


'''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()
_lowercase    :    Optional[Any]			     =  logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_lowercase    :    Tuple			     =  []
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__   ( A     :    Dict   ,     A     :    Optional[Any]   ,     A     :    Dict  ):




						'''simple docstring'''






						UpperCAmelCase         =			state_dict.pop(A  )
						UpperCAmelCase         =			val


def    lowerCamelCase__   ( A     :    Any  ):




						'''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__   ( A     :    Optional[int]  ):




						'''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__   ( A     :    Optional[int]   ,     A     :    Optional[Any]  ):




						'''simple docstring'''






						UpperCAmelCase       ,       UpperCAmelCase         =			image.size
						UpperCAmelCase         =			max(A   ,     A  )
						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__   ( A     :    Dict  ):




						'''simple docstring'''






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


@torch.no_grad()
def    lowerCamelCase__   ( A     :    str   ,     A     :    Optional[Any]   ,     A     :    List[Any]  ):




						'''simple docstring'''






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

						# load original state dict
						UpperCAmelCase         =			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         =			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         =			'''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(A  )
																		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(A  )
						model.load_state_dict(A  )
						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=A  )
						UpperCAmelCase         =			Image.open(A  ).convert('''RGB'''  )
						UpperCAmelCase         =			normalize(resize(A   ,     A  )  ).unsqueeze(0  )

						UpperCAmelCase         =			model(A  )

						if "detection" in checkpoint_url:
												UpperCAmelCase         =			(1, 15, 3)
												UpperCAmelCase         =			torch.tensor(
												    [[-6.7_897, -16.9_985, 6.7_937], [-8.0_186, -22.2_192, 6.9_677], [-7.3_117, -21.0_708, 7.4_055]]  )
												UpperCAmelCase         =			torch.tensor([[0.4_867, 0.1_767, 0.6_732], [0.6_718, 0.4_479, 0.3_830], [0.4_716, 0.1_760, 0.6_364]]  )

						else:
												UpperCAmelCase         =			(1, 1_25, 7)
												UpperCAmelCase         =			torch.tensor(
												    [[-18.1_430, -8.3_214, 4.8_274], [-18.4_685, -7.1_361, -4.2_667], [-26.3_693, -9.3_429, -4.9_962]]  )
												UpperCAmelCase         =			torch.tensor([[0.4_983, 0.5_595, 0.9_440], [0.4_916, 0.6_315, 0.5_954], [0.6_108, 0.8_637, 0.1_135]]  )

						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         =			(
												    '''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__":
	_lowercase    :    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."""
	)
	_lowercase    :    Optional[Any]			     =  parser.parse_args()
	convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)









'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_lowercase    :    Any			     =  {
    """configuration_gpt_bigcode""": ["""GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTBigCodeConfig"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase    :    Optional[int]			     =  [
	    """GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """GPTBigCodeForSequenceClassification""",
	    """GPTBigCodeForTokenClassification""",
	    """GPTBigCodeForCausalLM""",
	    """GPTBigCodeModel""",
	    """GPTBigCodePreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_bigcode import (
		    GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTBigCodeForCausalLM,
		    GPTBigCodeForSequenceClassification,
		    GPTBigCodeForTokenClassification,
		    GPTBigCodeModel,
		    GPTBigCodePreTrainedModel,
		)


else:
	import sys

	_lowercase    :    Tuple			     =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





