





'''simple docstring'''






A__				:   Any      =[4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A__				:   str      =[3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A__				:   str      ={
    0: '''Sunday''',
    1: '''Monday''',
    2: '''Tuesday''',
    3: '''Wednesday''',
    4: '''Thursday''',
    5: '''Friday''',
    6: '''Saturday''',
}
def    UpperCamelCase__      (		lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase  ):





      """simple docstring"""


      assert len(str(lowerCAmelCase  )  ) > 2, "year should be in YYYY format"
      assert 1 <= month <= 12, "month should be between 1 to 12"
      assert 1 <= day <= 31, "day should be between 1 to 31"

      # Doomsday algorithm:
      _lowerCAmelCase							     =							year // 1_00
      _lowerCAmelCase							     =							(5 * (century % 4) + 2) % 7
      _lowerCAmelCase							     =							year % 1_00
      _lowerCAmelCase							     =							centurian % 12
      _lowerCAmelCase							     =							(
          (centurian // 12) + centurian_m + (centurian_m // 4) + century_anchor
      ) % 7
      _lowerCAmelCase							     =							(
          DOOMSDAY_NOT_LEAP[month - 1]
          if (year % 4 != 0) or (centurian == 0 and (year % 4_00) == 0)
          else DOOMSDAY_LEAP[month - 1]
      )
      _lowerCAmelCase							     =							(dooms_day + day - day_anchor) % 7
      return WEEK_DAY_NAMES[week_day]


if __name__ == "__main__":
      import doctest

      doctest.testmod()








"""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()
__magic_name__       =			logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__magic_name__       =			[]
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				(				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = state_dict.pop(UpperCamelCase_				)
		__SCREAMING_SNAKE_CASE            = val



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = OrderedDict()
		for key, value in state_dict.items():
				if "backbone.0.body" in key:
						__SCREAMING_SNAKE_CASE            = key.replace("""backbone.0.body"""  ,				"""backbone.conv_encoder.model"""				)
						__SCREAMING_SNAKE_CASE            = value
				else:
						__SCREAMING_SNAKE_CASE            = value

		return new_state_dict



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = """"""

		# 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)
				__SCREAMING_SNAKE_CASE            = state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"				)
				__SCREAMING_SNAKE_CASE            = 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
				__SCREAMING_SNAKE_CASE            = in_proj_weight[:256, :]
				__SCREAMING_SNAKE_CASE            = in_proj_bias[:256]
				__SCREAMING_SNAKE_CASE            = in_proj_weight[256:512, :]
				__SCREAMING_SNAKE_CASE            = in_proj_bias[256:512]
				__SCREAMING_SNAKE_CASE            = in_proj_weight[-256:, :]
				__SCREAMING_SNAKE_CASE            = 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
				__SCREAMING_SNAKE_CASE            = state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"				)
				__SCREAMING_SNAKE_CASE            = 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
				__SCREAMING_SNAKE_CASE            = in_proj_weight[:256, :]
				__SCREAMING_SNAKE_CASE            = in_proj_bias[:256]
				__SCREAMING_SNAKE_CASE            = in_proj_weight[256:512, :]
				__SCREAMING_SNAKE_CASE            = in_proj_bias[256:512]
				__SCREAMING_SNAKE_CASE            = in_proj_weight[-256:, :]
				__SCREAMING_SNAKE_CASE            = in_proj_bias[-256:]
				# read in weights + bias of input projection layer of cross-attention
				__SCREAMING_SNAKE_CASE            = state_dict.pop(
				    f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"				)
				__SCREAMING_SNAKE_CASE            = 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
				__SCREAMING_SNAKE_CASE            = in_proj_weight_cross_attn[:256, :]
				__SCREAMING_SNAKE_CASE            = in_proj_bias_cross_attn[:256]
				__SCREAMING_SNAKE_CASE            = in_proj_weight_cross_attn[256:512, :]
				__SCREAMING_SNAKE_CASE            = in_proj_bias_cross_attn[256:512]
				__SCREAMING_SNAKE_CASE            = in_proj_weight_cross_attn[-256:, :]
				__SCREAMING_SNAKE_CASE            = in_proj_bias_cross_attn[-256:]



