


"""simple docstring"""
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					:       Union[str, Any]							   =							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[Any]							   =							[]
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 						lowercase				(	_snake_case		: List[str]	,  _snake_case		: str	,  _snake_case		: List[Any]	)	->str:
	"""simple docstring"""





	__snake_case      :		List[Any]         =							state_dict.pop(_snake_case	)
	__snake_case      :		Dict         =							val






def 						lowercase				(	_snake_case		: int	)	->int:
	"""simple docstring"""





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

	return new_state_dict






def 						lowercase				(	_snake_case		: int	,  _snake_case		: Dict=False	)	->int:
	"""simple docstring"""





	__snake_case      :		Dict         =							''''''
	if is_panoptic:
		__snake_case      :		Dict         =							'''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)
		__snake_case      :		Union[str, Any]         =							state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""	)
		__snake_case      :		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
		__snake_case      :		Optional[Any]         =							in_proj_weight[:256, :]
		__snake_case      :		int         =							in_proj_bias[:256]
		__snake_case      :		Tuple         =							in_proj_weight[256:512, :]
		__snake_case      :		Tuple         =							in_proj_bias[256:512]
		__snake_case      :		List[str]         =							in_proj_weight[-256:, :]
		__snake_case      :		List[Any]         =							in_proj_bias[-256:]
	# 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
		__snake_case      :		List[str]         =							state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""	)
		__snake_case      :		Tuple         =							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
		__snake_case      :		Union[str, Any]         =							in_proj_weight[:256, :]
		__snake_case      :		int         =							in_proj_bias[:256]
		__snake_case      :		List[str]         =							in_proj_weight[256:512, :]
		__snake_case      :		str         =							in_proj_bias[256:512]
		__snake_case      :		List[Any]         =							in_proj_weight[-256:, :]
		__snake_case      :		Tuple         =							in_proj_bias[-256:]
		# read in weights + bias of input projection layer of cross-attention
		__snake_case      :		str         =							state_dict.pop(
		    f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""	)
		__snake_case      :		List[Any]         =							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
		__snake_case      :		str         =							in_proj_weight_cross_attn[:256, :]
		__snake_case      :		Optional[int]         =							in_proj_bias_cross_attn[:256]
		__snake_case      :		Dict         =							in_proj_weight_cross_attn[256:512, :]
		__snake_case      :		Union[str, Any]         =							in_proj_bias_cross_attn[256:512]
		__snake_case      :		Dict         =							in_proj_weight_cross_attn[-256:, :]
		__snake_case      :		Any         =							in_proj_bias_cross_attn[-256:]






def 						lowercase				(	)	->List[str]:
	"""simple docstring"""





	__snake_case      :		Union[str, Any]         =							'''http://images.cocodataset.org/val2017/000000039769.jpg'''
	__snake_case      :		int         =							Image.open(requests.get(_snake_case	,  stream=_snake_case	).raw	)

	return im






@torch.no_grad()
def 						lowercase				(	_snake_case		: int	,  _snake_case		: Union[str, Any]	)	->Dict:
	"""simple docstring"""





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

	# load image processor
	__snake_case      :		Any         =							'''coco_panoptic''' if is_panoptic else '''coco_detection'''
	__snake_case      :		List[str]         =							DetrImageProcessor(format=_snake_case	)

