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 		UpperCamelCase_						(					self						) ->				List[Any]:






           """simple docstring"""
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()



     @property
     def 		UpperCamelCase_						(					self						) ->				Dict:






           """simple docstring"""
           _lowerCamelCase			    =				1
           _lowerCamelCase			    =				3
           _lowerCamelCase			    =				(32, 32)

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



     @property
     def 		UpperCamelCase_						(					self						) ->				Optional[Any]:






           """simple docstring"""
           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=A_				,				only_cross_attention=(True, True, False)				,				num_class_embeds=1_00				,				)
           return model



     @property
     def 		UpperCamelCase_						(					self						) ->				Optional[Any]:






           """simple docstring"""
           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 		UpperCamelCase_						(					self						) ->				List[Any]:






           """simple docstring"""
           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(A_						)



     def 		UpperCamelCase_						(					self						) ->				Optional[Any]:






           """simple docstring"""
           _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(A_						)						).convert('''RGB'''						).resize((64, 64)						)

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

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

           _lowerCamelCase			    =				output.images

           _lowerCamelCase			    =				torch.Generator(device=A_						).manual_seed(0						)
           _lowerCamelCase			    =				sd_pipe(
               [prompt]				,				image=A_				,				generator=A_				,				guidance_scale=6.0				,				noise_level=20				,				num_inference_steps=2				,				output_type='''np'''				,				return_dict=A_				,				)[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([0.3113, 0.3910, 0.4272, 0.4859, 0.5061, 0.4652, 0.5362, 0.5715, 0.5661]						)

           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						) ->				int:






           """simple docstring"""
           _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(A_						)						).convert('''RGB'''						).resize((64, 64)						)

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

           _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=A_						).manual_seed(0						)
           _lowerCamelCase			    =				sd_pipe(
               [prompt]				,				image=A_				,				generator=A_				,				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 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           _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(A_						)						).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=A_				,				low_res_scheduler=A_				,				scheduler=A_				,				vae=A_				,				text_encoder=A_				,				tokenizer=A_				,				max_noise_level=3_50				,				)
           _lowerCamelCase			    =				sd_pipe.to(A_						)
           sd_pipe.set_progress_bar_config(disable=A_						)

           _lowerCamelCase			    =				'''A painting of a squirrel eating a burger'''
           _lowerCamelCase			    =				torch.manual_seed(0						)
           _lowerCamelCase			    =				sd_pipe(
               [prompt]				,				image=A_				,				generator=A_				,				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 		UpperCamelCase_						(					self						) ->				Optional[Any]:






           """simple docstring"""
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()



     def 		UpperCamelCase_						(					self						) ->				Optional[int]:






           """simple docstring"""
           _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(A_						)
           pipe.to(A_						)
           pipe.set_progress_bar_config(disable=A_						)
           pipe.enable_attention_slicing()

           _lowerCamelCase			    =				'''a cat sitting on a park bench'''

           _lowerCamelCase			    =				torch.manual_seed(0						)
           _lowerCamelCase			    =				pipe(
               prompt=A_				,				image=A_				,				generator=A_				,				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 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           _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(
               A_				,				torch_dtype=torch.floataa				,				)
           pipe.to(A_						)
           pipe.set_progress_bar_config(disable=A_						)
           pipe.enable_attention_slicing()

           _lowerCamelCase			    =				'''a cat sitting on a park bench'''

           _lowerCamelCase			    =				torch.manual_seed(0						)
           _lowerCamelCase			    =				pipe(
               prompt=A_				,				image=A_				,				generator=A_				,				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 		UpperCamelCase_						(					self						) ->				List[str]:






           """simple docstring"""
           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(
               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()

           _lowerCamelCase			    =				'''a cat sitting on a park bench'''

           _lowerCamelCase			    =				torch.manual_seed(0						)
           _lowerCamelCase			    =				pipe(
               prompt=A_				,				image=A_				,				generator=A_				,				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 .data_collator import (
    DataCollatorForLanguageModeling,
    DataCollatorForPermutationLanguageModeling,
    DataCollatorForSeqaSeq,
    DataCollatorForSOP,
    DataCollatorForTokenClassification,
    DataCollatorForWholeWordMask,
    DataCollatorWithPadding,
    DefaultDataCollator,
    default_data_collator,
)
from .metrics import glue_compute_metrics, xnli_compute_metrics
from .processors import (
    DataProcessor,
    InputExample,
    InputFeatures,
    SingleSentenceClassificationProcessor,
    SquadExample,
    SquadFeatures,
    SquadVaProcessor,
    SquadVaProcessor,
    glue_convert_examples_to_features,
    glue_output_modes,
    glue_processors,
    glue_tasks_num_labels,
    squad_convert_examples_to_features,
    xnli_output_modes,
    xnli_processors,
    xnli_tasks_num_labels,
)