

"""simple docstring"""





import gc
import unittest

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

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

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


enable_full_determinism()




class 					_lowerCAmelCase  (	lowercase     ,unittest.TestCase   ):







 """simple docstring"""







 __UpperCAmelCase	:					Dict   		=	LDMTextToImagePipeline
 __UpperCAmelCase	:					str   		=	TEXT_TO_IMAGE_PARAMS - {
     "negative_prompt",
     "negative_prompt_embeds",
     "cross_attention_kwargs",
     "prompt_embeds",
 }
 __UpperCAmelCase	:					Optional[Any]   		=	PipelineTesterMixin.required_optional_params - {
     "num_images_per_prompt",
     "callback",
     "callback_steps",
 }
 __UpperCAmelCase	:					Union[str, Any]   		=	TEXT_TO_IMAGE_BATCH_PARAMS
 __UpperCAmelCase	:					List[str]   		=	False


 def 					_lowercase (			self							:			str	):
    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,
        "vqvae": vae,
        "bert": text_encoder,
        "tokenizer": tokenizer,
    }
    return components


 def 					_lowercase (			self							:			Union[str, Any],					UpperCAmelCase__							:			Dict,					UpperCAmelCase__							:			Union[str, Any]=0	):
    if str(UpperCAmelCase__	).startswith("mps"	):
       __lowercase								=						torch.manual_seed(UpperCAmelCase__	)
    else:
       __lowercase								=						torch.Generator(device=UpperCAmelCase__	).manual_seed(UpperCAmelCase__	)
    __lowercase								=						{
        "prompt": "A painting of a squirrel eating a burger",
        "generator": generator,
        "num_inference_steps": 2,
        "guidance_scale": 6.0,
        "output_type": "numpy",
    }
    return inputs



 def 					_lowercase (			self							:			int	):
    __lowercase								=						"cpu"  # ensure determinism for the device-dependent torch.Generator

    __lowercase								=						self.get_dummy_components()
    __lowercase								=						LDMTextToImagePipeline(**UpperCAmelCase__	)
    pipe.to(UpperCAmelCase__	)
    pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

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

    assert image.shape == (1, 1_6, 1_6, 3)
    __lowercase								=						np.array([0.6_101, 0.6_156, 0.5_622, 0.4_895, 0.6_661, 0.3_804, 0.5_748, 0.6_136, 0.5_014]	)

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







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







 """simple docstring"""







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


 def 					_lowercase (			self							:			int,					UpperCAmelCase__							:			Optional[int],					UpperCAmelCase__							:			Dict=torch.floataa,					UpperCAmelCase__							:			Dict=0	):
    __lowercase								=						torch.manual_seed(UpperCAmelCase__	)
    __lowercase								=						np.random.RandomState(UpperCAmelCase__	).standard_normal((1, 4, 3_2, 3_2)	)
    __lowercase								=						torch.from_numpy(UpperCAmelCase__	).to(device=UpperCAmelCase__,					dtype=UpperCAmelCase__	)
    __lowercase								=						{
        "prompt": "A painting of a squirrel eating a burger",
        "latents": latents,
        "generator": generator,
        "num_inference_steps": 3,
        "guidance_scale": 6.0,
        "output_type": "numpy",
    }
    return inputs



 def 					_lowercase (			self							:			Tuple	):
    __lowercase								=						LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256"	).to(UpperCAmelCase__	)
    pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

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

    assert image.shape == (1, 2_5_6, 2_5_6, 3)
    __lowercase								=						np.array([0.51_825, 0.52_850, 0.52_543, 0.54_258, 0.52_304, 0.52_569, 0.54_363, 0.55_276, 0.56_878]	)
    __lowercase								=						np.abs(expected_slice - image_slice	).max()
    assert max_diff < 1E-3







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







 """simple docstring"""







 def 					_lowercase (			self							:			List[str]	):
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()


 def 					_lowercase (			self							:			Union[str, Any],					UpperCAmelCase__							:			Optional[Any],					UpperCAmelCase__							:			List[str]=torch.floataa,					UpperCAmelCase__							:			List[str]=0	):
    __lowercase								=						torch.manual_seed(UpperCAmelCase__	)
    __lowercase								=						np.random.RandomState(UpperCAmelCase__	).standard_normal((1, 4, 3_2, 3_2)	)
    __lowercase								=						torch.from_numpy(UpperCAmelCase__	).to(device=UpperCAmelCase__,					dtype=UpperCAmelCase__	)
    __lowercase								=						{
        "prompt": "A painting of a squirrel eating a burger",
        "latents": latents,
        "generator": generator,
        "num_inference_steps": 5_0,
        "guidance_scale": 6.0,
        "output_type": "numpy",
    }
    return inputs



 def 					_lowercase (			self							:			int	):
    __lowercase								=						LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256"	).to(UpperCAmelCase__	)
    pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

    __lowercase								=						self.get_inputs(UpperCAmelCase__	)
    __lowercase								=						pipe(**UpperCAmelCase__	).images[0]

    __lowercase								=						load_numpy(
        "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy"	)
    __lowercase								=						np.abs(expected_image - image	).max()
    assert max_diff < 1E-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"	)




