


import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE			: List[str]				   =					logging.get_logger(__name__)



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_=False					)       ->		Tuple:
    _lowercase			:				Tuple 						=						[]
    for i in range(config.num_hidden_layers					):
        # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
        rename_keys.append((F'''blocks.{i}.norm1.weight''', F'''deit.encoder.layer.{i}.layernorm_before.weight''')					)
        rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''deit.encoder.layer.{i}.layernorm_before.bias''')					)
        rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''deit.encoder.layer.{i}.attention.output.dense.weight''')					)
        rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''deit.encoder.layer.{i}.attention.output.dense.bias''')					)
        rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''deit.encoder.layer.{i}.layernorm_after.weight''')					)
        rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''deit.encoder.layer.{i}.layernorm_after.bias''')					)
        rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''deit.encoder.layer.{i}.intermediate.dense.weight''')					)
        rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''deit.encoder.layer.{i}.intermediate.dense.bias''')					)
        rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''deit.encoder.layer.{i}.output.dense.weight''')					)
        rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''deit.encoder.layer.{i}.output.dense.bias''')					)

    # projection layer + position embeddings
    rename_keys.extend(
        [
            ('cls_token', 'deit.embeddings.cls_token'),
            ('dist_token', 'deit.embeddings.distillation_token'),
            ('patch_embed.proj.weight', 'deit.embeddings.patch_embeddings.projection.weight'),
            ('patch_embed.proj.bias', 'deit.embeddings.patch_embeddings.projection.bias'),
            ('pos_embed', 'deit.embeddings.position_embeddings'),
        ]					)

    if base_model:
        # layernorm + pooler
        rename_keys.extend(
            [
                ('norm.weight', 'layernorm.weight'),
                ('norm.bias', 'layernorm.bias'),
                ('pre_logits.fc.weight', 'pooler.dense.weight'),
                ('pre_logits.fc.bias', 'pooler.dense.bias'),
            ]					)

        # if just the base model, we should remove "deit" from all keys that start with "deit"
        _lowercase			:				List[str] 						=						[(pair[0], pair[1][4:]) if pair[1].startswith('deit'					) else pair for pair in rename_keys]
    else:
        # layernorm + classification heads
        rename_keys.extend(
            [
                ('norm.weight', 'deit.layernorm.weight'),
                ('norm.bias', 'deit.layernorm.bias'),
                ('head.weight', 'cls_classifier.weight'),
                ('head.bias', 'cls_classifier.bias'),
                ('head_dist.weight', 'distillation_classifier.weight'),
                ('head_dist.bias', 'distillation_classifier.bias'),
            ]					)

    return rename_keys



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_=False					)       ->		Optional[Any]:
    for i in range(config.num_hidden_layers					):
        if base_model:
            _lowercase			:				Dict 						=						''
        else:
            _lowercase			:				Union[str, Any] 						=						'deit.'
        # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
        _lowercase			:				Union[str, Any] 						=						state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''					)
        _lowercase			:				int 						=						state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''					)
        # next, add query, keys and values (in that order) to the state dict
        _lowercase			:				Union[str, Any] 						=						in_proj_weight[
            : config.hidden_size, :
        ]
        _lowercase			:				List[Any] 						=						in_proj_bias[: config.hidden_size]
        _lowercase			:				int 						=						in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        _lowercase			:				Dict 						=						in_proj_bias[
            config.hidden_size : config.hidden_size * 2
        ]
        _lowercase			:				Tuple 						=						in_proj_weight[
            -config.hidden_size :, :
        ]
        _lowercase			:				List[str] 						=						in_proj_bias[-config.hidden_size :]



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		str:
    _lowercase			:				Any 						=						dct.pop(lowerCamelCase_					)
    _lowercase			:				int 						=						val



def 							UpperCamelCase_( )       ->		Optional[int]:
    _lowercase			:				str 						=						'http://images.cocodataset.org/val2017/000000039769.jpg'
    _lowercase			:				Optional[int] 						=						Image.open(requests.get(lowerCamelCase_				,					stream=lowerCamelCase_					).raw					)
    return im



