






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        a       (					unittest.TestCase  ):



      """simple docstring"""


      def       UpperCAmelCase ( self   :     List[str]   )				-> str:
        __UpperCAmelCase				:      List[str]	 =    tempfile.mkdtemp()

        __UpperCAmelCase				:      Optional[int]	 =    BlipImageProcessor()
        __UpperCAmelCase				:      Any	 =    BertTokenizer.from_pretrained("""hf-internal-testing/tiny-random-BertModel"""   )

        __UpperCAmelCase				:      Optional[Any]	 =    BlipProcessor(__lowercase						, __lowercase   )

        processor.save_pretrained(self.tmpdirname   )
      def       UpperCAmelCase ( self   :     int						, **__lowercase   :     Optional[Any]   )				-> Any:
        return AutoProcessor.from_pretrained(self.tmpdirname						, **__lowercase   ).tokenizer
      def       UpperCAmelCase ( self   :     Optional[Any]						, **__lowercase   :     int   )				-> str:
        return AutoProcessor.from_pretrained(self.tmpdirname						, **__lowercase   ).image_processor
      def       UpperCAmelCase ( self   :     int   )				-> int:
        shutil.rmtree(self.tmpdirname   )
      def       UpperCAmelCase ( self   :     Union[str, Any]   )				-> Dict:
        __UpperCAmelCase				:      List[str]	 =    [np.random.randint(255						, size=(3, 30, 400)						, dtype=np.uinta   )]

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

        return image_inputs
      def       UpperCAmelCase ( self   :     int   )				-> Any:
        __UpperCAmelCase				:      int	 =    BlipProcessor(tokenizer=self.get_tokenizer()						, image_processor=self.get_image_processor()   )
        processor.save_pretrained(self.tmpdirname   )

        __UpperCAmelCase				:      str	 =    self.get_tokenizer(bos_token="""(BOS)"""						, eos_token="""(EOS)"""   )
        __UpperCAmelCase				:      Optional[Any]	 =    self.get_image_processor(do_normalize=__lowercase						, padding_value=1.0   )

        __UpperCAmelCase				:      List[str]	 =    BlipProcessor.from_pretrained(
            self.tmpdirname						, bos_token="""(BOS)"""						, eos_token="""(EOS)"""						, do_normalize=__lowercase						, padding_value=1.0   )

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

        self.assertEqual(processor.image_processor.to_json_string()						, image_processor_add_kwargs.to_json_string()   )
        self.assertIsInstance(processor.image_processor						, __lowercase   )
      def       UpperCAmelCase ( self   :     List[Any]   )				-> int:
        __UpperCAmelCase				:      List[Any]	 =    self.get_image_processor()
        __UpperCAmelCase				:      Dict	 =    self.get_tokenizer()

        __UpperCAmelCase				:      Optional[Any]	 =    BlipProcessor(tokenizer=__lowercase						, image_processor=__lowercase   )

        __UpperCAmelCase				:      Dict	 =    self.prepare_image_inputs()

        __UpperCAmelCase				:      str	 =    image_processor(__lowercase						, return_tensors="""np"""   )
        __UpperCAmelCase				:      Dict	 =    processor(images=__lowercase						, 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   :     Union[str, Any]   )				-> Dict:
        __UpperCAmelCase				:      Optional[Any]	 =    self.get_image_processor()
        __UpperCAmelCase				:      Dict	 =    self.get_tokenizer()

        __UpperCAmelCase				:      Union[str, Any]	 =    BlipProcessor(tokenizer=__lowercase						, image_processor=__lowercase   )

        __UpperCAmelCase				:      str	 =    """lower newer"""

        __UpperCAmelCase				:      int	 =    processor(text=__lowercase   )

        __UpperCAmelCase				:      Any	 =    tokenizer(__lowercase						, return_token_type_ids=__lowercase   )

        for key in encoded_tok.keys():
          self.assertListEqual(encoded_tok[key]						, encoded_processor[key]   )
      def       UpperCAmelCase ( self   :     Dict   )				-> Any:
        __UpperCAmelCase				:      Tuple	 =    self.get_image_processor()
        __UpperCAmelCase				:      Any	 =    self.get_tokenizer()

        __UpperCAmelCase				:      Dict	 =    BlipProcessor(tokenizer=__lowercase						, image_processor=__lowercase   )

        __UpperCAmelCase				:      Union[str, Any]	 =    """lower newer"""
        __UpperCAmelCase				:      Tuple	 =    self.prepare_image_inputs()

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

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

        # test if it raises when no input is passed
        with pytest.raises(__lowercase   ):
          processor()
      def       UpperCAmelCase ( self   :     str   )				-> List[Any]:
        __UpperCAmelCase				:      Optional[Any]	 =    self.get_image_processor()
        __UpperCAmelCase				:      List[str]	 =    self.get_tokenizer()

        __UpperCAmelCase				:      Union[str, Any]	 =    BlipProcessor(tokenizer=__lowercase						, image_processor=__lowercase   )

