


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_      : Optional[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        SCREAMING_SNAKE_CASE_						(			__A :			Any  ,    __A :			Dict  ,    __A :			Union[str, Any]				)	->		List[Any]:

       """simple docstring"""

       a_					:		List[str]							=  state_dict.pop(__A				)
       a_					:		Dict							=  val







def        SCREAMING_SNAKE_CASE_						(			__A :			Optional[Any]				)	->		Optional[Any]:

       """simple docstring"""

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

       return new_state_dict







def        SCREAMING_SNAKE_CASE_						(			__A :			Union[str, Any]				)	->		Dict:

       """simple docstring"""

       a_					:		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)
              a_					:		List[str]							=  state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""				)
              a_					:		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
              a_					:		List[Any]							=  in_proj_weight[:2_56, :]
              a_					:		Dict							=  in_proj_bias[:2_56]
              a_					:		int							=  in_proj_weight[2_56:5_12, :]
              a_					:		Tuple							=  in_proj_bias[2_56:5_12]
              a_					:		Dict							=  in_proj_weight[-2_56:, :]
              a_					:		Optional[Any]							=  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
              a_					:		Tuple							=  state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""				)
              a_					:		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
              a_					:		Optional[int]							=  in_proj_weight[:2_56, :]
              a_					:		Any							=  in_proj_bias[:2_56]
              a_					:		Optional[int]							=  in_proj_weight[2_56:5_12, :]
              a_					:		Union[str, Any]							=  in_proj_bias[2_56:5_12]
              a_					:		Dict							=  in_proj_weight[-2_56:, :]
              a_					:		List[str]							=  in_proj_bias[-2_56:]
              # read in weights + bias of input projection layer of cross-attention
              a_					:		Optional[int]							=  state_dict.pop(
                  F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""				)
              a_					:		List[str]							=  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
              a_					:		Tuple							=  in_proj_weight_cross_attn[:2_56, :]
              a_					:		Dict							=  in_proj_bias_cross_attn[:2_56]
              a_					:		Dict							=  in_proj_weight_cross_attn[2_56:5_12, :]
              a_					:		Optional[int]							=  in_proj_bias_cross_attn[2_56:5_12]
              a_					:		List[str]							=  in_proj_weight_cross_attn[-2_56:, :]
              a_					:		Optional[int]							=  in_proj_bias_cross_attn[-2_56:]







def        SCREAMING_SNAKE_CASE_						(			__A :			List[str]  ,    __A :			int				)	->		int:

       """simple docstring"""

       a_					:		Tuple							=  image.size
       a_					:		Tuple							=  max(__A  ,    __A				)
       a_					:		int							=  8_00 if 'detection' in checkpoint_url else 10_00
       a_					:		Any							=  target_max_size / current_max_size
       a_					:		Tuple							=  image.resize((int(round(scale * width				)				), int(round(scale * height				)				))				)

       return resized_image







def        SCREAMING_SNAKE_CASE_						(			__A :			List[Any]				)	->		Dict:

       """simple docstring"""

       a_					:		Dict							=  F.to_tensor(__A				)
       a_					:		int							=  F.normalize(__A  ,    mean=[0.485, 0.456, 0.406]  ,    std=[0.229, 0.224, 0.225]				)
       return image







@torch.no_grad()
def        SCREAMING_SNAKE_CASE_						(			__A :			Union[str, Any]  ,    __A :			List[str]  ,    __A :			List[Any]				)	->		Union[str, Any]:

       """simple docstring"""

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

       # load original state dict
       a_					:		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				)
       a_					:		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
       a_					:		List[Any]							=  'model.'
       for key in state_dict.copy().keys():
              if not key.startswith('class_labels_classifier'				) and not key.startswith('bbox_predictor'				):
                     a_					:		Union[str, Any]							=  state_dict.pop(__A				)
                     a_					:		Tuple							=  val
                      # create HuggingFace model and load state dict
       a_					:		List[str]							=  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:
              a_					:		List[str]							=  15
              a_					:		Tuple							=  2
              a_					:		Optional[Any]							=  {0: 'table', 1: 'table rotated'}
              a_					:		List[Any]							=  idalabel
              a_					:		List[Any]							=  {v: k for k, v in idalabel.items()}
       else:
              a_					:		Optional[Any]							=  1_25
              a_					:		Optional[Any]							=  6
              a_					:		Tuple							=  {
                  0: 'table',
                  1: 'table column',
                  2: 'table row',
                  3: 'table column header',
                  4: 'table projected row header',
                  5: 'table spanning cell',
              }
              a_					:		Dict							=  idalabel
              a_					:		List[str]							=  {v: k for k, v in idalabel.items()}

       a_					:		Dict							=  DetrImageProcessor(
           format='coco_detection'  ,    max_size=8_00 if 'detection' in checkpoint_url else 10_00				)
       a_					:		Optional[int]							=  TableTransformerForObjectDetection(__A				)
       model.load_state_dict(__A				)
       model.eval()

