


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class  __SCREAMING_SNAKE_CASE       (						__a  ,     unittest.TestCase):
     __SCREAMING_SNAKE_CASE  : Dict     =			KandinskyVaaControlnetImgaImgPipeline
     __SCREAMING_SNAKE_CASE  : Dict     =			['image_embeds', 'negative_image_embeds', 'image', 'hint']
     __SCREAMING_SNAKE_CASE  : int     =			['image_embeds', 'negative_image_embeds', 'image', 'hint']
     __SCREAMING_SNAKE_CASE  : Union[str, Any]     =			[
         'generator',
         'height',
         'width',
         'strength',
         'guidance_scale',
         'num_inference_steps',
         'return_dict',
         'guidance_scale',
         'num_images_per_prompt',
         'output_type',
         'return_dict',
     ]
     __SCREAMING_SNAKE_CASE  : Union[str, Any]     =			False


     @property
     def 							UpperCAmelCase__  (			self      :		List[Any] ):
       return 32


     @property
     def 							UpperCAmelCase__  (			self      :		str ):
       return 32


     @property
     def 							UpperCAmelCase__  (			self      :		Dict ):
       return self.time_input_dim


     @property
     def 							UpperCAmelCase__  (			self      :		Optional[int] ):
       return self.time_input_dim * 4


     @property
     def 							UpperCAmelCase__  (			self      :		int ):
       return 100


     @property
     def 							UpperCAmelCase__  (			self      :		Optional[int] ):
       torch.manual_seed(0 )

       _UpperCAmelCase												=     {
           "in_channels": 8,
           # Out channels is double in channels because predicts mean and variance
           "out_channels": 8,
           "addition_embed_type": "image_hint",
           "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
           "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
           "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
           "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
           "layers_per_block": 1,
           "encoder_hid_dim": self.text_embedder_hidden_size,
           "encoder_hid_dim_type": "image_proj",
           "cross_attention_dim": self.cross_attention_dim,
           "attention_head_dim": 4,
           "resnet_time_scale_shift": "scale_shift",
           "class_embed_type": None,
       }

       _UpperCAmelCase												=     UNetaDConditionModel(**_lowerCamelCase )
       return model


     @property
     def 							UpperCAmelCase__  (			self      :		str ):
       return {
           "block_out_channels": [32, 32, 64, 64],
           "down_block_types": [
               "DownEncoderBlock2D",
               "DownEncoderBlock2D",
               "DownEncoderBlock2D",
               "AttnDownEncoderBlock2D",
           ],
           "in_channels": 3,
           "latent_channels": 4,
           "layers_per_block": 1,
           "norm_num_groups": 8,
           "norm_type": "spatial",
           "num_vq_embeddings": 12,
           "out_channels": 3,
           "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
           "vq_embed_dim": 4,
       }


     @property
     def 							UpperCAmelCase__  (			self      :		Optional[int] ):
       torch.manual_seed(0 )
       _UpperCAmelCase												=     VQModel(**self.dummy_movq_kwargs )
       return model


     def 							UpperCAmelCase__  (			self      :		Optional[Any] ):
       _UpperCAmelCase												=     self.dummy_unet
       _UpperCAmelCase												=     self.dummy_movq

       _UpperCAmelCase												=     {
           "num_train_timesteps": 1_000,
           "beta_schedule": "linear",
           "beta_start": 0.00085,
           "beta_end": 0.012,
           "clip_sample": False,
           "set_alpha_to_one": False,
           "steps_offset": 0,
           "prediction_type": "epsilon",
           "thresholding": False,
       }

       _UpperCAmelCase												=     DDIMScheduler(**_lowerCamelCase )

       _UpperCAmelCase												=     {
           "unet": unet,
           "scheduler": scheduler,
           "movq": movq,
       }

       return components


     def 							UpperCAmelCase__  (			self      :		Optional[int]       ,  __UpperCamelCase      :		str       ,  __UpperCamelCase      :		Dict=0 ):
       _UpperCAmelCase												=     floats_tensor((1, self.text_embedder_hidden_size)       ,  rng=random.Random(_lowerCamelCase ) ).to(_lowerCamelCase )
       _UpperCAmelCase												=     floats_tensor((1, self.text_embedder_hidden_size)       ,  rng=random.Random(seed + 1 ) ).to(
           _lowerCamelCase )
       # create init_image
       _UpperCAmelCase												=     floats_tensor((1, 3, 64, 64)       ,  rng=random.Random(_lowerCamelCase ) ).to(_lowerCamelCase )
       _UpperCAmelCase												=     image.cpu().permute(0       ,  2       ,  3       ,  1 )[0]
       _UpperCAmelCase												=     Image.fromarray(np.uinta(_lowerCamelCase ) ).convert("RGB" ).resize((256, 256) )
       # create hint
       _UpperCAmelCase												=     floats_tensor((1, 3, 64, 64)       ,  rng=random.Random(_lowerCamelCase ) ).to(_lowerCamelCase )

