


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__lowerCAmelCase     							=			{
    '''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
    '''tokenization_canine''': ['''CanineTokenizer'''],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase     							=			[
        '''CANINE_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''CanineForMultipleChoice''',
        '''CanineForQuestionAnswering''',
        '''CanineForSequenceClassification''',
        '''CanineForTokenClassification''',
        '''CanineLayer''',
        '''CanineModel''',
        '''CaninePreTrainedModel''',
        '''load_tf_weights_in_canine''',
    ]


if TYPE_CHECKING:
    from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
    from .tokenization_canine import CanineTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_canine import (
            CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
            CanineForMultipleChoice,
            CanineForQuestionAnswering,
            CanineForSequenceClassification,
            CanineForTokenClassification,
            CanineLayer,
            CanineModel,
            CaninePreTrainedModel,
            load_tf_weights_in_canine,
        )


else:
    import sys

    __lowerCAmelCase     							=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)










'''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 				__snake_case     (					unittest.TestCase							):





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





	@property
	def   _snake_case   (		self		)      ->		str:
		snake_case__	       =   1
		snake_case__	       =   3
		snake_case__	       =   (32, 32)

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





	@property
	def   _snake_case   (		self		)      ->		Union[str, Any]:
		torch.manual_seed(0		)
		snake_case__	       =   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=UpperCamelCase_      ,       only_cross_attention=(True, True, False)      ,       num_class_embeds=100      ,       )
		return model





	@property
	def   _snake_case   (		self		)      ->		Optional[Any]:
		torch.manual_seed(0		)
		snake_case__	       =   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		)      ->		List[str]:
		torch.manual_seed(0		)
		snake_case__	       =   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=1000      ,       hidden_act='gelu'      ,       projection_dim=512      ,       )
		return CLIPTextModel(UpperCamelCase_		)





	def   _snake_case   (		self		)      ->		Union[str, Any]:
		snake_case__	       =   'cpu'  # ensure determinism for the device-dependent torch.Generator
		snake_case__	       =   self.dummy_cond_unet_upscale
		snake_case__	       =   DDPMScheduler()
		snake_case__	       =   DDIMScheduler(prediction_type='v_prediction'		)
		snake_case__	       =   self.dummy_vae
		snake_case__	       =   self.dummy_text_encoder
		snake_case__	       =   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)

		snake_case__	       =   self.dummy_image.cpu().permute(0      ,       2      ,       3      ,       1		)[0]
		snake_case__	       =   Image.fromarray(np.uinta(UpperCamelCase_		)		).convert('RGB'		).resize((64, 64)		)

		# make sure here that pndm scheduler skips prk
		snake_case__	       =   StableDiffusionUpscalePipeline(
		    unet=UpperCamelCase_      ,       low_res_scheduler=UpperCamelCase_      ,       scheduler=UpperCamelCase_      ,       vae=UpperCamelCase_      ,       text_encoder=UpperCamelCase_      ,       tokenizer=UpperCamelCase_      ,       max_noise_level=350      ,       )
		snake_case__	       =   sd_pipe.to(UpperCamelCase_		)
		sd_pipe.set_progress_bar_config(disable=UpperCamelCase_		)

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

		snake_case__	       =   output.images

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

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

		snake_case__	       =   low_res_image.size[0] * 4
		assert image.shape == (1, expected_height_width, expected_height_width, 3)
		snake_case__	       =   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		)      ->		List[str]:
		snake_case__	       =   'cpu'  # ensure determinism for the device-dependent torch.Generator
		snake_case__	       =   self.dummy_cond_unet_upscale
		snake_case__	       =   DDPMScheduler()
		snake_case__	       =   DDIMScheduler(prediction_type='v_prediction'		)
		snake_case__	       =   self.dummy_vae
		snake_case__	       =   self.dummy_text_encoder
		snake_case__	       =   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)

		snake_case__	       =   self.dummy_image.cpu().permute(0      ,       2      ,       3      ,       1		)[0]
		snake_case__	       =   Image.fromarray(np.uinta(UpperCamelCase_		)		).convert('RGB'		).resize((64, 64)		)

