
"""simple docstring"""


import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
							from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
							lowerCAmelCase			=		False

if is_vision_available():
							from PIL import Image

							from transformers import (
							    AutoProcessor,
							    PixaStructImageProcessor,
							    PixaStructProcessor,
							    PreTrainedTokenizerFast,
							    TaTokenizer,
							)

@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11   ,      reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""   ,      )
@require_vision
@require_torch
class 					A_			(  unittest.TestCase     ):



							"""simple docstring"""






							def    UpperCAmelCase__							(						self						:Tuple ):





										"""simple docstring"""

										lowerCamelCase__			:  Dict  =tempfile.mkdtemp()

										lowerCamelCase__			:  Any  =PixaStructImageProcessor()
										lowerCamelCase__			:  int  =TaTokenizer.from_pretrained('t5-small' )

										lowerCamelCase__			:  List[Any]  =PixaStructProcessor(lowerCamelCase_  ,							lowerCamelCase_ )

										processor.save_pretrained(self.tmpdirname )





							def    UpperCAmelCase__							(						self						:List[str]  ,							**lowerCamelCase_						:List[str] ):





										"""simple docstring"""

										return AutoProcessor.from_pretrained(self.tmpdirname  ,							**lowerCamelCase_ ).tokenizer





							def    UpperCAmelCase__							(						self						:Tuple  ,							**lowerCamelCase_						:int ):





										"""simple docstring"""

										return AutoProcessor.from_pretrained(self.tmpdirname  ,							**lowerCamelCase_ ).image_processor





							def    UpperCAmelCase__							(						self						:Optional[int] ):





										"""simple docstring"""

										shutil.rmtree(self.tmpdirname )





							def    UpperCAmelCase__							(						self						:Dict ):





										"""simple docstring"""

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

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

										return image_inputs





							def    UpperCAmelCase__							(						self						:Optional[Any] ):





										"""simple docstring"""

										lowerCamelCase__			:  Dict  =PixaStructProcessor(tokenizer=self.get_tokenizer()  ,							image_processor=self.get_image_processor() )
										processor.save_pretrained(self.tmpdirname )

										lowerCamelCase__			:  List[str]  =self.get_tokenizer(bos_token='(BOS)'  ,							eos_token='(EOS)' )
										lowerCamelCase__			:  Any  =self.get_image_processor(do_normalize=lowerCamelCase_  ,							padding_value=1.0 )

										lowerCamelCase__			:  Tuple  =PixaStructProcessor.from_pretrained(
										    self.tmpdirname  ,							bos_token='(BOS)'  ,							eos_token='(EOS)'  ,							do_normalize=lowerCamelCase_  ,							padding_value=1.0 )

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

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





							def    UpperCAmelCase__							(						self						:Union[str, Any] ):





										"""simple docstring"""

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

										lowerCamelCase__			:  Optional[Any]  =PixaStructProcessor(tokenizer=lowerCamelCase_  ,							image_processor=lowerCamelCase_ )

										lowerCamelCase__			:  Dict  =self.prepare_image_inputs()

										lowerCamelCase__			:  Optional[int]  =image_processor(lowerCamelCase_  ,							return_tensors='np' )
										lowerCamelCase__			:  Optional[int]  =processor(images=lowerCamelCase_  ,							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						:Any ):





										"""simple docstring"""

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

										lowerCamelCase__			:  List[Any]  =PixaStructProcessor(tokenizer=lowerCamelCase_  ,							image_processor=lowerCamelCase_ )

										lowerCamelCase__			:  Optional[int]  ='lower newer'

										lowerCamelCase__			:  int  =processor(text=lowerCamelCase_ )

										lowerCamelCase__			:  Tuple  =tokenizer(lowerCamelCase_  ,							return_token_type_ids=lowerCamelCase_  ,							add_special_tokens=lowerCamelCase_ )

