


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

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
     from .tokenization_mbartaa import MBartaaTokenizer
else:
     __UpperCAmelCase      			=  None


__UpperCAmelCase      			=  logging.get_logger(__name__)

__UpperCAmelCase      			=  {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

__UpperCAmelCase      			=  {
    """vocab_file""": {
        """facebook/mbart-large-50-one-to-many-mmt""": (
            """https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """facebook/mbart-large-50-one-to-many-mmt""": (
            """https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json"""
        ),
    },
}

__UpperCAmelCase      			=  {
    """facebook/mbart-large-50-one-to-many-mmt""": 1024,
}

# fmt: off
__UpperCAmelCase      			=  ["""ar_AR""", """cs_CZ""", """de_DE""", """en_XX""", """es_XX""", """et_EE""", """fi_FI""", """fr_XX""", """gu_IN""", """hi_IN""", """it_IT""", """ja_XX""", """kk_KZ""", """ko_KR""", """lt_LT""", """lv_LV""", """my_MM""", """ne_NP""", """nl_XX""", """ro_RO""", """ru_RU""", """si_LK""", """tr_TR""", """vi_VN""", """zh_CN""", """af_ZA""", """az_AZ""", """bn_IN""", """fa_IR""", """he_IL""", """hr_HR""", """id_ID""", """ka_GE""", """km_KH""", """mk_MK""", """ml_IN""", """mn_MN""", """mr_IN""", """pl_PL""", """ps_AF""", """pt_XX""", """sv_SE""", """sw_KE""", """ta_IN""", """te_IN""", """th_TH""", """tl_XX""", """uk_UA""", """ur_PK""", """xh_ZA""", """gl_ES""", """sl_SI"""]



class 							lowercase__(						__lowercase			):






      '''simple docstring'''







      snake_case__       =					VOCAB_FILES_NAMES
      snake_case__       =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      snake_case__       =					PRETRAINED_VOCAB_FILES_MAP
      snake_case__       =					['input_ids', 'attention_mask']
      snake_case__       =					MBartaaTokenizer

      snake_case__       =					[]
      snake_case__       =					[]




      def __init__(		self   ,			__SCREAMING_SNAKE_CASE=None   ,			__SCREAMING_SNAKE_CASE=None   ,			__SCREAMING_SNAKE_CASE=None   ,			__SCREAMING_SNAKE_CASE=None   ,			__SCREAMING_SNAKE_CASE="</s>"   ,			__SCREAMING_SNAKE_CASE="</s>"   ,			__SCREAMING_SNAKE_CASE="<s>"   ,			__SCREAMING_SNAKE_CASE="<unk>"   ,			__SCREAMING_SNAKE_CASE="<pad>"   ,			__SCREAMING_SNAKE_CASE="<mask>"   ,			**__SCREAMING_SNAKE_CASE   ,			)					->						Tuple:







         """simple docstring"""







         UpperCamelCase__ :			Optional[int] 						=AddedToken(__A   ,			lstrip=__A   ,			rstrip=__A) if isinstance(__A   ,			__A) else mask_token

         UpperCamelCase__ :			Optional[Any] 						=kwargs.get("additional_special_tokens"   ,			[])
         kwargs["additional_special_tokens"] += [
             code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
         ]

         super().__init__(
             __A   ,			src_lang=__A   ,			tgt_lang=__A   ,			tokenizer_file=__A   ,			eos_token=__A   ,			sep_token=__A   ,			cls_token=__A   ,			unk_token=__A   ,			pad_token=__A   ,			mask_token=__A   ,			**__A   ,			)

         UpperCamelCase__ :			List[Any] 						=vocab_file
         UpperCamelCase__ :			Optional[Any] 						=False if not self.vocab_file else True

         UpperCamelCase__ :			Dict 						={
             lang_code: self.convert_tokens_to_ids(__A) for lang_code in FAIRSEQ_LANGUAGE_CODES
         }

         UpperCamelCase__ :			Optional[int] 						=src_lang if src_lang is not None else "en_XX"
         UpperCamelCase__ :			Optional[int] 						=tgt_lang
         UpperCamelCase__ :			List[Any] 						=self.lang_code_to_id[self._src_lang]
         self.set_src_lang_special_tokens(self._src_lang)




      @property
      def    UpperCAmelCase							(		self)					->						str:







         """simple docstring"""







         return self._src_lang




      @src_lang.setter
      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE)					->						None:







         """simple docstring"""







         UpperCamelCase__ :			Any 						=new_src_lang
         self.set_src_lang_special_tokens(self._src_lang)




