
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
					from PIL import Image

					from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast





@require_vision
class 							UpperCAmelCase      (  unittest.TestCase  ):




	'''simple docstring'''





	def 				UpperCamelCase_  (       self					: Dict ):
					__A   			= tempfile.mkdtemp()

					__A   			= BlipImageProcessor()
					__A   			= BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-BertModel" )

					__A   			= BlipProcessor(A							,A )

					processor.save_pretrained(self.tmpdirname )






	def 				UpperCamelCase_  (       self					: Union[str, Any]							,**A					: int ):
					return AutoProcessor.from_pretrained(self.tmpdirname							,**A ).tokenizer






	def 				UpperCamelCase_  (       self					: List[str]							,**A					: Optional[Any] ):
					return AutoProcessor.from_pretrained(self.tmpdirname							,**A ).image_processor






	def 				UpperCamelCase_  (       self					: Optional[int] ):
					shutil.rmtree(self.tmpdirname )






	def 				UpperCamelCase_  (       self					: int ):
					__A   			= [np.random.randint(2_55							,size=(3, 30, 4_00)							,dtype=np.uinta )]

					__A   			= [Image.fromarray(np.moveaxis(A							,0							,-1 ) ) for x in image_inputs]

					return image_inputs






	def 				UpperCamelCase_  (       self					: List[Any] ):
					__A   			= BlipProcessor(tokenizer=self.get_tokenizer()							,image_processor=self.get_image_processor() )
					processor.save_pretrained(self.tmpdirname )

					__A   			= self.get_tokenizer(bos_token="(BOS)"							,eos_token="(EOS)" )
					__A   			= self.get_image_processor(do_normalize=A							,padding_value=1.0 )

					__A   			= BlipProcessor.from_pretrained(
					    self.tmpdirname							,bos_token="(BOS)"							,eos_token="(EOS)"							,do_normalize=A							,padding_value=1.0 )

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

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






	def 				UpperCamelCase_  (       self					: List[Any] ):
					__A   			= self.get_image_processor()
					__A   			= self.get_tokenizer()

					__A   			= BlipProcessor(tokenizer=A							,image_processor=A )

					__A   			= self.prepare_image_inputs()

					__A   			= image_processor(A							,return_tensors="np" )
					__A   			= processor(images=A							,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 				UpperCamelCase_  (       self					: Optional[int] ):
					__A   			= self.get_image_processor()
					__A   			= self.get_tokenizer()

					__A   			= BlipProcessor(tokenizer=A							,image_processor=A )

					__A   			= "lower newer"

					__A   			= processor(text=A )

					__A   			= tokenizer(A							,return_token_type_ids=A )

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






	def 				UpperCamelCase_  (       self					: List[Any] ):
					__A   			= self.get_image_processor()
					__A   			= self.get_tokenizer()

					__A   			= BlipProcessor(tokenizer=A							,image_processor=A )

					__A   			= "lower newer"
					__A   			= self.prepare_image_inputs()

					__A   			= processor(text=A							,images=A )

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

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






	def 				UpperCamelCase_  (       self					: Optional[int] ):
					__A   			= self.get_image_processor()
					__A   			= self.get_tokenizer()

					__A   			= BlipProcessor(tokenizer=A							,image_processor=A )

					__A   			= [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					__A   			= processor.batch_decode(A )
					__A   			= tokenizer.batch_decode(A )

					self.assertListEqual(A							,A )







	def 				UpperCamelCase_  (       self					: Union[str, Any] ):
					__A   			= self.get_image_processor()
					__A   			= self.get_tokenizer()

					__A   			= BlipProcessor(tokenizer=A							,image_processor=A )

					__A   			= "lower newer"
					__A   			= self.prepare_image_inputs()

					__A   			= processor(text=A							,images=A )

					# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
					self.assertListEqual(list(inputs.keys() )							,["pixel_values", "input_ids", "attention_mask"] )


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


SCREAMING_SNAKE_CASE							:List[Any]							 =				{}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:List[str]							 =				['BartphoTokenizer']

if TYPE_CHECKING:
					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_bartpho import BartphoTokenizer

else:
					import sys

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