



'''simple docstring'''


from typing import Optional

import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import (
    BackboneOutput,
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
    replace_return_docstrings,
)
from ...utils.backbone_utils import BackboneMixin
from .configuration_resnet import ResNetConfig


_lowerCamelCase	:       Dict      	=	logging.get_logger(__name__)

# General docstring
_lowerCamelCase	:       List[str]      	=	"ResNetConfig"

# Base docstring
_lowerCamelCase	:       Tuple      	=	"microsoft/resnet-50"
_lowerCamelCase	:       Union[str, Any]      	=	[1, 2048, 7, 7]

# Image classification docstring
_lowerCamelCase	:       Dict      	=	"microsoft/resnet-50"
_lowerCamelCase	:       Optional[int]      	=	"tiger cat"

_lowerCamelCase	:       str      	=	[
    "microsoft/resnet-50",
    # See all resnet models at https://huggingface.co/models?filter=resnet
]

class  SCREAMING_SNAKE_CASE (  nn.Module		):






    """simple docstring"""





    def __init__(  self      :					Dict  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					int = 3  ,						UpperCamelCase__      :					int = 1  ,						UpperCamelCase__      :					str = "relu" ):


       """simple docstring"""







       super().__init__()
       UpperCamelCase					=  nn.Convad(
           UpperCamelCase__  ,						UpperCamelCase__  ,						kernel_size=UpperCamelCase__  ,						stride=UpperCamelCase__  ,						padding=kernel_size // 2  ,						bias=UpperCamelCase__ )
       UpperCamelCase					=  nn.BatchNormad(UpperCamelCase__ )
       UpperCamelCase					=  ACTaFN[activation] if activation is not None else nn.Identity()






    def  A       (  self      :					Dict  ,						UpperCamelCase__      :					Tensor ):


       """simple docstring"""







       UpperCamelCase					=  self.convolution(UpperCamelCase__ )
       UpperCamelCase					=  self.normalization(UpperCamelCase__ )
       UpperCamelCase					=  self.activation(UpperCamelCase__ )
       return hidden_state

class  SCREAMING_SNAKE_CASE (  nn.Module		):






    """simple docstring"""





    def __init__(  self      :					Any  ,						UpperCamelCase__      :					ResNetConfig ):


       """simple docstring"""







       super().__init__()
       UpperCamelCase					=  ResNetConvLayer(
           config.num_channels  ,						config.embedding_size  ,						kernel_size=7  ,						stride=2  ,						activation=config.hidden_act )
       UpperCamelCase					=  nn.MaxPoolad(kernel_size=3  ,						stride=2  ,						padding=1 )
       UpperCamelCase					=  config.num_channels






    def  A       (  self      :					Union[str, Any]  ,						UpperCamelCase__      :					Tensor ):


       """simple docstring"""







       UpperCamelCase					=  pixel_values.shape[1]
       if num_channels != self.num_channels:
          raise ValueError(
              'Make sure that the channel dimension of the pixel values match with the one set in the configuration.' )
       UpperCamelCase					=  self.embedder(UpperCamelCase__ )
       UpperCamelCase					=  self.pooler(UpperCamelCase__ )
       return embedding

class  SCREAMING_SNAKE_CASE (  nn.Module		):






    """simple docstring"""





    def __init__(  self      :					Union[str, Any]  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					int = 2 ):


       """simple docstring"""







       super().__init__()
       UpperCamelCase					=  nn.Convad(UpperCamelCase__  ,						UpperCamelCase__  ,						kernel_size=1  ,						stride=UpperCamelCase__  ,						bias=UpperCamelCase__ )
       UpperCamelCase					=  nn.BatchNormad(UpperCamelCase__ )






    def  A       (  self      :					Any  ,						UpperCamelCase__      :					Tensor ):


       """simple docstring"""







       UpperCamelCase					=  self.convolution(UpperCamelCase__ )
       UpperCamelCase					=  self.normalization(UpperCamelCase__ )
       return hidden_state

class  SCREAMING_SNAKE_CASE (  nn.Module		):






    """simple docstring"""





    def __init__(  self      :					Optional[int]  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					int = 1  ,						UpperCamelCase__      :					str = "relu" ):