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE = None)					->						List[int]:







         """simple docstring"""







         if token_ids_a is None:
            return self.prefix_tokens + token_ids_a + self.suffix_tokens
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE = "en_XX"   ,			__SCREAMING_SNAKE_CASE = None   ,			__SCREAMING_SNAKE_CASE = "ro_RO"   ,			**__SCREAMING_SNAKE_CASE   ,			)					->						BatchEncoding:







         """simple docstring"""







         UpperCamelCase__ :			Dict 						=src_lang
         UpperCamelCase__ :			str 						=tgt_lang
         return super().prepare_seqaseq_batch(__A   ,			__A   ,			**__A)




      def    UpperCAmelCase							(		self)					->						Optional[Any]:







         """simple docstring"""







         return self.set_src_lang_special_tokens(self.src_lang)




      def    UpperCAmelCase							(		self)					->						Union[str, Any]:







         """simple docstring"""







         return self.set_tgt_lang_special_tokens(self.tgt_lang)




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE)					->						None:







         """simple docstring"""







         UpperCamelCase__ :			int 						=self.convert_tokens_to_ids(__A)
         UpperCamelCase__ :			Any 						=[self.cur_lang_code_id]
         UpperCamelCase__ :			Union[str, Any] 						=[self.eos_token_id]

         UpperCamelCase__ :			Optional[int] 						=self.convert_ids_to_tokens(self.prefix_tokens)
         UpperCamelCase__ :			List[Any] 						=self.convert_ids_to_tokens(self.suffix_tokens)

         UpperCamelCase__ :			Union[str, Any] 						=processors.TemplateProcessing(
             single=prefix_tokens_str + ["$A"] + suffix_tokens_str   ,			pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str   ,			special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str   ,			self.prefix_tokens + self.suffix_tokens))   ,			)




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE)					->						None:







         """simple docstring"""







         UpperCamelCase__ :			str 						=self.convert_tokens_to_ids(__A)
         UpperCamelCase__ :			Optional[Any] 						=[self.cur_lang_code_id]
         UpperCamelCase__ :			Dict 						=[self.eos_token_id]

         UpperCamelCase__ :			List[Any] 						=self.convert_ids_to_tokens(self.prefix_tokens)
         UpperCamelCase__ :			List[Any] 						=self.convert_ids_to_tokens(self.suffix_tokens)

         UpperCamelCase__ :			Tuple 						=processors.TemplateProcessing(
             single=prefix_tokens_str + ["$A"] + suffix_tokens_str   ,			pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str   ,			special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str   ,			self.prefix_tokens + self.suffix_tokens))   ,			)




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			**__SCREAMING_SNAKE_CASE)					->						Dict:







         """simple docstring"""







         if src_lang is None or tgt_lang is None:
            raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model")
         UpperCamelCase__ :			str 						=src_lang
         UpperCamelCase__ :			Optional[Any] 						=self(__A   ,			add_special_tokens=__A   ,			return_tensors=__A   ,			**__A)
         UpperCamelCase__ :			List[str] 						=self.convert_tokens_to_ids(__A)
         UpperCamelCase__ :			str 						=tgt_lang_id
         return inputs




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE = None)					->						Tuple[str]:







         """simple docstring"""







         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(__A):
            logger.error(F'''Vocabulary path ({save_directory}) should be a directory''')
            return
         UpperCamelCase__ :			Optional[Any] 						=os.path.join(
             __A   ,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])

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

         return (out_vocab_file,)


'''simple docstring'''

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DeformableDetrConfig, DeformableDetrForObjectDetection, DeformableDetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowercase_  		=			logging.get_logger(__name__)



def 				UpperCamelCase__      (      a__   ):


						'''simple docstring'''






						if "backbone.0.body" in orig_key:
												_lowerCAmelCase					    =orig_key.replace('backbone.0.body'      ,   'backbone.conv_encoder.model'   )
						if "transformer" in orig_key:
												_lowerCAmelCase					    =orig_key.replace('transformer.'      ,   ''   )
						if "norm1" in orig_key:
												if "encoder" in orig_key:
																		_lowerCAmelCase					    =orig_key.replace('norm1'      ,   'self_attn_layer_norm'   )
												else:
																		_lowerCAmelCase					    =orig_key.replace('norm1'      ,   'encoder_attn_layer_norm'   )
						if "norm2" in orig_key:
												if "encoder" in orig_key:
																		_lowerCAmelCase					    =orig_key.replace('norm2'      ,   'final_layer_norm'   )
												else:
																		_lowerCAmelCase					    =orig_key.replace('norm2'      ,   'self_attn_layer_norm'   )
						if "norm3" in orig_key:
												_lowerCAmelCase					    =orig_key.replace('norm3'      ,   'final_layer_norm'   )
						if "linear1" in orig_key:
												_lowerCAmelCase					    =orig_key.replace('linear1'      ,   'fc1'   )
						if "linear2" in orig_key:
												_lowerCAmelCase					    =orig_key.replace('linear2'      ,   'fc2'   )
						if "query_embed" in orig_key:
												_lowerCAmelCase					    =orig_key.replace('query_embed'      ,   'query_position_embeddings'   )
						if "cross_attn" in orig_key:
												_lowerCAmelCase					    =orig_key.replace('cross_attn'      ,   'encoder_attn'   )

