





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

from tokenizers import processors

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


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


__A       : Union[str, Any]						    = logging.get_logger(__name__)

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

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

__A       : Dict						    = {
    "facebook/mbart-large-50-one-to-many-mmt": 1_024,
}

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







class     lowerCamelCase(	__snake_case  ):
     '''simple docstring'''


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

     __magic_name__        = []
     __magic_name__        = []




     def __init__(						self							,    snake_case_=None							,    snake_case_=None							,    snake_case_=None							,    snake_case_=None							,    snake_case_="</s>"							,    snake_case_="</s>"							,    snake_case_="<s>"							,    snake_case_="<unk>"							,    snake_case_="<pad>"							,    snake_case_="<mask>"							,    **snake_case_							,    ):
         # Mask token behave like a normal word, i.e. include the space before it
         _A    =       AddedToken(snake_case_							,    lstrip=snake_case_							,    rstrip=snake_case_       ) if isinstance(snake_case_							,    snake_case_       ) else mask_token

         _A    =       kwargs.get('additional_special_tokens'							,    []       )
         kwargs["additional_special_tokens"] += [
             code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
         ]

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

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

         _A    =       {
             lang_code: self.convert_tokens_to_ids(snake_case_       ) for lang_code in FAIRSEQ_LANGUAGE_CODES
         }

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




     @property
     def 					lowerCAmelCase__		(						self       ):
         return self._src_lang




     @src_lang.setter
     def 					lowerCAmelCase__		(						self							,    snake_case_       ):
         _A    =       new_src_lang
         self.set_src_lang_special_tokens(self._src_lang       )




     def 					lowerCAmelCase__		(						self							,    snake_case_							,    snake_case_ = None       ):
         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							,    snake_case_							,    snake_case_ = "en_XX"							,    snake_case_ = None							,    snake_case_ = "ro_RO"							,    **snake_case_							,    ):
         _A    =       src_lang
         _A    =       tgt_lang
         return super().prepare_seqaseq_batch(snake_case_							,    snake_case_							,    **snake_case_       )




     def 					lowerCAmelCase__		(						self       ):
         return self.set_src_lang_special_tokens(self.src_lang       )




     def 					lowerCAmelCase__		(						self       ):
         return self.set_tgt_lang_special_tokens(self.tgt_lang       )




     def 					lowerCAmelCase__		(						self							,    snake_case_       ):
         _A    =       self.convert_tokens_to_ids(snake_case_       )
         _A    =       [self.cur_lang_code_id]
         _A    =       [self.eos_token_id]

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

         _A    =       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							,    snake_case_       ):
         _A    =       self.convert_tokens_to_ids(snake_case_       )
         _A    =       [self.cur_lang_code_id]
         _A    =       [self.eos_token_id]

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

         _A    =       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							,    snake_case_							,    snake_case_							,    snake_case_							,    snake_case_							,    **snake_case_       ):
         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(snake_case_							,    add_special_tokens=snake_case_							,    return_tensors=snake_case_							,    **snake_case_       )
         _A    =       self.convert_tokens_to_ids(snake_case_       )
         _A    =       tgt_lang_id
         return inputs




     def 					lowerCAmelCase__		(						self							,    snake_case_							,    snake_case_ = None       ):
         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(snake_case_       ):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory"       )
             return
         _A    =       os.path.join(
             snake_case_							,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']       )

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

         return (out_vocab_file,)












import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor







class     lowerCamelCase(	unittest.TestCase  ):
     '''simple docstring'''


     @property
     def 					lowerCAmelCase__		(						self       ):
         _A    =       1
         _A    =       3
         _A    =       8
         _A    =       8

         _A    =       torch.rand((batch_size, num_channels, height, width)       )

         return sample




     def 					lowerCAmelCase__		(						self							,    snake_case_       ):
         if isinstance(image[0]							,    PIL.Image.Image       ):
             return np.stack([np.array(snake_case_       ) for i in image]							,    axis=0       )
         elif isinstance(snake_case_							,    torch.Tensor       ):
             return image.cpu().numpy().transpose(0							,    2							,    3							,    1       )
         return image




     def 					lowerCAmelCase__		(						self       ):
         _A    =       VaeImageProcessor(do_resize=snake_case_							,    do_normalize=snake_case_       )