       if str(_lowerCamelCase ).startswith("mps" ):
         _UpperCAmelCase												=     torch.manual_seed(_lowerCamelCase )
       else:
         _UpperCAmelCase												=     torch.Generator(device=_lowerCamelCase ).manual_seed(_lowerCamelCase )
       _UpperCAmelCase												=     {
           "image": init_image,
           "image_embeds": image_embeds,
           "negative_image_embeds": negative_image_embeds,
           "hint": hint,
           "generator": generator,
           "height": 64,
           "width": 64,
           "num_inference_steps": 10,
           "guidance_scale": 7.0,
           "strength": 0.2,
           "output_type": "np",
       }
       return inputs



     def 							UpperCAmelCase__  (			self      :		Any ):
       _UpperCAmelCase												=     "cpu"

       _UpperCAmelCase												=     self.get_dummy_components()

       _UpperCAmelCase												=     self.pipeline_class(**_lowerCamelCase )
       _UpperCAmelCase												=     pipe.to(_lowerCamelCase )

       pipe.set_progress_bar_config(disable=_lowerCamelCase )

       _UpperCAmelCase												=     pipe(**self.get_dummy_inputs(_lowerCamelCase ) )
       _UpperCAmelCase												=     output.images

       _UpperCAmelCase												=     pipe(
           **self.get_dummy_inputs(_lowerCamelCase )       ,  return_dict=_lowerCamelCase       ,  )[0]

       _UpperCAmelCase												=     image[0, -3:, -3:, -1]
       _UpperCAmelCase												=     image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)

       _UpperCAmelCase												=     np.array(
           [0.54985034, 0.55509365, 0.52561504, 0.5570494, 0.5593818, 0.5263979, 0.50285643, 0.5069846, 0.51196736] )
       assert (
           np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
       ), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
       assert (
           np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
       ), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''





@slow
@require_torch_gpu
class  __SCREAMING_SNAKE_CASE       (						unittest.TestCase):


     def 							UpperCAmelCase__  (			self      :		int ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()



     def 							UpperCAmelCase__  (			self      :		Dict ):
       _UpperCAmelCase												=     load_numpy(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy" )

       _UpperCAmelCase												=     load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png" )
       _UpperCAmelCase												=     init_image.resize((512, 512) )

       _UpperCAmelCase												=     load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/kandinskyv22/hint_image_cat.png" )
       _UpperCAmelCase												=     torch.from_numpy(np.array(_lowerCamelCase ) ).float() / 255.0
       _UpperCAmelCase												=     hint.permute(2       ,  0       ,  1 ).unsqueeze(0 )

       _UpperCAmelCase												=     "A robot, 4k photo"

       _UpperCAmelCase												=     KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
           "kandinsky-community/kandinsky-2-2-prior"       ,  torch_dtype=torch.floataa )
       pipe_prior.to(_lowerCamelCase )

       _UpperCAmelCase												=     KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
           "kandinsky-community/kandinsky-2-2-controlnet-depth"       ,  torch_dtype=torch.floataa )
       _UpperCAmelCase												=     pipeline.to(_lowerCamelCase )

       pipeline.set_progress_bar_config(disable=_lowerCamelCase )

       _UpperCAmelCase												=     torch.Generator(device="cpu" ).manual_seed(0 )

       _UpperCAmelCase						,		_UpperCAmelCase												=     pipe_prior(
           _lowerCamelCase       ,  image=_lowerCamelCase       ,  strength=0.85       ,  generator=_lowerCamelCase       ,  negative_prompt=""       ,  ).to_tuple()

       _UpperCAmelCase												=     pipeline(
           image=_lowerCamelCase       ,  image_embeds=_lowerCamelCase       ,  negative_image_embeds=_lowerCamelCase       ,  hint=_lowerCamelCase       ,  generator=_lowerCamelCase       ,  num_inference_steps=100       ,  height=512       ,  width=512       ,  strength=0.5       ,  output_type="np"       ,  )

