import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class     snake_case_			(				UpperCamelCase_			, UpperCamelCase_			, UpperCamelCase_			, unittest.TestCase							):


							"""simple docstring"""





							__UpperCAmelCase       =StableDiffusionInstructPixaPixPipeline
							__UpperCAmelCase       =TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width""", """cross_attention_kwargs"""}
							__UpperCAmelCase       =TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							__UpperCAmelCase       =IMAGE_TO_IMAGE_IMAGE_PARAMS
							__UpperCAmelCase       =IMAGE_TO_IMAGE_IMAGE_PARAMS




							def        A__  ( self							):
												torch.manual_seed(0							)
												__lowerCAmelCase					=							UNetaDConditionModel(
												    block_out_channels=(3_2, 6_4)					,						layers_per_block=2					,						sample_size=3_2					,						in_channels=8					,						out_channels=4					,						down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')					,						up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')					,						cross_attention_dim=3_2					,						)
												__lowerCAmelCase					=							PNDMScheduler(skip_prk_steps=__A							)
												torch.manual_seed(0							)
												__lowerCAmelCase					=							AutoencoderKL(
												    block_out_channels=[3_2, 6_4]					,						in_channels=3					,						out_channels=3					,						down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']					,						up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']					,						latent_channels=4					,						)
												torch.manual_seed(0							)
												__lowerCAmelCase					=							CLIPTextConfig(
												    bos_token_id=0					,						eos_token_id=2					,						hidden_size=3_2					,						intermediate_size=3_7					,						layer_norm_eps=1e-0_5					,						num_attention_heads=4					,						num_hidden_layers=5					,						pad_token_id=1					,						vocab_size=1_0_0_0					,						)
												__lowerCAmelCase					=							CLIPTextModel(__A							)
												__lowerCAmelCase					=							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'							)

												__lowerCAmelCase					=							{
												    "unet": unet,
												    "scheduler": scheduler,
												    "vae": vae,
												    "text_encoder": text_encoder,
												    "tokenizer": tokenizer,
												    "safety_checker": None,
												    "feature_extractor": None,
												}
												return components




							def        A__  ( self					,						_A					,						_A=0							):
												__lowerCAmelCase					=							floats_tensor((1, 3, 3_2, 3_2)					,						rng=random.Random(__A							)							).to(__A							)
												__lowerCAmelCase					=							image.cpu().permute(0					,						2					,						3					,						1							)[0]
												__lowerCAmelCase					=							Image.fromarray(np.uinta(__A							)							).convert('RGB'							)
												if str(__A							).startswith('mps'							):
																	__lowerCAmelCase					=							torch.manual_seed(__A							)
												else:
																	__lowerCAmelCase					=							torch.Generator(device=__A							).manual_seed(__A							)
												__lowerCAmelCase					=							{
												    "prompt": "A painting of a squirrel eating a burger",
												    "image": image,
												    "generator": generator,
												    "num_inference_steps": 2,
												    "guidance_scale": 6.0,
												    "image_guidance_scale": 1,
												    "output_type": "numpy",
												}
												return inputs




							def        A__  ( self							):
												__lowerCAmelCase					=							"cpu"  # ensure determinism for the device-dependent torch.Generator
												__lowerCAmelCase					=							self.get_dummy_components()
												__lowerCAmelCase					=							StableDiffusionInstructPixaPixPipeline(**__A							)
												__lowerCAmelCase					=							sd_pipe.to(__A							)
												sd_pipe.set_progress_bar_config(disable=__A							)

												__lowerCAmelCase					=							self.get_dummy_inputs(__A							)
												__lowerCAmelCase					=							sd_pipe(**__A							).images
												__lowerCAmelCase					=							image[0, -3:, -3:, -1]
												assert image.shape == (1, 3_2, 3_2, 3)
												__lowerCAmelCase					=							np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815]							)

