



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

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





    '''simple docstring'''


    lowercase__		       =						state_dict.pop(_snake_case       )
    lowercase__		       =						val




def        lowerCamelCase  (				_snake_case  :						List[Any]       ):





    '''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        lowerCamelCase  (				_snake_case  :						List[str]       ):





    '''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[:256, :]
        lowercase__		       =						in_proj_bias[:256]
        lowercase__		       =						in_proj_weight[256:512, :]
        lowercase__		       =						in_proj_bias[256:512]
        lowercase__		       =						in_proj_weight[-256:, :]
        lowercase__		       =						in_proj_bias[-256:]
    # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
    for i in range(6       ):
        # read in weights + bias of input projection layer of self-attention
        lowercase__		       =						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[:256, :]
        lowercase__		       =						in_proj_bias[:256]
        lowercase__		       =						in_proj_weight[256:512, :]
        lowercase__		       =						in_proj_bias[256:512]
        lowercase__		       =						in_proj_weight[-256:, :]
        lowercase__		       =						in_proj_bias[-256:]
        # 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[:256, :]
        lowercase__		       =						in_proj_bias_cross_attn[:256]
        lowercase__		       =						in_proj_weight_cross_attn[256:512, :]
        lowercase__		       =						in_proj_bias_cross_attn[256:512]
        lowercase__		       =						in_proj_weight_cross_attn[-256:, :]
        lowercase__		       =						in_proj_bias_cross_attn[-256:]




def        lowerCamelCase  (				_snake_case  :						str     ,_snake_case  :						Optional[int]       ):





    '''simple docstring'''


    lowercase__       ,       lowercase__		       =						image.size
    lowercase__		       =						max(_snake_case     ,_snake_case       )
    lowercase__		       =						800 if "detection" in checkpoint_url else 1_000
    lowercase__		       =						target_max_size / current_max_size
    lowercase__		       =						image.resize((int(round(scale * width       )       ), int(round(scale * height       )       ))       )

    return resized_image




def        lowerCamelCase  (				_snake_case  :						Union[str, Any]       ):





    '''simple docstring'''


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




@torch.no_grad()
def        lowerCamelCase  (				_snake_case  :						List[Any]     ,_snake_case  :						Optional[Any]     ,_snake_case  :						Tuple       ):





    '''simple docstring'''


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

    # load original state dict
    lowercase__		       =						torch.hub.load_state_dict_from_url(_snake_case     ,map_location="cpu"       )
    # rename keys
    for src, dest in rename_keys:
        rename_key(_snake_case     ,_snake_case     ,_snake_case       )
    lowercase__		       =						rename_backbone_keys(_snake_case       )
    # query, key and value matrices need special treatment
    read_in_q_k_v(_snake_case       )
    # 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(_snake_case       )
            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__		       =						125
        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=800 if "detection" in checkpoint_url else 1_000       )
    lowercase__		       =						TableTransformerForObjectDetection(_snake_case       )
    model.load_state_dict(_snake_case       )
    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=_snake_case       )
    lowercase__		       =						Image.open(_snake_case       ).convert("RGB"       )
    lowercase__		       =						normalize(resize(_snake_case     ,_snake_case       )       ).unsqueeze(0       )

    lowercase__		       =						model(_snake_case       )

    if "detection" in checkpoint_url:
        lowercase__		       =						(1, 15, 3)
        lowercase__		       =						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]]       )
        lowercase__		       =						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:
        lowercase__		       =						(1, 125, 7)
        lowercase__		       =						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]]       )
        lowercase__		       =						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]     ,_snake_case     ,atol=1e-4       )
    assert torch.allclose(outputs.pred_boxes[0, :3, :3]     ,_snake_case     ,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(_snake_case       ).mkdir(exist_ok=_snake_case       )
        model.save_pretrained(_snake_case       )
        image_processor.save_pretrained(_snake_case       )

    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(_snake_case       )
        image_processor.push_to_hub(_snake_case       )


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









'''simple docstring'''





# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os
import platform

import numpy as np
import psutil
import torch

from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file

from ..utils import is_npu_available, is_xpu_available




def        lowerCamelCase  (				_snake_case  :						str=None       ):





    '''simple docstring'''


    if subparsers is not None:
        lowercase__		       =						subparsers.add_parser("env"       )
    else:
        lowercase__		       =						argparse.ArgumentParser("Accelerate env command"       )

    parser.add_argument(
        "--config_file"     ,default=_snake_case     ,help="The config file to use for the default values in the launching script."       )

    if subparsers is not None:
        parser.set_defaults(func=_snake_case       )
    return parser




def        lowerCamelCase  (				_snake_case  :						Tuple       ):





    '''simple docstring'''


    lowercase__		       =						torch.__version__
    lowercase__		       =						torch.cuda.is_available()
    lowercase__		       =						is_xpu_available()
    lowercase__		       =						is_npu_available()

    lowercase__		       =						"Not found"
    # Get the default from the config file.
    if args.config_file is not None or os.path.isfile(_snake_case       ):
        lowercase__		       =						load_config_from_file(args.config_file       ).to_dict()

    lowercase__		       =						{
        "`Accelerate` version": version,
        "Platform": platform.platform(),
        "Python version": platform.python_version(),
        "Numpy version": np.__version__,
        "PyTorch version (GPU?)": f'''{pt_version} ({pt_cuda_available})''',
        "PyTorch XPU available": str(_snake_case       ),
        "PyTorch NPU available": str(_snake_case       ),
        "System RAM": f'''{psutil.virtual_memory().total / 1_024 ** 3:.2f} GB''',
    }
    if pt_cuda_available:
        lowercase__		       =						torch.cuda.get_device_name()

    print("\nCopy-and-paste the text below in your GitHub issue\n"       )
    print("\n".join([f'''- {prop}: {val}''' for prop, val in info.items()]       )       )

    print("- `Accelerate` default config:" if args.config_file is None else "- `Accelerate` config passed:"       )
    lowercase__		       =						(
        "\n".join([f'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()]       )
        if isinstance(_snake_case     ,_snake_case       )
        else f'''\t{accelerate_config}'''
    )
    print(_snake_case       )

    lowercase__		       =						accelerate_config

    return info




def        lowerCamelCase  (				):





    '''simple docstring'''


    lowercase__		       =						env_command_parser()
    lowercase__		       =						parser.parse_args()
    env_command(_snake_case       )
    return 0


if __name__ == "__main__":
 raise SystemExit(main())




