
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 							__SCREAMING_SNAKE_CASE   (		SCREAMING_SNAKE_CASE       :      Optional[int]    ,							SCREAMING_SNAKE_CASE       :      str			)     ->						Dict:
				__lowercase								=					old_name

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

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

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

												__lowercase								=					"last_stage." + trimmed_name

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

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

				return new_name




def 							__SCREAMING_SNAKE_CASE   (		SCREAMING_SNAKE_CASE       :      Optional[int]    ,							SCREAMING_SNAKE_CASE       :      Any			)     ->						List[Any]:
				for key in checkpoint.copy().keys():
								__lowercase								=					checkpoint.pop(__lowerCAmelCase			)
								__lowercase								=					val

				return checkpoint




def 							__SCREAMING_SNAKE_CASE   (		)     ->						Any:
				__lowercase								=					"http://images.cocodataset.org/val2017/000000039769.jpg"
				__lowercase								=					Image.open(requests.get(__lowerCAmelCase    ,							stream=__lowerCAmelCase			).raw			)

				return image




def 							__SCREAMING_SNAKE_CASE   (		SCREAMING_SNAKE_CASE       :      Union[str, Any]    ,							SCREAMING_SNAKE_CASE       :      Tuple    ,							SCREAMING_SNAKE_CASE       :      Dict    ,							SCREAMING_SNAKE_CASE       :      str			)     ->						int:
				__lowercase								=					torch.load(__lowerCAmelCase    ,							map_location='cpu'			)["model"]
				__lowercase								=					EfficientFormerConfig.from_json_file(__lowerCAmelCase			)
				__lowercase								=					EfficientFormerForImageClassificationWithTeacher(__lowerCAmelCase			)
				__lowercase								=					"_".join(checkpoint_path.split('/'			)[-1].split('.'			)[0].split('_'			)[:-1]			)

				__lowercase								=					config.depths[-1] - config.num_metaad_blocks + 1
				__lowercase								=					convert_torch_checkpoint(__lowerCAmelCase    ,							__lowerCAmelCase			)

				model.load_state_dict(__lowerCAmelCase			)
				model.eval()

				__lowercase								=					{
				    "bilinear": PILImageResampling.BILINEAR,
				    "bicubic": PILImageResampling.BICUBIC,
				    "nearest": PILImageResampling.NEAREST,
				}

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

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

				assert torch.allclose(__lowerCAmelCase    ,							__lowerCAmelCase			)

				__lowercase								=					model(__lowerCAmelCase			)
				__lowercase								=					outputs.logits

				__lowercase								=					(1, 1000)

				if "l1" in model_name:
								__lowercase								=					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]    ,							__lowerCAmelCase    ,							atol=1E-3			)
								assert logits.shape == expected_shape
				elif "l3" in model_name:
								__lowercase								=					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]    ,							__lowerCAmelCase    ,							atol=1E-3			)
								assert logits.shape == expected_shape
				elif "l7" in model_name:
								__lowercase								=					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(__lowerCAmelCase			).mkdir(exist_ok=__lowerCAmelCase			)
				model.save_pretrained(__lowerCAmelCase			)
				print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""			)
				processor.save_pretrained(__lowerCAmelCase			)
				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=__lowerCAmelCase    ,							)
								processor.push_to_hub(
								    repo_id=F"""Bearnardd/{pytorch_dump_path}"""    ,							commit_message='Add image processor'    ,							use_temp_dir=__lowerCAmelCase    ,							)


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__							=				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)

							SCREAMING_SNAKE_CASE__							=				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 ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer


lowerCamelCase__					=   logging.get_logger(__name__)

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

lowerCamelCase__					=   {
    'vocab_file': {
        'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt',
        'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt',
        'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/vocab.txt',
        'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/vocab.txt',
        'bert-base-multilingual-uncased': (
            'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt'
        ),
        'bert-base-multilingual-cased': 'https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt',
        'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt',
        'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt',
        'bert-large-uncased-whole-word-masking': (
            'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt'
        ),
        'bert-large-cased-whole-word-masking': (
            'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt'
        ),
        'bert-large-uncased-whole-word-masking-finetuned-squad': (
            'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'
        ),
        'bert-large-cased-whole-word-masking-finetuned-squad': (
            'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'
        ),
        'bert-base-cased-finetuned-mrpc': (
            'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt'
        ),
        'bert-base-german-dbmdz-cased': 'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt',
        'bert-base-german-dbmdz-uncased': (
            'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt'
        ),
        'TurkuNLP/bert-base-finnish-cased-v1': (
            'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt'
        ),
        'TurkuNLP/bert-base-finnish-uncased-v1': (
            'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt'
        ),
        'wietsedv/bert-base-dutch-cased': (
            'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json',
        'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json',
        'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json',
        'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json',
        'bert-base-multilingual-uncased': (
            'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json'
        ),
        'bert-base-multilingual-cased': (
            'https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json'
        ),
        'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json',
        'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json',
        'bert-large-uncased-whole-word-masking': (
            'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json'
        ),
        'bert-large-cased-whole-word-masking': (
            'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json'
        ),
        'bert-large-uncased-whole-word-masking-finetuned-squad': (
            'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'
        ),
        'bert-large-cased-whole-word-masking-finetuned-squad': (
            'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'
        ),
        'bert-base-cased-finetuned-mrpc': (
            'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json'
        ),
        'bert-base-german-dbmdz-cased': (
            'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json'
        ),
        'bert-base-german-dbmdz-uncased': (
            'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json'
        ),
        'TurkuNLP/bert-base-finnish-cased-v1': (
            'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json'
        ),
        'TurkuNLP/bert-base-finnish-uncased-v1': (
            'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json'
        ),
        'wietsedv/bert-base-dutch-cased': (
            'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json'
        ),
    },
}

