

'''simple docstring'''





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

import sentencepiece as spm

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


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

__snake_case	: Optional[Any]       				=							{"vocab_file": "sentencepiece.bpe.model"}

__snake_case	: Dict       				=							{
    "vocab_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
    }
}

__snake_case	: str       				=							{
    "camembert-base": 512,
}

__snake_case	: Optional[int]       				=							"▁"
class        A  (	__UpperCAmelCase ):
						__UpperCAmelCase				:	List[Any]   =   VOCAB_FILES_NAMES
						__UpperCAmelCase				:	List[Any]   =   PRETRAINED_VOCAB_FILES_MAP
						__UpperCAmelCase				:	List[str]   =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__UpperCAmelCase				:	Union[str, Any]   =   ['''input_ids''', '''attention_mask''']





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

										_a        =   {} if sp_model_kwargs is None else sp_model_kwargs

										super().__init__(
										    bos_token=lowerCamelCase__	,    eos_token=lowerCamelCase__	,    unk_token=lowerCamelCase__	,    sep_token=lowerCamelCase__	,    cls_token=lowerCamelCase__	,    pad_token=lowerCamelCase__	,    mask_token=lowerCamelCase__	,    additional_special_tokens=lowerCamelCase__	,    sp_model_kwargs=self.sp_model_kwargs	,    **lowerCamelCase__	,    )
										_a        =   spm.SentencePieceProcessor(**self.sp_model_kwargs			)
										self.sp_model.Load(str(lowerCamelCase__			)			)
										_a        =   vocab_file
										# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
										# sentencepiece vocabulary (this is the case for <s> and </s>
										_a        =   {"""<s>NOTUSED""": 0, """<pad>""": 1, """</s>NOTUSED""": 2, """<unk>""": 3}
										_a        =   len(self.fairseq_tokens_to_ids			)
										_a        =   len(self.sp_model			) + len(self.fairseq_tokens_to_ids			)
										_a        =   {v: k for k, v in self.fairseq_tokens_to_ids.items()}





						def __lowerCAmelCase   (	self	,    snake_case_	,    snake_case_ = None			)			->							List[int]:

										if token_ids_a is None:
														return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
										_a        =   [self.cls_token_id]
										_a        =   [self.sep_token_id]
										return cls + token_ids_a + sep + sep + token_ids_a + sep





						def __lowerCAmelCase   (	self	,    snake_case_	,    snake_case_ = None	,    snake_case_ = False			)			->							List[int]:
										if already_has_special_tokens:
														return super().get_special_tokens_mask(
														    token_ids_a=lowerCamelCase__	,    token_ids_a=lowerCamelCase__	,    already_has_special_tokens=lowerCamelCase__			)

										if token_ids_a is None:
														return [1] + ([0] * len(lowerCamelCase__			)) + [1]
										return [1] + ([0] * len(lowerCamelCase__			)) + [1, 1] + ([0] * len(lowerCamelCase__			)) + [1]





						def __lowerCAmelCase   (	self	,    snake_case_	,    snake_case_ = None			)			->							List[int]:
										_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]





						@property
						def __lowerCAmelCase   (	self			)			->							Optional[Any]:
										return len(self.fairseq_tokens_to_ids			) + len(self.sp_model			)





						def __lowerCAmelCase   (	self			)			->							Dict:
										_a        =   {self.convert_ids_to_tokens(lowerCamelCase__			): i for i in range(self.vocab_size			)}
										vocab.update(self.added_tokens_encoder			)
										return vocab





						def __lowerCAmelCase   (	self	,    snake_case_			)			->							List[str]:
										return self.sp_model.encode(lowerCamelCase__	,    out_type=lowerCamelCase__			)





