'''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=  logging.get_logger(__name__)

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




    """simple docstring"""



    __UpperCamelCase	:	Tuple		    =  state_dict.pop(_a			)
    __UpperCamelCase	:	Dict		    =  val







def  _UpperCamelCase (     _a	:	Dict			):




    """simple docstring"""



    __UpperCamelCase	:	str		    =  OrderedDict()
    for key, value in state_dict.items():
        if "backbone.0.body" in key:
            __UpperCamelCase	:	List[Any]		    =  key.replace('backbone.0.body'				,     'backbone.conv_encoder.model'			)
            __UpperCamelCase	:	Any		    =  value
        else:
            __UpperCamelCase	:	List[Any]		    =  value

    return new_state_dict







def  _UpperCamelCase (     _a	:	Tuple			):




    """simple docstring"""



    __UpperCamelCase	:	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)
        __UpperCamelCase	:	List[str]		    =  state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""			)
        __UpperCamelCase	:	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
        __UpperCamelCase	:	List[Any]		    =  in_proj_weight[:2_5_6, :]
        __UpperCamelCase	:	List[str]		    =  in_proj_bias[:2_5_6]
        __UpperCamelCase	:	Union[str, Any]		    =  in_proj_weight[2_5_6:5_1_2, :]
        __UpperCamelCase	:	str		    =  in_proj_bias[2_5_6:5_1_2]
        __UpperCamelCase	:	Dict		    =  in_proj_weight[-2_5_6:, :]
        __UpperCamelCase	:	Optional[Any]		    =  in_proj_bias[-2_5_6:]
    # 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
        __UpperCamelCase	:	List[str]		    =  state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""			)
        __UpperCamelCase	:	str		    =  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
        __UpperCamelCase	:	Tuple		    =  in_proj_weight[:2_5_6, :]
        __UpperCamelCase	:	int		    =  in_proj_bias[:2_5_6]
        __UpperCamelCase	:	List[Any]		    =  in_proj_weight[2_5_6:5_1_2, :]
        __UpperCamelCase	:	Union[str, Any]		    =  in_proj_bias[2_5_6:5_1_2]
        __UpperCamelCase	:	Optional[Any]		    =  in_proj_weight[-2_5_6:, :]
        __UpperCamelCase	:	List[str]		    =  in_proj_bias[-2_5_6:]
        # read in weights + bias of input projection layer of cross-attention
        __UpperCamelCase	:	List[str]		    =  state_dict.pop(
            f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""			)
        __UpperCamelCase	:	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
        __UpperCamelCase	:	List[Any]		    =  in_proj_weight_cross_attn[:2_5_6, :]
        __UpperCamelCase	:	Optional[int]		    =  in_proj_bias_cross_attn[:2_5_6]
        __UpperCamelCase	:	List[Any]		    =  in_proj_weight_cross_attn[2_5_6:5_1_2, :]
        __UpperCamelCase	:	Tuple		    =  in_proj_bias_cross_attn[2_5_6:5_1_2]
        __UpperCamelCase	:	Optional[int]		    =  in_proj_weight_cross_attn[-2_5_6:, :]
        __UpperCamelCase	:	Tuple		    =  in_proj_bias_cross_attn[-2_5_6:]







def  _UpperCamelCase (     _a	:	List[Any]				,     _a	:	str			):




    """simple docstring"""



    __UpperCamelCase	:	str		    =  image.size
    __UpperCamelCase	:	int		    =  max(_a				,     _a			)
    __UpperCamelCase	:	List[str]		    =  8_0_0 if 'detection' in checkpoint_url else 1_0_0_0
    __UpperCamelCase	:	Union[str, Any]		    =  target_max_size / current_max_size
    __UpperCamelCase	:	Tuple		    =  image.resize((int(round(scale * width			)			), int(round(scale * height			)			))			)

    return resized_image







def  _UpperCamelCase (     _a	:	List[str]			):




    """simple docstring"""



    __UpperCamelCase	:	int		    =  F.to_tensor(_a			)
    __UpperCamelCase	:	str		    =  F.normalize(_a				,     mean=[0.485, 0.456, 0.406]				,     std=[0.229, 0.224, 0.225]			)
    return image







@torch.no_grad()
def  _UpperCamelCase (     _a	:	Union[str, Any]				,     _a	:	Union[str, Any]				,     _a	:	List[Any]			):




    """simple docstring"""



    logger.info('Converting model...'			)

    # load original state dict
    __UpperCamelCase	:	Any		    =  torch.hub.load_state_dict_from_url(_a				,     map_location='cpu'			)
    # rename keys
    for src, dest in rename_keys:
        rename_key(_a				,     _a				,     _a			)
    __UpperCamelCase	:	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
    __UpperCamelCase	:	Dict		    =  'model.'
    for key in state_dict.copy().keys():
        if not key.startswith('class_labels_classifier'			) and not key.startswith('bbox_predictor'			):
            __UpperCamelCase	:	str		    =  state_dict.pop(_a			)
            __UpperCamelCase	:	Tuple		    =  val
    # create HuggingFace model and load state dict
    __UpperCamelCase	:	List[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:
        __UpperCamelCase	:	Dict		    =  1_5
        __UpperCamelCase	:	int		    =  2
        __UpperCamelCase	:	str		    =  {0: 'table', 1: 'table rotated'}
        __UpperCamelCase	:	str		    =  idalabel
        __UpperCamelCase	:	int		    =  {v: k for k, v in idalabel.items()}
    else:
        __UpperCamelCase	:	Any		    =  1_2_5
        __UpperCamelCase	:	Tuple		    =  6
        __UpperCamelCase	:	List[Any]		    =  {
            0: 'table',
            1: 'table column',
            2: 'table row',
            3: 'table column header',
            4: 'table projected row header',
            5: 'table spanning cell',
        }
        __UpperCamelCase	:	Optional[Any]		    =  idalabel
        __UpperCamelCase	:	Union[str, Any]		    =  {v: k for k, v in idalabel.items()}