						return orig_key



def 				UpperCamelCase__      (      a__   ):


						'''simple docstring'''






						for i in range(6   ):
												# read in weights + bias of input projection layer of self-attention
												_lowerCAmelCase					    =state_dict.pop(F'''decoder.layers.{i}.self_attn.in_proj_weight'''   )
												_lowerCAmelCase					    =state_dict.pop(F'''decoder.layers.{i}.self_attn.in_proj_bias'''   )
												# next, add query, keys and values (in that order) to the state dict
												_lowerCAmelCase					    =in_proj_weight[:2_5_6, :]
												_lowerCAmelCase					    =in_proj_bias[:2_5_6]
												_lowerCAmelCase					    =in_proj_weight[2_5_6:5_1_2, :]
												_lowerCAmelCase					    =in_proj_bias[2_5_6:5_1_2]
												_lowerCAmelCase					    =in_proj_weight[-2_5_6:, :]
												_lowerCAmelCase					    =in_proj_bias[-2_5_6:]



def 				UpperCamelCase__      (      ):


						'''simple docstring'''






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

						return im



@torch.no_grad()
def 				UpperCamelCase__      (      a__      ,   a__      ,   a__      ,   a__      ,   a__      ,   a__      ,   a__      ,   ):


						'''simple docstring'''






						_lowerCAmelCase					    =DeformableDetrConfig()
						# set config attributes
						if single_scale:
												_lowerCAmelCase					    =1
						_lowerCAmelCase					    =dilation
						_lowerCAmelCase					    =with_box_refine
						_lowerCAmelCase					    =two_stage
						# set labels
						_lowerCAmelCase					    =9_1
						_lowerCAmelCase					    ='huggingface/label-files'
						_lowerCAmelCase					    ='coco-detection-id2label.json'
						_lowerCAmelCase					    =json.load(open(cached_download(hf_hub_url(a__      ,   a__      ,   repo_type='dataset'   )   )      ,   'r'   )   )
						_lowerCAmelCase					    ={int(a__   ): v for k, v in idalabel.items()}
						_lowerCAmelCase					    =idalabel
						_lowerCAmelCase					    ={v: k for k, v in idalabel.items()}

						# load image processor
						_lowerCAmelCase					    =DeformableDetrImageProcessor(format='coco_detection'   )

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

						logger.info('Converting model...'   )

						# load original state dict
						_lowerCAmelCase					    =torch.load(a__      ,   map_location='cpu'   )['model']
						# rename keys
						for key in state_dict.copy().keys():
												_lowerCAmelCase					    =state_dict.pop(a__   )
												_lowerCAmelCase					    =val
						# query, key and value matrices need special treatment
						read_in_q_k_v(a__   )
						# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
						_lowerCAmelCase					    ='model.'
						for key in state_dict.copy().keys():
												if not key.startswith('class_embed'   ) and not key.startswith('bbox_embed'   ):
																		_lowerCAmelCase					    =state_dict.pop(a__   )
																		_lowerCAmelCase					    =val
    # finally, create HuggingFace model and load state dict
						_lowerCAmelCase					    =DeformableDetrForObjectDetection(a__   )
						model.load_state_dict(a__   )
						model.eval()

						_lowerCAmelCase					    ='cuda' if torch.cuda.is_available() else 'cpu'
						model.to(a__   )
						# verify our conversion
						_lowerCAmelCase					    =model(pixel_values.to(a__   )   )