						def __lowerCAmelCase   (	self	,    snake_case_			)			->							str:
										if token in self.fairseq_tokens_to_ids:
														return self.fairseq_tokens_to_ids[token]
										elif self.sp_model.PieceToId(lowerCamelCase__			) == 0:
														# Convert sentence piece unk token to fairseq unk token index
														return self.unk_token_id
										return self.fairseq_offset + self.sp_model.PieceToId(lowerCamelCase__			)





						def __lowerCAmelCase   (	self	,    snake_case_			)			->							int:
										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	,    snake_case_			)			->							str:
										_a        =   []
										_a        =   """"""
										_a        =   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        =   True
																		_a        =   []
														else:
																		current_sub_tokens.append(lowerCamelCase__			)
																		_a        =   False
										out_string += self.sp_model.decode(lowerCamelCase__			)
										return out_string.strip()





						def __getstate__(	self			)			->							int:
										_a        =   self.__dict__.copy()
										_a        =   None
										return state





						def __setstate__(	self	,    snake_case_			)			->							int:
										_a        =   d

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

										_a        =   spm.SentencePieceProcessor(**self.sp_model_kwargs			)
										self.sp_model.Load(self.vocab_file			)





						def __lowerCAmelCase   (	self	,    snake_case_	,    snake_case_ = None			)			->							Tuple[str]:
										if not os.path.isdir(lowerCamelCase__			):
														logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''			)
														return
										_a        =   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        =   self.sp_model.serialized_model_proto()
																		fi.write(lowerCamelCase__			)

										return (out_vocab_file,)









"""simple docstring"""


import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / 'utils'))

from test_module.custom_image_processing import CustomImageProcessor  # noqa E402


__snake_case    					=					get_tests_dir('fixtures')

class 				_SCREAMING_SNAKE_CASE				(							unittest.TestCase  ):
   """simple docstring"""



   def 					UpperCAmelCase__(						self						)      ->							str:
    # A mock response for an HTTP head request to emulate server down
    lowercase__       :       List[str]				=						mock.Mock()
    lowercase__       :       Dict				=						500
    lowercase__       :       Any				=						{}
    lowercase__       :       int				=						HTTPError
    lowercase__       :       Optional[int]				=						{}

    # Download this model to make sure it's in the cache.
    lowercase__       :       Dict				=						ViTImageProcessor.from_pretrained("""hf-internal-testing/tiny-random-vit"""						)
    # Under the mock environment we get a 500 error when trying to reach the model.
    with mock.patch("""requests.Session.request""" ,     return_value=lowerCamelCase__						) as mock_head:
     lowercase__       :       Dict				=						ViTImageProcessor.from_pretrained("""hf-internal-testing/tiny-random-vit"""						)
     # This check we did call the fake head request
     mock_head.assert_called()


   def 					UpperCAmelCase__(						self						)      ->							Dict:
    # This test is for deprecated behavior and can be removed in v5
    lowercase__       :       Dict				=						ViTImageProcessor.from_pretrained(
        """https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json"""						)







   def 					UpperCAmelCase__(						self						)      ->							Tuple:
    with self.assertRaises(lowerCamelCase__						):
     # config is in subfolder, the following should not work without specifying the subfolder
     lowercase__       :       List[str]				=						AutoImageProcessor.from_pretrained("""hf-internal-testing/stable-diffusion-all-variants"""						)

    lowercase__       :       Any				=						AutoImageProcessor.from_pretrained(
        """hf-internal-testing/stable-diffusion-all-variants""" ,     subfolder="""feature_extractor"""						)

    self.assertIsNotNone(lowerCamelCase__						)

@is_staging_test
class 				_SCREAMING_SNAKE_CASE				(							unittest.TestCase  ):
   """simple docstring"""



   @classmethod
   def 					UpperCAmelCase__(						cls						)      ->							int:
    lowercase__       :       str				=						TOKEN
    HfFolder.save_token(lowerCamelCase__						)