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




							def        A__  ( self							):
												__lowerCAmelCase					=							"cpu"  # ensure determinism for the device-dependent torch.Generator
												__lowerCAmelCase					=							self.get_dummy_components()
												__lowerCAmelCase					=							StableDiffusionInstructPixaPixPipeline(**__A							)
												__lowerCAmelCase					=							sd_pipe.to(__A							)
												sd_pipe.set_progress_bar_config(disable=__A							)

												__lowerCAmelCase					=							self.get_dummy_inputs(__A							)
												__lowerCAmelCase					=							"french fries"
												__lowerCAmelCase					=							sd_pipe(**__A					,						negative_prompt=__A							)
												__lowerCAmelCase					=							output.images
												__lowerCAmelCase					=							image[0, -3:, -3:, -1]

												assert image.shape == (1, 3_2, 3_2, 3)
												__lowerCAmelCase					=							np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831]							)

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




							def        A__  ( self							):
												__lowerCAmelCase					=							"cpu"  # ensure determinism for the device-dependent torch.Generator
												__lowerCAmelCase					=							self.get_dummy_components()
												__lowerCAmelCase					=							StableDiffusionInstructPixaPixPipeline(**__A							)
												__lowerCAmelCase					=							sd_pipe.to(__A							)
												sd_pipe.set_progress_bar_config(disable=__A							)

												__lowerCAmelCase					=							self.get_dummy_inputs(__A							)
												__lowerCAmelCase					=							[inputs["prompt"]] * 2

												__lowerCAmelCase					=							np.array(inputs['image']							).astype(np.floataa							) / 2_5_5.0
												__lowerCAmelCase					=							torch.from_numpy(__A							).unsqueeze(0							).to(__A							)
												__lowerCAmelCase					=							image / 2 + 0.5
												__lowerCAmelCase					=							image.permute(0					,						3					,						1					,						2							)
												__lowerCAmelCase					=							image.repeat(2					,						1					,						1					,						1							)

												__lowerCAmelCase					=							sd_pipe(**__A							).images
												__lowerCAmelCase					=							image[-1, -3:, -3:, -1]

												assert image.shape == (2, 3_2, 3_2, 3)
												__lowerCAmelCase					=							np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579]							)

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




							def        A__  ( self							):
												__lowerCAmelCase					=							"cpu"  # ensure determinism for the device-dependent torch.Generator
												__lowerCAmelCase					=							self.get_dummy_components()
												__lowerCAmelCase					=							EulerAncestralDiscreteScheduler(
												    beta_start=0.0_0085					,						beta_end=0.012					,						beta_schedule='scaled_linear'							)
												__lowerCAmelCase					=							StableDiffusionInstructPixaPixPipeline(**__A							)
												__lowerCAmelCase					=							sd_pipe.to(__A							)
												sd_pipe.set_progress_bar_config(disable=__A							)

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

												__lowerCAmelCase					=							[round(__A					,						4							) for x in image_slice.flatten().tolist()]
												print(','.join([str(__A							) for x in slice]							)							)

												assert image.shape == (1, 3_2, 3_2, 3)
												__lowerCAmelCase					=							np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986]							)

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




							def        A__  ( self							):
												super().test_inference_batch_single_identical(expected_max_diff=3e-3							)




							def        A__  ( self							):
												__lowerCAmelCase					=							self.get_dummy_components()
												__lowerCAmelCase					=							StableDiffusionInstructPixaPixPipeline(**__A							)
												__lowerCAmelCase					=							VaeImageProcessor(do_resize=__A					,						do_normalize=__A							)
												__lowerCAmelCase					=							pipe.to(__A							)
												pipe.set_progress_bar_config(disable=__A							)

												__lowerCAmelCase					=							pipe(**self.get_dummy_inputs_by_type(__A					,						input_image_type='pt'							)							)[0]

												__lowerCAmelCase					=							components["vae"]
												__lowerCAmelCase					=							self.get_dummy_inputs_by_type(__A					,						input_image_type='pt'							)

												for image_param in self.image_latents_params:
																	if image_param in inputs.keys():
																						__lowerCAmelCase					=							vae.encode(inputs[image_param]							).latent_dist.mode()

