





"""simple docstring"""
import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer






class 					_UpperCAmelCase   (	lowercase_ ):
					UpperCamelCase      =							['''image_processor''', '''tokenizer''']
					UpperCamelCase      =							'''BlipImageProcessor'''
					UpperCamelCase      =							'''AutoTokenizer'''


					def __init__(					self			:Dict     ,       __UpperCamelCase			:Union[str, Any]     ,       __UpperCamelCase			:Dict     ,       __UpperCamelCase			:Any		):
												super().__init__(__UpperCamelCase     ,       __UpperCamelCase		)

												# add QFormer tokenizer
												A		=  qformer_tokenizer


					def __call__(					self			:Any     ,       __UpperCamelCase			:ImageInput = None     ,       __UpperCamelCase			:Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None     ,       __UpperCamelCase			:bool = True     ,       __UpperCamelCase			:Union[bool, str, PaddingStrategy] = False     ,       __UpperCamelCase			:Union[bool, str, TruncationStrategy] = None     ,       __UpperCamelCase			:Optional[int] = None     ,       __UpperCamelCase			:int = 0     ,       __UpperCamelCase			:Optional[int] = None     ,       __UpperCamelCase			:Optional[bool] = None     ,       __UpperCamelCase			:bool = False     ,       __UpperCamelCase			:bool = False     ,       __UpperCamelCase			:bool = False     ,       __UpperCamelCase			:bool = False     ,       __UpperCamelCase			:bool = False     ,       __UpperCamelCase			:bool = True     ,       __UpperCamelCase			:Optional[Union[str, TensorType]] = None     ,       **__UpperCamelCase			:List[str]     ,       ):
												if images is None and text is None:
																			raise ValueError("You have to specify at least images or text."		)

												A		=  BatchFeature()

												if text is not None:
																			A		=  self.tokenizer(
																			    text=__UpperCamelCase     ,       add_special_tokens=__UpperCamelCase     ,       padding=__UpperCamelCase     ,       truncation=__UpperCamelCase     ,       max_length=__UpperCamelCase     ,       stride=__UpperCamelCase     ,       pad_to_multiple_of=__UpperCamelCase     ,       return_attention_mask=__UpperCamelCase     ,       return_overflowing_tokens=__UpperCamelCase     ,       return_special_tokens_mask=__UpperCamelCase     ,       return_offsets_mapping=__UpperCamelCase     ,       return_token_type_ids=__UpperCamelCase     ,       return_length=__UpperCamelCase     ,       verbose=__UpperCamelCase     ,       return_tensors=__UpperCamelCase     ,       **__UpperCamelCase     ,       )
																			encoding.update(__UpperCamelCase		)
																			A		=  self.qformer_tokenizer(
																			    text=__UpperCamelCase     ,       add_special_tokens=__UpperCamelCase     ,       padding=__UpperCamelCase     ,       truncation=__UpperCamelCase     ,       max_length=__UpperCamelCase     ,       stride=__UpperCamelCase     ,       pad_to_multiple_of=__UpperCamelCase     ,       return_attention_mask=__UpperCamelCase     ,       return_overflowing_tokens=__UpperCamelCase     ,       return_special_tokens_mask=__UpperCamelCase     ,       return_offsets_mapping=__UpperCamelCase     ,       return_token_type_ids=__UpperCamelCase     ,       return_length=__UpperCamelCase     ,       verbose=__UpperCamelCase     ,       return_tensors=__UpperCamelCase     ,       **__UpperCamelCase     ,       )
																			A		=  qformer_text_encoding.pop("input_ids"		)
																			A		=  qformer_text_encoding.pop("attention_mask"		)

												if images is not None:
																			A		=  self.image_processor(__UpperCamelCase     ,       return_tensors=__UpperCamelCase		)
																			encoding.update(__UpperCamelCase		)

												return encoding


					def 	lowerCamelCase       (					self			:Any     ,       *__UpperCamelCase			:List[Any]     ,       **__UpperCamelCase			:str		):
												return self.tokenizer.batch_decode(*__UpperCamelCase     ,       **__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Union[str, Any]     ,       *__UpperCamelCase			:List[Any]     ,       **__UpperCamelCase			:Tuple		):
												return self.tokenizer.decode(*__UpperCamelCase     ,       **__UpperCamelCase		)


					@property
					# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
					def 	lowerCamelCase       (					self			:Optional[Any]		):
												A		=  self.tokenizer.model_input_names
												A		=  self.image_processor.model_input_names
												return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)


					def 	lowerCamelCase       (					self			:List[str]     ,       __UpperCamelCase			:Tuple     ,       **__UpperCamelCase			:List[Any]		):
												if os.path.isfile(__UpperCamelCase		):
																			raise ValueError(f"Provided path ({save_directory}) should be a directory, not a file"		)
												os.makedirs(__UpperCamelCase     ,       exist_ok=__UpperCamelCase		)
												A		=  os.path.join(__UpperCamelCase     ,       "qformer_tokenizer"		)
												self.qformer_tokenizer.save_pretrained(__UpperCamelCase		)
												return super().save_pretrained(__UpperCamelCase     ,       **__UpperCamelCase		)