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

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

	# load original model from torch hub
	__snake_case      :		List[Any]         =							torch.hub.load('''facebookresearch/detr'''	,  _snake_case	,  pretrained=_snake_case	).eval()
	__snake_case      :		Optional[int]         =							detr.state_dict()
	# rename keys
	for src, dest in rename_keys:
		if is_panoptic:
			__snake_case      :		List[str]         =							'''detr.''' + src
		rename_key(_snake_case	,  _snake_case	,  _snake_case	)
	__snake_case      :		str         =							rename_backbone_keys(_snake_case	)
	# query, key and value matrices need special treatment
	read_in_q_k_v(_snake_case	,  is_panoptic=_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
	__snake_case      :		List[str]         =							'''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'''	)
			):
				__snake_case      :		int         =							state_dict.pop(_snake_case	)
				__snake_case      :		Any         =							val
			elif "class_labels_classifier" in key or "bbox_predictor" in key:
				__snake_case      :		Tuple         =							state_dict.pop(_snake_case	)
				__snake_case      :		Any         =							val
			elif key.startswith('''bbox_attention'''	) or key.startswith('''mask_head'''	):
				continue
			else:
				__snake_case      :		Any         =							state_dict.pop(_snake_case	)
				__snake_case      :		List[str]         =							val
		else:
			if not key.startswith('''class_labels_classifier'''	) and not key.startswith('''bbox_predictor'''	):
				__snake_case      :		List[Any]         =							state_dict.pop(_snake_case	)
				__snake_case      :		int         =							val
    # finally, create HuggingFace model and load state dict
	__snake_case      :		Union[str, Any]         =							DetrForSegmentation(_snake_case	) if is_panoptic else DetrForObjectDetection(_snake_case	)
	model.load_state_dict(_snake_case	)
	model.eval()
	# verify our conversion
	__snake_case      :		int         =							detr(_snake_case	)
	__snake_case      :		Any         =							model(_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(_snake_case	).mkdir(exist_ok=_snake_case	)
	model.save_pretrained(_snake_case	)
	image_processor.save_pretrained(_snake_case	)


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE					:       Optional[int]							   =							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					:       Any							   =							parser.parse_args()
			convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)








import json
import os
import re
import warnings
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


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

UpperCAmelCase__			:  int					     =     {
    """source_spm""": """source.spm""",
    """target_spm""": """target.spm""",
    """vocab""": """vocab.json""",
    """target_vocab_file""": """target_vocab.json""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

UpperCAmelCase__			:  Tuple					     =     {
    """source_spm""": {
        """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm"""
    },
    """target_spm""": {
        """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm"""
    },
    """vocab""": {
        """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json"""
    },
    """tokenizer_config_file""": {
        """Helsinki-NLP/opus-mt-en-de""": (
            """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json"""
        )
    },
}

UpperCAmelCase__			:  int					     =     {"""Helsinki-NLP/opus-mt-en-de""": 512}
UpperCAmelCase__			:  Union[str, Any]					     =     {}







class 							a__							( UpperCAmelCase							):







  """simple docstring"""




  UpperCAmelCase__   :			Union[str, Any]      							=VOCAB_FILES_NAMES
  UpperCAmelCase__   :			List[Any]      							=PRETRAINED_VOCAB_FILES_MAP
  UpperCAmelCase__   :			int      							=PRETRAINED_INIT_CONFIGURATION
  UpperCAmelCase__   :			int      							=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  UpperCAmelCase__   :			Tuple      							=["""input_ids""", """attention_mask"""]
  UpperCAmelCase__   :			Tuple      							=re.compile(""">>.+<<"""							)  # type: re.Pattern


  def __init__(      self     :			Dict					,					UpperCAmelCase__     :			str					,					UpperCAmelCase__     :			Optional[Any]					,					UpperCAmelCase__     :			List[str]					,					UpperCAmelCase__     :			Union[str, Any]=None					,					UpperCAmelCase__     :			Optional[Any]=None					,					UpperCAmelCase__     :			Tuple=None					,					UpperCAmelCase__     :			Optional[Any]="<unk>"					,					UpperCAmelCase__     :			List[str]="</s>"					,					UpperCAmelCase__     :			Any="<pad>"					,					UpperCAmelCase__     :			Union[str, Any]=5_1_2					,					UpperCAmelCase__     :			Optional[Dict[str, Any]] = None					,					UpperCAmelCase__     :			Dict=False					,					**UpperCAmelCase__     :			List[str]					,					)       ->None:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   List[str]	  = {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            # bos_token=bos_token,  unused. Start decoding with config.decoder_start_token_id
            source_lang=UpperCAmelCase__					,					target_lang=UpperCAmelCase__					,					unk_token=UpperCAmelCase__					,					eos_token=UpperCAmelCase__					,					pad_token=UpperCAmelCase__					,					model_max_length=UpperCAmelCase__					,					sp_model_kwargs=self.sp_model_kwargs					,					target_vocab_file=UpperCAmelCase__					,					separate_vocabs=UpperCAmelCase__					,					**UpperCAmelCase__					,					)
        assert Path(UpperCAmelCase__		).exists(), f"cannot find spm source {source_spm}"

