
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()
__SCREAMING_SNAKE_CASE			:   List[str]         =     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			:   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}.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__							(       __magic_name__						:     Optional[int]							,  __magic_name__						:     int							,  __magic_name__						:     Optional[int]  ):


     '''simple docstring'''

     lowerCAmelCase		:					Any  	=     state_dict.pop(__magic_name__  )
     lowerCAmelCase		:					str  	=     val
def     UpperCAmelCase__							(       __magic_name__						:     str  ):


     '''simple docstring'''

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

     return new_state_dict
def     UpperCAmelCase__							(       __magic_name__						:     List[str]							,  __magic_name__						:     str=False  ):


     '''simple docstring'''

     lowerCAmelCase		:					str  	=     ''''''
     if is_panoptic:
          lowerCAmelCase		:					Optional[int]  	=     '''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[int]  	=     state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''  )
          lowerCAmelCase		:					Union[str, 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		:					Tuple  	=     in_proj_weight[:2_56, :]
          lowerCAmelCase		:					Optional[int]  	=     in_proj_bias[:2_56]
          lowerCAmelCase		:					List[str]  	=     in_proj_weight[2_56:5_12, :]
          lowerCAmelCase		:					str  	=     in_proj_bias[2_56:5_12]
          lowerCAmelCase		:					Optional[Any]  	=     in_proj_weight[-2_56:, :]
          lowerCAmelCase		:					Dict  	=     in_proj_bias[-2_56:]
     # 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		:					Optional[int]  	=     state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''  )
          lowerCAmelCase		:					Optional[Any]  	=     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		:					Tuple  	=     in_proj_weight[:2_56, :]
          lowerCAmelCase		:					Any  	=     in_proj_bias[:2_56]
          lowerCAmelCase		:					int  	=     in_proj_weight[2_56:5_12, :]
          lowerCAmelCase		:					Tuple  	=     in_proj_bias[2_56:5_12]
          lowerCAmelCase		:					int  	=     in_proj_weight[-2_56:, :]
          lowerCAmelCase		:					List[str]  	=     in_proj_bias[-2_56:]
          # read in weights + bias of input projection layer of cross-attention
          lowerCAmelCase		:					str  	=     state_dict.pop(
              f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''  )
          lowerCAmelCase		:					Tuple  	=     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		:					Optional[int]  	=     in_proj_weight_cross_attn[:2_56, :]
          lowerCAmelCase		:					Any  	=     in_proj_bias_cross_attn[:2_56]
          lowerCAmelCase		:					str  	=     in_proj_weight_cross_attn[2_56:5_12, :]
          lowerCAmelCase		:					Dict  	=     in_proj_bias_cross_attn[2_56:5_12]
          lowerCAmelCase		:					Any  	=     in_proj_weight_cross_attn[-2_56:, :]
          lowerCAmelCase		:					str  	=     in_proj_bias_cross_attn[-2_56:]
def     UpperCAmelCase__							(       ):


     '''simple docstring'''

     lowerCAmelCase		:					Dict  	=     '''http://images.cocodataset.org/val2017/000000039769.jpg'''
     lowerCAmelCase		:					Optional[int]  	=     Image.open(requests.get(__magic_name__							,  stream=__magic_name__  ).raw  )

     return im
@torch.no_grad()
def     UpperCAmelCase__							(       __magic_name__						:     Tuple							,  __magic_name__						:     Optional[int]  ):


     '''simple docstring'''

     lowerCAmelCase		:					Dict  	=     DetrConfig()
     # set backbone and dilation attributes
     if "resnet101" in model_name:
          lowerCAmelCase		:					Union[str, Any]  	=     '''resnet101'''
     if "dc5" in model_name:
          lowerCAmelCase		:					Any  	=     True
     lowerCAmelCase		:					List[Any]  	=     '''panoptic''' in model_name
     if is_panoptic:
          lowerCAmelCase		:					List[str]  	=     2_50
     else:
          lowerCAmelCase		:					Dict  	=     91
          lowerCAmelCase		:					List[Any]  	=     '''huggingface/label-files'''
          lowerCAmelCase		:					List[str]  	=     '''coco-detection-id2label.json'''
          lowerCAmelCase		:					Optional[Any]  	=     json.load(open(hf_hub_download(__magic_name__							,  __magic_name__							,  repo_type='''dataset'''  )							,  '''r'''  )  )
          lowerCAmelCase		:					int  	=     {int(__magic_name__  ): v for k, v in idalabel.items()}
          lowerCAmelCase		:					int  	=     idalabel
          lowerCAmelCase		:					str  	=     {v: k for k, v in idalabel.items()}

     # load image processor
     lowerCAmelCase		:					Any  	=     '''coco_panoptic''' if is_panoptic else '''coco_detection'''
     lowerCAmelCase		:					Dict  	=     DetrImageProcessor(format=__magic_name__  )

