




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 (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCamelCase	:		str							       =   logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_lowerCamelCase	:		List[str]							       =   []
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}.cross_attn.out_proj.weight''',
          F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
      )
  )
  rename_keys.append(
      (
          F'''transformer.decoder.layers.{i}.cross_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'''))

  # q, k, v projections in self/cross-attention in decoder for conditional DETR
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.sa_qcontent_proj.weight''', F'''decoder.layers.{i}.sa_qcontent_proj.weight''')
  )
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.sa_kcontent_proj.weight''', F'''decoder.layers.{i}.sa_kcontent_proj.weight''')
  )
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.sa_qpos_proj.weight''', F'''decoder.layers.{i}.sa_qpos_proj.weight''')
  )
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.sa_kpos_proj.weight''', F'''decoder.layers.{i}.sa_kpos_proj.weight''')
  )
  rename_keys.append((F'''transformer.decoder.layers.{i}.sa_v_proj.weight''', F'''decoder.layers.{i}.sa_v_proj.weight'''))
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.ca_qcontent_proj.weight''', F'''decoder.layers.{i}.ca_qcontent_proj.weight''')
  )
  # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.ca_kcontent_proj.weight''', F'''decoder.layers.{i}.ca_kcontent_proj.weight''')
  )
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.ca_kpos_proj.weight''', F'''decoder.layers.{i}.ca_kpos_proj.weight''')
  )
  rename_keys.append((F'''transformer.decoder.layers.{i}.ca_v_proj.weight''', F'''decoder.layers.{i}.ca_v_proj.weight'''))
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight''', F'''decoder.layers.{i}.ca_qpos_sine_proj.weight''')
  )

  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.sa_qcontent_proj.bias''', F'''decoder.layers.{i}.sa_qcontent_proj.bias''')
  )
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.sa_kcontent_proj.bias''', F'''decoder.layers.{i}.sa_kcontent_proj.bias''')
  )
  rename_keys.append((F'''transformer.decoder.layers.{i}.sa_qpos_proj.bias''', F'''decoder.layers.{i}.sa_qpos_proj.bias'''))
  rename_keys.append((F'''transformer.decoder.layers.{i}.sa_kpos_proj.bias''', F'''decoder.layers.{i}.sa_kpos_proj.bias'''))
  rename_keys.append((F'''transformer.decoder.layers.{i}.sa_v_proj.bias''', F'''decoder.layers.{i}.sa_v_proj.bias'''))
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.ca_qcontent_proj.bias''', F'''decoder.layers.{i}.ca_qcontent_proj.bias''')
  )
  # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.ca_kcontent_proj.bias''', F'''decoder.layers.{i}.ca_kcontent_proj.bias''')
  )
  rename_keys.append((F'''transformer.decoder.layers.{i}.ca_kpos_proj.bias''', F'''decoder.layers.{i}.ca_kpos_proj.bias'''))
  rename_keys.append((F'''transformer.decoder.layers.{i}.ca_v_proj.bias''', F'''decoder.layers.{i}.ca_v_proj.bias'''))
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias''', F'''decoder.layers.{i}.ca_qpos_sine_proj.bias''')
  )

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
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"""),
        ("""transformer.decoder.ref_point_head.layers.0.weight""", """decoder.ref_point_head.layers.0.weight"""),
        ("""transformer.decoder.ref_point_head.layers.0.bias""", """decoder.ref_point_head.layers.0.bias"""),
        ("""transformer.decoder.ref_point_head.layers.1.weight""", """decoder.ref_point_head.layers.1.weight"""),
        ("""transformer.decoder.ref_point_head.layers.1.bias""", """decoder.ref_point_head.layers.1.bias"""),
        ("""transformer.decoder.query_scale.layers.0.weight""", """decoder.query_scale.layers.0.weight"""),
        ("""transformer.decoder.query_scale.layers.0.bias""", """decoder.query_scale.layers.0.bias"""),
        ("""transformer.decoder.query_scale.layers.1.weight""", """decoder.query_scale.layers.1.weight"""),
        ("""transformer.decoder.query_scale.layers.1.bias""", """decoder.query_scale.layers.1.bias"""),
        ("""transformer.decoder.layers.0.ca_qpos_proj.weight""", """decoder.layers.0.ca_qpos_proj.weight"""),
        ("""transformer.decoder.layers.0.ca_qpos_proj.bias""", """decoder.layers.0.ca_qpos_proj.bias"""),
    ]
)







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ ,				lowercase_						)   ->       Optional[Any]:



       """simple docstring"""

       A__       =    state_dict.pop(lowercase_						)
       A__       =    val







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       List[str]:



       """simple docstring"""

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

       return new_state_dict







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_=False						)   ->       Dict:



