


"""simple docstring"""






import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
							from PIL import Image

							from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor





@require_vision
class      lowerCamelCase__   (     unittest.TestCase				):







					"""simple docstring"""


					def lowerCamelCase__							(       self    : str  ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[Any]      							=					tempfile.mkdtemp()

												__UpperCAmelCase   :						Tuple      							=					[
												    """[UNK]""",
												    """[CLS]""",
												    """[SEP]""",
												    """[PAD]""",
												    """[MASK]""",
												    """的""",
												    """价""",
												    """格""",
												    """是""",
												    """15""",
												    """便""",
												    """alex""",
												    """##andra""",
												    """，""",
												    """。""",
												    """-""",
												    """t""",
												    """shirt""",
												]
												__UpperCAmelCase   :						str      							=					os.path.join(self.tmpdirname ,       VOCAB_FILES_NAMES["""vocab_file"""]  )
												with open(self.vocab_file ,       """w""" ,       encoding="""utf-8"""  ) as vocab_writer:
																			vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]  )  )

												__UpperCAmelCase   :						Tuple      							=					{
												    """do_resize""": True,
												    """size""": {"""height""": 224, """width""": 224},
												    """do_center_crop""": True,
												    """crop_size""": {"""height""": 18, """width""": 18},
												    """do_normalize""": True,
												    """image_mean""": [0.48145466, 0.4578275, 0.40821073],
												    """image_std""": [0.26862954, 0.26130258, 0.27577711],
												    """do_convert_rgb""": True,
												}
												__UpperCAmelCase   :						str      							=					os.path.join(self.tmpdirname ,       UpperCamelCase  )
												with open(self.image_processor_file ,       """w""" ,       encoding="""utf-8"""  ) as fp:
																			json.dump(UpperCamelCase ,       UpperCamelCase  )






					def lowerCamelCase__							(       self    : Tuple ,       **UpperCamelCase    : Union[str, Any]  ):
												'''simple docstring'''






												return BertTokenizer.from_pretrained(self.tmpdirname ,       **UpperCamelCase  )






					def lowerCamelCase__							(       self    : List[str] ,       **UpperCamelCase    : int  ):
												'''simple docstring'''






												return BertTokenizerFast.from_pretrained(self.tmpdirname ,       **UpperCamelCase  )






					def lowerCamelCase__							(       self    : Any ,       **UpperCamelCase    : Union[str, Any]  ):
												'''simple docstring'''






												return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname ,       **UpperCamelCase  )






					def lowerCamelCase__							(       self    : List[Any]  ):
												'''simple docstring'''






												shutil.rmtree(self.tmpdirname  )






					def lowerCamelCase__							(       self    : str  ):
												'''simple docstring'''






												__UpperCAmelCase   :						str      							=					[np.random.randint(255 ,       size=(3, 30, 400) ,       dtype=np.uinta  )]

												__UpperCAmelCase   :						List[Any]      							=					[Image.fromarray(np.moveaxis(UpperCamelCase ,       0 ,       -1  )  ) for x in image_inputs]

												return image_inputs






					def lowerCamelCase__							(       self    : Union[str, Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						str      							=					self.get_tokenizer()
												__UpperCAmelCase   :						Tuple      							=					self.get_rust_tokenizer()
												__UpperCAmelCase   :						Dict      							=					self.get_image_processor()

												__UpperCAmelCase   :						Optional[Any]      							=					ChineseCLIPProcessor(tokenizer=UpperCamelCase ,       image_processor=UpperCamelCase  )
												processor_slow.save_pretrained(self.tmpdirname  )
												__UpperCAmelCase   :						Optional[Any]      							=					ChineseCLIPProcessor.from_pretrained(self.tmpdirname ,       use_fast=UpperCamelCase  )

