





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()
SCREAMING_SNAKE_CASE_						   =       logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
SCREAMING_SNAKE_CASE_						   =       []
for i in range(6):
      # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
      rename_keys.append(
          (F'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', F'''encoder.layers.{i}.self_attn.out_proj.weight''')
      )
      rename_keys.append(
          (F'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', F'''encoder.layers.{i}.self_attn.out_proj.bias''')
      )
      rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''encoder.layers.{i}.fc1.weight'''))
      rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''encoder.layers.{i}.fc1.bias'''))
      rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''encoder.layers.{i}.fc2.weight'''))
      rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''encoder.layers.{i}.fc2.bias'''))
      rename_keys.append(
          (F'''transformer.encoder.layers.{i}.norm1.weight''', F'''encoder.layers.{i}.self_attn_layer_norm.weight''')
      )
      rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.bias''', F'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
      rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.weight''', F'''encoder.layers.{i}.final_layer_norm.weight'''))
      rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''encoder.layers.{i}.final_layer_norm.bias'''))
      # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
      rename_keys.append(
          (F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', F'''decoder.layers.{i}.self_attn.out_proj.weight''')
      )
      rename_keys.append(
          (F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''decoder.layers.{i}.self_attn.out_proj.bias''')
      )
      rename_keys.append(
          (
              F'''transformer.decoder.layers.{i}.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    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ->   str:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						state_dict.pop(_SCREAMING_SNAKE_CASE	)
      SCREAMING_SNAKE_CASE		=						val



def    __lowercase   (      _SCREAMING_SNAKE_CASE	) ->   Optional[Any]:


      '''simple docstring'''


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

      return new_state_dict



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE=False	) ->   Optional[Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						""""""
      if is_panoptic:
            SCREAMING_SNAKE_CASE		=						"""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)
            SCREAMING_SNAKE_CASE		=						state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""	)
            SCREAMING_SNAKE_CASE		=						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
            SCREAMING_SNAKE_CASE		=						in_proj_weight[:2_56, :]
            SCREAMING_SNAKE_CASE		=						in_proj_bias[:2_56]
            SCREAMING_SNAKE_CASE		=						in_proj_weight[2_56:5_12, :]
            SCREAMING_SNAKE_CASE		=						in_proj_bias[2_56:5_12]
            SCREAMING_SNAKE_CASE		=						in_proj_weight[-2_56:, :]
            SCREAMING_SNAKE_CASE		=						in_proj_bias[-2_56:]



def    __lowercase   (      ) ->   List[str]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      SCREAMING_SNAKE_CASE		=						Image.open(requests.get(_SCREAMING_SNAKE_CASE ,						stream=_SCREAMING_SNAKE_CASE	).raw	)

      return im



@torch.no_grad()
def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ->   Any:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						ConditionalDetrConfig()
      # set backbone and dilation attributes
      if "resnet101" in model_name:
            SCREAMING_SNAKE_CASE		=						"""resnet101"""
      if "dc5" in model_name:
            SCREAMING_SNAKE_CASE		=						True
      SCREAMING_SNAKE_CASE		=						"""panoptic""" in model_name
      if is_panoptic:
            SCREAMING_SNAKE_CASE		=						2_50
      else:
            SCREAMING_SNAKE_CASE		=						91
            SCREAMING_SNAKE_CASE		=						"""huggingface/label-files"""
            SCREAMING_SNAKE_CASE		=						"""coco-detection-id2label.json"""
            SCREAMING_SNAKE_CASE		=						json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						repo_type="""dataset"""	) ,						"""r"""	)	)
            SCREAMING_SNAKE_CASE		=						{int(_SCREAMING_SNAKE_CASE	): v for k, v in idalabel.items()}
            SCREAMING_SNAKE_CASE		=						idalabel
            SCREAMING_SNAKE_CASE		=						{v: k for k, v in idalabel.items()}

      # load image processor
      SCREAMING_SNAKE_CASE		=						"""coco_panoptic""" if is_panoptic else """coco_detection"""
      SCREAMING_SNAKE_CASE		=						ConditionalDetrImageProcessor(format=_SCREAMING_SNAKE_CASE	)

      # prepare image
      SCREAMING_SNAKE_CASE		=						prepare_img()
      SCREAMING_SNAKE_CASE		=						image_processor(images=_SCREAMING_SNAKE_CASE ,						return_tensors="""pt"""	)
      SCREAMING_SNAKE_CASE		=						encoding["""pixel_values"""]

