

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    __UpperCAmelCase (	__a      :			Optional[Any]		,__a      :			List[str]		,__a      :			Any	)			->     int:

					"""simple docstring"""

					_a	:    int         =		state_dict.pop(__a	)
					_a	:    List[str]         =		val



def    __UpperCAmelCase (	__a      :			str	)			->     Optional[int]:

					"""simple docstring"""

					_a	:    Any         =		OrderedDict()
					for key, value in state_dict.items():
										if "backbone.0.body" in key:
															_a	:    int         =		key.replace('''backbone.0.body'''		,'''backbone.conv_encoder.model'''	)
															_a	:    List[str]         =		value
										else:
															_a	:    int         =		value

					return new_state_dict



def    __UpperCAmelCase (	__a      :			Optional[int]	)			->     Tuple:

					"""simple docstring"""

					_a	:    int         =		''''''

					# 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	:    Union[str, Any]         =		state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""	)
										_a	:    Tuple         =		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	:    Tuple         =		in_proj_weight[:256, :]
										_a	:    str         =		in_proj_bias[:256]
										_a	:    List[Any]         =		in_proj_weight[256:512, :]
										_a	:    Union[str, Any]         =		in_proj_bias[256:512]
										_a	:    Any         =		in_proj_weight[-256:, :]
										_a	:    int         =		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
										_a	:    List[str]         =		state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""	)
										_a	:    List[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	:    Any         =		in_proj_weight[:256, :]
										_a	:    List[Any]         =		in_proj_bias[:256]
										_a	:    Optional[int]         =		in_proj_weight[256:512, :]
										_a	:    Tuple         =		in_proj_bias[256:512]
										_a	:    Tuple         =		in_proj_weight[-256:, :]
										_a	:    str         =		in_proj_bias[-256:]
										# read in weights + bias of input projection layer of cross-attention
										_a	:    Any         =		state_dict.pop(
										    F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""	)
										_a	:    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	:    Any         =		in_proj_weight_cross_attn[:256, :]
										_a	:    Any         =		in_proj_bias_cross_attn[:256]
										_a	:    List[str]         =		in_proj_weight_cross_attn[256:512, :]
										_a	:    Optional[Any]         =		in_proj_bias_cross_attn[256:512]
										_a	:    Union[str, Any]         =		in_proj_weight_cross_attn[-256:, :]
										_a	:    List[str]         =		in_proj_bias_cross_attn[-256:]



def    __UpperCAmelCase (	__a      :			List[Any]		,__a      :			Optional[int]	)			->     int:

					"""simple docstring"""

					_a      ,					_a	:    Optional[int]         =		image.size
					_a	:    Union[str, Any]         =		max(__a		,__a	)
					_a	:    str         =		800 if '''detection''' in checkpoint_url else 1_000
					_a	:    Optional[int]         =		target_max_size / current_max_size
					_a	:    Optional[Any]         =		image.resize((int(round(scale * width	)	), int(round(scale * height	)	))	)

					return resized_image



def    __UpperCAmelCase (	__a      :			List[str]	)			->     Dict:

					"""simple docstring"""

					_a	:    Optional[int]         =		F.to_tensor(__a	)
					_a	:    Union[str, Any]         =		F.normalize(__a		,mean=[0.4_85, 0.4_56, 0.4_06]		,std=[0.2_29, 0.2_24, 0.2_25]	)
					return image



@torch.no_grad()
def    __UpperCAmelCase (	__a      :			Union[str, Any]		,__a      :			Tuple		,__a      :			str	)			->     Any:

					"""simple docstring"""