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





							def    UpperCAmelCase__							(						self						:Optional[int] ):





										"""simple docstring"""

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

										lowerCamelCase__			:  List[Any]  =PixaStructProcessor(tokenizer=lowerCamelCase_  ,							image_processor=lowerCamelCase_ )

										lowerCamelCase__			:  Optional[int]  ='lower newer'
										lowerCamelCase__			:  Union[str, Any]  =self.prepare_image_inputs()

										lowerCamelCase__			:  str  =processor(text=lowerCamelCase_  ,							images=lowerCamelCase_ )

										self.assertListEqual(
										    list(inputs.keys() )  ,							['flattened_patches', 'attention_mask', 'decoder_attention_mask', 'decoder_input_ids'] )

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





							def    UpperCAmelCase__							(						self						:Optional[Any] ):





										"""simple docstring"""

										lowerCamelCase__			:  int  =self.get_image_processor()
										lowerCamelCase__			:  List[str]  =self.get_tokenizer()

										lowerCamelCase__			:  Optional[int]  =PixaStructProcessor(tokenizer=lowerCamelCase_  ,							image_processor=lowerCamelCase_ )

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

										lowerCamelCase__			:  Dict  =processor(text=lowerCamelCase_  ,							images=lowerCamelCase_ )

										lowerCamelCase__			:  Optional[Any]  =[512, 1_024, 2_048, 4_096]
										lowerCamelCase__			:  Any  =[770, 770, 770, 770]
										# with text
										for i, max_patch in enumerate(lowerCamelCase_ ):
													lowerCamelCase__			:  Dict  =processor(text=lowerCamelCase_  ,							images=lowerCamelCase_  ,							max_patches=lowerCamelCase_ )
													self.assertEqual(inputs['flattened_patches'][0].shape[0]  ,							lowerCamelCase_ )
													self.assertEqual(inputs['flattened_patches'][0].shape[1]  ,							expected_hidden_size[i] )

										# without text input
										for i, max_patch in enumerate(lowerCamelCase_ ):
													lowerCamelCase__			:  Union[str, Any]  =processor(images=lowerCamelCase_  ,							max_patches=lowerCamelCase_ )
													self.assertEqual(inputs['flattened_patches'][0].shape[0]  ,							lowerCamelCase_ )
													self.assertEqual(inputs['flattened_patches'][0].shape[1]  ,							expected_hidden_size[i] )





							def    UpperCAmelCase__							(						self						:Optional[int] ):





										"""simple docstring"""

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

										lowerCamelCase__			:  Tuple  =PixaStructProcessor(tokenizer=lowerCamelCase_  ,							image_processor=lowerCamelCase_ )

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

										lowerCamelCase__			:  Dict  =processor.batch_decode(lowerCamelCase_ )
										lowerCamelCase__			:  List[Any]  =tokenizer.batch_decode(lowerCamelCase_ )

										self.assertListEqual(lowerCamelCase_  ,							lowerCamelCase_ )





							def    UpperCAmelCase__							(						self						:List[Any] ):





										"""simple docstring"""

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

										lowerCamelCase__			:  List[Any]  =PixaStructProcessor(tokenizer=lowerCamelCase_  ,							image_processor=lowerCamelCase_ )

										lowerCamelCase__			:  Optional[int]  ='lower newer'
										lowerCamelCase__			:  Union[str, Any]  =self.prepare_image_inputs()

										lowerCamelCase__			:  Union[str, Any]  =processor(text=lowerCamelCase_  ,							images=lowerCamelCase_ )

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

										lowerCamelCase__			:  int  =processor(text=lowerCamelCase_ )

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

"""simple docstring"""


from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


lowerCAmelCase			=		logging.get_logger(__name__)  # pylint: disable=invalid-name

class 					A_			(  A__     ):



							"""simple docstring"""






