



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

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

def 			_lowerCAmelCase				(       lowercase_						):
						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				(       lowercase_						):
						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[:256, :]
												UpperCAmelCase					=			in_proj_bias[:256]
												UpperCAmelCase					=			in_proj_weight[256:512, :]
												UpperCAmelCase					=			in_proj_bias[256:512]
												UpperCAmelCase					=			in_proj_weight[-256:, :]
												UpperCAmelCase					=			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
												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[:256, :]
												UpperCAmelCase					=			in_proj_bias[:256]
												UpperCAmelCase					=			in_proj_weight[256:512, :]
												UpperCAmelCase					=			in_proj_bias[256:512]
												UpperCAmelCase					=			in_proj_weight[-256:, :]
												UpperCAmelCase					=			in_proj_bias[-256:]
												# 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[:256, :]
												UpperCAmelCase					=			in_proj_bias_cross_attn[:256]
												UpperCAmelCase					=			in_proj_weight_cross_attn[256:512, :]
												UpperCAmelCase					=			in_proj_bias_cross_attn[256:512]
												UpperCAmelCase					=			in_proj_weight_cross_attn[-256:, :]
												UpperCAmelCase					=			in_proj_bias_cross_attn[-256:]

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

						return resized_image

def 			_lowerCAmelCase				(       lowercase_						):
						UpperCAmelCase					=			F.to_tensor(lowercase_						)
						UpperCAmelCase					=			F.normalize(lowercase_		,     mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]		,     std=[0.2_2_9, 0.2_2_4, 0.2_2_5]						)
						return image

@torch.no_grad()
def 			_lowerCAmelCase				(       lowercase_		,     lowercase_		,     lowercase_						):
						logger.info('Converting model...'						)

						# load original state dict
						UpperCAmelCase					=			torch.hub.load_state_dict_from_url(lowercase_		,     map_location='cpu'						)
						# rename keys
						for src, dest in rename_keys:
												rename_key(lowercase_		,     lowercase_		,     lowercase_						)
						UpperCAmelCase					=			rename_backbone_keys(lowercase_						)
						# query, key and value matrices need special treatment
						read_in_q_k_v(lowercase_						)
						# 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(lowercase_						)
																		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					=			125
												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=800 if 'detection' in checkpoint_url else 1000						)
						UpperCAmelCase					=			TableTransformerForObjectDetection(lowercase_						)
						model.load_state_dict(lowercase_						)
						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=lowercase_						)
						UpperCAmelCase					=			Image.open(lowercase_						).convert('RGB'						)
						UpperCAmelCase					=			normalize(resize(lowercase_		,     lowercase_						)						).unsqueeze(0						)

						UpperCAmelCase					=			model(lowercase_						)

						if "detection" in checkpoint_url:
												UpperCAmelCase					=			(1, 15, 3)
												UpperCAmelCase					=			torch.tensor(
												    [[-6.7_8_9_7, -1_6.9_9_8_5, 6.7_9_3_7], [-8.0_1_8_6, -2_2.2_1_9_2, 6.9_6_7_7], [-7.3_1_1_7, -2_1.0_7_0_8, 7.4_0_5_5]]						)
												UpperCAmelCase					=			torch.tensor([[0.4_8_6_7, 0.1_7_6_7, 0.6_7_3_2], [0.6_7_1_8, 0.4_4_7_9, 0.3_8_3_0], [0.4_7_1_6, 0.1_7_6_0, 0.6_3_6_4]]						)

						else:
												UpperCAmelCase					=			(1, 125, 7)
												UpperCAmelCase					=			torch.tensor(
												    [[-1_8.1_4_3_0, -8.3_2_1_4, 4.8_2_7_4], [-1_8.4_6_8_5, -7.1_3_6_1, -4.2_6_6_7], [-2_6.3_6_9_3, -9.3_4_2_9, -4.9_9_6_2]]						)
												UpperCAmelCase					=			torch.tensor([[0.4_9_8_3, 0.5_5_9_5, 0.9_4_4_0], [0.4_9_1_6, 0.6_3_1_5, 0.5_9_5_4], [0.6_1_0_8, 0.8_6_3_7, 0.1_1_3_5]]						)

						assert outputs.logits.shape == expected_shape
						assert torch.allclose(outputs.logits[0, :3, :3]		,     lowercase_		,     atol=1e-4						)
						assert torch.allclose(outputs.pred_boxes[0, :3, :3]		,     lowercase_		,     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(lowercase_						).mkdir(exist_ok=lowercase_						)
												model.save_pretrained(lowercase_						)
												image_processor.save_pretrained(lowercase_						)

