'''simple docstring'''






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





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Any   ,		__snake_case							:			List[Any]   ,		__snake_case							:			Optional[Any]   ,		__snake_case							:			Tuple				):
     _A    		=  original_name.split('.'				)[0]
     _A    		=  key.split('.'				)
     _A    		=  int(key_list[key_list.index(lowerCamelCase_				) - 2]				)
     _A    		=  int(key_list[key_list.index(lowerCamelCase_				) - 1]				)
     _A    		=  orig_block_num - offset

     _A    		=  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		(      __snake_case							:			Union[str, Any]				):
     _A    		=  OrderedDict()
     _A    		=  0, 0
     for key, value in state_dict.items():
          if key.startswith('network'				):
               _A    		=  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
               _A    		=  key[: key.find('proj'				)]
               _A    		=  key.replace(lowerCamelCase_   ,		F'patch_embeddings.{total_embed_found}.'				)
               _A    		=  key.replace('proj'   ,		'projection'				)
               if key.endswith('bias'				):
                    total_embed_found += 1
          if "patch_embeddings" in key:
               _A    		=  """poolformer.encoder.""" + key
          if "mlp.fc1" in key:
               _A    		=  replace_key_with_offset(lowerCamelCase_   ,		lowerCamelCase_   ,		'mlp.fc1'   ,		'output.conv1'				)
          if "mlp.fc2" in key:
               _A    		=  replace_key_with_offset(lowerCamelCase_   ,		lowerCamelCase_   ,		'mlp.fc2'   ,		'output.conv2'				)
          if "norm1" in key:
               _A    		=  replace_key_with_offset(lowerCamelCase_   ,		lowerCamelCase_   ,		'norm1'   ,		'before_norm'				)
          if "norm2" in key:
               _A    		=  replace_key_with_offset(lowerCamelCase_   ,		lowerCamelCase_   ,		'norm2'   ,		'after_norm'				)
          if "layer_scale_1" in key:
               _A    		=  replace_key_with_offset(lowerCamelCase_   ,		lowerCamelCase_   ,		'layer_scale_1'   ,		'layer_scale_1'				)
          if "layer_scale_2" in key:
               _A    		=  replace_key_with_offset(lowerCamelCase_   ,		lowerCamelCase_   ,		'layer_scale_2'   ,		'layer_scale_2'				)
          if "head" in key:
               _A    		=  key.replace('head'   ,		'classifier'				)
          _A    		=  value
     return new_state_dict





def  _SCREAMING_SNAKE_CASE		(      ):
     _A    		=  """http://images.cocodataset.org/val2017/000000039769.jpg"""
     _A    		=  Image.open(requests.get(lowerCamelCase_   ,		stream=lowerCamelCase_				).raw				)

     return image





@torch.no_grad()
def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Tuple   ,		__snake_case							:			Optional[int]   ,		__snake_case							:			Any				):
     _A    		=  PoolFormerConfig()

     # set attributes based on model_name
     _A    		=  """huggingface/label-files"""
     _A    		=  model_name[-3:]
     _A    		=  1_0_0_0
     _A    		=  """imagenet-1k-id2label.json"""
     _A    		=  (1, 1_0_0_0)

     # set config attributes
     _A    		=  json.load(open(hf_hub_download(lowerCamelCase_   ,		lowerCamelCase_   ,		repo_type='dataset'				)   ,		'r'				)				)
     _A    		=  {int(lowerCamelCase_				): v for k, v in idalabel.items()}
     _A    		=  idalabel
     _A    		=  {v: k for k, v in idalabel.items()}
     if size == "s12":
          _A    		=  [2, 2, 6, 2]
          _A    		=  [6_4, 1_2_8, 3_2_0, 5_1_2]
          _A    		=  4.0
          _A    		=  0.9
     elif size == "s24":
          _A    		=  [4, 4, 1_2, 4]
          _A    		=  [6_4, 1_2_8, 3_2_0, 5_1_2]
          _A    		=  4.0
          _A    		=  0.9
     elif size == "s36":
          _A    		=  [6, 6, 1_8, 6]
          _A    		=  [6_4, 1_2_8, 3_2_0, 5_1_2]
          _A    		=  4.0
          _A    		=  1e-6
          _A    		=  0.9
     elif size == "m36":
          _A    		=  [6, 6, 1_8, 6]
          _A    		=  [9_6, 1_9_2, 3_8_4, 7_6_8]
          _A    		=  4.0
          _A    		=  1e-6
          _A    		=  0.95
     elif size == "m48":
          _A    		=  [8, 8, 2_4, 8]
          _A    		=  [9_6, 1_9_2, 3_8_4, 7_6_8]
          _A    		=  4.0
          _A    		=  1e-6
          _A    		=  0.95
     else:
          raise ValueError(F'Size {size} not supported'				)

