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





def        a_  (   _lowerCAmelCase						:				Any     ,				_lowerCAmelCase						:				List[Any]     ,				_lowerCAmelCase						:				List[Any]     ,				_lowerCAmelCase						:				Optional[int]  ):





       '''simple docstring'''

       lowercase__    :	Tuple     					=			original_name.split('.'  )[0]
       lowercase__    :	Tuple     					=			key.split('.'  )
       lowercase__    :	Optional[Any]     					=			int(key_list[key_list.index(_A  ) - 2]  )
       lowercase__    :	int     					=			int(key_list[key_list.index(_A  ) - 1]  )
       lowercase__    :	Any     					=			orig_block_num - offset

       lowercase__    :	List[Any]     					=			key.replace(f"""{orig_block_num}.{layer_num}.{original_name}"""     ,				f"""block.{new_block_num}.{layer_num}.{new_name}"""  )
       return key





def        a_  (   _lowerCAmelCase						:				Optional[int]  ):





       '''simple docstring'''

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





def        a_  (   ):





       '''simple docstring'''

       lowercase__    :	List[Any]     					=			'http://images.cocodataset.org/val2017/000000039769.jpg'
       lowercase__    :	int     					=			Image.open(requests.get(_A     ,				stream=_A  ).raw  )

       return image





@torch.no_grad()
def        a_  (   _lowerCAmelCase						:				Dict     ,				_lowerCAmelCase						:				Any     ,				_lowerCAmelCase						:				Optional[int]  ):





       '''simple docstring'''

       lowercase__    :	Any     					=			PoolFormerConfig()

       # set attributes based on model_name
       lowercase__    :	Any     					=			'huggingface/label-files'
       lowercase__    :	Optional[int]     					=			model_name[-3:]
       lowercase__    :	Optional[Any]     					=			1000
       lowercase__    :	Tuple     					=			'imagenet-1k-id2label.json'
       lowercase__    :	Optional[Any]     					=			(1, 1000)

       # set config attributes
       lowercase__    :	Any     					=			json.load(open(hf_hub_download(_A     ,				_A     ,				repo_type='dataset'  )     ,				'r'  )  )
       lowercase__    :	Dict     					=			{int(_A  ): v for k, v in idalabel.items()}
       lowercase__    :	Union[str, Any]     					=			idalabel
       lowercase__    :	List[str]     					=			{v: k for k, v in idalabel.items()}
       if size == "s12":
              lowercase__    :	List[str]     					=			[2, 2, 6, 2]
              lowercase__    :	Dict     					=			[64, 128, 320, 512]
              lowercase__    :	Any     					=			4.0
              lowercase__    :	Optional[Any]     					=			0.9
       elif size == "s24":
              lowercase__    :	Optional[Any]     					=			[4, 4, 12, 4]
              lowercase__    :	List[Any]     					=			[64, 128, 320, 512]
              lowercase__    :	Optional[int]     					=			4.0
              lowercase__    :	int     					=			0.9
       elif size == "s36":
              lowercase__    :	Dict     					=			[6, 6, 18, 6]
              lowercase__    :	Optional[Any]     					=			[64, 128, 320, 512]
              lowercase__    :	int     					=			4.0
              lowercase__    :	str     					=			1E-6
              lowercase__    :	Any     					=			0.9
       elif size == "m36":
              lowercase__    :	List[Any]     					=			[6, 6, 18, 6]
              lowercase__    :	List[str]     					=			[96, 192, 384, 768]
              lowercase__    :	Union[str, Any]     					=			4.0
              lowercase__    :	int     					=			1E-6
              lowercase__    :	Union[str, Any]     					=			0.9_5
       elif size == "m48":
              lowercase__    :	str     					=			[8, 8, 24, 8]
              lowercase__    :	Union[str, Any]     					=			[96, 192, 384, 768]
              lowercase__    :	Optional[Any]     					=			4.0
              lowercase__    :	Any     					=			1E-6
              lowercase__    :	Dict     					=			0.9_5
       else:
              raise ValueError(f"""Size {size} not supported"""  )

       # load image processor
       lowercase__    :	Optional[int]     					=			PoolFormerImageProcessor(crop_pct=_A  )

       # Prepare image
       lowercase__    :	Any     					=			prepare_img()
       lowercase__    :	Union[str, Any]     					=			image_processor(images=_A     ,				return_tensors='pt'  ).pixel_values

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

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

       # rename keys
       lowercase__    :	Dict     					=			rename_keys(_A  )

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

       # Define image processor
       lowercase__    :	Union[str, Any]     					=			PoolFormerImageProcessor(crop_pct=_A  )
       lowercase__    :	Any     					=			image_processor(images=prepare_img()     ,				return_tensors='pt'  ).pixel_values

       # forward pass
       lowercase__    :	Tuple     					=			model(_A  )
       lowercase__    :	Any     					=			outputs.logits

       # define expected logit slices for different models
       if size == "s12":
              lowercase__    :	Any     					=			torch.tensor([-0.3_0_4_5, -0.6_7_5_8, -0.4_8_6_9]  )
       elif size == "s24":
              lowercase__    :	Tuple     					=			torch.tensor([0.4_4_0_2, -0.1_3_7_4, -0.8_0_4_5]  )
       elif size == "s36":
              lowercase__    :	List[str]     					=			torch.tensor([-0.6_0_8_0, -0.5_1_3_3, -0.5_8_9_8]  )
       elif size == "m36":
              lowercase__    :	Tuple     					=			torch.tensor([0.3_9_5_2, 0.2_2_6_3, -1.2_6_6_8]  )
       elif size == "m48":
              lowercase__    :	Tuple     					=			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__":
       _UpperCamelCase     :		Optional[int]											=					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     :		Union[str, Any]											=					parser.parse_args()
       convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)



