

'''simple docstring'''

import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


lowercase__         =      logging.get_logger(__name__)

lowercase__         =      "▁"

lowercase__         =      {
    "vocab_file": "vocab.json",
    "spm_file": "sentencepiece.bpe.model",
    "tokenizer_config_file": "tokenizer_config.json",
}

lowercase__         =      {
    "vocab_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json",
    },
    "spm_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_config_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json",
    },
}

lowercase__         =      {
    "facebook/m2m100_418M": 1024,
}

# fmt: off
lowercase__         =      {
    "m2m100": ["af", "am", "ar", "ast", "az", "ba", "be", "bg", "bn", "br", "bs", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "es", "et", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "ht", "hu", "hy", "id", "ig", "ilo", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "lb", "lg", "ln", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "ns", "oc", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "so", "sq", "sr", "ss", "su", "sv", "sw", "ta", "th", "tl", "tn", "tr", "uk", "ur", "uz", "vi", "wo", "xh", "yi", "yo", "zh", "zu"],
    "wmt21": ["en", "ha", "is", "ja", "cs", "ru", "zh", "de"]
}




class 	A_			(    _snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:     List[str]  		=  VOCAB_FILES_NAMES
     UpperCAmelCase_						:     Optional[Any]  		=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase_						:     Any  		=  PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase_						:     int  		=  ["""input_ids""", """attention_mask"""]

     UpperCAmelCase_						:     List[int]  		=  []
     UpperCAmelCase_						:     List[int]  		=  []


     def __init__(		self			:					List[Any]					,      lowercase_			:					List[Any]					,      lowercase_			:					Dict					,      lowercase_			:					int=None					,      lowercase_			:					Any=None					,      lowercase_			:					Optional[Any]="<s>"					,      lowercase_			:					Tuple="</s>"					,      lowercase_			:					List[str]="</s>"					,      lowercase_			:					Optional[Any]="<pad>"					,      lowercase_			:					Optional[Any]="<unk>"					,      lowercase_			:					Optional[Any]="m2m100"					,      lowercase_			:					Optional[Dict[str, Any]] = None					,      lowercase_			:					List[str]=8					,      **lowercase_			:					Tuple					,      )						->     None:
      UpperCAmelCase	:     Union[str, Any]						 =			{} if sp_model_kwargs is None else sp_model_kwargs

      UpperCAmelCase	:     Any						 =			language_codes
      UpperCAmelCase	:     Any						 =			FAIRSEQ_LANGUAGE_CODES[language_codes]
      UpperCAmelCase	:     List[Any]						 =			{lang_code: f"""__{lang_code}__""" for lang_code in fairseq_language_code}

      UpperCAmelCase	:     Optional[int]						 =			kwargs.get('additional_special_tokens'					,      []	)
      kwargs["additional_special_tokens"] += [
          self.get_lang_token(lowercase_	)
          for lang_code in fairseq_language_code
          if self.get_lang_token(lowercase_	) not in kwargs["additional_special_tokens"]
      ]

      super().__init__(
          src_lang=lowercase_					,      tgt_lang=lowercase_					,      bos_token=lowercase_					,      eos_token=lowercase_					,      sep_token=lowercase_					,      unk_token=lowercase_					,      pad_token=lowercase_					,      language_codes=lowercase_					,      sp_model_kwargs=self.sp_model_kwargs					,      num_madeup_words=lowercase_					,      **lowercase_					,      )

      UpperCAmelCase	:     Union[str, Any]						 =			vocab_file
      UpperCAmelCase	:     Union[str, Any]						 =			load_json(lowercase_	)
      UpperCAmelCase	:     Optional[int]						 =			{v: k for k, v in self.encoder.items()}
      UpperCAmelCase	:     Dict						 =			spm_file
      UpperCAmelCase	:     Union[str, Any]						 =			load_spm(lowercase_					,      self.sp_model_kwargs	)

      UpperCAmelCase	:     Optional[Any]						 =			len(self.encoder	)

      UpperCAmelCase	:     str						 =			{
          self.get_lang_token(lowercase_	): self.encoder_size + i for i, lang_code in enumerate(lowercase_	)
      }
      UpperCAmelCase	:     str						 =			{lang_code: self.encoder_size + i for i, lang_code in enumerate(lowercase_	)}
      UpperCAmelCase	:     str						 =			{v: k for k, v in self.lang_token_to_id.items()}

      UpperCAmelCase	:     int						 =			src_lang if src_lang is not None else 'en'
      UpperCAmelCase	:     List[Any]						 =			tgt_lang
      UpperCAmelCase	:     str						 =			self.get_lang_id(self._src_lang	)
      self.set_src_lang_special_tokens(self._src_lang	)

