'''simple docstring'''




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DeformableDetrConfig, DeformableDetrForObjectDetection, DeformableDetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__snake_case   :      List[str]						    =  logging.get_logger(__name__)





def      _UpperCAmelCase    (							_UpperCamelCase    :	List[str]   )	->      str:
    if "backbone.0.body" in orig_key:
        A_     =   orig_key.replace('''backbone.0.body''',   '''backbone.conv_encoder.model'''   )
    if "transformer" in orig_key:
        A_     =   orig_key.replace('''transformer.''',   ''''''   )
    if "norm1" in orig_key:
        if "encoder" in orig_key:
            A_     =   orig_key.replace('''norm1''',   '''self_attn_layer_norm'''   )
        else:
            A_     =   orig_key.replace('''norm1''',   '''encoder_attn_layer_norm'''   )
    if "norm2" in orig_key:
        if "encoder" in orig_key:
            A_     =   orig_key.replace('''norm2''',   '''final_layer_norm'''   )
        else:
            A_     =   orig_key.replace('''norm2''',   '''self_attn_layer_norm'''   )
    if "norm3" in orig_key:
        A_     =   orig_key.replace('''norm3''',   '''final_layer_norm'''   )
    if "linear1" in orig_key:
        A_     =   orig_key.replace('''linear1''',   '''fc1'''   )
    if "linear2" in orig_key:
        A_     =   orig_key.replace('''linear2''',   '''fc2'''   )
    if "query_embed" in orig_key:
        A_     =   orig_key.replace('''query_embed''',   '''query_position_embeddings'''   )
    if "cross_attn" in orig_key:
        A_     =   orig_key.replace('''cross_attn''',   '''encoder_attn'''   )

    return orig_key





def      _UpperCAmelCase    (							_UpperCamelCase    :	Optional[int]   )	->      Optional[Any]:
    # transformer decoder self-attention layers
    for i in range(6   ):
        # read in weights + bias of input projection layer of self-attention
        A_     =   state_dict.pop(F'''decoder.layers.{i}.self_attn.in_proj_weight'''   )
        A_     =   state_dict.pop(F'''decoder.layers.{i}.self_attn.in_proj_bias'''   )
        # next, add query, keys and values (in that order) to the state dict
        A_     =   in_proj_weight[:2_56, :]
        A_     =   in_proj_bias[:2_56]
        A_     =   in_proj_weight[2_56:5_12, :]
        A_     =   in_proj_bias[2_56:5_12]
        A_     =   in_proj_weight[-2_56:, :]
        A_     =   in_proj_bias[-2_56:]





def      _UpperCAmelCase    (							)	->      List[str]:
    A_     =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    A_     =   Image.open(requests.get(_UpperCamelCase,   stream=_UpperCamelCase   ).raw   )

    return im





@torch.no_grad()
def      _UpperCAmelCase    (							_UpperCamelCase    :	Dict,   _UpperCamelCase    :	Tuple,   _UpperCamelCase    :	int,   _UpperCamelCase    :	List[Any],   _UpperCamelCase    :	Any,   _UpperCamelCase    :	Any,   _UpperCamelCase    :	List[str],   )	->      Dict:
    A_     =   DeformableDetrConfig()
    # set config attributes
    if single_scale:
        A_     =   1
    A_     =   dilation
    A_     =   with_box_refine
    A_     =   two_stage
    # set labels
    A_     =   91
    A_     =   '''huggingface/label-files'''
    A_     =   '''coco-detection-id2label.json'''
    A_     =   json.load(open(cached_download(hf_hub_url(_UpperCamelCase,   _UpperCamelCase,   repo_type='''dataset'''   )   ),   '''r'''   )   )
    A_     =   {int(_UpperCamelCase   ): v for k, v in idalabel.items()}
    A_     =   idalabel
    A_     =   {v: k for k, v in idalabel.items()}

    # load image processor
    A_     =   DeformableDetrImageProcessor(format='''coco_detection'''   )

    # prepare image
    A_     =   prepare_img()
    A_     =   image_processor(images=_UpperCamelCase,   return_tensors='''pt'''   )
    A_     =   encoding['''pixel_values''']

    logger.info('''Converting model...'''   )

