



'''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


lowerCamelCase							:Dict       = get_tests_dir('''fixtures''')







class      _lowerCAmelCase							(      unittest.TestCase			):


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

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


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


	def    _a     (self							):
				with self.assertRaises(lowercase							):
							# config is in subfolder, the following should not work without specifying the subfolder
							A_     :						Optional[Any]     =					AutoImageProcessor.from_pretrained("""hf-internal-testing/stable-diffusion-all-variants"""							)

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

				self.assertIsNotNone(lowercase							)







@is_staging_test
class      _lowerCAmelCase							(      unittest.TestCase			):


	@classmethod
	def    _a     (cls							):
				A_     :						Optional[Any]     =					TOKEN
				HfFolder.save_token(lowercase							)


	@classmethod
	def    _a     (cls							):
				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    _a     (self							):
				A_     :						Union[str, Any]     =					ViTImageProcessor.from_pretrained(lowercase							)
				image_processor.push_to_hub("""test-image-processor"""	,				use_auth_token=self._token							)

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

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

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


	def    _a     (self							):
				A_     :						Optional[int]     =					ViTImageProcessor.from_pretrained(lowercase							)
				image_processor.push_to_hub("""valid_org/test-image-processor"""	,				use_auth_token=self._token							)

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

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

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


	def    _a     (self							):
				CustomImageProcessor.register_for_auto_class()
				A_     :						Tuple     =					CustomImageProcessor.from_pretrained(lowercase							)

				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"""}	,				)

				A_     :						Any     =					AutoImageProcessor.from_pretrained(
				    F'{USER}/test-dynamic-image-processor'	,				trust_remote_code=lowercase							)
				# 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"""							)




'''simple docstring'''




import os
import unicodedata
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 SPIECE_UNDERLINE, logging


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

lowerCamelCase							:Union[str, Any]       = {'''vocab_file''': '''spiece.model'''}

lowerCamelCase							:Tuple       = {
    '''vocab_file''': {
        '''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model''',
    }
}







class      _lowerCAmelCase							(      __UpperCAmelCase			):


	def __init__(self	,				lowercase	,				lowercase=False	,				lowercase=True	,				lowercase=False	,				lowercase="<s>"	,				lowercase="</s>"	,				lowercase="<unk>"	,				lowercase="<sep>"	,				lowercase="<pad>"	,				lowercase="<cls>"	,				lowercase="<mask>"	,				lowercase=["<eop>", "<eod>"]	,				lowercase = None	,				**lowercase	,				):
				A_     :						Optional[int]     =					AddedToken(lowercase	,				lstrip=lowercase	,				rstrip=lowercase							) if isinstance(lowercase	,				lowercase							) else mask_token

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

				super().__init__(
				    do_lower_case=lowercase	,				remove_space=lowercase	,				keep_accents=lowercase	,				bos_token=lowercase	,				eos_token=lowercase	,				unk_token=lowercase	,				sep_token=lowercase	,				pad_token=lowercase	,				cls_token=lowercase	,				mask_token=lowercase	,				additional_special_tokens=lowercase	,				sp_model_kwargs=self.sp_model_kwargs	,				**lowercase	,				)

				A_     :						List[str]     =					3

				A_     :						List[str]     =					do_lower_case
				A_     :						Tuple     =					remove_space
				A_     :						Tuple     =					keep_accents
				A_     :						Tuple     =					vocab_file

				A_     :						int     =					spm.SentencePieceProcessor(**self.sp_model_kwargs							)
				self.sp_model.Load(lowercase							)

				try:
							import jieba
				except ModuleNotFoundError as error:
							raise error.__class__(
							    """You need to install jieba to use CpmTokenizer or CpmTokenizerFast. """
							    """See https://pypi.org/project/jieba/ for installation."""							)
				A_     :						Tuple     =					jieba
				A_     :						int     =					str.maketrans(""" \n"""	,				"""\u2582\u2583"""							)