      UpperCAmelCase	:     Any						 =			num_madeup_words


     @property
     def        UpperCAmelCase_	(		self			:					Optional[int]	)						->     int:
      return len(self.encoder	) + len(self.lang_token_to_id	)


     @property
     def        UpperCAmelCase_	(		self			:					Tuple	)						->     str:
      return self._src_lang


     @src_lang.setter
     def        UpperCAmelCase_	(		self			:					Union[str, Any]					,      lowercase_			:					str	)						->     None:
      UpperCAmelCase	:     List[str]						 =			new_src_lang
      self.set_src_lang_special_tokens(self._src_lang	)


     def        UpperCAmelCase_	(		self			:					Tuple					,      lowercase_			:					str	)						->     List[str]:
      return self.sp_model.encode(lowercase_					,      out_type=lowercase_	)


     def        UpperCAmelCase_	(		self			:					List[Any]					,      lowercase_			:					Optional[Any]	)						->     Dict:
      if token in self.lang_token_to_id:
       return self.lang_token_to_id[token]
      return self.encoder.get(lowercase_					,      self.encoder[self.unk_token]	)


     def        UpperCAmelCase_	(		self			:					Union[str, Any]					,      lowercase_			:					int	)						->     str:
      if index in self.id_to_lang_token:
       return self.id_to_lang_token[index]
      return self.decoder.get(lowercase_					,      self.unk_token	)


     def        UpperCAmelCase_	(		self			:					Any					,      lowercase_			:					str	)						->     List[str]:
      UpperCAmelCase	:     Union[str, Any]						 =			[]
      UpperCAmelCase	:     Dict						 =			''
      for token in tokens:
       # make sure that special tokens are not decoded using sentencepiece model
       if token in self.all_special_tokens:
        out_string += self.sp_model.decode(lowercase_	) + token
        UpperCAmelCase	:     Optional[int]						 =			[]
       else:
        current_sub_tokens.append(lowercase_	)
      out_string += self.sp_model.decode(lowercase_	)
      return out_string.strip()


     def        UpperCAmelCase_	(		self			:					List[str]					,      lowercase_			:					List[int]					,      lowercase_			:					Optional[List[int]] = None					,      lowercase_			:					bool = False	)						->     List[int]:

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

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


     def        UpperCAmelCase_	(		self			:					Optional[Any]					,      lowercase_			:					List[int]					,      lowercase_			:					Optional[List[int]] = None	)						->     List[int]:
      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        UpperCAmelCase_	(		self			:					Dict	)						->     Dict:
      UpperCAmelCase	:     Any						 =			{self.convert_ids_to_tokens(lowercase_	): i for i in range(self.vocab_size	)}
      vocab.update(self.added_tokens_encoder	)
      return vocab


     def __getstate__(		self			:					Dict	)						->     Dict:
      UpperCAmelCase	:     Tuple						 =			self.__dict__.copy()
      UpperCAmelCase	:     str						 =			None
      return state


     def __setstate__(		self			:					int					,      lowercase_			:					Dict	)						->     None:
      UpperCAmelCase	:     Tuple						 =			d

      # for backward compatibility
      if not hasattr(self					,      'sp_model_kwargs'	):
       UpperCAmelCase	:     Union[str, Any]						 =			{}

      UpperCAmelCase	:     Tuple						 =			load_spm(self.spm_file					,      self.sp_model_kwargs	)


     def        UpperCAmelCase_	(		self			:					Dict					,      lowercase_			:					str					,      lowercase_			:					Optional[str] = None	)						->     Tuple[str]:
      UpperCAmelCase	:     List[Any]						 =			Path(lowercase_	)
      if not save_dir.is_dir():
       raise OSError(f"""{save_directory} should be a directory"""	)
      UpperCAmelCase	:     Optional[Any]						 =			save_dir / (
          (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file']
      )
      UpperCAmelCase	:     List[Any]						 =			save_dir / (
          (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['spm_file']
      )

      save_json(self.encoder					,      lowercase_	)

      if os.path.abspath(self.spm_file	) != os.path.abspath(lowercase_	) and os.path.isfile(self.spm_file	):
       copyfile(self.spm_file					,      lowercase_	)
      elif not os.path.isfile(self.spm_file	):
       with open(lowercase_					,      'wb'	) as fi:
        UpperCAmelCase	:     List[str]						 =			self.sp_model.serialized_model_proto()
        fi.write(lowercase_	)

      return (str(lowercase_	), str(lowercase_	))


