





"""simple docstring"""



import argparse
from collections import OrderedDict
from pathlib import Path

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F

from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging


logging.set_verbosity_info()
a_					=     logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
a_					=     []
for i in range(6):
       # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
       rename_keys.append(
           (f'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', f'''encoder.layers.{i}.self_attn.out_proj.weight''')
       )
       rename_keys.append(
           (f'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', f'''encoder.layers.{i}.self_attn.out_proj.bias''')
       )
       rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.weight''', f'''encoder.layers.{i}.fc1.weight'''))
       rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.bias''', f'''encoder.layers.{i}.fc1.bias'''))
       rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.weight''', f'''encoder.layers.{i}.fc2.weight'''))
       rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.bias''', f'''encoder.layers.{i}.fc2.bias'''))
       rename_keys.append(
           (f'''transformer.encoder.layers.{i}.norm1.weight''', f'''encoder.layers.{i}.self_attn_layer_norm.weight''')
       )
       rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.bias''', f'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
       rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.weight''', f'''encoder.layers.{i}.final_layer_norm.weight'''))
       rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.bias''', f'''encoder.layers.{i}.final_layer_norm.bias'''))
       # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
       rename_keys.append(
           (f'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', f'''decoder.layers.{i}.self_attn.out_proj.weight''')
       )
       rename_keys.append(
           (f'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', f'''decoder.layers.{i}.self_attn.out_proj.bias''')
       )
       rename_keys.append(
           (
               f'''transformer.decoder.layers.{i}.multihead_attn.out_proj.weight''',
               f'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
           )
       )
       rename_keys.append(
           (
               f'''transformer.decoder.layers.{i}.multihead_attn.out_proj.bias''',
               f'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
           )
       )
       rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.weight''', f'''decoder.layers.{i}.fc1.weight'''))
       rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.bias''', f'''decoder.layers.{i}.fc1.bias'''))
       rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.weight''', f'''decoder.layers.{i}.fc2.weight'''))
       rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.bias''', f'''decoder.layers.{i}.fc2.bias'''))
       rename_keys.append(
           (f'''transformer.decoder.layers.{i}.norm1.weight''', f'''decoder.layers.{i}.self_attn_layer_norm.weight''')
       )
       rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.bias''', f'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
       rename_keys.append(
           (f'''transformer.decoder.layers.{i}.norm2.weight''', f'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')
       )
       rename_keys.append(
           (f'''transformer.decoder.layers.{i}.norm2.bias''', f'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')
       )
       rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.weight''', f'''decoder.layers.{i}.final_layer_norm.weight'''))
       rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.bias''', f'''decoder.layers.{i}.final_layer_norm.bias'''))

# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ("""input_proj.weight""", """input_projection.weight"""),
        ("""input_proj.bias""", """input_projection.bias"""),
        ("""query_embed.weight""", """query_position_embeddings.weight"""),
        ("""transformer.encoder.norm.weight""", """encoder.layernorm.weight"""),
        ("""transformer.encoder.norm.bias""", """encoder.layernorm.bias"""),
        ("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
        ("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
        ("""class_embed.weight""", """class_labels_classifier.weight"""),
        ("""class_embed.bias""", """class_labels_classifier.bias"""),
        ("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
        ("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
        ("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
        ("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
        ("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
        ("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
    ]
)







def 						__lowercase	(  snake_case_    :    Tuple							,snake_case_    :    Union[str, Any]							,snake_case_    :    str		)  ->Union[str, Any]:



    '''simple docstring'''







    __A       :		Any							=	state_dict.pop(snake_case_		)
    __A       :		Any							=	val







def 						__lowercase	(  snake_case_    :    Union[str, Any]		)  ->Tuple:



    '''simple docstring'''







    __A       :		Optional[Any]							=	OrderedDict()
    for key, value in state_dict.items():
        if "backbone.0.body" in key:
            __A       :		Any							=	key.replace('''backbone.0.body'''							,'''backbone.conv_encoder.model'''		)
            __A       :		str							=	value
        else:
            __A       :		Optional[int]							=	value

    return new_state_dict







def 						__lowercase	(  snake_case_    :    Union[str, Any]		)  ->Union[str, Any]:



    '''simple docstring'''







