


"""simple docstring"""


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, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()
class   _UpperCamelCase  (   unittest.TestCase		):
							'''simple docstring'''







							def 			snake_case				(     self	):
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()

							@property
							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			1
										__lowerCAmelCase				    =			3
										__lowerCAmelCase				    =			(32, 32)

										__lowerCAmelCase				    =			floats_tensor((batch_size, num_channels) + sizes    ,   rng=random.Random(0	)	).to(__lowerCamelCase	)
										return image

							@property
							def 			snake_case				(     self	):
										torch.manual_seed(0	)
										__lowerCAmelCase				    =			UNetaDConditionModel(
										    block_out_channels=(32, 32, 64)    ,   layers_per_block=2    ,   sample_size=32    ,   in_channels=7    ,   out_channels=4    ,   down_block_types=("DownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D")    ,   up_block_types=("CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "UpBlock2D")    ,   cross_attention_dim=32    ,   attention_head_dim=8    ,   use_linear_projection=__lowerCamelCase    ,   only_cross_attention=(True, True, False)    ,   num_class_embeds=1_00    ,   )
										return model

							@property
							def 			snake_case				(     self	):
										torch.manual_seed(0	)
										__lowerCAmelCase				    =			AutoencoderKL(
										    block_out_channels=[32, 32, 64]    ,   in_channels=3    ,   out_channels=3    ,   down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D"]    ,   up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"]    ,   latent_channels=4    ,   )
										return model

							@property
							def 			snake_case				(     self	):
										torch.manual_seed(0	)
										__lowerCAmelCase				    =			CLIPTextConfig(
										    bos_token_id=0    ,   eos_token_id=2    ,   hidden_size=32    ,   intermediate_size=37    ,   layer_norm_eps=1e-0_5    ,   num_attention_heads=4    ,   num_hidden_layers=5    ,   pad_token_id=1    ,   vocab_size=10_00    ,   hidden_act="gelu"    ,   projection_dim=5_12    ,   )
										return CLIPTextModel(__lowerCamelCase	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			"cpu"  # ensure determinism for the device-dependent torch.Generator
										__lowerCAmelCase				    =			self.dummy_cond_unet_upscale
										__lowerCAmelCase				    =			DDPMScheduler()
										__lowerCAmelCase				    =			DDIMScheduler(prediction_type="v_prediction"	)
										__lowerCAmelCase				    =			self.dummy_vae
										__lowerCAmelCase				    =			self.dummy_text_encoder
										__lowerCAmelCase				    =			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

										__lowerCAmelCase				    =			self.dummy_image.cpu().permute(0    ,   2    ,   3    ,   1	)[0]
										__lowerCAmelCase				    =			Image.fromarray(np.uinta(__lowerCamelCase	)	).convert("RGB"	).resize((64, 64)	)

										# make sure here that pndm scheduler skips prk
										__lowerCAmelCase				    =			StableDiffusionUpscalePipeline(
										    unet=__lowerCamelCase    ,   low_res_scheduler=__lowerCamelCase    ,   scheduler=__lowerCamelCase    ,   vae=__lowerCamelCase    ,   text_encoder=__lowerCamelCase    ,   tokenizer=__lowerCamelCase    ,   max_noise_level=3_50    ,   )
										__lowerCAmelCase				    =			sd_pipe.to(__lowerCamelCase	)
										sd_pipe.set_progress_bar_config(disable=__lowerCamelCase	)

										__lowerCAmelCase				    =			"A painting of a squirrel eating a burger"
										__lowerCAmelCase				    =			torch.Generator(device=__lowerCamelCase	).manual_seed(0	)
										__lowerCAmelCase				    =			sd_pipe(
										    [prompt]    ,   image=__lowerCamelCase    ,   generator=__lowerCamelCase    ,   guidance_scale=6.0    ,   noise_level=20    ,   num_inference_steps=2    ,   output_type="np"    ,   )

										__lowerCAmelCase				    =			output.images

										__lowerCAmelCase				    =			torch.Generator(device=__lowerCamelCase	).manual_seed(0	)
										__lowerCAmelCase				    =			sd_pipe(
										    [prompt]    ,   image=__lowerCamelCase    ,   generator=__lowerCamelCase    ,   guidance_scale=6.0    ,   noise_level=20    ,   num_inference_steps=2    ,   output_type="np"    ,   return_dict=__lowerCamelCase    ,   )[0]

										__lowerCAmelCase				    =			image[0, -3:, -3:, -1]
										__lowerCAmelCase				    =			image_from_tuple[0, -3:, -3:, -1]

