


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     lowercase	(    a     , a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:Optional[int]				     =						old_name

		if "patch_embed" in old_name:
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						old_name.split("." )

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

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

						SCREAMING_SNAKE_CASE_				:Tuple				     =						"last_stage." + trimmed_name

		elif "network" in old_name and re.search(R".\d."     , a ):
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						old_name.replace("network"     , "intermediate_stages" )

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

		return new_name


def     lowercase	(    a     , a ):



		'''simple docstring'''




		for key in checkpoint.copy().keys():
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						checkpoint.pop(a )
				SCREAMING_SNAKE_CASE_				:str				     =						val

		return checkpoint


def     lowercase	(    ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:Dict				     =						"http://images.cocodataset.org/val2017/000000039769.jpg"
		SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						Image.open(requests.get(a     , stream=a ).raw )

		return image


def     lowercase	(    a     , a     , a     , a ):



		'''simple docstring'''




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

		SCREAMING_SNAKE_CASE_				:List[Any]				     =						config.depths[-1] - config.num_metaad_blocks + 1
		SCREAMING_SNAKE_CASE_				:List[Any]				     =						convert_torch_checkpoint(a     , a )

		model.load_state_dict(a )
		model.eval()

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

		# prepare image
		SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						prepare_img()
		SCREAMING_SNAKE_CASE_				:List[Any]				     =						256
		SCREAMING_SNAKE_CASE_				:str				     =						224
		SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						EfficientFormerImageProcessor(
		    size={"shortest_edge": image_size}     , crop_size={"height": crop_size, "width": crop_size}     , resample=pillow_resamplings["bicubic"]     , )
		SCREAMING_SNAKE_CASE_				:Any				     =						processor(images=a     , return_tensors="pt" ).pixel_values

		# original processing pipeline
		SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						Compose(
		    [
		        Resize(a     , interpolation=pillow_resamplings["bicubic"] ),
		        CenterCrop(a ),
		        ToTensor(),
		        Normalize(a     , a ),
		    ] )
		SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						image_transforms(a ).unsqueeze(0 )

		assert torch.allclose(a     , a )

		SCREAMING_SNAKE_CASE_				:int				     =						model(a )
		SCREAMING_SNAKE_CASE_				:List[Any]				     =						outputs.logits

		SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						(1, 1000)

		if "l1" in model_name:
				SCREAMING_SNAKE_CASE_				:int				     =						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:
				SCREAMING_SNAKE_CASE_				:Any				     =						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:
				SCREAMING_SNAKE_CASE_				:str				     =						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__":
		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,
		)










import os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


SCREAMING_SNAKE_CASE__     					=			datasets.logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__     					=			"\\n@inproceedings{bleurt,\n  title={BLEURT: Learning Robust Metrics for Text Generation},\n  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},\n  booktitle={ACL},\n  year={2020},\n  url={https://arxiv.org/abs/2004.04696}\n}\n"

SCREAMING_SNAKE_CASE__     					=			"\\nBLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)\nand then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune\nit for your specific application (the latter is expected to perform better).\n\nSee the project's README at https://github.com/google-research/bleurt#readme for more information.\n"

SCREAMING_SNAKE_CASE__     					=			"\nBLEURT score.\n\nArgs:\n    `predictions` (list of str): prediction/candidate sentences\n    `references` (list of str): reference sentences\n    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.\n\nReturns:\n    'scores': List of scores.\nExamples:\n\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> bleurt = datasets.load_metric(\"bleurt\")\n    >>> results = bleurt.compute(predictions=predictions, references=references)\n    >>> print([round(v, 2) for v in results[\"scores\"]])\n    [1.03, 1.04]\n"

SCREAMING_SNAKE_CASE__     					=			{
    "bleurt-tiny-128": "https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip",
    "bleurt-tiny-512": "https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip",
    "bleurt-base-128": "https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip",
    "bleurt-base-512": "https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip",
    "bleurt-large-128": "https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip",
    "bleurt-large-512": "https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip",
    "BLEURT-20-D3": "https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip",
    "BLEURT-20-D6": "https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip",
    "BLEURT-20-D12": "https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip",
    "BLEURT-20": "https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip",
}



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,		_KWARGS_DESCRIPTION							)
class 						_UpperCAmelCase      (     datasets.Metric							):






		def _snake_case		(						self				:     List[str]):
				return datasets.MetricInfo(
				    description=_DESCRIPTION   ,  citation=_CITATION   ,  homepage="https://github.com/google-research/bleurt"   ,  inputs_description=_KWARGS_DESCRIPTION   ,  features=datasets.Features(
				        {
				            "predictions": datasets.Value("string"   ,  id="sequence"),
				            "references": datasets.Value("string"   ,  id="sequence"),
				        })   ,  codebase_urls=["https://github.com/google-research/bleurt"]   ,  reference_urls=["https://github.com/google-research/bleurt", "https://arxiv.org/abs/2004.04696"]   ,  )






		def _snake_case		(						self				:     Any   ,  UpperCAmelCase				:     Any):
				# check that config name specifies a valid BLEURT model
				if self.config_name == "default":
						logger.warning(
						    "Using default BLEURT-Base checkpoint for sequence maximum length 128. "
						    "You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512').")
						SCREAMING_SNAKE_CASE_				:str				     =						"bleurt-base-128"

				if self.config_name.lower() in CHECKPOINT_URLS:
						SCREAMING_SNAKE_CASE_				:Dict				     =						self.config_name.lower()

				elif self.config_name.upper() in CHECKPOINT_URLS:
						SCREAMING_SNAKE_CASE_				:Dict				     =						self.config_name.upper()

				else:
						raise KeyError(
						    F"{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}")

				# download the model checkpoint specified by self.config_name and set up the scorer
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name])
				SCREAMING_SNAKE_CASE_				:Dict				     =						score.BleurtScorer(os.path.join(UpperCAmelCase   ,  UpperCAmelCase))







		def _snake_case		(						self				:     Union[str, Any]   ,  UpperCAmelCase				:     List[str]   ,  UpperCAmelCase				:     Union[str, Any]):
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						self.scorer.score(references=UpperCAmelCase   ,  candidates=UpperCAmelCase)
				return {"scores": scores}