       """simple docstring"""







       super().__init__()
       UpperCamelCase					=  in_channels != out_channels or stride != 1
       UpperCamelCase					=  (
           ResNetShortCut(UpperCamelCase__  ,						UpperCamelCase__  ,						stride=UpperCamelCase__ ) if should_apply_shortcut else nn.Identity()
       )
       UpperCamelCase					=  nn.Sequential(
           ResNetConvLayer(UpperCamelCase__  ,						UpperCamelCase__  ,						stride=UpperCamelCase__ )  ,						ResNetConvLayer(UpperCamelCase__  ,						UpperCamelCase__  ,						activation=UpperCamelCase__ )  ,						)
       UpperCamelCase					=  ACTaFN[activation]






    def  A       (  self      :					str  ,						UpperCamelCase__      :					Union[str, Any] ):


       """simple docstring"""







       UpperCamelCase					=  hidden_state
       UpperCamelCase					=  self.layer(UpperCamelCase__ )
       UpperCamelCase					=  self.shortcut(UpperCamelCase__ )
       hidden_state += residual
       UpperCamelCase					=  self.activation(UpperCamelCase__ )
       return hidden_state

class  SCREAMING_SNAKE_CASE (  nn.Module		):






    """simple docstring"""





    def __init__(  self      :					Optional[Any]  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					int = 1  ,						UpperCamelCase__      :					str = "relu"  ,						UpperCamelCase__      :					int = 4 ):


       """simple docstring"""







       super().__init__()
       UpperCamelCase					=  in_channels != out_channels or stride != 1
       UpperCamelCase					=  out_channels // reduction
       UpperCamelCase					=  (
           ResNetShortCut(UpperCamelCase__  ,						UpperCamelCase__  ,						stride=UpperCamelCase__ ) if should_apply_shortcut else nn.Identity()
       )
       UpperCamelCase					=  nn.Sequential(
           ResNetConvLayer(UpperCamelCase__  ,						UpperCamelCase__  ,						kernel_size=1 )  ,						ResNetConvLayer(UpperCamelCase__  ,						UpperCamelCase__  ,						stride=UpperCamelCase__ )  ,						ResNetConvLayer(UpperCamelCase__  ,						UpperCamelCase__  ,						kernel_size=1  ,						activation=UpperCamelCase__ )  ,						)
       UpperCamelCase					=  ACTaFN[activation]






    def  A       (  self      :					Dict  ,						UpperCamelCase__      :					Optional[Any] ):


       """simple docstring"""







       UpperCamelCase					=  hidden_state
       UpperCamelCase					=  self.layer(UpperCamelCase__ )
       UpperCamelCase					=  self.shortcut(UpperCamelCase__ )
       hidden_state += residual
       UpperCamelCase					=  self.activation(UpperCamelCase__ )
       return hidden_state

class  SCREAMING_SNAKE_CASE (  nn.Module		):






    """simple docstring"""





    def __init__(  self      :					List[str]  ,						UpperCamelCase__      :					ResNetConfig  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					int = 2  ,						UpperCamelCase__      :					int = 2  ,						):


       """simple docstring"""







       super().__init__()

       UpperCamelCase					=  ResNetBottleNeckLayer if config.layer_type == 'bottleneck' else ResNetBasicLayer

       UpperCamelCase					=  nn.Sequential(
           # downsampling is done in the first layer with stride of 2
           layer(UpperCamelCase__  ,						UpperCamelCase__  ,						stride=UpperCamelCase__  ,						activation=config.hidden_act )  ,						*[layer(UpperCamelCase__  ,						UpperCamelCase__  ,						activation=config.hidden_act ) for _ in range(depth - 1 )]  ,						)






    def  A       (  self      :					Dict  ,						UpperCamelCase__      :					Tensor ):


       """simple docstring"""







       UpperCamelCase					=  input
       for layer in self.layers:
          UpperCamelCase					=  layer(UpperCamelCase__ )
       return hidden_state

class  SCREAMING_SNAKE_CASE (  nn.Module		):






    """simple docstring"""





    def __init__(  self      :					Dict  ,						UpperCamelCase__      :					ResNetConfig ):