"""simple docstring"""





import unittest

import numpy as np

from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
       import jax.numpy as jnp

       from transformers.models.albert.modeling_flax_albert import (
           FlaxAlbertForMaskedLM,
           FlaxAlbertForMultipleChoice,
           FlaxAlbertForPreTraining,
           FlaxAlbertForQuestionAnswering,
           FlaxAlbertForSequenceClassification,
           FlaxAlbertForTokenClassification,
           FlaxAlbertModel,
       )


class   UpperCAmelCase_			(					unittest.TestCase):


      def __init__(     self	,    a	,    a=1_3	,    a=7	,    a=True	,    a=True	,    a=True	,    a=True	,    a=9_9	,    a=3_2	,    a=5	,    a=4	,    a=3_7	,    a="gelu"	,    a=0.1	,    a=0.1	,    a=5_1_2	,    a=1_6	,    a=2	,    a=0.02	,    a=4	,    ) ->      Dict:
             lowercase__    :	Optional[Any]     					=			parent
             lowercase__    :	Dict     					=			batch_size
             lowercase__    :	List[Any]     					=			seq_length
             lowercase__    :	int     					=			is_training
             lowercase__    :	str     					=			use_attention_mask
             lowercase__    :	Dict     					=			use_token_type_ids
             lowercase__    :	Optional[int]     					=			use_labels
             lowercase__    :	Tuple     					=			vocab_size
             lowercase__    :	List[str]     					=			hidden_size
             lowercase__    :	Union[str, Any]     					=			num_hidden_layers
             lowercase__    :	int     					=			num_attention_heads
             lowercase__    :	Dict     					=			intermediate_size
             lowercase__    :	List[str]     					=			hidden_act
             lowercase__    :	Dict     					=			hidden_dropout_prob
             lowercase__    :	Tuple     					=			attention_probs_dropout_prob
             lowercase__    :	List[str]     					=			max_position_embeddings
             lowercase__    :	int     					=			type_vocab_size
             lowercase__    :	List[str]     					=			type_sequence_label_size
             lowercase__    :	Union[str, Any]     					=			initializer_range
             lowercase__    :	Optional[int]     					=			num_choices


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             lowercase__    :	List[Any]     					=			ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size							)

             lowercase__    :	str     					=			None
             if self.use_attention_mask:
                    lowercase__    :	Optional[Any]     					=			random_attention_mask([self.batch_size, self.seq_length]							)

             lowercase__    :	List[str]     					=			None
             if self.use_token_type_ids:
                    lowercase__    :	Optional[int]     					=			ids_tensor([self.batch_size, self.seq_length]	,    self.type_vocab_size							)

             lowercase__    :	Any     					=			AlbertConfig(
                 vocab_size=self.vocab_size	,    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	,    max_position_embeddings=self.max_position_embeddings	,    type_vocab_size=self.type_vocab_size	,    is_decoder=a	,    initializer_range=self.initializer_range	,    )

             return config, input_ids, token_type_ids, attention_mask


      def 	_UpperCAmelCase (     self							) ->      Any:
             lowercase__    :	Optional[int]     					=			self.prepare_config_and_inputs()
             lowercase__					,   lowercase__					,   lowercase__					,   lowercase__    :	Tuple     					=			config_and_inputs
             lowercase__    :	Union[str, Any]     					=			{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
             return config, inputs_dict





@require_flax
class   UpperCAmelCase_			(					_a				,							unittest.TestCase):
      lowerCamelCase__	:					Tuple										=						(
          (
              FlaxAlbertModel,
              FlaxAlbertForPreTraining,
              FlaxAlbertForMaskedLM,
              FlaxAlbertForMultipleChoice,
              FlaxAlbertForQuestionAnswering,
              FlaxAlbertForSequenceClassification,
              FlaxAlbertForTokenClassification,
              FlaxAlbertForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )


      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             lowercase__    :	Union[str, Any]     					=			FlaxAlbertModelTester(self							)


      @slow
      def 	_UpperCAmelCase (     self							) ->      str:
             for model_class_name in self.all_model_classes:
                    lowercase__    :	str     					=			model_class_name.from_pretrained('albert-base-v2'							)
                    lowercase__    :	Tuple     					=			model(np.ones((1, 1)							)							)
                    self.assertIsNotNone(a							)





@require_flax
class   UpperCAmelCase_			(					unittest.TestCase):


      @slow
      def 	_UpperCAmelCase (     self							) ->      Tuple:
             lowercase__    :	str     					=			FlaxAlbertModel.from_pretrained('albert-base-v2'							)
             lowercase__    :	Optional[int]     					=			np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]							)
             lowercase__    :	Optional[Any]     					=			np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
             lowercase__    :	Any     					=			model(a	,    attention_mask=a							)[0]
             lowercase__    :	Tuple     					=			(1, 1_1, 7_6_8)
             self.assertEqual(output.shape	,    a							)
             lowercase__    :	Optional[Any]     					=			np.array(
                 [[[-0.6_513, 1.5_035, -0.2_766], [-0.6_515, 1.5_046, -0.2_780], [-0.6_512, 1.5_049, -0.2_784]]]							)

             self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]	,    a	,    atol=1e-4							)							)