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

					# load original state dict
					_a	:    List[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	:    List[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	:    int         =		'''model.'''
					for key in state_dict.copy().keys():
										if not key.startswith('''class_labels_classifier'''	) and not key.startswith('''bbox_predictor'''	):
															_a	:    Dict         =		state_dict.pop(__a	)
															_a	:    int         =		val
    # create HuggingFace model and load state dict
					_a	:    Union[str, Any]         =		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	:    int         =		15
										_a	:    int         =		2
										_a	:    Union[str, Any]         =		{0: '''table''', 1: '''table rotated'''}
										_a	:    Optional[int]         =		idalabel
										_a	:    Optional[int]         =		{v: k for k, v in idalabel.items()}
					else:
										_a	:    Any         =		125
										_a	:    Union[str, Any]         =		6
										_a	:    List[str]         =		{
										    0: '''table''',
										    1: '''table column''',
										    2: '''table row''',
										    3: '''table column header''',
										    4: '''table projected row header''',
										    5: '''table spanning cell''',
										}
										_a	:    Union[str, Any]         =		idalabel
										_a	:    Dict         =		{v: k for k, v in idalabel.items()}

					_a	:    List[Any]         =		DetrImageProcessor(
					    format='''coco_detection'''		,max_size=800 if '''detection''' in checkpoint_url else 1_000	)
					_a	:    List[Any]         =		TableTransformerForObjectDetection(__a	)
					model.load_state_dict(__a	)
					model.eval()

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

					_a	:    Optional[int]         =		model(__a	)

					if "detection" in checkpoint_url:
										_a	:    List[Any]         =		(1, 15, 3)
										_a	:    str         =		torch.tensor(
										    [[-6.78_97, -16.99_85, 6.79_37], [-8.01_86, -22.21_92, 6.96_77], [-7.31_17, -21.07_08, 7.40_55]]	)
										_a	:    Tuple         =		torch.tensor([[0.48_67, 0.17_67, 0.67_32], [0.67_18, 0.44_79, 0.38_30], [0.47_16, 0.17_60, 0.63_64]]	)

					else:
										_a	:    Optional[Any]         =		(1, 125, 7)
										_a	:    Optional[Any]         =		torch.tensor(
										    [[-18.14_30, -8.32_14, 4.82_74], [-18.46_85, -7.13_61, -4.26_67], [-26.36_93, -9.34_29, -4.99_62]]	)
										_a	:    Dict         =		torch.tensor([[0.49_83, 0.55_95, 0.94_40], [0.49_16, 0.63_15, 0.59_54], [0.61_08, 0.86_37, 0.11_35]]	)

					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__":
			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)






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


a__				=		logging.get_logger(__name__)



def    __UpperCAmelCase (	__a      :			Dict	)			->     Tuple:

					"""simple docstring"""

					_a	:    Any         =		R'''\w+[.]\d+'''
					_a	:    Union[str, Any]         =		re.findall(__a		,__a	)
					for pat in pats:
										_a	:    int         =		key.replace(__a		,'''_'''.join(pat.split('''.'''	)	)	)
					return key



def    __UpperCAmelCase (	__a      :			List[str]		,__a      :			Union[str, Any]		,__a      :			Optional[int]	)			->     Tuple:

					"""simple docstring"""

					_a	:    Dict         =		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)
					):
										_a	:    Dict         =		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:
										_a	:    Optional[int]         =		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:
										_a	:    Union[str, Any]         =		pt_tuple_key[:-1] + ('''embedding''',)
										return renamed_pt_tuple_key, pt_tensor

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

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

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

					# old PyTorch layer norm bias
					_a	:    Union[str, Any]         =		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    __UpperCAmelCase (	__a      :			Dict		,__a      :			str		,__a      :			str=42	)			->     Optional[int]:

					"""simple docstring"""

					_a	:    Tuple         =		{k: v.numpy() for k, v in pt_state_dict.items()}

					# Step 2: Since the model is stateless, get random Flax params
					_a	:    List[Any]         =		flax_model.init_weights(PRNGKey(__a	)	)

					_a	:    Optional[int]         =		flatten_dict(__a	)
					_a	:    Tuple         =		{}

					# Need to change some parameters name to match Flax names
					for pt_key, pt_tensor in pt_state_dict.items():
										_a	:    List[str]         =		rename_key(__a	)
										_a	:    Optional[Any]         =		tuple(renamed_pt_key.split('''.'''	)	)

										# Correctly rename weight parameters
										_a      ,					_a	:    List[str]         =		rename_key_and_reshape_tensor(__a		,__a		,__a	)

										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
										_a	:    Dict         =		jnp.asarray(__a	)

					return unflatten_dict(__a	)



