
import tempfile
import unittest

import numpy as np

from diffusers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionPipeline,
    PNDMScheduler,
)
from diffusers.utils.testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
	import onnxruntime as ort





class 							UpperCAmelCase				(  A__      ,      unittest.TestCase  ):




	'''simple docstring'''





	snake_case_    =					'hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'






	def     UpperCamelCase_  (	self :			Any							,A :			Union[str, Any]=0			):
					__A             =			np.random.RandomState(lowerCamelCase__			)
					__A             =			{
					    "prompt": "A painting of a squirrel eating a burger",
					    "generator": generator,
					    "num_inference_steps": 2,
					    "guidance_scale": 7.5,
					    "output_type": "numpy",
					}
					return inputs






	def     UpperCamelCase_  (	self :			Dict			):
					__A             =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,provider="CPUExecutionProvider"			)
					pipe.set_progress_bar_config(disable=lowerCamelCase__			)

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

					assert image.shape == (1, 1_28, 1_28, 3)
					__A             =			np.array([0.6_50_72, 0.5_84_92, 0.4_82_19, 0.5_55_21, 0.5_31_80, 0.5_59_39, 0.5_06_97, 0.3_98_00, 0.4_64_55]			)

					assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2






	def     UpperCamelCase_  (	self :			Dict			):
					__A             =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,provider="CPUExecutionProvider"			)
					__A             =			PNDMScheduler.from_config(pipe.scheduler.config							,skip_prk_steps=lowerCamelCase__			)
					pipe.set_progress_bar_config(disable=lowerCamelCase__			)

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

					assert image.shape == (1, 1_28, 1_28, 3)
					__A             =			np.array([0.6_58_63, 0.5_94_25, 0.4_93_26, 0.5_63_13, 0.5_38_75, 0.5_66_27, 0.5_10_65, 0.3_97_77, 0.4_63_30]			)

					assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2






	def     UpperCamelCase_  (	self :			Any			):
					__A             =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,provider="CPUExecutionProvider"			)
					__A             =			LMSDiscreteScheduler.from_config(pipe.scheduler.config			)
					pipe.set_progress_bar_config(disable=lowerCamelCase__			)

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

					assert image.shape == (1, 1_28, 1_28, 3)
					__A             =			np.array([0.5_37_55, 0.6_07_86, 0.4_74_02, 0.4_94_88, 0.5_18_69, 0.4_98_19, 0.4_79_85, 0.3_89_57, 0.4_42_79]			)

					assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2






	def     UpperCamelCase_  (	self :			str			):
					__A             =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,provider="CPUExecutionProvider"			)
					__A             =			EulerDiscreteScheduler.from_config(pipe.scheduler.config			)
					pipe.set_progress_bar_config(disable=lowerCamelCase__			)

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

					assert image.shape == (1, 1_28, 1_28, 3)
					__A             =			np.array([0.5_37_55, 0.6_07_86, 0.4_74_02, 0.4_94_88, 0.5_18_69, 0.4_98_19, 0.4_79_85, 0.3_89_57, 0.4_42_79]			)

					assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2






	def     UpperCamelCase_  (	self :			List[str]			):
					__A             =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,provider="CPUExecutionProvider"			)
					__A             =			EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config			)
					pipe.set_progress_bar_config(disable=lowerCamelCase__			)

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

					assert image.shape == (1, 1_28, 1_28, 3)
					__A             =			np.array([0.5_38_17, 0.6_08_12, 0.4_73_84, 0.4_95_30, 0.5_18_94, 0.4_98_14, 0.4_79_84, 0.3_89_58, 0.4_42_71]			)

					assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2






	def     UpperCamelCase_  (	self :			int			):
					__A             =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,provider="CPUExecutionProvider"			)
					__A             =			DPMSolverMultistepScheduler.from_config(pipe.scheduler.config			)
					pipe.set_progress_bar_config(disable=lowerCamelCase__			)

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

					assert image.shape == (1, 1_28, 1_28, 3)
					__A             =			np.array([0.5_38_95, 0.6_08_08, 0.4_79_33, 0.4_96_08, 0.5_18_86, 0.4_99_50, 0.4_80_53, 0.3_89_57, 0.4_42_00]			)

					assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2






	def     UpperCamelCase_  (	self :			Union[str, Any]			):
					__A             =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,provider="CPUExecutionProvider"			)
					pipe.set_progress_bar_config(disable=lowerCamelCase__			)

					__A             =			self.get_dummy_inputs()
					__A             =			3 * [inputs["prompt"]]

					# forward
					__A             =			pipe(**lowerCamelCase__			)
					__A             =			output.images[0, -3:, -3:, -1]

					__A             =			self.get_dummy_inputs()
					__A             =			3 * [inputs.pop("prompt"			)]

					__A             =			pipe.tokenizer(
					    lowerCamelCase__							,padding="max_length"							,max_length=pipe.tokenizer.model_max_length							,truncation=lowerCamelCase__							,return_tensors="np"							,)
					__A             =			text_inputs["input_ids"]

