
"""simple docstring"""
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def 				a__   (   a   :				int		):

  """simple docstring"""







  return EnvironmentCommand()






class      _UpperCAmelCase				(					_snake_case):
    @staticmethod
    def 					lowerCamelCase__	(    snake_case_     ):
      _snake_case     :						List[str]						=   parser.add_parser("env"     )
      download_parser.set_defaults(func=snake_case_     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Union[str, Any]						=   huggingface_hub.__version__

      _snake_case     :						int						=   "not installed"
      _snake_case     :						Tuple						=   "NA"
      if is_torch_available():
        import torch

        _snake_case     :						int						=   torch.__version__
        _snake_case     :						int						=   torch.cuda.is_available()

      _snake_case     :						List[str]						=   "not installed"
      if is_transformers_available():
        import transformers

        _snake_case     :						Optional[Any]						=   transformers.__version__

      _snake_case     :						Tuple						=   "not installed"
      if is_accelerate_available():
        import accelerate

        _snake_case     :						Optional[int]						=   accelerate.__version__

      _snake_case     :						List[str]						=   "not installed"
      if is_xformers_available():
        import xformers

        _snake_case     :						Any						=   xformers.__version__

      _snake_case     :						int						=   {
          "`diffusers` version": version,
          "Platform": platform.platform(),
          "Python version": platform.python_version(),
          "PyTorch version (GPU?)": F'{pt_version} ({pt_cuda_available})',
          "Huggingface_hub version": hub_version,
          "Transformers version": transformers_version,
          "Accelerate version": accelerate_version,
          "xFormers version": xformers_version,
          "Using GPU in script?": "<fill in>",
          "Using distributed or parallel set-up in script?": "<fill in>",
      }

      print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n"     )
      print(self.format_dict(snake_case_     )     )

      return info


    @staticmethod
    def 					lowerCamelCase__	(    snake_case_     ):
      return "\n".join([F'- {prop}: {val}' for prop, val in d.items()]     ) + "\n"



"""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()
_a      :			Tuple					=							logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_a      :			int					=							[]
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   :				List[str]       ,     a   :				int       ,     a   :				int		):

  """simple docstring"""







  _snake_case     :						Union[str, Any]						=   state_dict.pop(a		)
  _snake_case     :						Union[str, Any]						=   val
def 				a__   (   a   :				Tuple		):

  """simple docstring"""







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

  return new_state_dict
def 				a__   (   a   :				int		):

  """simple docstring"""







  _snake_case     :						Any						=   ""

  # 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     :						str						=   state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'		)
    _snake_case     :						str						=   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     :						int						=   in_proj_weight[:256, :]
    _snake_case     :						List[str]						=   in_proj_bias[:256]
    _snake_case     :						Optional[Any]						=   in_proj_weight[256:512, :]
    _snake_case     :						List[str]						=   in_proj_bias[256:512]
    _snake_case     :						Dict						=   in_proj_weight[-256:, :]
    _snake_case     :						Dict						=   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     :						List[Any]						=   state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'		)
    _snake_case     :						Union[str, 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
    _snake_case     :						Union[str, Any]						=   in_proj_weight[:256, :]
    _snake_case     :						Tuple						=   in_proj_bias[:256]
    _snake_case     :						int						=   in_proj_weight[256:512, :]
    _snake_case     :						int						=   in_proj_bias[256:512]
    _snake_case     :						Dict						=   in_proj_weight[-256:, :]
    _snake_case     :						str						=   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     :						Optional[int]						=   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     :						Dict						=   in_proj_weight_cross_attn[:256, :]
    _snake_case     :						Any						=   in_proj_bias_cross_attn[:256]
    _snake_case     :						Union[str, Any]						=   in_proj_weight_cross_attn[256:512, :]
    _snake_case     :						Optional[int]						=   in_proj_bias_cross_attn[256:512]
    _snake_case     :						Any						=   in_proj_weight_cross_attn[-256:, :]
    _snake_case     :						str						=   in_proj_bias_cross_attn[-256:]
def 				a__   (   a   :				str       ,     a   :				int		):

  """simple docstring"""







  _snake_case			,					_snake_case     :						List[str]						=   image.size
  _snake_case     :						Dict						=   max(a       ,     a		)
  _snake_case     :						Union[str, Any]						=   800 if "detection" in checkpoint_url else 1_000
  _snake_case     :						Any						=   target_max_size / current_max_size
  _snake_case     :						str						=   image.resize((int(round(scale * width		)		), int(round(scale * height		)		))		)

  return resized_image
def 				a__   (   a   :				str		):

  """simple docstring"""







  _snake_case     :						str						=   F.to_tensor(a		)
  _snake_case     :						Union[str, 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[Any]       ,     a   :				Any       ,     a   :				Union[str, Any]		):

  """simple docstring"""







  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     :						Union[str, Any]						=   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     :						int						=   "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     :						Any						=   val
    # create HuggingFace model and load state dict
  _snake_case     :						Tuple						=   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     :						Any						=   15
    _snake_case     :						int						=   2
    _snake_case     :						Optional[Any]						=   {0: "table", 1: "table rotated"}
    _snake_case     :						Union[str, Any]						=   idalabel
    _snake_case     :						Union[str, Any]						=   {v: k for k, v in idalabel.items()}
  else:
    _snake_case     :						Any						=   125
    _snake_case     :						Union[str, 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     :						Any						=   idalabel
    _snake_case     :						Optional[int]						=   {v: k for k, v in idalabel.items()}

  _snake_case     :						Union[str, Any]						=   DetrImageProcessor(
      format="coco_detection"       ,     max_size=800 if "detection" in checkpoint_url else 1_000		)
  _snake_case     :						str						=   TableTransformerForObjectDetection(a		)
  model.load_state_dict(a		)
  model.eval()

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

  _snake_case     :						str						=   model(a		)

  if "detection" in checkpoint_url:
    _snake_case     :						int						=   (1, 15, 3)
    _snake_case     :						List[str]						=   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     :						Union[str, Any]						=   (1, 125, 7)
    _snake_case     :						str						=   torch.tensor(
        [[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]]		)
    _snake_case     :						Optional[Any]						=   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     :						int						=   (
        "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__":
    _a      :			Tuple					=							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."""
    )
    _a      :			Any					=							parser.parse_args()
    convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)