        SCREAMING_SNAKE_CASE			:   Any	  = separate_vocabs
        SCREAMING_SNAKE_CASE			:   Optional[int]	  = load_json(UpperCAmelCase__		)
        if self.unk_token not in self.encoder:
              raise KeyError("""<unk> token must be in vocab"""		)
        assert self.pad_token in self.encoder

        if separate_vocabs:
              SCREAMING_SNAKE_CASE			:   str	  = load_json(UpperCAmelCase__		)
              SCREAMING_SNAKE_CASE			:   str	  = {v: k for k, v in self.target_encoder.items()}
              SCREAMING_SNAKE_CASE			:   Tuple	  = []
        else:
              SCREAMING_SNAKE_CASE			:   List[str]	  = {v: k for k, v in self.encoder.items()}
              SCREAMING_SNAKE_CASE			:   list	  = [k for k in self.encoder if k.startswith(""">>"""		) and k.endswith("""<<"""		)]

        SCREAMING_SNAKE_CASE			:   Dict	  = source_lang
        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = target_lang
        SCREAMING_SNAKE_CASE			:   str	  = [source_spm, target_spm]

        # load SentencePiece model for pre-processing
        SCREAMING_SNAKE_CASE			:   int	  = load_spm(UpperCAmelCase__					,					self.sp_model_kwargs		)
        SCREAMING_SNAKE_CASE			:   Dict	  = load_spm(UpperCAmelCase__					,					self.sp_model_kwargs		)
        SCREAMING_SNAKE_CASE			:   Optional[int]	  = self.spm_source
        SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = self.encoder

        # Multilingual target side: default to using first supported language code.

        self._setup_normalizer()


  def 			_lowercase   (      self     :			Optional[int]		)       ->Union[str, Any]:
        """simple docstring"""







        try:
              from sacremoses import MosesPunctNormalizer

              SCREAMING_SNAKE_CASE			:   Optional[int]	  = MosesPunctNormalizer(self.source_lang		).normalize
        except (ImportError, FileNotFoundError):
              warnings.warn("""Recommended: pip install sacremoses."""		)
              SCREAMING_SNAKE_CASE			:   Optional[int]	  = lambda UpperCAmelCase__		: x


  def 			_lowercase   (      self     :			Tuple					,					UpperCAmelCase__     :			str		)       ->str:
        """simple docstring"""







        return self.punc_normalizer(UpperCAmelCase__		) if x else ""


  def 			_lowercase   (      self     :			Union[str, Any]					,					UpperCAmelCase__     :			Tuple		)       ->Dict:
        """simple docstring"""







        return self.current_encoder.get(UpperCAmelCase__					,					self.current_encoder[self.unk_token]		)


  def 			_lowercase   (      self     :			Any					,					UpperCAmelCase__     :			str		)       ->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   Dict	  = self.language_code_re.match(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE			:   list	  = [match.group(0		)] if match else []
        return code, self.language_code_re.sub(""""""					,					UpperCAmelCase__		)


  def 			_lowercase   (      self     :			List[str]					,					UpperCAmelCase__     :			str		)       ->List[str]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE			:   str	  = self.remove_language_code(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE			:   int	  = self.current_spm.encode(UpperCAmelCase__					,					out_type=UpperCAmelCase__		)
        return code + pieces


