
"""simple docstring"""
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_a      :			Optional[Any]					=							logging.get_logger(__name__)

_a      :			str					=							{
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """quantizer.weight_proj""": """quantizer.weight_proj""",
    """quantizer.vars""": """quantizer.codevectors""",
    """project_q""": """project_q""",
    """final_proj""": """project_hid""",
    """w2v_encoder.proj""": """ctc_proj""",
    """mask_emb""": """masked_spec_embed""",
}
_a      :			List[str]					=							[
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]
def 				a__   (   a   :				Any       ,     a   :				Optional[int]       ,     a   :				Any       ,     a   :				Dict       ,     a   :				List[str]       ,     a   :				Any		):

  """simple docstring"""







  for attribute in key.split("."		):
    if is_finetuned:
      if attribute in ["quantizer", "project_q", "project_hid"]:
        # those layers are only relevant for pretraining and should be dropped
        return

      if attribute == "ctc_proj":
        # we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
        _snake_case     :						Dict						=   "lm_head"

    _snake_case     :						Union[str, Any]						=   getattr(a       ,     a		)

  if weight_type is not None:
    _snake_case     :						List[str]						=   getattr(a       ,     a		).shape
  else:
    _snake_case     :						Union[str, Any]						=   hf_pointer.shape

  assert hf_shape == value.shape, (
      f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
      f' {value.shape} for {full_name}'
  )

  if weight_type == "weight":
    _snake_case     :						Any						=   value
  elif weight_type == "weight_g":
    _snake_case     :						List[Any]						=   value
  elif weight_type == "weight_v":
    _snake_case     :						List[Any]						=   value
  elif weight_type == "bias":
    _snake_case     :						Tuple						=   value
  else:
    _snake_case     :						str						=   value

  logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'		)
def 				a__   (   a   :				str       ,     a   :				Optional[int]       ,     a   :				Any		):

  """simple docstring"""







  _snake_case     :						str						=   []
  _snake_case     :						Dict						=   fairseq_model.state_dict()

  _snake_case     :						Dict						=   hf_model.unispeech.feature_extractor

  for name, value in fairseq_dict.items():
    _snake_case     :						str						=   False
    if "conv_layers" in name:
      load_conv_layer(
          a       ,     a       ,     a       ,     a       ,     hf_model.config.feat_extract_norm == "group"       ,     )
      _snake_case     :						Union[str, Any]						=   True
    else:
      for key, mapped_key in MAPPING.items():
        _snake_case     :						Union[str, Any]						=   "unispeech." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
        if key in name or key.split("w2v_model."		)[-1] == name.split("."		)[0]:
          _snake_case     :						Any						=   True
          if "*" in mapped_key:
            _snake_case     :						Optional[Any]						=   name.split(a		)[0].split("."		)[-2]
            _snake_case     :						str						=   mapped_key.replace("*"       ,     a		)
          if "weight_g" in name:
            _snake_case     :						List[Any]						=   "weight_g"
          elif "weight_v" in name:
            _snake_case     :						str						=   "weight_v"
          elif "bias" in name:
            _snake_case     :						List[str]						=   "bias"
          elif "weight" in name:
            # TODO: don't match quantizer.weight_proj
            _snake_case     :						List[str]						=   "weight"
          else:
            _snake_case     :						str						=   None
          set_recursively(a       ,     a       ,     a       ,     a       ,     a       ,     a		)
        continue
    if not is_used:
      unused_weights.append(a		)

  logger.warning(f'Unused weights: {unused_weights}'		)
def 				a__   (   a   :				int       ,     a   :				Any       ,     a   :				int       ,     a   :				str       ,     a   :				str		):

  """simple docstring"""







  _snake_case     :						Optional[int]						=   full_name.split("conv_layers."		)[-1]
  _snake_case     :						Dict						=   name.split("."		)
  _snake_case     :						Optional[Any]						=   int(items[0]		)
  _snake_case     :						Optional[int]						=   int(items[1]		)

  if type_id == 0:
    if "bias" in name:
      assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
          f'{full_name} has size {value.shape}, but'
          f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
      )
      _snake_case     :						Tuple						=   value
      logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'		)
    elif "weight" in name:
      assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
          f'{full_name} has size {value.shape}, but'
          f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
      )
      _snake_case     :						str						=   value
      logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'		)
  elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
    if "bias" in name:
      assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
          f'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
          " found."
      )
      _snake_case     :						int						=   value
      logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'		)
    elif "weight" in name:
      assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
          f'{full_name} has size {value.shape}, but'
          f' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
      )
      _snake_case     :						Any						=   value
      logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'		)
  else:
    unused_weights.append(a		)
@torch.no_grad()
def 				a__   (   a   :				Any       ,     a   :				List[str]       ,     a   :				int=None       ,     a   :				Union[str, Any]=None       ,     a   :				str=True		):

