





from __future__ import annotations

import unittest

from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers.models.distilbert.modeling_tf_distilbert import (
	    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
	    TFDistilBertForMaskedLM,
	    TFDistilBertForMultipleChoice,
	    TFDistilBertForQuestionAnswering,
	    TFDistilBertForSequenceClassification,
	    TFDistilBertForTokenClassification,
	    TFDistilBertModel,
	)




class   UpperCamelCase_       :


							def __init__(							self    :Tuple							,				__A    :Any							,				)   ->   Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      parent
									SCREAMING_SNAKE_CASE__			=      13
									SCREAMING_SNAKE_CASE__			=      7
									SCREAMING_SNAKE_CASE__			=      True
									SCREAMING_SNAKE_CASE__			=      True
									SCREAMING_SNAKE_CASE__			=      False
									SCREAMING_SNAKE_CASE__			=      True
									SCREAMING_SNAKE_CASE__			=      99
									SCREAMING_SNAKE_CASE__			=      32
									SCREAMING_SNAKE_CASE__			=      2
									SCREAMING_SNAKE_CASE__			=      4
									SCREAMING_SNAKE_CASE__			=      37
									SCREAMING_SNAKE_CASE__			=      """gelu"""
									SCREAMING_SNAKE_CASE__			=      0.1
									SCREAMING_SNAKE_CASE__			=      0.1
									SCREAMING_SNAKE_CASE__			=      512
									SCREAMING_SNAKE_CASE__			=      16
									SCREAMING_SNAKE_CASE__			=      2
									SCREAMING_SNAKE_CASE__			=      0.0_2
									SCREAMING_SNAKE_CASE__			=      3
									SCREAMING_SNAKE_CASE__			=      4
									SCREAMING_SNAKE_CASE__			=      None


							def      _snake_case  (							self    :Optional[Any]		)   ->   str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      ids_tensor([self.batch_size, self.seq_length]							,				self.vocab_size		)

									SCREAMING_SNAKE_CASE__			=      None
									if self.use_input_mask:
											SCREAMING_SNAKE_CASE__			=      random_attention_mask([self.batch_size, self.seq_length]		)

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

									SCREAMING_SNAKE_CASE__			=      DistilBertConfig(
									    vocab_size=self.vocab_size							,				dim=self.hidden_size							,				n_layers=self.num_hidden_layers							,				n_heads=self.num_attention_heads							,				hidden_dim=self.intermediate_size							,				hidden_act=self.hidden_act							,				dropout=self.hidden_dropout_prob							,				attention_dropout=self.attention_probs_dropout_prob							,				max_position_embeddings=self.max_position_embeddings							,				initializer_range=self.initializer_range							,				)

									return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels


							def      _snake_case  (							self    :List[str]							,				__A    :List[Any]							,				__A    :Dict							,				__A    :int							,				__A    :Optional[Any]							,				__A    :Any							,				__A    :List[str]		)   ->   List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      TFDistilBertModel(config=__A		)
									SCREAMING_SNAKE_CASE__			=      {"""input_ids""": input_ids, """attention_mask""": input_mask}

									SCREAMING_SNAKE_CASE__			=      model(__A		)

									SCREAMING_SNAKE_CASE__			=      [input_ids, input_mask]

									SCREAMING_SNAKE_CASE__			=      model(__A		)

									self.parent.assertEqual(result.last_hidden_state.shape							,				(self.batch_size, self.seq_length, self.hidden_size)		)


							def      _snake_case  (							self    :Tuple							,				__A    :Optional[int]							,				__A    :List[str]							,				__A    :Union[str, Any]							,				__A    :Any							,				__A    :List[Any]							,				__A    :Tuple		)   ->   str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      TFDistilBertForMaskedLM(config=__A		)
									SCREAMING_SNAKE_CASE__			=      {"""input_ids""": input_ids, """attention_mask""": input_mask}
									SCREAMING_SNAKE_CASE__			=      model(__A		)
									self.parent.assertEqual(result.logits.shape							,				(self.batch_size, self.seq_length, self.vocab_size)		)