		# make sure here that pndm scheduler skips prk
		snake_case__	       =   StableDiffusionUpscalePipeline(
		    unet=UpperCamelCase_      ,       low_res_scheduler=UpperCamelCase_      ,       scheduler=UpperCamelCase_      ,       vae=UpperCamelCase_      ,       text_encoder=UpperCamelCase_      ,       tokenizer=UpperCamelCase_      ,       max_noise_level=350      ,       )
		snake_case__	       =   sd_pipe.to(UpperCamelCase_		)
		sd_pipe.set_progress_bar_config(disable=UpperCamelCase_		)

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

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





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

		snake_case__	       =   self.dummy_image.cpu().permute(0      ,       2      ,       3      ,       1		)[0]
		snake_case__	       =   Image.fromarray(np.uinta(UpperCamelCase_		)		).convert('RGB'		).resize((64, 64)		)

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

		# make sure here that pndm scheduler skips prk
		snake_case__	       =   StableDiffusionUpscalePipeline(
		    unet=UpperCamelCase_      ,       low_res_scheduler=UpperCamelCase_      ,       scheduler=UpperCamelCase_      ,       vae=UpperCamelCase_      ,       text_encoder=UpperCamelCase_      ,       tokenizer=UpperCamelCase_      ,       max_noise_level=350      ,       )
		snake_case__	       =   sd_pipe.to(UpperCamelCase_		)
		sd_pipe.set_progress_bar_config(disable=UpperCamelCase_		)

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

		snake_case__	       =   low_res_image.size[0] * 4
		assert image.shape == (1, expected_height_width, expected_height_width, 3)






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





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





	def   _snake_case   (		self		)      ->		Optional[int]:
		snake_case__	       =   load_image(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
		    '/sd2-upscale/low_res_cat.png'		)
		snake_case__	       =   load_numpy(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
		    '/upsampled_cat.npy'		)

		snake_case__	       =   'stabilityai/stable-diffusion-x4-upscaler'
		snake_case__	       =   StableDiffusionUpscalePipeline.from_pretrained(UpperCamelCase_		)
		pipe.to(UpperCamelCase_		)
		pipe.set_progress_bar_config(disable=UpperCamelCase_		)
		pipe.enable_attention_slicing()

		snake_case__	       =   'a cat sitting on a park bench'

		snake_case__	       =   torch.manual_seed(0		)
		snake_case__	       =   pipe(
		    prompt=UpperCamelCase_      ,       image=UpperCamelCase_      ,       generator=UpperCamelCase_      ,       output_type='np'      ,       )
		snake_case__	       =   output.images[0]

		assert image.shape == (512, 512, 3)
		assert np.abs(expected_image - image		).max() < 1E-3





	def   _snake_case   (		self		)      ->		List[Any]:
		snake_case__	       =   load_image(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
		    '/sd2-upscale/low_res_cat.png'		)
		snake_case__	       =   load_numpy(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
		    '/upsampled_cat_fp16.npy'		)

		snake_case__	       =   'stabilityai/stable-diffusion-x4-upscaler'
		snake_case__	       =   StableDiffusionUpscalePipeline.from_pretrained(
		    UpperCamelCase_      ,       torch_dtype=torch.floataa      ,       )
		pipe.to(UpperCamelCase_		)
		pipe.set_progress_bar_config(disable=UpperCamelCase_		)
		pipe.enable_attention_slicing()

		snake_case__	       =   'a cat sitting on a park bench'

		snake_case__	       =   torch.manual_seed(0		)
		snake_case__	       =   pipe(
		    prompt=UpperCamelCase_      ,       image=UpperCamelCase_      ,       generator=UpperCamelCase_      ,       output_type='np'      ,       )
		snake_case__	       =   output.images[0]

		assert image.shape == (512, 512, 3)
		assert np.abs(expected_image - image		).max() < 5E-1





	def   _snake_case   (		self		)      ->		int:
		torch.cuda.empty_cache()
		torch.cuda.reset_max_memory_allocated()
		torch.cuda.reset_peak_memory_stats()

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

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

		snake_case__	       =   'a cat sitting on a park bench'

		snake_case__	       =   torch.manual_seed(0		)
		snake_case__	       =   pipe(
		    prompt=UpperCamelCase_      ,       image=UpperCamelCase_      ,       generator=UpperCamelCase_      ,       num_inference_steps=5      ,       output_type='np'      ,       )

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