   @classmethod
   def 					UpperCAmelCase__(						cls						)      ->							List[str]:
    try:
     delete_repo(token=cls._token ,     repo_id="""test-image-processor"""						)
    except HTTPError:
     pass

    try:
     delete_repo(token=cls._token ,     repo_id="""valid_org/test-image-processor-org"""						)
    except HTTPError:
     pass

    try:
     delete_repo(token=cls._token ,     repo_id="""test-dynamic-image-processor"""						)
    except HTTPError:
     pass


   def 					UpperCAmelCase__(						self						)      ->							int:
    lowercase__       :       List[str]				=						ViTImageProcessor.from_pretrained(lowerCamelCase__						)
    image_processor.push_to_hub("""test-image-processor""" ,     use_auth_token=self._token						)

    lowercase__       :       str				=						ViTImageProcessor.from_pretrained(F'''{USER}/test-image-processor'''						)
    for k, v in image_processor.__dict__.items():
     self.assertEqual(lowerCamelCase__ ,     getattr(lowerCamelCase__ ,     lowerCamelCase__						)						)

    # Reset repo
    delete_repo(token=self._token ,     repo_id="""test-image-processor"""						)

    # Push to hub via save_pretrained
    with tempfile.TemporaryDirectory() as tmp_dir:
     image_processor.save_pretrained(
         lowerCamelCase__ ,     repo_id="""test-image-processor""" ,     push_to_hub=lowerCamelCase__ ,     use_auth_token=self._token						)

    lowercase__       :       Union[str, Any]				=						ViTImageProcessor.from_pretrained(F'''{USER}/test-image-processor'''						)
    for k, v in image_processor.__dict__.items():
     self.assertEqual(lowerCamelCase__ ,     getattr(lowerCamelCase__ ,     lowerCamelCase__						)						)


   def 					UpperCAmelCase__(						self						)      ->							Optional[int]:
    lowercase__       :       Tuple				=						ViTImageProcessor.from_pretrained(lowerCamelCase__						)
    image_processor.push_to_hub("""valid_org/test-image-processor""" ,     use_auth_token=self._token						)

    lowercase__       :       Any				=						ViTImageProcessor.from_pretrained("""valid_org/test-image-processor"""						)
    for k, v in image_processor.__dict__.items():
     self.assertEqual(lowerCamelCase__ ,     getattr(lowerCamelCase__ ,     lowerCamelCase__						)						)

    # Reset repo
    delete_repo(token=self._token ,     repo_id="""valid_org/test-image-processor"""						)

    # Push to hub via save_pretrained
    with tempfile.TemporaryDirectory() as tmp_dir:
     image_processor.save_pretrained(
         lowerCamelCase__ ,     repo_id="""valid_org/test-image-processor-org""" ,     push_to_hub=lowerCamelCase__ ,     use_auth_token=self._token						)

    lowercase__       :       List[Any]				=						ViTImageProcessor.from_pretrained("""valid_org/test-image-processor-org"""						)
    for k, v in image_processor.__dict__.items():
     self.assertEqual(lowerCamelCase__ ,     getattr(lowerCamelCase__ ,     lowerCamelCase__						)						)







   def 					UpperCAmelCase__(						self						)      ->							Dict:
    CustomImageProcessor.register_for_auto_class()
    lowercase__       :       List[str]				=						CustomImageProcessor.from_pretrained(lowerCamelCase__						)

    image_processor.push_to_hub("""test-dynamic-image-processor""" ,     use_auth_token=self._token						)

    # This has added the proper auto_map field to the config
    self.assertDictEqual(
        image_processor.auto_map ,     {"""AutoImageProcessor""": """custom_image_processing.CustomImageProcessor"""} ,     )

    lowercase__       :       Dict				=						AutoImageProcessor.from_pretrained(
        F'''{USER}/test-dynamic-image-processor''' ,     trust_remote_code=lowerCamelCase__						)
    # Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
    self.assertEqual(new_image_processor.__class__.__name__ ,     """CustomImageProcessor"""						)