



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 (       lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__						):

				"""simple docstring"""


				lowercase__					:   List[str]													=					state_dict.pop(lowerCamelCase__						)
				lowercase__					:   Dict													=					val


def  __lowerCamelCase (       lowerCamelCase__						):

				"""simple docstring"""


				lowercase__					:   Any													=					OrderedDict()
				for key, value in state_dict.items():
								if "backbone.0.body" in key:
												lowercase__					:   Optional[int]													=					key.replace("backbone.0.body"					,			"backbone.conv_encoder.model"						)
												lowercase__					:   Optional[int]													=					value
								else:
												lowercase__					:   Dict													=					value

				return new_state_dict


def  __lowerCamelCase (       lowerCamelCase__						):

				"""simple docstring"""


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


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__						):

				"""simple docstring"""


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

				return resized_image


def  __lowerCamelCase (       lowerCamelCase__						):

				"""simple docstring"""


				lowercase__					:   Tuple													=					F.to_tensor(lowerCamelCase__						)
				lowercase__					:   Any													=					F.normalize(lowerCamelCase__					,			mean=[0.485, 0.456, 0.406]					,			std=[0.229, 0.224, 0.225]						)
				return image


@torch.no_grad()
def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__						):

				"""simple docstring"""


				logger.info("Converting model..."						)

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

				lowercase__					:   List[str]													=					DetrImageProcessor(
				    format="coco_detection"					,			max_size=800 if "detection" in checkpoint_url else 1_000						)
				lowercase__					:   Any													=					TableTransformerForObjectDetection(lowerCamelCase__						)
				model.load_state_dict(lowerCamelCase__						)
				model.eval()

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

				lowercase__					:   Dict													=					model(lowerCamelCase__						)

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

				else:
								lowercase__					:   List[str]													=					(1, 125, 7)
								lowercase__					:   int													=					torch.tensor(
								    [[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]]						)
								lowercase__					:   Union[str, Any]													=					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]					,			lowerCamelCase__					,			atol=1e-4						)
				assert torch.allclose(outputs.pred_boxes[0, :3, :3]					,			lowerCamelCase__					,			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(lowerCamelCase__						).mkdir(exist_ok=lowerCamelCase__						)
								model.save_pretrained(lowerCamelCase__						)
								image_processor.save_pretrained(lowerCamelCase__						)

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


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)









import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
				import optax
				from flax.training.common_utils import onehot

				from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
				from transformers.models.ta.modeling_flax_ta import shift_tokens_right

@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 				snake_case__(unittest.TestCase      ):

					"""simple docstring"""






					@slow
					def 			snake_case					(							self :     Optional[int]					):
									lowercase__					:   Dict													=					FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small"					)
									lowercase__					:   Dict													=					AutoTokenizer.from_pretrained("google/mt5-small"					)

									lowercase__					:   Optional[Any]													=					tokenizer("Hello there"				,       return_tensors="np"					).input_ids
									lowercase__					:   Optional[Any]													=					tokenizer("Hi I am"				,       return_tensors="np"					).input_ids

									lowercase__					:   int													=					shift_tokens_right(SCREAMING_SNAKE_CASE				,       model.config.pad_token_id				,       model.config.decoder_start_token_id					)

									lowercase__					:   Tuple													=					model(SCREAMING_SNAKE_CASE				,       decoder_input_ids=SCREAMING_SNAKE_CASE					).logits
									lowercase__					:   Dict													=					optax.softmax_cross_entropy(SCREAMING_SNAKE_CASE				,       onehot(SCREAMING_SNAKE_CASE				,       logits.shape[-1]					)					).mean()

									lowercase__					:   Union[str, Any]													=					-(labels.shape[-1] * loss.item())

									lowercase__					:   Union[str, Any]													=					-84.9_127
									self.assertTrue(abs(mtf_score - EXPECTED_SCORE					) < 1E-4					)




