



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 	_lowercase							(   unittest.TestCase	):





	"""simple docstring"""




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()




	@property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   1
						a					     =   3
						a					     =   (32, 32)

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




	@property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						torch.manual_seed(0		)
						a					     =   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=100		,       )
						return model




	@property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						torch.manual_seed(0		)
						a					     =   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		):
						"""simple docstring"""

						torch.manual_seed(0		)
						a					     =   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(lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

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

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

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

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

						a					     =   output.images

						a					     =   torch.Generator(device=lowerCamelCase_		).manual_seed(0		)
						a					     =   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]

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

						a					     =   low_res_image.size[0] * 4
						assert image.shape == (1, expected_height_width, expected_height_width, 3)
						a					     =   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		):
						"""simple docstring"""

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

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

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

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

						a					     =   torch.Generator(device=lowerCamelCase_		).manual_seed(0		)
						a					     =   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"		,       )
						a					     =   output.images
						assert image.shape[0] == 2




	@unittest.skipIf(torch_device != "cuda"		,       "This test requires a GPU"		)
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.dummy_cond_unet_upscale
						a					     =   DDPMScheduler()
						a					     =   DDIMScheduler(prediction_type="v_prediction"		)
						a					     =   self.dummy_vae
						a					     =   self.dummy_text_encoder
						a					     =   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

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

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

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

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

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




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





	"""simple docstring"""




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

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

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

						a					     =   "a cat sitting on a park bench"

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

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

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

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

						a					     =   "a cat sitting on a park bench"

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

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						torch.cuda.empty_cache()
						torch.cuda.reset_max_memory_allocated()
						torch.cuda.reset_peak_memory_stats()

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

						a					     =   "stabilityai/stable-diffusion-x4-upscaler"
						a					     =   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()

						a					     =   "a cat sitting on a park bench"

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

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





import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


_lowercase: Union[str, Any]      			=		get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
				_lowercase: List[str]      			=		json.load(f)



@require_torch
class 	_lowercase							(   unittest.TestCase	):





	"""simple docstring"""




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

						return FSMTTokenizer.from_pretrained(lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   FSMTForConditionalGeneration.from_pretrained(lowerCamelCase_		).to(lowerCamelCase_		)
						if torch_device == "cuda":
											model.half()
						return model




	@parameterized.expand(
	    [
	        ["en-ru", 26.0],
	        ["ru-en", 22.0],
	        ["en-de", 22.0],
	        ["de-en", 29.0],
	    ]		)
	@slow
	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   F'''facebook/wmt19-{pair}'''
						a					     =   self.get_tokenizer(lowerCamelCase_		)
						a					     =   self.get_model(lowerCamelCase_		)

						a					     =   bleu_data[pair]["src"]
						a					     =   bleu_data[pair]["tgt"]

						a					     =   tokenizer(lowerCamelCase_		,       return_tensors="pt"		,       truncation=lowerCamelCase_		,       padding="longest"		).to(lowerCamelCase_		)
						a					     =   model.generate(
						    input_ids=batch.input_ids		,       num_beams=8		,       )
						a					     =   tokenizer.batch_decode(
						    lowerCamelCase_		,       skip_special_tokens=lowerCamelCase_		,       clean_up_tokenization_spaces=lowerCamelCase_		)
						a					     =   calculate_bleu(lowerCamelCase_		,       lowerCamelCase_		)
						print(lowerCamelCase_		)
						self.assertGreaterEqual(scores["bleu"]		,       lowerCamelCase_		)
