





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	:	Optional[int]		     =       logging.get_logger(__name__)

UpperCAmelCase	:	List[str]		     =       """▁"""

UpperCAmelCase	:	Union[str, Any]		     =       {"""vocab_file""": """sentencepiece.bpe.model"""}

UpperCAmelCase	:	Tuple		     =       {
    """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"""
        ),
    }
}

UpperCAmelCase	:	str		     =       {
    """facebook/mbart-large-50-one-to-many-mmt""": 1024,
}

# fmt: off
UpperCAmelCase	:	Tuple		     =       ["""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       (    UpperCamelCase__):
      _lowercase				:					Union[str, Any]						=			VOCAB_FILES_NAMES
      _lowercase				:					Dict						=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _lowercase				:					List[str]						=			PRETRAINED_VOCAB_FILES_MAP
      _lowercase				:					int						=			["""input_ids""", """attention_mask"""]

      _lowercase				:					List[int]						=			[]
      _lowercase				:					List[int]						=			[]







      def __init__(    self		,      lowerCAmelCase__		,      lowerCAmelCase__=None		,      lowerCAmelCase__=None		,      lowerCAmelCase__="</s>"		,      lowerCAmelCase__="</s>"		,      lowerCAmelCase__="<s>"		,      lowerCAmelCase__="<unk>"		,      lowerCAmelCase__="<pad>"		,      lowerCAmelCase__="<mask>"		,      lowerCAmelCase__ = None		,      **lowerCAmelCase__		,      )   ->						None:



        '''simple docstring'''




        a__	:					Any										=AddedToken(lowerCAmelCase__		,      lstrip=lowerCAmelCase__		,      rstrip=lowerCAmelCase__							) if isinstance(lowerCAmelCase__		,      lowerCAmelCase__							) else mask_token

        a__	:					str										={} if sp_model_kwargs is None else sp_model_kwargs

        a__	:					Dict										=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__(
            src_lang=lowerCAmelCase__		,      tgt_lang=lowerCAmelCase__		,      eos_token=lowerCAmelCase__		,      unk_token=lowerCAmelCase__		,      sep_token=lowerCAmelCase__		,      cls_token=lowerCAmelCase__		,      pad_token=lowerCAmelCase__		,      mask_token=lowerCAmelCase__		,      sp_model_kwargs=self.sp_model_kwargs		,      **lowerCAmelCase__		,      )

        a__	:					Union[str, Any]										=spm.SentencePieceProcessor(**self.sp_model_kwargs							)
        self.sp_model.Load(str(lowerCAmelCase__							)							)
        a__	:					str										=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>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
        # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

        # Mimic fairseq token-to-id alignment for the first 4 token
        a__	:					Optional[int]										={"<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__	:					Dict										=1

        a__	:					List[Any]										=len(self.sp_model							)
        a__	:					List[str]										={
            code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(lowerCAmelCase__							)
        }
        a__	:					Optional[int]										={v: k for k, v in self.lang_code_to_id.items()}
        a__	:					Any										=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__	:					Tuple										={v: k for k, v in self.fairseq_tokens_to_ids.items()}

        a__	:					Optional[int]										=src_lang if src_lang is not None else "en_XX"
        a__	:					int										=self.lang_code_to_id[self._src_lang]
        a__	:					Dict										=tgt_lang
        self.set_src_lang_special_tokens(self._src_lang							)







      @property
      def    _lowercase  (    self							)   ->						int:



        '''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    _lowercase  (    self							)   ->						str:



        '''simple docstring'''




        return self._src_lang







      @src_lang.setter
      def    _lowercase  (    self		,      lowerCAmelCase__							)   ->						None:



        '''simple docstring'''




        a__	:					Tuple										=new_src_lang
        self.set_src_lang_special_tokens(self._src_lang							)







      def __getstate__(    self							)   ->						Dict:



        '''simple docstring'''




        a__	:					Tuple										=self.__dict__.copy()
        a__	:					List[Any]										=None
        return state







      def __setstate__(    self		,      lowerCAmelCase__							)   ->						None:



        '''simple docstring'''




        a__	:					Union[str, Any]										=d

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

        a__	:					str										=spm.SentencePieceProcessor(**self.sp_model_kwargs							)
        self.sp_model.Load(self.vocab_file							)







      def    _lowercase  (    self							)   ->						Dict:



        '''simple docstring'''




        a__	:					Union[str, Any]										={self.convert_ids_to_tokens(lowerCAmelCase__							): i for i in range(self.vocab_size							)}
        vocab.update(self.added_tokens_encoder							)
        return vocab







      def    _lowercase  (    self		,      lowerCAmelCase__							)   ->						List[str]:



        '''simple docstring'''




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







      def    _lowercase  (    self		,      lowerCAmelCase__							)   ->						int:



        '''simple docstring'''




        if token in self.fairseq_tokens_to_ids:
          return self.fairseq_tokens_to_ids[token]
        a__	:					Union[str, Any]										=self.sp_model.PieceToId(lowerCAmelCase__							)