    # load original state dict
    A_     =   torch.load(_UpperCamelCase,   map_location='''cpu'''   )['''model''']
    # rename keys
    for key in state_dict.copy().keys():
        A_     =   state_dict.pop(_UpperCamelCase   )
        A_     =   val
    # query, key and value matrices need special treatment
    read_in_q_k_v(_UpperCamelCase   )
    # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
    A_     =   '''model.'''
    for key in state_dict.copy().keys():
        if not key.startswith('''class_embed'''   ) and not key.startswith('''bbox_embed'''   ):
            A_     =   state_dict.pop(_UpperCamelCase   )
            A_     =   val
    # finally, create HuggingFace model and load state dict
    A_     =   DeformableDetrForObjectDetection(_UpperCamelCase   )
    model.load_state_dict(_UpperCamelCase   )
    model.eval()

    A_     =   '''cuda''' if torch.cuda.is_available() else '''cpu'''
    model.to(_UpperCamelCase   )
    # verify our conversion
    A_     =   model(pixel_values.to(_UpperCamelCase   )   )

    A_     =   torch.tensor(
        [[-9.6_6_4_5, -4.3_4_4_9, -5.8_7_0_5], [-9.7_0_3_5, -3.8_5_0_4, -5.0_7_2_4], [-1_0.5_6_3_4, -5.3_3_7_9, -7.5_1_1_6]]   )
    A_     =   torch.tensor([[0.8_6_9_3, 0.2_2_8_9, 0.2_4_9_2], [0.3_1_5_0, 0.5_4_8_9, 0.5_8_4_5], [0.5_5_6_3, 0.7_5_8_0, 0.8_5_1_8]]   )

    if single_scale:
        A_     =   torch.tensor(
            [[-9.9_0_5_1, -4.2_5_4_1, -6.4_8_5_2], [-9.6_9_4_7, -4.0_8_5_4, -6.8_0_3_3], [-1_0.0_6_6_5, -5.8_4_7_0, -7.7_0_0_3]]   )
        A_     =   torch.tensor([[0.7_2_9_2, 0.4_9_9_1, 0.5_5_3_2], [0.7_9_5_9, 0.2_4_2_6, 0.4_2_3_6], [0.7_5_8_2, 0.3_5_1_8, 0.4_4_5_1]]   )

    if single_scale and dilation:
        A_     =   torch.tensor(
            [[-8.9_6_5_2, -4.1_0_7_4, -5.6_6_3_5], [-9.0_5_9_6, -4.9_4_4_7, -6.6_0_7_5], [-1_0.1_1_7_8, -4.5_2_7_5, -6.2_6_7_1]]   )
        A_     =   torch.tensor([[0.7_6_6_5, 0.4_1_3_0, 0.4_7_6_9], [0.8_3_6_4, 0.1_8_4_1, 0.3_3_9_1], [0.6_2_6_1, 0.3_8_9_5, 0.7_9_7_8]]   )

    if with_box_refine:
        A_     =   torch.tensor(
            [[-8.8_8_9_5, -5.4_1_8_7, -6.8_1_5_3], [-8.4_7_0_6, -6.1_6_6_8, -7.6_1_8_4], [-9.0_0_4_2, -5.5_3_5_9, -6.9_1_4_1]]   )
        A_     =   torch.tensor([[0.7_8_2_8, 0.2_2_0_8, 0.4_3_2_3], [0.0_8_9_2, 0.5_9_9_6, 0.1_3_1_9], [0.5_5_2_4, 0.6_3_8_9, 0.8_9_1_4]]   )

    if with_box_refine and two_stage:
        A_     =   torch.tensor(
            [[-6.7_1_0_8, -4.3_2_1_3, -6.3_7_7_7], [-8.9_0_1_4, -6.1_7_9_9, -6.7_2_4_0], [-6.9_3_1_5, -4.4_7_3_5, -6.2_2_9_8]]   )
        A_     =   torch.tensor([[0.2_5_8_3, 0.5_4_9_9, 0.4_6_8_3], [0.7_6_5_2, 0.9_0_6_8, 0.4_8_8_2], [0.5_4_9_0, 0.2_7_6_3, 0.0_5_6_4]]   )

    print('''Logits:''',   outputs.logits[0, :3, :3]   )

    assert torch.allclose(outputs.logits[0, :3, :3],   expected_logits.to(_UpperCamelCase   ),   atol=1E-4   )
    assert torch.allclose(outputs.pred_boxes[0, :3, :3],   expected_boxes.to(_UpperCamelCase   ),   atol=1E-4   )

    print('''Everything ok!'''   )

