
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,
					    BertTokenizerFast,
					    BlipImageProcessor,
					    GPTaTokenizer,
					    InstructBlipProcessor,
					    PreTrainedTokenizerFast,
					)







@require_vision
class  UpperCamelCase__		(    unittest.TestCase   ):







	'''simple docstring'''





	def   __snake_case				(			self		):
					A__  :	Optional[int]         =							tempfile.mkdtemp()

					A__  :	Optional[int]         =							BlipImageProcessor()
					A__  :	Union[str, Any]         =							GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model'''		)
					A__  :	List[str]         =							BertTokenizerFast.from_pretrained('''hf-internal-testing/tiny-random-bert'''		)

					A__  :	Union[str, Any]         =							InstructBlipProcessor(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		)

					processor.save_pretrained(self.tmpdirname		)





	def   __snake_case				(			self				,       **UpperCamelCase__		):
					return AutoProcessor.from_pretrained(self.tmpdirname				,       **UpperCamelCase__		).tokenizer





	def   __snake_case				(			self				,       **UpperCamelCase__		):
					return AutoProcessor.from_pretrained(self.tmpdirname				,       **UpperCamelCase__		).image_processor





	def   __snake_case				(			self				,       **UpperCamelCase__		):
					return AutoProcessor.from_pretrained(self.tmpdirname				,       **UpperCamelCase__		).qformer_tokenizer





	def   __snake_case				(			self		):
					shutil.rmtree(self.tmpdirname		)





	def   __snake_case				(			self		):
					A__  :	Optional[int]         =							[np.random.randint(255				,       size=(3, 30, 400)				,       dtype=np.uinta		)]

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

					return image_inputs





	def   __snake_case				(			self		):
					A__  :	List[str]         =							InstructBlipProcessor(
					    tokenizer=self.get_tokenizer()				,       image_processor=self.get_image_processor()				,       qformer_tokenizer=self.get_qformer_tokenizer()				,       )
					processor.save_pretrained(self.tmpdirname		)

					A__  :	Dict         =							self.get_tokenizer(bos_token='''(BOS)'''				,       eos_token='''(EOS)'''		)
					A__  :	Dict         =							self.get_image_processor(do_normalize=UpperCamelCase__				,       padding_value=1.0		)

					A__  :	Tuple         =							InstructBlipProcessor.from_pretrained(
					    self.tmpdirname				,       bos_token='''(BOS)'''				,       eos_token='''(EOS)'''				,       do_normalize=UpperCamelCase__				,       padding_value=1.0		)

					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__		)
					self.assertIsInstance(processor.qformer_tokenizer				,       UpperCamelCase__		)





	def   __snake_case				(			self		):
					A__  :	Dict         =							self.get_image_processor()
					A__  :	Any         =							self.get_tokenizer()
					A__  :	Optional[int]         =							self.get_qformer_tokenizer()

					A__  :	List[str]         =							InstructBlipProcessor(
					    tokenizer=UpperCamelCase__				,       image_processor=UpperCamelCase__				,       qformer_tokenizer=UpperCamelCase__		)

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

					A__  :	List[Any]         =							image_processor(UpperCamelCase__				,       return_tensors='''np'''		)
					A__  :	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   __snake_case				(			self		):
					A__  :	Optional[int]         =							self.get_image_processor()
					A__  :	str         =							self.get_tokenizer()
					A__  :	List[Any]         =							self.get_qformer_tokenizer()

					A__  :	Union[str, Any]         =							InstructBlipProcessor(
					    tokenizer=UpperCamelCase__				,       image_processor=UpperCamelCase__				,       qformer_tokenizer=UpperCamelCase__		)

					A__  :	List[Any]         =							'''lower newer'''

					A__  :	Union[str, Any]         =							processor(text=UpperCamelCase__		)

					A__  :	List[Any]         =							tokenizer(UpperCamelCase__				,       return_token_type_ids=UpperCamelCase__		)
					A__  :	Dict         =							qformer_tokenizer(UpperCamelCase__				,       return_token_type_ids=UpperCamelCase__		)

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

					for key in encoded_tokens_qformer.keys():
									self.assertListEqual(encoded_tokens_qformer[key]				,       encoded_processor['''qformer_''' + key]		)





	def   __snake_case				(			self		):
					A__  :	List[str]         =							self.get_image_processor()
					A__  :	Optional[Any]         =							self.get_tokenizer()
					A__  :	List[Any]         =							self.get_qformer_tokenizer()

					A__  :	int         =							InstructBlipProcessor(
					    tokenizer=UpperCamelCase__				,       image_processor=UpperCamelCase__				,       qformer_tokenizer=UpperCamelCase__		)

					A__  :	Optional[int]         =							'''lower newer'''
					A__  :	Optional[int]         =							self.prepare_image_inputs()

					A__  :	Any         =							processor(text=UpperCamelCase__				,       images=UpperCamelCase__		)

					self.assertListEqual(
					    list(inputs.keys()		)				,       ['''input_ids''', '''attention_mask''', '''qformer_input_ids''', '''qformer_attention_mask''', '''pixel_values''']				,       )

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





	def   __snake_case				(			self		):
					A__  :	List[str]         =							self.get_image_processor()
					A__  :	Tuple         =							self.get_tokenizer()
					A__  :	str         =							self.get_qformer_tokenizer()

					A__  :	Any         =							InstructBlipProcessor(
					    tokenizer=UpperCamelCase__				,       image_processor=UpperCamelCase__				,       qformer_tokenizer=UpperCamelCase__		)

					A__  :	Tuple         =							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					A__  :	Optional[int]         =							processor.batch_decode(UpperCamelCase__		)
					A__  :	Union[str, Any]         =							tokenizer.batch_decode(UpperCamelCase__		)

					self.assertListEqual(UpperCamelCase__				,       UpperCamelCase__		)





	def   __snake_case				(			self		):
					A__  :	Optional[int]         =							self.get_image_processor()
					A__  :	Optional[int]         =							self.get_tokenizer()
					A__  :	str         =							self.get_qformer_tokenizer()

					A__  :	Optional[int]         =							InstructBlipProcessor(
					    tokenizer=UpperCamelCase__				,       image_processor=UpperCamelCase__				,       qformer_tokenizer=UpperCamelCase__		)

					A__  :	Optional[Any]         =							'''lower newer'''
					A__  :	Any         =							self.prepare_image_inputs()

					A__  :	Dict         =							processor(text=UpperCamelCase__				,       images=UpperCamelCase__		)

					self.assertListEqual(
					    list(inputs.keys()		)				,       ['''input_ids''', '''attention_mask''', '''qformer_input_ids''', '''qformer_attention_mask''', '''pixel_values''']				,       )

from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
					if not (is_transformers_available() and is_torch_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
					from .pipeline_kandinsky import KandinskyPipeline
					from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
					from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
					from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
					from .text_encoder import MultilingualCLIP