


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()
__snake_case            =   logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__snake_case            =   []
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 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		List[Any]       ,  SCREAMING_SNAKE_CASE_   :		Tuple       ,  SCREAMING_SNAKE_CASE_   :		Optional[int]	):





			"""simple docstring"""




			UpperCamelCase         =					state_dict.pop(SCREAMING_SNAKE_CASE_	)
			UpperCamelCase         =					val






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Optional[Any]	):





			"""simple docstring"""




			UpperCamelCase         =					OrderedDict()
			for key, value in state_dict.items():
						if "backbone.0.body" in key:
									UpperCamelCase         =					key.replace("""backbone.0.body"""       ,  """backbone.conv_encoder.model"""	)
									UpperCamelCase         =					value
						else:
									UpperCamelCase         =					value

			return new_state_dict






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Union[str, Any]       ,  SCREAMING_SNAKE_CASE_   :		Optional[Any]=False	):





			"""simple docstring"""




			UpperCamelCase         =					""""""
			if is_panoptic:
						UpperCamelCase         =					"""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)
						UpperCamelCase         =					state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'	)
						UpperCamelCase         =					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
						UpperCamelCase         =					in_proj_weight[:256, :]
						UpperCamelCase         =					in_proj_bias[:256]
						UpperCamelCase         =					in_proj_weight[256:512, :]
						UpperCamelCase         =					in_proj_bias[256:512]
						UpperCamelCase         =					in_proj_weight[-256:, :]
						UpperCamelCase         =					in_proj_bias[-256:]






def 							_lowercase    (		):





			"""simple docstring"""




			UpperCamelCase         =					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
			UpperCamelCase         =					Image.open(requests.get(SCREAMING_SNAKE_CASE_       ,  stream=SCREAMING_SNAKE_CASE_	).raw	)

			return im






@torch.no_grad()
def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		str       ,  SCREAMING_SNAKE_CASE_   :		str	):





			"""simple docstring"""




			UpperCamelCase         =					ConditionalDetrConfig()
			# set backbone and dilation attributes
			if "resnet101" in model_name:
						UpperCamelCase         =					"""resnet101"""
			if "dc5" in model_name:
						UpperCamelCase         =					True
			UpperCamelCase         =					"""panoptic""" in model_name
			if is_panoptic:
						UpperCamelCase         =					250
			else:
						UpperCamelCase         =					91
						UpperCamelCase         =					"""huggingface/label-files"""
						UpperCamelCase         =					"""coco-detection-id2label.json"""
						UpperCamelCase         =					json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_       ,  repo_type="""dataset"""	)       ,  """r"""	)	)
						UpperCamelCase         =					{int(SCREAMING_SNAKE_CASE_	): v for k, v in idalabel.items()}
						UpperCamelCase         =					idalabel
						UpperCamelCase         =					{v: k for k, v in idalabel.items()}

			# load image processor
			UpperCamelCase         =					"""coco_panoptic""" if is_panoptic else """coco_detection"""
			UpperCamelCase         =					ConditionalDetrImageProcessor(format=SCREAMING_SNAKE_CASE_	)

			# prepare image
			UpperCamelCase         =					prepare_img()
			UpperCamelCase         =					image_processor(images=SCREAMING_SNAKE_CASE_       ,  return_tensors="""pt"""	)
			UpperCamelCase         =					encoding["""pixel_values"""]