       _UpperCAmelCase												=     output.images[0]

       assert image.shape == (512, 512, 3)

       assert_mean_pixel_difference(_lowerCamelCase       ,  _lowerCamelCase )




'''simple docstring'''

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
A_     :						Dict    							=logging.get_logger(__name__)



def snake_case_      (					__snake_case  :							Any)			->					Tuple:
   # initialize config
   if "resnet-50" in model_name:
      lowerCAmelCase_			=      ResNetConfig.from_pretrained('''microsoft/resnet-50''')
   elif "resnet-101" in model_name:
      lowerCAmelCase_			=      ResNetConfig.from_pretrained('''microsoft/resnet-101''')
   else:
      raise ValueError('''Model name should include either resnet50 or resnet101''')

   lowerCAmelCase_			=      DetrConfig(use_timm_backbone=__snake_case	,   backbone_config=__snake_case)

   # set label attributes
   lowerCAmelCase_			=      '''panoptic''' in model_name
   if is_panoptic:
      lowerCAmelCase_			=      250
   else:
      lowerCAmelCase_			=      91
      lowerCAmelCase_			=      '''huggingface/label-files'''
      lowerCAmelCase_			=      '''coco-detection-id2label.json'''
      lowerCAmelCase_			=      json.load(open(hf_hub_download(__snake_case	,   __snake_case	,   repo_type='''dataset''')	,   '''r'''))
      lowerCAmelCase_			=      {int(__snake_case): v for k, v in idalabel.items()}
      lowerCAmelCase_			=      idalabel
      lowerCAmelCase_			=      {v: k for k, v in idalabel.items()}

   return config, is_panoptic



def snake_case_      (					__snake_case  :							Optional[Any])			->					Tuple:
   # here we list all keys to be renamed (original name on the left, our name on the right)
   lowerCAmelCase_			=      []

   # stem
   # fmt: off
   rename_keys.append(('''backbone.0.body.conv1.weight''', '''backbone.conv_encoder.model.embedder.embedder.convolution.weight'''))
   rename_keys.append(('''backbone.0.body.bn1.weight''', '''backbone.conv_encoder.model.embedder.embedder.normalization.weight'''))
   rename_keys.append(('''backbone.0.body.bn1.bias''', '''backbone.conv_encoder.model.embedder.embedder.normalization.bias'''))
   rename_keys.append(('''backbone.0.body.bn1.running_mean''', '''backbone.conv_encoder.model.embedder.embedder.normalization.running_mean'''))
   rename_keys.append(('''backbone.0.body.bn1.running_var''', '''backbone.conv_encoder.model.embedder.embedder.normalization.running_var'''))
   # stages
   for stage_idx in range(len(config.backbone_config.depths)):
      for layer_idx in range(config.backbone_config.depths[stage_idx]):
         # shortcut
         if layer_idx == 0:
            rename_keys.append(
                (
                    F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight''',
                    F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight''',
                ))
            rename_keys.append(
                (
                    F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight''',
                    F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight''',
                ))
            rename_keys.append(
                (
                    F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias''',
                    F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias''',
                ))
            rename_keys.append(
                (
                    F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean''',
                    F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean''',
                ))
            rename_keys.append(
                (
                    F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var''',
                    F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var''',
                ))
         # 3 convs
         for i in range(3):
            rename_keys.append(
                (
                    F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight''',
                    F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight''',
                ))
            rename_keys.append(
                (
                    F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight''',
                    F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight''',
                ))
            rename_keys.append(
                (
                    F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias''',
                    F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias''',
                ))
            rename_keys.append(
                (
                    F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean''',
                    F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean''',
                ))
            rename_keys.append(
                (
                    F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var''',
                    F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var''',
                ))
    # fmt: on

   for i in range(config.encoder_layers):
      # 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 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.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'''),
       ])

   return rename_keys



def snake_case_      (					__snake_case  :							List[str]	,   __snake_case  :							Tuple	,   __snake_case  :							Optional[Any])			->					List[str]:
   lowerCAmelCase_			=      state_dict.pop(__snake_case)
   lowerCAmelCase_			=      val



def snake_case_      (					__snake_case  :							List[Any]	,   __snake_case  :							Optional[int]=False)			->					str:
   lowerCAmelCase_			=      ''''''
   if is_panoptic:
      lowerCAmelCase_			=      '''detr.'''