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

        __UpperCAmelCase				:      str	 =    processor.batch_decode(__lowercase   )
        __UpperCAmelCase				:      Dict	 =    tokenizer.batch_decode(__lowercase   )

        self.assertListEqual(__lowercase						, __lowercase   )




      def       UpperCAmelCase ( self   :     Tuple   )				-> Optional[Any]:
        __UpperCAmelCase				:      int	 =    self.get_image_processor()
        __UpperCAmelCase				:      List[str]	 =    self.get_tokenizer()

        __UpperCAmelCase				:      Dict	 =    BlipProcessor(tokenizer=__lowercase						, image_processor=__lowercase   )

        __UpperCAmelCase				:      List[Any]	 =    """lower newer"""
        __UpperCAmelCase				:      List[str]	 =    self.prepare_image_inputs()

        __UpperCAmelCase				:      Optional[Any]	 =    processor(text=__lowercase						, images=__lowercase   )

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




from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import (
    BaseOutput,
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_onnx_available,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)



@dataclass
class    a_ (			_a    ):
			a	:			Union[List[PIL.Image.Image], np.ndarray]
			a	:			Optional[List[bool]]


try:
		if not (is_transformers_available() and is_torch_available()):
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
		from .pipeline_cycle_diffusion import CycleDiffusionPipeline
		from .pipeline_stable_diffusion import StableDiffusionPipeline
		from .pipeline_stable_diffusion_attend_and_excite import StableDiffusionAttendAndExcitePipeline
		from .pipeline_stable_diffusion_imgaimg import StableDiffusionImgaImgPipeline
		from .pipeline_stable_diffusion_inpaint import StableDiffusionInpaintPipeline
		from .pipeline_stable_diffusion_inpaint_legacy import StableDiffusionInpaintPipelineLegacy
		from .pipeline_stable_diffusion_instruct_pixapix import StableDiffusionInstructPixaPixPipeline
		from .pipeline_stable_diffusion_latent_upscale import StableDiffusionLatentUpscalePipeline
		from .pipeline_stable_diffusion_ldmad import StableDiffusionLDMaDPipeline
		from .pipeline_stable_diffusion_model_editing import StableDiffusionModelEditingPipeline
		from .pipeline_stable_diffusion_panorama import StableDiffusionPanoramaPipeline
		from .pipeline_stable_diffusion_paradigms import StableDiffusionParadigmsPipeline
		from .pipeline_stable_diffusion_sag import StableDiffusionSAGPipeline
		from .pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
		from .pipeline_stable_unclip import StableUnCLIPPipeline
		from .pipeline_stable_unclip_imgaimg import StableUnCLIPImgaImgPipeline
		from .safety_checker import StableDiffusionSafetyChecker
		from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer

try:
		if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ...utils.dummy_torch_and_transformers_objects import StableDiffusionImageVariationPipeline
else:
		from .pipeline_stable_diffusion_image_variation import StableDiffusionImageVariationPipeline


try:
		if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.26.0''')):
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ...utils.dummy_torch_and_transformers_objects import (
		    StableDiffusionDepthaImgPipeline,
		    StableDiffusionDiffEditPipeline,
		    StableDiffusionPixaPixZeroPipeline,
		)
else:
		from .pipeline_stable_diffusion_depthaimg import StableDiffusionDepthaImgPipeline
		from .pipeline_stable_diffusion_diffedit import StableDiffusionDiffEditPipeline
		from .pipeline_stable_diffusion_pixapix_zero import StableDiffusionPixaPixZeroPipeline


try:
		if not (
		    is_torch_available()
		    and is_transformers_available()
		    and is_k_diffusion_available()
		    and is_k_diffusion_version('''>=''', '''0.0.12''')
		):
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ...utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
		from .pipeline_stable_diffusion_k_diffusion import StableDiffusionKDiffusionPipeline

try:
		if not (is_transformers_available() and is_onnx_available()):
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ...utils.dummy_onnx_objects import *  # noqa F403
else:
		from .pipeline_onnx_stable_diffusion import OnnxStableDiffusionPipeline, StableDiffusionOnnxPipeline
		from .pipeline_onnx_stable_diffusion_imgaimg import OnnxStableDiffusionImgaImgPipeline
		from .pipeline_onnx_stable_diffusion_inpaint import OnnxStableDiffusionInpaintPipeline
		from .pipeline_onnx_stable_diffusion_inpaint_legacy import OnnxStableDiffusionInpaintPipelineLegacy
		from .pipeline_onnx_stable_diffusion_upscale import OnnxStableDiffusionUpscalePipeline

if is_transformers_available() and is_flax_available():
		import flax



		@flax.struct.dataclass
		class    a_ (			_a    ):
					a	:			np.ndarray
					a	:			List[bool]







		from ...schedulers.scheduling_pndm_flax import PNDMSchedulerState
		from .pipeline_flax_stable_diffusion import FlaxStableDiffusionPipeline
		from .pipeline_flax_stable_diffusion_imgaimg import FlaxStableDiffusionImgaImgPipeline
		from .pipeline_flax_stable_diffusion_inpaint import FlaxStableDiffusionInpaintPipeline
		from .safety_checker_flax import FlaxStableDiffusionSafetyChecker