





"""simple docstring"""






import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import ImageGPTImageProcessor




class 			__lowercase     (     unittest.TestCase							):







					'''simple docstring'''




					def __init__(  self		,     _UpperCAmelCase		,     _UpperCAmelCase=7		,     _UpperCAmelCase=3		,     _UpperCAmelCase=18		,     _UpperCAmelCase=30		,     _UpperCAmelCase=400		,     _UpperCAmelCase=True		,     _UpperCAmelCase=None		,     _UpperCAmelCase=True		,     ):
									__a	:  Tuple         =					size if size is not None else {'height': 18, 'width': 18}
									__a	:  Tuple         =					parent
									__a	:  Dict         =					batch_size
									__a	:  Tuple         =					num_channels
									__a	:  Union[str, Any]         =					image_size
									__a	:  Dict         =					min_resolution
									__a	:  Dict         =					max_resolution
									__a	:  Optional[int]         =					do_resize
									__a	:  Dict         =					size
									__a	:  Any         =					do_normalize







					def 	_lowerCamelCase						(  self       ):
									return {
									    # here we create 2 clusters for the sake of simplicity
									    "clusters": np.asarray(
									        [
									            [0.8_8_6_6_4_4_3_6_3_4_0_3_3_2_0_3, 0.6_6_1_8_8_2_9_3_6_9_5_4_4_9_8_3, 0.3_8_9_1_7_4_6_4_0_1_7_8_6_8_0_4],
									            [-0.6_0_4_2_5_5_9_1_4_6_8_8_1_1_0_4, -0.0_2_2_9_5_0_0_8_8_6_0_5_2_8_4_6_9, 0.5_4_2_3_7_9_7_3_6_9_0_0_3_2_9_6],
									        ]       ),
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_normalize": self.do_normalize,
									}






@require_torch
@require_vision
class 			__lowercase     (     __UpperCamelCase				, unittest.TestCase							):







					'''simple docstring'''




					__lowerCAmelCase					=					ImageGPTImageProcessor if is_vision_available() else None







					def 	_lowerCamelCase						(  self       ):
									__a	:  Optional[Any]         =					ImageGPTImageProcessingTester(self       )







					@property
					def 	_lowerCamelCase						(  self       ):
									return self.image_processor_tester.prepare_image_processor_dict()







					def 	_lowerCamelCase						(  self       ):
									__a	:  int         =					self.image_processing_class(**self.image_processor_dict       )
									self.assertTrue(hasattr(_UpperCAmelCase		,     '''clusters'''       )       )
									self.assertTrue(hasattr(_UpperCAmelCase		,     '''do_resize'''       )       )
									self.assertTrue(hasattr(_UpperCAmelCase		,     '''size'''       )       )
									self.assertTrue(hasattr(_UpperCAmelCase		,     '''do_normalize'''       )       )







					def 	_lowerCamelCase						(  self       ):
									__a	:  Optional[int]         =					self.image_processing_class.from_dict(self.image_processor_dict       )
									self.assertEqual(image_processor.size		,     {'''height''': 18, '''width''': 18}       )

									__a	:  Dict         =					self.image_processing_class.from_dict(self.image_processor_dict		,     size=42       )
									self.assertEqual(image_processor.size		,     {'''height''': 42, '''width''': 42}       )







					def 	_lowerCamelCase						(  self       ):
									__a	:  Tuple         =					self.image_processing_class(**self.image_processor_dict       )
									__a	:  Dict         =					json.loads(image_processor.to_json_string()       )
									for key, value in self.image_processor_dict.items():
													if key == "clusters":
																	self.assertTrue(np.array_equal(_UpperCAmelCase		,     obj[key]       )       )
													else:
																	self.assertEqual(obj[key]		,     _UpperCAmelCase       )







					def 	_lowerCamelCase						(  self       ):
									__a	:  Union[str, Any]         =					self.image_processing_class(**self.image_processor_dict       )

									with tempfile.TemporaryDirectory() as tmpdirname:
													__a	:  int         =					os.path.join(_UpperCAmelCase		,     '''image_processor.json'''       )
													image_processor_first.to_json_file(_UpperCAmelCase       )
													__a	:  List[Any]         =					self.image_processing_class.from_json_file(_UpperCAmelCase       ).to_dict()