         _A    =       self.dummy_sample
         _A    =       self.to_np(snake_case_       )

         for output_type in ["pt", "np", "pil"]:
             _A    =       image_processor.postprocess(
                 image_processor.preprocess(snake_case_       )							,    output_type=snake_case_							,    )
             _A    =       self.to_np(snake_case_       )
             _A    =       (input_np * 255).round() if output_type == 'pil' else input_np
             assert (
                 np.abs(in_np - out_np       ).max() < 1E-6
             ), F"decoded output does not match input for output_type {output_type}"




     def 					lowerCAmelCase__		(						self       ):
         _A    =       VaeImageProcessor(do_resize=snake_case_							,    do_normalize=snake_case_       )
         _A    =       self.dummy_sample.cpu().numpy().transpose(0							,    2							,    3							,    1       )

         for output_type in ["pt", "np", "pil"]:
             _A    =       image_processor.postprocess(image_processor.preprocess(snake_case_       )							,    output_type=snake_case_       )

             _A    =       self.to_np(snake_case_       )
             _A    =       (input_np * 255).round() if output_type == 'pil' else input_np
             assert (
                 np.abs(in_np - out_np       ).max() < 1E-6
             ), F"decoded output does not match input for output_type {output_type}"




     def 					lowerCAmelCase__		(						self       ):
         _A    =       VaeImageProcessor(do_resize=snake_case_							,    do_normalize=snake_case_       )

         _A    =       self.dummy_sample.cpu().numpy().transpose(0							,    2							,    3							,    1       )
         _A    =       image_processor.numpy_to_pil(snake_case_       )

         for output_type in ["pt", "np", "pil"]:
             _A    =       image_processor.postprocess(image_processor.preprocess(snake_case_       )							,    output_type=snake_case_       )
             for i, o in zip(snake_case_							,    snake_case_       ):
                 _A    =       np.array(snake_case_       )
                 _A    =       self.to_np(snake_case_       ) if output_type == 'pil' else (self.to_np(snake_case_       ) * 255).round()
                 assert (
                     np.abs(in_np - out_np       ).max() < 1E-6
                 ), F"decoded output does not match input for output_type {output_type}"




     def 					lowerCAmelCase__		(						self       ):
         _A    =       VaeImageProcessor(do_resize=snake_case_							,    do_normalize=snake_case_       )

         _A    =       self.dummy_sample
         _A    =       input_pt_ad.squeeze(0       )

         _A    =       image_processor.postprocess(
             image_processor.preprocess(snake_case_       )							,    output_type='np'							,    )
         _A    =       image_processor.postprocess(
             image_processor.preprocess(snake_case_       )							,    output_type='np'							,    )

         _A    =       self.to_np(self.dummy_sample       )
         _A    =       input_np_ad.squeeze(0       )

         _A    =       image_processor.postprocess(
             image_processor.preprocess(snake_case_       )							,    output_type='np'							,    )
         _A    =       image_processor.postprocess(
             image_processor.preprocess(snake_case_       )							,    output_type='np'							,    )

         assert np.abs(out_pt_ad - out_pt_ad       ).max() < 1E-6
         assert np.abs(out_np_ad - out_np_ad       ).max() < 1E-6




     def 					lowerCAmelCase__		(						self       ):
         _A    =       VaeImageProcessor(do_resize=snake_case_							,    do_normalize=snake_case_       )

         _A    =       self.dummy_sample
         _A    =       list(snake_case_       )

         _A    =       image_processor.postprocess(
             image_processor.preprocess(snake_case_       )							,    output_type='np'							,    )

         _A    =       image_processor.postprocess(
             image_processor.preprocess(snake_case_       )							,    output_type='np'							,    )

         _A    =       self.to_np(self.dummy_sample       )
         list(snake_case_       )

         _A    =       image_processor.postprocess(
             image_processor.preprocess(snake_case_       )							,    output_type='np'							,    )

         _A    =       image_processor.postprocess(
             image_processor.preprocess(snake_case_       )							,    output_type='np'							,    )

         assert np.abs(out_pt_ad - out_pt_list       ).max() < 1E-6
         assert np.abs(out_np_ad - out_np_list       ).max() < 1E-6