	@property
	# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
	def    _a     (self							):
				return len(self.sp_model							)


	def    _a     (self							):
				A_     :						str     =					{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							):
				A_     :						Optional[Any]     =					self.__dict__.copy()
				A_     :						Tuple     =					None
				return state


	def __setstate__(self	,				lowercase							):
				A_     :						Tuple     =					d

				# for backward compatibility
				if not hasattr(self	,				"""sp_model_kwargs"""							):
							A_     :						List[Any]     =					{}

				A_     :						int     =					spm.SentencePieceProcessor(**self.sp_model_kwargs							)
				self.sp_model.Load(self.vocab_file							)


	def    _a     (self	,				lowercase							):
				if self.remove_space:
							A_     :						Union[str, Any]     =					""" """.join(inputs.strip().split()							)
				else:
							A_     :						Optional[int]     =					inputs
				A_     :						List[str]     =					outputs.replace("""``"""	,				"""\""""							).replace("""''"""	,				"""\""""							)

				if not self.keep_accents:
							A_     :						str     =					unicodedata.normalize("""NFKD"""	,				lowercase							)
							A_     :						Union[str, Any]     =					"""""".join([c for c in outputs if not unicodedata.combining(lowercase							)]							)
				if self.do_lower_case:
							A_     :						int     =					outputs.lower()

				return outputs


	def    _a     (self	,				lowercase							):
				A_     :						Optional[int]     =					self.preprocess_text(lowercase							)
				A_     :						Dict     =					self.sp_model.encode(lowercase	,				out_type=lowercase							)
				A_     :						List[Any]     =					[]
				for piece in pieces:
							if len(lowercase							) > 1 and piece[-1] == str(""","""							) and piece[-2].isdigit():
										A_     :						Union[str, Any]     =					self.sp_model.EncodeAsPieces(piece[:-1].replace(lowercase	,				""""""							)							)
										if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
													if len(cur_pieces[0]							) == 1:
																A_     :						Dict     =					cur_pieces[1:]
													else:
																A_     :						str     =					cur_pieces[0][1:]
										cur_pieces.append(piece[-1]							)
										new_pieces.extend(lowercase							)
							else:
										new_pieces.append(lowercase							)

				return new_pieces


	def    _a     (self	,				lowercase							):
				return self.sp_model.PieceToId(lowercase							)


	def    _a     (self	,				lowercase							):
				return self.sp_model.IdToPiece(lowercase							)


	def    _a     (self	,				lowercase							):
				A_     :						int     =					"""""".join(lowercase							).replace(lowercase	,				""" """							).strip()
				return out_string


	def    _a     (self	,				lowercase	,				lowercase = None							):
				A_     :						List[str]     =					[self.sep_token_id]
				A_     :						Any     =					[self.cls_token_id]
				if token_ids_a is None:
							return token_ids_a + sep + cls
				return token_ids_a + sep + token_ids_a + sep + cls


	def    _a     (self	,				lowercase	,				lowercase = None	,				lowercase = False							):

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

				if token_ids_a is not None:
							return ([0] * len(lowercase							)) + [1] + ([0] * len(lowercase							)) + [1, 1]
				return ([0] * len(lowercase							)) + [1, 1]


	def    _a     (self	,				lowercase	,				lowercase = None							):
				A_     :						str     =					[self.sep_token_id]
				A_     :						str     =					[2]

				if token_ids_a is None:
							return len(token_ids_a + sep							) * [0] + cls_segment_id
				return len(token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1] + cls_segment_id


	def    _a     (self	,				lowercase	,				lowercase = None							):
				if not os.path.isdir(lowercase							):
							logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
							return
				A_     :						Tuple     =					os.path.join(
				    lowercase	,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)

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

				return (out_vocab_file,)


	def    _a     (self	,				*lowercase	,				**lowercase							):
				A_     :						Any     =					super()._decode(*lowercase	,				**lowercase							)
				A_     :						int     =					text.replace(""" """	,				""""""							).replace("""\u2582"""	,				""" """							).replace("""\u2583"""	,				"""\n"""							)
				return text