


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    :						List[str]     =							logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__UpperCamelCase    :						List[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   _UpperCAmelCase       (     UpperCAmelCase      :			Tuple						,	UpperCAmelCase      :			Any						,	UpperCAmelCase      :			Optional[Any]      ):
      """simple docstring"""




      __lowerCamelCase     :      Optional[int]       						=						state_dict.pop(_lowerCamelCase      )
      __lowerCamelCase     :      Optional[int]       						=						val





def   _UpperCAmelCase       (     UpperCAmelCase      :			Optional[int]      ):
      """simple docstring"""




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

      return new_state_dict





def   _UpperCAmelCase       (     UpperCAmelCase      :			Any      ):
      """simple docstring"""




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





def   _UpperCAmelCase       (     UpperCAmelCase      :			List[Any]						,	UpperCAmelCase      :			Union[str, Any]      ):
      """simple docstring"""




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

      return resized_image





def   _UpperCAmelCase       (     UpperCAmelCase      :			List[str]      ):
      """simple docstring"""




      __lowerCamelCase     :      Dict       						=						F.to_tensor(_lowerCamelCase      )
      __lowerCamelCase     :      Dict       						=						F.normalize(_lowerCamelCase						,	mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]						,	std=[0.2_2_9, 0.2_2_4, 0.2_2_5]      )
      return image





@torch.no_grad()
def   _UpperCAmelCase       (     UpperCAmelCase      :			List[str]						,	UpperCAmelCase      :			Any						,	UpperCAmelCase      :			str      ):
      """simple docstring"""




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

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

      __lowerCamelCase     :      int       						=						DetrImageProcessor(
          format="""coco_detection"""						,	max_size=800 if """detection""" in checkpoint_url else 1_000      )
      __lowerCamelCase     :      List[Any]       						=						TableTransformerForObjectDetection(_lowerCamelCase      )
      model.load_state_dict(_lowerCamelCase      )
      model.eval()

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

      __lowerCamelCase     :      str       						=						model(_lowerCamelCase      )

      if "detection" in checkpoint_url:
            __lowerCamelCase     :      List[Any]       						=						(1, 15, 3)
            __lowerCamelCase     :      Union[str, Any]       						=						torch.tensor(
                [[-6.7_8_9_7, -1_6.9_9_8_5, 6.7_9_3_7], [-8.0_1_8_6, -2_2.2_1_9_2, 6.9_6_7_7], [-7.3_1_1_7, -2_1.0_7_0_8, 7.4_0_5_5]]      )
            __lowerCamelCase     :      Optional[int]       						=						torch.tensor([[0.4_8_6_7, 0.1_7_6_7, 0.6_7_3_2], [0.6_7_1_8, 0.4_4_7_9, 0.3_8_3_0], [0.4_7_1_6, 0.1_7_6_0, 0.6_3_6_4]]      )

      else:
            __lowerCamelCase     :      Tuple       						=						(1, 125, 7)
            __lowerCamelCase     :      Union[str, Any]       						=						torch.tensor(
                [[-1_8.1_4_3_0, -8.3_2_1_4, 4.8_2_7_4], [-1_8.4_6_8_5, -7.1_3_6_1, -4.2_6_6_7], [-2_6.3_6_9_3, -9.3_4_2_9, -4.9_9_6_2]]      )
            __lowerCamelCase     :      List[Any]       						=						torch.tensor([[0.4_9_8_3, 0.5_5_9_5, 0.9_4_4_0], [0.4_9_1_6, 0.6_3_1_5, 0.5_9_5_4], [0.6_1_0_8, 0.8_6_3_7, 0.1_1_3_5]]      )

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

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


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







# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCamelCase    :						Any     =							{
    'configuration_mgp_str': ['MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MgpstrConfig'],
    'processing_mgp_str': ['MgpstrProcessor'],
    'tokenization_mgp_str': ['MgpstrTokenizer'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __UpperCamelCase    :						Tuple     =							[
        'MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST',
        'MgpstrModel',
        'MgpstrPreTrainedModel',
        'MgpstrForSceneTextRecognition',
    ]

if TYPE_CHECKING:
    from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
    from .processing_mgp_str import MgpstrProcessor
    from .tokenization_mgp_str import MgpstrTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mgp_str import (
            MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
            MgpstrForSceneTextRecognition,
            MgpstrModel,
            MgpstrPreTrainedModel,
        )
else:
    import sys

    __UpperCamelCase    :						Optional[Any]     =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