												__UpperCAmelCase   :						Optional[Any]      							=					ChineseCLIPProcessor(tokenizer=UpperCamelCase ,       image_processor=UpperCamelCase  )
												processor_fast.save_pretrained(self.tmpdirname  )
												__UpperCAmelCase   :						List[str]      							=					ChineseCLIPProcessor.from_pretrained(self.tmpdirname  )

												self.assertEqual(processor_slow.tokenizer.get_vocab() ,       tokenizer_slow.get_vocab()  )
												self.assertEqual(processor_fast.tokenizer.get_vocab() ,       tokenizer_fast.get_vocab()  )
												self.assertEqual(tokenizer_slow.get_vocab() ,       tokenizer_fast.get_vocab()  )
												self.assertIsInstance(processor_slow.tokenizer ,       UpperCamelCase  )
												self.assertIsInstance(processor_fast.tokenizer ,       UpperCamelCase  )

												self.assertEqual(processor_slow.image_processor.to_json_string() ,       image_processor.to_json_string()  )
												self.assertEqual(processor_fast.image_processor.to_json_string() ,       image_processor.to_json_string()  )
												self.assertIsInstance(processor_slow.image_processor ,       UpperCamelCase  )
												self.assertIsInstance(processor_fast.image_processor ,       UpperCamelCase  )






					def lowerCamelCase__							(       self    : str  ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[str]      							=					ChineseCLIPProcessor(tokenizer=self.get_tokenizer() ,       image_processor=self.get_image_processor()  )
												processor.save_pretrained(self.tmpdirname  )

												__UpperCAmelCase   :						List[str]      							=					self.get_tokenizer(cls_token="""(CLS)""" ,       sep_token="""(SEP)"""  )
												__UpperCAmelCase   :						Dict      							=					self.get_image_processor(do_normalize=UpperCamelCase  )

												__UpperCAmelCase   :						Dict      							=					ChineseCLIPProcessor.from_pretrained(
												    self.tmpdirname ,       cls_token="""(CLS)""" ,       sep_token="""(SEP)""" ,       do_normalize=UpperCamelCase  )

												self.assertEqual(processor.tokenizer.get_vocab() ,       tokenizer_add_kwargs.get_vocab()  )
												self.assertIsInstance(processor.tokenizer ,       UpperCamelCase  )

												self.assertEqual(processor.image_processor.to_json_string() ,       image_processor_add_kwargs.to_json_string()  )
												self.assertIsInstance(processor.image_processor ,       UpperCamelCase  )






					def lowerCamelCase__							(       self    : List[Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Any      							=					self.get_image_processor()
												__UpperCAmelCase   :						Union[str, Any]      							=					self.get_tokenizer()

												__UpperCAmelCase   :						Dict      							=					ChineseCLIPProcessor(tokenizer=UpperCamelCase ,       image_processor=UpperCamelCase  )

												__UpperCAmelCase   :						List[Any]      							=					self.prepare_image_inputs()

												__UpperCAmelCase   :						Dict      							=					image_processor(UpperCamelCase ,       return_tensors="""np"""  )
												__UpperCAmelCase   :						List[str]      							=					processor(images=UpperCamelCase ,       return_tensors="""np"""  )

												for key in input_feat_extract.keys():
																			self.assertAlmostEqual(input_feat_extract[key].sum() ,       input_processor[key].sum() ,       delta=1e-2  )






					def lowerCamelCase__							(       self    : List[Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Optional[int]      							=					self.get_image_processor()
												__UpperCAmelCase   :						Tuple      							=					self.get_tokenizer()

												__UpperCAmelCase   :						Tuple      							=					ChineseCLIPProcessor(tokenizer=UpperCamelCase ,       image_processor=UpperCamelCase  )

												__UpperCAmelCase   :						Optional[int]      							=					"""Alexandra，T-shirt的价格是15便士。"""

												__UpperCAmelCase   :						Dict      							=					processor(text=UpperCamelCase  )

												__UpperCAmelCase   :						str      							=					tokenizer(UpperCamelCase  )

												for key in encoded_tok.keys():
																			self.assertListEqual(encoded_tok[key] ,       encoded_processor[key]  )






					def lowerCamelCase__							(       self    : List[Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Dict      							=					self.get_image_processor()
												__UpperCAmelCase   :						Tuple      							=					self.get_tokenizer()