							SCREAMING_SNAKE_CASE_			   =     ["""safety_checker""", """feature_extractor"""]





							def __init__(						self						:Optional[Any]  ,							lowerCamelCase_						:AutoencoderKL  ,							lowerCamelCase_						:CLIPTextModel  ,							lowerCamelCase_						:CLIPTokenizer  ,							lowerCamelCase_						:UNetaDConditionModel  ,							lowerCamelCase_						:Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]  ,							lowerCamelCase_						:StableDiffusionSafetyChecker  ,							lowerCamelCase_						:CLIPImageProcessor  ,							lowerCamelCase_						:bool = True  ,							):





										"""simple docstring"""

										super().__init__()
										if hasattr(scheduler.config  ,							'steps_offset' ) and scheduler.config.steps_offset != 1:
													lowerCamelCase__			:  Dict  =(
													    f"""The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"""
													    f""" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure """
													    'to update the config accordingly as leaving `steps_offset` might led to incorrect results'
													    ' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'
													    ' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'
													    ' file'
													)
													deprecate('steps_offset!=1'  ,							'1.0.0'  ,							lowerCamelCase_  ,							standard_warn=lowerCamelCase_ )
													lowerCamelCase__			:  Tuple  =dict(scheduler.config )
													lowerCamelCase__			:  str  =1
													lowerCamelCase__			:  List[Any]  =FrozenDict(lowerCamelCase_ )

										self.register_modules(
										    vae=lowerCamelCase_  ,							text_encoder=lowerCamelCase_  ,							tokenizer=lowerCamelCase_  ,							unet=lowerCamelCase_  ,							scheduler=lowerCamelCase_  ,							safety_checker=lowerCamelCase_  ,							feature_extractor=lowerCamelCase_  ,							)
										self.register_to_config(requires_safety_checker=lowerCamelCase_ )





							@property
							def    UpperCAmelCase__							(						self						:Any ):





										"""simple docstring"""

										return {k: getattr(self  ,							lowerCamelCase_ ) for k in self.config.keys() if not k.startswith('_' )}





							def    UpperCAmelCase__							(						self						:Optional[Any]  ,							lowerCamelCase_						:Optional[Union[str, int]] = "auto" ):





										"""simple docstring"""

										if slice_size == "auto":
													# half the attention head size is usually a good trade-off between
													# speed and memory
													lowerCamelCase__			:  List[Any]  =self.unet.config.attention_head_dim // 2
										self.unet.set_attention_slice(lowerCamelCase_ )





							def    UpperCAmelCase__							(						self						:Any ):





										"""simple docstring"""

										self.enable_attention_slicing(lowerCamelCase_ )





							@torch.no_grad()
							def    UpperCAmelCase__							(						self						:Optional[int]  ,							lowerCamelCase_						:Union[str, List[str]]  ,							lowerCamelCase_						:Union[torch.FloatTensor, PIL.Image.Image]  ,							lowerCamelCase_						:Union[torch.FloatTensor, PIL.Image.Image]  ,							lowerCamelCase_						:float = 0.8  ,							lowerCamelCase_						:Optional[int] = 50  ,							lowerCamelCase_						:Optional[float] = 7.5  ,							lowerCamelCase_						:Optional[Union[str, List[str]]] = None  ,							lowerCamelCase_						:Optional[int] = 1  ,							lowerCamelCase_						:Optional[float] = 0.0  ,							lowerCamelCase_						:Optional[torch.Generator] = None  ,							lowerCamelCase_						:Optional[str] = "pil"  ,							lowerCamelCase_						:bool = True  ,							lowerCamelCase_						:Optional[Callable[[int, int, torch.FloatTensor], None]] = None  ,							lowerCamelCase_						:int = 1  ,							):





