



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()
SCREAMING_SNAKE_CASE_:Optional[int]					=						logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
SCREAMING_SNAKE_CASE_:Optional[Any]					=						[]
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      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase		)	->  Optional[int]:



    """simple docstring"""






    A			:	Union[str, Any]										=       state_dict.pop(_lowerCAmelCase		)
    A			:	Any										=       val




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  Optional[Any]:



    """simple docstring"""






    A			:	Tuple										=       OrderedDict()
    for key, value in state_dict.items():
        if "backbone.0.body" in key:
            A			:	int										=       key.replace("""backbone.0.body"""				,					"""backbone.conv_encoder.model"""		)
            A			:	Any										=       value
        else:
            A			:	List[Any]										=       value

    return new_state_dict




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  List[Any]:



    """simple docstring"""






    A			:	str										=       """"""

    # 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			:	int										=       state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''		)
        A			:	Tuple										=       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			:	Tuple										=       in_proj_weight[:256, :]
        A			:	str										=       in_proj_bias[:256]
        A			:	str										=       in_proj_weight[256:512, :]
        A			:	List[Any]										=       in_proj_bias[256:512]
        A			:	Any										=       in_proj_weight[-256:, :]
        A			:	Optional[Any]										=       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			:	Tuple										=       state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''		)
        A			:	Optional[Any]										=       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			:	Union[str, Any]										=       in_proj_weight[:256, :]
        A			:	Optional[int]										=       in_proj_bias[:256]
        A			:	str										=       in_proj_weight[256:512, :]
        A			:	Union[str, Any]										=       in_proj_bias[256:512]
        A			:	int										=       in_proj_weight[-256:, :]
        A			:	Union[str, Any]										=       in_proj_bias[-256:]
        # read in weights + bias of input projection layer of cross-attention
        A			:	str										=       state_dict.pop(
            f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''		)
        A			:	List[str]										=       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			:	List[str]										=       in_proj_weight_cross_attn[256:512, :]
        A			:	List[Any]										=       in_proj_bias_cross_attn[256:512]
        A			:	List[str]										=       in_proj_weight_cross_attn[-256:, :]
        A			:	List[str]										=       in_proj_bias_cross_attn[-256:]




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase		)	->  Optional[Any]:



    """simple docstring"""






    A					,							A			:	Optional[Any]										=       image.size
    A			:	Dict										=       max(_lowerCAmelCase				,					_lowerCAmelCase		)
    A			:	List[Any]										=       800 if """detection""" in checkpoint_url else 1000
    A			:	str										=       target_max_size / current_max_size
    A			:	List[Any]										=       image.resize((int(round(scale * width		)		), int(round(scale * height		)		))		)

    return resized_image




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  Dict:



    """simple docstring"""






    A			:	Tuple										=       F.to_tensor(_lowerCAmelCase		)
    A			:	Optional[Any]										=       F.normalize(_lowerCAmelCase				,					mean=[0.485, 0.456, 0.406]				,					std=[0.229, 0.224, 0.225]		)
    return image




@torch.no_grad()
def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase		)	->  Optional[int]:



    """simple docstring"""






    logger.info("""Converting model..."""		)

    # load original state dict
    A			:	List[Any]										=       torch.hub.load_state_dict_from_url(_lowerCAmelCase				,					map_location="""cpu"""		)
    # rename keys
    for src, dest in rename_keys:
        rename_key(_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase		)
    A			:	int										=       rename_backbone_keys(_lowerCAmelCase		)
    # query, key and value matrices need special treatment
    read_in_q_k_v(_lowerCAmelCase		)
    # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
    A			:	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(_lowerCAmelCase		)
            A			:	int										=       val
    # create HuggingFace model and load state dict
    A			:	str										=       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			:	List[Any]										=       15
        A			:	Any										=       2
        A			:	Dict										=       {0: """table""", 1: """table rotated"""}
        A			:	Any										=       idalabel
        A			:	Any										=       {v: k for k, v in idalabel.items()}
    else:
        A			:	int										=       125
        A			:	str										=       6
        A			:	List[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			:	Optional[int]										=       DetrImageProcessor(
        format="""coco_detection"""				,					max_size=800 if """detection""" in checkpoint_url else 1000		)
    A			:	Dict										=       TableTransformerForObjectDetection(_lowerCAmelCase		)
    model.load_state_dict(_lowerCAmelCase		)
    model.eval()

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

    A			:	Dict										=       model(_lowerCAmelCase		)

    if "detection" in checkpoint_url:
        A			:	Tuple										=       (1, 15, 3)
        A			:	str										=       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			:	str										=       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			:	Union[str, Any]										=       (1, 125, 7)
        A			:	Dict										=       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			:	str										=       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]				,					_lowerCAmelCase				,					atol=1e-4		)
    assert torch.allclose(outputs.pred_boxes[0, :3, :3]				,					_lowerCAmelCase				,					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(_lowerCAmelCase		).mkdir(exist_ok=_lowerCAmelCase		)
        model.save_pretrained(_lowerCAmelCase		)
        image_processor.save_pretrained(_lowerCAmelCase		)

    if push_to_hub:
        # Push model to HF hub
        logger.info("""Pushing model to the hub..."""		)
        A			:	Optional[int]										=       (
            """microsoft/table-transformer-detection"""
            if """detection""" in checkpoint_url
            else """microsoft/table-transformer-structure-recognition"""
        )
        model.push_to_hub(_lowerCAmelCase		)
        image_processor.push_to_hub(_lowerCAmelCase		)


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_:Union[str, Any]					=						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."""
     )
     SCREAMING_SNAKE_CASE_:Union[str, Any]					=						parser.parse_args()
     convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)







import requests

SCREAMING_SNAKE_CASE_:List[str]					=						""""""  # <-- Put your OpenWeatherMap appid here!
SCREAMING_SNAKE_CASE_:Dict					=						"""https://api.openweathermap.org/data/2.5/"""




def      __UpperCamelCase     (  _lowerCAmelCase = "Chicago"				,					_lowerCAmelCase = APPID		)	->  dict:



    """simple docstring"""






    return requests.get(URL_BASE + """weather"""				,					params=locals()		).json()




def      __UpperCamelCase     (  _lowerCAmelCase = "Kolkata, India"				,					_lowerCAmelCase = APPID		)	->  dict:



    """simple docstring"""






    return requests.get(URL_BASE + """forecast"""				,					params=locals()		).json()




def      __UpperCamelCase     (  _lowerCAmelCase = 55.68				,					_lowerCAmelCase = 12.57				,					_lowerCAmelCase = APPID		)	->  dict:



    """simple docstring"""






    return requests.get(URL_BASE + """onecall"""				,					params=locals()		).json()


if __name__ == "__main__":
     from pprint import pprint

     while True:
          SCREAMING_SNAKE_CASE_:int					=						input("""Enter a location:""").strip()
          if location:
               pprint(current_weather(location))
          else:
               break


