"""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):


      def 		_UpperCAmelCase       (   self							)       ->					Any:
             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()


      @property
      def 		_UpperCAmelCase       (   self							)       ->					str:
             lowercase__			:		Any       =	1
             lowercase__			:		str       =	3
             lowercase__			:		Dict       =	(3_2, 3_2)

             lowercase__			:		Any       =	floats_tensor((batch_size, num_channels) + sizes		,			rng=random.Random(0							)							).to(_a							)
             return image


      @property
      def 		_UpperCAmelCase       (   self							)       ->					int:
             torch.manual_seed(0							)
             lowercase__			:		List[Any]       =	UNetaDConditionModel(
                 block_out_channels=(3_2, 3_2, 6_4)		,			layers_per_block=2		,			sample_size=3_2		,			in_channels=7		,			out_channels=4		,			down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D', 'CrossAttnDownBlock2D')		,			up_block_types=('CrossAttnUpBlock2D', 'CrossAttnUpBlock2D', 'UpBlock2D')		,			cross_attention_dim=3_2		,			attention_head_dim=8		,			use_linear_projection=_a		,			only_cross_attention=(True, True, False)		,			num_class_embeds=1_0_0		,			)
             return model


      @property
      def 		_UpperCAmelCase       (   self							)       ->					List[Any]:
             torch.manual_seed(0							)
             lowercase__			:		Dict       =	AutoencoderKL(
                 block_out_channels=[3_2, 3_2, 6_4]		,			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 		_UpperCAmelCase       (   self							)       ->					Union[str, Any]:
             torch.manual_seed(0							)
             lowercase__			:		Tuple       =	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		,			hidden_act='gelu'		,			projection_dim=5_1_2		,			)
             return CLIPTextModel(_a							)


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

             lowercase__			:		str       =	self.dummy_image.cpu().permute(0		,			2		,			3		,			1							)[0]
             lowercase__			:		Tuple       =	Image.fromarray(np.uinta(_a							)							).convert('RGB'							).resize((6_4, 6_4)							)

             # make sure here that pndm scheduler skips prk
             lowercase__			:		Any       =	StableDiffusionUpscalePipeline(
                 unet=_a		,			low_res_scheduler=_a		,			scheduler=_a		,			vae=_a		,			text_encoder=_a		,			tokenizer=_a		,			max_noise_level=3_5_0		,			)
             lowercase__			:		Dict       =	sd_pipe.to(_a							)
             sd_pipe.set_progress_bar_config(disable=_a							)

             lowercase__			:		Any       =	'A painting of a squirrel eating a burger'
             lowercase__			:		List[str]       =	torch.Generator(device=_a							).manual_seed(0							)
             lowercase__			:		Optional[int]       =	sd_pipe(
                 [prompt]		,			image=_a		,			generator=_a		,			guidance_scale=6.0		,			noise_level=2_0		,			num_inference_steps=2		,			output_type='np'		,			)

             lowercase__			:		List[str]       =	output.images

             lowercase__			:		int       =	torch.Generator(device=_a							).manual_seed(0							)
             lowercase__			:		List[Any]       =	sd_pipe(
                 [prompt]		,			image=_a		,			generator=_a		,			guidance_scale=6.0		,			noise_level=2_0		,			num_inference_steps=2		,			output_type='np'		,			return_dict=_a		,			)[0]

             lowercase__			:		List[Any]       =	image[0, -3:, -3:, -1]
             lowercase__			:		Optional[int]       =	image_from_tuple[0, -3:, -3:, -1]

             lowercase__			:		Tuple       =	low_res_image.size[0] * 4
             assert image.shape == (1, expected_height_width, expected_height_width, 3)
             lowercase__			:		Dict       =	np.array([0.3_113, 0.3_910, 0.4_272, 0.4_859, 0.5_061, 0.4_652, 0.5_362, 0.5_715, 0.5_661]							)

             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 		_UpperCAmelCase       (   self							)       ->					str:
             lowercase__			:		Any       =	'cpu'  # ensure determinism for the device-dependent torch.Generator
             lowercase__			:		Dict       =	self.dummy_cond_unet_upscale
             lowercase__			:		Dict       =	DDPMScheduler()
             lowercase__			:		List[Any]       =	DDIMScheduler(prediction_type='v_prediction'							)
             lowercase__			:		Optional[Any]       =	self.dummy_vae
             lowercase__			:		str       =	self.dummy_text_encoder
             lowercase__			:		Dict       =	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'							)

             lowercase__			:		Any       =	self.dummy_image.cpu().permute(0		,			2		,			3		,			1							)[0]
             lowercase__			:		List[Any]       =	Image.fromarray(np.uinta(_a							)							).convert('RGB'							).resize((6_4, 6_4)							)