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

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

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

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

     # rename keys
     _A    		=  rename_keys(lowerCamelCase_				)

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

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

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

     # define expected logit slices for different models
     if size == "s12":
          _A    		=  torch.tensor([-0.30_45, -0.67_58, -0.48_69]				)
     elif size == "s24":
          _A    		=  torch.tensor([0.44_02, -0.13_74, -0.80_45]				)
     elif size == "s36":
          _A    		=  torch.tensor([-0.60_80, -0.51_33, -0.58_98]				)
     elif size == "m36":
          _A    		=  torch.tensor([0.39_52, 0.22_63, -1.26_68]				)
     elif size == "m48":
          _A    		=  torch.tensor([0.11_67, -0.06_56, -0.34_23]				)
     else:
          raise ValueError(F'Size {size} not supported'				)

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

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


if __name__ == "__main__":
   _UpperCAmelCase    :     Tuple         =       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.'''
   )
   _UpperCAmelCase    :     Optional[Any]         =       parser.parse_args()
   convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)










'''simple docstring'''




import shutil
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_tf_cross_test,
    require_tf,
    require_torch,
    require_torchvision,
    require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available


if is_vision_available():
							from PIL import Image

							from transformers import AutoProcessor, SamImageProcessor, SamProcessor

if is_torch_available():
							import torch

if is_tf_available():
							import tensorflow as tf




@require_vision
@require_torchvision
class      UpperCamelCase__					(					unittest.TestCase			):




					"""simple docstring"""


					def lowerCamelCase_			(						self				:			Any						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			tempfile.mkdtemp()
							SCREAMING_SNAKE_CASE :				List[Any]		=			SamImageProcessor()
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			SamProcessor(lowerCamelCase_						)
							processor.save_pretrained(self.tmpdirname						)


					def lowerCamelCase_			(						self				:			Dict      ,						**lowerCamelCase_				:			List[str]						):

							'''simple docstring'''



							return AutoProcessor.from_pretrained(self.tmpdirname      ,						**lowerCamelCase_						).image_processor


					def lowerCamelCase_			(						self				:			Dict						):

							'''simple docstring'''



							shutil.rmtree(self.tmpdirname						)


					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Optional[int]		=			[np.random.randint(2_55      ,						size=(3, 30, 4_00)      ,						dtype=np.uinta						)]

							SCREAMING_SNAKE_CASE :				Optional[int]		=			[Image.fromarray(np.moveaxis(lowerCamelCase_      ,						0      ,						-1						)						) for x in image_inputs]

							return image_inputs


					def lowerCamelCase_			(						self				:			Any						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[Any]		=			SamProcessor(image_processor=self.get_image_processor()						)
							processor.save_pretrained(self.tmpdirname						)

							SCREAMING_SNAKE_CASE :				int		=			self.get_image_processor(do_normalize=lowerCamelCase_      ,						padding_value=1.0						)

							SCREAMING_SNAKE_CASE :				Tuple		=			SamProcessor.from_pretrained(self.tmpdirname      ,						do_normalize=lowerCamelCase_      ,						padding_value=1.0						)

							self.assertEqual(processor.image_processor.to_json_string()      ,						image_processor_add_kwargs.to_json_string()						)
							self.assertIsInstance(processor.image_processor      ,						lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			Tuple						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Optional[Any]		=			self.get_image_processor()

							SCREAMING_SNAKE_CASE :				List[Any]		=			SamProcessor(image_processor=lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Optional[int]		=			self.prepare_image_inputs()

							SCREAMING_SNAKE_CASE :				List[str]		=			image_processor(lowerCamelCase_      ,						return_tensors="""np"""						)
							SCREAMING_SNAKE_CASE :				Dict		=			processor(images=lowerCamelCase_      ,						return_tensors="""np"""						)

							input_feat_extract.pop("""original_sizes"""						)  # pop original_sizes as it is popped in the processor
							input_feat_extract.pop("""reshaped_input_sizes"""						)  # pop original_sizes as it is popped in the processor

							for key in input_feat_extract.keys():
									self.assertAlmostEqual(input_feat_extract[key].sum()      ,						input_processor[key].sum()      ,						delta=1e-2						)



					@require_torch
					def lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[str]		=			self.get_image_processor()

							SCREAMING_SNAKE_CASE :				Optional[Any]		=			SamProcessor(image_processor=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Any		=			[torch.ones((1, 3, 5, 5)						)]

							SCREAMING_SNAKE_CASE :				int		=			[[17_64, 26_46]]

							SCREAMING_SNAKE_CASE :				Any		=			[[6_83, 10_24]]
							SCREAMING_SNAKE_CASE :				str		=			processor.post_process_masks(lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_						)
							self.assertEqual(masks[0].shape      ,						(1, 3, 17_64, 26_46)						)

