



'''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()
_lowerCAmelCase      						=	logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_lowerCAmelCase      						=	[]
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    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  List[Any]							      =					state_dict.pop(UpperCamelCase			)
    lowerCAmelCase__ :  Tuple							      =					val
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





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

    return new_state_dict
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  Tuple							      =					""""""

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



    """simple docstring"""





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

    return resized_image
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  Tuple							      =					F.to_tensor(UpperCamelCase			)
    lowerCAmelCase__ :  Union[str, Any]							      =					F.normalize(UpperCamelCase							,    mean=[0.485, 0.456, 0.406]							,    std=[0.229, 0.224, 0.225]			)
    return image
@torch.no_grad()
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





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

    # load original state dict
    lowerCAmelCase__ :  str							      =					torch.hub.load_state_dict_from_url(UpperCamelCase							,    map_location="""cpu"""			)
    # rename keys
    for src, dest in rename_keys:
        rename_key(UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase			)
    lowerCAmelCase__ :  Tuple							      =					rename_backbone_keys(UpperCamelCase			)
    # query, key and value matrices need special treatment
    read_in_q_k_v(UpperCamelCase			)
    # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
    lowerCAmelCase__ :  List[str]							      =					"""model."""
    for key in state_dict.copy().keys():
        if not key.startswith("""class_labels_classifier"""			) and not key.startswith("""bbox_predictor"""			):
            lowerCAmelCase__ :  Optional[Any]							      =					state_dict.pop(UpperCamelCase			)
            lowerCAmelCase__ :  int							      =					val
    # create HuggingFace model and load state dict
    lowerCAmelCase__ :  List[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:
        lowerCAmelCase__ :  int							      =					15
        lowerCAmelCase__ :  Tuple							      =					2
        lowerCAmelCase__ :  Optional[Any]							      =					{0: """table""", 1: """table rotated"""}
        lowerCAmelCase__ :  Union[str, Any]							      =					idalabel
        lowerCAmelCase__ :  Tuple							      =					{v: k for k, v in idalabel.items()}
    else:
        lowerCAmelCase__ :  Optional[Any]							      =					125
        lowerCAmelCase__ :  int							      =					6
        lowerCAmelCase__ :  Union[str, Any]							      =					{
            0: """table""",
            1: """table column""",
            2: """table row""",
            3: """table column header""",
            4: """table projected row header""",
            5: """table spanning cell""",
        }
        lowerCAmelCase__ :  Any							      =					idalabel
        lowerCAmelCase__ :  Any							      =					{v: k for k, v in idalabel.items()}

    lowerCAmelCase__ :  List[Any]							      =					DetrImageProcessor(
        format="""coco_detection"""							,    max_size=800 if """detection""" in checkpoint_url else 1000			)
    lowerCAmelCase__ :  Optional[int]							      =					TableTransformerForObjectDetection(UpperCamelCase			)
    model.load_state_dict(UpperCamelCase			)
    model.eval()

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

    lowerCAmelCase__ :  Tuple							      =					model(UpperCamelCase			)

    if "detection" in checkpoint_url:
        lowerCAmelCase__ :  List[str]							      =					(1, 15, 3)
        lowerCAmelCase__ :  Optional[Any]							      =					torch.tensor(
            [[-6.7897, -16.9985, 6.7937], [-8.0186, -22.2192, 6.9677], [-7.3117, -21.0708, 7.4055]]			)
        lowerCAmelCase__ :  Optional[Any]							      =					torch.tensor([[0.4867, 0.1767, 0.6732], [0.6718, 0.4479, 0.3830], [0.4716, 0.1760, 0.6364]]			)

    else:
        lowerCAmelCase__ :  Any							      =					(1, 125, 7)
        lowerCAmelCase__ :  str							      =					torch.tensor(
            [[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]]			)
        lowerCAmelCase__ :  List[str]							      =					torch.tensor([[0.4983, 0.5595, 0.9440], [0.4916, 0.6315, 0.5954], [0.6108, 0.8637, 0.1135]]			)

    assert outputs.logits.shape == expected_shape
    assert torch.allclose(outputs.logits[0, :3, :3]							,    UpperCamelCase							,    atol=1e-4			)
    assert torch.allclose(outputs.pred_boxes[0, :3, :3]							,    UpperCamelCase							,    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(UpperCamelCase			).mkdir(exist_ok=UpperCamelCase			)
        model.save_pretrained(UpperCamelCase			)
        image_processor.save_pretrained(UpperCamelCase			)

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


if __name__ == "__main__":
 _lowerCAmelCase      						=	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.'''
 )
 _lowerCAmelCase      						=	parser.parse_args()
 convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)








'''simple docstring'''







import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class 			_a  (	__a ):
       __a				:     int   				=					["""image_processor""", """tokenizer"""]
       __a				:     Union[str, Any]   				=					"""ChineseCLIPImageProcessor"""
       __a				:     List[Any]   				=					("""BertTokenizer""", """BertTokenizerFast""")
       def __init__(	self						:	Dict   ,				lowercase						:	Union[str, Any]=None   ,				lowercase						:	Dict=None   ,				**lowercase						:	Optional[Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		None
           if "feature_extractor" in kwargs:
               warnings.warn(
                   '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                   ''' instead.'''   ,				lowercase   ,				)
               UpperCAmelCase			       =		kwargs.pop('''feature_extractor'''				)

           UpperCAmelCase			       =		image_processor if image_processor is not None else feature_extractor
           if image_processor is None:
               raise ValueError('''You need to specify an `image_processor`.'''				)
           if tokenizer is None:
               raise ValueError('''You need to specify a `tokenizer`.'''				)

           super().__init__(lowercase   ,				lowercase				)
           UpperCAmelCase			       =		self.image_processor
       def __call__(	self						:	Tuple   ,				lowercase						:	Optional[Any]=None   ,				lowercase						:	Union[str, Any]=None   ,				lowercase						:	int=None   ,				**lowercase						:	Dict				):



           '''simple docstring'''

           if text is None and images is None:
               raise ValueError('''You have to specify either text or images. Both cannot be none.'''				)

           if text is not None:
               UpperCAmelCase			       =		self.tokenizer(lowercase   ,				return_tensors=lowercase   ,				**lowercase				)

           if images is not None:
               UpperCAmelCase			       =		self.image_processor(lowercase   ,				return_tensors=lowercase   ,				**lowercase				)

           if text is not None and images is not None:
               UpperCAmelCase			       =		image_features.pixel_values
               return encoding
           elif text is not None:
               return encoding
           else:
               return BatchEncoding(data=dict(**lowercase				)   ,				tensor_type=lowercase				)
       def     A      (	self						:	int   ,				*lowercase						:	Tuple   ,				**lowercase						:	List[str]				):



           '''simple docstring'''

           return self.tokenizer.batch_decode(*lowercase   ,				**lowercase				)
       def     A      (	self						:	Optional[Any]   ,				*lowercase						:	int   ,				**lowercase						:	Optional[int]				):



           '''simple docstring'''

           return self.tokenizer.decode(*lowercase   ,				**lowercase				)
       @property
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.tokenizer.model_input_names
           UpperCAmelCase			       =		self.image_processor.model_input_names
           return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)







       @property
       def     A      (	self						:	List[Any]				):



           '''simple docstring'''

           warnings.warn(
               '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''   ,				lowercase   ,				)
           return self.image_processor_class




