


import argparse
import json
import pickle
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


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






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		str	):





			"""simple docstring"""




			UpperCamelCase         =					SwinConfig.from_pretrained(
			    """microsoft/swin-tiny-patch4-window7-224"""       ,  out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]	)
			UpperCamelCase         =					MaskFormerConfig(backbone_config=SCREAMING_SNAKE_CASE_	)

			UpperCamelCase         =					"""huggingface/label-files"""
			if "ade20k-full" in model_name:
						# this should be ok
						UpperCamelCase         =					847
						UpperCamelCase         =					"""maskformer-ade20k-full-id2label.json"""
			elif "ade" in model_name:
						# this should be ok
						UpperCamelCase         =					150
						UpperCamelCase         =					"""ade20k-id2label.json"""
			elif "coco-stuff" in model_name:
						# this should be ok
						UpperCamelCase         =					171
						UpperCamelCase         =					"""maskformer-coco-stuff-id2label.json"""
			elif "coco" in model_name:
						# TODO
						UpperCamelCase         =					133
						UpperCamelCase         =					"""coco-panoptic-id2label.json"""
			elif "cityscapes" in model_name:
						# this should be ok
						UpperCamelCase         =					19
						UpperCamelCase         =					"""cityscapes-id2label.json"""
			elif "vistas" in model_name:
						# this should be ok
						UpperCamelCase         =					65
						UpperCamelCase         =					"""mapillary-vistas-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()}

			return config






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Optional[Any]	):





			"""simple docstring"""




			UpperCamelCase         =					[]
			# stem
			# fmt: off
			rename_keys.append(("""backbone.patch_embed.proj.weight""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight""")	)
			rename_keys.append(("""backbone.patch_embed.proj.bias""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias""")	)
			rename_keys.append(("""backbone.patch_embed.norm.weight""", """model.pixel_level_module.encoder.model.embeddings.norm.weight""")	)
			rename_keys.append(("""backbone.patch_embed.norm.bias""", """model.pixel_level_module.encoder.model.embeddings.norm.bias""")	)
			# stages
			for i in range(len(config.backbone_config.depths	)	):
						for j in range(config.backbone_config.depths[i]	):
									rename_keys.append((f'backbone.layers.{i}.blocks.{j}.norm1.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight')	)
									rename_keys.append((f'backbone.layers.{i}.blocks.{j}.norm1.bias', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias')	)
									rename_keys.append((f'backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table')	)
									rename_keys.append((f'backbone.layers.{i}.blocks.{j}.attn.relative_position_index', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index')	)
									rename_keys.append((f'backbone.layers.{i}.blocks.{j}.attn.proj.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight')	)
									rename_keys.append((f'backbone.layers.{i}.blocks.{j}.attn.proj.bias', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias')	)
									rename_keys.append((f'backbone.layers.{i}.blocks.{j}.norm2.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight')	)
									rename_keys.append((f'backbone.layers.{i}.blocks.{j}.norm2.bias', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias')	)
									rename_keys.append((f'backbone.layers.{i}.blocks.{j}.mlp.fc1.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight')	)
									rename_keys.append((f'backbone.layers.{i}.blocks.{j}.mlp.fc1.bias', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias')	)
									rename_keys.append((f'backbone.layers.{i}.blocks.{j}.mlp.fc2.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight')	)
									rename_keys.append((f'backbone.layers.{i}.blocks.{j}.mlp.fc2.bias', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias')	)

						if i < 3:
									rename_keys.append((f'backbone.layers.{i}.downsample.reduction.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight')	)
									rename_keys.append((f'backbone.layers.{i}.downsample.norm.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight')	)
									rename_keys.append((f'backbone.layers.{i}.downsample.norm.bias', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias')	)
						rename_keys.append((f'backbone.norm{i}.weight', f'model.pixel_level_module.encoder.hidden_states_norms.{i}.weight')	)
						rename_keys.append((f'backbone.norm{i}.bias', f'model.pixel_level_module.encoder.hidden_states_norms.{i}.bias')	)

			# FPN
			rename_keys.append(("""sem_seg_head.layer_4.weight""", """model.pixel_level_module.decoder.fpn.stem.0.weight""")	)
			rename_keys.append(("""sem_seg_head.layer_4.norm.weight""", """model.pixel_level_module.decoder.fpn.stem.1.weight""")	)
			rename_keys.append(("""sem_seg_head.layer_4.norm.bias""", """model.pixel_level_module.decoder.fpn.stem.1.bias""")	)
			for source_index, target_index in zip(range(3       ,  0       ,  -1	)       ,  range(0       ,  3	)	):
						rename_keys.append((f'sem_seg_head.adapter_{source_index}.weight', f'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight')	)
						rename_keys.append((f'sem_seg_head.adapter_{source_index}.norm.weight', f'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight')	)
						rename_keys.append((f'sem_seg_head.adapter_{source_index}.norm.bias', f'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias')	)
						rename_keys.append((f'sem_seg_head.layer_{source_index}.weight', f'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight')	)
						rename_keys.append((f'sem_seg_head.layer_{source_index}.norm.weight', f'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight')	)
						rename_keys.append((f'sem_seg_head.layer_{source_index}.norm.bias', f'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias')	)
			rename_keys.append(("""sem_seg_head.mask_features.weight""", """model.pixel_level_module.decoder.mask_projection.weight""")	)
			rename_keys.append(("""sem_seg_head.mask_features.bias""", """model.pixel_level_module.decoder.mask_projection.bias""")	)

