


"""simple docstring"""

from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder


# @@protoc_insertion_point(imports)

UpperCAmelCase__  							=_symbol_database.Default()


UpperCAmelCase__  							=_descriptor_pool.Default().AddSerializedFile(
    B"\n\x19sentencepiece_model.proto\x12\rsentencepiece\"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12\"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12\"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18  \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18\" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse\"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32\".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL\"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03"
)

UpperCAmelCase__  							=globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, "sentencepiece_model_pb2", _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
							UpperCAmelCase__  							=None
							UpperCAmelCase__  							=B"H\003"
							# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
							# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
							# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
							# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
							# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
							UpperCAmelCase__  							=45
							UpperCAmelCase__  							=1581
							UpperCAmelCase__  							=1517
							UpperCAmelCase__  							=1570
							UpperCAmelCase__  							=1584
							UpperCAmelCase__  							=1793
							UpperCAmelCase__  							=1795
							UpperCAmelCase__  							=1916
							UpperCAmelCase__  							=1864
							UpperCAmelCase__  							=1905
							UpperCAmelCase__  							=1919
							UpperCAmelCase__  							=2429
							UpperCAmelCase__  							=2208
							UpperCAmelCase__  							=2418
							UpperCAmelCase__  							=2323
							UpperCAmelCase__  							=2407
# @@protoc_insertion_point(module_scope)




"""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


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import ChineseCLIPImageProcessor





class    lowerCamelCase__						(		unittest.TestCase    ):
      def __init__(		self	:	List[Any]      ,       A_	:	List[str]      ,       A_	:	str=7      ,       A_	:	Optional[int]=3      ,       A_	:	str=1_8      ,       A_	:	Tuple=3_0      ,       A_	:	Optional[Any]=4_0_0      ,       A_	:	Union[str, Any]=True      ,       A_	:	str=None      ,       A_	:	str=True      ,       A_	:	Optional[Any]=None      ,       A_	:	List[Any]=True      ,       A_	:	Union[str, Any]=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]      ,       A_	:	str=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]      ,       A_	:	List[str]=True      ,       ):






       '''simple docstring'''


       __lowercase									=	size if size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
       __lowercase									=	crop_size if crop_size is not None else {"""height""": 1_8, """width""": 1_8}
       __lowercase									=	parent
       __lowercase									=	batch_size
       __lowercase									=	num_channels
       __lowercase									=	image_size
       __lowercase									=	min_resolution
       __lowercase									=	max_resolution
       __lowercase									=	do_resize
       __lowercase									=	size
       __lowercase									=	do_center_crop
       __lowercase									=	crop_size
       __lowercase									=	do_normalize
       __lowercase									=	image_mean
       __lowercase									=	image_std
       __lowercase									=	do_convert_rgb
      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]							):






       '''simple docstring'''


       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_center_crop": self.do_center_crop,
           "crop_size": self.crop_size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_convert_rgb": self.do_convert_rgb,
       }






      def SCREAMING_SNAKE_CASE_	(		self	:	str      ,       A_	:	List[Any]=False      ,       A_	:	List[Any]=False      ,       A_	:	Optional[Any]=False							):






       '''simple docstring'''


       assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

       if equal_resolution:
        __lowercase									=	[]
        for i in range(self.batch_size							):
         image_inputs.append(
             np.random.randint(
                 2_5_5      ,       size=(self.num_channels, self.max_resolution, self.max_resolution)      ,       dtype=np.uinta							)							)
       else:
        __lowercase									=	[]
        for i in range(self.batch_size							):
         __lowercase							,		__lowercase									=	np.random.choice(np.arange(self.min_resolution      ,       self.max_resolution							)      ,       2							)
         image_inputs.append(np.random.randint(2_5_5      ,       size=(self.num_channels, width, height)      ,       dtype=np.uinta							)							)

       if not numpify and not torchify:
        # PIL expects the channel dimension as last dimension
        __lowercase									=	[Image.fromarray(np.moveaxis(A_      ,       0      ,       -1							)							) for x in image_inputs]

       if torchify:
        __lowercase									=	[torch.from_numpy(A_							) for x in image_inputs]

       return image_inputs






@require_torch
@require_vision
class    lowerCamelCase__						(		_a			,   unittest.TestCase    ):
      a :     Union[str, Any]											=		ChineseCLIPImageProcessor if is_vision_available() else None
      def SCREAMING_SNAKE_CASE_	(		self	:	Tuple							):






       '''simple docstring'''


       __lowercase									=	ChineseCLIPImageProcessingTester(self      ,       do_center_crop=A_							)
      @property
      def SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]							):






       '''simple docstring'''


       return self.image_processor_tester.prepare_image_processor_dict()
      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]							):






       '''simple docstring'''


