

'''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()
UpperCamelCase_    						=       logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
UpperCamelCase_    						=       []
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			(   __magic_name__						:     List[Any]   , __magic_name__						:     Any   , __magic_name__						:     List[str]						)				->  Union[str, Any]:
						lowercase :							Optional[int]    		=state_dict.pop(__magic_name__						)
						lowercase :							List[str]    		=val






def 				_lowerCAmelCase			(   __magic_name__						:     Optional[int]						)				->  Optional[Any]:
						lowercase :							str    		=OrderedDict()
						for key, value in state_dict.items():
												if "backbone.0.body" in key:
																		lowercase :							Optional[Any]    		=key.replace('''backbone.0.body'''   , '''backbone.conv_encoder.model'''						)
																		lowercase :							Optional[Any]    		=value
												else:
																		lowercase :							Any    		=value

						return new_state_dict






def 				_lowerCAmelCase			(   __magic_name__						:     int						)				->  Dict:
						lowercase :							Tuple    		=''''''

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






def 				_lowerCAmelCase			(   __magic_name__						:     Union[str, Any]   , __magic_name__						:     int						)				->  Optional[Any]:
						lowercase		,	lowercase :							Optional[int]    		=image.size
						lowercase :							Union[str, Any]    		=max(__magic_name__   , __magic_name__						)
						lowercase :							List[str]    		=800 if '''detection''' in checkpoint_url else 1000
						lowercase :							List[str]    		=target_max_size / current_max_size
						lowercase :							List[Any]    		=image.resize((int(round(scale * width						)						), int(round(scale * height						)						))						)

						return resized_image






def 				_lowerCAmelCase			(   __magic_name__						:     Optional[int]						)				->  Dict:
						lowercase :							Tuple    		=F.to_tensor(__magic_name__						)
						lowercase :							int    		=F.normalize(__magic_name__   , 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			(   __magic_name__						:     Optional[int]   , __magic_name__						:     Optional[Any]   , __magic_name__						:     Dict						)				->  Tuple:
						logger.info('''Converting model...'''						)

						# load original state dict
						lowercase :							Any    		=torch.hub.load_state_dict_from_url(__magic_name__   , map_location='''cpu'''						)
						# rename keys
						for src, dest in rename_keys:
												rename_key(__magic_name__   , __magic_name__   , __magic_name__						)
						lowercase :							List[Any]    		=rename_backbone_keys(__magic_name__						)
						# query, key and value matrices need special treatment
						read_in_q_k_v(__magic_name__						)
						# 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 :							Optional[int]    		=state_dict.pop(__magic_name__						)
																		lowercase :							str    		=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 :							Any    		=15
												lowercase :							Tuple    		=2
												lowercase :							Tuple    		={0: '''table''', 1: '''table rotated'''}
												lowercase :							Any    		=idalabel
												lowercase :							Optional[int]    		={v: k for k, v in idalabel.items()}
						else:
												lowercase :							List[Any]    		=125
												lowercase :							Any    		=6
												lowercase :							List[str]    		={
												    0: '''table''',
												    1: '''table column''',
												    2: '''table row''',
												    3: '''table column header''',
												    4: '''table projected row header''',
												    5: '''table spanning cell''',
												}
												lowercase :							int    		=idalabel
												lowercase :							str    		={v: k for k, v in idalabel.items()}

						lowercase :							Tuple    		=DetrImageProcessor(
						    format='''coco_detection'''   , max_size=800 if '''detection''' in checkpoint_url else 1000						)
						lowercase :							Optional[int]    		=TableTransformerForObjectDetection(__magic_name__						)
						model.load_state_dict(__magic_name__						)
						model.eval()

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

						lowercase :							List[str]    		=model(__magic_name__						)

						if "detection" in checkpoint_url:
												lowercase :							Any    		=(1, 15, 3)
												lowercase :							List[str]    		=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]]						)
												lowercase :							List[str]    		=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:
												lowercase :							Dict    		=(1, 125, 7)
												lowercase :							Dict    		=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]]						)
												lowercase :							Any    		=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]   , __magic_name__   , atol=1E-4						)
						assert torch.allclose(outputs.pred_boxes[0, :3, :3]   , __magic_name__   , 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(__magic_name__						).mkdir(exist_ok=__magic_name__						)
												model.save_pretrained(__magic_name__						)
												image_processor.save_pretrained(__magic_name__						)

						if push_to_hub:
												# Push model to HF hub
												logger.info('''Pushing model to the hub...'''						)
												lowercase :							Optional[Any]    		=(
												    '''microsoft/table-transformer-detection'''
												    if '''detection''' in checkpoint_url
												    else '''microsoft/table-transformer-structure-recognition'''
												)
												model.push_to_hub(__magic_name__						)
												image_processor.push_to_hub(__magic_name__						)


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








'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase_    						=       logging.get_logger(__name__)

UpperCamelCase_    						=       {
    """facebook/s2t-wav2vec2-large-en-de""": (
        """https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"""
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}





class 		__SCREAMING_SNAKE_CASE							(			lowercase__      ):
				lowerCamelCase_		       =    'speech_to_text_2'
				lowerCamelCase_		       =    ['past_key_values']
				lowerCamelCase_		       =    {'num_attention_heads': 'decoder_attention_heads', 'hidden_size': 'd_model'}







				def __init__(       self     :		int		,					UpperCAmelCase__     :		Dict=10000		,					UpperCAmelCase__     :		Optional[int]=6		,					UpperCAmelCase__     :		str=2048		,					UpperCAmelCase__     :		Optional[Any]=4		,					UpperCAmelCase__     :		Union[str, Any]=0.0		,					UpperCAmelCase__     :		Optional[Any]=True		,					UpperCAmelCase__     :		Optional[Any]="relu"		,					UpperCAmelCase__     :		List[str]=256		,					UpperCAmelCase__     :		Union[str, Any]=0.1		,					UpperCAmelCase__     :		List[Any]=0.0		,					UpperCAmelCase__     :		Optional[Any]=0.0		,					UpperCAmelCase__     :		Tuple=0.02		,					UpperCAmelCase__     :		int=2		,					UpperCAmelCase__     :		str=True		,					UpperCAmelCase__     :		str=1		,					UpperCAmelCase__     :		Optional[Any]=0		,					UpperCAmelCase__     :		Optional[int]=2		,					UpperCAmelCase__     :		List[Any]=1024		,					**UpperCAmelCase__     :		Dict		,					):
										'''simple docstring'''
										lowercase :							List[str]    		=vocab_size
										lowercase :							Optional[int]    		=d_model
										lowercase :							Optional[Any]    		=decoder_ffn_dim
										lowercase :							Any    		=decoder_layers
										lowercase :							Dict    		=decoder_attention_heads
										lowercase :							List[Any]    		=dropout
										lowercase :							List[Any]    		=attention_dropout
										lowercase :							Any    		=activation_dropout
										lowercase :							Optional[Any]    		=activation_function
										lowercase :							Optional[int]    		=init_std
										lowercase :							Dict    		=decoder_layerdrop
										lowercase :							Optional[int]    		=use_cache
										lowercase :							Optional[Any]    		=decoder_layers
										lowercase :							List[str]    		=scale_embedding  # scale factor will be sqrt(d_model) if True
										lowercase :							str    		=max_target_positions

										super().__init__(
										    pad_token_id=UpperCAmelCase__		,					bos_token_id=UpperCAmelCase__		,					eos_token_id=UpperCAmelCase__		,					decoder_start_token_id=UpperCAmelCase__		,					**UpperCAmelCase__		,					)