    # Save model and image processor
    logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''   )
    Path(_UpperCamelCase   ).mkdir(exist_ok=_UpperCamelCase   )
    model.save_pretrained(_UpperCamelCase   )
    image_processor.save_pretrained(_UpperCamelCase   )

    # Push to hub
    if push_to_hub:
        A_     =   '''deformable-detr'''
        model_name += "-single-scale" if single_scale else ""
        model_name += "-dc5" if dilation else ""
        model_name += "-with-box-refine" if with_box_refine else ""
        model_name += "-two-stage" if two_stage else ""
        print('''Pushing model to hub...'''   )
        model.push_to_hub(repo_path_or_name=_UpperCamelCase,   organization='''nielsr''',   commit_message='''Add model'''   )


if __name__ == "__main__":
  __snake_case   :      str						    =  argparse.ArgumentParser()

  parser.add_argument(
      '--checkpoint_path',
      type=str,
      default='/home/niels/checkpoints/deformable_detr/r50_deformable_detr-checkpoint.pth',
      help='Path to Pytorch checkpoint (.pth file) you\'d like to convert.',
  )
  parser.add_argument('--single_scale', action='store_true', help='Whether to set config.num_features_levels = 1.')
  parser.add_argument('--dilation', action='store_true', help='Whether to set config.dilation=True.')
  parser.add_argument('--with_box_refine', action='store_true', help='Whether to set config.with_box_refine=True.')
  parser.add_argument('--two_stage', action='store_true', help='Whether to set config.two_stage=True.')
  parser.add_argument(
      '--pytorch_dump_folder_path',
      default=None,
      type=str,
      required=True,
      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.'
  )
  __snake_case   :      Optional[Any]						    =  parser.parse_args()
  convert_deformable_detr_checkpoint(
      args.checkpoint_path,
      args.single_scale,
      args.dilation,
      args.with_box_refine,
      args.two_stage,
      args.pytorch_dump_folder_path,
      args.push_to_hub,
  )







'''simple docstring'''




import tempfile
import unittest

from diffusers import (
    DDIMScheduler,
    DDPMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    PNDMScheduler,
    logging,
)
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.utils.testing_utils import CaptureLogger




class 	__UpperCAmelCase				(				_UpperCamelCase     ):





       '''simple docstring'''



       __lowercase					:					str    =				'config.json'

       @register_to_config
       def __init__(      self    ,     _SCREAMING_SNAKE_CASE=2    ,     _SCREAMING_SNAKE_CASE=5    ,     _SCREAMING_SNAKE_CASE=(2, 5)    ,     _SCREAMING_SNAKE_CASE="for diffusion"    ,     _SCREAMING_SNAKE_CASE=[1, 3]    ,     )		->     Union[str, Any]:
           pass




class 	__UpperCAmelCase				(				_UpperCamelCase     ):





       '''simple docstring'''



       __lowercase					:					Tuple    =				'config.json'

       @register_to_config
       def __init__(      self    ,     _SCREAMING_SNAKE_CASE=2    ,     _SCREAMING_SNAKE_CASE=5    ,     _SCREAMING_SNAKE_CASE=(2, 5)    ,     _SCREAMING_SNAKE_CASE="for diffusion"    ,     _SCREAMING_SNAKE_CASE=[1, 3]    ,     )		->     Union[str, Any]:
           pass




class 	__UpperCAmelCase				(				_UpperCamelCase     ):





       '''simple docstring'''



       __lowercase					:					Optional[Any]    =				'config.json'

       @register_to_config
       def __init__(      self    ,     _SCREAMING_SNAKE_CASE=2    ,     _SCREAMING_SNAKE_CASE=5    ,     _SCREAMING_SNAKE_CASE=(2, 5)    ,     _SCREAMING_SNAKE_CASE="for diffusion"    ,     _SCREAMING_SNAKE_CASE=[1, 3]    ,     _SCREAMING_SNAKE_CASE=[1, 3]    ,     )		->     Dict:
           pass




class 	__UpperCAmelCase				(				_UpperCamelCase     ):