             # make sure here that pndm scheduler skips prk
             lowercase__			:		List[str]       =	StableDiffusionUpscalePipeline(
                 unet=_a		,			low_res_scheduler=_a		,			scheduler=_a		,			vae=_a		,			text_encoder=_a		,			tokenizer=_a		,			max_noise_level=3_5_0		,			)
             lowercase__			:		Dict       =	sd_pipe.to(_a							)
             sd_pipe.set_progress_bar_config(disable=_a							)

             lowercase__			:		int       =	'A painting of a squirrel eating a burger'
             lowercase__			:		int       =	sd_pipe(
                 2 * [prompt]		,			image=2 * [low_res_image]		,			guidance_scale=6.0		,			noise_level=2_0		,			num_inference_steps=2		,			output_type='np'		,			)
             lowercase__			:		Tuple       =	output.images
             assert image.shape[0] == 2

             lowercase__			:		Dict       =	torch.Generator(device=_a							).manual_seed(0							)
             lowercase__			:		List[Any]       =	sd_pipe(
                 [prompt]		,			image=_a		,			generator=_a		,			num_images_per_prompt=2		,			guidance_scale=6.0		,			noise_level=2_0		,			num_inference_steps=2		,			output_type='np'		,			)
             lowercase__			:		str       =	output.images
             assert image.shape[0] == 2


      @unittest.skipIf(torch_device != 'cuda'		,			'This test requires a GPU'							)
      def 		_UpperCAmelCase       (   self							)       ->					Tuple:
             lowercase__			:		List[str]       =	self.dummy_cond_unet_upscale
             lowercase__			:		Dict       =	DDPMScheduler()
             lowercase__			:		Optional[Any]       =	DDIMScheduler(prediction_type='v_prediction'							)
             lowercase__			:		Union[str, Any]       =	self.dummy_vae
             lowercase__			:		Optional[Any]       =	self.dummy_text_encoder
             lowercase__			:		Tuple       =	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'							)

             lowercase__			:		Optional[int]       =	self.dummy_image.cpu().permute(0		,			2		,			3		,			1							)[0]
             lowercase__			:		List[Any]       =	Image.fromarray(np.uinta(_a							)							).convert('RGB'							).resize((6_4, 6_4)							)

             # put models in fp16, except vae as it overflows in fp16
             lowercase__			:		Optional[int]       =	unet.half()
             lowercase__			:		List[Any]       =	text_encoder.half()

             # make sure here that pndm scheduler skips prk
             lowercase__			:		Any       =	StableDiffusionUpscalePipeline(
                 unet=_a		,			low_res_scheduler=_a		,			scheduler=_a		,			vae=_a		,			text_encoder=_a		,			tokenizer=_a		,			max_noise_level=3_5_0		,			)
             lowercase__			:		Optional[Any]       =	sd_pipe.to(_a							)
             sd_pipe.set_progress_bar_config(disable=_a							)

             lowercase__			:		Any       =	'A painting of a squirrel eating a burger'
             lowercase__			:		str       =	torch.manual_seed(0							)
             lowercase__			:		Union[str, Any]       =	sd_pipe(
                 [prompt]		,			image=_a		,			generator=_a		,			num_inference_steps=2		,			output_type='np'		,			).images

             lowercase__			:		Union[str, Any]       =	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):


      def 		_UpperCAmelCase       (   self							)       ->					Tuple:
             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()


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

             lowercase__			:		Dict       =	'stabilityai/stable-diffusion-x4-upscaler'
             lowercase__			:		Dict       =	StableDiffusionUpscalePipeline.from_pretrained(_a							)
             pipe.to(_a							)
             pipe.set_progress_bar_config(disable=_a							)
             pipe.enable_attention_slicing()

             lowercase__			:		Optional[Any]       =	'a cat sitting on a park bench'

             lowercase__			:		Tuple       =	torch.manual_seed(0							)
             lowercase__			:		Any       =	pipe(
                 prompt=_a		,			image=_a		,			generator=_a		,			output_type='np'		,			)
             lowercase__			:		Optional[Any]       =	output.images[0]

             assert image.shape == (5_1_2, 5_1_2, 3)
             assert np.abs(expected_image - image							).max() < 1e-3


