([0.4_4_0_2, -0.1_3_7_4, -0.8_0_4_5]       )
     elif size == "s36":
          SCREAMING_SNAKE_CASE__					=					torch.tensor([-0.6_0_8_0, -0.5_1_3_3, -0.5_8_9_8]       )
     elif size == "m36":
          SCREAMING_SNAKE_CASE__					=					torch.tensor([0.3_9_5_2, 0.2_2_6_3, -1.2_6_6_8]       )
     elif size == "m48":
          SCREAMING_SNAKE_CASE__					=					torch.tensor([0.1_1_6_7, -0.0_6_5_6, -0.3_4_2_3]       )
     else:
          raise ValueError(F'''Size {size} not supported'''       )

     # verify logits
     assert logits.shape == expected_shape
     assert torch.allclose(logits[0, :3]   ,	_A   ,	atol=1e-2       )

     # finally, save model and image processor
     logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''       )
     Path(_A       ).mkdir(exist_ok=_A       )
     model.save_pretrained(_A       )
     print(F'''Saving image processor to {pytorch_dump_folder_path}'''       )
     image_processor.save_pretrained(_A       )


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE  :						Union[str, Any]         =    argparse.ArgumentParser()

       parser.add_argument(
           '''--model_name''',
           default='''poolformer_s12''',
           type=str,
           help='''Name of the model you\'d like to convert.''',
       )
       parser.add_argument(
           '''--checkpoint_path''', default=None, type=str, help='''Path to the original PyTorch checkpoint (.pth file).'''
       )
       parser.add_argument(
           '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
       )
       _SCREAMING_SNAKE_CASE  :						Tuple         =    parser.parse_args()
       convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)


