


'''simple docstring'''






import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling


def  _snake_case       (					A			,   A	)						->   str:
							lowerCAmelCase__	       =     old_name

							if "patch_embed" in old_name:
														lowerCAmelCase__       , lowerCAmelCase__       , lowerCAmelCase__	       =     old_name.split('''.'''	)

														if layer == "0":
																					lowerCAmelCase__	       =     old_name.replace('''0'''			,   '''convolution1'''	)
														elif layer == "1":
																					lowerCAmelCase__	       =     old_name.replace('''1'''			,   '''batchnorm_before'''	)
														elif layer == "3":
																					lowerCAmelCase__	       =     old_name.replace('''3'''			,   '''convolution2'''	)
														else:
																					lowerCAmelCase__	       =     old_name.replace('''4'''			,   '''batchnorm_after'''	)

							if "network" in old_name and re.search(R'''\d\.\d'''			,   A	):
														lowerCAmelCase__	       =     R'''\b\d{2}\b'''
														if bool(re.search(A			,   A	)	):
																					lowerCAmelCase__	       =     re.search(R'''\d\.\d\d.'''			,   A	).group()
														else:
																					lowerCAmelCase__	       =     re.search(R'''\d\.\d.'''			,   A	).group()
														if int(match[0]	) < 6:
																					lowerCAmelCase__	       =     old_name.replace(A			,   ''''''	)
																					lowerCAmelCase__	       =     trimmed_name.replace('''network'''			,   match[0] + '''.meta4D_layers.blocks.''' + match[2:-1]	)
																					lowerCAmelCase__	       =     '''intermediate_stages.''' + trimmed_name
														else:
																					lowerCAmelCase__	       =     old_name.replace(A			,   ''''''	)
																					if int(match[2]	) < num_meta4D_last_stage:
																												lowerCAmelCase__	       =     trimmed_name.replace('''network'''			,   '''meta4D_layers.blocks.''' + match[2]	)
																					else:
																												lowerCAmelCase__	       =     str(int(match[2]	) - num_meta4D_last_stage	)
																												lowerCAmelCase__	       =     trimmed_name.replace('''network'''			,   '''meta3D_layers.blocks.''' + layer_index	)
																												if "norm1" in old_name:
																																			lowerCAmelCase__	       =     trimmed_name.replace('''norm1'''			,   '''layernorm1'''	)
																												elif "norm2" in old_name:
																																			lowerCAmelCase__	       =     trimmed_name.replace('''norm2'''			,   '''layernorm2'''	)
																												elif "fc1" in old_name:
																																			lowerCAmelCase__	       =     trimmed_name.replace('''fc1'''			,   '''linear_in'''	)
																												elif "fc2" in old_name:
																																			lowerCAmelCase__	       =     trimmed_name.replace('''fc2'''			,   '''linear_out'''	)

																					lowerCAmelCase__	       =     '''last_stage.''' + trimmed_name

							elif "network" in old_name and re.search(R'''.\d.'''			,   A	):
														lowerCAmelCase__	       =     old_name.replace('''network'''			,   '''intermediate_stages'''	)

							if "fc" in new_name:
														lowerCAmelCase__	       =     new_name.replace('''fc'''			,   '''convolution'''	)
							elif ("norm1" in new_name) and ("layernorm1" not in new_name):
														lowerCAmelCase__	       =     new_name.replace('''norm1'''			,   '''batchnorm_before'''	)
							elif ("norm2" in new_name) and ("layernorm2" not in new_name):
														lowerCAmelCase__	       =     new_name.replace('''norm2'''			,   '''batchnorm_after'''	)
							if "proj" in new_name:
														lowerCAmelCase__	       =     new_name.replace('''proj'''			,   '''projection'''	)
							if "dist_head" in new_name:
														lowerCAmelCase__	       =     new_name.replace('''dist_head'''			,   '''distillation_classifier'''	)
							elif "head" in new_name:
														lowerCAmelCase__	       =     new_name.replace('''head'''			,   '''classifier'''	)
							elif "patch_embed" in new_name:
														lowerCAmelCase__	       =     '''efficientformer.''' + new_name
							elif new_name == "norm.weight" or new_name == "norm.bias":
														lowerCAmelCase__	       =     new_name.replace('''norm'''			,   '''layernorm'''	)
														lowerCAmelCase__	       =     '''efficientformer.''' + new_name
							else:
														lowerCAmelCase__	       =     '''efficientformer.encoder.''' + new_name

							return new_name


def  _snake_case       (					A			,   A	)						->   Optional[int]:
							for key in checkpoint.copy().keys():
														lowerCAmelCase__	       =     checkpoint.pop(A	)
														lowerCAmelCase__	       =     val

							return checkpoint


def  _snake_case       (					)						->   Any:
							lowerCAmelCase__	       =     '''http://images.cocodataset.org/val2017/000000039769.jpg'''
							lowerCAmelCase__	       =     Image.open(requests.get(A			,   stream=A	).raw	)