       '''simple docstring'''



       __lowercase					:					Optional[Any]    =				'config.json'

       @register_to_config
       def __init__(      self    ,     _SCREAMING_SNAKE_CASE=2    ,     _SCREAMING_SNAKE_CASE=5    ,     _SCREAMING_SNAKE_CASE=(2, 5)    ,     _SCREAMING_SNAKE_CASE="for diffusion"    ,     _SCREAMING_SNAKE_CASE=[1, 5]    ,     _SCREAMING_SNAKE_CASE=[5, 4]    ,     )		->     List[str]:
           pass




class 	__UpperCAmelCase				(				unittest.TestCase     ):





       '''simple docstring'''



       def 					__A       (      self )		->     Optional[int]:
           with self.assertRaises(_SCREAMING_SNAKE_CASE ):
               ConfigMixin.load_config('''dummy_path''' )

       def 					__A       (      self )		->     Union[str, Any]:
           A_     =   SampleObject()
           A_     =   obj.config
           assert config["a"] == 2
           assert config["b"] == 5
           assert config["c"] == (2, 5)
           assert config["d"] == "for diffusion"
           assert config["e"] == [1, 3]

           # init ignore private arguments
           A_     =   SampleObject(_name_or_path='''lalala''' )
           A_     =   obj.config
           assert config["a"] == 2
           assert config["b"] == 5
           assert config["c"] == (2, 5)
           assert config["d"] == "for diffusion"
           assert config["e"] == [1, 3]

           # can override default
           A_     =   SampleObject(c=6 )
           A_     =   obj.config
           assert config["a"] == 2
           assert config["b"] == 5
           assert config["c"] == 6
           assert config["d"] == "for diffusion"
           assert config["e"] == [1, 3]

           # can use positional arguments.
           A_     =   SampleObject(1    ,     c=6 )
           A_     =   obj.config
           assert config["a"] == 1
           assert config["b"] == 5
           assert config["c"] == 6
           assert config["d"] == "for diffusion"
           assert config["e"] == [1, 3]

       def 					__A       (      self )		->     int:
           A_     =   SampleObject()
           A_     =   obj.config

           assert config["a"] == 2
           assert config["b"] == 5
           assert config["c"] == (2, 5)
           assert config["d"] == "for diffusion"
           assert config["e"] == [1, 3]

           with tempfile.TemporaryDirectory() as tmpdirname:
               obj.save_config(_SCREAMING_SNAKE_CASE )
               A_     =   SampleObject.from_config(SampleObject.load_config(_SCREAMING_SNAKE_CASE ) )
               A_     =   new_obj.config

           # unfreeze configs
           A_     =   dict(_SCREAMING_SNAKE_CASE )
           A_     =   dict(_SCREAMING_SNAKE_CASE )

           assert config.pop('''c''' ) == (2, 5)  # instantiated as tuple
           assert new_config.pop('''c''' ) == [2, 5]  # saved & loaded as list because of json
           config.pop('''_use_default_values''' )
           assert config == new_config

       def 					__A       (      self )		->     List[Any]:
           A_     =   logging.get_logger('''diffusers.configuration_utils''' )
           # 30 for warning
           logger.setLevel(30 )

           with CaptureLogger(_SCREAMING_SNAKE_CASE ) as cap_logger:
               A_     =   DDIMScheduler.from_pretrained(
                   '''hf-internal-testing/tiny-stable-diffusion-torch'''    ,     subfolder='''scheduler''' )

           assert ddim.__class__ == DDIMScheduler
           # no warning should be thrown
           assert cap_logger.out == ""

       def 					__A       (      self )		->     List[str]:
           A_     =   logging.get_logger('''diffusers.configuration_utils''' )
           # 30 for warning
           logger.setLevel(30 )

           with CaptureLogger(_SCREAMING_SNAKE_CASE ) as cap_logger:
               A_     =   EulerDiscreteScheduler.from_pretrained(
                   '''hf-internal-testing/tiny-stable-diffusion-torch'''    ,     subfolder='''scheduler''' )

           assert euler.__class__ == EulerDiscreteScheduler
           # no warning should be thrown
           assert cap_logger.out == ""

