






import math
from typing import Callable, List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
from diffusers.schedulers import DDIMScheduler, DDPMScheduler, LMSDiscreteScheduler, PNDMScheduler

def   __lowercase       (			snake_case,	snake_case,	snake_case=[]			):







				"""simple docstring"""






				__magic_name__		:int   =   size[0] - overlap_pixels * 2
				__magic_name__		:List[Any]   =   size[1] - overlap_pixels * 2
				for letter in ["l", "r"]:
								if letter in remove_borders:
												size_x += overlap_pixels
				for letter in ["t", "b"]:
								if letter in remove_borders:
												size_y += overlap_pixels
				__magic_name__		:List[str]   =   np.ones((size_y, size_x),	dtype=np.uinta			) * 2_5_5
				__magic_name__		:str   =   np.pad(snake_case,	mode='''linear_ramp''',	pad_width=snake_case,	end_values=0			)

				if "l" in remove_borders:
								__magic_name__		:Union[str, Any]   =   mask[:, overlap_pixels : mask.shape[1]]
				if "r" in remove_borders:
								__magic_name__		:Optional[int]   =   mask[:, 0 : mask.shape[1] - overlap_pixels]
				if "t" in remove_borders:
								__magic_name__		:str   =   mask[overlap_pixels : mask.shape[0], :]
				if "b" in remove_borders:
								__magic_name__		:Union[str, Any]   =   mask[0 : mask.shape[0] - overlap_pixels, :]
				return mask

def   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				return max(snake_case,	min(snake_case,	snake_case			)			)

def   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				return (
				    clamp(rect[0],	min[0],	max[0]			),
				    clamp(rect[1],	min[1],	max[1]			),
				    clamp(rect[2],	min[0],	max[0]			),
				    clamp(rect[3],	min[1],	max[1]			),
				)

def   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Optional[Any]   =   list(snake_case			)
				rect[0] -= overlap
				rect[1] -= overlap
				rect[2] += overlap
				rect[3] += overlap
				__magic_name__		:Union[str, Any]   =   clamp_rect(snake_case,	[0, 0],	[image_size[0], image_size[1]]			)
				return rect

def   __lowercase       (			snake_case,	snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:List[str]   =   Image.new('''RGB''',	(tile.size[0] + original_slice, tile.size[1])			)
				result.paste(
				    original_image.resize((tile.size[0], tile.size[1]),	Image.BICUBIC			).crop(
				        (slice_x, 0, slice_x + original_slice, tile.size[1])			),	(0, 0),	)
				result.paste(snake_case,	(original_slice, 0)			)
				return result

def   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Tuple   =   (original_image_slice * 4, 0, tile.size[0], tile.size[1])
				__magic_name__		:Optional[Any]   =   tile.crop(snake_case			)
				return tile

def   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:int   =   n % d
				return n - divisor
class     lowerCamelCase_					(   lowerCamelCase			):







				def __init__(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase = 3_5_0							,       ):
								"""simple docstring"""
								super().__init__(
								    vae=__UpperCAmelCase							,       text_encoder=__UpperCAmelCase							,       tokenizer=__UpperCAmelCase							,       unet=__UpperCAmelCase							,       low_res_scheduler=__UpperCAmelCase							,       scheduler=__UpperCAmelCase							,       max_noise_level=__UpperCAmelCase							,       )