										__lowerCAmelCase				    =			low_res_image.size[0] * 4
										assert image.shape == (1, expected_height_width, expected_height_width, 3)
										__lowerCAmelCase				    =			np.array([0.3_1_1_3, 0.3_9_1_0, 0.4_2_7_2, 0.4_8_5_9, 0.5_0_6_1, 0.4_6_5_2, 0.5_3_6_2, 0.5_7_1_5, 0.5_6_6_1]	)

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

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			"cpu"  # ensure determinism for the device-dependent torch.Generator
										__lowerCAmelCase				    =			self.dummy_cond_unet_upscale
										__lowerCAmelCase				    =			DDPMScheduler()
										__lowerCAmelCase				    =			DDIMScheduler(prediction_type="v_prediction"	)
										__lowerCAmelCase				    =			self.dummy_vae
										__lowerCAmelCase				    =			self.dummy_text_encoder
										__lowerCAmelCase				    =			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

										__lowerCAmelCase				    =			self.dummy_image.cpu().permute(0    ,   2    ,   3    ,   1	)[0]
										__lowerCAmelCase				    =			Image.fromarray(np.uinta(__lowerCamelCase	)	).convert("RGB"	).resize((64, 64)	)

										# make sure here that pndm scheduler skips prk
										__lowerCAmelCase				    =			StableDiffusionUpscalePipeline(
										    unet=__lowerCamelCase    ,   low_res_scheduler=__lowerCamelCase    ,   scheduler=__lowerCamelCase    ,   vae=__lowerCamelCase    ,   text_encoder=__lowerCamelCase    ,   tokenizer=__lowerCamelCase    ,   max_noise_level=3_50    ,   )
										__lowerCAmelCase				    =			sd_pipe.to(__lowerCamelCase	)
										sd_pipe.set_progress_bar_config(disable=__lowerCamelCase	)

										__lowerCAmelCase				    =			"A painting of a squirrel eating a burger"
										__lowerCAmelCase				    =			sd_pipe(
										    2 * [prompt]    ,   image=2 * [low_res_image]    ,   guidance_scale=6.0    ,   noise_level=20    ,   num_inference_steps=2    ,   output_type="np"    ,   )
										__lowerCAmelCase				    =			output.images
										assert image.shape[0] == 2

										__lowerCAmelCase				    =			torch.Generator(device=__lowerCamelCase	).manual_seed(0	)
										__lowerCAmelCase				    =			sd_pipe(
										    [prompt]    ,   image=__lowerCamelCase    ,   generator=__lowerCamelCase    ,   num_images_per_prompt=2    ,   guidance_scale=6.0    ,   noise_level=20    ,   num_inference_steps=2    ,   output_type="np"    ,   )
										__lowerCAmelCase				    =			output.images
										assert image.shape[0] == 2



							@unittest.skipIf(torch_device != "cuda"    ,   "This test requires a GPU"	)
							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.dummy_cond_unet_upscale
										__lowerCAmelCase				    =			DDPMScheduler()
										__lowerCAmelCase				    =			DDIMScheduler(prediction_type="v_prediction"	)
										__lowerCAmelCase				    =			self.dummy_vae
										__lowerCAmelCase				    =			self.dummy_text_encoder
										__lowerCAmelCase				    =			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

										__lowerCAmelCase				    =			self.dummy_image.cpu().permute(0    ,   2    ,   3    ,   1	)[0]
										__lowerCAmelCase				    =			Image.fromarray(np.uinta(__lowerCamelCase	)	).convert("RGB"	).resize((64, 64)	)

										# put models in fp16, except vae as it overflows in fp16
										__lowerCAmelCase				    =			unet.half()
										__lowerCAmelCase				    =			text_encoder.half()

										# make sure here that pndm scheduler skips prk
										__lowerCAmelCase				    =			StableDiffusionUpscalePipeline(
										    unet=__lowerCamelCase    ,   low_res_scheduler=__lowerCamelCase    ,   scheduler=__lowerCamelCase    ,   vae=__lowerCamelCase    ,   text_encoder=__lowerCamelCase    ,   tokenizer=__lowerCamelCase    ,   max_noise_level=3_50    ,   )
										__lowerCAmelCase				    =			sd_pipe.to(__lowerCamelCase	)
										sd_pipe.set_progress_bar_config(disable=__lowerCamelCase	)

										__lowerCAmelCase				    =			"A painting of a squirrel eating a burger"
										__lowerCAmelCase				    =			torch.manual_seed(0	)
										__lowerCAmelCase				    =			sd_pipe(
										    [prompt]    ,   image=__lowerCamelCase    ,   generator=__lowerCamelCase    ,   num_inference_steps=2    ,   output_type="np"    ,   ).images

										__lowerCAmelCase				    =			low_res_image.size[0] * 4
										assert image.shape == (1, expected_height_width, expected_height_width, 3)
@slow
@require_torch_gpu
class   _UpperCamelCase  (   unittest.TestCase		):
							'''simple docstring'''







							def 			snake_case				(     self	):
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/sd2-upscale/low_res_cat.png"	)
										__lowerCAmelCase				    =			load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
										    "/upsampled_cat.npy"	)