							def      _snake_case  (							self    :Tuple							,				__A    :int							,				__A    :str							,				__A    :Union[str, Any]							,				__A    :Optional[Any]							,				__A    :Any							,				__A    :List[str]		)   ->   Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      TFDistilBertForQuestionAnswering(config=__A		)
									SCREAMING_SNAKE_CASE__			=      {
									    """input_ids""": input_ids,
									    """attention_mask""": input_mask,
									}
									SCREAMING_SNAKE_CASE__			=      model(__A		)
									self.parent.assertEqual(result.start_logits.shape							,				(self.batch_size, self.seq_length)		)
									self.parent.assertEqual(result.end_logits.shape							,				(self.batch_size, self.seq_length)		)


							def      _snake_case  (							self    :Dict							,				__A    :Optional[Any]							,				__A    :Optional[Any]							,				__A    :Dict							,				__A    :Optional[int]							,				__A    :List[Any]							,				__A    :Tuple		)   ->   Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.num_labels
									SCREAMING_SNAKE_CASE__			=      TFDistilBertForSequenceClassification(__A		)
									SCREAMING_SNAKE_CASE__			=      {"""input_ids""": input_ids, """attention_mask""": input_mask}
									SCREAMING_SNAKE_CASE__			=      model(__A		)
									self.parent.assertEqual(result.logits.shape							,				(self.batch_size, self.num_labels)		)


							def      _snake_case  (							self    :Union[str, Any]							,				__A    :List[str]							,				__A    :Any							,				__A    :Optional[int]							,				__A    :int							,				__A    :str							,				__A    :Union[str, Any]		)   ->   List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.num_choices
									SCREAMING_SNAKE_CASE__			=      TFDistilBertForMultipleChoice(__A		)
									SCREAMING_SNAKE_CASE__			=      tf.tile(tf.expand_dims(__A							,				1		)							,				(1, self.num_choices, 1)		)
									SCREAMING_SNAKE_CASE__			=      tf.tile(tf.expand_dims(__A							,				1		)							,				(1, self.num_choices, 1)		)
									SCREAMING_SNAKE_CASE__			=      {
									    """input_ids""": multiple_choice_inputs_ids,
									    """attention_mask""": multiple_choice_input_mask,
									}
									SCREAMING_SNAKE_CASE__			=      model(__A		)
									self.parent.assertEqual(result.logits.shape							,				(self.batch_size, self.num_choices)		)


							def      _snake_case  (							self    :List[Any]							,				__A    :Any							,				__A    :Any							,				__A    :int							,				__A    :List[Any]							,				__A    :str							,				__A    :Dict		)   ->   str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.num_labels
									SCREAMING_SNAKE_CASE__			=      TFDistilBertForTokenClassification(__A		)
									SCREAMING_SNAKE_CASE__			=      {"""input_ids""": input_ids, """attention_mask""": input_mask}
									SCREAMING_SNAKE_CASE__			=      model(__A		)
									self.parent.assertEqual(result.logits.shape							,				(self.batch_size, self.seq_length, self.num_labels)		)


							def      _snake_case  (							self    :Optional[int]		)   ->   List[str]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.prepare_config_and_inputs()
									((SCREAMING_SNAKE_CASE__)	,						(SCREAMING_SNAKE_CASE__)	,						(SCREAMING_SNAKE_CASE__)	,						(SCREAMING_SNAKE_CASE__)	,						(SCREAMING_SNAKE_CASE__)	,						(SCREAMING_SNAKE_CASE__))			=      config_and_inputs
									SCREAMING_SNAKE_CASE__			=      {"""input_ids""": input_ids, """attention_mask""": input_mask}
									return config, inputs_dict




