

"""simple docstring"""





import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class 					_lowerCAmelCase  (	lowercase     ,lowercase     ,unittest.TestCase   ):







 """simple docstring"""







 __UpperCAmelCase	:					List[str]   		=	StableDiffusionSAGPipeline
 __UpperCAmelCase	:					Dict   		=	TEXT_TO_IMAGE_PARAMS
 __UpperCAmelCase	:					int   		=	TEXT_TO_IMAGE_BATCH_PARAMS
 __UpperCAmelCase	:					List[Any]   		=	TEXT_TO_IMAGE_IMAGE_PARAMS
 __UpperCAmelCase	:					List[str]   		=	TEXT_TO_IMAGE_IMAGE_PARAMS
 __UpperCAmelCase	:					Optional[int]   		=	False


 def 					_lowercase (			self							:			Any	):
    torch.manual_seed(0	)
    __lowercase								=						UNetaDConditionModel(
        block_out_channels=(3_2, 6_4),					layers_per_block=2,					sample_size=3_2,					in_channels=4,					out_channels=4,					down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),					up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),					cross_attention_dim=3_2,					)
    __lowercase								=						DDIMScheduler(
        beta_start=0.00_085,					beta_end=0.012,					beta_schedule="scaled_linear",					clip_sample=UpperCAmelCase__,					set_alpha_to_one=UpperCAmelCase__,					)
    torch.manual_seed(0	)
    __lowercase								=						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	)
    __lowercase								=						CLIPTextConfig(
        bos_token_id=0,					eos_token_id=2,					hidden_size=3_2,					intermediate_size=3_7,					layer_norm_eps=1E-05,					num_attention_heads=4,					num_hidden_layers=5,					pad_token_id=1,					vocab_size=1_0_0_0,					)
    __lowercase								=						CLIPTextModel(UpperCAmelCase__	)
    __lowercase								=						CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

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


 def 					_lowercase (			self							:			Optional[int],					UpperCAmelCase__							:			str,					UpperCAmelCase__							:			Optional[int]=0	):
    if str(UpperCAmelCase__	).startswith("mps"	):
       __lowercase								=						torch.manual_seed(UpperCAmelCase__	)
    else:
       __lowercase								=						torch.Generator(device=UpperCAmelCase__	).manual_seed(UpperCAmelCase__	)
    __lowercase								=						{
        "prompt": ".",
        "generator": generator,
        "num_inference_steps": 2,
        "guidance_scale": 1.0,
        "sag_scale": 1.0,
        "output_type": "numpy",
    }
    return inputs



 def 					_lowercase (			self							:			Tuple	):
    super().test_inference_batch_single_identical(expected_max_diff=3E-3	)







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







 """simple docstring"""







 def 					_lowercase (			self							:			List[Any]	):
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()


 def 					_lowercase (			self							:			int	):
    __lowercase								=						StableDiffusionSAGPipeline.from_pretrained("CompVis/stable-diffusion-v1-4"	)
    __lowercase								=						sag_pipe.to(UpperCAmelCase__	)
    sag_pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

    __lowercase								=						"."
    __lowercase								=						torch.manual_seed(0	)
    __lowercase								=						sag_pipe(
        [prompt],					generator=UpperCAmelCase__,					guidance_scale=7.5,					sag_scale=1.0,					num_inference_steps=2_0,					output_type="np"	)

    __lowercase								=						output.images

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

    assert image.shape == (1, 5_1_2, 5_1_2, 3)
    __lowercase								=						np.array([0.1_568, 0.1_738, 0.1_695, 0.1_693, 0.1_507, 0.1_705, 0.1_547, 0.1_751, 0.1_949]	)

    assert np.abs(image_slice.flatten() - expected_slice	).max() < 5E-2


 def 					_lowercase (			self							:			Union[str, Any]	):
    __lowercase								=						StableDiffusionSAGPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"	)
    __lowercase								=						sag_pipe.to(UpperCAmelCase__	)
    sag_pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

    __lowercase								=						"."
    __lowercase								=						torch.manual_seed(0	)
    __lowercase								=						sag_pipe(
        [prompt],					generator=UpperCAmelCase__,					guidance_scale=7.5,					sag_scale=1.0,					num_inference_steps=2_0,					output_type="np"	)

    __lowercase								=						output.images

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

    assert image.shape == (1, 5_1_2, 5_1_2, 3)
    __lowercase								=						np.array([0.3_459, 0.2_876, 0.2_537, 0.3_002, 0.2_671, 0.2_160, 0.3_026, 0.2_262, 0.2_371]	)

    assert np.abs(image_slice.flatten() - expected_slice	).max() < 5E-2



 def 					_lowercase (			self							:			int	):
    __lowercase								=						StableDiffusionSAGPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"	)
    __lowercase								=						sag_pipe.to(UpperCAmelCase__	)
    sag_pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

    __lowercase								=						"."
    __lowercase								=						torch.manual_seed(0	)
    __lowercase								=						sag_pipe(
        [prompt],					width=7_6_8,					height=5_1_2,					generator=UpperCAmelCase__,					guidance_scale=7.5,					sag_scale=1.0,					num_inference_steps=2_0,					output_type="np",					)

    __lowercase								=						output.images

    assert image.shape == (1, 5_1_2, 7_6_8, 3)







"""simple docstring"""





import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin




class 					_lowerCAmelCase  (	unittest.TestCase     ,lowercase   ):







 """simple docstring"""







 def 					_lowercase (			self							:			List[Any]	):
    __lowercase								=						load_tool("text-classification"	)
    self.tool.setup()
    __lowercase								=						load_tool("text-classification",					remote=UpperCAmelCase__	)


 def 					_lowercase (			self							:			str	):
    __lowercase								=						self.tool("That's quite cool",					["positive", "negative"]	)
    self.assertEqual(UpperCAmelCase__,					"positive"	)


 def 					_lowercase (			self							:			str	):
    __lowercase								=						self.remote_tool("That's quite cool",					["positive", "negative"]	)
    self.assertEqual(UpperCAmelCase__,					"positive"	)


 def 					_lowercase (			self							:			List[str]	):
    __lowercase								=						self.tool(text="That's quite cool",					labels=["positive", "negative"]	)
    self.assertEqual(UpperCAmelCase__,					"positive"	)



 def 					_lowercase (			self							:			Tuple	):
    __lowercase								=						self.remote_tool(text="That's quite cool",					labels=["positive", "negative"]	)
    self.assertEqual(UpperCAmelCase__,					"positive"	)