  """simple docstring"""







  if config_path is not None:
    _snake_case     :						Optional[int]						=   UniSpeechConfig.from_pretrained(a		)
  else:
    _snake_case     :						Optional[int]						=   UniSpeechConfig()

  if is_finetuned:
    if dict_path:
      _snake_case     :						Any						=   Dictionary.load_from_json(a		)

      # important change bos & pad token id since CTC symbol is <pad> and
      # not <s> as in fairseq
      _snake_case     :						Optional[Any]						=   target_dict.pad_index
      _snake_case     :						List[Any]						=   target_dict.bos_index
      _snake_case     :						Union[str, Any]						=   target_dict.eos_index
      _snake_case     :						int						=   len(target_dict.symbols		)
      _snake_case     :						Any						=   os.path.join(a       ,     "vocab.json"		)
      if not os.path.isdir(a		):
        logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(a		)		)
        return
      os.makedirs(a       ,     exist_ok=a		)
      _snake_case     :						Any						=   target_dict.indices

      # fairseq has the <pad> and <s> switched
      _snake_case     :						Optional[int]						=   42
      _snake_case     :						List[Any]						=   43
      with open(a       ,     "w"       ,     encoding="utf-8"		) as vocab_handle:
        json.dump(a       ,     a		)
      _snake_case     :						List[str]						=   WavaVecaPhonemeCTCTokenizer(
          a       ,     unk_token=target_dict.unk_word       ,     pad_token=target_dict.pad_word       ,     bos_token=target_dict.bos_word       ,     eos_token=target_dict.eos_word       ,     word_delimiter_token="|"       ,     do_lower_case=a       ,     )
      _snake_case     :						Optional[Any]						=   True if config.feat_extract_norm == "layer" else False
      _snake_case     :						str						=   WavaVecaFeatureExtractor(
          feature_size=1       ,     sampling_rate=16_000       ,     padding_value=0       ,     do_normalize=a       ,     return_attention_mask=a       ,     )
      _snake_case     :						Optional[Any]						=   WavaVecaProcessor(feature_extractor=a       ,     tokenizer=a		)
      processor.save_pretrained(a		)

    _snake_case     :						Any						=   UniSpeechForCTC(a		)
  else:
    _snake_case     :						List[Any]						=   UniSpeechForPreTraining(a		)

  if is_finetuned:
    _snake_case     :						Tuple						=   fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]       ,     arg_overrides={"data": "/".join(dict_path.split("/"		)[:-1]		), "w2v_path": checkpoint_path}		)
  else:
    _snake_case     :						Tuple						=   fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]		)

  _snake_case     :						Optional[int]						=   model[0].eval()

  recursively_load_weights(a       ,     a       ,     a		)

  hf_unispeech.save_pretrained(a		)


if __name__ == "__main__":
    _a      :			Dict					=							argparse.ArgumentParser()
    parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
    parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
    parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
    parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
    parser.add_argument(
        """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
    )
    _a      :			Optional[int]					=							parser.parse_args()
    convert_unispeech_checkpoint(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
    )



"""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()
_a      :			Tuple					=							logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_a      :			int					=							[]
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   :				List[str]       ,     a   :				int       ,     a   :				int		):

  """simple docstring"""







  _snake_case     :						Union[str, Any]						=   state_dict.pop(a		)
  _snake_case     :						Union[str, Any]						=   val
def 				a__   (   a   :				Tuple		):

  """simple docstring"""







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

  return new_state_dict
def 				a__   (   a   :				int		):

  """simple docstring"""







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

  """simple docstring"""







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

  return resized_image
def 				a__   (   a   :				str		):

  """simple docstring"""







  _snake_case     :						str						=   F.to_tensor(a		)
  _snake_case     :						Union[str, Any]						=   F.normalize(a       ,     mean=[0.485, 0.456, 0.406]       ,     std=[0.229, 0.224, 0.225]		)
  return image
@torch.no_grad()
def 				a__   (   a   :				Optional[Any]       ,     a   :				Any       ,     a   :				Union[str, Any]		):

  """simple docstring"""







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

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

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

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

  _snake_case     :						str						=   model(a		)

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

  else:
    _snake_case     :						Union[str, Any]						=   (1, 125, 7)
    _snake_case     :						str						=   torch.tensor(
        [[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]]		)
    _snake_case     :						Optional[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]       ,     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..."		)
    _snake_case     :						int						=   (
        "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      :			Tuple					=							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      :			Any					=							parser.parse_args()
    convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)