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

             lowercase__			:		str       =	'stabilityai/stable-diffusion-x4-upscaler'
             lowercase__			:		int       =	StableDiffusionUpscalePipeline.from_pretrained(
                 _a		,			torch_dtype=torch.floataa		,			)
             pipe.to(_a							)
             pipe.set_progress_bar_config(disable=_a							)
             pipe.enable_attention_slicing()

             lowercase__			:		str       =	'a cat sitting on a park bench'

             lowercase__			:		List[Any]       =	torch.manual_seed(0							)
             lowercase__			:		Union[str, Any]       =	pipe(
                 prompt=_a		,			image=_a		,			generator=_a		,			output_type='np'		,			)
             lowercase__			:		Any       =	output.images[0]

             assert image.shape == (5_1_2, 5_1_2, 3)
             assert np.abs(expected_image - image							).max() < 5e-1


      def 		_UpperCAmelCase       (   self							)       ->					Dict:
             torch.cuda.empty_cache()
             torch.cuda.reset_max_memory_allocated()
             torch.cuda.reset_peak_memory_stats()

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

             lowercase__			:		Dict       =	'stabilityai/stable-diffusion-x4-upscaler'
             lowercase__			:		List[Any]       =	StableDiffusionUpscalePipeline.from_pretrained(
                 _a		,			torch_dtype=torch.floataa		,			)
             pipe.to(_a							)
             pipe.set_progress_bar_config(disable=_a							)
             pipe.enable_attention_slicing(1							)
             pipe.enable_sequential_cpu_offload()

             lowercase__			:		List[Any]       =	'a cat sitting on a park bench'

             lowercase__			:		Optional[int]       =	torch.manual_seed(0							)
             lowercase__			:		Optional[Any]       =	pipe(
                 prompt=_a		,			image=_a		,			generator=_a		,			num_inference_steps=5		,			output_type='np'		,			)

             lowercase__			:		Any       =	torch.cuda.max_memory_allocated()
             # make sure that less than 2.9 GB is allocated
             assert mem_bytes < 2.9 * 1_0**9




'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase :      Any					 =     {
    "configuration_funnel": ["FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP", "FunnelConfig"],
    "convert_funnel_original_tf_checkpoint_to_pytorch": [],
    "tokenization_funnel": ["FunnelTokenizer"],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      Optional[Any]					 =     ["FunnelTokenizerFast"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      List[str]					 =     [
         "FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST",
         "FunnelBaseModel",
         "FunnelForMaskedLM",
         "FunnelForMultipleChoice",
         "FunnelForPreTraining",
         "FunnelForQuestionAnswering",
         "FunnelForSequenceClassification",
         "FunnelForTokenClassification",
         "FunnelModel",
         "FunnelPreTrainedModel",
         "load_tf_weights_in_funnel",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      Union[str, Any]					 =     [
         "TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFFunnelBaseModel",
         "TFFunnelForMaskedLM",
         "TFFunnelForMultipleChoice",
         "TFFunnelForPreTraining",
         "TFFunnelForQuestionAnswering",
         "TFFunnelForSequenceClassification",
         "TFFunnelForTokenClassification",
         "TFFunnelModel",
         "TFFunnelPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
     from .tokenization_funnel import FunnelTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_funnel_fast import FunnelTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_funnel import (
              FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
              FunnelBaseModel,
              FunnelForMaskedLM,
              FunnelForMultipleChoice,
              FunnelForPreTraining,
              FunnelForQuestionAnswering,
              FunnelForSequenceClassification,
              FunnelForTokenClassification,
              FunnelModel,
              FunnelPreTrainedModel,
              load_tf_weights_in_funnel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_funnel import (
              TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFFunnelBaseModel,
              TFFunnelForMaskedLM,
              TFFunnelForMultipleChoice,
              TFFunnelForPreTraining,
              TFFunnelForQuestionAnswering,
              TFFunnelForSequenceClassification,
              TFFunnelForTokenClassification,
              TFFunnelModel,
              TFFunnelPreTrainedModel,
          )

else:
     import sys

     lowerCamelCase :      Optional[Any]					 =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