    __UpperCamelCase	:	Any		    =  DetrImageProcessor(
        format='coco_detection'				,     max_size=8_0_0 if 'detection' in checkpoint_url else 1_0_0_0			)
    __UpperCamelCase	:	Any		    =  TableTransformerForObjectDetection(_a			)
    model.load_state_dict(_a			)
    model.eval()

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

    __UpperCamelCase	:	List[Any]		    =  model(_a			)

    if "detection" in checkpoint_url:
        __UpperCamelCase	:	List[Any]		    =  (1, 1_5, 3)
        __UpperCamelCase	:	int		    =  torch.tensor(
            [[-6.7897, -16.9985, 6.7937], [-8.0186, -22.2192, 6.9677], [-7.3117, -21.0708, 7.4055]]			)
        __UpperCamelCase	:	List[Any]		    =  torch.tensor([[0.4867, 0.1767, 0.6732], [0.6718, 0.4479, 0.3830], [0.4716, 0.1760, 0.6364]]			)

    else:
        __UpperCamelCase	:	int		    =  (1, 1_2_5, 7)
        __UpperCamelCase	:	List[Any]		    =  torch.tensor(
            [[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]]			)
        __UpperCamelCase	:	str		    =  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...'			)
        __UpperCamelCase	:	Union[str, 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__":
       a=  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=  parser.parse_args()
       convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)




'''simple docstring'''
import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin






class 			__lowercase		(						_lowerCamelCase			,							_lowerCamelCase						):







     """simple docstring"""






     @register_to_config
     def __init__(		self     ,			*,
         _lowerCamelCase = 4     ,			_lowerCamelCase = 7_6_8     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			):
         super().__init__()

         __UpperCamelCase	:	Optional[int]		    =  nn.Parameter(torch.zeros(_lowerCamelCase    )    )

         # parameters for additional clip time embeddings
         __UpperCamelCase	:	Dict		    =  nn.Linear(_lowerCamelCase     ,			_lowerCamelCase    )
         __UpperCamelCase	:	int		    =  nn.Linear(_lowerCamelCase     ,			_lowerCamelCase    )

         # parameters for encoder hidden states
         __UpperCamelCase	:	Optional[Any]		    =  clip_extra_context_tokens
         __UpperCamelCase	:	Dict		    =  nn.Linear(
             _lowerCamelCase     ,			self.clip_extra_context_tokens * cross_attention_dim    )
         __UpperCamelCase	:	str		    =  nn.Linear(_lowerCamelCase     ,			_lowerCamelCase    )
         __UpperCamelCase	:	List[str]		    =  nn.LayerNorm(_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			*, _lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         if do_classifier_free_guidance:
             # Add the classifier free guidance embeddings to the image embeddings
             __UpperCamelCase	:	int		    =  image_embeddings.shape[0]
             __UpperCamelCase	:	Optional[Any]		    =  self.learned_classifier_free_guidance_embeddings.unsqueeze(0    )
             __UpperCamelCase	:	List[Any]		    =  classifier_free_guidance_embeddings.expand(
                 _lowerCamelCase     ,			-1    )
             __UpperCamelCase	:	Optional[Any]		    =  torch.cat([classifier_free_guidance_embeddings, image_embeddings]     ,			dim=0    )

         # The image embeddings batch size and the text embeddings batch size are equal
         assert image_embeddings.shape[0] == prompt_embeds.shape[0]

         __UpperCamelCase	:	Optional[Any]		    =  prompt_embeds.shape[0]

         # "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
         # adding CLIP embeddings to the existing timestep embedding, ...
         __UpperCamelCase	:	Optional[int]		    =  self.embedding_proj(_lowerCamelCase    )
         __UpperCamelCase	:	List[str]		    =  self.clip_image_embeddings_project_to_time_embeddings(_lowerCamelCase    )
         __UpperCamelCase	:	Optional[int]		    =  time_projected_image_embeddings + time_projected_prompt_embeds

         # ... and by projecting CLIP embeddings into four
         # extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
         __UpperCamelCase	:	Optional[int]		    =  self.clip_extra_context_tokens_proj(_lowerCamelCase    )
         __UpperCamelCase	:	Tuple		    =  clip_extra_context_tokens.reshape(_lowerCamelCase     ,			-1     ,			self.clip_extra_context_tokens    )
         __UpperCamelCase	:	Dict		    =  clip_extra_context_tokens.permute(0     ,			2     ,			1    )

         __UpperCamelCase	:	Union[str, Any]		    =  self.encoder_hidden_states_proj(_lowerCamelCase    )
         __UpperCamelCase	:	Optional[Any]		    =  self.text_encoder_hidden_states_norm(_lowerCamelCase    )
         __UpperCamelCase	:	Tuple		    =  torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]     ,			dim=1    )

         return text_encoder_hidden_states, additive_clip_time_embeddings