       __lowercase									=	self.image_processing_class(**self.image_processor_dict							)
       self.assertTrue(hasattr(A_      ,       """do_resize"""							)							)
       self.assertTrue(hasattr(A_      ,       """size"""							)							)
       self.assertTrue(hasattr(A_      ,       """do_center_crop"""							)							)
       self.assertTrue(hasattr(A_      ,       """center_crop"""							)							)
       self.assertTrue(hasattr(A_      ,       """do_normalize"""							)							)
       self.assertTrue(hasattr(A_      ,       """image_mean"""							)							)
       self.assertTrue(hasattr(A_      ,       """image_std"""							)							)
       self.assertTrue(hasattr(A_      ,       """do_convert_rgb"""							)							)
      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]							):






       '''simple docstring'''


       __lowercase									=	self.image_processing_class.from_dict(self.image_processor_dict							)
       self.assertEqual(image_processor.size      ,       {"""height""": 2_2_4, """width""": 2_2_4}							)
       self.assertEqual(image_processor.crop_size      ,       {"""height""": 1_8, """width""": 1_8}							)

       __lowercase									=	self.image_processing_class.from_dict(self.image_processor_dict      ,       size=4_2      ,       crop_size=8_4							)
       self.assertEqual(image_processor.size      ,       {"""shortest_edge""": 4_2}							)
       self.assertEqual(image_processor.crop_size      ,       {"""height""": 8_4, """width""": 8_4}							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[Any]							):






       '''simple docstring'''


       pass
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]							):






       '''simple docstring'''


       __lowercase									=	self.image_processing_class(**self.image_processor_dict							)
       # create random PIL images
       __lowercase									=	self.image_processor_tester.prepare_inputs(equal_resolution=A_							)
       for image in image_inputs:
        self.assertIsInstance(A_      ,       Image.Image							)

       # Test not batched input
       __lowercase									=	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
       __lowercase									=	image_processing(A_      ,       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 SCREAMING_SNAKE_CASE_	(		self	:	Any							):






       '''simple docstring'''


       __lowercase									=	self.image_processing_class(**self.image_processor_dict							)
       # create random numpy tensors
       __lowercase									=	self.image_processor_tester.prepare_inputs(equal_resolution=A_      ,       numpify=A_							)
       for image in image_inputs:
        self.assertIsInstance(A_      ,       np.ndarray							)

       # Test not batched input
       __lowercase									=	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
       __lowercase									=	image_processing(A_      ,       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 SCREAMING_SNAKE_CASE_	(		self	:	Dict							):






       '''simple docstring'''


       __lowercase									=	self.image_processing_class(**self.image_processor_dict							)
       # create random PyTorch tensors
       __lowercase									=	self.image_processor_tester.prepare_inputs(equal_resolution=A_      ,       torchify=A_							)
       for image in image_inputs:
        self.assertIsInstance(A_      ,       torch.Tensor							)

       # Test not batched input
       __lowercase									=	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
       __lowercase									=	image_processing(A_      ,       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"""],
           )      ,       )






@require_torch
@require_vision
class    lowerCamelCase__						(		_a			,   unittest.TestCase    ):
      a :     Optional[Any]											=		ChineseCLIPImageProcessor if is_vision_available() else None
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[Any]							):






       '''simple docstring'''


       __lowercase									=	ChineseCLIPImageProcessingTester(self      ,       num_channels=4      ,       do_center_crop=A_							)
       __lowercase									=	3
      @property
      def SCREAMING_SNAKE_CASE_	(		self	:	str							):






       '''simple docstring'''


       return self.image_processor_tester.prepare_image_processor_dict()
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]							):






       '''simple docstring'''


       __lowercase									=	self.image_processing_class(**self.image_processor_dict							)
       self.assertTrue(hasattr(A_      ,       """do_resize"""							)							)
       self.assertTrue(hasattr(A_      ,       """size"""							)							)
       self.assertTrue(hasattr(A_      ,       """do_center_crop"""							)							)
       self.assertTrue(hasattr(A_      ,       """center_crop"""							)							)
       self.assertTrue(hasattr(A_      ,       """do_normalize"""							)							)
       self.assertTrue(hasattr(A_      ,       """image_mean"""							)							)
       self.assertTrue(hasattr(A_      ,       """image_std"""							)							)
       self.assertTrue(hasattr(A_      ,       """do_convert_rgb"""							)							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]							):






       '''simple docstring'''


       pass






      def SCREAMING_SNAKE_CASE_	(		self	:	str							):






       '''simple docstring'''


       __lowercase									=	self.image_processing_class(**self.image_processor_dict							)
       # create random PIL images
       __lowercase									=	self.image_processor_tester.prepare_inputs(equal_resolution=A_							)
       for image in image_inputs:
        self.assertIsInstance(A_      ,       Image.Image							)

       # Test not batched input
       __lowercase									=	image_processing(image_inputs[0]      ,       return_tensors="""pt"""							).pixel_values
       self.assertEqual(
           encoded_images.shape      ,       (
               1,
               self.expected_encoded_image_num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )      ,       )

       # Test batched
       __lowercase									=	image_processing(A_      ,       return_tensors="""pt"""							).pixel_values
       self.assertEqual(
           encoded_images.shape      ,       (
               self.image_processor_tester.batch_size,
               self.expected_encoded_image_num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )      ,       )