					@classmethod
					def 	lowerCamelCase       (					cls			:Tuple     ,       __UpperCamelCase			:Dict     ,       **__UpperCamelCase			:Tuple		):
												A		=  AutoTokenizer.from_pretrained(__UpperCamelCase     ,       subfolder="qformer_tokenizer"		)
												A		=  cls._get_arguments_from_pretrained(__UpperCamelCase     ,       **__UpperCamelCase		)
												args.append(__UpperCamelCase		)
												return cls(*__UpperCamelCase		)









"""simple docstring"""
import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
		import onnxruntime as ort






class 					_UpperCAmelCase   (	lowercase_     ,							unittest.TestCase ):
					UpperCamelCase      =							'''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''


					def 	lowerCamelCase       (					self			:Optional[int]     ,       __UpperCamelCase			:Union[str, Any]=0		):
												A		=  floats_tensor((1, 3, 1_28, 1_28)     ,       rng=random.Random(__UpperCamelCase		)		)
												A		=  np.random.RandomState(__UpperCamelCase		)
												A		=  {
												    "prompt": "A painting of a squirrel eating a burger",
												    "image": image,
												    "generator": generator,
												    "num_inference_steps": 3,
												    "strength": 0.75,
												    "guidance_scale": 7.5,
												    "output_type": "numpy",
												}
												return inputs


					def 	lowerCamelCase       (					self			:Any		):
												A		=  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint     ,       provider="CPUExecutionProvider"		)
												pipe.set_progress_bar_config(disable=__UpperCamelCase		)

												A		=  self.get_dummy_inputs()
												A		=  pipe(**__UpperCamelCase		).images
												A		=  image[0, -3:, -3:, -1].flatten()

												assert image.shape == (1, 1_28, 1_28, 3)
												A		=  np.array([0.69_643, 0.58_484, 0.50_314, 0.58_760, 0.55_368, 0.59_643, 0.51_529, 0.41_217, 0.49_087]		)
												assert np.abs(image_slice - expected_slice		).max() < 1e-1


					def 	lowerCamelCase       (					self			:Dict		):
												A		=  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint     ,       provider="CPUExecutionProvider"		)
												A		=  PNDMScheduler.from_config(pipe.scheduler.config     ,       skip_prk_steps=__UpperCamelCase		)
												pipe.set_progress_bar_config(disable=__UpperCamelCase		)

												A		=  self.get_dummy_inputs()
												A		=  pipe(**__UpperCamelCase		).images
												A		=  image[0, -3:, -3:, -1]

												assert image.shape == (1, 1_28, 1_28, 3)
												A		=  np.array([0.61_737, 0.54_642, 0.53_183, 0.54_465, 0.52_742, 0.60_525, 0.49_969, 0.40_655, 0.48_154]		)

												assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-1


					def 	lowerCamelCase       (					self			:Optional[Any]		):
												A		=  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint     ,       provider="CPUExecutionProvider"		)
												A		=  LMSDiscreteScheduler.from_config(pipe.scheduler.config		)
												pipe.set_progress_bar_config(disable=__UpperCamelCase		)

												# warmup pass to apply optimizations
												A		=  pipe(**self.get_dummy_inputs()		)

												A		=  self.get_dummy_inputs()
												A		=  pipe(**__UpperCamelCase		).images
												A		=  image[0, -3:, -3:, -1]

												assert image.shape == (1, 1_28, 1_28, 3)
												A		=  np.array([0.52_761, 0.59_977, 0.49_033, 0.49_619, 0.54_282, 0.50_311, 0.47_600, 0.40_918, 0.45_203]		)

												assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-1


					def 	lowerCamelCase       (					self			:Dict		):
												A		=  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint     ,       provider="CPUExecutionProvider"		)
												A		=  EulerDiscreteScheduler.from_config(pipe.scheduler.config		)
												pipe.set_progress_bar_config(disable=__UpperCamelCase		)

												A		=  self.get_dummy_inputs()
												A		=  pipe(**__UpperCamelCase		).images
												A		=  image[0, -3:, -3:, -1]

												assert image.shape == (1, 1_28, 1_28, 3)
												A		=  np.array([0.52_911, 0.60_004, 0.49_229, 0.49_805, 0.54_502, 0.50_680, 0.47_777, 0.41_028, 0.45_304]		)

												assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-1


					def 	lowerCamelCase       (					self			:Optional[Any]		):
												A		=  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint     ,       provider="CPUExecutionProvider"		)
												A		=  EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config		)
												pipe.set_progress_bar_config(disable=__UpperCamelCase		)