							return image


def  _snake_case       (					A			,   A			,   A			,   A	)						->   List[str]:
							lowerCAmelCase__	       =     torch.load(A			,   map_location='''cpu'''	)['''model''']
							lowerCAmelCase__	       =     EfficientFormerConfig.from_json_file(A	)
							lowerCAmelCase__	       =     EfficientFormerForImageClassificationWithTeacher(A	)
							lowerCAmelCase__	       =     '''_'''.join(checkpoint_path.split('''/'''	)[-1].split('''.'''	)[0].split('''_'''	)[:-1]	)

							lowerCAmelCase__	       =     config.depths[-1] - config.num_metaad_blocks + 1
							lowerCAmelCase__	       =     convert_torch_checkpoint(A			,   A	)

							model.load_state_dict(A	)
							model.eval()

							lowerCAmelCase__	       =     {
							    '''bilinear''': PILImageResampling.BILINEAR,
							    '''bicubic''': PILImageResampling.BICUBIC,
							    '''nearest''': PILImageResampling.NEAREST,
							}

							# prepare image
							lowerCAmelCase__	       =     prepare_img()
							lowerCAmelCase__	       =     256
							lowerCAmelCase__	       =     224
							lowerCAmelCase__	       =     EfficientFormerImageProcessor(
							    size={'''shortest_edge''': image_size}			,   crop_size={'''height''': crop_size, '''width''': crop_size}			,   resample=pillow_resamplings['''bicubic''']			,   )
							lowerCAmelCase__	       =     processor(images=A			,   return_tensors='''pt'''	).pixel_values

							# original processing pipeline
							lowerCAmelCase__	       =     Compose(
							    [
							        Resize(A			,   interpolation=pillow_resamplings['''bicubic''']	),
							        CenterCrop(A	),
							        ToTensor(),
							        Normalize(A			,   A	),
							    ]	)
							lowerCAmelCase__	       =     image_transforms(A	).unsqueeze(0	)

							assert torch.allclose(A			,   A	)

							lowerCAmelCase__	       =     model(A	)
							lowerCAmelCase__	       =     outputs.logits

							lowerCAmelCase__	       =     (1, 1000)

							if "l1" in model_name:
														lowerCAmelCase__	       =     torch.Tensor(
														    [-0.1_312, 0.4_353, -1.0_499, -0.5_124, 0.4_183, -0.6_793, -1.3_777, -0.0_893, -0.7_358, -2.4_328]	)
														assert torch.allclose(logits[0, :10]			,   A			,   atol=1E-3	)
														assert logits.shape == expected_shape
							elif "l3" in model_name:
														lowerCAmelCase__	       =     torch.Tensor(
														    [-1.3_150, -1.5_456, -1.2_556, -0.8_496, -0.7_127, -0.7_897, -0.9_728, -0.3_052, 0.3_751, -0.3_127]	)
														assert torch.allclose(logits[0, :10]			,   A			,   atol=1E-3	)
														assert logits.shape == expected_shape
							elif "l7" in model_name:
														lowerCAmelCase__	       =     torch.Tensor(
														    [-1.0_283, -1.4_131, -0.5_644, -1.3_115, -0.5_785, -1.2_049, -0.7_528, 0.1_992, -0.3_822, -0.0_878]	)
														assert logits.shape == expected_shape
							else:
														raise ValueError(
														    F"""Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7"""	)

							# Save Checkpoints
							Path(A	).mkdir(exist_ok=A	)
							model.save_pretrained(A	)
							print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""	)
							processor.save_pretrained(A	)
							print(F"""Processor successfuly saved at {pytorch_dump_path}"""	)

							if push_to_hub:
														print('''Pushing model to the hub...'''	)

														model.push_to_hub(
														    repo_id=F"""Bearnardd/{pytorch_dump_path}"""			,   commit_message='''Add model'''			,   use_temp_dir=A			,   )
														processor.push_to_hub(
														    repo_id=F"""Bearnardd/{pytorch_dump_path}"""			,   commit_message='''Add image processor'''			,   use_temp_dir=A			,   )


if __name__ == "__main__":
		__UpperCAmelCase       =							argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--pytorch_model_path''',
		    default=None,
		    type=str,
		    required=True,
		    help='''Path to EfficientFormer pytorch checkpoint.''',
		)
		parser.add_argument(
		    '''--config_file''',
		    default=None,
		    type=str,
		    required=True,
		    help='''The json file for EfficientFormer model config.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)

		parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')
		parser.add_argument(
		    '''--no-push_to_hub''',
		    dest='''push_to_hub''',
		    action='''store_false''',
		    help='''Do not push model and image processor to the hub''',
		)
		parser.set_defaults(push_to_hub=True)

		__UpperCAmelCase       =							parser.parse_args()
		convert_efficientformer_checkpoint(
		    checkpoint_path=args.pytorch_model_path,
		    efficientformer_config_file=args.config_file,
		    pytorch_dump_path=args.pytorch_dump_path,
		    push_to_hub=args.push_to_hub,
		)



'''simple docstring'''