										__lowerCAmelCase				    =			"stabilityai/stable-diffusion-x4-upscaler"
										__lowerCAmelCase				    =			StableDiffusionUpscalePipeline.from_pretrained(__lowerCamelCase	)
										pipe.to(__lowerCamelCase	)
										pipe.set_progress_bar_config(disable=__lowerCamelCase	)
										pipe.enable_attention_slicing()

										__lowerCAmelCase				    =			"a cat sitting on a park bench"

										__lowerCAmelCase				    =			torch.manual_seed(0	)
										__lowerCAmelCase				    =			pipe(
										    prompt=__lowerCamelCase    ,   image=__lowerCamelCase    ,   generator=__lowerCamelCase    ,   output_type="np"    ,   )
										__lowerCAmelCase				    =			output.images[0]

										assert image.shape == (5_12, 5_12, 3)
										assert np.abs(expected_image - image	).max() < 1e-3

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/sd2-upscale/low_res_cat.png"	)
										__lowerCAmelCase				    =			load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
										    "/upsampled_cat_fp16.npy"	)

										__lowerCAmelCase				    =			"stabilityai/stable-diffusion-x4-upscaler"
										__lowerCAmelCase				    =			StableDiffusionUpscalePipeline.from_pretrained(
										    __lowerCamelCase    ,   torch_dtype=torch.floataa    ,   )
										pipe.to(__lowerCamelCase	)
										pipe.set_progress_bar_config(disable=__lowerCamelCase	)
										pipe.enable_attention_slicing()

										__lowerCAmelCase				    =			"a cat sitting on a park bench"

										__lowerCAmelCase				    =			torch.manual_seed(0	)
										__lowerCAmelCase				    =			pipe(
										    prompt=__lowerCamelCase    ,   image=__lowerCamelCase    ,   generator=__lowerCamelCase    ,   output_type="np"    ,   )
										__lowerCAmelCase				    =			output.images[0]

										assert image.shape == (5_12, 5_12, 3)
										assert np.abs(expected_image - image	).max() < 5e-1



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

										__lowerCAmelCase				    =			load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/sd2-upscale/low_res_cat.png"	)

										__lowerCAmelCase				    =			"stabilityai/stable-diffusion-x4-upscaler"
										__lowerCAmelCase				    =			StableDiffusionUpscalePipeline.from_pretrained(
										    __lowerCamelCase    ,   torch_dtype=torch.floataa    ,   )
										pipe.to(__lowerCamelCase	)
										pipe.set_progress_bar_config(disable=__lowerCamelCase	)
										pipe.enable_attention_slicing(1	)
										pipe.enable_sequential_cpu_offload()

										__lowerCAmelCase				    =			"a cat sitting on a park bench"

										__lowerCAmelCase				    =			torch.manual_seed(0	)
										__lowerCAmelCase				    =			pipe(
										    prompt=__lowerCamelCase    ,   image=__lowerCamelCase    ,   generator=__lowerCamelCase    ,   num_inference_steps=5    ,   output_type="np"    ,   )

										__lowerCAmelCase				    =			torch.cuda.max_memory_allocated()
										# make sure that less than 2.9 GB is allocated
										assert mem_bytes < 2.9 * 10**9








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


lowerCAmelCase__      		= {
    'configuration_speecht5': [
        'SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP',
        'SpeechT5Config',
        'SpeechT5HifiGanConfig',
    ],
    'feature_extraction_speecht5': ['SpeechT5FeatureExtractor'],
    'processing_speecht5': ['SpeechT5Processor'],
}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__      		= ['SpeechT5Tokenizer']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__      		= [
        'SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST',
        'SpeechT5ForSpeechToText',
        'SpeechT5ForSpeechToSpeech',
        'SpeechT5ForTextToSpeech',
        'SpeechT5Model',
        'SpeechT5PreTrainedModel',
        'SpeechT5HifiGan',
    ]

if TYPE_CHECKING:
    from .configuration_speechta import (
        SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
        SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
        SpeechTaConfig,
        SpeechTaHifiGanConfig,
    )
    from .feature_extraction_speechta import SpeechTaFeatureExtractor
    from .processing_speechta import SpeechTaProcessor

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_speechta import SpeechTaTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_speechta import (
            SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
            SpeechTaForSpeechToSpeech,
            SpeechTaForSpeechToText,
            SpeechTaForTextToSpeech,
            SpeechTaHifiGan,
            SpeechTaModel,
            SpeechTaPreTrainedModel,
        )

else:
    import sys

    lowerCAmelCase__      		= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