										"""simple docstring"""

										return StableDiffusionInpaintPipelineLegacy(**self.components )(
										    prompt=lowerCamelCase_  ,							image=lowerCamelCase_  ,							mask_image=lowerCamelCase_  ,							strength=lowerCamelCase_  ,							num_inference_steps=lowerCamelCase_  ,							guidance_scale=lowerCamelCase_  ,							negative_prompt=lowerCamelCase_  ,							num_images_per_prompt=lowerCamelCase_  ,							eta=lowerCamelCase_  ,							generator=lowerCamelCase_  ,							output_type=lowerCamelCase_  ,							return_dict=lowerCamelCase_  ,							callback=lowerCamelCase_  ,							)





							@torch.no_grad()
							def    UpperCAmelCase__							(						self						:Any  ,							lowerCamelCase_						:Union[str, List[str]]  ,							lowerCamelCase_						:Union[torch.FloatTensor, PIL.Image.Image]  ,							lowerCamelCase_						:float = 0.8  ,							lowerCamelCase_						:Optional[int] = 50  ,							lowerCamelCase_						:Optional[float] = 7.5  ,							lowerCamelCase_						:Optional[Union[str, List[str]]] = None  ,							lowerCamelCase_						:Optional[int] = 1  ,							lowerCamelCase_						:Optional[float] = 0.0  ,							lowerCamelCase_						:Optional[torch.Generator] = None  ,							lowerCamelCase_						:Optional[str] = "pil"  ,							lowerCamelCase_						:bool = True  ,							lowerCamelCase_						:Optional[Callable[[int, int, torch.FloatTensor], None]] = None  ,							lowerCamelCase_						:int = 1  ,							**lowerCamelCase_						:str  ,							):





										"""simple docstring"""

										return StableDiffusionImgaImgPipeline(**self.components )(
										    prompt=lowerCamelCase_  ,							image=lowerCamelCase_  ,							strength=lowerCamelCase_  ,							num_inference_steps=lowerCamelCase_  ,							guidance_scale=lowerCamelCase_  ,							negative_prompt=lowerCamelCase_  ,							num_images_per_prompt=lowerCamelCase_  ,							eta=lowerCamelCase_  ,							generator=lowerCamelCase_  ,							output_type=lowerCamelCase_  ,							return_dict=lowerCamelCase_  ,							callback=lowerCamelCase_  ,							callback_steps=lowerCamelCase_  ,							)





							@torch.no_grad()
							def    UpperCAmelCase__							(						self						:Optional[int]  ,							lowerCamelCase_						:Union[str, List[str]]  ,							lowerCamelCase_						:int = 512  ,							lowerCamelCase_						:int = 512  ,							lowerCamelCase_						:int = 50  ,							lowerCamelCase_						:float = 7.5  ,							lowerCamelCase_						:Optional[Union[str, List[str]]] = None  ,							lowerCamelCase_						:Optional[int] = 1  ,							lowerCamelCase_						:float = 0.0  ,							lowerCamelCase_						:Optional[torch.Generator] = None  ,							lowerCamelCase_						:Optional[torch.FloatTensor] = None  ,							lowerCamelCase_						:Optional[str] = "pil"  ,							lowerCamelCase_						:bool = True  ,							lowerCamelCase_						:Optional[Callable[[int, int, torch.FloatTensor], None]] = None  ,							lowerCamelCase_						:int = 1  ,							):





										"""simple docstring"""

										return StableDiffusionPipeline(**self.components )(
										    prompt=lowerCamelCase_  ,							height=lowerCamelCase_  ,							width=lowerCamelCase_  ,							num_inference_steps=lowerCamelCase_  ,							guidance_scale=lowerCamelCase_  ,							negative_prompt=lowerCamelCase_  ,							num_images_per_prompt=lowerCamelCase_  ,							eta=lowerCamelCase_  ,							generator=lowerCamelCase_  ,							latents=lowerCamelCase_  ,							output_type=lowerCamelCase_  ,							return_dict=lowerCamelCase_  ,							callback=lowerCamelCase_  ,							callback_steps=lowerCamelCase_  ,							)