
'''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder


# @@protoc_insertion_point(imports)

lowerCAmelCase					:    Dict           = _symbol_database.Default()


lowerCAmelCase					:    Union[str, Any]           = _descriptor_pool.Default().AddSerializedFile(
    B'\n\x19sentencepiece_model.proto\x12\rsentencepiece"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18  \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03'
)

lowerCAmelCase					:    Optional[Any]           = globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'sentencepiece_model_pb2', _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
						lowerCAmelCase					:    Optional[int]           = None
						lowerCAmelCase					:    str           = B'H\003'
						# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
						# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
						# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
						# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
						# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
						lowerCAmelCase					:    Optional[int]           = 45
						lowerCAmelCase					:    Tuple           = 15_81
						lowerCAmelCase					:    Tuple           = 15_17
						lowerCAmelCase					:    Tuple           = 15_70
						lowerCAmelCase					:    Union[str, Any]           = 15_84
						lowerCAmelCase					:    Optional[int]           = 17_93
						lowerCAmelCase					:    int           = 17_95
						lowerCAmelCase					:    Dict           = 19_16
						lowerCAmelCase					:    List[Any]           = 18_64
						lowerCAmelCase					:    Any           = 19_05
						lowerCAmelCase					:    Any           = 19_19
						lowerCAmelCase					:    str           = 24_29
						lowerCAmelCase					:    str           = 22_08
						lowerCAmelCase					:    Any           = 24_18
						lowerCAmelCase					:    Dict           = 23_23
						lowerCAmelCase					:    Optional[int]           = 24_07
# @@protoc_insertion_point(module_scope)






"""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()
_a								=						logging.get_logger(__name__)



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case,	__snake_case      )				->						List[str]:


					"""simple docstring"""





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

					_UpperCamelCase									=    key.replace(F'''{orig_block_num}.{layer_num}.{original_name}''',	F'''block.{new_block_num}.{layer_num}.{new_name}'''      )
					return key



def  lowerCamelCase__						(     __snake_case      )				->						str:


					"""simple docstring"""





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



def  lowerCamelCase__						(     )				->						Optional[Any]:


					"""simple docstring"""





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

					return image



@torch.no_grad()
def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case      )				->						int:


					"""simple docstring"""





					_UpperCamelCase									=    PoolFormerConfig()

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

					# set config attributes
					_UpperCamelCase									=    json.load(open(hf_hub_download(__snake_case,	__snake_case,	repo_type='''dataset'''      ),	'''r'''      )      )
					_UpperCamelCase									=    {int(__snake_case      ): v for k, v in idalabel.items()}
					_UpperCamelCase									=    idalabel
					_UpperCamelCase									=    {v: k for k, v in idalabel.items()}
					if size == "s12":
										_UpperCamelCase									=    [2, 2, 6, 2]
										_UpperCamelCase									=    [64, 1_28, 3_20, 5_12]
										_UpperCamelCase									=    4.0
										_UpperCamelCase									=    0.9
					elif size == "s24":
										_UpperCamelCase									=    [4, 4, 12, 4]
										_UpperCamelCase									=    [64, 1_28, 3_20, 5_12]
										_UpperCamelCase									=    4.0
										_UpperCamelCase									=    0.9
					elif size == "s36":
										_UpperCamelCase									=    [6, 6, 18, 6]
										_UpperCamelCase									=    [64, 1_28, 3_20, 5_12]
										_UpperCamelCase									=    4.0
										_UpperCamelCase									=    1e-6
										_UpperCamelCase									=    0.9
					elif size == "m36":
										_UpperCamelCase									=    [6, 6, 18, 6]
										_UpperCamelCase									=    [96, 1_92, 3_84, 7_68]
										_UpperCamelCase									=    4.0
										_UpperCamelCase									=    1e-6
										_UpperCamelCase									=    0.95
					elif size == "m48":
										_UpperCamelCase									=    [8, 8, 24, 8]
										_UpperCamelCase									=    [96, 1_92, 3_84, 7_68]
										_UpperCamelCase									=    4.0
										_UpperCamelCase									=    1e-6
										_UpperCamelCase									=    0.95
					else:
										raise ValueError(F'''Size {size} not supported'''      )

					# load image processor
					_UpperCamelCase									=    PoolFormerImageProcessor(crop_pct=__snake_case      )

					# Prepare image
					_UpperCamelCase									=    prepare_img()
					_UpperCamelCase									=    image_processor(images=__snake_case,	return_tensors='''pt'''      ).pixel_values

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

					# load original state dict
					_UpperCamelCase									=    torch.load(__snake_case,	map_location=torch.device('''cpu'''      )      )

					# rename keys
					_UpperCamelCase									=    rename_keys(__snake_case      )

					# create HuggingFace model and load state dict
					_UpperCamelCase									=    PoolFormerForImageClassification(__snake_case      )
					model.load_state_dict(__snake_case      )
					model.eval()

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

					# forward pass
					_UpperCamelCase									=    model(__snake_case      )
					_UpperCamelCase									=    outputs.logits

					# define expected logit slices for different models
					if size == "s12":
										_UpperCamelCase									=    torch.tensor([-0.3045, -0.6758, -0.4869]      )
					elif size == "s24":
										_UpperCamelCase									=    torch.tensor([0.4402, -0.1374, -0.8045]      )
					elif size == "s36":
										_UpperCamelCase									=    torch.tensor([-0.6080, -0.5133, -0.5898]      )
					elif size == "m36":
										_UpperCamelCase									=    torch.tensor([0.3952, 0.2263, -1.2668]      )
					elif size == "m48":
										_UpperCamelCase									=    torch.tensor([0.1167, -0.0656, -0.3423]      )
					else:
										raise ValueError(F'''Size {size} not supported'''      )

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

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


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