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								torch.manual_seed(0  )
								__magic_name__		:Optional[Any]   =   (
								    min(image.size[0] - (tile_size + original_image_slice)							,       x * tile_size  ),
								    min(image.size[1] - (tile_size + original_image_slice)							,       y * tile_size  ),
								    min(image.size[0]							,       (x + 1) * tile_size  ),
								    min(image.size[1]							,       (y + 1) * tile_size  ),
								)
								__magic_name__		:Dict   =   add_overlap_rect(__UpperCAmelCase							,       __UpperCAmelCase							,       image.size  )
								__magic_name__		:Optional[int]   =   image.crop(__UpperCAmelCase  )
								__magic_name__		:Union[str, Any]   =   ((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
								__magic_name__		:List[str]   =   translated_slice_x - (original_image_slice / 2)
								__magic_name__		:Tuple   =   max(0							,       __UpperCAmelCase  )
								__magic_name__		:int   =   squeeze_tile(__UpperCAmelCase							,       __UpperCAmelCase							,       __UpperCAmelCase							,       __UpperCAmelCase  )
								__magic_name__		:List[Any]   =   to_input.size
								__magic_name__		:str   =   to_input.resize((tile_size, tile_size)							,       Image.BICUBIC  )
								__magic_name__		:Optional[int]   =   super(__UpperCAmelCase							,       self  ).__call__(image=__UpperCAmelCase							,       **__UpperCAmelCase  ).images[0]
								__magic_name__		:str   =   upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)							,       Image.BICUBIC  )
								__magic_name__		:Any   =   unsqueeze_tile(__UpperCAmelCase							,       __UpperCAmelCase  )
								__magic_name__		:Dict   =   upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)							,       Image.BICUBIC  )
								__magic_name__		:List[Any]   =   []
								if x == 0:
												remove_borders.append('''l'''  )
								elif crop_rect[2] == image.size[0]:
												remove_borders.append('''r'''  )
								if y == 0:
												remove_borders.append('''t'''  )
								elif crop_rect[3] == image.size[1]:
												remove_borders.append('''b'''  )
								__magic_name__		:List[str]   =   Image.fromarray(
								    make_transparency_mask(
								        (upscaled_tile.size[0], upscaled_tile.size[1])							,       tile_border * 4							,       remove_borders=__UpperCAmelCase  )							,       mode='''L'''							,       )
								final_image.paste(
								    __UpperCAmelCase							,       (crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4)							,       __UpperCAmelCase  )







				@torch.no_grad()
				def __call__(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase = 7_5							,       __lowerCAmelCase = 9.0							,       __lowerCAmelCase = 5_0							,       __lowerCAmelCase = None							,       __lowerCAmelCase = 1							,       __lowerCAmelCase = 0.0							,       __lowerCAmelCase = None							,       __lowerCAmelCase = None							,       __lowerCAmelCase = None							,       __lowerCAmelCase = 1							,       __lowerCAmelCase = 1_2_8							,       __lowerCAmelCase = 3_2							,       __lowerCAmelCase = 3_2							,       ):
								"""simple docstring"""
								__magic_name__		:Dict   =   Image.new('''RGB'''							,       (image.size[0] * 4, image.size[1] * 4)  )
								__magic_name__		:Optional[Any]   =   math.ceil(image.size[0] / tile_size  )
								__magic_name__		:List[Any]   =   math.ceil(image.size[1] / tile_size  )
								__magic_name__		:int   =   tcx * tcy
								__magic_name__		:Any   =   0
								for y in range(__UpperCAmelCase  ):
												for x in range(__UpperCAmelCase  ):
																self._process_tile(
																    __UpperCAmelCase							,       __UpperCAmelCase							,       __UpperCAmelCase							,       __UpperCAmelCase							,       __UpperCAmelCase							,       __UpperCAmelCase							,       __UpperCAmelCase							,       prompt=__UpperCAmelCase							,       num_inference_steps=__UpperCAmelCase							,       guidance_scale=__UpperCAmelCase							,       noise_level=__UpperCAmelCase							,       negative_prompt=__UpperCAmelCase							,       num_images_per_prompt=__UpperCAmelCase							,       eta=__UpperCAmelCase							,       generator=__UpperCAmelCase							,       latents=__UpperCAmelCase							,       )
																current_count += 1
																if callback is not None:
																				callback({'''progress''': current_count / total_tile_count, '''image''': final_image}  )
								return final_image







def   __lowercase       (			):