       """simple docstring"""

       A__       =    ''''''
       if is_panoptic:
              A__       =    '''conditional_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)
              A__       =    state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""						)
              A__       =    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
              A__       =    in_proj_weight[:256, :]
              A__       =    in_proj_bias[:256]
              A__       =    in_proj_weight[256:512, :]
              A__       =    in_proj_bias[256:512]
              A__       =    in_proj_weight[-256:, :]
              A__       =    in_proj_bias[-256:]







def SCREAMING_SNAKE_CASE				(       )   ->       List[Any]:



       """simple docstring"""

       A__       =    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
       A__       =    Image.open(requests.get(lowercase_ ,				stream=lowercase_						).raw						)

       return im







@torch.no_grad()
def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       Tuple:



       """simple docstring"""

       A__       =    ConditionalDetrConfig()
       # set backbone and dilation attributes
       if "resnet101" in model_name:
              A__       =    '''resnet101'''
       if "dc5" in model_name:
              A__       =    True
       A__       =    '''panoptic''' in model_name
       if is_panoptic:
              A__       =    250
       else:
              A__       =    91
              A__       =    '''huggingface/label-files'''
              A__       =    '''coco-detection-id2label.json'''
              A__       =    json.load(open(hf_hub_download(lowercase_ ,				lowercase_ ,				repo_type='''dataset'''						) ,				'''r'''						)						)
              A__       =    {int(lowercase_						): v for k, v in idalabel.items()}
              A__       =    idalabel
              A__       =    {v: k for k, v in idalabel.items()}

       # load image processor
       A__       =    '''coco_panoptic''' if is_panoptic else '''coco_detection'''
       A__       =    ConditionalDetrImageProcessor(format=lowercase_						)

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

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

       # load original model from torch hub
       A__       =    torch.hub.load('''DeppMeng/ConditionalDETR''' ,				lowercase_ ,				pretrained=lowercase_						).eval()
       A__       =    conditional_detr.state_dict()
       # rename keys
       for src, dest in rename_keys:
              if is_panoptic:
                     A__       =    '''conditional_detr.''' + src
              rename_key(lowercase_ ,				lowercase_ ,				lowercase_						)
       A__       =    rename_backbone_keys(lowercase_						)
       # query, key and value matrices need special treatment
       read_in_q_k_v(lowercase_ ,				is_panoptic=lowercase_						)
       # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
       A__       =    '''conditional_detr.model.''' if is_panoptic else '''model.'''
       for key in state_dict.copy().keys():
              if is_panoptic:
                     if (
                         key.startswith('''conditional_detr'''						)
                         and not key.startswith('''class_labels_classifier'''						)
                         and not key.startswith('''bbox_predictor'''						)
                     ):
                            A__       =    state_dict.pop(lowercase_						)
                            A__       =    val
                     elif "class_labels_classifier" in key or "bbox_predictor" in key:
                            A__       =    state_dict.pop(lowercase_						)
                            A__       =    val
                     elif key.startswith('''bbox_attention'''						) or key.startswith('''mask_head'''						):
                            continue
                     else:
                            A__       =    state_dict.pop(lowercase_						)
                            A__       =    val
              else:
                     if not key.startswith('''class_labels_classifier'''						) and not key.startswith('''bbox_predictor'''						):
                            A__       =    state_dict.pop(lowercase_						)
                            A__       =    val
    # finally, create HuggingFace model and load state dict
       A__       =    ConditionalDetrForSegmentation(lowercase_						) if is_panoptic else ConditionalDetrForObjectDetection(lowercase_						)
       model.load_state_dict(lowercase_						)
       model.eval()
       model.push_to_hub(repo_id=lowercase_ ,				organization='''DepuMeng''' ,				commit_message='''Add model'''						)
       # verify our conversion
       A__       =    conditional_detr(lowercase_						)
       A__       =    model(lowercase_						)
       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(lowercase_						).mkdir(exist_ok=lowercase_						)
       model.save_pretrained(lowercase_						)
       image_processor.save_pretrained(lowercase_						)


if __name__ == "__main__":
  _lowerCamelCase	:		Dict							       =   argparse.ArgumentParser()

  parser.add_argument(
      """--model_name""",
      default="""conditional_detr_resnet50""",
      type=str,
      help="""Name of the CONDITIONAL_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."""
  )
  _lowerCamelCase	:		Optional[Any]							       =   parser.parse_args()
  convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)



'''simple docstring'''




from typing import Optional, Tuple

import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule






def 				snake_case__							(			_A:					Union[str, Any]      ,   _A:					Tuple      ,   _A:					Any=1e-12 )    ->	str:

		'''simple docstring'''


		lowerCAmelCase   					=  jnp.divide(emb_a.T      ,   jnp.clip(jnp.linalg.norm(_A      ,   axis=1 )      ,   a_min=_A ) ).T
		lowerCAmelCase   					=  jnp.divide(emb_a.T      ,   jnp.clip(jnp.linalg.norm(_A      ,   axis=1 )      ,   a_min=_A ) ).T
		return jnp.matmul(_A      ,   norm_emb_a.T )






class 				a__(					nn.Module	):


			'''simple docstring'''






			UpperCAmelCase_			:     CLIPConfig
			UpperCAmelCase_			:     jnp.dtype      =       jnp.floataa



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  FlaxCLIPVisionModule(self.config.vision_config)
					lowerCAmelCase   					=  nn.Dense(self.config.projection_dim       ,    use_bias=__lowerCAmelCase       ,    dtype=self.dtype)

					lowerCAmelCase   					=  self.param("""concept_embeds"""       ,    jax.nn.initializers.ones       ,    (17, self.config.projection_dim))
					lowerCAmelCase   					=  self.param(
					    """special_care_embeds"""       ,    jax.nn.initializers.ones       ,    (3, self.config.projection_dim))

					lowerCAmelCase   					=  self.param("""concept_embeds_weights"""       ,    jax.nn.initializers.ones       ,    (17,))
					lowerCAmelCase   					=  self.param("""special_care_embeds_weights"""       ,    jax.nn.initializers.ones       ,    (3,))







			def __call__(   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  self.vision_model(__lowerCAmelCase)[1]
					lowerCAmelCase   					=  self.visual_projection(__lowerCAmelCase)

					lowerCAmelCase   					=  jax_cosine_distance(__lowerCAmelCase       ,    self.special_care_embeds)
					lowerCAmelCase   					=  jax_cosine_distance(__lowerCAmelCase       ,    self.concept_embeds)

					# increase this value to create a stronger `nfsw` filter
					# at the cost of increasing the possibility of filtering benign image inputs
					lowerCAmelCase   					=  0.0

					lowerCAmelCase   					=  special_cos_dist - self.special_care_embeds_weights[None, :] + adjustment
					lowerCAmelCase   					=  jnp.round(__lowerCAmelCase       ,    3)
					lowerCAmelCase   					=  jnp.any(special_scores > 0       ,    axis=1       ,    keepdims=__lowerCAmelCase)
					# Use a lower threshold if an image has any special care concept
					lowerCAmelCase   					=  is_special_care * 0.01

					lowerCAmelCase   					=  cos_dist - self.concept_embeds_weights[None, :] + special_adjustment
					lowerCAmelCase   					=  jnp.round(__lowerCAmelCase       ,    3)
					lowerCAmelCase   					=  jnp.any(concept_scores > 0       ,    axis=1)

					return has_nsfw_concepts







class 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






			UpperCAmelCase_			:     int      =       CLIPConfig
			UpperCAmelCase_			:     Any      =       '''clip_input'''
			UpperCAmelCase_			:     List[str]      =       FlaxStableDiffusionSafetyCheckerModule



			def __init__(   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = 0       ,    __lowerCAmelCase = jnp.floataa       ,    __lowerCAmelCase = True       ,    **__lowerCAmelCase       ,    ):




					"""simple docstring"""


					if input_shape is None:
							lowerCAmelCase   					=  (1, 224, 224, 3)
					lowerCAmelCase   					=  self.module_class(config=__lowerCAmelCase       ,    dtype=__lowerCAmelCase       ,    **__lowerCAmelCase)
					super().__init__(__lowerCAmelCase       ,    __lowerCAmelCase       ,    input_shape=__lowerCAmelCase       ,    seed=__lowerCAmelCase       ,    dtype=__lowerCAmelCase       ,    _do_init=_do_init)



			def 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase = None):




					"""simple docstring"""


					lowerCAmelCase   					=  jax.random.normal(__lowerCAmelCase       ,    __lowerCAmelCase)

					lowerCAmelCase					,     lowerCAmelCase   					=  jax.random.split(__lowerCAmelCase)
					lowerCAmelCase   					=  {"""params""": params_rng, """dropout""": dropout_rng}

					lowerCAmelCase   					=  self.module.init(__lowerCAmelCase       ,    __lowerCAmelCase)["""params"""]

					return random_params







			def __call__(   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase = None       ,    ):




					"""simple docstring"""


					lowerCAmelCase   					=  jnp.transpose(__lowerCAmelCase       ,    (0, 2, 3, 1))

					return self.module.apply(
					    {"""params""": params or self.params}       ,    jnp.array(__lowerCAmelCase       ,    dtype=jnp.floataa)       ,    rngs={}       ,    )