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

      # load original model from torch hub
      SCREAMING_SNAKE_CASE		=						torch.hub.load("""DeppMeng/ConditionalDETR""" ,						_SCREAMING_SNAKE_CASE ,						pretrained=_SCREAMING_SNAKE_CASE	).eval()
      SCREAMING_SNAKE_CASE		=						conditional_detr.state_dict()
      # rename keys
      for src, dest in rename_keys:
            if is_panoptic:
                  SCREAMING_SNAKE_CASE		=						"""conditional_detr.""" + src
            rename_key(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	)
      SCREAMING_SNAKE_CASE		=						rename_backbone_keys(_SCREAMING_SNAKE_CASE	)
      # query, key and value matrices need special treatment
      read_in_q_k_v(_SCREAMING_SNAKE_CASE ,						is_panoptic=_SCREAMING_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
      SCREAMING_SNAKE_CASE		=						"""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"""	)
                  ):
                        SCREAMING_SNAKE_CASE		=						state_dict.pop(_SCREAMING_SNAKE_CASE	)
                        SCREAMING_SNAKE_CASE		=						val
                  elif "class_labels_classifier" in key or "bbox_predictor" in key:
                        SCREAMING_SNAKE_CASE		=						state_dict.pop(_SCREAMING_SNAKE_CASE	)
                        SCREAMING_SNAKE_CASE		=						val
                  elif key.startswith("""bbox_attention"""	) or key.startswith("""mask_head"""	):
                        continue
                  else:
                        SCREAMING_SNAKE_CASE		=						state_dict.pop(_SCREAMING_SNAKE_CASE	)
                        SCREAMING_SNAKE_CASE		=						val
            else:
                  if not key.startswith("""class_labels_classifier"""	) and not key.startswith("""bbox_predictor"""	):
                        SCREAMING_SNAKE_CASE		=						state_dict.pop(_SCREAMING_SNAKE_CASE	)
                        SCREAMING_SNAKE_CASE		=						val
                        # finally, create HuggingFace model and load state dict
      SCREAMING_SNAKE_CASE		=						ConditionalDetrForSegmentation(_SCREAMING_SNAKE_CASE	) if is_panoptic else ConditionalDetrForObjectDetection(_SCREAMING_SNAKE_CASE	)
      model.load_state_dict(_SCREAMING_SNAKE_CASE	)
      model.eval()
      model.push_to_hub(repo_id=_SCREAMING_SNAKE_CASE ,						organization="""DepuMeng""" ,						commit_message="""Add model"""	)
      # verify our conversion
      SCREAMING_SNAKE_CASE		=						conditional_detr(_SCREAMING_SNAKE_CASE	)
      SCREAMING_SNAKE_CASE		=						model(_SCREAMING_SNAKE_CASE	)
      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(_SCREAMING_SNAKE_CASE	).mkdir(exist_ok=_SCREAMING_SNAKE_CASE	)
      model.save_pretrained(_SCREAMING_SNAKE_CASE	)
      image_processor.save_pretrained(_SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE_						   =       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."""
      )
      SCREAMING_SNAKE_CASE_						   =       parser.parse_args()
      convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)




from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
      import numpy as np
      import tensorflow as tf

      from transformers import TFCamembertModel





@require_tf
@require_sentencepiece
@require_tokenizers
class 							A__		(	unittest.TestCase):
      @slow
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Union[str, Any]							=      TFCamembertModel.from_pretrained("""jplu/tf-camembert-base"""      )

       lowerCamelCase		:	Any							=      tf.convert_to_tensor(
           [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]    ,  dtype=tf.intaa    ,  )  # J'aime le camembert !"

       lowerCamelCase		:	str							=      model(__magic_name__      )["""last_hidden_state"""]
       lowerCamelCase		:	Union[str, Any]							=      tf.TensorShape((1, 1_0, 7_6_8)      )
       self.assertEqual(output.shape    ,  __magic_name__      )
       # compare the actual values for a slice.
       lowerCamelCase		:	Dict							=      tf.convert_to_tensor(
           [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]]    ,  dtype=tf.floataa    ,  )
       # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
       # camembert.eval()
       # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

       self.assertTrue(np.allclose(output[:, :3, :3].numpy()    ,  expected_slice.numpy()    ,  atol=1e-4      )      )