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



def snake_case_      (					)			->					str:
   lowerCAmelCase_			=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
   lowerCAmelCase_			=      Image.open(requests.get(__snake_case	,   stream=__snake_case).raw)

   return im



@torch.no_grad()
def snake_case_      (					__snake_case  :							Union[str, Any]	,   __snake_case  :							List[Any]=None	,   __snake_case  :							Any=False)			->					Optional[Any]:
   lowerCAmelCase_	,lowerCAmelCase_			=      get_detr_config(__snake_case)

   # load original model from torch hub
   lowerCAmelCase_			=      {
       '''detr-resnet-50''': '''detr_resnet50''',
       '''detr-resnet-101''': '''detr_resnet101''',
   }
   logger.info(F'''Converting model {model_name}...''')
   lowerCAmelCase_			=      torch.hub.load('''facebookresearch/detr'''	,   model_name_to_original_name[model_name]	,   pretrained=__snake_case).eval()
   lowerCAmelCase_			=      detr.state_dict()
   # rename keys
   for src, dest in create_rename_keys(__snake_case):
      if is_panoptic:
         lowerCAmelCase_			=      '''detr.''' + src
      rename_key(__snake_case	,   __snake_case	,   __snake_case)
   # query, key and value matrices need special treatment
   read_in_q_k_v(__snake_case	,   is_panoptic=__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
   lowerCAmelCase_			=      '''detr.model.''' if is_panoptic else '''model.'''
   for key in state_dict.copy().keys():
      if is_panoptic:
         if (
             key.startswith('''detr''')
             and not key.startswith('''class_labels_classifier''')
             and not key.startswith('''bbox_predictor''')
         ):
            lowerCAmelCase_			=      state_dict.pop(__snake_case)
            lowerCAmelCase_			=      val
         elif "class_labels_classifier" in key or "bbox_predictor" in key:
            lowerCAmelCase_			=      state_dict.pop(__snake_case)
            lowerCAmelCase_			=      val
         elif key.startswith('''bbox_attention''') or key.startswith('''mask_head'''):
            continue
         else:
            lowerCAmelCase_			=      state_dict.pop(__snake_case)
            lowerCAmelCase_			=      val
      else:
         if not key.startswith('''class_labels_classifier''') and not key.startswith('''bbox_predictor'''):
            lowerCAmelCase_			=      state_dict.pop(__snake_case)
            lowerCAmelCase_			=      val

    # finally, create HuggingFace model and load state dict
   lowerCAmelCase_			=      DetrForSegmentation(__snake_case) if is_panoptic else DetrForObjectDetection(__snake_case)
   model.load_state_dict(__snake_case)
   model.eval()

   # verify our conversion on an image
   lowerCAmelCase_			=      '''coco_panoptic''' if is_panoptic else '''coco_detection'''
   lowerCAmelCase_			=      DetrImageProcessor(format=__snake_case)

   lowerCAmelCase_			=      processor(images=prepare_img()	,   return_tensors='''pt''')
   lowerCAmelCase_			=      encoding['''pixel_values''']

   lowerCAmelCase_			=      detr(__snake_case)
   lowerCAmelCase_			=      model(__snake_case)

   assert torch.allclose(outputs.logits	,   original_outputs['''pred_logits''']	,   atol=1E-3)
   assert torch.allclose(outputs.pred_boxes	,   original_outputs['''pred_boxes''']	,   atol=1E-3)
   if is_panoptic:
      assert torch.allclose(outputs.pred_masks	,   original_outputs['''pred_masks''']	,   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)
      processor.save_pretrained(__snake_case)

   if push_to_hub:
      # Upload model and image processor to the hub
      logger.info('''Uploading PyTorch model and image processor to the hub...''')
      model.push_to_hub(F'''nielsr/{model_name}''')
      processor.push_to_hub(F'''nielsr/{model_name}''')


if __name__ == "__main__":
 A_     :						Any    							=argparse.ArgumentParser()

 parser.add_argument(
     '''--model_name''',
     default='''detr-resnet-50''',
     type=str,
     choices=['''detr-resnet-50''', '''detr-resnet-101'''],
     help='''Name of the DETR model 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 to push the model to the hub or not.''')
 A_     :						Optional[int]    							=parser.parse_args()
 convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





