
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()
lowercase__          =logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowercase__          =[]
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        __UpperCamelCase					(    lowerCAmelCase__							:    Optional[Any]  ,       lowerCAmelCase__							:    Optional[Any]  ,       lowerCAmelCase__							:    Any  ):
 __a			:  Optional[Any]		    =  state_dict.pop(lowerCAmelCase__  )
 __a			:  int		    =  val




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

 return new_state_dict




def        __UpperCamelCase					(    lowerCAmelCase__							:    Tuple  ,       lowerCAmelCase__							:    Any=False  ):
 __a			:  Any		    =  ''''''
 if is_panoptic:
  __a			:  Optional[Any]		    =  '''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			:  List[Any]		    =  state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"  )
  __a			:  Tuple		    =  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			:  List[str]		    =  in_proj_weight[:2_5_6, :]
  __a			:  str		    =  in_proj_bias[:2_5_6]
  __a			:  Optional[Any]		    =  in_proj_weight[2_5_6:5_1_2, :]
  __a			:  List[Any]		    =  in_proj_bias[2_5_6:5_1_2]
  __a			:  Optional[Any]		    =  in_proj_weight[-2_5_6:, :]
  __a			:  int		    =  in_proj_bias[-2_5_6:]




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

 return im




@torch.no_grad()
def        __UpperCamelCase					(    lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    Dict  ):
 __a			:  Optional[Any]		    =  ConditionalDetrConfig()
 # set backbone and dilation attributes
 if "resnet101" in model_name:
  __a			:  Tuple		    =  '''resnet101'''
 if "dc5" in model_name:
  __a			:  int		    =  True
 __a			:  List[Any]		    =  '''panoptic''' in model_name
 if is_panoptic:
  __a			:  Optional[Any]		    =  2_5_0
 else:
  __a			:  Optional[int]		    =  9_1
  __a			:  str		    =  '''huggingface/label-files'''
  __a			:  Optional[int]		    =  '''coco-detection-id2label.json'''
  __a			:  Tuple		    =  json.load(open(hf_hub_download(lowerCAmelCase__  ,       lowerCAmelCase__  ,       repo_type='''dataset'''  )  ,       '''r'''  )  )
  __a			:  List[Any]		    =  {int(lowerCAmelCase__  ): v for k, v in idalabel.items()}
  __a			:  int		    =  idalabel
  __a			:  Union[str, Any]		    =  {v: k for k, v in idalabel.items()}

 # load image processor
 __a			:  Optional[int]		    =  '''coco_panoptic''' if is_panoptic else '''coco_detection'''
 __a			:  Optional[int]		    =  ConditionalDetrImageProcessor(format=lowerCAmelCase__  )

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

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

 # load original model from torch hub
 __a			:  List[str]		    =  torch.hub.load('''DeppMeng/ConditionalDETR'''  ,       lowerCAmelCase__  ,       pretrained=lowerCAmelCase__  ).eval()
 __a			:  Tuple		    =  conditional_detr.state_dict()
 # rename keys
 for src, dest in rename_keys:
  if is_panoptic:
   __a			:  Any		    =  '''conditional_detr.''' + src
  rename_key(lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  )
 __a			:  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
 __a			:  Optional[Any]		    =  '''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			:  int		    =  state_dict.pop(lowerCAmelCase__  )
    __a			:  Optional[Any]		    =  val
   elif "class_labels_classifier" in key or "bbox_predictor" in key:
    __a			:  Union[str, Any]		    =  state_dict.pop(lowerCAmelCase__  )
    __a			:  Any		    =  val
   elif key.startswith('''bbox_attention'''  ) or key.startswith('''mask_head'''  ):
    continue
   else:
    __a			:  str		    =  state_dict.pop(lowerCAmelCase__  )
    __a			:  str		    =  val
  else:
   if not key.startswith('''class_labels_classifier'''  ) and not key.startswith('''bbox_predictor'''  ):
    __a			:  Optional[int]		    =  state_dict.pop(lowerCAmelCase__  )
    __a			:  str		    =  val
    # finally, create HuggingFace model and load state dict
 __a			:  List[Any]		    =  ConditionalDetrForSegmentation(lowerCAmelCase__  ) if is_panoptic else ConditionalDetrForObjectDetection(lowerCAmelCase__  )
 model.load_state_dict(lowerCAmelCase__  )
 model.eval()
 model.push_to_hub(repo_id=lowerCAmelCase__  ,       organization='''DepuMeng'''  ,       commit_message='''Add model'''  )
 # verify our conversion
 __a			:  Any		    =  conditional_detr(lowerCAmelCase__  )
 __a			:  str		    =  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__          =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.'
      )
      lowercase__          =parser.parse_args()
      convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)


import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin



class 		UpperCamelCase__  (	__lowercase   ,unittest.TestCase   ):
   _SCREAMING_SNAKE_CASE				:							Union[str, Any]     	=				PhobertTokenizer
   _SCREAMING_SNAKE_CASE				:							int     	=				False
   def 				lowerCAmelCase      (self			: Optional[int]       ):
    super().setUp()

    # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
    __a			:  Optional[int]		    =  ['''T@@''', '''i''', '''I''', '''R@@''', '''r''', '''e@@''']
    __a			:  Tuple		    =  dict(zip(snake_case_ ,    range(len(snake_case_       )       )       )       )
    __a			:  int		    =  ['''#version: 0.2''', '''l à</w>''']
    __a			:  List[Any]		    =  {'''unk_token''': '''<unk>'''}

    __a			:  Any		    =  os.path.join(self.tmpdirname ,    VOCAB_FILES_NAMES['''vocab_file''']       )
    __a			:  List[Any]		    =  os.path.join(self.tmpdirname ,    VOCAB_FILES_NAMES['''merges_file''']       )

    with open(self.vocab_file ,    '''w''' ,    encoding='''utf-8'''       ) as fp:
     for token in vocab_tokens:
      fp.write(f"{token} {vocab_tokens[token]}\n"       )
    with open(self.merges_file ,    '''w''' ,    encoding='''utf-8'''       ) as fp:
     fp.write('''\n'''.join(snake_case_       )       )
   def 				lowerCAmelCase      (self			: Union[str, Any] ,    **snake_case_			: List[str]       ):
    kwargs.update(self.special_tokens_map       )
    return PhobertTokenizer.from_pretrained(self.tmpdirname ,    **snake_case_       )
   def 				lowerCAmelCase      (self			: Any ,    snake_case_			: Dict       ):
    __a			:  Union[str, Any]		    =  '''Tôi là VinAI Research'''
    __a			:  int		    =  '''T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'''
    return input_text, output_text







   def 				lowerCAmelCase      (self			: Optional[Any]       ):
    __a			:  Optional[int]		    =  PhobertTokenizer(self.vocab_file ,    self.merges_file ,    **self.special_tokens_map       )
    __a			:  Any		    =  '''Tôi là VinAI Research'''
    __a			:  Union[str, Any]		    =  '''T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'''.split()
    __a			:  List[str]		    =  tokenizer.tokenize(snake_case_       )
    print(snake_case_       )
    self.assertListEqual(snake_case_ ,    snake_case_       )

    __a			:  str		    =  tokens + [tokenizer.unk_token]

    __a			:  str		    =  [4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_       ) ,    snake_case_       )
