
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  :					List[Any]						    =							logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowercase  :					int						    =							[]
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 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Optional[int]:
       lowercase							:     List[str]       	=      state_dict.pop(SCREAMING_SNAKE_CASE__				)
       lowercase							:     Optional[int]       	=      val







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    str:
       lowercase							:     str       	=      OrderedDict()
       for key, value in state_dict.items():
              if "backbone.0.body" in key:
                     lowercase							:     Tuple       	=      key.replace("""backbone.0.body"""					,	"""backbone.conv_encoder.model"""				)
                     lowercase							:     List[str]       	=      value
              else:
                     lowercase							:     Union[str, Any]       	=      value

       return new_state_dict







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=False				) ->    Optional[Any]:
       lowercase							:     List[str]       	=      """"""
       if is_panoptic:
              lowercase							:     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)
              lowercase							:     int       	=      state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"				)
              lowercase							:     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
              lowercase							:     str       	=      in_proj_weight[:256, :]
              lowercase							:     List[Any]       	=      in_proj_bias[:256]
              lowercase							:     Union[str, Any]       	=      in_proj_weight[256:512, :]
              lowercase							:     Optional[int]       	=      in_proj_bias[256:512]
              lowercase							:     List[Any]       	=      in_proj_weight[-256:, :]
              lowercase							:     str       	=      in_proj_bias[-256:]







def 						_snake_case(  ) ->    Optional[int]:
       lowercase							:     Dict       	=      """http://images.cocodataset.org/val2017/000000039769.jpg"""
       lowercase							:     List[Any]       	=      Image.open(requests.get(SCREAMING_SNAKE_CASE__					,	stream=SCREAMING_SNAKE_CASE__				).raw				)

       return im







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

       # load image processor
       lowercase							:     str       	=      """coco_panoptic""" if is_panoptic else """coco_detection"""
       lowercase							:     List[Any]       	=      ConditionalDetrImageProcessor(format=SCREAMING_SNAKE_CASE__				)

       # prepare image
       lowercase							:     List[str]       	=      prepare_img()
       lowercase							:     Tuple       	=      image_processor(images=SCREAMING_SNAKE_CASE__					,	return_tensors="""pt"""				)
       lowercase							:     Tuple       	=      encoding["""pixel_values"""]

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

       # load original model from torch hub
       lowercase							:     List[Any]       	=      torch.hub.load("""DeppMeng/ConditionalDETR"""					,	SCREAMING_SNAKE_CASE__					,	pretrained=SCREAMING_SNAKE_CASE__				).eval()
       lowercase							:     Dict       	=      conditional_detr.state_dict()
       # rename keys
       for src, dest in rename_keys:
              if is_panoptic:
                     lowercase							:     str       	=      """conditional_detr.""" + src
              rename_key(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       lowercase							:     List[str]       	=      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
       lowercase							:     Tuple       	=      """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"""				)
                     ):
                            lowercase							:     str       	=      state_dict.pop(SCREAMING_SNAKE_CASE__				)
                            lowercase							:     Optional[int]       	=      val
                     elif "class_labels_classifier" in key or "bbox_predictor" in key:
                            lowercase							:     Dict       	=      state_dict.pop(SCREAMING_SNAKE_CASE__				)
                            lowercase							:     Tuple       	=      val
                     elif key.startswith("""bbox_attention"""				) or key.startswith("""mask_head"""				):
                            continue
                     else:
                            lowercase							:     List[str]       	=      state_dict.pop(SCREAMING_SNAKE_CASE__				)
                            lowercase							:     str       	=      val
              else:
                     if not key.startswith("""class_labels_classifier"""				) and not key.startswith("""bbox_predictor"""				):
                            lowercase							:     Dict       	=      state_dict.pop(SCREAMING_SNAKE_CASE__				)
                            lowercase							:     List[Any]       	=      val
    # finally, create HuggingFace model and load state dict
       lowercase							:     int       	=      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
       lowercase							:     List[str]       	=      conditional_detr(SCREAMING_SNAKE_CASE__				)
       lowercase							:     List[str]       	=      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__":
  lowercase  :					Tuple						    =							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  :					Optional[int]						    =							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 __snake_case			(			a ,  unittest.TestCase		):
   UpperCAmelCase__  :    Any          =  PhobertTokenizer
   UpperCAmelCase__  :    List[str]          =  False







   def lowerCamelCase						(      self		:						str):

        """simple docstring"""


        super().setUp()

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

        UpperCAmelCase_          =							os.path.join(self.tmpdirname					,						VOCAB_FILES_NAMES['''vocab_file'''])
        UpperCAmelCase_          =							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		:						int					,						**_snake_case		:						Any):

        """simple docstring"""


        kwargs.update(self.special_tokens_map)
        return PhobertTokenizer.from_pretrained(self.tmpdirname					,						**_snake_case)







   def lowerCamelCase						(      self		:						Optional[Any]					,						_snake_case		:						Union[str, Any]):

        """simple docstring"""


        UpperCAmelCase_          =							'''Tôi là VinAI Research'''
        UpperCAmelCase_          =							'''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		:						List[str]):

        """simple docstring"""


        UpperCAmelCase_          =							PhobertTokenizer(self.vocab_file					,						self.merges_file					,						**self.special_tokens_map)
        UpperCAmelCase_          =							'''Tôi là VinAI Research'''
        UpperCAmelCase_          =							'''T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'''.split()
        UpperCAmelCase_          =							tokenizer.tokenize(_snake_case)
        print(_snake_case)
        self.assertListEqual(_snake_case					,						_snake_case)

        UpperCAmelCase_          =							tokens + [tokenizer.unk_token]

        UpperCAmelCase_          =							[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)






