

from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Sequence, Value
from .base import TaskTemplate






@dataclass(frozen=lowerCAmelCase_      )
class 	__magic_name__    (			lowerCAmelCase_      ):
			# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
			SCREAMING_SNAKE_CASE							   =						field(default='question-answering-extractive'  ,    metadata={'include_in_asdict_even_if_is_default': True}      )
			SCREAMING_SNAKE_CASE							   =						Features({'question': Value('string'      ), 'context': Value('string'      )}      )
			SCREAMING_SNAKE_CASE							   =						Features(
			    {
			        'answers': Sequence(
			            {
			                'text': Value('string'      ),
			                'answer_start': Value('int32'      ),
			            }      )
			    }      )
			SCREAMING_SNAKE_CASE							   =						"question"
			SCREAMING_SNAKE_CASE							   =						"context"
			SCREAMING_SNAKE_CASE							   =						"answers"





			@property
			def 		__magic_name__    (		self					)   -> Dict[str, str]:




						'''simple docstring'''
						return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}









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        UpperCamelCase_(							_snake_case   :  int       ,     _snake_case   :  int	):




			"""simple docstring"""






			__a        =old_name

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

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

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

									__a        ='last_stage.' + trimmed_name

			elif "network" in old_name and re.search(r'.\d.'       ,     _snake_case	):
						__a        =old_name.replace('network'       ,     'intermediate_stages'	)

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

			return new_name






def        UpperCamelCase_(							_snake_case   :  List[str]       ,     _snake_case   :  Dict	):




			"""simple docstring"""






			for key in checkpoint.copy().keys():
						__a        =checkpoint.pop(_snake_case	)
						__a        =val

			return checkpoint






def        UpperCamelCase_(							):




			"""simple docstring"""






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

			return image






def        UpperCamelCase_(							_snake_case   :  Path       ,     _snake_case   :  Path       ,     _snake_case   :  Path       ,     _snake_case   :  bool	):




			"""simple docstring"""






			__a        =torch.load(_snake_case       ,     map_location='cpu'	)['model']
			__a        =EfficientFormerConfig.from_json_file(_snake_case	)
			__a        =EfficientFormerForImageClassificationWithTeacher(_snake_case	)
			__a        ='_'.join(checkpoint_path.split('/'	)[-1].split('.'	)[0].split('_'	)[:-1]	)

			__a        =config.depths[-1] - config.num_metaad_blocks + 1
			__a        =convert_torch_checkpoint(_snake_case       ,     _snake_case	)

			model.load_state_dict(_snake_case	)
			model.eval()

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

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

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

			assert torch.allclose(_snake_case       ,     _snake_case	)

			__a        =model(_snake_case	)
			__a        =outputs.logits

			__a        =(1, 1000)

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


if __name__ == "__main__":
						_lowerCAmelCase					:			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)

						_lowerCAmelCase					:			Union[str, Any]  				=			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,
						)