lowerCamelCase__					=   {
    'bert-base-uncased': 512,
    'bert-large-uncased': 512,
    'bert-base-cased': 512,
    'bert-large-cased': 512,
    'bert-base-multilingual-uncased': 512,
    'bert-base-multilingual-cased': 512,
    'bert-base-chinese': 512,
    'bert-base-german-cased': 512,
    'bert-large-uncased-whole-word-masking': 512,
    'bert-large-cased-whole-word-masking': 512,
    'bert-large-uncased-whole-word-masking-finetuned-squad': 512,
    'bert-large-cased-whole-word-masking-finetuned-squad': 512,
    'bert-base-cased-finetuned-mrpc': 512,
    'bert-base-german-dbmdz-cased': 512,
    'bert-base-german-dbmdz-uncased': 512,
    'TurkuNLP/bert-base-finnish-cased-v1': 512,
    'TurkuNLP/bert-base-finnish-uncased-v1': 512,
    'wietsedv/bert-base-dutch-cased': 512,
}

lowerCamelCase__					=   {
    'bert-base-uncased': {'do_lower_case': True},
    'bert-large-uncased': {'do_lower_case': True},
    'bert-base-cased': {'do_lower_case': False},
    'bert-large-cased': {'do_lower_case': False},
    'bert-base-multilingual-uncased': {'do_lower_case': True},
    'bert-base-multilingual-cased': {'do_lower_case': False},
    'bert-base-chinese': {'do_lower_case': False},
    'bert-base-german-cased': {'do_lower_case': False},
    'bert-large-uncased-whole-word-masking': {'do_lower_case': True},
    'bert-large-cased-whole-word-masking': {'do_lower_case': False},
    'bert-large-uncased-whole-word-masking-finetuned-squad': {'do_lower_case': True},
    'bert-large-cased-whole-word-masking-finetuned-squad': {'do_lower_case': False},
    'bert-base-cased-finetuned-mrpc': {'do_lower_case': False},
    'bert-base-german-dbmdz-cased': {'do_lower_case': False},
    'bert-base-german-dbmdz-uncased': {'do_lower_case': True},
    'TurkuNLP/bert-base-finnish-cased-v1': {'do_lower_case': False},
    'TurkuNLP/bert-base-finnish-uncased-v1': {'do_lower_case': True},
    'wietsedv/bert-base-dutch-cased': {'do_lower_case': False},
}






class 		lowerCAmelCase__	(	UpperCAmelCase__		):
			lowerCAmelCase       :		Union[str, Any]				      =   VOCAB_FILES_NAMES
			lowerCAmelCase       :		Dict				      =   PRETRAINED_VOCAB_FILES_MAP
			lowerCAmelCase       :		Tuple				      =   PRETRAINED_INIT_CONFIGURATION
			lowerCAmelCase       :		Optional[int]				      =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCAmelCase       :		Any				      =   BertTokenizer




			def __init__(						self     :   int		,     lowerCamelCase__     :   Optional[Any]=None		,     lowerCamelCase__     :   Optional[int]=None		,     lowerCamelCase__     :   str=True		,     lowerCamelCase__     :   Tuple="[UNK]"		,     lowerCamelCase__     :   str="[SEP]"		,     lowerCamelCase__     :   Optional[Any]="[PAD]"		,     lowerCamelCase__     :   List[str]="[CLS]"		,     lowerCamelCase__     :   Union[str, Any]="[MASK]"		,     lowerCamelCase__     :   str=True		,     lowerCamelCase__     :   Dict=None		,     **lowerCamelCase__     :   Union[str, Any]		,     )      ->Tuple:



					'''simple docstring'''







					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__		,     )

					_UpperCAmelCase						:				Optional[Any]	    =					json.loads(self.backend_tokenizer.normalizer.__getstate__()       )
					if (
					    normalizer_state.get("lowercase"		,     lowerCamelCase__       ) != do_lower_case
					    or normalizer_state.get("strip_accents"		,     lowerCamelCase__       ) != strip_accents
					    or normalizer_state.get("handle_chinese_chars"		,     lowerCamelCase__       ) != tokenize_chinese_chars
					):
							_UpperCAmelCase						:				str	    =					getattr(lowerCamelCase__		,     normalizer_state.pop("type"       )       )
							_UpperCAmelCase						:				Optional[Any]	    =					do_lower_case
							_UpperCAmelCase						:				Any	    =					strip_accents
							_UpperCAmelCase						:				List[Any]	    =					tokenize_chinese_chars
							_UpperCAmelCase						:				int	    =					normalizer_class(**lowerCamelCase__       )

					_UpperCAmelCase						:				Union[str, Any]	    =					do_lower_case




			def 							lowerCAmelCase__			(						self     :   List[str]		,     lowerCamelCase__     :   List[str]		,     lowerCamelCase__     :   str=None       )      ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				List[str]	    =					[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 							lowerCAmelCase__			(						self     :   List[Any]		,     lowerCamelCase__     :   List[int]		,     lowerCamelCase__     :   Optional[List[int]] = None       )      ->List[int]:



					'''simple docstring'''







					_UpperCAmelCase						:				Tuple	    =					[self.sep_token_id]
					_UpperCAmelCase						:				int	    =					[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 							lowerCAmelCase__			(						self     :   List[Any]		,     lowerCamelCase__     :   str		,     lowerCamelCase__     :   Optional[str] = None       )      ->Tuple[str]:



					'''simple docstring'''







					_UpperCAmelCase						:				List[str]	    =					self._tokenizer.model.save(lowerCamelCase__		,     name=lowerCamelCase__       )
					return tuple(lowerCamelCase__       )