				"""simple docstring"""






				__magic_name__		:List[str]   =   '''stabilityai/stable-diffusion-x4-upscaler'''
				__magic_name__		:Optional[int]   =   StableDiffusionTiledUpscalePipeline.from_pretrained(snake_case,	revision='''fp16''',	torch_dtype=torch.floataa			)
				__magic_name__		:List[str]   =   pipe.to('''cuda'''			)
				__magic_name__		:Optional[int]   =   Image.open('''../../docs/source/imgs/diffusers_library.jpg'''			)

				def callback(snake_case			):
								print(f'''progress: {obj["progress"]:.4f}'''			)
								obj["image"].save('''diffusers_library_progress.jpg'''			)

				__magic_name__		:Union[str, Any]   =   pipe(image=snake_case,	prompt='''Black font, white background, vector''',	noise_level=4_0,	callback=snake_case			)
				final_image.save('''diffusers_library.jpg'''			)


if __name__ == "__main__":
					main()














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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase__							  =						logging.get_logger(__name__)

UpperCAmelCase__							  =						"▁"

UpperCAmelCase__							  =						{"vocab_file": "sentencepiece.bpe.model"}

UpperCAmelCase__							  =						{
    "vocab_file": {
        "facebook/nllb-200-distilled-600M": (
            "https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model"
        ),
    }
}

UpperCAmelCase__							  =						{
    "facebook/nllb-200-distilled-600M": 1024,
}

# fmt: off
UpperCAmelCase__							  =						["ace_Arab", "ace_Latn", "acm_Arab", "acq_Arab", "aeb_Arab", "afr_Latn", "ajp_Arab", "aka_Latn", "amh_Ethi", "apc_Arab", "arb_Arab", "ars_Arab", "ary_Arab", "arz_Arab", "asm_Beng", "ast_Latn", "awa_Deva", "ayr_Latn", "azb_Arab", "azj_Latn", "bak_Cyrl", "bam_Latn", "ban_Latn", "bel_Cyrl", "bem_Latn", "ben_Beng", "bho_Deva", "bjn_Arab", "bjn_Latn", "bod_Tibt", "bos_Latn", "bug_Latn", "bul_Cyrl", "cat_Latn", "ceb_Latn", "ces_Latn", "cjk_Latn", "ckb_Arab", "crh_Latn", "cym_Latn", "dan_Latn", "deu_Latn", "dik_Latn", "dyu_Latn", "dzo_Tibt", "ell_Grek", "eng_Latn", "epo_Latn", "est_Latn", "eus_Latn", "ewe_Latn", "fao_Latn", "pes_Arab", "fij_Latn", "fin_Latn", "fon_Latn", "fra_Latn", "fur_Latn", "fuv_Latn", "gla_Latn", "gle_Latn", "glg_Latn", "grn_Latn", "guj_Gujr", "hat_Latn", "hau_Latn", "heb_Hebr", "hin_Deva", "hne_Deva", "hrv_Latn", "hun_Latn", "hye_Armn", "ibo_Latn", "ilo_Latn", "ind_Latn", "isl_Latn", "ita_Latn", "jav_Latn", "jpn_Jpan", "kab_Latn", "kac_Latn", "kam_Latn", "kan_Knda", "kas_Arab", "kas_Deva", "kat_Geor", "knc_Arab", "knc_Latn", "kaz_Cyrl", "kbp_Latn", "kea_Latn", "khm_Khmr", "kik_Latn", "kin_Latn", "kir_Cyrl", "kmb_Latn", "kon_Latn", "kor_Hang", "kmr_Latn", "lao_Laoo", "lvs_Latn", "lij_Latn", "lim_Latn", "lin_Latn", "lit_Latn", "lmo_Latn", "ltg_Latn", "ltz_Latn", "lua_Latn", "lug_Latn", "luo_Latn", "lus_Latn", "mag_Deva", "mai_Deva", "mal_Mlym", "mar_Deva", "min_Latn", "mkd_Cyrl", "plt_Latn", "mlt_Latn", "mni_Beng", "khk_Cyrl", "mos_Latn", "mri_Latn", "zsm_Latn", "mya_Mymr", "nld_Latn", "nno_Latn", "nob_Latn", "npi_Deva", "nso_Latn", "nus_Latn", "nya_Latn", "oci_Latn", "gaz_Latn", "ory_Orya", "pag_Latn", "pan_Guru", "pap_Latn", "pol_Latn", "por_Latn", "prs_Arab", "pbt_Arab", "quy_Latn", "ron_Latn", "run_Latn", "rus_Cyrl", "sag_Latn", "san_Deva", "sat_Beng", "scn_Latn", "shn_Mymr", "sin_Sinh", "slk_Latn", "slv_Latn", "smo_Latn", "sna_Latn", "snd_Arab", "som_Latn", "sot_Latn", "spa_Latn", "als_Latn", "srd_Latn", "srp_Cyrl", "ssw_Latn", "sun_Latn", "swe_Latn", "swh_Latn", "szl_Latn", "tam_Taml", "tat_Cyrl", "tel_Telu", "tgk_Cyrl", "tgl_Latn", "tha_Thai", "tir_Ethi", "taq_Latn", "taq_Tfng", "tpi_Latn", "tsn_Latn", "tso_Latn", "tuk_Latn", "tum_Latn", "tur_Latn", "twi_Latn", "tzm_Tfng", "uig_Arab", "ukr_Cyrl", "umb_Latn", "urd_Arab", "uzn_Latn", "vec_Latn", "vie_Latn", "war_Latn", "wol_Latn", "xho_Latn", "ydd_Hebr", "yor_Latn", "yue_Hant", "zho_Hans", "zho_Hant", "zul_Latn"]