					__A             =			pipe.text_encoder(input_ids=text_inputs.astype(np.intaa			)			)[0]

					__A             =			prompt_embeds

					# forward
					__A             =			pipe(**lowerCamelCase__			)
					__A             =			output.images[0, -3:, -3:, -1]

					assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()			).max() < 1E-4







	def     UpperCamelCase_  (	self :			List[Any]			):
					__A             =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,provider="CPUExecutionProvider"			)
					pipe.set_progress_bar_config(disable=lowerCamelCase__			)

					__A             =			self.get_dummy_inputs()
					__A             =			3 * ["this is a negative prompt"]
					__A             =			negative_prompt
					__A             =			3 * [inputs["prompt"]]

					# forward
					__A             =			pipe(**lowerCamelCase__			)
					__A             =			output.images[0, -3:, -3:, -1]

					__A             =			self.get_dummy_inputs()
					__A             =			3 * [inputs.pop("prompt"			)]

					__A             =			[]
					for p in [prompt, negative_prompt]:
									__A             =			pipe.tokenizer(
									    lowerCamelCase__							,padding="max_length"							,max_length=pipe.tokenizer.model_max_length							,truncation=lowerCamelCase__							,return_tensors="np"							,)
									__A             =			text_inputs["input_ids"]

									embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.intaa			)			)[0]			)

					__A      ,							__A             =			embeds

					# forward
					__A             =			pipe(**lowerCamelCase__			)
					__A             =			output.images[0, -3:, -3:, -1]

					assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()			).max() < 1E-4





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




	'''simple docstring'''





	@property
	def     UpperCamelCase_  (	self :			str			):
					return (
					    "CUDAExecutionProvider",
					    {
					        "gpu_mem_limit": "15000000000",  # 15GB
					        "arena_extend_strategy": "kSameAsRequested",
					    },
					)






	@property
	def     UpperCamelCase_  (	self :			Union[str, Any]			):
					__A             =			ort.SessionOptions()
					__A             =			False
					return options






	def     UpperCamelCase_  (	self :			Optional[Any]			):
					# using the PNDM scheduler by default
					__A             =			OnnxStableDiffusionPipeline.from_pretrained(
					    "CompVis/stable-diffusion-v1-4"							,revision="onnx"							,safety_checker=lowerCamelCase__							,feature_extractor=lowerCamelCase__							,provider=self.gpu_provider							,sess_options=self.gpu_options							,)
					sd_pipe.set_progress_bar_config(disable=lowerCamelCase__			)

					__A             =			"A painting of a squirrel eating a burger"
					np.random.seed(0			)
					__A             =			sd_pipe([prompt]							,guidance_scale=6.0							,num_inference_steps=10							,output_type="np"			)
					__A             =			output.images

					__A             =			image[0, -3:, -3:, -1]

					assert image.shape == (1, 5_12, 5_12, 3)
					__A             =			np.array([0.04_52, 0.03_90, 0.00_87, 0.03_50, 0.06_17, 0.03_64, 0.05_44, 0.05_23, 0.07_20]			)

					assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-3






	def     UpperCamelCase_  (	self :			str			):
					__A             =			DDIMScheduler.from_pretrained(
					    "runwayml/stable-diffusion-v1-5"							,subfolder="scheduler"							,revision="onnx"			)
					__A             =			OnnxStableDiffusionPipeline.from_pretrained(
					    "runwayml/stable-diffusion-v1-5"							,revision="onnx"							,scheduler=lowerCamelCase__							,safety_checker=lowerCamelCase__							,feature_extractor=lowerCamelCase__							,provider=self.gpu_provider							,sess_options=self.gpu_options							,)
					sd_pipe.set_progress_bar_config(disable=lowerCamelCase__			)

					__A             =			"open neural network exchange"
					__A             =			np.random.RandomState(0			)
					__A             =			sd_pipe([prompt]							,guidance_scale=7.5							,num_inference_steps=10							,generator=lowerCamelCase__							,output_type="np"			)
					__A             =			output.images
					__A             =			image[0, -3:, -3:, -1]

					assert image.shape == (1, 5_12, 5_12, 3)
					__A             =			np.array([0.28_67, 0.19_74, 0.14_81, 0.72_94, 0.72_51, 0.66_67, 0.41_94, 0.56_42, 0.64_86]			)

					assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-3






	def     UpperCamelCase_  (	self :			Dict			):
					__A             =			LMSDiscreteScheduler.from_pretrained(
					    "runwayml/stable-diffusion-v1-5"							,subfolder="scheduler"							,revision="onnx"			)
					__A             =			OnnxStableDiffusionPipeline.from_pretrained(
					    "runwayml/stable-diffusion-v1-5"							,revision="onnx"							,scheduler=lowerCamelCase__							,safety_checker=lowerCamelCase__							,feature_extractor=lowerCamelCase__							,provider=self.gpu_provider							,sess_options=self.gpu_options							,)
					sd_pipe.set_progress_bar_config(disable=lowerCamelCase__			)