    __A       :		List[Any]							=	''''''

    # first: transformer encoder
    for i in range(6		):
        # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
        __A       :		Optional[Any]							=	state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""		)
        __A       :		Union[str, Any]							=	state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""		)
        # next, add query, keys and values (in that order) to the state dict
        __A       :		Union[str, Any]							=	in_proj_weight[:256, :]
        __A       :		Optional[int]							=	in_proj_bias[:256]
        __A       :		str							=	in_proj_weight[256:512, :]
        __A       :		Optional[Any]							=	in_proj_bias[256:512]
        __A       :		Tuple							=	in_proj_weight[-256:, :]
        __A       :		List[str]							=	in_proj_bias[-256:]
    # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
    for i in range(6		):
        # read in weights + bias of input projection layer of self-attention
        __A       :		List[str]							=	state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""		)
        __A       :		Optional[int]							=	state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"""		)
        # next, add query, keys and values (in that order) to the state dict
        __A       :		Optional[Any]							=	in_proj_weight[:256, :]
        __A       :		int							=	in_proj_bias[:256]
        __A       :		Any							=	in_proj_weight[256:512, :]
        __A       :		List[Any]							=	in_proj_bias[256:512]
        __A       :		Any							=	in_proj_weight[-256:, :]
        __A       :		Optional[int]							=	in_proj_bias[-256:]
        # read in weights + bias of input projection layer of cross-attention
        __A       :		List[str]							=	state_dict.pop(
            F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""		)
        __A       :		List[Any]							=	state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"""		)
        # next, add query, keys and values (in that order) of cross-attention to the state dict
        __A       :		int							=	in_proj_weight_cross_attn[:256, :]
        __A       :		str							=	in_proj_bias_cross_attn[:256]
        __A       :		str							=	in_proj_weight_cross_attn[256:512, :]
        __A       :		List[Any]							=	in_proj_bias_cross_attn[256:512]
        __A       :		Tuple							=	in_proj_weight_cross_attn[-256:, :]
        __A       :		Tuple							=	in_proj_bias_cross_attn[-256:]







def 						__lowercase	(  snake_case_    :    Any							,snake_case_    :    List[str]		)  ->int:



    '''simple docstring'''







    __A					,			__A       :		Tuple							=	image.size
    __A       :		str							=	max(snake_case_							,snake_case_		)
    __A       :		str							=	800 if '''detection''' in checkpoint_url else 1000
    __A       :		Tuple							=	target_max_size / current_max_size
    __A       :		Union[str, Any]							=	image.resize((int(round(scale * width		)		), int(round(scale * height		)		))		)

    return resized_image







def 						__lowercase	(  snake_case_    :    Optional[int]		)  ->str:



    '''simple docstring'''







    __A       :		Tuple							=	F.to_tensor(snake_case_		)
    __A       :		str							=	F.normalize(snake_case_							,mean=[0.485, 0.456, 0.406]							,std=[0.229, 0.224, 0.225]		)
    return image







@torch.no_grad()
def 						__lowercase	(  snake_case_    :    Optional[int]							,snake_case_    :    Tuple							,snake_case_    :    Optional[Any]		)  ->List[str]:



    '''simple docstring'''







    logger.info('''Converting model...'''		)

    # load original state dict
    __A       :		Optional[Any]							=	torch.hub.load_state_dict_from_url(snake_case_							,map_location='''cpu'''		)
    # rename keys
    for src, dest in rename_keys:
        rename_key(snake_case_							,snake_case_							,snake_case_		)
    __A       :		Dict							=	rename_backbone_keys(snake_case_		)
    # query, key and value matrices need special treatment
    read_in_q_k_v(snake_case_		)
    # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
    __A       :		Optional[int]							=	'''model.'''
    for key in state_dict.copy().keys():
        if not key.startswith('''class_labels_classifier'''		) and not key.startswith('''bbox_predictor'''		):
            __A       :		Union[str, Any]							=	state_dict.pop(snake_case_		)
            __A       :		str							=	val
    # create HuggingFace model and load state dict
    __A       :		Dict							=	TableTransformerConfig(
        backbone='''resnet18'''							,mask_loss_coefficient=1							,dice_loss_coefficient=1							,ce_loss_coefficient=1							,bbox_loss_coefficient=5							,giou_loss_coefficient=2							,eos_coefficient=0.4							,class_cost=1							,bbox_cost=5							,giou_cost=2							,)