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	    =    []
      __snake_case	    =    []







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




       """simple docstring"""


       a						    = AddedToken(__UpperCAmelCase					,   lstrip=__UpperCAmelCase					,   rstrip=__UpperCAmelCase    ) if isinstance(__UpperCAmelCase					,   __UpperCAmelCase    ) else mask_token

       a						    = {} if sp_model_kwargs is None else sp_model_kwargs
       a						    = legacy_behaviour

       super().__init__(
           bos_token=__UpperCAmelCase					,   eos_token=__UpperCAmelCase					,   unk_token=__UpperCAmelCase					,   sep_token=__UpperCAmelCase					,   cls_token=__UpperCAmelCase					,   pad_token=__UpperCAmelCase					,   mask_token=__UpperCAmelCase					,   tokenizer_file=__UpperCAmelCase					,   src_lang=__UpperCAmelCase					,   tgt_lang=__UpperCAmelCase					,   additional_special_tokens=__UpperCAmelCase					,   sp_model_kwargs=self.sp_model_kwargs					,   legacy_behaviour=__UpperCAmelCase					,   **__UpperCAmelCase					,   )

       a						    = spm.SentencePieceProcessor(**self.sp_model_kwargs    )
       self.sp_model.Load(str(__UpperCAmelCase    )    )
       a						    = vocab_file
       # Original fairseq vocab and spm vocab must be "aligned":
       # Vocab    |    0    |    1    |   2    |    3    |  4   |  5   |  6   |   7  |   8  |  9
       # -------- | ------- | ------- | ------ | ------- | ---- | ---- | ---- | ---- | ---- | ----
       # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
       # spm      | '<unk>' | '<s>'   | '</s>' | 'an'    | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'

       # Mimic fairseq token-to-id alignment for the first 4 token
       a						    = {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

       # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
       a						    = 1

       a						    = len(self.sp_model    )
       a						    = {
           code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(__UpperCAmelCase    )
       }
       a						    = {v: k for k, v in self.lang_code_to_id.items()}
       a						    = len(self.sp_model    ) + len(self.lang_code_to_id    ) + self.fairseq_offset

       self.fairseq_tokens_to_ids.update(self.lang_code_to_id    )
       a						    = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
       a						    = list(self.lang_code_to_id.keys()    )

