"""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()
lowerCAmelCase_:					Optional[Any]     =					logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowerCAmelCase_:					Any     =					[]
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 					__a   (		A    ,				A    ,				A      ):






     '''simple docstring'''



     lowercase__				 =							state_dict.pop(_lowercase      )
     lowercase__				 =							val







def 					__a   (		A      ):






     '''simple docstring'''



     lowercase__				 =							OrderedDict()
     for key, value in state_dict.items():
          if "backbone.0.body" in key:
               lowercase__				 =							key.replace("backbone.0.body"    ,				"backbone.conv_encoder.model"      )
               lowercase__				 =							value
          else:
               lowercase__				 =							value

     return new_state_dict







def 					__a   (		A      ):






     '''simple docstring'''



     lowercase__				 =							''''''

     # 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__				 =							state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''      )
          lowercase__				 =							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__				 =							in_proj_weight[:2_56, :]
          lowercase__				 =							in_proj_bias[:2_56]
          lowercase__				 =							in_proj_weight[2_56:5_12, :]
          lowercase__				 =							in_proj_bias[2_56:5_12]
          lowercase__				 =							in_proj_weight[-2_56:, :]
          lowercase__				 =							in_proj_bias[-2_56:]
     # 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__				 =							state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''      )
          lowercase__				 =							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__				 =							in_proj_weight[:2_56, :]
          lowercase__				 =							in_proj_bias[:2_56]
          lowercase__				 =							in_proj_weight[2_56:5_12, :]
          lowercase__				 =							in_proj_bias[2_56:5_12]
          lowercase__				 =							in_proj_weight[-2_56:, :]
          lowercase__				 =							in_proj_bias[-2_56:]
          # read in weights + bias of input projection layer of cross-attention
          lowercase__				 =							state_dict.pop(
              f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''      )
          lowercase__				 =							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__				 =							in_proj_weight_cross_attn[:2_56, :]
          lowercase__				 =							in_proj_bias_cross_attn[:2_56]
          lowercase__				 =							in_proj_weight_cross_attn[2_56:5_12, :]
          lowercase__				 =							in_proj_bias_cross_attn[2_56:5_12]
          lowercase__				 =							in_proj_weight_cross_attn[-2_56:, :]
          lowercase__				 =							in_proj_bias_cross_attn[-2_56:]







def 					__a   (		A    ,				A      ):






     '''simple docstring'''



     lowercase__				 =							image.size
     lowercase__				 =							max(_lowercase    ,				_lowercase      )
     lowercase__				 =							8_00 if '''detection''' in checkpoint_url else 10_00
     lowercase__				 =							target_max_size / current_max_size
     lowercase__				 =							image.resize((int(round(scale * width      )      ), int(round(scale * height      )      ))      )

     return resized_image







def 					__a   (		A      ):






     '''simple docstring'''



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







@torch.no_grad()
def 					__a   (		A    ,				A    ,				A      ):






     '''simple docstring'''



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

     # load original state dict
     lowercase__				 =							torch.hub.load_state_dict_from_url(_lowercase    ,				map_location="cpu"      )
     # rename keys
     for src, dest in rename_keys:
          rename_key(_lowercase    ,				_lowercase    ,				_lowercase      )
     lowercase__				 =							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
     lowercase__				 =							'''model.'''
     for key in state_dict.copy().keys():
          if not key.startswith("class_labels_classifier"      ) and not key.startswith("bbox_predictor"      ):
               lowercase__				 =							state_dict.pop(_lowercase      )
               lowercase__				 =							val
    # create HuggingFace model and load state dict
     lowercase__				 =							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__				 =							15
          lowercase__				 =							2
          lowercase__				 =							{0: '''table''', 1: '''table rotated'''}
          lowercase__				 =							idalabel
          lowercase__				 =							{v: k for k, v in idalabel.items()}
     else:
          lowercase__				 =							1_25
          lowercase__				 =							6
          lowercase__				 =							{
              0: '''table''',
              1: '''table column''',
              2: '''table row''',
              3: '''table column header''',
              4: '''table projected row header''',
              5: '''table spanning cell''',
          }
          lowercase__				 =							idalabel
          lowercase__				 =							{v: k for k, v in idalabel.items()}

     lowercase__				 =							DetrImageProcessor(
         format="coco_detection"    ,				max_size=8_00 if "detection" in checkpoint_url else 10_00      )
     lowercase__				 =							TableTransformerForObjectDetection(_lowercase      )
     model.load_state_dict(_lowercase      )
     model.eval()

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

     lowercase__				 =							model(_lowercase      )

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

     else:
          lowercase__				 =							(1, 1_25, 7)
          lowercase__				 =							torch.tensor(
              [[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]]      )
          lowercase__				 =							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]    ,				_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..."      )
          lowercase__				 =							(
              '''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__":
 lowerCAmelCase_:					str     =					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_:					Optional[int]     =					parser.parse_args()
 convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)