						_lowerCAmelCase					    =torch.tensor(
						    [[-9.6_645, -4.3_449, -5.8_705], [-9.7_035, -3.8_504, -5.0_724], [-10.5_634, -5.3_379, -7.5_116]]   )
						_lowerCAmelCase					    =torch.tensor([[0.8_693, 0.2_289, 0.2_492], [0.3_150, 0.5_489, 0.5_845], [0.5_563, 0.7_580, 0.8_518]]   )

						if single_scale:
												_lowerCAmelCase					    =torch.tensor(
												    [[-9.9_051, -4.2_541, -6.4_852], [-9.6_947, -4.0_854, -6.8_033], [-10.0_665, -5.8_470, -7.7_003]]   )
												_lowerCAmelCase					    =torch.tensor([[0.7_292, 0.4_991, 0.5_532], [0.7_959, 0.2_426, 0.4_236], [0.7_582, 0.3_518, 0.4_451]]   )

						if single_scale and dilation:
												_lowerCAmelCase					    =torch.tensor(
												    [[-8.9_652, -4.1_074, -5.6_635], [-9.0_596, -4.9_447, -6.6_075], [-10.1_178, -4.5_275, -6.2_671]]   )
												_lowerCAmelCase					    =torch.tensor([[0.7_665, 0.4_130, 0.4_769], [0.8_364, 0.1_841, 0.3_391], [0.6_261, 0.3_895, 0.7_978]]   )

						if with_box_refine:
												_lowerCAmelCase					    =torch.tensor(
												    [[-8.8_895, -5.4_187, -6.8_153], [-8.4_706, -6.1_668, -7.6_184], [-9.0_042, -5.5_359, -6.9_141]]   )
												_lowerCAmelCase					    =torch.tensor([[0.7_828, 0.2_208, 0.4_323], [0.0_892, 0.5_996, 0.1_319], [0.5_524, 0.6_389, 0.8_914]]   )

						if with_box_refine and two_stage:
												_lowerCAmelCase					    =torch.tensor(
												    [[-6.7_108, -4.3_213, -6.3_777], [-8.9_014, -6.1_799, -6.7_240], [-6.9_315, -4.4_735, -6.2_298]]   )
												_lowerCAmelCase					    =torch.tensor([[0.2_583, 0.5_499, 0.4_683], [0.7_652, 0.9_068, 0.4_882], [0.5_490, 0.2_763, 0.0_564]]   )

						print('Logits:'      ,   outputs.logits[0, :3, :3]   )

						assert torch.allclose(outputs.logits[0, :3, :3]      ,   expected_logits.to(a__   )      ,   atol=1E-4   )
						assert torch.allclose(outputs.pred_boxes[0, :3, :3]      ,   expected_boxes.to(a__   )      ,   atol=1E-4   )

						print('Everything ok!'   )

						# Save model and image processor
						logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''   )
						Path(a__   ).mkdir(exist_ok=a__   )
						model.save_pretrained(a__   )
						image_processor.save_pretrained(a__   )

						# Push to hub
						if push_to_hub:
												_lowerCAmelCase					    ='deformable-detr'
												model_name += "-single-scale" if single_scale else ""
												model_name += "-dc5" if dilation else ""
												model_name += "-with-box-refine" if with_box_refine else ""
												model_name += "-two-stage" if two_stage else ""
												print('Pushing model to hub...'   )
												model.push_to_hub(repo_path_or_name=a__      ,   organization='nielsr'      ,   commit_message='Add model'   )


if __name__ == "__main__":
							lowercase_  		=			argparse.ArgumentParser()

							parser.add_argument(
							    '''--checkpoint_path''',
							    type=str,
							    default='''/home/niels/checkpoints/deformable_detr/r50_deformable_detr-checkpoint.pth''',
							    help='''Path to Pytorch checkpoint (.pth file) you\'d like to convert.''',
							)
							parser.add_argument('''--single_scale''', action='''store_true''', help='''Whether to set config.num_features_levels = 1.''')
							parser.add_argument('''--dilation''', action='''store_true''', help='''Whether to set config.dilation=True.''')
							parser.add_argument('''--with_box_refine''', action='''store_true''', help='''Whether to set config.with_box_refine=True.''')
							parser.add_argument('''--two_stage''', action='''store_true''', help='''Whether to set config.two_stage=True.''')
							parser.add_argument(
							    '''--pytorch_dump_folder_path''',
							    default=None,
							    type=str,
							    required=True,
							    help='''Path to the folder to output PyTorch model.''',
							)
							parser.add_argument(
							    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
							)
							lowercase_  		=			parser.parse_args()
							convert_deformable_detr_checkpoint(
							    args.checkpoint_path,
							    args.single_scale,
							    args.dilation,
							    args.with_box_refine,
							    args.two_stage,
							    args.pytorch_dump_folder_path,
							    args.push_to_hub,
							)





