
"""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 				lowerCamelCase__		(      __snake_case,					__snake_case,					__snake_case     )							->		Union[str, Any]:


					"""simple docstring"""





					_UpperCamelCase										=  state_dict.pop(__snake_case     )
					_UpperCamelCase										=  val



def 				lowerCamelCase__		(      __snake_case     )							->		Tuple:


					"""simple docstring"""





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

					return new_state_dict



def 				lowerCamelCase__		(      __snake_case     )							->		str:


					"""simple docstring"""





					_UpperCamelCase										=  ''''''

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



def 				lowerCamelCase__		(      __snake_case,					__snake_case     )							->		Any:


					"""simple docstring"""





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

					return resized_image



def 				lowerCamelCase__		(      __snake_case     )							->		Optional[Any]:


					"""simple docstring"""





					_UpperCamelCase										=  F.to_tensor(__snake_case     )
					_UpperCamelCase										=  F.normalize(__snake_case,					mean=[0.485, 0.456, 0.406],					std=[0.229, 0.224, 0.225]     )
					return image



@torch.no_grad()
def 				lowerCamelCase__		(      __snake_case,					__snake_case,					__snake_case     )							->		Any:


					"""simple docstring"""





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

					# load original state dict
					_UpperCamelCase										=  torch.hub.load_state_dict_from_url(__snake_case,					map_location='''cpu'''     )
					# rename keys
					for src, dest in rename_keys:
										rename_key(__snake_case,					__snake_case,					__snake_case     )
					_UpperCamelCase										=  rename_backbone_keys(__snake_case     )
					# query, key and value matrices need special treatment
					read_in_q_k_v(__snake_case     )
					# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
					_UpperCamelCase										=  '''model.'''
					for key in state_dict.copy().keys():
										if not key.startswith('''class_labels_classifier'''     ) and not key.startswith('''bbox_predictor'''     ):
															_UpperCamelCase										=  state_dict.pop(__snake_case     )
															_UpperCamelCase										=  val
    # create HuggingFace model and load state dict
					_UpperCamelCase										=  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										=  15
										_UpperCamelCase										=  2
										_UpperCamelCase										=  {0: '''table''', 1: '''table rotated'''}
										_UpperCamelCase										=  idalabel
										_UpperCamelCase										=  {v: k for k, v in idalabel.items()}
					else:
										_UpperCamelCase										=  1_25
										_UpperCamelCase										=  6
										_UpperCamelCase										=  {
										    0: '''table''',
										    1: '''table column''',
										    2: '''table row''',
										    3: '''table column header''',
										    4: '''table projected row header''',
										    5: '''table spanning cell''',
										}
										_UpperCamelCase										=  idalabel
										_UpperCamelCase										=  {v: k for k, v in idalabel.items()}

					_UpperCamelCase										=  DetrImageProcessor(
					    format='''coco_detection''',					max_size=8_00 if '''detection''' in checkpoint_url else 10_00     )
					_UpperCamelCase										=  TableTransformerForObjectDetection(__snake_case     )
					model.load_state_dict(__snake_case     )
					model.eval()

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

					_UpperCamelCase										=  model(__snake_case     )

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

					else:
										_UpperCamelCase										=  (1, 1_25, 7)
										_UpperCamelCase										=  torch.tensor(
										    [[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]]     )
										_UpperCamelCase										=  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],					__snake_case,					atol=1e-4     )
					assert torch.allclose(outputs.pred_boxes[0, :3, :3],					__snake_case,					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(__snake_case     ).mkdir(exist_ok=__snake_case     )
										model.save_pretrained(__snake_case     )
										image_processor.save_pretrained(__snake_case     )

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


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"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_a														=		logging.get_logger(__name__)

_a														=		{
    """microsoft/biogpt""": """https://huggingface.co/microsoft/biogpt/resolve/main/config.json""",
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				'biogpt'



		def __init__( self    ,		__a=4_23_84    ,		__a=10_24    ,		__a=24    ,		__a=16    ,		__a=40_96    ,		__a="gelu"    ,		__a=0.1    ,		__a=0.1    ,		__a=10_24    ,		__a=0.02    ,		__a=1e-12    ,		__a=True    ,		__a=True    ,		__a=0.0    ,		__a=0.0    ,		__a=1    ,		__a=0    ,		__a=2    ,		**__a    ,		)	->	Dict:






							'''simple docstring'''
							_UpperCamelCase										=  vocab_size
							_UpperCamelCase										=  max_position_embeddings
							_UpperCamelCase										=  hidden_size
							_UpperCamelCase										=  num_hidden_layers
							_UpperCamelCase										=  num_attention_heads
							_UpperCamelCase										=  intermediate_size
							_UpperCamelCase										=  hidden_act
							_UpperCamelCase										=  hidden_dropout_prob
							_UpperCamelCase										=  attention_probs_dropout_prob
							_UpperCamelCase										=  initializer_range
							_UpperCamelCase										=  layer_norm_eps
							_UpperCamelCase										=  scale_embedding
							_UpperCamelCase										=  use_cache
							_UpperCamelCase										=  layerdrop
							_UpperCamelCase										=  activation_dropout
							super().__init__(pad_token_id=__a    ,		bos_token_id=__a    ,		eos_token_id=__a    ,		**__a)