  def 			_lowercase   (      self     :			Dict					,					UpperCAmelCase__     :			int		)       ->str:
        """simple docstring"""







        return self.decoder.get(UpperCAmelCase__					,					self.unk_token		)


  def 			_lowercase   (      self     :			List[Any]					,					UpperCAmelCase__     :			List[str]					,					**UpperCAmelCase__     :			List[Any]		)       ->Dict:
        """simple docstring"""







        return super().batch_decode(UpperCAmelCase__					,					**UpperCAmelCase__		)


  def 			_lowercase   (      self     :			Tuple					,					UpperCAmelCase__     :			str					,					**UpperCAmelCase__     :			List[Any]		)       ->Optional[int]:
        """simple docstring"""







        return super().decode(UpperCAmelCase__					,					**UpperCAmelCase__		)


  def 			_lowercase   (      self     :			int					,					UpperCAmelCase__     :			List[str]		)       ->str:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   Tuple	  = self.spm_source if self._decode_use_source_tokenizer else self.spm_target
        SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = []
        SCREAMING_SNAKE_CASE			:   Tuple	  = """"""
        for token in tokens:
              # make sure that special tokens are not decoded using sentencepiece model
              if token in self.all_special_tokens:
                    out_string += sp_model.decode_pieces(UpperCAmelCase__		) + token + " "
                    SCREAMING_SNAKE_CASE			:   List[str]	  = []
              else:
                    current_sub_tokens.append(UpperCAmelCase__		)
        out_string += sp_model.decode_pieces(UpperCAmelCase__		)
        return out_string.strip()


  def 			_lowercase   (      self     :			int					,					UpperCAmelCase__     :			Tuple					,					UpperCAmelCase__     :			Union[str, Any]=None		)       ->List[int]:
        """simple docstring"""







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


  def 			_lowercase   (      self     :			Tuple		)       ->Tuple:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   int	  = self.spm_source
        SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = self.encoder


  def 			_lowercase   (      self     :			Optional[int]		)       ->Union[str, Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   List[Any]	  = self.spm_target
        if self.separate_vocabs:
              SCREAMING_SNAKE_CASE			:   List[Any]	  = self.target_encoder


  @property
  def 			_lowercase   (      self     :			List[Any]		)       ->int:
        """simple docstring"""







        return len(self.encoder		)


  def 			_lowercase   (      self     :			Tuple					,					UpperCAmelCase__     :			str					,					UpperCAmelCase__     :			Optional[str] = None		)       ->Tuple[str]:
        """simple docstring"""







        if not os.path.isdir(UpperCAmelCase__		):
              logger.error(f"Vocabulary path ({save_directory}) should be a directory"		)
              return
        SCREAMING_SNAKE_CASE			:   Tuple	  = []

        if self.separate_vocabs:
              SCREAMING_SNAKE_CASE			:   Dict	  = os.path.join(
                  UpperCAmelCase__					,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab"""]					,					)
              SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = os.path.join(
                  UpperCAmelCase__					,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""target_vocab_file"""]					,					)
              save_json(self.encoder					,					UpperCAmelCase__		)
              save_json(self.target_encoder					,					UpperCAmelCase__		)
              saved_files.append(UpperCAmelCase__		)
              saved_files.append(UpperCAmelCase__		)
        else:
              SCREAMING_SNAKE_CASE			:   Tuple	  = os.path.join(
                  UpperCAmelCase__					,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab"""]		)
              save_json(self.encoder					,					UpperCAmelCase__		)
              saved_files.append(UpperCAmelCase__		)

