
import os

import pytest
from attr import dataclass


__lowerCAmelCase					   =       'us-east-1'  # defaults region





@dataclass
class     lowerCamelCase      :
	UpperCamelCase_  :					str
	UpperCamelCase_  :					Optional[int]    		=	'arn:aws:iam::558105141721:role/sagemaker_execution_role'
	UpperCamelCase_  :					List[str]    		=	{
	    'task_name': 'mnli',
	    'per_device_train_batch_size': 1_6,
	    'per_device_eval_batch_size': 1_6,
	    'do_train': True,
	    'do_eval': True,
	    'do_predict': True,
	    'output_dir': '/opt/ml/model',
	    'overwrite_output_dir': True,
	    'max_steps': 5_0_0,
	    'save_steps': 5_5_0_0,
	}
	UpperCamelCase_  :					Any    		=	{**hyperparameters, 'max_steps': 1_0_0_0}







	@property
	def  snake_case__     (     self      :Optional[int]			)		->      str:







				"""simple docstring"""






				if self.framework == "pytorch":
							return [
							    {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
							    {"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
							    {"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
							]
				else:
							return [
							    {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
							    {"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
							    {"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
							]







	@property
	def  snake_case__     (     self      :str			)		->      str:







				"""simple docstring"""






				return f"""{self.framework}-transfromers-test"""







	@property
	def  snake_case__     (     self      :str			)		->      str:







				"""simple docstring"""






				return f"""./tests/sagemaker/scripts/{self.framework}"""







	@property
	def  snake_case__     (     self      :Tuple			)		->      str:







				"""simple docstring"""






				if self.framework == "pytorch":
							return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
				else:
							return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"



@pytest.fixture(scope='''class'''	)
def    a      ( a	)     ->List[Any]:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= SageMakerTestEnvironment(framework=request.cls.framework	)

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    a      ( a		,						a	)     ->Dict:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= old_name

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

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

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

									SCREAMING_SNAKE_CASE     						= '''last_stage.''' + trimmed_name

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

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

			return new_name


def    a      ( a		,						a	)     ->Any:




			'''simple docstring'''


			for key in checkpoint.copy().keys():
						SCREAMING_SNAKE_CASE     						= checkpoint.pop(a	)
						SCREAMING_SNAKE_CASE     						= val

			return checkpoint


def    a      ( )     ->List[Any]:




			'''simple docstring'''


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

			return image


def    a      ( a		,						a		,						a		,						a	)     ->Tuple:




			'''simple docstring'''


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

			SCREAMING_SNAKE_CASE     						= config.depths[-1] - config.num_metaad_blocks + 1
			SCREAMING_SNAKE_CASE     						= convert_torch_checkpoint(a		,						a	)

			model.load_state_dict(a	)
			model.eval()

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

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

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

			assert torch.allclose(a		,						a	)

			SCREAMING_SNAKE_CASE     						= model(a	)
			SCREAMING_SNAKE_CASE     						= outputs.logits

			SCREAMING_SNAKE_CASE     						= (1, 1000)

			if "l1" in model_name:
						SCREAMING_SNAKE_CASE     						= torch.Tensor(
						    [-0.13_12, 0.43_53, -1.04_99, -0.51_24, 0.41_83, -0.67_93, -1.37_77, -0.08_93, -0.73_58, -2.43_28]	)
						assert torch.allclose(logits[0, :10]		,						a		,						atol=1E-3	)
						assert logits.shape == expected_shape
			elif "l3" in model_name:
						SCREAMING_SNAKE_CASE     						= torch.Tensor(
						    [-1.31_50, -1.54_56, -1.25_56, -0.84_96, -0.71_27, -0.78_97, -0.97_28, -0.30_52, 0.37_51, -0.31_27]	)
						assert torch.allclose(logits[0, :10]		,						a		,						atol=1E-3	)
						assert logits.shape == expected_shape
			elif "l7" in model_name:
						SCREAMING_SNAKE_CASE     						= torch.Tensor(
						    [-1.02_83, -1.41_31, -0.56_44, -1.31_15, -0.57_85, -1.20_49, -0.75_28, 0.19_92, -0.38_22, -0.08_78]	)
						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__":
							__lowerCAmelCase					   =       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					   =       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,
							)