												A		=  self.get_dummy_inputs()
												A		=  pipe(**__UpperCamelCase		).images
												A		=  image[0, -3:, -3:, -1]

												assert image.shape == (1, 1_28, 1_28, 3)
												A		=  np.array([0.52_911, 0.60_004, 0.49_229, 0.49_805, 0.54_502, 0.50_680, 0.47_777, 0.41_028, 0.45_304]		)

												assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-1


					def 	lowerCamelCase       (					self			:Union[str, Any]		):
												A		=  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint     ,       provider="CPUExecutionProvider"		)
												A		=  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config		)
												pipe.set_progress_bar_config(disable=__UpperCamelCase		)

												A		=  self.get_dummy_inputs()
												A		=  pipe(**__UpperCamelCase		).images
												A		=  image[0, -3:, -3:, -1]

												assert image.shape == (1, 1_28, 1_28, 3)
												A		=  np.array([0.65_331, 0.58_277, 0.48_204, 0.56_059, 0.53_665, 0.56_235, 0.50_969, 0.40_009, 0.46_552]		)

												assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-1







@nightly
@require_onnxruntime
@require_torch_gpu
class 					_UpperCAmelCase   (	unittest.TestCase ):


					@property
					def 	lowerCamelCase       (					self			:Optional[Any]		):
												return (
												    "CUDAExecutionProvider",
												    {
												        "gpu_mem_limit": "15000000000",  # 15GB
												        "arena_extend_strategy": "kSameAsRequested",
												    },
												)


					@property
					def 	lowerCamelCase       (					self			:Optional[int]		):
												A		=  ort.SessionOptions()
												A		=  False
												return options


					def 	lowerCamelCase       (					self			:Dict		):
												A		=  load_image(
												    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
												    "/img2img/sketch-mountains-input.jpg"		)
												A		=  init_image.resize((7_68, 5_12)		)
												# using the PNDM scheduler by default
												A		=  OnnxStableDiffusionImgaImgPipeline.from_pretrained(
												    "CompVis/stable-diffusion-v1-4"     ,       revision="onnx"     ,       safety_checker=__UpperCamelCase     ,       feature_extractor=__UpperCamelCase     ,       provider=self.gpu_provider     ,       sess_options=self.gpu_options     ,       )
												pipe.set_progress_bar_config(disable=__UpperCamelCase		)

												A		=  "A fantasy landscape, trending on artstation"

												A		=  np.random.RandomState(0		)
												A		=  pipe(
												    prompt=__UpperCamelCase     ,       image=__UpperCamelCase     ,       strength=0.75     ,       guidance_scale=7.5     ,       num_inference_steps=10     ,       generator=__UpperCamelCase     ,       output_type="np"     ,       )
												A		=  output.images
												A		=  images[0, 2_55:2_58, 3_83:3_86, -1]

												assert images.shape == (1, 5_12, 7_68, 3)
												A		=  np.array([0.4_909, 0.5_059, 0.5_372, 0.4_623, 0.4_876, 0.5_049, 0.4_820, 0.4_956, 0.5_019]		)
												# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

												assert np.abs(image_slice.flatten() - expected_slice		).max() < 2e-2


					def 	lowerCamelCase       (					self			:Any		):
												A		=  load_image(
												    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
												    "/img2img/sketch-mountains-input.jpg"		)
												A		=  init_image.resize((7_68, 5_12)		)
												A		=  LMSDiscreteScheduler.from_pretrained(
												    "runwayml/stable-diffusion-v1-5"     ,       subfolder="scheduler"     ,       revision="onnx"		)
												A		=  OnnxStableDiffusionImgaImgPipeline.from_pretrained(
												    "runwayml/stable-diffusion-v1-5"     ,       revision="onnx"     ,       scheduler=__UpperCamelCase     ,       safety_checker=__UpperCamelCase     ,       feature_extractor=__UpperCamelCase     ,       provider=self.gpu_provider     ,       sess_options=self.gpu_options     ,       )
												pipe.set_progress_bar_config(disable=__UpperCamelCase		)

												A		=  "A fantasy landscape, trending on artstation"

												A		=  np.random.RandomState(0		)
												A		=  pipe(
												    prompt=__UpperCamelCase     ,       image=__UpperCamelCase     ,       strength=0.75     ,       guidance_scale=7.5     ,       num_inference_steps=20     ,       generator=__UpperCamelCase     ,       output_type="np"     ,       )
												A		=  output.images
												A		=  images[0, 2_55:2_58, 3_83:3_86, -1]

												assert images.shape == (1, 5_12, 7_68, 3)
												A		=  np.array([0.8_043, 0.926, 0.9_581, 0.8_119, 0.8_954, 0.913, 0.7_209, 0.7_463, 0.7_431]		)
												# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

												assert np.abs(image_slice.flatten() - expected_slice		).max() < 2e-2


