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

# here we list all keys to be renamed (original name on the left, our name on the right)
UpperCAmelCase    					=   []
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 (       A_							:					Union[str, Any]    ,							A_							:					str    ,							A_							:					Optional[int]       ):
						snake_case       :     Tuple =    state_dict.pop(A_       )
						snake_case       :     List[Any] =    val

def      A (       A_							:					Tuple       ):
						snake_case       :     Dict =    OrderedDict()
						for key, value in state_dict.items():
												if "backbone.0.body" in key:
																		snake_case       :     str =    key.replace('''backbone.0.body'''    ,							'''backbone.conv_encoder.model'''       )
																		snake_case       :     Any =    value
												else:
																		snake_case       :     Union[str, Any] =    value

						return new_state_dict

def      A (       A_							:					Optional[Any]       ):
						snake_case       :     Optional[int] =    ''''''

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

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

						return resized_image

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

@torch.no_grad()
def      A (       A_							:					Optional[int]    ,							A_							:					Any    ,							A_							:					Dict       ):
						logger.info('''Converting model...'''       )

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

						snake_case       :     int =    DetrImageProcessor(
						    format='''coco_detection'''    ,							max_size=800 if '''detection''' in checkpoint_url else 1000       )
						snake_case       :     Any =    TableTransformerForObjectDetection(A_       )
						model.load_state_dict(A_       )
						model.eval()

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

						snake_case       :     str =    model(A_       )

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

						else:
												snake_case       :     str =    (1, 125, 7)
												snake_case       :     Union[str, Any] =    torch.tensor(
												    [[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]]       )
												snake_case       :     Dict =    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]    ,							A_    ,							atol=1e-4       )
						assert torch.allclose(outputs.pred_boxes[0, :3, :3]    ,							A_    ,							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(A_       ).mkdir(exist_ok=A_       )
												model.save_pretrained(A_       )
												image_processor.save_pretrained(A_       )

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


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



'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCAmelCase    					=   {
    "configuration_swiftformer": [
        "SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SwiftFormerConfig",
        "SwiftFormerOnnxConfig",
    ]
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCAmelCase    					=   [
						    "SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "SwiftFormerForImageClassification",
						    "SwiftFormerModel",
						    "SwiftFormerPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_swiftformer import (
						    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    SwiftFormerConfig,
						    SwiftFormerOnnxConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_swiftformer import (
												    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SwiftFormerForImageClassification,
												    SwiftFormerModel,
												    SwiftFormerPreTrainedModel,
												)

else:
						import sys

						UpperCAmelCase    					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