       """simple docstring"""







       super().__init__()
       UpperCamelCase					=  nn.ModuleList([] )
       # based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
       self.stages.append(
           ResNetStage(
               UpperCamelCase__  ,						config.embedding_size  ,						config.hidden_sizes[0]  ,						stride=2 if config.downsample_in_first_stage else 1  ,						depth=config.depths[0]  ,						) )
       UpperCamelCase					=  zip(config.hidden_sizes  ,						config.hidden_sizes[1:] )
       for (in_channels, out_channels), depth in zip(UpperCamelCase__  ,						config.depths[1:] ):
          self.stages.append(ResNetStage(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						depth=UpperCamelCase__ ) )






    def  A       (  self      :					Tuple  ,						UpperCamelCase__      :					Tensor  ,						UpperCamelCase__      :					bool = False  ,						UpperCamelCase__      :					bool = True ):


       """simple docstring"""







       UpperCamelCase					=  () if output_hidden_states else None

       for stage_module in self.stages:
          if output_hidden_states:
             UpperCamelCase					=  hidden_states + (hidden_state,)

          UpperCamelCase					=  stage_module(UpperCamelCase__ )

       if output_hidden_states:
          UpperCamelCase					=  hidden_states + (hidden_state,)

       if not return_dict:
          return tuple(v for v in [hidden_state, hidden_states] if v is not None )

       return BaseModelOutputWithNoAttention(
           last_hidden_state=UpperCamelCase__  ,						hidden_states=UpperCamelCase__  ,						)

class  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  ResNetConfig
    _SCREAMING_SNAKE_CASE	  =  """resnet"""
    _SCREAMING_SNAKE_CASE	  =  """pixel_values"""
    _SCREAMING_SNAKE_CASE	  =  True






    def  A       (  self      :					List[str]  ,						UpperCamelCase__      :					Dict ):


       """simple docstring"""







       if isinstance(UpperCamelCase__  ,						nn.Convad ):
          nn.init.kaiming_normal_(module.weight  ,						mode='fan_out'  ,						nonlinearity='relu' )
       elif isinstance(UpperCamelCase__  ,						(nn.BatchNormad, nn.GroupNorm) ):
          nn.init.constant_(module.weight  ,						1 )
          nn.init.constant_(module.bias  ,						0 )






    def  A       (  self      :					Dict  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					str=False ):


       """simple docstring"""







       if isinstance(UpperCamelCase__  ,						UpperCamelCase__ ):
          UpperCamelCase					=  value

_lowerCamelCase	:       Union[str, Any]      	=	R"\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"

_lowerCamelCase	:       Any      	=	R"\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConvNextImageProcessor.__call__`] for details.\n\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n"

@add_start_docstrings(
    """The bare ResNet model outputting raw features without any specific head on top."""   ,						_a   ,						)
class  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    def __init__(  self      :					str  ,						UpperCamelCase__      :					Any ):


       """simple docstring"""







       super().__init__(UpperCamelCase__ )
       UpperCamelCase					=  config
       UpperCamelCase					=  ResNetEmbeddings(UpperCamelCase__ )
       UpperCamelCase					=  ResNetEncoder(UpperCamelCase__ )
       UpperCamelCase					=  nn.AdaptiveAvgPoolad((1, 1) )
       # Initialize weights and apply final processing
       self.post_init()






    @add_start_docstrings_to_model_forward(UpperCamelCase__ )
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC  ,						output_type=UpperCamelCase__  ,						config_class=_CONFIG_FOR_DOC  ,						modality='vision'  ,						expected_output=_EXPECTED_OUTPUT_SHAPE  ,						)
    def  A       (  self      :					str  ,						UpperCamelCase__      :					Tensor  ,						UpperCamelCase__      :					Optional[bool] = None  ,						UpperCamelCase__      :					Optional[bool] = None ):


       """simple docstring"""







       UpperCamelCase					=  (
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       UpperCamelCase					=  return_dict if return_dict is not None else self.config.use_return_dict

       UpperCamelCase					=  self.embedder(UpperCamelCase__ )

       UpperCamelCase					=  self.encoder(
           UpperCamelCase__  ,						output_hidden_states=UpperCamelCase__  ,						return_dict=UpperCamelCase__ )

       UpperCamelCase					=  encoder_outputs[0]

