
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()
lowerCamelCase__          =		logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowerCamelCase__          =		[]
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__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		str:
			lowerCAmelCase__      :      Dict         =		state_dict.pop(_UpperCamelCase     )
			lowerCAmelCase__      :      str         =		val
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		Tuple:
			lowerCAmelCase__      :      str         =		OrderedDict()
			for key, value in state_dict.items():
						if "backbone.0.body" in key:
									lowerCAmelCase__      :      Optional[int]         =		key.replace('backbone.0.body'       ,							'backbone.conv_encoder.model'     )
									lowerCAmelCase__      :      Optional[int]         =		value
						else:
									lowerCAmelCase__      :      Optional[Any]         =		value

			return new_state_dict
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		List[str]:
			lowerCAmelCase__      :      List[Any]         =		""""""

			# 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)
						lowerCAmelCase__      :      Optional[int]         =		state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''     )
						lowerCAmelCase__      :      str         =		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
						lowerCAmelCase__      :      List[str]         =		in_proj_weight[:256, :]
						lowerCAmelCase__      :      str         =		in_proj_bias[:256]
						lowerCAmelCase__      :      Tuple         =		in_proj_weight[256:512, :]
						lowerCAmelCase__      :      Optional[int]         =		in_proj_bias[256:512]
						lowerCAmelCase__      :      List[Any]         =		in_proj_weight[-256:, :]
						lowerCAmelCase__      :      List[Any]         =		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
						lowerCAmelCase__      :      str         =		state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''     )
						lowerCAmelCase__      :      Union[str, 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
						lowerCAmelCase__      :      Union[str, Any]         =		in_proj_weight[:256, :]
						lowerCAmelCase__      :      List[Any]         =		in_proj_bias[:256]
						lowerCAmelCase__      :      Tuple         =		in_proj_weight[256:512, :]
						lowerCAmelCase__      :      Dict         =		in_proj_bias[256:512]
						lowerCAmelCase__      :      Optional[Any]         =		in_proj_weight[-256:, :]
						lowerCAmelCase__      :      Union[str, Any]         =		in_proj_bias[-256:]
						# read in weights + bias of input projection layer of cross-attention
						lowerCAmelCase__      :      List[Any]         =		state_dict.pop(
						    F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''     )
						lowerCAmelCase__      :      Dict         =		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
						lowerCAmelCase__      :      Tuple         =		in_proj_weight_cross_attn[:256, :]
						lowerCAmelCase__      :      int         =		in_proj_bias_cross_attn[:256]
						lowerCAmelCase__      :      Optional[Any]         =		in_proj_weight_cross_attn[256:512, :]
						lowerCAmelCase__      :      List[Any]         =		in_proj_bias_cross_attn[256:512]
						lowerCAmelCase__      :      Union[str, Any]         =		in_proj_weight_cross_attn[-256:, :]
						lowerCAmelCase__      :      Tuple         =		in_proj_bias_cross_attn[-256:]
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		Optional[int]:
			lowerCAmelCase__      :      Tuple         =		image.size
			lowerCAmelCase__      :      Optional[Any]         =		max(_UpperCamelCase       ,							_UpperCamelCase     )
			lowerCAmelCase__      :      str         =		800 if """detection""" in checkpoint_url else 1_000
			lowerCAmelCase__      :      Dict         =		target_max_size / current_max_size
			lowerCAmelCase__      :      str         =		image.resize((int(round(scale * width     )     ), int(round(scale * height     )     ))     )

			return resized_image
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		Tuple:
			lowerCAmelCase__      :      str         =		F.to_tensor(_UpperCamelCase     )
			lowerCAmelCase__      :      List[str]         =		F.normalize(_UpperCamelCase       ,							mean=[0.485, 0.456, 0.406]       ,							std=[0.229, 0.224, 0.225]     )
			return image
@torch.no_grad()
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		int:
			logger.info('Converting model...'     )

			# load original state dict
			lowerCAmelCase__      :      Union[str, Any]         =		torch.hub.load_state_dict_from_url(_UpperCamelCase       ,							map_location='cpu'     )
			# rename keys
			for src, dest in rename_keys:
						rename_key(_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase     )
			lowerCAmelCase__      :      Any         =		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
			lowerCAmelCase__      :      Optional[Any]         =		"""model."""
			for key in state_dict.copy().keys():
						if not key.startswith('class_labels_classifier'     ) and not key.startswith('bbox_predictor'     ):
									lowerCAmelCase__      :      Optional[int]         =		state_dict.pop(_UpperCamelCase     )
									lowerCAmelCase__      :      Optional[int]         =		val
    # create HuggingFace model and load state dict
			lowerCAmelCase__      :      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:
						lowerCAmelCase__      :      List[str]         =		15
						lowerCAmelCase__      :      Any         =		2
						lowerCAmelCase__      :      List[str]         =		{0: """table""", 1: """table rotated"""}
						lowerCAmelCase__      :      List[str]         =		idalabel
						lowerCAmelCase__      :      List[str]         =		{v: k for k, v in idalabel.items()}
			else:
						lowerCAmelCase__      :      List[str]         =		125
						lowerCAmelCase__      :      int         =		6
						lowerCAmelCase__      :      Optional[int]         =		{
						    0: """table""",
						    1: """table column""",
						    2: """table row""",
						    3: """table column header""",
						    4: """table projected row header""",
						    5: """table spanning cell""",
						}
						lowerCAmelCase__      :      List[Any]         =		idalabel
						lowerCAmelCase__      :      List[Any]         =		{v: k for k, v in idalabel.items()}

			lowerCAmelCase__      :      Any         =		DetrImageProcessor(
			    format='coco_detection'       ,							max_size=800 if 'detection' in checkpoint_url else 1_000     )
			lowerCAmelCase__      :      str         =		TableTransformerForObjectDetection(_UpperCamelCase     )
			model.load_state_dict(_UpperCamelCase     )
			model.eval()

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

			lowerCAmelCase__      :      int         =		model(_UpperCamelCase     )

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

			else:
						lowerCAmelCase__      :      int         =		(1, 125, 7)
						lowerCAmelCase__      :      Dict         =		torch.tensor(
						    [[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]]     )
						lowerCAmelCase__      :      Tuple         =		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]       ,							_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...'     )
						lowerCAmelCase__      :      Any         =		(
						    """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__":
					lowerCamelCase__          =		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."""
					)
					lowerCamelCase__          =		parser.parse_args()
					convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)



"""simple docstring"""






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCAmelCase    :     Union[str, Any]   						=		{'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase    :     int   						=		[
							    'MRA_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'MraForMaskedLM',
							    'MraForMultipleChoice',
							    'MraForQuestionAnswering',
							    'MraForSequenceClassification',
							    'MraForTokenClassification',
							    'MraLayer',
							    'MraModel',
							    'MraPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mra import (
														    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MraForMaskedLM,
														    MraForMultipleChoice,
														    MraForQuestionAnswering,
														    MraForSequenceClassification,
														    MraForTokenClassification,
														    MraLayer,
														    MraModel,
														    MraPreTrainedModel,
														)
else:
							import sys

							UpperCAmelCase    :     Tuple   						=		_LazyModule(__name__, globals()['__file__'], _import_structure)