     def        UpperCAmelCase_	(		self			:					Dict					,      lowercase_			:					List[str]					,      lowercase_			:					str = "en"					,      lowercase_			:					Optional[List[str]] = None					,      lowercase_			:					str = "ro"					,      **lowercase_			:					Tuple					,      )						->     BatchEncoding:
      UpperCAmelCase	:     List[str]						 =			src_lang
      UpperCAmelCase	:     Dict						 =			tgt_lang
      self.set_src_lang_special_tokens(self.src_lang	)
      return super().prepare_seqaseq_batch(lowercase_					,      lowercase_					,      **lowercase_	)


     def        UpperCAmelCase_	(		self			:					Optional[Any]					,      lowercase_			:					Union[str, Any]					,      lowercase_			:					Optional[str]					,      lowercase_			:					Optional[str]					,      **lowercase_			:					str	)						->     Union[str, Any]:
      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	:     List[Any]						 =			src_lang
      UpperCAmelCase	:     int						 =			self(lowercase_					,      add_special_tokens=lowercase_					,      **lowercase_	)
      UpperCAmelCase	:     Tuple						 =			self.get_lang_id(lowercase_	)
      UpperCAmelCase	:     str						 =			tgt_lang_id
      return inputs


     def        UpperCAmelCase_	(		self			:					List[Any]	)						->     Tuple:
      self.set_src_lang_special_tokens(self.src_lang	)


     def        UpperCAmelCase_	(		self			:					int	)						->     int:
      self.set_tgt_lang_special_tokens(self.tgt_lang	)


     def        UpperCAmelCase_	(		self			:					int					,      lowercase_			:					str	)						->     None:
      UpperCAmelCase	:     Optional[Any]						 =			self.get_lang_token(lowercase_	)
      UpperCAmelCase	:     Tuple						 =			self.lang_token_to_id[lang_token]
      UpperCAmelCase	:     int						 =			[self.cur_lang_id]
      UpperCAmelCase	:     Any						 =			[self.eos_token_id]


     def        UpperCAmelCase_	(		self			:					List[Any]					,      lowercase_			:					str	)						->     None:
      UpperCAmelCase	:     Any						 =			self.get_lang_token(lowercase_	)
      UpperCAmelCase	:     int						 =			self.lang_token_to_id[lang_token]
      UpperCAmelCase	:     int						 =			[self.cur_lang_id]
      UpperCAmelCase	:     List[str]						 =			[self.eos_token_id]


     def        UpperCAmelCase_	(		self			:					str					,      lowercase_			:					str	)						->     str:
      return self.lang_code_to_token[lang]




     def        UpperCAmelCase_	(		self			:					Any					,      lowercase_			:					str	)						->     int:
      UpperCAmelCase	:     str						 =			self.get_lang_token(lowercase_	)
      return self.lang_token_to_id[lang_token]




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     Optional[int]						 =			sentencepiece.SentencePieceProcessor(**UpperCAmelCase_     )
 spm.Load(str(UpperCAmelCase_     )     )
 return spm




def 				UpperCamelCase(					UpperCAmelCase_     ):
 with open(UpperCAmelCase_  ,	'r'     ) as f:
  return json.load(UpperCAmelCase_     )




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 with open(UpperCAmelCase_  ,	'w'     ) as f:
  json.dump(UpperCAmelCase_  ,	UpperCAmelCase_  ,	indent=2     )







'''simple docstring'''

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 PoolFormerImageProcessor




class 	A_			(    unittest.TestCase							):






     '''simple docstring'''



     def __init__(		self			:					Any					,      lowercase_			:					str					,      lowercase_			:					Union[str, Any]=7					,      lowercase_			:					Union[str, Any]=3					,      lowercase_			:					int=30					,      lowercase_			:					Tuple=400					,      lowercase_			:					Tuple=True					,      lowercase_			:					Optional[int]=None					,      lowercase_			:					List[str]=0.9					,      lowercase_			:					Tuple=None					,      lowercase_			:					Union[str, Any]=True					,      lowercase_			:					int=[0.5, 0.5, 0.5]					,      lowercase_			:					List[str]=[0.5, 0.5, 0.5]					,      )						->     Tuple:
      UpperCAmelCase	:     Optional[int]						 =			size if size is not None else {'shortest_edge': 30}
      UpperCAmelCase	:     int						 =			crop_size if crop_size is not None else {'height': 30, 'width': 30}
      UpperCAmelCase	:     Tuple						 =			parent
      UpperCAmelCase	:     Optional[Any]						 =			batch_size
      UpperCAmelCase	:     int						 =			num_channels
      UpperCAmelCase	:     int						 =			min_resolution
      UpperCAmelCase	:     Optional[int]						 =			max_resolution
      UpperCAmelCase	:     str						 =			do_resize_and_center_crop
      UpperCAmelCase	:     int						 =			size
      UpperCAmelCase	:     Dict						 =			crop_pct
      UpperCAmelCase	:     Union[str, Any]						 =			crop_size
      UpperCAmelCase	:     Optional[int]						 =			do_normalize
      UpperCAmelCase	:     Optional[Any]						 =			image_mean
      UpperCAmelCase	:     Optional[Any]						 =			image_std




     def        UpperCAmelCase_	(		self			:					str	)						->     int:
      return {
          "size": self.size,
          "do_resize_and_center_crop": self.do_resize_and_center_crop,
          "crop_pct": self.crop_pct,
          "crop_size": self.crop_size,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
      }