    if "detection" in checkpoint_url:
        __A       :		Optional[Any]							=	15
        __A       :		Union[str, Any]							=	2
        __A       :		Optional[int]							=	{0: '''table''', 1: '''table rotated'''}
        __A       :		List[str]							=	idalabel
        __A       :		Any							=	{v: k for k, v in idalabel.items()}
    else:
        __A       :		str							=	125
        __A       :		Optional[int]							=	6
        __A       :		Optional[Any]							=	{
            0: '''table''',
            1: '''table column''',
            2: '''table row''',
            3: '''table column header''',
            4: '''table projected row header''',
            5: '''table spanning cell''',
        }
        __A       :		Optional[Any]							=	idalabel
        __A       :		Tuple							=	{v: k for k, v in idalabel.items()}

    __A       :		List[Any]							=	DetrImageProcessor(
        format='''coco_detection'''							,max_size=800 if '''detection''' in checkpoint_url else 1000		)
    __A       :		Dict							=	TableTransformerForObjectDetection(snake_case_		)
    model.load_state_dict(snake_case_		)
    model.eval()

    # verify our conversion
    __A       :		Optional[Any]							=	'''example_pdf.png''' if '''detection''' in checkpoint_url else '''example_table.png'''
    __A       :		Dict							=	hf_hub_download(repo_id='''nielsr/example-pdf'''							,repo_type='''dataset'''							,filename=snake_case_		)
    __A       :		Tuple							=	Image.open(snake_case_		).convert('''RGB'''		)
    __A       :		Optional[int]							=	normalize(resize(snake_case_							,snake_case_		)		).unsqueeze(0		)

    __A       :		Optional[Any]							=	model(snake_case_		)

    if "detection" in checkpoint_url:
        __A       :		List[str]							=	(1, 15, 3)
        __A       :		Any							=	torch.tensor(
            [[-6.7_897, -16.9_985, 6.7_937], [-8.0_186, -22.2_192, 6.9_677], [-7.3_117, -21.0_708, 7.4_055]]		)
        __A       :		Optional[Any]							=	torch.tensor([[0.4_867, 0.1_767, 0.6_732], [0.6_718, 0.4_479, 0.3_830], [0.4_716, 0.1_760, 0.6_364]]		)

    else:
        __A       :		Optional[Any]							=	(1, 125, 7)
        __A       :		Optional[Any]							=	torch.tensor(
            [[-18.1_430, -8.3_214, 4.8_274], [-18.4_685, -7.1_361, -4.2_667], [-26.3_693, -9.3_429, -4.9_962]]		)
        __A       :		int							=	torch.tensor([[0.4_983, 0.5_595, 0.9_440], [0.4_916, 0.6_315, 0.5_954], [0.6_108, 0.8_637, 0.1_135]]		)

    assert outputs.logits.shape == expected_shape
    assert torch.allclose(outputs.logits[0, :3, :3]							,snake_case_							,atol=1e-4		)
    assert torch.allclose(outputs.pred_boxes[0, :3, :3]							,snake_case_							,atol=1e-4		)
    print('''Looks ok!'''		)

    if pytorch_dump_folder_path is not None:
        # Save model and image processor
        logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""		)
        Path(snake_case_		).mkdir(exist_ok=snake_case_		)
        model.save_pretrained(snake_case_		)
        image_processor.save_pretrained(snake_case_		)

    if push_to_hub:
        # Push model to HF hub
        logger.info('''Pushing model to the hub...'''		)
        __A       :		Dict							=	(
            '''microsoft/table-transformer-detection'''
            if '''detection''' in checkpoint_url
            else '''microsoft/table-transformer-structure-recognition'''
        )
        model.push_to_hub(snake_case_		)
        image_processor.push_to_hub(snake_case_		)


if __name__ == "__main__":
       a_					=     argparse.ArgumentParser()

       parser.add_argument(
           """--checkpoint_url""",
           default="""https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
           type=str,
           choices=[
               """https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
               """https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth""",
           ],
           help="""URL of the Table Transformer checkpoint you'd like to convert.""",
       )
       parser.add_argument(
           """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
       )
       parser.add_argument(
           """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
       )
       a_					=     parser.parse_args()
       convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)











"""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>""")