@torch.no_grad()
def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_					)       ->		Optional[Any]:
    _lowercase			:				Tuple 						=						DeiTConfig()
    # all deit models have fine-tuned heads
    _lowercase			:				List[Any] 						=						False
    # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
    _lowercase			:				int 						=						1000
    _lowercase			:				List[str] 						=						'huggingface/label-files'
    _lowercase			:				List[str] 						=						'imagenet-1k-id2label.json'
    _lowercase			:				Optional[Any] 						=						json.load(open(hf_hub_download(lowerCamelCase_				,					lowerCamelCase_				,					repo_type='dataset'					)				,					'r'					)					)
    _lowercase			:				Tuple 						=						{int(lowerCamelCase_					): v for k, v in idalabel.items()}
    _lowercase			:				List[str] 						=						idalabel
    _lowercase			:				Tuple 						=						{v: k for k, v in idalabel.items()}
    _lowercase			:				Optional[Any] 						=						int(deit_name[-6:-4]					)
    _lowercase			:				Union[str, Any] 						=						int(deit_name[-3:]					)
    # size of the architecture
    if deit_name[9:].startswith('tiny'					):
        _lowercase			:				List[Any] 						=						192
        _lowercase			:				Union[str, Any] 						=						768
        _lowercase			:				Union[str, Any] 						=						12
        _lowercase			:				List[str] 						=						3
    elif deit_name[9:].startswith('small'					):
        _lowercase			:				Tuple 						=						384
        _lowercase			:				List[str] 						=						1536
        _lowercase			:				List[Any] 						=						12
        _lowercase			:				List[Any] 						=						6
    if deit_name[9:].startswith('base'					):
        pass
    elif deit_name[4:].startswith('large'					):
        _lowercase			:				List[Any] 						=						1024
        _lowercase			:				Tuple 						=						4096
        _lowercase			:				Union[str, Any] 						=						24
        _lowercase			:				Optional[Any] 						=						16

    # load original model from timm
    _lowercase			:				Union[str, Any] 						=						timm.create_model(lowerCamelCase_				,					pretrained=lowerCamelCase_					)
    timm_model.eval()

    # load state_dict of original model, remove and rename some keys
    _lowercase			:				List[str] 						=						timm_model.state_dict()
    _lowercase			:				List[Any] 						=						create_rename_keys(lowerCamelCase_				,					lowerCamelCase_					)
    for src, dest in rename_keys:
        rename_key(lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)
    read_in_q_k_v(lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)

    # load HuggingFace model
    _lowercase			:				Dict 						=						DeiTForImageClassificationWithTeacher(lowerCamelCase_					).eval()
    model.load_state_dict(lowerCamelCase_					)

    # Check outputs on an image, prepared by DeiTImageProcessor
    _lowercase			:				str 						=						int(
        (256 / 224) * config.image_size					)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
    _lowercase			:				Optional[Any] 						=						DeiTImageProcessor(size=lowerCamelCase_				,					crop_size=config.image_size					)
    _lowercase			:				List[str] 						=						image_processor(images=prepare_img()				,					return_tensors='pt'					)
    _lowercase			:				Union[str, Any] 						=						encoding['pixel_values']
    _lowercase			:				Tuple 						=						model(lowerCamelCase_					)

    _lowercase			:				Optional[int] 						=						timm_model(lowerCamelCase_					)
    assert timm_logits.shape == outputs.logits.shape
    assert torch.allclose(lowerCamelCase_				,					outputs.logits				,					atol=1e-3					)

    Path(lowerCamelCase_					).mkdir(exist_ok=lowerCamelCase_					)
    print(F'''Saving model {deit_name} to {pytorch_dump_folder_path}'''					)
    model.save_pretrained(lowerCamelCase_					)
    print(F'''Saving image processor to {pytorch_dump_folder_path}'''					)
    image_processor.save_pretrained(lowerCamelCase_					)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE			: Union[str, Any]				   =					argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--deit_name",
     default="vit_deit_base_distilled_patch16_224",
     type=str,
     help="Name of the DeiT timm model you'd like to convert.",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
 )

 SCREAMING_SNAKE_CASE			: List[Any]				   =					parser.parse_args()
 convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)









"""simple docstring"""



import requests

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







def 						__lowercase	(  snake_case_    :    str		)  ->None:



    '''simple docstring'''







    __A       :		str							=	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>""")




