


"""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


A     :   Any			    =			logging.get_logger(__name__)

A     :   int			    =			{"vocab_file": "sentencepiece.bpe.model"}

A     :   str			    =			{
    "vocab_file": {
        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez-orangesum-title": (
            "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"
        ),
    },
}

A     :   int			    =			{
    "moussaKam/mbarthez": 1_0_2_4,
    "moussaKam/barthez": 1_0_2_4,
    "moussaKam/barthez-orangesum-title": 1_0_2_4,
}

A     :   int			    =			"▁"
class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							__UpperCAmelCase						:    Any				      =VOCAB_FILES_NAMES
							__UpperCAmelCase						:    int				      =PRETRAINED_VOCAB_FILES_MAP
							__UpperCAmelCase						:    int				      =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__UpperCAmelCase						:    Dict				      =["""input_ids""", """attention_mask"""]

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

										__lowerCAmelCase				    =			{} if sp_model_kwargs is None else sp_model_kwargs

										super().__init__(
										    bos_token=__a    ,   eos_token=__a    ,   unk_token=__a    ,   sep_token=__a    ,   cls_token=__a    ,   pad_token=__a    ,   mask_token=__a    ,   sp_model_kwargs=self.sp_model_kwargs    ,   **__a    ,   )

										__lowerCAmelCase				    =			vocab_file
										__lowerCAmelCase				    =			spm.SentencePieceProcessor(**self.sp_model_kwargs	)
										self.sp_model.Load(str(__a	)	)

										__lowerCAmelCase				    =			{"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

										__lowerCAmelCase				    =			len(self.sp_model	) - 1
										__lowerCAmelCase				    =			{v: k for k, v in self.fairseq_tokens_to_ids.items()}

							def 			snake_case				(     self    ,   __a    ,   __a = None	):

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

							def 			snake_case				(     self    ,   __a    ,   __a = None    ,   __a = False	):
										if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=__a    ,   token_ids_a=__a    ,   already_has_special_tokens=__a	)

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

							def 			snake_case				(     self    ,   __a    ,   __a = None	):
										__lowerCAmelCase				    =			[self.sep_token_id]
										__lowerCAmelCase				    =			[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 			snake_case				(     self	):
										return len(self.sp_model	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			{self.convert_ids_to_tokens(__a	): i for i in range(self.vocab_size	)}
										vocab.update(self.added_tokens_encoder	)
										return vocab

							def 			snake_case				(     self    ,   __a	):
										return self.sp_model.encode(__a    ,   out_type=__a	)

							def 			snake_case				(     self    ,   __a	):
										if token in self.fairseq_tokens_to_ids:
													return self.fairseq_tokens_to_ids[token]
										__lowerCAmelCase				    =			self.sp_model.PieceToId(__a	)

										return spm_id if spm_id else self.unk_token_id

							def 			snake_case				(     self    ,   __a	):
										if index in self.fairseq_ids_to_tokens:
													return self.fairseq_ids_to_tokens[index]
										return self.sp_model.IdToPiece(__a	)

							def 			snake_case				(     self    ,   __a	):
										__lowerCAmelCase				    =			[]
										__lowerCAmelCase				    =			""
										__lowerCAmelCase				    =			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(__a	) + token
																__lowerCAmelCase				    =			True
																__lowerCAmelCase				    =			[]
													else:
																current_sub_tokens.append(__a	)
																__lowerCAmelCase				    =			False
										out_string += self.sp_model.decode(__a	)
										return out_string.strip()

							def __getstate__(     self	):
										__lowerCAmelCase				    =			self.__dict__.copy()
										__lowerCAmelCase				    =			None
										return state

							def __setstate__(     self    ,   __a	):
										__lowerCAmelCase				    =			d

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

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



							def 			snake_case				(     self    ,   __a    ,   __a = None	):
										if not os.path.isdir(__a	):
													logger.error(f"Vocabulary path ({save_directory}) should be a directory"	)
													return
										__lowerCAmelCase				    =			os.path.join(
										    __a    ,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)

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

										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


a   :			List[str]					  =    get_tests_dir("""fixtures""")

class 			UpperCamelCase_ (							unittest.TestCase			):





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

								# Download this model to make sure it's in the cache.
								UpperCAmelCase     :		Optional[int]							     =		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=A    ) as mock_head:
															UpperCAmelCase     :		Optional[int]							     =		ViTImageProcessor.from_pretrained("""hf-internal-testing/tiny-random-vit"""    )
															# This check we did call the fake head request
															mock_head.assert_called()





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






	def  _lowercase(  self    )			->			Union[str, Any]:
								with self.assertRaises(A    ):
															# config is in subfolder, the following should not work without specifying the subfolder
															UpperCAmelCase     :		Any							     =		AutoImageProcessor.from_pretrained("""hf-internal-testing/stable-diffusion-all-variants"""    )

								UpperCAmelCase     :		Union[str, Any]							     =		AutoImageProcessor.from_pretrained(
								    """hf-internal-testing/stable-diffusion-all-variants"""		,		subfolder="""feature_extractor"""    )

								self.assertIsNotNone(A    )




@is_staging_test
class 			UpperCamelCase_ (							unittest.TestCase			):





	@classmethod
	def  _lowercase(  cls    )			->			Dict:
								UpperCAmelCase     :		Tuple							     =		TOKEN
								HfFolder.save_token(A    )





	@classmethod
	def  _lowercase(  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  _lowercase(  self    )			->			Optional[int]:
								UpperCAmelCase     :		Union[str, Any]							     =		ViTImageProcessor.from_pretrained(A    )
								image_processor.push_to_hub("""test-image-processor"""		,		use_auth_token=self._token    )

								UpperCAmelCase     :		Optional[int]							     =		ViTImageProcessor.from_pretrained(f'''{USER}/test-image-processor'''    )
								for k, v in image_processor.__dict__.items():
															self.assertEqual(A		,		getattr(A		,		A    )    )

								# 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(
															    A		,		repo_id="""test-image-processor"""		,		push_to_hub=A		,		use_auth_token=self._token    )

								UpperCAmelCase     :		Tuple							     =		ViTImageProcessor.from_pretrained(f'''{USER}/test-image-processor'''    )
								for k, v in image_processor.__dict__.items():
															self.assertEqual(A		,		getattr(A		,		A    )    )





	def  _lowercase(  self    )			->			List[str]:
								UpperCAmelCase     :		List[str]							     =		ViTImageProcessor.from_pretrained(A    )
								image_processor.push_to_hub("""valid_org/test-image-processor"""		,		use_auth_token=self._token    )

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

								# 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(
															    A		,		repo_id="""valid_org/test-image-processor-org"""		,		push_to_hub=A		,		use_auth_token=self._token    )

								UpperCAmelCase     :		int							     =		ViTImageProcessor.from_pretrained("""valid_org/test-image-processor-org"""    )
								for k, v in image_processor.__dict__.items():
															self.assertEqual(A		,		getattr(A		,		A    )    )






	def  _lowercase(  self    )			->			Optional[int]:
								CustomImageProcessor.register_for_auto_class()
								UpperCAmelCase     :		Optional[Any]							     =		CustomImageProcessor.from_pretrained(A    )

								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"""}		,		)

								UpperCAmelCase     :		Union[str, Any]							     =		AutoImageProcessor.from_pretrained(
								    f'''{USER}/test-dynamic-image-processor'''		,		trust_remote_code=A    )
								# 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"""    )

