
import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, 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  _lowerCamelCase			(lowercase_							,					unittest.TestCase					):
     lowercase__				  =			KandinskyPipeline
     lowercase__				  =			[
         """prompt""",
         """image_embeds""",
         """negative_image_embeds""",
     ]
     lowercase__				  =			["""prompt""", """negative_prompt""", """image_embeds""", """negative_image_embeds"""]
     lowercase__				  =			[
         """generator""",
         """height""",
         """width""",
         """latents""",
         """guidance_scale""",
         """negative_prompt""",
         """num_inference_steps""",
         """return_dict""",
         """guidance_scale""",
         """num_images_per_prompt""",
         """output_type""",
         """return_dict""",
     ]
     lowercase__				  =			False

     @property
     def 						__lowerCamelCase				(  self    ):
            return 32

     @property
     def 						__lowerCamelCase				(  self    ):
            return 32

     @property
     def 						__lowerCamelCase				(  self    ):
            return self.time_input_dim

     @property
     def 						__lowerCamelCase				(  self    ):
            return self.time_input_dim * 4

     @property
     def 						__lowerCamelCase				(  self    ):
            return 100

     @property
     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'    )
            return tokenizer

     @property
     def 						__lowerCamelCase				(  self    ):
            torch.manual_seed(0    )
            __snake_case						       =   MCLIPConfig(
                numDims=self.cross_attention_dim							,       transformerDimensions=self.text_embedder_hidden_size							,       hidden_size=self.text_embedder_hidden_size							,       intermediate_size=37							,       num_attention_heads=4							,       num_hidden_layers=5							,       vocab_size=1_005							,       )

            __snake_case						       =   MultilingualCLIP(SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   text_encoder.eval()

            return text_encoder

     @property
     def 						__lowerCamelCase				(  self    ):
            torch.manual_seed(0    )

            __snake_case						       =   {
                'in_channels': 4,
                # Out channels is double in channels because predicts mean and variance
                'out_channels': 8,
                'addition_embed_type': 'text_image',
                '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': 'text_image_proj',
                'cross_attention_dim': self.cross_attention_dim,
                'attention_head_dim': 4,
                'resnet_time_scale_shift': 'scale_shift',
                'class_embed_type': None,
            }

            __snake_case						       =   UNetaDConditionModel(**SCREAMING_SNAKE_CASE_    )
            return model

     @property
     def 						__lowerCamelCase				(  self    ):
            return {
                "block_out_channels": [32, 64],
                "down_block_types": ["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",
                ],
                "vq_embed_dim": 4,
            }

     @property
     def 						__lowerCamelCase				(  self    ):
            torch.manual_seed(0    )
            __snake_case						       =   VQModel(**self.dummy_movq_kwargs    )
            return model

     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   self.dummy_text_encoder
            __snake_case						       =   self.dummy_tokenizer
            __snake_case						       =   self.dummy_unet
            __snake_case						       =   self.dummy_movq

            __snake_case						       =   DDIMScheduler(
                num_train_timesteps=1_000							,       beta_schedule='linear'							,       beta_start=0.0_0_0_8_5							,       beta_end=0.0_1_2							,       clip_sample=SCREAMING_SNAKE_CASE_							,       set_alpha_to_one=SCREAMING_SNAKE_CASE_							,       steps_offset=1							,       prediction_type='epsilon'							,       thresholding=SCREAMING_SNAKE_CASE_							,       )

            __snake_case						       =   {
                'text_encoder': text_encoder,
                'tokenizer': tokenizer,
                'unet': unet,
                'scheduler': scheduler,
                'movq': movq,
            }
            return components

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_=0    ):
            __snake_case						       =   floats_tensor((1, self.cross_attention_dim)							,       rng=random.Random(SCREAMING_SNAKE_CASE_    )    ).to(SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   floats_tensor((1, self.cross_attention_dim)							,       rng=random.Random(seed + 1    )    ).to(SCREAMING_SNAKE_CASE_    )
            if str(SCREAMING_SNAKE_CASE_    ).startswith('mps'    ):
                   __snake_case						       =   torch.manual_seed(SCREAMING_SNAKE_CASE_    )
            else:
                   __snake_case						       =   torch.Generator(device=SCREAMING_SNAKE_CASE_    ).manual_seed(SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   {
                'prompt': 'horse',
                'image_embeds': image_embeds,
                'negative_image_embeds': negative_image_embeds,
                'generator': generator,
                'height': 64,
                'width': 64,
                'guidance_scale': 4.0,
                'num_inference_steps': 2,
                'output_type': 'np',
            }
            return inputs