def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = image.size
		__SCREAMING_SNAKE_CASE            = max(UpperCamelCase_  ,				UpperCamelCase_				)
		__SCREAMING_SNAKE_CASE            = 800 if """detection""" in checkpoint_url else 1000
		__SCREAMING_SNAKE_CASE            = target_max_size / current_max_size
		__SCREAMING_SNAKE_CASE            = image.resize((int(round(scale * width				)				), int(round(scale * height				)				))				)

		return resized_image



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = F.to_tensor(UpperCamelCase_				)
		__SCREAMING_SNAKE_CASE            = F.normalize(UpperCamelCase_  ,				mean=[0.485, 0.456, 0.406]  ,				std=[0.229, 0.224, 0.225]				)
		return image



@torch.no_grad()
def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_				):
		logger.info("""Converting model..."""				)

		# load original state dict
		__SCREAMING_SNAKE_CASE            = torch.hub.load_state_dict_from_url(UpperCamelCase_  ,				map_location="""cpu"""				)
		# rename keys
		for src, dest in rename_keys:
				rename_key(UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_				)
		__SCREAMING_SNAKE_CASE            = rename_backbone_keys(UpperCamelCase_				)
		# query, key and value matrices need special treatment
		read_in_q_k_v(UpperCamelCase_				)
		# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
		__SCREAMING_SNAKE_CASE            = """model."""
		for key in state_dict.copy().keys():
				if not key.startswith("""class_labels_classifier"""				) and not key.startswith("""bbox_predictor"""				):
						__SCREAMING_SNAKE_CASE            = state_dict.pop(UpperCamelCase_				)
						__SCREAMING_SNAKE_CASE            = val
    # create HuggingFace model and load state dict
		__SCREAMING_SNAKE_CASE            = 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:
				__SCREAMING_SNAKE_CASE            = 15
				__SCREAMING_SNAKE_CASE            = 2
				__SCREAMING_SNAKE_CASE            = {0: """table""", 1: """table rotated"""}
				__SCREAMING_SNAKE_CASE            = idalabel
				__SCREAMING_SNAKE_CASE            = {v: k for k, v in idalabel.items()}
		else:
				__SCREAMING_SNAKE_CASE            = 125
				__SCREAMING_SNAKE_CASE            = 6
				__SCREAMING_SNAKE_CASE            = {
				    0: """table""",
				    1: """table column""",
				    2: """table row""",
				    3: """table column header""",
				    4: """table projected row header""",
				    5: """table spanning cell""",
				}
				__SCREAMING_SNAKE_CASE            = idalabel
				__SCREAMING_SNAKE_CASE            = {v: k for k, v in idalabel.items()}

		__SCREAMING_SNAKE_CASE            = DetrImageProcessor(
		    format="""coco_detection"""  ,				max_size=800 if """detection""" in checkpoint_url else 1000				)
		__SCREAMING_SNAKE_CASE            = TableTransformerForObjectDetection(UpperCamelCase_				)
		model.load_state_dict(UpperCamelCase_				)
		model.eval()

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

		__SCREAMING_SNAKE_CASE            = model(UpperCamelCase_				)

		if "detection" in checkpoint_url:
				__SCREAMING_SNAKE_CASE            = (1, 15, 3)
				__SCREAMING_SNAKE_CASE            = 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]]				)
				__SCREAMING_SNAKE_CASE            = 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:
				__SCREAMING_SNAKE_CASE            = (1, 125, 7)
				__SCREAMING_SNAKE_CASE            = 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]]				)
				__SCREAMING_SNAKE_CASE            = 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]  ,				UpperCamelCase_  ,				atol=1e-4				)
		assert torch.allclose(outputs.pred_boxes[0, :3, :3]  ,				UpperCamelCase_  ,				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(UpperCamelCase_				).mkdir(exist_ok=UpperCamelCase_				)
				model.save_pretrained(UpperCamelCase_				)
				image_processor.save_pretrained(UpperCamelCase_				)

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


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