        # 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    _lowercase  (    self		,      lowerCAmelCase__							)   ->						str:



        '''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    _lowercase  (    self		,      lowerCAmelCase__							)   ->						Dict:



        '''simple docstring'''




        a__	:					str										=[]
        a__	:					Tuple										=""
        a__	:					str										=False
        for token in tokens:
          # make sure that special tokens are not decoded using sentencepiece model
          if token in self.all_special_tokens:
            if not prev_is_special:
              out_string += " "
            out_string += self.sp_model.decode(lowerCAmelCase__							) + token
            a__	:					Optional[int]										=True
            a__	:					Dict										=[]
          else:
            current_sub_tokens.append(lowerCAmelCase__							)
            a__	:					Optional[Any]										=False
        out_string += self.sp_model.decode(lowerCAmelCase__							)
        return out_string.strip()







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__ = None							)   ->						Tuple[str]:



        '''simple docstring'''




        if not os.path.isdir(lowerCAmelCase__							):
          logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''							)
          return
        a__	:					List[str]										=os.path.join(
            lowerCAmelCase__		,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]							)

        if os.path.abspath(self.vocab_file							) != os.path.abspath(lowerCAmelCase__							) and os.path.isfile(self.vocab_file							):
          copyfile(self.vocab_file		,      lowerCAmelCase__							)
        elif not os.path.isfile(self.vocab_file							):
          with open(lowerCAmelCase__		,      "wb"							) as fi:
            a__	:					Optional[int]										=self.sp_model.serialized_model_proto()
            fi.write(lowerCAmelCase__							)

        return (out_vocab_file,)







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = False							)   ->						List[int]:



        '''simple docstring'''




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

        a__	:					List[str]										=[1] * len(self.prefix_tokens							)
        a__	:					List[str]										=[1] * len(self.suffix_tokens							)
        if token_ids_a is None:
          return prefix_ones + ([0] * len(lowerCAmelCase__							)) + suffix_ones
        return prefix_ones + ([0] * len(lowerCAmelCase__							)) + ([0] * len(lowerCAmelCase__							)) + suffix_ones







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__ = 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    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__		,      **lowerCAmelCase__							)   ->						List[str]:



        '''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__	:					Any										=src_lang
        a__	:					Optional[int]										=self(lowerCAmelCase__		,      add_special_tokens=lowerCAmelCase__		,      return_tensors=lowerCAmelCase__		,      **lowerCAmelCase__							)
        a__	:					Any										=self.convert_tokens_to_ids(lowerCAmelCase__							)
        a__	:					int										=tgt_lang_id
        return inputs







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__ = "en_XX"		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = "ro_RO"		,      **lowerCAmelCase__		,      )   ->						BatchEncoding:



        '''simple docstring'''




        a__	:					Optional[Any]										=src_lang
        a__	:					Optional[int]										=tgt_lang
        return super().prepare_seqaseq_batch(lowerCAmelCase__		,      lowerCAmelCase__		,      **lowerCAmelCase__							)







      def    _lowercase  (    self							)   ->						Tuple:



        '''simple docstring'''




        return self.set_src_lang_special_tokens(self.src_lang							)







      def    _lowercase  (    self							)   ->						str:



        '''simple docstring'''




        return self.set_tgt_lang_special_tokens(self.tgt_lang							)







      def    _lowercase  (    self		,      lowerCAmelCase__							)   ->						None:



        '''simple docstring'''




        a__	:					Tuple										=self.lang_code_to_id[src_lang]
        a__	:					Optional[Any]										=[self.cur_lang_code_id]
        a__	:					Optional[int]										=[self.eos_token_id]







      def    _lowercase  (    self		,      lowerCAmelCase__							)   ->						None:



        '''simple docstring'''




        a__	:					Tuple										=self.lang_code_to_id[tgt_lang]
        a__	:					List[Any]										=[self.cur_lang_code_id]
        a__	:					str										=[self.eos_token_id]













import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import MobileViTImageProcessor







class 				__lowerCAmelCase       (    unittest.TestCase):







      def __init__(    self		,      lowerCAmelCase__		,      lowerCAmelCase__=7		,      lowerCAmelCase__=3		,      lowerCAmelCase__=1_8		,      lowerCAmelCase__=3_0		,      lowerCAmelCase__=4_0_0		,      lowerCAmelCase__=True		,      lowerCAmelCase__=None		,      lowerCAmelCase__=True		,      lowerCAmelCase__=None		,      lowerCAmelCase__=True		,      )   ->						Optional[Any]:



        '''simple docstring'''




        a__	:					Union[str, Any]										=size if size is not None else {"shortest_edge": 2_0}
        a__	:					List[str]										=crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
        a__	:					Tuple										=parent
        a__	:					Union[str, Any]										=batch_size
        a__	:					List[str]										=num_channels
        a__	:					List[Any]										=image_size
        a__	:					str										=min_resolution
        a__	:					Optional[int]										=max_resolution
        a__	:					Tuple										=do_resize
        a__	:					Union[str, Any]										=size
        a__	:					List[Any]										=do_center_crop
        a__	:					List[str]										=crop_size
        a__	:					Optional[int]										=do_flip_channel_order







      def    _lowercase  (    self							)   ->						Optional[int]:



        '''simple docstring'''




        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
            "do_flip_channel_order": self.do_flip_channel_order,
        }







@require_torch
@require_vision
class 				__lowerCAmelCase       (    UpperCamelCase__						,				unittest.TestCase):
      _lowercase				:					int						=			MobileViTImageProcessor if is_vision_available() else None







      def    _lowercase  (    self							)   ->						Optional[Any]:



        '''simple docstring'''




        a__	:					Tuple										=MobileViTImageProcessingTester(self							)







      @property
      def    _lowercase  (    self							)   ->						List[str]:



        '''simple docstring'''




        return self.image_processor_tester.prepare_image_processor_dict()







      def    _lowercase  (    self							)   ->						List[str]:



        '''simple docstring'''




        a__	:					str										=self.image_processing_class(**self.image_processor_dict							)
        self.assertTrue(hasattr(lowerCAmelCase__		,      "do_resize"							)							)
        self.assertTrue(hasattr(lowerCAmelCase__		,      "size"							)							)
        self.assertTrue(hasattr(lowerCAmelCase__		,      "do_center_crop"							)							)
        self.assertTrue(hasattr(lowerCAmelCase__		,      "center_crop"							)							)
        self.assertTrue(hasattr(lowerCAmelCase__		,      "do_flip_channel_order"							)							)







      def    _lowercase  (    self							)   ->						Optional[int]:



        '''simple docstring'''




        a__	:					List[Any]										=self.image_processing_class.from_dict(self.image_processor_dict							)
        self.assertEqual(image_processor.size		,      {"shortest_edge": 2_0}							)
        self.assertEqual(image_processor.crop_size		,      {"height": 1_8, "width": 1_8}							)

        a__	:					Union[str, Any]										=self.image_processing_class.from_dict(self.image_processor_dict		,      size=4_2		,      crop_size=8_4							)
        self.assertEqual(image_processor.size		,      {"shortest_edge": 4_2}							)
        self.assertEqual(image_processor.crop_size		,      {"height": 8_4, "width": 8_4}							)







      def    _lowercase  (    self							)   ->						Optional[Any]:



        '''simple docstring'''




        pass







      def    _lowercase  (    self							)   ->						Tuple:



        '''simple docstring'''




        a__	:					List[str]										=self.image_processing_class(**self.image_processor_dict							)
        # create random PIL images
        a__	:					Any										=prepare_image_inputs(self.image_processor_tester		,      equal_resolution=lowerCAmelCase__							)
        for image in image_inputs:
          self.assertIsInstance(lowerCAmelCase__		,      Image.Image							)

        # Test not batched input
        a__	:					Tuple										=image_processing(image_inputs[0]		,      return_tensors="pt"							).pixel_values
        self.assertEqual(
            encoded_images.shape		,      (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )		,      )

        # Test batched
        a__	:					List[Any]										=image_processing(lowerCAmelCase__		,      return_tensors="pt"							).pixel_values
        self.assertEqual(
            encoded_images.shape		,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )		,      )







      def    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        a__	:					Optional[Any]										=self.image_processing_class(**self.image_processor_dict							)
        # create random numpy tensors
        a__	:					List[Any]										=prepare_image_inputs(self.image_processor_tester		,      equal_resolution=lowerCAmelCase__		,      numpify=lowerCAmelCase__							)
        for image in image_inputs:
          self.assertIsInstance(lowerCAmelCase__		,      np.ndarray							)

        # Test not batched input
        a__	:					Tuple										=image_processing(image_inputs[0]		,      return_tensors="pt"							).pixel_values
        self.assertEqual(
            encoded_images.shape		,      (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )		,      )

        # Test batched
        a__	:					int										=image_processing(lowerCAmelCase__		,      return_tensors="pt"							).pixel_values
        self.assertEqual(
            encoded_images.shape		,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )		,      )







      def    _lowercase  (    self							)   ->						Optional[Any]:



        '''simple docstring'''




        a__	:					int										=self.image_processing_class(**self.image_processor_dict							)
        # create random PyTorch tensors
        a__	:					Tuple										=prepare_image_inputs(self.image_processor_tester		,      equal_resolution=lowerCAmelCase__		,      torchify=lowerCAmelCase__							)
        for image in image_inputs:
          self.assertIsInstance(lowerCAmelCase__		,      torch.Tensor							)

        # Test not batched input
        a__	:					List[str]										=image_processing(image_inputs[0]		,      return_tensors="pt"							).pixel_values
        self.assertEqual(
            encoded_images.shape		,      (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )		,      )

        # Test batched
        a__	:					List[str]										=image_processing(lowerCAmelCase__		,      return_tensors="pt"							).pixel_values
        self.assertEqual(
            encoded_images.shape		,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )		,      )