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


__UpperCAmelCase       =							logging.get_logger(__name__)

__UpperCAmelCase       =							{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__UpperCAmelCase       =							{
    '''vocab_file''': {
        '''junnyu/roformer_chinese_small''': '''https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt''',
        '''junnyu/roformer_chinese_base''': '''https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt''',
        '''junnyu/roformer_chinese_char_small''': (
            '''https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_chinese_char_base''': (
            '''https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_small_discriminator''': (
            '''https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_small_generator''': (
            '''https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt'''
        ),
    }
}

__UpperCAmelCase       =							{
    '''junnyu/roformer_chinese_small''': 1_536,
    '''junnyu/roformer_chinese_base''': 1_536,
    '''junnyu/roformer_chinese_char_small''': 512,
    '''junnyu/roformer_chinese_char_base''': 512,
    '''junnyu/roformer_small_discriminator''': 128,
    '''junnyu/roformer_small_generator''': 128,
}


__UpperCAmelCase       =							{
    '''junnyu/roformer_chinese_small''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_base''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_char_small''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_char_base''': {'''do_lower_case''': True},
    '''junnyu/roformer_small_discriminator''': {'''do_lower_case''': True},
    '''junnyu/roformer_small_generator''': {'''do_lower_case''': True},
}




class 		a__    (							a__  ):





							'''simple docstring'''






							lowercase__      :       int     						=				VOCAB_FILES_NAMES
							lowercase__      :       Optional[int]     						=				PRETRAINED_VOCAB_FILES_MAP
							lowercase__      :       Any     						=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase__      :       Union[str, Any]     						=				PRETRAINED_INIT_CONFIGURATION
							lowercase__      :       Tuple     						=				RoFormerTokenizer







							def __init__(		self ,   lowerCamelCase_=None ,   lowerCamelCase_=None ,   lowerCamelCase_=True ,   lowerCamelCase_="[UNK]" ,   lowerCamelCase_="[SEP]" ,   lowerCamelCase_="[PAD]" ,   lowerCamelCase_="[CLS]" ,   lowerCamelCase_="[MASK]" ,   lowerCamelCase_=True ,   lowerCamelCase_=None ,   **lowerCamelCase_ ,   )	->							Tuple:
														super().__init__(
														    lowerCamelCase_ ,   tokenizer_file=lowerCamelCase_ ,   do_lower_case=lowerCamelCase_ ,   unk_token=lowerCamelCase_ ,   sep_token=lowerCamelCase_ ,   pad_token=lowerCamelCase_ ,   cls_token=lowerCamelCase_ ,   mask_token=lowerCamelCase_ ,   tokenize_chinese_chars=lowerCamelCase_ ,   strip_accents=lowerCamelCase_ ,   **lowerCamelCase_ ,   )

														lowerCAmelCase__	       =     json.loads(self.backend_tokenizer.normalizer.__getstate__()       )
														if (
														    pre_tok_state.get('''lowercase''' ,   lowerCamelCase_       ) != do_lower_case
														    or pre_tok_state.get('''strip_accents''' ,   lowerCamelCase_       ) != strip_accents
														):
																					lowerCAmelCase__	       =     getattr(lowerCamelCase_ ,   pre_tok_state.pop('''type'''       )       )
																					lowerCAmelCase__	       =     do_lower_case
																					lowerCAmelCase__	       =     strip_accents
																					lowerCAmelCase__	       =     pre_tok_class(**lowerCamelCase_       )

														lowerCAmelCase__	       =     do_lower_case







							def __getstate__(		self       )	->							Any:
														lowerCAmelCase__	       =     self.__dict__.copy()
														lowerCAmelCase__	       =     BertPreTokenizer()
														return state







							def __setstate__(		self ,   lowerCamelCase_       )	->							List[Any]:
														lowerCAmelCase__	       =     d
														lowerCAmelCase__	       =     self.__dict__['''_tokenizer'''].get_vocab()
														lowerCAmelCase__	       =     PreTokenizer.custom(JiebaPreTokenizer(lowerCamelCase_       )       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_=None       )	->							Union[str, Any]:
														lowerCAmelCase__	       =     [self.cls_token_id] + token_ids_a + [self.sep_token_id]

														if token_ids_a:
																					output += token_ids_a + [self.sep_token_id]

														return output







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ = None       )	->							List[int]:
														lowerCAmelCase__	       =     [self.sep_token_id]
														lowerCAmelCase__	       =     [self.cls_token_id]
														if token_ids_a is None:
																					return len(cls + token_ids_a + sep       ) * [0]
														return len(cls + token_ids_a + sep       ) * [0] + len(token_ids_a + sep       ) * [1]







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ = None       )	->							Tuple[str]:
														lowerCAmelCase__	       =     self._tokenizer.model.save(lowerCamelCase_ ,   name=lowerCamelCase_       )
														return tuple(lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_=None ,   lowerCamelCase_=None ,   lowerCamelCase_=False ,   **lowerCamelCase_ ,   )	->							Union[str, Any]:
														lowerCAmelCase__	       =     BertPreTokenizer()
														return super().save_pretrained(lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ ,   **lowerCamelCase_       )