"""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 					__a   (		A    ,				A    ,				A      ):






     '''simple docstring'''



     lowercase__				 =							state_dict.pop(A      )
     lowercase__				 =							val







def 					__a   (		A      ):






     '''simple docstring'''



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

     return new_state_dict







def 					__a   (		A      ):






     '''simple docstring'''



     lowercase__				 =							""

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







def 					__a   (		A    ,				A      ):






     '''simple docstring'''



     lowercase__ ,						lowercase__				 =							image.size
     lowercase__				 =							max(A    ,				A      )
     lowercase__				 =							8_00 if "detection" in checkpoint_url else 10_00
     lowercase__				 =							target_max_size / current_max_size
     lowercase__				 =							image.resize((int(round(scale * width      )      ), int(round(scale * height      )      ))      )

     return resized_image







def 					__a   (		A      ):






     '''simple docstring'''



     lowercase__				 =							F.to_tensor(A      )
     lowercase__				 =							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    ,				A    ,				A      ):






     '''simple docstring'''



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

     # load original state dict
     lowercase__				 =							torch.hub.load_state_dict_from_url(A    ,				map_location="cpu"      )
     # rename keys
     for src, dest in rename_keys:
          rename_key(A    ,				A    ,				A      )
     lowercase__				 =							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
     lowercase__				 =							"model."
     for key in state_dict.copy().keys():
          if not key.startswith("class_labels_classifier"      ) and not key.startswith("bbox_predictor"      ):
               lowercase__				 =							state_dict.pop(A      )
               lowercase__				 =							val
    # create HuggingFace model and load state dict
     lowercase__				 =							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:
          lowercase__				 =							15
          lowercase__				 =							2
          lowercase__				 =							{0: "table", 1: "table rotated"}
          lowercase__				 =							idalabel
          lowercase__				 =							{v: k for k, v in idalabel.items()}
     else:
          lowercase__				 =							1_25
          lowercase__				 =							6
          lowercase__				 =							{
              0: "table",
              1: "table column",
              2: "table row",
              3: "table column header",
              4: "table projected row header",
              5: "table spanning cell",
          }
          lowercase__				 =							idalabel
          lowercase__				 =							{v: k for k, v in idalabel.items()}

     lowercase__				 =							DetrImageProcessor(
         format="coco_detection"    ,				max_size=8_00 if "detection" in checkpoint_url else 10_00      )
     lowercase__				 =							TableTransformerForObjectDetection(A      )
     model.load_state_dict(A      )
     model.eval()

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

     lowercase__				 =							model(A      )

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

     else:
          lowercase__				 =							(1, 1_25, 7)
          lowercase__				 =							torch.tensor(
              [[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]]      )
          lowercase__				 =							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..."      )
          lowercase__				 =							(
              "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__":
 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"""


from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


try:
 if not (is_transformers_available() and is_torch_available()):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
 from .notes_encoder import SpectrogramNotesEncoder
 from .continous_encoder import SpectrogramContEncoder
 from .pipeline_spectrogram_diffusion import (
     SpectrogramContEncoder,
     SpectrogramDiffusionPipeline,
     TaFilmDecoder,
 )

try:
 if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ...utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
 from .midi_utils import MidiProcessor