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

       a						    = src_lang if src_lang is not None else '''eng_Latn'''
       a						    = self.lang_code_to_id[self._src_lang]
       a						    = tgt_lang
       self.set_src_lang_special_tokens(self._src_lang    )







      def __getstate__( self :		str    )       ->Any:




       """simple docstring"""


       a						    = self.__dict__.copy()
       a						    = None
       a						    = self.sp_model.serialized_model_proto()
       return state







      def __setstate__( self :		List[str]					,   __UpperCAmelCase :		Optional[Any]    )       ->int:




       """simple docstring"""


       a						    = d

       # for backward compatibility
       if not hasattr(self					,   '''sp_model_kwargs'''    ):
        a						    = {}

       a						    = spm.SentencePieceProcessor(**self.sp_model_kwargs    )
       self.sp_model.LoadFromSerializedProto(self.sp_model_proto    )







      @property
      def  __lowerCAmelCase   ( self :		Tuple    )       ->Optional[Any]:




       """simple docstring"""


       return len(self.sp_model    ) + len(self.lang_code_to_id    ) + self.fairseq_offset + 1  # Plus 1 for the mask token







      @property
      def  __lowerCAmelCase   ( self :		Any    )       ->str:




       """simple docstring"""


       return self._src_lang







      @src_lang.setter
      def  __lowerCAmelCase   ( self :		List[Any]					,   __UpperCAmelCase :		str    )       ->None:




       """simple docstring"""


       a						    = new_src_lang
       self.set_src_lang_special_tokens(self._src_lang    )







      def  __lowerCAmelCase   ( self :		Any					,   __UpperCAmelCase :		List[int]					,   __UpperCAmelCase :		Optional[List[int]] = None					,   __UpperCAmelCase :		bool = False    )       ->List[int]:




       """simple docstring"""


       if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=__UpperCAmelCase					,   token_ids_a=__UpperCAmelCase					,   already_has_special_tokens=__UpperCAmelCase    )

       a						    = [1] * len(self.prefix_tokens    )
       a						    = [1] * len(self.suffix_tokens    )
       if token_ids_a is None:
        return prefix_ones + ([0] * len(__UpperCAmelCase    )) + suffix_ones
       return prefix_ones + ([0] * len(__UpperCAmelCase    )) + ([0] * len(__UpperCAmelCase    )) + suffix_ones







      def  __lowerCAmelCase   ( self :		Any					,   __UpperCAmelCase :		List[int]					,   __UpperCAmelCase :		Optional[List[int]] = 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  __lowerCAmelCase   ( self :		int					,   __UpperCAmelCase :		List[int]					,   __UpperCAmelCase :		Optional[List[int]] = None    )       ->List[int]:




       """simple docstring"""


       a						    = [self.sep_token_id]
       a						    = [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 :		str					,   __UpperCAmelCase :		Optional[Any]					,   __UpperCAmelCase :		str					,   __UpperCAmelCase :		Optional[str]					,   __UpperCAmelCase :		Optional[str]					,   **__UpperCAmelCase :		Tuple    )       ->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'''    )
       a						    = src_lang
       a						    = self(__UpperCAmelCase					,   add_special_tokens=__UpperCAmelCase					,   return_tensors=__UpperCAmelCase					,   **__UpperCAmelCase    )
       a						    = self.convert_tokens_to_ids(__UpperCAmelCase    )
       a						    = tgt_lang_id
       return inputs







      def  __lowerCAmelCase   ( self :		Optional[int]    )       ->Union[str, Any]:




       """simple docstring"""


       a						    = {self.convert_ids_to_tokens(__UpperCAmelCase    ): i for i in range(self.vocab_size    )}
       vocab.update(self.added_tokens_encoder    )
       return vocab







      def  __lowerCAmelCase   ( self :		List[Any]					,   __UpperCAmelCase :		str    )       ->List[str]:




       """simple docstring"""


       return self.sp_model.encode(__UpperCAmelCase					,   out_type=__UpperCAmelCase    )







      def  __lowerCAmelCase   ( self :		Optional[int]					,   __UpperCAmelCase :		str    )       ->List[Any]:




       """simple docstring"""


       if token in self.fairseq_tokens_to_ids:
        return self.fairseq_tokens_to_ids[token]
       a						    = self.sp_model.PieceToId(__UpperCAmelCase    )

       # Need to return unknown token if the SP model returned 0
       return spm_id + self.fairseq_offset if spm_id else self.unk_token_id







      def  __lowerCAmelCase   ( self :		Union[str, Any]					,   __UpperCAmelCase :		int    )       ->Optional[Any]:




       """simple docstring"""


       if index in self.fairseq_ids_to_tokens:
        return self.fairseq_ids_to_tokens[index]
       return self.sp_model.IdToPiece(index - self.fairseq_offset    )







      def  __lowerCAmelCase   ( self :		Tuple					,   __UpperCAmelCase :		List[str]    )       ->List[Any]:




       """simple docstring"""


       a						    = ''''''.join(__UpperCAmelCase    ).replace(__UpperCAmelCase					,   ''' '''    ).strip()
       return out_string







      def  __lowerCAmelCase   ( self :		str					,   __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
       a						    = os.path.join(
           __UpperCAmelCase					,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )

       if os.path.abspath(self.vocab_file    ) != os.path.abspath(__UpperCAmelCase    ) and os.path.isfile(self.vocab_file    ):
        copyfile(self.vocab_file					,   __UpperCAmelCase    )
       elif not os.path.isfile(self.vocab_file    ):
        with open(__UpperCAmelCase					,   '''wb'''    ) as fi:
         a						    = self.sp_model.serialized_model_proto()
         fi.write(__UpperCAmelCase    )

       return (out_vocab_file,)







      def  __lowerCAmelCase   ( self :		List[Any]					,   __UpperCAmelCase :		List[str]					,   __UpperCAmelCase :		str = "eng_Latn"					,   __UpperCAmelCase :		Optional[List[str]] = None					,   __UpperCAmelCase :		str = "fra_Latn"					,   **__UpperCAmelCase :		Optional[int]					,   )       ->BatchEncoding:




       """simple docstring"""


       a						    = src_lang
       a						    = tgt_lang
       return super().prepare_seqaseq_batch(__UpperCAmelCase					,   __UpperCAmelCase					,   **__UpperCAmelCase    )







      def  __lowerCAmelCase   ( self :		Dict    )       ->List[str]:




       """simple docstring"""


       return self.set_src_lang_special_tokens(self.src_lang    )







      def  __lowerCAmelCase   ( self :		Any    )       ->Any:




       """simple docstring"""


       return self.set_tgt_lang_special_tokens(self.tgt_lang    )







      def  __lowerCAmelCase   ( self :		Any					,   __UpperCAmelCase :		Tuple    )       ->None:




       """simple docstring"""


       a						    = self.lang_code_to_id[src_lang]
       if self.legacy_behaviour:
        a						    = []
        a						    = [self.eos_token_id, self.cur_lang_code]
       else:
        a						    = [self.cur_lang_code]
        a						    = [self.eos_token_id]







      def  __lowerCAmelCase   ( self :		List[str]					,   __UpperCAmelCase :		str    )       ->None:




       """simple docstring"""


       a						    = self.lang_code_to_id[lang]
       if self.legacy_behaviour:
        a						    = []
        a						    = [self.eos_token_id, self.cur_lang_code]
       else:
        a						    = [self.cur_lang_code]
        a						    = [self.eos_token_id]