     # prepare image
     lowerCAmelCase		:					Optional[Any]  	=     prepare_img()
     lowerCAmelCase		:					Optional[int]  	=     image_processor(images=__magic_name__							,  return_tensors='''pt'''  )
     lowerCAmelCase		:					Union[str, Any]  	=     encoding['''pixel_values''']

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

     # load original model from torch hub
     lowerCAmelCase		:					Tuple  	=     torch.hub.load('''facebookresearch/detr'''							,  __magic_name__							,  pretrained=__magic_name__  ).eval()
     lowerCAmelCase		:					List[str]  	=     detr.state_dict()
     # rename keys
     for src, dest in rename_keys:
          if is_panoptic:
               lowerCAmelCase		:					Union[str, Any]  	=     '''detr.''' + src
          rename_key(__magic_name__							,  __magic_name__							,  __magic_name__  )
     lowerCAmelCase		:					str  	=     rename_backbone_keys(__magic_name__  )
     # query, key and value matrices need special treatment
     read_in_q_k_v(__magic_name__							,  is_panoptic=__magic_name__  )
     # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
     lowerCAmelCase		:					Union[str, Any]  	=     '''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(__magic_name__  )
                    lowerCAmelCase		:					Union[str, Any]  	=     val
               elif "class_labels_classifier" in key or "bbox_predictor" in key:
                    lowerCAmelCase		:					Tuple  	=     state_dict.pop(__magic_name__  )
                    lowerCAmelCase		:					Optional[int]  	=     val
               elif key.startswith('''bbox_attention'''  ) or key.startswith('''mask_head'''  ):
                    continue
               else:
                    lowerCAmelCase		:					Any  	=     state_dict.pop(__magic_name__  )
                    lowerCAmelCase		:					int  	=     val
          else:
               if not key.startswith('''class_labels_classifier'''  ) and not key.startswith('''bbox_predictor'''  ):
                    lowerCAmelCase		:					Any  	=     state_dict.pop(__magic_name__  )
                    lowerCAmelCase		:					Optional[Any]  	=     val
    # finally, create HuggingFace model and load state dict
     lowerCAmelCase		:					List[Any]  	=     DetrForSegmentation(__magic_name__  ) if is_panoptic else DetrForObjectDetection(__magic_name__  )
     model.load_state_dict(__magic_name__  )
     model.eval()
     # verify our conversion
     lowerCAmelCase		:					Union[str, Any]  	=     detr(__magic_name__  )
     lowerCAmelCase		:					int  	=     model(__magic_name__  )
     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(__magic_name__  ).mkdir(exist_ok=__magic_name__  )
     model.save_pretrained(__magic_name__  )
     image_processor.save_pretrained(__magic_name__  )


if __name__ == "__main__":
  __SCREAMING_SNAKE_CASE			:   Optional[Any]         =     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.'
  )
  __SCREAMING_SNAKE_CASE			:   Union[str, Any]         =     parser.parse_args()
  convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)


import os
from shutil import copyfile
from typing import TYPE_CHECKING, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
from ...utils.versions import require_version


if TYPE_CHECKING:
		from transformers.pipelines.conversational import Conversation

require_version('tokenizers>=0.13.3')

if is_sentencepiece_available():
		from .tokenization_llama import LlamaTokenizer
else:
		__SCREAMING_SNAKE_CASE			:   int         =     None

__SCREAMING_SNAKE_CASE			:   List[Any]         =     logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE			:   Optional[int]         =     {'vocab_file': 'tokenizer.model', 'tokenizer_file': 'tokenizer.json'}

__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE			:   Dict         =     '[INST]', '[/INST]'
__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE			:   List[Any]         =     '<<SYS>>\n', '\n<</SYS>>\n\n'

# fmt: off
__SCREAMING_SNAKE_CASE			:   List[Any]         =     'You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\\nanswers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\\nthat your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not\\ncorrect. If you don\'t know the answer to a question, please don\'t share false information.'


class 		__magic_name__   (						snake_case    ):
		_lowerCAmelCase		=	VOCAB_FILES_NAMES
		_lowerCAmelCase		=	LlamaTokenizer
		_lowerCAmelCase		=	"left"
		_lowerCAmelCase		=	["input_ids", "attention_mask"]