							SCREAMING_SNAKE_CASE :				str		=			processor.post_process_masks(
							    lowerCamelCase_      ,						torch.tensor(lowerCamelCase_						)      ,						torch.tensor(lowerCamelCase_						)						)
							self.assertEqual(masks[0].shape      ,						(1, 3, 17_64, 26_46)						)

							# should also work with np
							SCREAMING_SNAKE_CASE :				Optional[int]		=			[np.ones((1, 3, 5, 5)						)]
							SCREAMING_SNAKE_CASE :				Optional[int]		=			processor.post_process_masks(lowerCamelCase_      ,						np.array(lowerCamelCase_						)      ,						np.array(lowerCamelCase_						)						)

							self.assertEqual(masks[0].shape      ,						(1, 3, 17_64, 26_46)						)

							SCREAMING_SNAKE_CASE :				Tuple		=			[[1, 0], [0, 1]]
							with self.assertRaises(lowerCamelCase_						):
									SCREAMING_SNAKE_CASE :				Tuple		=			processor.post_process_masks(lowerCamelCase_      ,						np.array(lowerCamelCase_						)      ,						np.array(lowerCamelCase_						)						)







@require_vision
@require_tf
class      UpperCamelCase__					(					unittest.TestCase			):




					"""simple docstring"""


					def lowerCamelCase_			(						self				:			Tuple						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			tempfile.mkdtemp()
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			SamImageProcessor()
							SCREAMING_SNAKE_CASE :				Optional[int]		=			SamProcessor(lowerCamelCase_						)
							processor.save_pretrained(self.tmpdirname						)


					def lowerCamelCase_			(						self				:			Any      ,						**lowerCamelCase_				:			str						):

							'''simple docstring'''



							return AutoProcessor.from_pretrained(self.tmpdirname      ,						**lowerCamelCase_						).image_processor


					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



							shutil.rmtree(self.tmpdirname						)


					def lowerCamelCase_			(						self				:			Union[str, Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Any		=			[np.random.randint(2_55      ,						size=(3, 30, 4_00)      ,						dtype=np.uinta						)]

							SCREAMING_SNAKE_CASE :				str		=			[Image.fromarray(np.moveaxis(lowerCamelCase_      ,						0      ,						-1						)						) for x in image_inputs]

							return image_inputs


					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			SamProcessor(image_processor=self.get_image_processor()						)
							processor.save_pretrained(self.tmpdirname						)

							SCREAMING_SNAKE_CASE :				Optional[Any]		=			self.get_image_processor(do_normalize=lowerCamelCase_      ,						padding_value=1.0						)

							SCREAMING_SNAKE_CASE :				List[Any]		=			SamProcessor.from_pretrained(self.tmpdirname      ,						do_normalize=lowerCamelCase_      ,						padding_value=1.0						)

							self.assertEqual(processor.image_processor.to_json_string()      ,						image_processor_add_kwargs.to_json_string()						)
							self.assertIsInstance(processor.image_processor      ,						lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			Optional[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				str		=			self.get_image_processor()

							SCREAMING_SNAKE_CASE :				Dict		=			SamProcessor(image_processor=lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				int		=			self.prepare_image_inputs()

							SCREAMING_SNAKE_CASE :				Optional[Any]		=			image_processor(lowerCamelCase_      ,						return_tensors="""np"""						)
							SCREAMING_SNAKE_CASE :				List[Any]		=			processor(images=lowerCamelCase_      ,						return_tensors="""np"""						)

							input_feat_extract.pop("""original_sizes"""						)  # pop original_sizes as it is popped in the processor
							input_feat_extract.pop("""reshaped_input_sizes"""						)  # pop reshaped_input_sizes as it is popped in the processor

							for key in input_feat_extract.keys():
									self.assertAlmostEqual(input_feat_extract[key].sum()      ,						input_processor[key].sum()      ,						delta=1e-2						)



					@require_tf
					def lowerCamelCase_			(						self				:			Dict						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Dict		=			self.get_image_processor()

							SCREAMING_SNAKE_CASE :				Tuple		=			SamProcessor(image_processor=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			[tf.ones((1, 3, 5, 5)						)]

							SCREAMING_SNAKE_CASE :				Dict		=			[[17_64, 26_46]]

							SCREAMING_SNAKE_CASE :				Any		=			[[6_83, 10_24]]
							SCREAMING_SNAKE_CASE :				str		=			processor.post_process_masks(lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_      ,						return_tensors="""tf"""						)
							self.assertEqual(masks[0].shape      ,						(1, 3, 17_64, 26_46)						)