"""simple docstring"""


from math import ceil
from typing import List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging


lowerCAmelCase_:					Union[str, Any]     =					logging.get_logger(__name__)






class    a__    (   _a	):
   snake_case_							=      ["audio_values", "audio_mask"]


   def __init__(     self,				_UpperCAmelCase=2048,				_UpperCAmelCase=1,				_UpperCAmelCase=[16, 16],				_UpperCAmelCase=128,				_UpperCAmelCase=4_4100,				_UpperCAmelCase=86,				_UpperCAmelCase=2048,				_UpperCAmelCase=0.0,				**_UpperCAmelCase,				):
        '''simple docstring'''



        super().__init__(
            feature_size=_UpperCAmelCase,				sampling_rate=_UpperCAmelCase,				padding_value=_UpperCAmelCase,				**_UpperCAmelCase,				)

        lowercase__				 =							spectrogram_length
        lowercase__				 =							num_channels
        lowercase__				 =							patch_size
        lowercase__				 =							feature_size // self.patch_size[1]
        lowercase__				 =							n_fft
        lowercase__				 =							sampling_rate // hop_length_to_sampling_rate
        lowercase__				 =							sampling_rate
        lowercase__				 =							padding_value
        lowercase__				 =							mel_filter_bank(
            num_frequency_bins=1 + n_fft // 2,				num_mel_filters=_UpperCAmelCase,				min_frequency=0.0,				max_frequency=22_050.0,				sampling_rate=_UpperCAmelCase,				norm="slaney",				mel_scale="slaney",				).T


   def 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							spectrogram(
            _UpperCAmelCase,				window_function(self.n_fft,				"hann"  ),				frame_length=self.n_fft,				hop_length=self.hop_length,				power=2.0,				mel_filters=self.mel_filters.T,				log_mel="dB",				db_range=80.0,				)
        lowercase__				 =							log_spec[:, :-1]
        lowercase__				 =							log_spec - 20.0
        lowercase__				 =							np.clip(log_spec / 40.0,				-2.0,				0.0  ) + 1.0
        return log_spec


   def __call__(     self,				_UpperCAmelCase,				_UpperCAmelCase = None,				_UpperCAmelCase = True,				_UpperCAmelCase = None,				_UpperCAmelCase = False,				_UpperCAmelCase = False,				**_UpperCAmelCase,				):
        '''simple docstring'''



        if sampling_rate is not None:
             if sampling_rate != self.sampling_rate:
                  raise ValueError(
                      "This feature extractor is set to support sampling rate"
                      F''' of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled'''
                      F''' with {self.sampling_rate} and not {sampling_rate}.'''  )
        else:
             logger.warning(
                 "It is strongly recommended to pass the `sampling_rate` argument to this function. "
                 "Failing to do so can result in silent errors that might be hard to debug."  )

        lowercase__				 =							isinstance(_UpperCAmelCase,				np.ndarray  ) and len(raw_speech.shape  ) > 1
        if is_batched_numpy and len(raw_speech.shape  ) > 2:
             raise ValueError(F'''Only mono-channel audio is supported for input to {self}'''  )
        lowercase__				 =							is_batched_numpy or (
            isinstance(_UpperCAmelCase,				(list, tuple)  ) and (isinstance(raw_speech[0],				(np.ndarray, tuple, list)  ))
        )
        if is_batched:
             lowercase__				 =							[np.asarray([speech],				dtype=np.floataa  ).T for speech in raw_speech]
        elif not is_batched and not isinstance(_UpperCAmelCase,				np.ndarray  ):
             lowercase__				 =							np.asarray(_UpperCAmelCase,				dtype=np.floataa  )
        elif isinstance(_UpperCAmelCase,				np.ndarray  ) and raw_speech.dtype is np.dtype(np.floataa  ):
             lowercase__				 =							raw_speech.astype(np.floataa  )
        # always return batch
        if not is_batched:
             lowercase__				 =							[np.asarray([raw_speech]  ).T]

        # Convert audio signals to log mel spectrograms, truncate by time axis
        lowercase__				 =							[
            self._np_extract_fbank_features(waveform.squeeze()  ).T[: self.spectrogram_length] for waveform in raw_speech
        ]
        if isinstance(audio_features[0],				_UpperCAmelCase  ):
             lowercase__				 =							[np.asarray(_UpperCAmelCase,				dtype=np.floataa  ) for feature in audio_features]

        # Create audio attention mask
        lowercase__				 =							max(
            [ceil(feature.shape[0] / self.patch_size[0]  ) * self.freq_len for feature in audio_features]  )  # The maximum number of audio patches in a batch
        if return_attention_mask:
             lowercase__				 =							[
                 (ceil(feature.shape[0] / self.patch_size[0]  ) * self.freq_len) * [1]
                 + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]  ) * self.freq_len) * [0]
                 for feature in audio_features
             ]
             lowercase__				 =							np.array(_UpperCAmelCase  ).astype(np.floataa  )

        # convert into correct format for padding
        lowercase__				 =							max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
        lowercase__				 =							np.ones([len(_UpperCAmelCase  ), 1, max_time_len, self.feature_size]  ).astype(np.floataa  )
        lowercase__				 =							padded_audio_features * self.padding_value
        for i in range(len(_UpperCAmelCase  )  ):
             lowercase__				 =							audio_features[i]
             lowercase__				 =							feature

        # return as BatchFeature
        if return_attention_mask:
             lowercase__				 =							{"audio_values": padded_audio_features, "audio_mask": audio_mask}
        else:
             lowercase__				 =							{"audio_values": padded_audio_features}

        lowercase__				 =							BatchFeature(data=_UpperCAmelCase,				tensor_type=_UpperCAmelCase  )
        return encoded_inputs