												__lowerCAmelCase					=							pipe(**__A							)[0]

												__lowerCAmelCase					=							np.abs(out - out_latents_inputs							).max()
												self.assertLess(__A					,						1e-4					,						'passing latents as image input generate different result from passing image'							)





@slow
@require_torch_gpu
class     snake_case_			(				unittest.TestCase							):


							"""simple docstring"""





							def        A__  ( self							):
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()




							def        A__  ( self					,						_A=0							):
												__lowerCAmelCase					=							torch.manual_seed(__A							)
												__lowerCAmelCase					=							load_image(
												    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'							)
												__lowerCAmelCase					=							{
												    "prompt": "turn him into a cyborg",
												    "image": image,
												    "generator": generator,
												    "num_inference_steps": 3,
												    "guidance_scale": 7.5,
												    "image_guidance_scale": 1.0,
												    "output_type": "numpy",
												}
												return inputs




							def        A__  ( self							):
												__lowerCAmelCase					=							StableDiffusionInstructPixaPixPipeline.from_pretrained(
												    'timbrooks/instruct-pix2pix'					,						safety_checker=__A							)
												pipe.to(__A							)
												pipe.set_progress_bar_config(disable=__A							)
												pipe.enable_attention_slicing()

												__lowerCAmelCase					=							self.get_inputs()
												__lowerCAmelCase					=							pipe(**__A							).images
												__lowerCAmelCase					=							image[0, -3:, -3:, -1].flatten()

												assert image.shape == (1, 5_1_2, 5_1_2, 3)
												__lowerCAmelCase					=							np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555]							)

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




							def        A__  ( self							):
												__lowerCAmelCase					=							StableDiffusionInstructPixaPixPipeline.from_pretrained(
												    'timbrooks/instruct-pix2pix'					,						safety_checker=__A							)
												__lowerCAmelCase					=							LMSDiscreteScheduler.from_config(pipe.scheduler.config							)
												pipe.to(__A							)
												pipe.set_progress_bar_config(disable=__A							)
												pipe.enable_attention_slicing()

												__lowerCAmelCase					=							self.get_inputs()
												__lowerCAmelCase					=							pipe(**__A							).images
												__lowerCAmelCase					=							image[0, -3:, -3:, -1].flatten()

												assert image.shape == (1, 5_1_2, 5_1_2, 3)
												__lowerCAmelCase					=							np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301]							)

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




							def        A__  ( self							):
												__lowerCAmelCase					=							StableDiffusionInstructPixaPixPipeline.from_pretrained(
												    'timbrooks/instruct-pix2pix'					,						safety_checker=__A							)
												__lowerCAmelCase					=							DDIMScheduler.from_config(pipe.scheduler.config							)
												pipe.to(__A							)
												pipe.set_progress_bar_config(disable=__A							)
												pipe.enable_attention_slicing()

												__lowerCAmelCase					=							self.get_inputs()
												__lowerCAmelCase					=							pipe(**__A							).images
												__lowerCAmelCase					=							image[0, -3:, -3:, -1].flatten()

												assert image.shape == (1, 5_1_2, 5_1_2, 3)
												__lowerCAmelCase					=							np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753]							)

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




							def        A__  ( self							):
												__lowerCAmelCase					=							0

												def callback_fn(_A					,						_A					,						_A							) -> None:
																	__lowerCAmelCase					=							True
																	nonlocal number_of_steps
																	number_of_steps += 1
																	if step == 1:
																						__lowerCAmelCase					=							latents.detach().cpu().numpy()
																						assert latents.shape == (1, 4, 6_4, 6_4)
																						__lowerCAmelCase					=							latents[0, -3:, -3:, -1]
																						__lowerCAmelCase					=							np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983]							)

																						assert np.abs(latents_slice.flatten() - expected_slice							).max() < 5e-2
																	elif step == 2:
																						__lowerCAmelCase					=							latents.detach().cpu().numpy()
																						assert latents.shape == (1, 4, 6_4, 6_4)
																						__lowerCAmelCase					=							latents[0, -3:, -3:, -1]
																						__lowerCAmelCase					=							np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115]							)