						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(lowercase_						)
												image_processor.push_to_hub(lowercase_						)


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










"""simple docstring"""


import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


snake_case_    					=	logging.get_logger(__name__)

def 			_lowerCAmelCase				(       lowercase_						):
						UpperCAmelCase					=			R'\w+[.]\d+'
						UpperCAmelCase					=			re.findall(lowercase_		,     lowercase_						)
						for pat in pats:
												UpperCAmelCase					=			key.replace(lowercase_		,     '_'.join(pat.split('.'						)						)						)
						return key

def 			_lowerCAmelCase				(       lowercase_		,     lowercase_		,     lowercase_						):
						UpperCAmelCase					=			pt_tuple_key[:-1] + ('scale',)
						if (
						    any('norm' in str_ for str_ in pt_tuple_key						)
						    and (pt_tuple_key[-1] == "bias")
						    and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
						    and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
						):
												UpperCAmelCase					=			pt_tuple_key[:-1] + ('scale',)
												return renamed_pt_tuple_key, pt_tensor
						elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
												UpperCAmelCase					=			pt_tuple_key[:-1] + ('scale',)
												return renamed_pt_tuple_key, pt_tensor

						# embedding
						if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
												UpperCAmelCase					=			pt_tuple_key[:-1] + ('embedding',)
												return renamed_pt_tuple_key, pt_tensor

						# conv layer
						UpperCAmelCase					=			pt_tuple_key[:-1] + ('kernel',)
						if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
												UpperCAmelCase					=			pt_tensor.transpose(2		,     3		,     1		,     0						)
												return renamed_pt_tuple_key, pt_tensor

						# linear layer
						UpperCAmelCase					=			pt_tuple_key[:-1] + ('kernel',)
						if pt_tuple_key[-1] == "weight":
												UpperCAmelCase					=			pt_tensor.T
												return renamed_pt_tuple_key, pt_tensor

						# old PyTorch layer norm weight
						UpperCAmelCase					=			pt_tuple_key[:-1] + ('weight',)
						if pt_tuple_key[-1] == "gamma":
												return renamed_pt_tuple_key, pt_tensor

						# old PyTorch layer norm bias
						UpperCAmelCase					=			pt_tuple_key[:-1] + ('bias',)
						if pt_tuple_key[-1] == "beta":
												return renamed_pt_tuple_key, pt_tensor

						return pt_tuple_key, pt_tensor






def 			_lowerCAmelCase				(       lowercase_		,     lowercase_		,     lowercase_=42						):
						# Step 1: Convert pytorch tensor to numpy
						UpperCAmelCase					=			{k: v.numpy() for k, v in pt_state_dict.items()}

						# Step 2: Since the model is stateless, get random Flax params
						UpperCAmelCase					=			flax_model.init_weights(PRNGKey(lowercase_						)						)

						UpperCAmelCase					=			flatten_dict(lowercase_						)
						UpperCAmelCase					=			{}

						# Need to change some parameters name to match Flax names
						for pt_key, pt_tensor in pt_state_dict.items():
												UpperCAmelCase					=			rename_key(lowercase_						)
												UpperCAmelCase					=			tuple(renamed_pt_key.split('.'						)						)

												# Correctly rename weight parameters
												UpperCAmelCase				,   UpperCAmelCase					=			rename_key_and_reshape_tensor(lowercase_		,     lowercase_		,     lowercase_						)

												if flax_key in random_flax_state_dict:
																		if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
																								raise ValueError(
																								    F"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
																								    F"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""						)

        # also add unexpected weight so that warning is thrown
												UpperCAmelCase					=			jnp.asarray(lowercase_						)

						return unflatten_dict(lowercase_						)