     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   'cpu'

            __snake_case						       =   self.get_dummy_components()

            __snake_case						       =   self.pipeline_class(**SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   pipe.to(SCREAMING_SNAKE_CASE_    )

            pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_    )

            __snake_case						       =   pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE_    )    )
            __snake_case						       =   output.images

            __snake_case						       =   pipe(
                **self.get_dummy_inputs(SCREAMING_SNAKE_CASE_    )							,       return_dict=SCREAMING_SNAKE_CASE_							,       )[0]

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

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

            __snake_case						       =   np.array(
                [0.3_2_8_6_6_3, 1.0, 0.2_3_2_1_6_8_7_3, 1.0, 0.9_2_7_1_7_5_6_4, 0.4_6_3_9_0_4_6, 0.9_6_8_9_4_7_7_7, 0.3_1_7_1_3_3_7_8, 0.6_2_9_3_9_5_3]    )

            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  _lowerCamelCase			(unittest.TestCase					):

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

     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   load_numpy(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                '/kandinsky/kandinsky_text2img_cat_fp16.npy'    )

            __snake_case						       =   KandinskyPriorPipeline.from_pretrained(
                'kandinsky-community/kandinsky-2-1-prior'							,       torch_dtype=torch.floataa    )
            pipe_prior.to(SCREAMING_SNAKE_CASE_    )

            __snake_case						       =   KandinskyPipeline.from_pretrained('kandinsky-community/kandinsky-2-1'							,       torch_dtype=torch.floataa    )
            __snake_case						       =   pipeline.to(SCREAMING_SNAKE_CASE_    )
            pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_    )

            __snake_case						       =   'red cat, 4k photo'

            __snake_case						       =   torch.Generator(device='cuda'    ).manual_seed(0    )
            __snake_case						       =   pipe_prior(
                SCREAMING_SNAKE_CASE_							,       generator=SCREAMING_SNAKE_CASE_							,       num_inference_steps=5							,       negative_prompt=''							,       ).to_tuple()

            __snake_case						       =   torch.Generator(device='cuda'    ).manual_seed(0    )
            __snake_case						       =   pipeline(
                SCREAMING_SNAKE_CASE_							,       image_embeds=SCREAMING_SNAKE_CASE_							,       negative_image_embeds=SCREAMING_SNAKE_CASE_							,       generator=SCREAMING_SNAKE_CASE_							,       num_inference_steps=100							,       output_type='np'							,       )

            __snake_case						       =   output.images[0]

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

            assert_mean_pixel_difference(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )





"""simple docstring"""







import argparse
import json
from collections import OrderedDict
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
from transformers.utils import logging


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

# here we list all keys to be renamed (original name on the left, our name on the right)
lowercase__						:							Union[str, Any]        =      []
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 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"""),
    ]
)

def 							UpperCamelCase_       (       lowerCAmelCase__ :							str       ,  lowerCAmelCase__ :							Dict       ,  lowerCAmelCase__ :							Optional[Any]		)		->  List[Any]:

  """simple docstring"""




  lowerCAmelCase_  :							Dict								=					state_dict.pop(lowerCAmelCase__		)
  lowerCAmelCase_  :							Union[str, Any]								=					val

def 							UpperCamelCase_       (       lowerCAmelCase__ :							Union[str, Any]		)		->  Any:

  """simple docstring"""




  lowerCAmelCase_  :							Tuple								=					OrderedDict()
  for key, value in state_dict.items():
    if "backbone.0.body" in key:
      lowerCAmelCase_  :							List[str]								=					key.replace('backbone.0.body'       ,  'backbone.conv_encoder.model'		)
      lowerCAmelCase_  :							Optional[int]								=					value
    else:
      lowerCAmelCase_  :							Optional[Any]								=					value

  return new_state_dict

def 							UpperCamelCase_       (       lowerCAmelCase__ :							Dict       ,  lowerCAmelCase__ :							List[Any]=False		)		->  Optional[int]:

  """simple docstring"""




  lowerCAmelCase_  :							Union[str, Any]								=					''
  if is_panoptic:
    lowerCAmelCase_  :							str								=					'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_  :							Optional[Any]								=					state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"		)
    lowerCAmelCase_  :							Any								=					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_  :							Optional[int]								=					in_proj_weight[:256, :]
    lowerCAmelCase_  :							int								=					in_proj_bias[:256]
    lowerCAmelCase_  :							Optional[Any]								=					in_proj_weight[256:512, :]
    lowerCAmelCase_  :							Union[str, Any]								=					in_proj_bias[256:512]
    lowerCAmelCase_  :							List[Any]								=					in_proj_weight[-256:, :]
    lowerCAmelCase_  :							int								=					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_  :							List[str]								=					state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"		)
    lowerCAmelCase_  :							str								=					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_  :							Any								=					in_proj_weight[:256, :]
    lowerCAmelCase_  :							List[Any]								=					in_proj_bias[:256]
    lowerCAmelCase_  :							List[Any]								=					in_proj_weight[256:512, :]
    lowerCAmelCase_  :							Tuple								=					in_proj_bias[256:512]
    lowerCAmelCase_  :							Any								=					in_proj_weight[-256:, :]
    lowerCAmelCase_  :							Tuple								=					in_proj_bias[-256:]
    # read in weights + bias of input projection layer of cross-attention
    lowerCAmelCase_  :							Optional[Any]								=					state_dict.pop(
        f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"		)
    lowerCAmelCase_  :							Optional[int]								=					state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"		)
    # next, add query, keys and values (in that order) of cross-attention to the state dict
    lowerCAmelCase_  :							int								=					in_proj_weight_cross_attn[:256, :]
    lowerCAmelCase_  :							Optional[Any]								=					in_proj_bias_cross_attn[:256]
    lowerCAmelCase_  :							Any								=					in_proj_weight_cross_attn[256:512, :]
    lowerCAmelCase_  :							int								=					in_proj_bias_cross_attn[256:512]
    lowerCAmelCase_  :							Dict								=					in_proj_weight_cross_attn[-256:, :]
    lowerCAmelCase_  :							Optional[Any]								=					in_proj_bias_cross_attn[-256:]

def 							UpperCamelCase_       (       )		->  Any:

  """simple docstring"""




  lowerCAmelCase_  :							Optional[Any]								=					'http://images.cocodataset.org/val2017/000000039769.jpg'
  lowerCAmelCase_  :							List[str]								=					Image.open(requests.get(lowerCAmelCase__       ,  stream=lowerCAmelCase__		).raw		)

  return im

@torch.no_grad()
def 							UpperCamelCase_       (       lowerCAmelCase__ :							Any       ,  lowerCAmelCase__ :							Tuple		)		->  str:

  """simple docstring"""




  lowerCAmelCase_  :							int								=					DetrConfig()
  # set backbone and dilation attributes
  if "resnet101" in model_name:
    lowerCAmelCase_  :							int								=					'resnet101'
  if "dc5" in model_name:
    lowerCAmelCase_  :							Any								=					True
  lowerCAmelCase_  :							Tuple								=					'panoptic' in model_name
  if is_panoptic:
    lowerCAmelCase_  :							int								=					250
  else:
    lowerCAmelCase_  :							Optional[int]								=					91
    lowerCAmelCase_  :							int								=					'huggingface/label-files'
    lowerCAmelCase_  :							Dict								=					'coco-detection-id2label.json'
    lowerCAmelCase_  :							List[str]								=					json.load(open(hf_hub_download(lowerCAmelCase__       ,  lowerCAmelCase__       ,  repo_type='dataset'		)       ,  'r'		)		)
    lowerCAmelCase_  :							str								=					{int(lowerCAmelCase__		): v for k, v in idalabel.items()}
    lowerCAmelCase_  :							Union[str, Any]								=					idalabel
    lowerCAmelCase_  :							int								=					{v: k for k, v in idalabel.items()}