@require_tf
class   UpperCamelCase_       (     UpperCamelCase__				,  UpperCamelCase__				,  unittest.TestCase    ):
							lowerCamelCase_           =					(
							    (
							        TFDistilBertModel,
							        TFDistilBertForMaskedLM,
							        TFDistilBertForQuestionAnswering,
							        TFDistilBertForSequenceClassification,
							        TFDistilBertForTokenClassification,
							        TFDistilBertForMultipleChoice,
							    )
							    if is_tf_available()
							    else None
							)
							lowerCamelCase_           =					(
							    {
							        "feature-extraction": TFDistilBertModel,
							        "fill-mask": TFDistilBertForMaskedLM,
							        "question-answering": TFDistilBertForQuestionAnswering,
							        "text-classification": TFDistilBertForSequenceClassification,
							        "token-classification": TFDistilBertForTokenClassification,
							        "zero-shot": TFDistilBertForSequenceClassification,
							    }
							    if is_tf_available()
							    else {}
							)
							lowerCamelCase_           =					False
							lowerCamelCase_           =					False


							def      _snake_case  (							self    :Tuple		)   ->   Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      TFDistilBertModelTester(self		)
									SCREAMING_SNAKE_CASE__			=      ConfigTester(self							,				config_class=__A							,				dim=37		)


							def      _snake_case  (							self    :List[str]		)   ->   Optional[int]:



									"""simple docstring"""



									self.config_tester.run_common_tests()


							def      _snake_case  (							self    :Optional[Any]		)   ->   Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_distilbert_model(*__A		)


							def      _snake_case  (							self    :List[str]		)   ->   str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_distilbert_for_masked_lm(*__A		)


							def      _snake_case  (							self    :List[Any]		)   ->   Dict:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_distilbert_for_question_answering(*__A		)


							def      _snake_case  (							self    :List[str]		)   ->   Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_distilbert_for_sequence_classification(*__A		)


							def      _snake_case  (							self    :Dict		)   ->   str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_distilbert_for_multiple_choice(*__A		)


							def      _snake_case  (							self    :str		)   ->   int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_distilbert_for_token_classification(*__A		)


							@slow
							def      _snake_case  (							self    :List[Any]		)   ->   Dict:



									"""simple docstring"""



									for model_name in list(TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]		):
											SCREAMING_SNAKE_CASE__			=      TFDistilBertModel.from_pretrained(__A		)
											self.assertIsNotNone(__A		)




@require_tf
class   UpperCamelCase_       (     unittest.TestCase    ):


							@slow
							def      _snake_case  (							self    :List[str]		)   ->   Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      TFDistilBertModel.from_pretrained("""distilbert-base-uncased"""		)
									SCREAMING_SNAKE_CASE__			=      tf.constant([[0, 1, 2, 3, 4, 5]]		)
									SCREAMING_SNAKE_CASE__			=      model(__A		)[0]

									SCREAMING_SNAKE_CASE__			=      [1, 6, 768]
									self.assertEqual(output.shape							,				__A		)

									SCREAMING_SNAKE_CASE__			=      tf.constant(
									    [
									        [
									            [0.1_9_2_6_1_8_8_5, -0.1_3_7_3_2_9_5_5, 0.4_1_1_9_7_9_9],
									            [0.2_2_1_5_0_1_5_6, -0.0_7_4_2_2_6_6_1, 0.3_9_0_3_7_2_0_4],
									            [0.2_2_7_5_6_0_1_8, -0.0_8_9_6_4_1_4, 0.3_7_0_1_4_6_7],
									        ]
									    ]		)
									tf.debugging.assert_near(output[:, :3, :3]							,				__A							,				atol=1E-4		)






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()
_lowerCamelCase   				=			logging.get_logger(__name__)






