


"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCamelCase_       :       Optional[int]      						=  {
    """configuration_mobilenet_v2""": [
        """MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MobileNetV2Config""",
        """MobileNetV2OnnxConfig""",
    ],
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_       :       List[Any]      						=  ["""MobileNetV2FeatureExtractor"""]
       lowerCamelCase_       :       Optional[Any]      						=  ["""MobileNetV2ImageProcessor"""]


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_       :       Any      						=  [
           """MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """MobileNetV2ForImageClassification""",
           """MobileNetV2ForSemanticSegmentation""",
           """MobileNetV2Model""",
           """MobileNetV2PreTrainedModel""",
           """load_tf_weights_in_mobilenet_v2""",
       ]


if TYPE_CHECKING:
       from .configuration_mobilenet_va import (
           MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
           MobileNetVaConfig,
           MobileNetVaOnnxConfig,
       )

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
              from .image_processing_mobilenet_va import MobileNetVaImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mobilenet_va import (
                  MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MobileNetVaForImageClassification,
                  MobileNetVaForSemanticSegmentation,
                  MobileNetVaModel,
                  MobileNetVaPreTrainedModel,
                  load_tf_weights_in_mobilenet_va,
              )


else:
       import sys

       lowerCamelCase_       :       str      						=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)










"""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_       :       int      						=  logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowerCamelCase_       :       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 _A						(						lowercase	,   lowercase	,   lowercase		):







    """simple docstring"""
    a					=state_dict.pop(lowercase		)
    a					=val
def _A						(						lowercase		):







    """simple docstring"""
    a					=OrderedDict()
    for key, value in state_dict.items():
        if "backbone.0.body" in key:
            a					=key.replace('''backbone.0.body'''	,   '''backbone.conv_encoder.model'''		)
            a					=value
        else:
            a					=value

    return new_state_dict
def _A						(						lowercase		):







    """simple docstring"""
    a					=''''''

    # 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					=state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''		)
        a					=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					=in_proj_weight[:2_56, :]
        a					=in_proj_bias[:2_56]
        a					=in_proj_weight[2_56:5_12, :]
        a					=in_proj_bias[2_56:5_12]
        a					=in_proj_weight[-2_56:, :]
        a					=in_proj_bias[-2_56:]
    # 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					=state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''		)
        a					=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					=in_proj_weight[:2_56, :]
        a					=in_proj_bias[:2_56]
        a					=in_proj_weight[2_56:5_12, :]
        a					=in_proj_bias[2_56:5_12]
        a					=in_proj_weight[-2_56:, :]
        a					=in_proj_bias[-2_56:]
        # read in weights + bias of input projection layer of cross-attention
        a					=state_dict.pop(
            f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''		)
        a					=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					=in_proj_weight_cross_attn[:2_56, :]
        a					=in_proj_bias_cross_attn[:2_56]
        a					=in_proj_weight_cross_attn[2_56:5_12, :]
        a					=in_proj_bias_cross_attn[2_56:5_12]
        a					=in_proj_weight_cross_attn[-2_56:, :]
        a					=in_proj_bias_cross_attn[-2_56:]
def _A						(						lowercase	,   lowercase		):







    """simple docstring"""
    a		,				a					=image.size
    a					=max(lowercase	,   lowercase		)
    a					=8_00 if '''detection''' in checkpoint_url else 10_00
    a					=target_max_size / current_max_size
    a					=image.resize((int(round(scale * width		)		), int(round(scale * height		)		))		)

    return resized_image
def _A						(						lowercase		):







    """simple docstring"""
    a					=F.to_tensor(lowercase		)
    a					=F.normalize(lowercase	,   mean=[0.485, 0.456, 0.406]	,   std=[0.229, 0.224, 0.225]		)
    return image
@torch.no_grad()
def _A						(						lowercase	,   lowercase	,   lowercase		):







    """simple docstring"""
    logger.info('''Converting model...'''		)

    # load original state dict
    a					=torch.hub.load_state_dict_from_url(lowercase	,   map_location='''cpu'''		)
    # rename keys
    for src, dest in rename_keys:
        rename_key(lowercase	,   lowercase	,   lowercase		)
    a					=rename_backbone_keys(lowercase		)
    # query, key and value matrices need special treatment
    read_in_q_k_v(lowercase		)
    # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
    a					='''model.'''
    for key in state_dict.copy().keys():
        if not key.startswith('''class_labels_classifier'''		) and not key.startswith('''bbox_predictor'''		):
            a					=state_dict.pop(lowercase		)
            a					=val
    # create HuggingFace model and load state dict
    a					=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					=15
        a					=2
        a					={0: '''table''', 1: '''table rotated'''}
        a					=idalabel
        a					={v: k for k, v in idalabel.items()}
    else:
        a					=1_25
        a					=6
        a					={
            0: '''table''',
            1: '''table column''',
            2: '''table row''',
            3: '''table column header''',
            4: '''table projected row header''',
            5: '''table spanning cell''',
        }
        a					=idalabel
        a					={v: k for k, v in idalabel.items()}

    a					=DetrImageProcessor(
        format='''coco_detection'''	,   max_size=8_00 if '''detection''' in checkpoint_url else 10_00		)
    a					=TableTransformerForObjectDetection(lowercase		)
    model.load_state_dict(lowercase		)
    model.eval()

    # verify our conversion
    a					='''example_pdf.png''' if '''detection''' in checkpoint_url else '''example_table.png'''
    a					=hf_hub_download(repo_id='''nielsr/example-pdf'''	,   repo_type='''dataset'''	,   filename=lowercase		)
    a					=Image.open(lowercase		).convert('''RGB'''		)
    a					=normalize(resize(lowercase	,   lowercase		)		).unsqueeze(0		)

    a					=model(lowercase		)

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

    else:
        a					=(1, 1_25, 7)
        a					=torch.tensor(
            [[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]]		)
        a					=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]	,   lowercase	,   atol=1E-4		)
    assert torch.allclose(outputs.pred_boxes[0, :3, :3]	,   lowercase	,   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(lowercase		).mkdir(exist_ok=lowercase		)
        model.save_pretrained(lowercase		)
        image_processor.save_pretrained(lowercase		)

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


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