       def 					__A       (      self )		->     Optional[Any]:
           A_     =   logging.get_logger('''diffusers.configuration_utils''' )
           # 30 for warning
           logger.setLevel(30 )

           with CaptureLogger(_SCREAMING_SNAKE_CASE ) as cap_logger:
               A_     =   EulerAncestralDiscreteScheduler.from_pretrained(
                   '''hf-internal-testing/tiny-stable-diffusion-torch'''    ,     subfolder='''scheduler''' )

           assert euler.__class__ == EulerAncestralDiscreteScheduler
           # no warning should be thrown
           assert cap_logger.out == ""

       def 					__A       (      self )		->     Optional[int]:
           A_     =   logging.get_logger('''diffusers.configuration_utils''' )
           # 30 for warning
           logger.setLevel(30 )

           with CaptureLogger(_SCREAMING_SNAKE_CASE ) as cap_logger:
               A_     =   PNDMScheduler.from_pretrained(
                   '''hf-internal-testing/tiny-stable-diffusion-torch'''    ,     subfolder='''scheduler''' )

           assert pndm.__class__ == PNDMScheduler
           # no warning should be thrown
           assert cap_logger.out == ""

       def 					__A       (      self )		->     Tuple:
           A_     =   logging.get_logger('''diffusers.configuration_utils''' )
           # 30 for warning
           logger.setLevel(30 )

           with CaptureLogger(_SCREAMING_SNAKE_CASE ) as cap_logger:
               A_     =   DDPMScheduler.from_pretrained(
                   '''hf-internal-testing/tiny-stable-diffusion-torch'''    ,     subfolder='''scheduler'''    ,     prediction_type='''sample'''    ,     beta_end=8    ,     )

           with CaptureLogger(_SCREAMING_SNAKE_CASE ) as cap_logger_a:
               A_     =   DDPMScheduler.from_pretrained('''google/ddpm-celebahq-256'''    ,     beta_start=88 )

           assert ddpm.__class__ == DDPMScheduler
           assert ddpm.config.prediction_type == "sample"
           assert ddpm.config.beta_end == 8
           assert ddpm_a.config.beta_start == 88

           # no warning should be thrown
           assert cap_logger.out == ""
           assert cap_logger_a.out == ""

       def 					__A       (      self )		->     List[Any]:
           A_     =   logging.get_logger('''diffusers.configuration_utils''' )
           # 30 for warning
           logger.setLevel(30 )

           with CaptureLogger(_SCREAMING_SNAKE_CASE ) as cap_logger:
               A_     =   DPMSolverMultistepScheduler.from_pretrained(
                   '''hf-internal-testing/tiny-stable-diffusion-torch'''    ,     subfolder='''scheduler''' )

           assert dpm.__class__ == DPMSolverMultistepScheduler
           # no warning should be thrown
           assert cap_logger.out == ""

       def 					__A       (      self )		->     Optional[int]:
           # let's first save a config that should be in the form
           #    a=2,
           #    b=5,
           #    c=(2, 5),
           #    d="for diffusion",
           #    e=[1, 3],

           A_     =   SampleObject()

           A_     =   {k: v for k, v in config.config.items() if not k.startswith('''_''' )}

           # make sure that default config has all keys in `_use_default_values`
           assert set(config_dict.keys() ) == set(config.config._use_default_values )

           with tempfile.TemporaryDirectory() as tmpdirname:
               config.save_config(_SCREAMING_SNAKE_CASE )

               # now loading it with SampleObject2 should put f into `_use_default_values`
               A_     =   SampleObjecta.from_config(_SCREAMING_SNAKE_CASE )

               assert "f" in config._use_default_values
               assert config.f == [1, 3]

           # now loading the config, should **NOT** use [1, 3] for `f`, but the default [1, 4] value
           # **BECAUSE** it is part of `config._use_default_values`
           A_     =   SampleObjecta.from_config(config.config )
           assert new_config.f == [5, 4]

           config.config._use_default_values.pop()
           A_     =   SampleObjecta.from_config(config.config )
           assert new_config_a.f == [1, 3]

           # Nevertheless "e" should still be correctly loaded to [1, 3] from SampleObject2 instead of defaulting to [1, 5]
           assert new_config_a.e == [1, 3]