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

       a_					:		str							=  model(__A				)

       if "detection" in checkpoint_url:
              a_					:		Dict							=  (1, 15, 3)
              a_					:		Tuple							=  torch.tensor(
                  [[-6.7897, -16.99_85, 6.7937], [-8.0186, -22.21_92, 6.9677], [-7.3117, -21.07_08, 7.4055]]				)
              a_					:		int							=  torch.tensor([[0.4867, 0.1767, 0.6732], [0.6718, 0.4479, 0.3830], [0.4716, 0.1760, 0.6364]]				)

       else:
              a_					:		Union[str, Any]							=  (1, 1_25, 7)
              a_					:		Dict							=  torch.tensor(
                  [[-18.14_30, -8.3214, 4.8274], [-18.46_85, -7.1361, -4.2667], [-26.36_93, -9.3429, -4.9962]]				)
              a_					:		List[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...'				)
              a_					:		Tuple							=  (
                  '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_      : List[str]  					=       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_      : Tuple  					=       parser.parse_args()
   convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)



import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
   a_    					=       argparse.ArgumentParser(
       description=(
           'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned'
           ' Distillation'
       )
   )
   parser.add_argument('--model_type', default='roberta', choices=['roberta', 'gpt2'])
   parser.add_argument('--model_name', default='roberta-large', type=str)
   parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_roberta_048131723.pth', type=str)
   parser.add_argument('--vocab_transform', action='store_true')
   a_    					=       parser.parse_args()

   if args.model_type == "roberta":
      a_    					=       RobertaForMaskedLM.from_pretrained(args.model_name)
      a_    					=       'roberta'
   elif args.model_type == "gpt2":
      a_    					=       GPTaLMHeadModel.from_pretrained(args.model_name)
      a_    					=       'transformer'

   a_    					=       model.state_dict()
   a_    					=       {}

   # Embeddings #
   if args.model_type == "gpt2":
      for param_name in ["wte.weight", "wpe.weight"]:
         a_    					=       state_dict[F"""{prefix}.{param_name}"""]
   else:
      for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
         a_    					=       F"""{prefix}.embeddings.{w}.weight"""
         a_    					=       state_dict[param_name]
      for w in ["weight", "bias"]:
         a_    					=       F"""{prefix}.embeddings.LayerNorm.{w}"""
         a_    					=       state_dict[param_name]

    # Transformer Blocks #
   a_    					=       0
   for teacher_idx in [0, 2, 4, 7, 9, 11]:
      if args.model_type == "gpt2":
         for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
            for w in ["weight", "bias"]:
               a_    					=       state_dict[
                   F"""{prefix}.h.{teacher_idx}.{layer}.{w}"""
               ]
         a_    					=       state_dict[F"""{prefix}.h.{teacher_idx}.attn.bias"""]
      else:
         for layer in [
             "attention.self.query",
             "attention.self.key",
             "attention.self.value",
             "attention.output.dense",
             "attention.output.LayerNorm",
             "intermediate.dense",
             "output.dense",
             "output.LayerNorm",
         ]:
            for w in ["weight", "bias"]:
               a_    					=       state_dict[
                   F"""{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}"""
               ]
      std_idx += 1

   # Language Modeling Head ###s
   if args.model_type == "roberta":
      for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
         a_    					=       state_dict[F"""{layer}"""]
      if args.vocab_transform:
         for w in ["weight", "bias"]:
            a_    					=       state_dict[F"""lm_head.dense.{w}"""]
            a_    					=       state_dict[F"""lm_head.layer_norm.{w}"""]
   elif args.model_type == "gpt2":
      for w in ["weight", "bias"]:
         a_    					=       state_dict[F"""{prefix}.ln_f.{w}"""]
      a_    					=       state_dict['lm_head.weight']

   print(F"""N layers selected for distillation: {std_idx}""")
   print(F"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")

   print(F"""Save transferred checkpoint to {args.dump_checkpoint}.""")
   torch.save(compressed_sd, args.dump_checkpoint)