									__a	:  Union[str, Any]         =					image_processor_first.to_dict()
									for key, value in image_processor_first.items():
													if key == "clusters":
																	self.assertTrue(np.array_equal(_UpperCAmelCase		,     image_processor_second[key]       )       )
													else:
																	self.assertEqual(image_processor_first[key]		,     _UpperCAmelCase       )







					def 	_lowerCamelCase						(  self       ):
									__a	:  List[str]         =					self.image_processing_class(**self.image_processor_dict       )

									with tempfile.TemporaryDirectory() as tmpdirname:
													image_processor_first.save_pretrained(_UpperCAmelCase       )
													__a	:  Dict         =					self.image_processing_class.from_pretrained(_UpperCAmelCase       ).to_dict()

									__a	:  Dict         =					image_processor_first.to_dict()
									for key, value in image_processor_first.items():
													if key == "clusters":
																	self.assertTrue(np.array_equal(_UpperCAmelCase		,     image_processor_second[key]       )       )
													else:
																	self.assertEqual(image_processor_first[key]		,     _UpperCAmelCase       )







					@unittest.skip('''ImageGPT requires clusters at initialization'''       )
					def 	_lowerCamelCase						(  self       ):
									pass






def       __A (     )						->						Optional[int]:
				__a	:  str         =					load_dataset('''hf-internal-testing/fixtures_image_utils'''				,    split='''test''')

				__a	:  int         =					Image.open(dataset[4]['''file'''])
				__a	:  Tuple         =					Image.open(dataset[5]['''file'''])

				__a	:  Tuple         =					[imagea, imagea]

				return images






@require_vision
@require_torch
class 			__lowercase     (     unittest.TestCase							):







					'''simple docstring'''




					@slow
					def 	_lowerCamelCase						(  self       ):
									__a	:  Any         =					ImageGPTImageProcessor.from_pretrained('''openai/imagegpt-small'''       )

									__a	:  List[Any]         =					prepare_images()

									# test non-batched
									__a	:  Optional[int]         =					image_processing(images[0]		,     return_tensors='''pt'''       )

									self.assertIsInstance(encoding.input_ids		,     torch.LongTensor       )
									self.assertEqual(encoding.input_ids.shape		,     (1, 1024)       )

									__a	:  Optional[int]         =					[306, 191, 191]
									self.assertEqual(encoding.input_ids[0, :3].tolist()		,     _UpperCAmelCase       )

									# test batched
									__a	:  int         =					image_processing(_UpperCAmelCase		,     return_tensors='''pt'''       )

									self.assertIsInstance(encoding.input_ids		,     torch.LongTensor       )
									self.assertEqual(encoding.input_ids.shape		,     (2, 1024)       )

									__a	:  Dict         =					[303, 13, 13]
									self.assertEqual(encoding.input_ids[1, -3:].tolist()		,     _UpperCAmelCase       )






"""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 logging


A	     =					logging.get_logger(__name__)
A	     =					{'''vocab_file''': '''spiece.model'''}

A	     =					{
    '''vocab_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/spiece.model''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/spiece.model''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/spiece.model''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model''',
    }
}

A	     =					{
    '''albert-base-v1''': 512,
    '''albert-large-v1''': 512,
    '''albert-xlarge-v1''': 512,
    '''albert-xxlarge-v1''': 512,
    '''albert-base-v2''': 512,
    '''albert-large-v2''': 512,
    '''albert-xlarge-v2''': 512,
    '''albert-xxlarge-v2''': 512,
}

A	     =					'''▁'''




class 			__lowercase     (     _UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase					=					VOCAB_FILES_NAMES
					__lowerCAmelCase					=					PRETRAINED_VOCAB_FILES_MAP
					__lowerCAmelCase					=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES







					def __init__(  self		,     _UpperCAmelCase		,     _UpperCAmelCase=True		,     _UpperCAmelCase=True		,     _UpperCAmelCase=False		,     _UpperCAmelCase="[CLS]"		,     _UpperCAmelCase="[SEP]"		,     _UpperCAmelCase="<unk>"		,     _UpperCAmelCase="[SEP]"		,     _UpperCAmelCase="<pad>"		,     _UpperCAmelCase="[CLS]"		,     _UpperCAmelCase="[MASK]"		,     _UpperCAmelCase = None		,     **_UpperCAmelCase		,     ):
									# Mask token behave like a normal word, i.e. include the space before it and
									# is included in the raw text, there should be a match in a non-normalized sentence.
									__a	:  int         =					(
									    AddedToken(_UpperCAmelCase		,     lstrip=_UpperCAmelCase		,     rstrip=_UpperCAmelCase		,     normalized=_UpperCAmelCase       )
									    if isinstance(_UpperCAmelCase		,     _UpperCAmelCase       )
									    else mask_token
									)