							SCREAMING_SNAKE_CASE :				Optional[int]		=			processor.post_process_masks(
							    lowerCamelCase_      ,						tf.convert_to_tensor(lowerCamelCase_						)      ,						tf.convert_to_tensor(lowerCamelCase_						)      ,						return_tensors="""tf"""      ,						)
							self.assertEqual(masks[0].shape      ,						(1, 3, 17_64, 26_46)						)

							# should also work with np
							SCREAMING_SNAKE_CASE :				Optional[int]		=			[np.ones((1, 3, 5, 5)						)]
							SCREAMING_SNAKE_CASE :				List[str]		=			processor.post_process_masks(
							    lowerCamelCase_      ,						np.array(lowerCamelCase_						)      ,						np.array(lowerCamelCase_						)      ,						return_tensors="""tf"""						)

							self.assertEqual(masks[0].shape      ,						(1, 3, 17_64, 26_46)						)

							SCREAMING_SNAKE_CASE :				str		=			[[1, 0], [0, 1]]
							with self.assertRaises(tf.errors.InvalidArgumentError						):
									SCREAMING_SNAKE_CASE :				Tuple		=			processor.post_process_masks(
									    lowerCamelCase_      ,						np.array(lowerCamelCase_						)      ,						np.array(lowerCamelCase_						)      ,						return_tensors="""tf"""						)







@require_vision
@require_torchvision
class      UpperCamelCase__					(					unittest.TestCase			):




					"""simple docstring"""


					def lowerCamelCase_			(						self				:			Tuple						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				int		=			tempfile.mkdtemp()
							SCREAMING_SNAKE_CASE :				int		=			SamImageProcessor()
							SCREAMING_SNAKE_CASE :				str		=			SamProcessor(lowerCamelCase_						)
							processor.save_pretrained(self.tmpdirname						)


					def lowerCamelCase_			(						self				:			Tuple      ,						**lowerCamelCase_				:			List[Any]						):

							'''simple docstring'''



							return AutoProcessor.from_pretrained(self.tmpdirname      ,						**lowerCamelCase_						).image_processor


					def lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							shutil.rmtree(self.tmpdirname						)


					def lowerCamelCase_			(						self				:			Dict						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Optional[int]		=			[np.random.randint(2_55      ,						size=(3, 30, 4_00)      ,						dtype=np.uinta						)]

							SCREAMING_SNAKE_CASE :				Dict		=			[Image.fromarray(np.moveaxis(lowerCamelCase_      ,						0      ,						-1						)						) for x in image_inputs]

							return image_inputs


					@is_pt_tf_cross_test
					def lowerCamelCase_			(						self				:			Optional[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				str		=			self.get_image_processor()

							SCREAMING_SNAKE_CASE :				Dict		=			SamProcessor(image_processor=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Any		=			np.random.randint(0      ,						2      ,						size=(1, 3, 5, 5)						).astype(np.floataa						)
							SCREAMING_SNAKE_CASE :				str		=			[tf.convert_to_tensor(lowerCamelCase_						)]
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			[torch.tensor(lowerCamelCase_						)]

							SCREAMING_SNAKE_CASE :				Tuple		=			[[17_64, 26_46]]

							SCREAMING_SNAKE_CASE :				Optional[Any]		=			[[6_83, 10_24]]
							SCREAMING_SNAKE_CASE :				Tuple		=			processor.post_process_masks(
							    lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_      ,						return_tensors="""tf"""						)
							SCREAMING_SNAKE_CASE :				Any		=			processor.post_process_masks(
							    lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_      ,						return_tensors="""pt"""						)

							self.assertTrue(np.all(tf_masks[0].numpy() == pt_masks[0].numpy()						)						)



					@is_pt_tf_cross_test
					def lowerCamelCase_			(						self				:			Tuple						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			self.get_image_processor()

							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			SamProcessor(image_processor=lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Any		=			self.prepare_image_inputs()

							SCREAMING_SNAKE_CASE :				int		=			image_processor(lowerCamelCase_      ,						return_tensors="""pt"""						)["""pixel_values"""].numpy()
							SCREAMING_SNAKE_CASE :				Tuple		=			processor(images=lowerCamelCase_      ,						return_tensors="""pt"""						)["""pixel_values"""].numpy()

							SCREAMING_SNAKE_CASE :				Dict		=			image_processor(lowerCamelCase_      ,						return_tensors="""tf"""						)["""pixel_values"""].numpy()
							SCREAMING_SNAKE_CASE :				Dict		=			processor(images=lowerCamelCase_      ,						return_tensors="""tf"""						)["""pixel_values"""].numpy()

							self.assertTrue(np.allclose(lowerCamelCase_      ,						lowerCamelCase_						)						)
							self.assertTrue(np.allclose(lowerCamelCase_      ,						lowerCamelCase_						)						)
							self.assertTrue(np.allclose(lowerCamelCase_      ,						lowerCamelCase_						)						)