        for spm_save_filename, spm_orig_path, spm_model in zip(
            [VOCAB_FILES_NAMES["""source_spm"""], VOCAB_FILES_NAMES["""target_spm"""]]					,					self.spm_files					,					[self.spm_source, self.spm_target]					,					):
              SCREAMING_SNAKE_CASE			:   Any	  = os.path.join(
                  UpperCAmelCase__					,					(filename_prefix + """-""" if filename_prefix else """""") + spm_save_filename		)
              if os.path.abspath(UpperCAmelCase__		) != os.path.abspath(UpperCAmelCase__		) and os.path.isfile(UpperCAmelCase__		):
                    copyfile(UpperCAmelCase__					,					UpperCAmelCase__		)
                    saved_files.append(UpperCAmelCase__		)
              elif not os.path.isfile(UpperCAmelCase__		):
                    with open(UpperCAmelCase__					,					"""wb"""		) as fi:
                          SCREAMING_SNAKE_CASE			:   List[str]	  = spm_model.serialized_model_proto()
                          fi.write(UpperCAmelCase__		)
                    saved_files.append(UpperCAmelCase__		)

        return tuple(UpperCAmelCase__		)


  def 			_lowercase   (      self     :			int		)       ->Dict:
        """simple docstring"""







        return self.get_src_vocab()


  def 			_lowercase   (      self     :			str		)       ->Union[str, Any]:
        """simple docstring"""







        return dict(self.encoder					,					**self.added_tokens_encoder		)


  def 			_lowercase   (      self     :			str		)       ->Tuple:
        """simple docstring"""







        return dict(self.target_encoder					,					**self.added_tokens_decoder		)


  def __getstate__(      self     :			Optional[int]		)       ->Dict:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   Dict	  = self.__dict__.copy()
        state.update(
            {k: None for k in ["""spm_source""", """spm_target""", """current_spm""", """punc_normalizer""", """target_vocab_file"""]}		)
        return state


  def __setstate__(      self     :			List[Any]					,					UpperCAmelCase__     :			Dict		)       ->None:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   List[str]	  = d

        # for backward compatibility
        if not hasattr(self					,					"""sp_model_kwargs"""		):
              SCREAMING_SNAKE_CASE			:   Optional[int]	  = {}

        SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE			:   List[Any]	  = (load_spm(UpperCAmelCase__					,					self.sp_model_kwargs		) for f in self.spm_files)
        SCREAMING_SNAKE_CASE			:   Any	  = self.spm_source
        self._setup_normalizer()


  def 			_lowercase   (      self     :			Dict					,					*UpperCAmelCase__     :			str					,					**UpperCAmelCase__     :			List[str]		)       ->Dict:
        """simple docstring"""







        return 1


  def 			_lowercase   (      self     :			List[Any]					,					UpperCAmelCase__     :			Dict		)       ->List[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = set(self.all_special_ids		)  # call it once instead of inside list comp
        all_special_ids.remove(self.unk_token_id		)  # <unk> is only sometimes special
        return [1 if x in all_special_ids else 0 for x in seq]




  def 			_lowercase   (      self     :			Tuple					,					UpperCAmelCase__     :			List					,					UpperCAmelCase__     :			Optional[List] = None					,					UpperCAmelCase__     :			bool = False		)       ->List[int]:
        """simple docstring"""







        if already_has_special_tokens:
              return self._special_token_mask(UpperCAmelCase__		)
        elif token_ids_a is None:
              return self._special_token_mask(UpperCAmelCase__		) + [1]
        else:
              return self._special_token_mask(token_ids_a + token_ids_a		) + [1]






def        __lowercase   (  _A    ,     _A      )  ->    sentencepiece.SentencePieceProcessor:
      SCREAMING_SNAKE_CASE			:   List[str]	  = sentencepiece.SentencePieceProcessor(**_A      )
      spm.Load(_A      )
      return spm






def        __lowercase   (  _A    ,     _A      )  ->    None:
      with open(_A    ,     """w"""      ) as f:
            json.dump(_A    ,     _A    ,     indent=2      )







def        __lowercase   (  _A      )  ->    Union[Dict, List]:
      with open(_A    ,     """r"""      ) as f:
            return json.load(_A      )