									__a	:  Any         =					{} if sp_model_kwargs is None else sp_model_kwargs

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

									__a	:  Tuple         =					do_lower_case
									__a	:  Optional[Any]         =					remove_space
									__a	:  Optional[Any]         =					keep_accents
									__a	:  Union[str, Any]         =					vocab_file

									__a	:  List[str]         =					spm.SentencePieceProcessor(**self.sp_model_kwargs       )
									self.sp_model.Load(_UpperCAmelCase       )







					@property
					def 	_lowerCamelCase						(  self       ):
									return len(self.sp_model       )







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







					def __getstate__(  self       ):
									__a	:  str         =					self.__dict__.copy()
									__a	:  Tuple         =					None
									return state







					def __setstate__(  self		,     _UpperCAmelCase       ):
									__a	:  Any         =					d

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

									__a	:  Dict         =					spm.SentencePieceProcessor(**self.sp_model_kwargs       )
									self.sp_model.Load(self.vocab_file       )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):
									if self.remove_space:
													__a	:  Any         =					''' '''.join(inputs.strip().split()       )
									else:
													__a	:  Tuple         =					inputs
									__a	:  Union[str, Any]         =					outputs.replace('''``'''		,     '''"'''       ).replace('''\'\''''		,     '''"'''       )

									if not self.keep_accents:
													__a	:  List[str]         =					unicodedata.normalize('''NFKD'''		,     _UpperCAmelCase       )
													__a	:  Optional[int]         =					''''''.join([c for c in outputs if not unicodedata.combining(_UpperCAmelCase       )]       )
									if self.do_lower_case:
													__a	:  Optional[Any]         =					outputs.lower()

									return outputs







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):
									__a	:  int         =					self.preprocess_text(_UpperCAmelCase       )
									__a	:  Tuple         =					self.sp_model.encode(_UpperCAmelCase		,     out_type=_UpperCAmelCase       )
									__a	:  int         =					[]
									for piece in pieces:
													if len(_UpperCAmelCase       ) > 1 and piece[-1] == str(''','''       ) and piece[-2].isdigit():
																	__a	:  List[str]         =					self.sp_model.EncodeAsPieces(piece[:-1].replace(_UpperCAmelCase		,     ''''''       )       )
																	if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																					if len(cur_pieces[0]       ) == 1:
																									__a	:  Tuple         =					cur_pieces[1:]
																					else:
																									__a	:  Optional[Any]         =					cur_pieces[0][1:]
																	cur_pieces.append(piece[-1]       )
																	new_pieces.extend(_UpperCAmelCase       )
													else:
																	new_pieces.append(_UpperCAmelCase       )

									return new_pieces







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):
									return self.sp_model.PieceToId(_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):
									return self.sp_model.IdToPiece(_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):
									__a	:  List[str]         =					[]
									__a	:  str         =					''''''
									__a	:  Any         =					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(_UpperCAmelCase       ) + token
																	__a	:  Tuple         =					True
																	__a	:  Tuple         =					[]
													else:
																	current_sub_tokens.append(_UpperCAmelCase       )
																	__a	:  Optional[int]         =					False
									out_string += self.sp_model.decode(_UpperCAmelCase       )
									return out_string.strip()







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase = None       ):
									__a	:  int         =					[self.sep_token_id]
									__a	:  Union[str, Any]         =					[self.cls_token_id]
									if token_ids_a is None:
													return cls + token_ids_a + sep
									return cls + token_ids_a + sep + token_ids_a + sep







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase = None		,     _UpperCAmelCase = False       ):

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

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







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase = None       ):
									__a	:  Union[str, Any]         =					[self.sep_token_id]
									__a	:  Tuple         =					[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       ) * [0] + len(token_ids_a + sep       ) * [1]







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

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

									return (out_vocab_file,)