       UpperCamelCase					=  self.pooler(UpperCamelCase__ )

       if not return_dict:
          return (last_hidden_state, pooled_output) + encoder_outputs[1:]

       return BaseModelOutputWithPoolingAndNoAttention(
           last_hidden_state=UpperCamelCase__  ,						pooler_output=UpperCamelCase__  ,						hidden_states=encoder_outputs.hidden_states  ,						)

@add_start_docstrings(
    """
    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    """   ,						_a   ,						)
class  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    def __init__(  self      :					int  ,						UpperCamelCase__      :					Dict ):


       """simple docstring"""







       super().__init__(UpperCamelCase__ )
       UpperCamelCase					=  config.num_labels
       UpperCamelCase					=  ResNetModel(UpperCamelCase__ )
       # classification head
       UpperCamelCase					=  nn.Sequential(
           nn.Flatten()  ,						nn.Linear(config.hidden_sizes[-1]  ,						config.num_labels ) if config.num_labels > 0 else nn.Identity()  ,						)
       # initialize weights and apply final processing
       self.post_init()






    @add_start_docstrings_to_model_forward(UpperCamelCase__ )
    @add_code_sample_docstrings(
        checkpoint=_IMAGE_CLASS_CHECKPOINT  ,						output_type=UpperCamelCase__  ,						config_class=_CONFIG_FOR_DOC  ,						expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT  ,						)
    def  A       (  self      :					List[Any]  ,						UpperCamelCase__      :					Optional[torch.FloatTensor] = None  ,						UpperCamelCase__      :					Optional[torch.LongTensor] = None  ,						UpperCamelCase__      :					Optional[bool] = None  ,						UpperCamelCase__      :					Optional[bool] = None  ,						):


       """simple docstring"""







       UpperCamelCase					=  return_dict if return_dict is not None else self.config.use_return_dict

       UpperCamelCase					=  self.resnet(UpperCamelCase__  ,						output_hidden_states=UpperCamelCase__  ,						return_dict=UpperCamelCase__ )

       UpperCamelCase					=  outputs.pooler_output if return_dict else outputs[1]

       UpperCamelCase					=  self.classifier(UpperCamelCase__ )

       UpperCamelCase					=  None

       if labels is not None:
          if self.config.problem_type is None:
             if self.num_labels == 1:
                UpperCamelCase					=  'regression'
             elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                UpperCamelCase					=  'single_label_classification'
             else:
                UpperCamelCase					=  'multi_label_classification'
          if self.config.problem_type == "regression":
             UpperCamelCase					=  MSELoss()
             if self.num_labels == 1:
                UpperCamelCase					=  loss_fct(logits.squeeze()  ,						labels.squeeze() )
             else:
                UpperCamelCase					=  loss_fct(UpperCamelCase__  ,						UpperCamelCase__ )
          elif self.config.problem_type == "single_label_classification":
             UpperCamelCase					=  CrossEntropyLoss()
             UpperCamelCase					=  loss_fct(logits.view(-1  ,						self.num_labels )  ,						labels.view(-1 ) )
          elif self.config.problem_type == "multi_label_classification":
             UpperCamelCase					=  BCEWithLogitsLoss()
             UpperCamelCase					=  loss_fct(UpperCamelCase__  ,						UpperCamelCase__ )

       if not return_dict:
          UpperCamelCase					=  (logits,) + outputs[2:]
          return (loss,) + output if loss is not None else output

       return ImageClassifierOutputWithNoAttention(loss=UpperCamelCase__  ,						logits=UpperCamelCase__  ,						hidden_states=outputs.hidden_states )

@add_start_docstrings(
    """
    ResNet backbone, to be used with frameworks like DETR and MaskFormer.
    """   ,						_a   ,						)
class  SCREAMING_SNAKE_CASE (  _a   ,						_a		):






    """simple docstring"""





    def __init__(  self      :					Dict  ,						UpperCamelCase__      :					Tuple ):


       """simple docstring"""







       super().__init__(UpperCamelCase__ )
       super()._init_backbone(UpperCamelCase__ )

       UpperCamelCase					=  [config.embedding_size] + config.hidden_sizes
       UpperCamelCase					=  ResNetEmbeddings(UpperCamelCase__ )
       UpperCamelCase					=  ResNetEncoder(UpperCamelCase__ )