					__A             =			"open neural network exchange"
					__A             =			np.random.RandomState(0			)
					__A             =			sd_pipe([prompt]							,guidance_scale=7.5							,num_inference_steps=10							,generator=lowerCamelCase__							,output_type="np"			)
					__A             =			output.images
					__A             =			image[0, -3:, -3:, -1]

					assert image.shape == (1, 5_12, 5_12, 3)
					__A             =			np.array([0.23_06, 0.19_59, 0.15_93, 0.65_49, 0.63_94, 0.54_08, 0.50_65, 0.60_10, 0.61_61]			)

					assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-3






	def     UpperCamelCase_  (	self :			Any			):
					__A             =			0

					def test_callback_fn(A :			Dict							,A :			Any							,A :			Optional[int]			) -> None:
									__A             =			True
									nonlocal number_of_steps
									number_of_steps += 1
									if step == 0:
													assert latents.shape == (1, 4, 64, 64)
													__A             =			latents[0, -3:, -3:, -1]
													__A             =			np.array(
													    [-0.67_72, -0.38_35, -1.24_56, 0.19_05, -1.09_74, 0.69_67, -1.93_53, 0.01_78, 1.01_67]			)

													assert np.abs(latents_slice.flatten() - expected_slice			).max() < 1E-3
									elif step == 5:
													assert latents.shape == (1, 4, 64, 64)
													__A             =			latents[0, -3:, -3:, -1]
													__A             =			np.array(
													    [-0.33_51, 0.22_41, -0.18_37, -0.23_25, -0.65_77, 0.33_93, -0.02_41, 0.58_99, 1.38_75]			)

													assert np.abs(latents_slice.flatten() - expected_slice			).max() < 1E-3

					__A             =			False

					__A             =			OnnxStableDiffusionPipeline.from_pretrained(
					    "runwayml/stable-diffusion-v1-5"							,revision="onnx"							,safety_checker=lowerCamelCase__							,feature_extractor=lowerCamelCase__							,provider=self.gpu_provider							,sess_options=self.gpu_options							,)
					pipe.set_progress_bar_config(disable=lowerCamelCase__			)

					__A             =			"Andromeda galaxy in a bottle"

					__A             =			np.random.RandomState(0			)
					pipe(
					    prompt=lowerCamelCase__							,num_inference_steps=5							,guidance_scale=7.5							,generator=lowerCamelCase__							,callback=lowerCamelCase__							,callback_steps=1							,)
					assert test_callback_fn.has_been_called
					assert number_of_steps == 6







	def     UpperCamelCase_  (	self :			Optional[Any]			):
					__A             =			OnnxStableDiffusionPipeline.from_pretrained(
					    "runwayml/stable-diffusion-v1-5"							,revision="onnx"							,safety_checker=lowerCamelCase__							,feature_extractor=lowerCamelCase__							,provider=self.gpu_provider							,sess_options=self.gpu_options							,)
					assert isinstance(lowerCamelCase__							,lowerCamelCase__			)
					assert pipe.safety_checker is None

					__A             =			pipe("example prompt"							,num_inference_steps=2			).images[0]
					assert image is not None

					# check that there's no error when saving a pipeline with one of the models being None
					with tempfile.TemporaryDirectory() as tmpdirname:
									pipe.save_pretrained(lowerCamelCase__			)
									__A             =			OnnxStableDiffusionPipeline.from_pretrained(lowerCamelCase__			)

					# sanity check that the pipeline still works
					assert pipe.safety_checker is None
					__A             =			pipe("example prompt"							,num_inference_steps=2			).images[0]
					assert image is not None


# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					"facebook/bart-large-mnli"
	snake_case_    =					(
	    "This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which "
	    "should be the text to classify, and `labels`, which should be the list of labels to use for classification. "
	    "It returns the most likely label in the list of provided `labels` for the input text."
	)
	snake_case_    =					"text_classifier"
	snake_case_    =					AutoTokenizer
	snake_case_    =					AutoModelForSequenceClassification

	snake_case_    =					["text", ["text"]]
	snake_case_    =					["text"]






	def     UpperCamelCase_  (	self :			str			):
					super().setup()
					__A             =			self.model.config
					__A             =			-1
					for idx, label in config.idalabel.items():
									if label.lower().startswith("entail"			):
													__A             =			int(A			)
					if self.entailment_id == -1:
									raise ValueError("Could not determine the entailment ID from the model config, please pass it at init."			)






	def     UpperCamelCase_  (	self :			int							,A :			Optional[Any]							,A :			Dict			):
					__A             =			labels
					return self.pre_processor(
					    [text] * len(A			)							,[f'''This example is {label}''' for label in labels]							,return_tensors="pt"							,padding="max_length"							,)







	def     UpperCamelCase_  (	self :			Union[str, Any]							,A :			Tuple			):
					__A             =			outputs.logits
					__A             =			torch.argmax(logits[:, 2]			).item()
					return self._labels[label_id]