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

			# load original model from torch hub
			UpperCamelCase         =					torch.hub.load("""DeppMeng/ConditionalDETR"""       ,  SCREAMING_SNAKE_CASE_       ,  pretrained=SCREAMING_SNAKE_CASE_	).eval()
			UpperCamelCase         =					conditional_detr.state_dict()
			# rename keys
			for src, dest in rename_keys:
						if is_panoptic:
									UpperCamelCase         =					"""conditional_detr.""" + src
						rename_key(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)
			UpperCamelCase         =					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
			UpperCamelCase         =					"""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"""	)
									):
												UpperCamelCase         =					state_dict.pop(SCREAMING_SNAKE_CASE_	)
												UpperCamelCase         =					val
									elif "class_labels_classifier" in key or "bbox_predictor" in key:
												UpperCamelCase         =					state_dict.pop(SCREAMING_SNAKE_CASE_	)
												UpperCamelCase         =					val
									elif key.startswith("""bbox_attention"""	) or key.startswith("""mask_head"""	):
												continue
									else:
												UpperCamelCase         =					state_dict.pop(SCREAMING_SNAKE_CASE_	)
												UpperCamelCase         =					val
						else:
									if not key.startswith("""class_labels_classifier"""	) and not key.startswith("""bbox_predictor"""	):
												UpperCamelCase         =					state_dict.pop(SCREAMING_SNAKE_CASE_	)
												UpperCamelCase         =					val
    # finally, create HuggingFace model and load state dict
			UpperCamelCase         =					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
			UpperCamelCase         =					conditional_detr(SCREAMING_SNAKE_CASE_	)
			UpperCamelCase         =					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__":
			__snake_case            =   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."
			)
			__snake_case            =   parser.parse_args()
			convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)








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_mbart import MBartTokenizer
else:
			__snake_case            =   None


__snake_case            =   logging.get_logger(__name__)


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

__snake_case            =   {
    "vocab_file": {
        "facebook/mbart-large-en-ro": (
            "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"
        ),
        "facebook/mbart-large-cc25": (
            "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "facebook/mbart-large-en-ro": "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json",
        "facebook/mbart-large-cc25": "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json",
    },
}

__snake_case            =   {
    "facebook/mbart-large-en-ro": 1_024,
    "facebook/mbart-large-cc25": 1_024,
}

# fmt: off
__snake_case            =   ["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"]




class    UpperCAmelCase					(       __snake_case    ):
			lowercase						= VOCAB_FILES_NAMES
			lowercase						= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowercase						= PRETRAINED_VOCAB_FILES_MAP
			lowercase						= ["""input_ids""", """attention_mask"""]
			lowercase						= MBartTokenizer

			lowercase						= []
			lowercase						= []




			def __init__(						self							:		Optional[int]       ,       __magic_name__							:		str=None       ,       __magic_name__							:		Optional[int]=None       ,       __magic_name__							:		Dict="<s>"       ,       __magic_name__							:		Union[str, Any]="</s>"       ,       __magic_name__							:		List[str]="</s>"       ,       __magic_name__							:		Optional[int]="<s>"       ,       __magic_name__							:		int="<unk>"       ,       __magic_name__							:		str="<pad>"       ,       __magic_name__							:		List[str]="<mask>"       ,       __magic_name__							:		Tuple=None       ,       __magic_name__							:		Dict=None       ,       __magic_name__							:		Dict=None       ,       **__magic_name__							:		Tuple       ,       ):







						"""simple docstring"""







						UpperCamelCase         =					AddedToken(__magic_name__       ,       lstrip=__magic_name__       ,       rstrip=__magic_name__						) if isinstance(__magic_name__       ,       __magic_name__						) else mask_token

						super().__init__(
						    vocab_file=__magic_name__       ,       tokenizer_file=__magic_name__       ,       bos_token=__magic_name__       ,       eos_token=__magic_name__       ,       sep_token=__magic_name__       ,       cls_token=__magic_name__       ,       unk_token=__magic_name__       ,       pad_token=__magic_name__       ,       mask_token=__magic_name__       ,       src_lang=__magic_name__       ,       tgt_lang=__magic_name__       ,       additional_special_tokens=__magic_name__       ,       **__magic_name__       ,       )

						UpperCamelCase         =					vocab_file
						UpperCamelCase         =					False if not self.vocab_file else True

						UpperCamelCase         =					FAIRSEQ_LANGUAGE_CODES.copy()

						if additional_special_tokens is not None:
									# Only add those special tokens if they are not already there.
									_additional_special_tokens.extend(
									    [t for t in additional_special_tokens if t not in _additional_special_tokens]						)

						self.add_special_tokens({"""additional_special_tokens""": _additional_special_tokens}						)
						UpperCamelCase         =					{
						    lang_code: self.convert_tokens_to_ids(__magic_name__						) for lang_code in FAIRSEQ_LANGUAGE_CODES
						}

