
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  :  List[Any]    ,							__lowerCAmelCase  :  int				):




			"""simple docstring"""


			lowerCAmelCase_						=	old_name

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

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

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

									lowerCAmelCase_						=	"last_stage." + trimmed_name

			elif "network" in old_name and re.search(r".\d."    ,							__lowerCAmelCase				):
						lowerCAmelCase_						=	old_name.replace("network"    ,							"intermediate_stages"				)

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

			return new_name






def    lowerCamelCase__				(				__lowerCAmelCase  :  Optional[Any]    ,							__lowerCAmelCase  :  List[str]				):




			"""simple docstring"""


			for key in checkpoint.copy().keys():
						lowerCAmelCase_						=	checkpoint.pop(__lowerCAmelCase				)
						lowerCAmelCase_						=	val

			return checkpoint






def    lowerCamelCase__				(				):




			"""simple docstring"""


			lowerCAmelCase_						=	"http://images.cocodataset.org/val2017/000000039769.jpg"
			lowerCAmelCase_						=	Image.open(requests.get(__lowerCAmelCase    ,							stream=__lowerCAmelCase				).raw				)

			return image






def    lowerCamelCase__				(				__lowerCAmelCase  :  Path    ,							__lowerCAmelCase  :  Path    ,							__lowerCAmelCase  :  Path    ,							__lowerCAmelCase  :  bool				):




			"""simple docstring"""


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

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

			model.load_state_dict(__lowerCAmelCase				)
			model.eval()

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

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

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

			assert torch.allclose(__lowerCAmelCase    ,							__lowerCAmelCase				)

			lowerCAmelCase_						=	model(__lowerCAmelCase				)
			lowerCAmelCase_						=	outputs.logits

			lowerCAmelCase_						=	(1, 1000)

			if "l1" in model_name:
						lowerCAmelCase_						=	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:
						lowerCAmelCase_						=	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:
						lowerCAmelCase_						=	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__":
			_A   =     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)

			_A   =     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 requests

_A   =     "https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="






def    lowerCamelCase__				(				__lowerCAmelCase  :  str				):




			"""simple docstring"""


			lowerCAmelCase_						=	requests.get(_NEWS_API + bbc_news_api_key				).json()
			# each article in the list is a dict
			for i, article in enumerate(bbc_news_page["articles"]    ,							1				):
						print(F"""{i}.) {article['title']}"""				)


if __name__ == "__main__":
			fetch_bbc_news(bbc_news_api_key="<Your BBC News API key goes here>")