																						assert np.abs(latents_slice.flatten() - expected_slice							).max() < 5e-2

												__lowerCAmelCase					=							False

												__lowerCAmelCase					=							StableDiffusionInstructPixaPixPipeline.from_pretrained(
												    'timbrooks/instruct-pix2pix'					,						safety_checker=__A					,						torch_dtype=torch.floataa							)
												__lowerCAmelCase					=							pipe.to(__A							)
												pipe.set_progress_bar_config(disable=__A							)
												pipe.enable_attention_slicing()

												__lowerCAmelCase					=							self.get_inputs()
												pipe(**__A					,						callback=__A					,						callback_steps=1							)
												assert callback_fn.has_been_called
												assert number_of_steps == 3




							def        A__  ( self							):
												torch.cuda.empty_cache()
												torch.cuda.reset_max_memory_allocated()
												torch.cuda.reset_peak_memory_stats()

												__lowerCAmelCase					=							StableDiffusionInstructPixaPixPipeline.from_pretrained(
												    'timbrooks/instruct-pix2pix'					,						safety_checker=__A					,						torch_dtype=torch.floataa							)
												__lowerCAmelCase					=							pipe.to(__A							)
												pipe.set_progress_bar_config(disable=__A							)
												pipe.enable_attention_slicing(1							)
												pipe.enable_sequential_cpu_offload()

												__lowerCAmelCase					=							self.get_inputs()
												__lowerCAmelCase					=							pipe(**__A							)

												__lowerCAmelCase					=							torch.cuda.max_memory_allocated()
												# make sure that less than 2.2 GB is allocated
												assert mem_bytes < 2.2 * 1_0**9




							def        A__  ( self							):
												__lowerCAmelCase					=							self.get_inputs()
												# resize to resolution that is divisible by 8 but not 16 or 32
												__lowerCAmelCase					=							inputs["image"].resize((5_0_4, 5_0_4)							)

												__lowerCAmelCase					=							"timbrooks/instruct-pix2pix"
												__lowerCAmelCase					=							StableDiffusionInstructPixaPixPipeline.from_pretrained(
												    __A					,						safety_checker=__A					,						)
												pipe.to(__A							)
												pipe.set_progress_bar_config(disable=__A							)
												pipe.enable_attention_slicing()

												__lowerCAmelCase					=							pipe(**__A							)
												__lowerCAmelCase					=							output.images[0]

												__lowerCAmelCase					=							image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]

												assert image.shape == (5_0_4, 5_0_4, 3)
												__lowerCAmelCase					=							np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590]							)

												assert np.abs(image_slice.flatten() - expected_slice							).max() < 5e-3









# 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     snake_case_			(				_a							):


							"""simple docstring"""





							__UpperCAmelCase       ="""facebook/bart-large-mnli"""
							__UpperCAmelCase       =(
							    """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."""
							)
							__UpperCAmelCase       ="""text_classifier"""
							__UpperCAmelCase       =AutoTokenizer
							__UpperCAmelCase       =AutoModelForSequenceClassification

							__UpperCAmelCase       =["""text""", ["""text"""]]
							__UpperCAmelCase       =["""text"""]




							def        A__  ( self							):
												super().setup()
												__lowerCAmelCase					=							self.model.config
												__lowerCAmelCase					=							-1
												for idx, label in config.idalabel.items():
																	if label.lower().startswith('entail'							):
																						__lowerCAmelCase					=							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        A__  ( self					,						_A					,						_A							):
												__lowerCAmelCase					=							labels
												return self.pre_processor(
												    [text] * len(_A							)					,						[F"""This example is {label}""" for label in labels]					,						return_tensors='pt'					,						padding='max_length'					,						)




							def        A__  ( self					,						_A							):
												__lowerCAmelCase					=							outputs.logits
												__lowerCAmelCase					=							torch.argmax(logits[:, 2]							).item()
												return self._labels[label_id]