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Union[str, Any]					,   UpperCamelCase__:							List[Any]					,   UpperCamelCase__:							Optional[Any]					,   UpperCamelCase__:							Optional[Any]    ):
		SCREAMING_SNAKE_CASE__			=      original_name.split("""."""    )[0]
		SCREAMING_SNAKE_CASE__			=      key.split("""."""    )
		SCREAMING_SNAKE_CASE__			=      int(key_list[key_list.index(UpperCamelCase__    ) - 2]    )
		SCREAMING_SNAKE_CASE__			=      int(key_list[key_list.index(UpperCamelCase__    ) - 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 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							int    ):
		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(UpperCamelCase__					,   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(UpperCamelCase__					,   UpperCamelCase__					,   """mlp.fc1"""					,   """output.conv1"""    )
				if "mlp.fc2" in key:
						SCREAMING_SNAKE_CASE__			=      replace_key_with_offset(UpperCamelCase__					,   UpperCamelCase__					,   """mlp.fc2"""					,   """output.conv2"""    )
				if "norm1" in key:
						SCREAMING_SNAKE_CASE__			=      replace_key_with_offset(UpperCamelCase__					,   UpperCamelCase__					,   """norm1"""					,   """before_norm"""    )
				if "norm2" in key:
						SCREAMING_SNAKE_CASE__			=      replace_key_with_offset(UpperCamelCase__					,   UpperCamelCase__					,   """norm2"""					,   """after_norm"""    )
				if "layer_scale_1" in key:
						SCREAMING_SNAKE_CASE__			=      replace_key_with_offset(UpperCamelCase__					,   UpperCamelCase__					,   """layer_scale_1"""					,   """layer_scale_1"""    )
				if "layer_scale_2" in key:
						SCREAMING_SNAKE_CASE__			=      replace_key_with_offset(UpperCamelCase__					,   UpperCamelCase__					,   """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 				SCREAMING_SNAKE_CASE__							(       ):
		SCREAMING_SNAKE_CASE__			=      """http://images.cocodataset.org/val2017/000000039769.jpg"""
		SCREAMING_SNAKE_CASE__			=      Image.open(requests.get(UpperCamelCase__					,   stream=UpperCamelCase__    ).raw    )

		return image






@torch.no_grad()
def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							List[str]					,   UpperCamelCase__:							Optional[int]					,   UpperCamelCase__:							Any    ):
		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__			=      1_000
		SCREAMING_SNAKE_CASE__			=      """imagenet-1k-id2label.json"""
		SCREAMING_SNAKE_CASE__			=      (1, 1_000)

		# set config attributes
		SCREAMING_SNAKE_CASE__			=      json.load(open(hf_hub_download(UpperCamelCase__					,   UpperCamelCase__					,   repo_type="""dataset"""    )					,   """r"""    )    )
		SCREAMING_SNAKE_CASE__			=      {int(UpperCamelCase__    ): 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, 128, 320, 512]
				SCREAMING_SNAKE_CASE__			=      4.0
				SCREAMING_SNAKE_CASE__			=      0.9
		elif size == "s24":
				SCREAMING_SNAKE_CASE__			=      [4, 4, 12, 4]
				SCREAMING_SNAKE_CASE__			=      [64, 128, 320, 512]
				SCREAMING_SNAKE_CASE__			=      4.0
				SCREAMING_SNAKE_CASE__			=      0.9
		elif size == "s36":
				SCREAMING_SNAKE_CASE__			=      [6, 6, 18, 6]
				SCREAMING_SNAKE_CASE__			=      [64, 128, 320, 512]
				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, 192, 384, 768]
				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, 192, 384, 768]
				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=UpperCamelCase__    )

		# Prepare image
		SCREAMING_SNAKE_CASE__			=      prepare_img()
		SCREAMING_SNAKE_CASE__			=      image_processor(images=UpperCamelCase__					,   return_tensors="""pt"""    ).pixel_values

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

		# load original state dict
		SCREAMING_SNAKE_CASE__			=      torch.load(UpperCamelCase__					,   map_location=torch.device("""cpu"""    )    )

		# rename keys
		SCREAMING_SNAKE_CASE__			=      rename_keys(UpperCamelCase__    )

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

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

		# forward pass
		SCREAMING_SNAKE_CASE__			=      model(UpperCamelCase__    )
		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]					,   UpperCamelCase__					,   atol=1e-2    )

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


if __name__ == "__main__":
	_lowerCamelCase   				=			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.'
	)
	_lowerCamelCase   				=			parser.parse_args()
	convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)