  # load image processor
  lowerCAmelCase_  :							Optional[int]								=					'coco_panoptic' if is_panoptic else 'coco_detection'
  lowerCAmelCase_  :							List[Any]								=					DetrImageProcessor(format=lowerCAmelCase__		)

  # prepare image
  lowerCAmelCase_  :							str								=					prepare_img()
  lowerCAmelCase_  :							Tuple								=					image_processor(images=lowerCAmelCase__       ,  return_tensors='pt'		)
  lowerCAmelCase_  :							int								=					encoding['pixel_values']

  logger.info(f"Converting model {model_name}..."		)

  # load original model from torch hub
  lowerCAmelCase_  :							int								=					torch.hub.load('facebookresearch/detr'       ,  lowerCAmelCase__       ,  pretrained=lowerCAmelCase__		).eval()
  lowerCAmelCase_  :							Optional[Any]								=					detr.state_dict()
  # rename keys
  for src, dest in rename_keys:
    if is_panoptic:
      lowerCAmelCase_  :							List[Any]								=					'detr.' + src
    rename_key(lowerCAmelCase__       ,  lowerCAmelCase__       ,  lowerCAmelCase__		)
  lowerCAmelCase_  :							Optional[int]								=					rename_backbone_keys(lowerCAmelCase__		)
  # query, key and value matrices need special treatment
  read_in_q_k_v(lowerCAmelCase__       ,  is_panoptic=lowerCAmelCase__		)
  # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
  lowerCAmelCase_  :							List[str]								=					'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_  :							Optional[int]								=					state_dict.pop(lowerCAmelCase__		)
        lowerCAmelCase_  :							Any								=					val
      elif "class_labels_classifier" in key or "bbox_predictor" in key:
        lowerCAmelCase_  :							str								=					state_dict.pop(lowerCAmelCase__		)
        lowerCAmelCase_  :							str								=					val
      elif key.startswith('bbox_attention'		) or key.startswith('mask_head'		):
        continue
      else:
        lowerCAmelCase_  :							Optional[int]								=					state_dict.pop(lowerCAmelCase__		)
        lowerCAmelCase_  :							Dict								=					val
    else:
      if not key.startswith('class_labels_classifier'		) and not key.startswith('bbox_predictor'		):
        lowerCAmelCase_  :							str								=					state_dict.pop(lowerCAmelCase__		)
        lowerCAmelCase_  :							str								=					val
    # finally, create HuggingFace model and load state dict
  lowerCAmelCase_  :							List[str]								=					DetrForSegmentation(lowerCAmelCase__		) if is_panoptic else DetrForObjectDetection(lowerCAmelCase__		)
  model.load_state_dict(lowerCAmelCase__		)
  model.eval()
  # verify our conversion
  lowerCAmelCase_  :							Tuple								=					detr(lowerCAmelCase__		)
  lowerCAmelCase_  :							List[Any]								=					model(lowerCAmelCase__		)
  assert torch.allclose(outputs.logits       ,  original_outputs['pred_logits']       ,  atol=1e-4		)
  assert torch.allclose(outputs.pred_boxes       ,  original_outputs['pred_boxes']       ,  atol=1e-4		)
  if is_panoptic:
    assert torch.allclose(outputs.pred_masks       ,  original_outputs['pred_masks']       ,  atol=1e-4		)

  # Save model and image processor
  logger.info(f"Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."		)
  Path(lowerCAmelCase__		).mkdir(exist_ok=lowerCAmelCase__		)
  model.save_pretrained(lowerCAmelCase__		)
  image_processor.save_pretrained(lowerCAmelCase__		)


if __name__ == "__main__":
     lowercase__						:							Tuple        =      argparse.ArgumentParser()

     parser.add_argument(
         """--model_name""", default="""detr_resnet50""", type=str, 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."""
     )
     lowercase__						:							Tuple        =      parser.parse_args()
     convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)