       # initialize weights and apply final processing
       self.post_init()






    @add_start_docstrings_to_model_forward(UpperCamelCase__ )
    @replace_return_docstrings(output_type=UpperCamelCase__  ,						config_class=_CONFIG_FOR_DOC )
    def  A       (  self      :					Any  ,						UpperCamelCase__      :					Tensor  ,						UpperCamelCase__      :					Optional[bool] = None  ,						UpperCamelCase__      :					Optional[bool] = None ):


       """simple docstring"""







       UpperCamelCase					=  return_dict if return_dict is not None else self.config.use_return_dict
       UpperCamelCase					=  (
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )

       UpperCamelCase					=  self.embedder(UpperCamelCase__ )

       UpperCamelCase					=  self.encoder(UpperCamelCase__  ,						output_hidden_states=UpperCamelCase__  ,						return_dict=UpperCamelCase__ )

       UpperCamelCase					=  outputs.hidden_states

       UpperCamelCase					=  ()
       for idx, stage in enumerate(self.stage_names ):
          if stage in self.out_features:
             feature_maps += (hidden_states[idx],)

       if not return_dict:
          UpperCamelCase					=  (feature_maps,)
          if output_hidden_states:
             output += (outputs.hidden_states,)
          return output

       return BackboneOutput(
           feature_maps=UpperCamelCase__  ,						hidden_states=outputs.hidden_states if output_hidden_states else None  ,						attentions=UpperCamelCase__  ,						)






'''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	:       int      	=	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__    )       ->	Dict:






   """simple docstring"""
   UpperCamelCase					=  state_dict.pop(A__    )
   UpperCamelCase					=  val







def 				__lowerCamelCase				(   A__    )       ->	int:






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

   return new_state_dict







def 				__lowerCamelCase				(   A__    )       ->	Dict:






   """simple docstring"""
   UpperCamelCase					=  ''

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







def 				__lowerCamelCase				(   A__    ,						A__    )       ->	Optional[int]:






   """simple docstring"""
   UpperCamelCase				,      UpperCamelCase					=  image.size
   UpperCamelCase					=  max(A__    ,						A__    )
   UpperCamelCase					=  800 if 'detection' in checkpoint_url else 1_000
   UpperCamelCase					=  target_max_size / current_max_size
   UpperCamelCase					=  image.resize((int(round(scale * width    )    ), int(round(scale * height    )    ))    )

   return resized_image







def 				__lowerCamelCase				(   A__    )       ->	List[Any]:






   """simple docstring"""
   UpperCamelCase					=  F.to_tensor(A__    )
   UpperCamelCase					=  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__    )       ->	Optional[Any]:






   """simple docstring"""
   logger.info('Converting model...'    )

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

   UpperCamelCase					=  DetrImageProcessor(
       format='coco_detection'    ,						max_size=800 if 'detection' in checkpoint_url else 1_000    )
   UpperCamelCase					=  TableTransformerForObjectDetection(A__    )
   model.load_state_dict(A__    )
   model.eval()

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

   UpperCamelCase					=  model(A__    )

   if "detection" in checkpoint_url:
      UpperCamelCase					=  (1, 15, 3)
      UpperCamelCase					=  torch.tensor(
          [[-6.7_897, -16.9_985, 6.7_937], [-8.0_186, -22.2_192, 6.9_677], [-7.3_117, -21.0_708, 7.4_055]]    )
      UpperCamelCase					=  torch.tensor([[0.4_867, 0.1_767, 0.6_732], [0.6_718, 0.4_479, 0.3_830], [0.4_716, 0.1_760, 0.6_364]]    )

   else:
      UpperCamelCase					=  (1, 125, 7)
      UpperCamelCase					=  torch.tensor(
          [[-18.1_430, -8.3_214, 4.8_274], [-18.4_685, -7.1_361, -4.2_667], [-26.3_693, -9.3_429, -4.9_962]]    )
      UpperCamelCase					=  torch.tensor([[0.4_983, 0.5_595, 0.9_440], [0.4_916, 0.6_315, 0.5_954], [0.6_108, 0.8_637, 0.1_135]]    )

   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...'    )
      UpperCamelCase					=  (
          '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	:       List[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	:       int      	=	parser.parse_args()
   convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)

