



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 	lowerCAmelCase__		(    lowerCamelCase_		:		Optional[Any]       ,lowerCamelCase_		:		List[Any]):





				'''simple docstring'''

				lowerCAmelCase__     :				str												=     old_name

				if "patch_embed" in old_name:
								lowerCAmelCase__     :				List[str]												=     old_name.split('''.''')

								if layer == "0":
												lowerCAmelCase__     :				Union[str, Any]												=     old_name.replace('''0'''       ,'''convolution1''')
								elif layer == "1":
												lowerCAmelCase__     :				Any												=     old_name.replace('''1'''       ,'''batchnorm_before''')
								elif layer == "3":
												lowerCAmelCase__     :				List[Any]												=     old_name.replace('''3'''       ,'''convolution2''')
								else:
												lowerCAmelCase__     :				Any												=     old_name.replace('''4'''       ,'''batchnorm_after''')

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

												lowerCAmelCase__     :				str												=     'last_stage.' + trimmed_name

				elif "network" in old_name and re.search(r'''.\d.'''       ,SCREAMING_SNAKE_CASE_):
								lowerCAmelCase__     :				Optional[Any]												=     old_name.replace('''network'''       ,'''intermediate_stages''')

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

				return new_name






def 	lowerCAmelCase__		(    lowerCamelCase_		:		Tuple       ,lowerCamelCase_		:		List[str]):





				'''simple docstring'''

				for key in checkpoint.copy().keys():
								lowerCAmelCase__     :				Any												=     checkpoint.pop(SCREAMING_SNAKE_CASE_)
								lowerCAmelCase__     :				Any												=     val

				return checkpoint






def 	lowerCAmelCase__		(    ):





				'''simple docstring'''

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

				return image






def 	lowerCAmelCase__		(    lowerCamelCase_		:		Path       ,lowerCamelCase_		:		Path       ,lowerCamelCase_		:		Path       ,lowerCamelCase_		:		bool):





				'''simple docstring'''

				lowerCAmelCase__     :				int												=     torch.load(SCREAMING_SNAKE_CASE_       ,map_location='''cpu''')['model']
				lowerCAmelCase__     :				str												=     EfficientFormerConfig.from_json_file(SCREAMING_SNAKE_CASE_)
				lowerCAmelCase__     :				Union[str, Any]												=     EfficientFormerForImageClassificationWithTeacher(SCREAMING_SNAKE_CASE_)
				lowerCAmelCase__     :				List[str]												=     '_'.join(checkpoint_path.split('''/''')[-1].split('''.''')[0].split('''_''')[:-1])

				lowerCAmelCase__     :				Dict												=     config.depths[-1] - config.num_metaad_blocks + 1
				lowerCAmelCase__     :				str												=     convert_torch_checkpoint(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_)

				model.load_state_dict(SCREAMING_SNAKE_CASE_)
				model.eval()

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

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

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

				assert torch.allclose(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_)

				lowerCAmelCase__     :				Any												=     model(SCREAMING_SNAKE_CASE_)
				lowerCAmelCase__     :				List[str]												=     outputs.logits

				lowerCAmelCase__     :				List[str]												=     (1, 1000)

				if "l1" in model_name:
								lowerCAmelCase__     :				Union[str, Any]												=     torch.Tensor(
								    [-0.1312, 0.4353, -1.0499, -0.5124, 0.4183, -0.6793, -1.3777, -0.0893, -0.7358, -2.4328])
								assert torch.allclose(logits[0, :10]       ,SCREAMING_SNAKE_CASE_       ,atol=1E-3)
								assert logits.shape == expected_shape
				elif "l3" in model_name:
								lowerCAmelCase__     :				Dict												=     torch.Tensor(
								    [-1.3150, -1.5456, -1.2556, -0.8496, -0.7127, -0.7897, -0.9728, -0.3052, 0.3751, -0.3127])
								assert torch.allclose(logits[0, :10]       ,SCREAMING_SNAKE_CASE_       ,atol=1E-3)
								assert logits.shape == expected_shape
				elif "l7" in model_name:
								lowerCAmelCase__     :				List[str]												=     torch.Tensor(
								    [-1.0283, -1.4131, -0.5644, -1.3115, -0.5785, -1.2049, -0.7528, 0.1992, -0.3822, -0.0878])
								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(SCREAMING_SNAKE_CASE_).mkdir(exist_ok=SCREAMING_SNAKE_CASE_)
				model.save_pretrained(SCREAMING_SNAKE_CASE_)
				print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}""")
				processor.save_pretrained(SCREAMING_SNAKE_CASE_)
				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=SCREAMING_SNAKE_CASE_       ,)
								processor.push_to_hub(
								    repo_id=f"""Bearnardd/{pytorch_dump_path}"""       ,commit_message='''Add image processor'''       ,use_temp_dir=SCREAMING_SNAKE_CASE_       ,)


if __name__ == "__main__":
							__snake_case				:		Optional[int]										=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)

							__snake_case				:		Tuple										=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,
							)




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case_			  = logging.get_logger(__name__)

snake_case_			  = {
    '''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/config.json''',
    '''distilbert-base-uncased-distilled-squad''': (
        '''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json'''
    ),
    '''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/config.json''',
    '''distilbert-base-cased-distilled-squad''': (
        '''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json'''
    ),
    '''distilbert-base-german-cased''': '''https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json''',
    '''distilbert-base-multilingual-cased''': (
        '''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json'''
    ),
    '''distilbert-base-uncased-finetuned-sst-2-english''': (
        '''https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json'''
    ),
}


class       SCREAMING_SNAKE_CASE__					(__snake_case     ):
    __lowerCamelCase						:       Any														=     """distilbert"""
    __lowerCamelCase						:       List[Any]														=     {
        """hidden_size""": """dim""",
        """num_attention_heads""": """n_heads""",
        """num_hidden_layers""": """n_layers""",
    }





    def __init__(      self						,   a=3_0522						,   a=512						,   a=False						,   a=6						,   a=12						,   a=768						,   a=4 * 768						,   a=0.1						,   a=0.1						,   a="gelu"						,   a=0.02						,   a=0.1						,   a=0.2						,   a=0						,   **a						,   ):
         lowercase__    :   int      =       vocab_size
         lowercase__    :   int      =       max_position_embeddings
         lowercase__    :   List[str]      =       sinusoidal_pos_embds
         lowercase__    :   Tuple      =       n_layers
         lowercase__    :   Optional[int]      =       n_heads
         lowercase__    :   List[Any]      =       dim
         lowercase__    :   Any      =       hidden_dim
         lowercase__    :   Union[str, Any]      =       dropout
         lowercase__    :   Optional[int]      =       attention_dropout
         lowercase__    :   List[Any]      =       activation
         lowercase__    :   Union[str, Any]      =       initializer_range
         lowercase__    :   Dict      =       qa_dropout
         lowercase__    :   Tuple      =       seq_classif_dropout
         super().__init__(**a						,   pad_token_id=a)



class       SCREAMING_SNAKE_CASE__					(__snake_case     ):





    @property
    def       snake_case_				(      self):
         if self.task == "multiple-choice":
              lowercase__    :   List[str]      =       {0: 'batch', 1: 'choice', 2: 'sequence'}
         else:
              lowercase__    :   Tuple      =       {0: 'batch', 1: 'sequence'}
         return OrderedDict(
             [
                 ('input_ids', dynamic_axis),
                 ('attention_mask', dynamic_axis),
             ])