						UpperCamelCase         =					src_lang if src_lang is not None else """en_XX"""
						UpperCamelCase         =					self.convert_tokens_to_ids(self._src_lang						)
						UpperCamelCase         =					tgt_lang
						self.set_src_lang_special_tokens(self._src_lang						)




			@property
			def   lowerCamelCase_					(						self							:		int						):







						"""simple docstring"""







						return self._src_lang




			@src_lang.setter
			def   lowerCamelCase_					(						self							:		List[str]       ,       __magic_name__							:		str						):







						"""simple docstring"""







						UpperCamelCase         =					new_src_lang
						self.set_src_lang_special_tokens(self._src_lang						)




			def   lowerCamelCase_					(						self							:		Optional[Any]       ,       __magic_name__							:		List[int]       ,       __magic_name__							:		Optional[List[int]] = None						):







						"""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   lowerCamelCase_					(						self							:		int       ,       __magic_name__							:		List[int]       ,       __magic_name__							:		Optional[List[int]] = None						):







						"""simple docstring"""







						UpperCamelCase         =					[self.sep_token_id]
						UpperCamelCase         =					[self.cls_token_id]

						if token_ids_a is None:
									return len(cls + token_ids_a + sep						) * [0]
						return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]




			def   lowerCamelCase_					(						self							:		int       ,       __magic_name__							:		List[Any]       ,       __magic_name__							:		str       ,       __magic_name__							:		Optional[str]       ,       __magic_name__							:		Optional[str]       ,       **__magic_name__							:		Tuple						):







						"""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         =					src_lang
						UpperCamelCase         =					self(__magic_name__       ,       add_special_tokens=__magic_name__       ,       return_tensors=__magic_name__       ,       **__magic_name__						)
						UpperCamelCase         =					self.convert_tokens_to_ids(__magic_name__						)
						UpperCamelCase         =					tgt_lang_id
						return inputs




			def   lowerCamelCase_					(						self							:		Dict       ,       __magic_name__							:		List[str]       ,       __magic_name__							:		str = "en_XX"       ,       __magic_name__							:		Optional[List[str]] = None       ,       __magic_name__							:		str = "ro_RO"       ,       **__magic_name__							:		int       ,       ):







						"""simple docstring"""







						UpperCamelCase         =					src_lang
						UpperCamelCase         =					tgt_lang
						return super().prepare_seqaseq_batch(__magic_name__       ,       __magic_name__       ,       **__magic_name__						)




			def   lowerCamelCase_					(						self							:		Dict						):







						"""simple docstring"""







						return self.set_src_lang_special_tokens(self.src_lang						)




			def   lowerCamelCase_					(						self							:		Any						):







						"""simple docstring"""







						return self.set_tgt_lang_special_tokens(self.tgt_lang						)




			def   lowerCamelCase_					(						self							:		int       ,       __magic_name__							:		Union[str, Any]						):







						"""simple docstring"""







						UpperCamelCase         =					self.convert_tokens_to_ids(__magic_name__						)
						UpperCamelCase         =					[]
						UpperCamelCase         =					[self.eos_token_id, self.cur_lang_code]

						UpperCamelCase         =					self.convert_ids_to_tokens(self.prefix_tokens						)
						UpperCamelCase         =					self.convert_ids_to_tokens(self.suffix_tokens						)

						UpperCamelCase         =					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   lowerCamelCase_					(						self							:		Tuple       ,       __magic_name__							:		str						):







						"""simple docstring"""







						UpperCamelCase         =					self.convert_tokens_to_ids(__magic_name__						)
						UpperCamelCase         =					[]
						UpperCamelCase         =					[self.eos_token_id, self.cur_lang_code]

						UpperCamelCase         =					self.convert_ids_to_tokens(self.prefix_tokens						)
						UpperCamelCase         =					self.convert_ids_to_tokens(self.suffix_tokens						)

						UpperCamelCase         =					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   lowerCamelCase_					(						self							:		Any       ,       __magic_name__							:		str       ,       __magic_name__							:		Optional[str] = None						):







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

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

						return (out_vocab_file,)