			# Transformer decoder
			for idx in range(config.decoder_config.decoder_layers	):
						# self-attention out projection
						rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight', f'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight')	)
						rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias', f'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias')	)
						# cross-attention out projection
						rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight', f'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight')	)
						rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias', f'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias')	)
						# MLP 1
						rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight', f'model.transformer_module.decoder.layers.{idx}.fc1.weight')	)
						rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias', f'model.transformer_module.decoder.layers.{idx}.fc1.bias')	)
						# MLP 2
						rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight', f'model.transformer_module.decoder.layers.{idx}.fc2.weight')	)
						rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias', f'model.transformer_module.decoder.layers.{idx}.fc2.bias')	)
						# layernorm 1 (self-attention layernorm)
						rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight', f'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight')	)
						rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias', f'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias')	)
						# layernorm 2 (cross-attention layernorm)
						rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight', f'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight')	)
						rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias', f'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias')	)
						# layernorm 3 (final layernorm)
						rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight', f'model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight')	)
						rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias', f'model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias')	)

			rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.weight""", """model.transformer_module.decoder.layernorm.weight""")	)
			rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.bias""", """model.transformer_module.decoder.layernorm.bias""")	)

			# heads on top
			rename_keys.append(("""sem_seg_head.predictor.query_embed.weight""", """model.transformer_module.queries_embedder.weight""")	)

			rename_keys.append(("""sem_seg_head.predictor.input_proj.weight""", """model.transformer_module.input_projection.weight""")	)
			rename_keys.append(("""sem_seg_head.predictor.input_proj.bias""", """model.transformer_module.input_projection.bias""")	)

			rename_keys.append(("""sem_seg_head.predictor.class_embed.weight""", """class_predictor.weight""")	)
			rename_keys.append(("""sem_seg_head.predictor.class_embed.bias""", """class_predictor.bias""")	)

			for i in range(3	):
						rename_keys.append((f'sem_seg_head.predictor.mask_embed.layers.{i}.weight', f'mask_embedder.{i}.0.weight')	)
						rename_keys.append((f'sem_seg_head.predictor.mask_embed.layers.{i}.bias', f'mask_embedder.{i}.0.bias')	)
			# fmt: on

			return rename_keys






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Any       ,  SCREAMING_SNAKE_CASE_   :		Optional[Any]       ,  SCREAMING_SNAKE_CASE_   :		Optional[Any]	):





			"""simple docstring"""




			UpperCamelCase         =					dct.pop(SCREAMING_SNAKE_CASE_	)
			UpperCamelCase         =					val






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		int       ,  SCREAMING_SNAKE_CASE_   :		List[Any]	):





			"""simple docstring"""




			UpperCamelCase         =					[int(backbone_config.embed_dim * 2**i	) for i in range(len(backbone_config.depths	)	)]
			for i in range(len(backbone_config.depths	)	):
						UpperCamelCase         =					num_features[i]
						for j in range(backbone_config.depths[i]	):
									# fmt: off
									# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
									UpperCamelCase         =					state_dict.pop(f'backbone.layers.{i}.blocks.{j}.attn.qkv.weight'	)
									UpperCamelCase         =					state_dict.pop(f'backbone.layers.{i}.blocks.{j}.attn.qkv.bias'	)
									# next, add query, keys and values (in that order) to the state dict
									UpperCamelCase         =					in_proj_weight[:dim, :]
									UpperCamelCase         =					in_proj_bias[: dim]
									UpperCamelCase         =					in_proj_weight[
									    dim : dim * 2, :
									]
									UpperCamelCase         =					in_proj_bias[
									    dim : dim * 2
									]
									UpperCamelCase         =					in_proj_weight[
									    -dim :, :
									]
									UpperCamelCase         =					in_proj_bias[-dim :]
									# fmt: on






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		str       ,  SCREAMING_SNAKE_CASE_   :		int	):





			"""simple docstring"""