		def __init__(							self    :	List[Any]						,  lowerCamelCase__    :	int=None						,  lowerCamelCase__    :	Union[str, Any]=None						,  lowerCamelCase__    :	List[str]=False						,  lowerCamelCase__    :	int="<unk>"						,  lowerCamelCase__    :	int="<s>"						,  lowerCamelCase__    :	str="</s>"						,  lowerCamelCase__    :	Optional[Any]=True						,  lowerCamelCase__    :	int=False						,  **lowerCamelCase__    :	List[str]						,  ):
							super().__init__(
							    vocab_file=lowerCamelCase__						,  tokenizer_file=lowerCamelCase__						,  clean_up_tokenization_spaces=lowerCamelCase__						,  unk_token=lowerCamelCase__						,  bos_token=lowerCamelCase__						,  eos_token=lowerCamelCase__						,  **lowerCamelCase__						,  )
							lowerCAmelCase		:					Optional[int]  	=     add_bos_token
							lowerCAmelCase		:					int  	=     add_eos_token
							self.update_post_processor()

							lowerCAmelCase		:					List[Any]  	=     vocab_file
							lowerCAmelCase		:					List[str]  	=     False if not self.vocab_file else True





		def       _A							(							self    :	Tuple				):
							lowerCAmelCase		:					List[str]  	=     self.bos_token
							lowerCAmelCase		:					Optional[Any]  	=     self.bos_token_id

							lowerCAmelCase		:					Optional[Any]  	=     self.eos_token
							lowerCAmelCase		:					List[str]  	=     self.eos_token_id

							lowerCAmelCase		:					Dict  	=     f'''{(bos+":0 ") * self.add_bos_token}$A:0{(" "+eos+":0") * self.add_eos_token}'''
							lowerCAmelCase		:					Optional[int]  	=     f'''{single}{(" "+bos+":1") * self.add_bos_token} $B:1{(" "+eos+":1") * self.add_eos_token}'''

							lowerCAmelCase		:					Any  	=     []
							if self.add_bos_token:
												special_tokens.append((bos, bos_token_id)				)
							if self.add_eos_token:
												special_tokens.append((eos, eos_token_id)				)
							lowerCAmelCase		:					Union[str, Any]  	=     processors.TemplateProcessing(
							    single=lowerCamelCase__						,  pair=lowerCamelCase__						,  special_tokens=lowerCamelCase__				)





		@property
		def       _A							(							self    :	str				):
							return self._add_eos_token





		@property
		def       _A							(							self    :	Dict				):
							return self._add_bos_token





		@add_eos_token.setter
		def       _A							(							self    :	Union[str, Any]						,  lowerCamelCase__    :	str				):
							lowerCAmelCase		:					Any  	=     value
							self.update_post_processor()





		@add_bos_token.setter
		def       _A							(							self    :	Dict						,  lowerCamelCase__    :	Optional[Any]				):
							lowerCAmelCase		:					List[Any]  	=     value
							self.update_post_processor()





		def       _A							(							self    :	int						,  lowerCamelCase__    :	str						,  lowerCamelCase__    :	Optional[str] = None				):
							if not self.can_save_slow_tokenizer:
												raise ValueError(
												    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
												    '''tokenizer.'''				)

							if not os.path.isdir(lowerCamelCase__				):
												logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''				)
												return
							lowerCAmelCase		:					str  	=     os.path.join(
							    lowerCamelCase__						,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)

							if os.path.abspath(self.vocab_file				) != os.path.abspath(lowerCamelCase__				):
												copyfile(self.vocab_file						,  lowerCamelCase__				)

							return (out_vocab_file,)





		def       _A							(							self    :	Any						,  lowerCamelCase__    :	"Conversation"				):
							lowerCAmelCase		:					int  	=     list(conversation.iter_texts()				)
							if not all([is_user for is_user, msg in dialogue[::2]]				) or not all(
							    [not is_user for is_user, msg in dialogue[1::2]]				):
												raise ValueError(
												    '''The model only supports \'user\' and \'assistant\' roles, starting with user and alternating (u/a/u/a/u...)'''				)

							lowerCAmelCase		:					List[str]  	=     []
							if len(conversation.past_user_inputs				) > 0:
												if not conversation.past_user_inputs[0].startswith(lowerCamelCase__				) or E_SYS not in conversation.past_user_inputs[0]:
																	lowerCAmelCase		:					Tuple  	=     (
																	    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
																	)
							elif not dialogue[0][1].startswith(lowerCamelCase__				) or E_SYS not in dialogue[0][1]:
												lowerCAmelCase		:					Optional[Any]  	=     (dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

							dialog_tokens += sum(
							    [
							        [self.bos_token_id]
							        + self.encode(
							            f'''{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} '''						,  add_special_tokens=lowerCamelCase__				)
							        + [self.eos_token_id]
							        for prompt, answer in zip(dialogue[::2]						,  dialogue[1::2]				)
							    ]						,  []						,  )
							if not (dialogue[-1][0]):
												raise ValueError(f'''Last message must be from user, got {dialogue[-1]["role"]}'''				)
							dialog_tokens += [self.bos_token_id] + self.encode(
							    f'''{B_INST} {(dialogue[-1][1]).strip()} {E_INST}'''						,  add_special_tokens=lowerCamelCase__				)
							return dialog_tokens