												__UpperCAmelCase   :						int      							=					ChineseCLIPProcessor(tokenizer=UpperCamelCase ,       image_processor=UpperCamelCase  )

												__UpperCAmelCase   :						Tuple      							=					"""Alexandra，T-shirt的价格是15便士。"""
												__UpperCAmelCase   :						List[str]      							=					self.prepare_image_inputs()

												__UpperCAmelCase   :						int      							=					processor(text=UpperCamelCase ,       images=UpperCamelCase  )

												self.assertListEqual(list(inputs.keys()  ) ,       ["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""]  )

												# test if it raises when no input is passed
												with pytest.raises(UpperCamelCase  ):
																			processor()






					def lowerCamelCase__							(       self    : int  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Optional[Any]      							=					self.get_image_processor()
												__UpperCAmelCase   :						Tuple      							=					self.get_tokenizer()

												__UpperCAmelCase   :						Any      							=					ChineseCLIPProcessor(tokenizer=UpperCamelCase ,       image_processor=UpperCamelCase  )

												__UpperCAmelCase   :						Optional[int]      							=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

												__UpperCAmelCase   :						Union[str, Any]      							=					processor.batch_decode(UpperCamelCase  )
												__UpperCAmelCase   :						Tuple      							=					tokenizer.batch_decode(UpperCamelCase  )

												self.assertListEqual(UpperCamelCase ,       UpperCamelCase  )






					def lowerCamelCase__							(       self    : List[str]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Any      							=					self.get_image_processor()
												__UpperCAmelCase   :						List[Any]      							=					self.get_tokenizer()

												__UpperCAmelCase   :						Optional[int]      							=					ChineseCLIPProcessor(tokenizer=UpperCamelCase ,       image_processor=UpperCamelCase  )

												__UpperCAmelCase   :						List[str]      							=					"""Alexandra，T-shirt的价格是15便士。"""
												__UpperCAmelCase   :						Union[str, Any]      							=					self.prepare_image_inputs()

												__UpperCAmelCase   :						Dict      							=					processor(text=UpperCamelCase ,       images=UpperCamelCase  )

												self.assertListEqual(list(inputs.keys()  ) ,       processor.model_input_names  )




"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase    :     Any   						=		{
    'configuration_blenderbot': [
        'BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BlenderbotConfig',
        'BlenderbotOnnxConfig',
    ],
    'tokenization_blenderbot': ['BlenderbotTokenizer'],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase    :     Optional[int]   						=		['BlenderbotTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase    :     Optional[Any]   						=		[
							    'BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'BlenderbotForCausalLM',
							    'BlenderbotForConditionalGeneration',
							    'BlenderbotModel',
							    'BlenderbotPreTrainedModel',
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase    :     Optional[int]   						=		[
							    'TFBlenderbotForConditionalGeneration',
							    'TFBlenderbotModel',
							    'TFBlenderbotPreTrainedModel',
							]


try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase    :     Optional[int]   						=		[
							    'FlaxBlenderbotForConditionalGeneration',
							    'FlaxBlenderbotModel',
							    'FlaxBlenderbotPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_blenderbot import (
							    BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    BlenderbotConfig,
							    BlenderbotOnnxConfig,
							)
							from .tokenization_blenderbot import BlenderbotTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_blenderbot import (
														    BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BlenderbotForCausalLM,
														    BlenderbotForConditionalGeneration,
														    BlenderbotModel,
														    BlenderbotPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_blenderbot import (
														    TFBlenderbotForConditionalGeneration,
														    TFBlenderbotModel,
														    TFBlenderbotPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_blenderbot import (
														    FlaxBlenderbotForConditionalGeneration,
														    FlaxBlenderbotModel,
														    FlaxBlenderbotPreTrainedModel,
														)

else:
							import sys

							UpperCAmelCase    :     Any   						=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
