



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:      Tuple		 =		logging.get_logger(__name__)

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

  return new_state_dict
def    lowerCamelCase__ (		_A       ):
  a		:      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)
    a		:      str       =  state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""       )
    a		:      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
    a		:      int       =  in_proj_weight[:256, :]
    a		:      List[str]       =  in_proj_bias[:256]
    a		:      Optional[Any]       =  in_proj_weight[256:512, :]
    a		:      List[str]       =  in_proj_bias[256:512]
    a		:      Dict       =  in_proj_weight[-256:, :]
    a		:      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
    a		:      List[Any]       =  state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""       )
    a		:      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
    a		:      Union[str, Any]       =  in_proj_weight[:256, :]
    a		:      Tuple       =  in_proj_bias[:256]
    a		:      int       =  in_proj_weight[256:512, :]
    a		:      int       =  in_proj_bias[256:512]
    a		:      Dict       =  in_proj_weight[-256:, :]
    a		:      str       =  in_proj_bias[-256:]
    # read in weights + bias of input projection layer of cross-attention
    a		:      Dict       =  state_dict.pop(
        f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""       )
    a		:      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
    a		:      Dict       =  in_proj_weight_cross_attn[:256, :]
    a		:      Any       =  in_proj_bias_cross_attn[:256]
    a		:      Union[str, Any]       =  in_proj_weight_cross_attn[256:512, :]
    a		:      Optional[int]       =  in_proj_bias_cross_attn[256:512]
    a		:      Any       =  in_proj_weight_cross_attn[-256:, :]
    a		:      str       =  in_proj_bias_cross_attn[-256:]
def    lowerCamelCase__ (		_A				,    _A       ):
  a		:      List[str]       =  image.size
  a		:      Dict       =  max(_A				,    _A       )
  a		:      Union[str, Any]       =  800 if "detection" in checkpoint_url else 1000
  a		:      Any       =  target_max_size / current_max_size
  a		:      str       =  image.resize((int(round(scale * width       )       ), int(round(scale * height       )       ))       )

  return resized_image
def    lowerCamelCase__ (		_A       ):
  a		:      str       =  F.to_tensor(_A       )
  a		:      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    lowerCamelCase__ (		_A				,    _A				,    _A       ):
  logger.info('Converting model...'       )

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

  a		:      Union[str, Any]       =  DetrImageProcessor(
      format='coco_detection'				,    max_size=800 if 'detection' in checkpoint_url else 1000       )
  a		:      str       =  TableTransformerForObjectDetection(_A       )
  model.load_state_dict(_A       )
  model.eval()

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

  a		:      str       =  model(_A       )

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

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




'''simple docstring'''






from collections import UserDict
from typing import Union

import numpy as np
import requests

from ..utils import (
    add_end_docstrings,
    logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline


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

@add_end_docstrings(lowerCamelCase__			)
class 							a__(      lowerCamelCase__			):







    def __init__(   self				:						Union[str, Any]		,    **__snake_case				:						List[str]			):
      super().__init__(**__snake_case			)

      if self.framework != "pt":
        raise ValueError(F"""The {self.__class__} is only available in PyTorch."""			)
      # No specific FOR_XXX available yet







    def __call__(   self				:						Any		,    __snake_case				:						Union[np.ndarray, bytes, str]		,    **__snake_case				:						Optional[Any]			):
      return super().__call__(__snake_case		,    **__snake_case			)







    def     lowercase_ (   self				:						Dict		,    **__snake_case				:						Optional[int]			):
      a		:      str       =  {}
      if "candidate_labels" in kwargs:
        a		:      Optional[int]       =  kwargs['candidate_labels']
      if "hypothesis_template" in kwargs:
        a		:      Any       =  kwargs['hypothesis_template']

      return preprocess_params, {}, {}







    def     lowercase_ (   self				:						int		,    __snake_case				:						Optional[Any]		,    __snake_case				:						List[Any]=None		,    __snake_case				:						List[str]="This is a sound of {}."			):
      if isinstance(__snake_case		,    __snake_case			):
        if audio.startswith('http://'			) or audio.startswith('https://'			):
          # We need to actually check for a real protocol, otherwise it's impossible to use a local file
          # like http_huggingface_co.png
          a		:      List[Any]       =  requests.get(__snake_case			).content
        else:
          with open(__snake_case		,    'rb'			) as f:
            a		:      List[str]       =  f.read()

      if isinstance(__snake_case		,    __snake_case			):
        a		:      Optional[Any]       =  ffmpeg_read(__snake_case		,    self.feature_extractor.sampling_rate			)

      if not isinstance(__snake_case		,    np.ndarray			):
        raise ValueError('We expect a numpy ndarray as input'			)
      if len(audio.shape			) != 1:
        raise ValueError('We expect a single channel audio input for ZeroShotAudioClassificationPipeline'			)

      a		:      str       =  self.feature_extractor(
          [audio]		,    sampling_rate=self.feature_extractor.sampling_rate		,    return_tensors='pt'			)
      a		:      Tuple       =  candidate_labels
      a		:      Union[str, Any]       =  [hypothesis_template.format(__snake_case			) for x in candidate_labels]
      a		:      int       =  self.tokenizer(__snake_case		,    return_tensors=self.framework		,    padding=__snake_case			)
      a		:      Any       =  [text_inputs]
      return inputs







    def     lowercase_ (   self				:						Optional[Any]		,    __snake_case				:						str			):
      a		:      Any       =  model_inputs.pop('candidate_labels'			)
      a		:      List[Any]       =  model_inputs.pop('text_inputs'			)
      if isinstance(text_inputs[0]		,    __snake_case			):
        a		:      str       =  text_inputs[0]
      else:
        # Batching case.
        a		:      Dict       =  text_inputs[0][0]

      a		:      List[Any]       =  self.model(**__snake_case		,    **__snake_case			)

      a		:      Any       =  {
          'candidate_labels': candidate_labels,
          'logits': outputs.logits_per_audio,
      }
      return model_outputs







    def     lowercase_ (   self				:						Union[str, Any]		,    __snake_case				:						str			):
      a		:      Optional[int]       =  model_outputs.pop('candidate_labels'			)
      a		:      List[str]       =  model_outputs['logits'][0]

      if self.framework == "pt":
        a		:      str       =  logits.softmax(dim=0			)
        a		:      List[Any]       =  probs.tolist()
      else:
        raise ValueError('`tf` framework not supported.'			)

      a		:      Dict       =  [
          {'score': score, 'label': candidate_label}
          for score, candidate_label in sorted(zip(__snake_case		,    __snake_case			)		,    key=lambda __snake_case			: -x[0]			)
      ]
      return result