@require_torch
@require_vision
class 	A_			(    _snake_case	,       unittest.TestCase							):






     '''simple docstring'''



     UpperCAmelCase_						:     Tuple  		=  PoolFormerImageProcessor if is_vision_available() else None


     def        UpperCAmelCase_	(		self			:					Dict	)						->     str:
      UpperCAmelCase	:     Any						 =			PoolFormerImageProcessingTester(self	)


     @property
     def        UpperCAmelCase_	(		self			:					List[Any]	)						->     Union[str, Any]:
      return self.image_processor_tester.prepare_image_processor_dict()


     def        UpperCAmelCase_	(		self			:					Tuple	)						->     str:
      UpperCAmelCase	:     Any						 =			self.image_processing_class(**self.image_processor_dict	)
      self.assertTrue(hasattr(lowercase_					,      'do_resize_and_center_crop'	)	)
      self.assertTrue(hasattr(lowercase_					,      'size'	)	)
      self.assertTrue(hasattr(lowercase_					,      'crop_pct'	)	)
      self.assertTrue(hasattr(lowercase_					,      'do_normalize'	)	)
      self.assertTrue(hasattr(lowercase_					,      'image_mean'	)	)
      self.assertTrue(hasattr(lowercase_					,      'image_std'	)	)


     def        UpperCAmelCase_	(		self			:					Optional[Any]	)						->     Tuple:
      UpperCAmelCase	:     List[str]						 =			self.image_processing_class.from_dict(self.image_processor_dict	)
      self.assertEqual(image_processor.size					,      {'shortest_edge': 30}	)
      self.assertEqual(image_processor.crop_size					,      {'height': 30, 'width': 30}	)

      UpperCAmelCase	:     Any						 =			self.image_processing_class.from_dict(self.image_processor_dict					,      size=42					,      crop_size=84	)
      self.assertEqual(image_processor.size					,      {'shortest_edge': 42}	)
      self.assertEqual(image_processor.crop_size					,      {'height': 84, 'width': 84}	)


     def        UpperCAmelCase_	(		self			:					Union[str, Any]	)						->     str:
      pass


     def        UpperCAmelCase_	(		self			:					List[Any]	)						->     List[str]:
      # Initialize image_processing
      UpperCAmelCase	:     int						 =			self.image_processing_class(**self.image_processor_dict	)
      # create random PIL images
      UpperCAmelCase	:     Any						 =			prepare_image_inputs(self.image_processor_tester					,      equal_resolution=lowercase_	)
      for image in image_inputs:
       self.assertIsInstance(lowercase_					,      Image.Image	)

      # Test not batched input
      UpperCAmelCase	:     Dict						 =			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
      UpperCAmelCase	:     str						 =			image_processing(lowercase_					,      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        UpperCAmelCase_	(		self			:					List[Any]	)						->     Dict:
      # Initialize image_processing
      UpperCAmelCase	:     int						 =			self.image_processing_class(**self.image_processor_dict	)
      # create random numpy tensors
      UpperCAmelCase	:     Dict						 =			prepare_image_inputs(self.image_processor_tester					,      equal_resolution=lowercase_					,      numpify=lowercase_	)
      for image in image_inputs:
       self.assertIsInstance(lowercase_					,      np.ndarray	)

      # Test not batched input
      UpperCAmelCase	:     Optional[Any]						 =			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
      UpperCAmelCase	:     Optional[Any]						 =			image_processing(lowercase_					,      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        UpperCAmelCase_	(		self			:					str	)						->     Dict:
      # Initialize image_processing
      UpperCAmelCase	:     Optional[Any]						 =			self.image_processing_class(**self.image_processor_dict	)
      # create random PyTorch tensors
      UpperCAmelCase	:     Tuple						 =			prepare_image_inputs(self.image_processor_tester					,      equal_resolution=lowercase_					,      torchify=lowercase_	)
      for image in image_inputs:
       self.assertIsInstance(lowercase_					,      torch.Tensor	)

      # Test not batched input
      UpperCAmelCase	:     int						 =			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
      UpperCAmelCase	:     Optional[int]						 =			image_processing(lowercase_					,      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'],
          )					,      )