			UpperCamelCase         =					config.decoder_config.hidden_size
			for idx in range(config.decoder_config.decoder_layers	):
						# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
						UpperCamelCase         =					state_dict.pop(f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'	)
						UpperCamelCase         =					state_dict.pop(f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias'	)
						# next, add query, keys and values (in that order) to the state dict
						UpperCamelCase         =					in_proj_weight[: hidden_size, :]
						UpperCamelCase         =					in_proj_bias[:config.hidden_size]
						UpperCamelCase         =					in_proj_weight[hidden_size : hidden_size * 2, :]
						UpperCamelCase         =					in_proj_bias[hidden_size : hidden_size * 2]
						UpperCamelCase         =					in_proj_weight[-hidden_size :, :]
						UpperCamelCase         =					in_proj_bias[-hidden_size :]
						# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
						UpperCamelCase         =					state_dict.pop(f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'	)
						UpperCamelCase         =					state_dict.pop(f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias'	)
						# next, add query, keys and values (in that order) to the state dict
						UpperCamelCase         =					in_proj_weight[: hidden_size, :]
						UpperCamelCase         =					in_proj_bias[:config.hidden_size]
						UpperCamelCase         =					in_proj_weight[hidden_size : hidden_size * 2, :]
						UpperCamelCase         =					in_proj_bias[hidden_size : hidden_size * 2]
						UpperCamelCase         =					in_proj_weight[-hidden_size :, :]
						UpperCamelCase         =					in_proj_bias[-hidden_size :]
			# fmt: on






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       ,  SCREAMING_SNAKE_CASE_   :		str       ,  SCREAMING_SNAKE_CASE_   :		bool = False	):





			"""simple docstring"""




			UpperCamelCase         =					get_maskformer_config(SCREAMING_SNAKE_CASE_	)

			# load original state_dict
			with open(SCREAMING_SNAKE_CASE_       ,  """rb"""	) as f:
						UpperCamelCase         =					pickle.load(SCREAMING_SNAKE_CASE_	)
			UpperCamelCase         =					data["""model"""]

			# for name, param in state_dict.items():
			#     print(name, param.shape)

			# rename keys
			UpperCamelCase         =					create_rename_keys(SCREAMING_SNAKE_CASE_	)
			for src, dest in rename_keys:
						rename_key(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)
			read_in_swin_q_k_v(SCREAMING_SNAKE_CASE_       ,  config.backbone_config	)
			read_in_decoder_q_k_v(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)

			# update to torch tensors
			for key, value in state_dict.items():
						UpperCamelCase         =					torch.from_numpy(SCREAMING_SNAKE_CASE_	)

			# load 🤗 model
			UpperCamelCase         =					MaskFormerForInstanceSegmentation(SCREAMING_SNAKE_CASE_	)
			model.eval()

			for name, param in model.named_parameters():
						print(SCREAMING_SNAKE_CASE_       ,  param.shape	)

			UpperCamelCase  ,  UpperCamelCase         =					model.load_state_dict(SCREAMING_SNAKE_CASE_       ,  strict=SCREAMING_SNAKE_CASE_	)
			assert missing_keys == [
			    "model.pixel_level_module.encoder.model.layernorm.weight",
			    "model.pixel_level_module.encoder.model.layernorm.bias",
			]
			assert len(SCREAMING_SNAKE_CASE_	) == 0, f'Unexpected keys: {unexpected_keys}'

			# verify results
			UpperCamelCase         =					prepare_img()
			if "vistas" in model_name:
						UpperCamelCase         =					65
			elif "cityscapes" in model_name:
						UpperCamelCase         =					65_535
			else:
						UpperCamelCase         =					255
			UpperCamelCase         =					True if """ade""" in model_name else False
			UpperCamelCase         =					MaskFormerImageProcessor(ignore_index=SCREAMING_SNAKE_CASE_       ,  reduce_labels=SCREAMING_SNAKE_CASE_	)

			UpperCamelCase         =					image_processor(SCREAMING_SNAKE_CASE_       ,  return_tensors="""pt"""	)

			UpperCamelCase         =					model(**SCREAMING_SNAKE_CASE_	)

			print("""Logits:"""       ,  outputs.class_queries_logits[0, :3, :3]	)

			if model_name == "maskformer-swin-tiny-ade":
						UpperCamelCase         =					torch.tensor(
						    [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]]	)
			assert torch.allclose(outputs.class_queries_logits[0, :3, :3]       ,  SCREAMING_SNAKE_CASE_       ,  atol=1e-4	)
			print("""Looks ok!"""	)

			if pytorch_dump_folder_path is not None:
						print(f'Saving 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 push_to_hub:
						print("""Pushing model and image processor to the hub..."""	)
						model.push_to_hub(f'nielsr/{model_name}'	)
						image_processor.push_to_hub(f'nielsr/{model_name}'	)


if __name__ == "__main__":
			__snake_case            =   argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--model_name",
			    default="maskformer-swin-tiny-ade",
			    type=str,
			    help=("Name of the MaskFormer model you'd like to convert",),
			)
			parser.add_argument(
			    "--checkpoint_path",
			    default="/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl",
			    type=str,
			    help="Path to the original state dict (.pth file).",
			)
			parser.add_argument(
			    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
			)
			parser.add_argument(
			    "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
			)

			__snake_case            =   parser.parse_args()
			convert_maskformer_checkpoint(
			    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
			)








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,)




