



"""simple docstring"""
import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class 							lowerCamelCase_(   __snake_case,				__snake_case,				unittest.TestCase    ):





					'''simple docstring'''



					lowercase__   :       List[str]										= StableDiffusionXLImgaImgPipeline
					lowercase__   :       Dict										= TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
					lowercase__   :       Union[str, Any]										= PipelineTesterMixin.required_optional_params - {'latents'}
					lowercase__   :       Dict										= TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
					lowercase__   :       List[str]										= IMAGE_TO_IMAGE_IMAGE_PARAMS
					lowercase__   :       Union[str, Any]										= IMAGE_TO_IMAGE_IMAGE_PARAMS

					def 			snake_case__      (    self   ):
									torch.manual_seed(0   )
									_lowerCamelCase        =				UNetaDConditionModel(
									    block_out_channels=(3_2, 6_4)				,       layers_per_block=2				,       sample_size=3_2				,       in_channels=4				,       out_channels=4				,       down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')				,       up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')				,       attention_head_dim=(2, 4)				,       use_linear_projection=_lowercase				,       addition_embed_type='''text_time'''				,       addition_time_embed_dim=8				,       transformer_layers_per_block=(1, 2)				,       projection_class_embeddings_input_dim=8_0				,       cross_attention_dim=6_4				,       )
									_lowerCamelCase        =				EulerDiscreteScheduler(
									    beta_start=0.0_0_0_8_5				,       beta_end=0.0_1_2				,       steps_offset=1				,       beta_schedule='''scaled_linear'''				,       timestep_spacing='''leading'''				,       )
									torch.manual_seed(0   )
									_lowerCamelCase        =				AutoencoderKL(
									    block_out_channels=[3_2, 6_4]				,       in_channels=3				,       out_channels=3				,       down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']				,       up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']				,       latent_channels=4				,       sample_size=1_2_8				,       )
									torch.manual_seed(0   )
									_lowerCamelCase        =				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=3_2				,       )
									_lowerCamelCase        =				CLIPTextModel(_lowercase   )
									_lowerCamelCase        =				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				,       local_files_only=_lowercase   )

									_lowerCamelCase        =				CLIPTextModelWithProjection(_lowercase   )
									_lowerCamelCase        =				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				,       local_files_only=_lowercase   )

									_lowerCamelCase        =				{
									    """unet""": unet,
									    """scheduler""": scheduler,
									    """vae""": vae,
									    """text_encoder""": text_encoder,
									    """tokenizer""": tokenizer,
									    """text_encoder_2""": text_encoder_a,
									    """tokenizer_2""": tokenizer_a,
									    # "safety_checker": None,
									    # "feature_extractor": None,
									}
									return components

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__=0   ):
									_lowerCamelCase        =				floats_tensor((1, 3, 3_2, 3_2)				,       rng=random.Random(_lowercase   )   ).to(_lowercase   )
									_lowerCamelCase        =				image / 2 + 0.5
									if str(_lowercase   ).startswith('''mps'''   ):
													_lowerCamelCase        =				torch.manual_seed(_lowercase   )
									else:
													_lowerCamelCase        =				torch.Generator(device=_lowercase   ).manual_seed(_lowercase   )
									_lowerCamelCase        =				{
									    """prompt""": """A painting of a squirrel eating a burger""",
									    """image""": image,
									    """generator""": generator,
									    """num_inference_steps""": 2,
									    """guidance_scale""": 5.0,
									    """output_type""": """numpy""",
									    """strength""": 0.7_5,
									}
									return inputs

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				"""cpu"""  # ensure determinism for the device-dependent torch.Generator
									_lowerCamelCase        =				self.get_dummy_components()
									_lowerCamelCase        =				StableDiffusionXLImgaImgPipeline(**_lowercase   )
									_lowerCamelCase        =				sd_pipe.to(_lowercase   )
									sd_pipe.set_progress_bar_config(disable=_lowercase   )

									_lowerCamelCase        =				self.get_dummy_inputs(_lowercase   )
									_lowerCamelCase        =				sd_pipe(**_lowercase   ).images
									_lowerCamelCase        =				image[0, -3:, -3:, -1]

									assert image.shape == (1, 3_2, 3_2, 3)

									_lowerCamelCase        =				np.array([0.4_6_5_6, 0.4_8_4_0, 0.4_4_3_9, 0.6_6_9_8, 0.5_5_7_4, 0.4_5_2_4, 0.5_7_9_9, 0.5_9_4_3, 0.5_1_6_5]   )

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

					def 			snake_case__      (    self   ):
									super().test_attention_slicing_forward_pass(expected_max_diff=3e-3   )

					def 			snake_case__      (    self   ):
									super().test_inference_batch_single_identical(expected_max_diff=3e-3   )

					def 			snake_case__      (    self   ):
									pass





					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.get_dummy_components()
									_lowerCamelCase        =				StableDiffusionXLImgaImgPipeline(**_lowercase   )
									_lowerCamelCase        =				sd_pipe.to(_lowercase   )
									_lowerCamelCase        =				sd_pipe.to(_lowercase   )
									sd_pipe.set_progress_bar_config(disable=_lowercase   )

									# forward without prompt embeds
									_lowerCamelCase        =				self.get_dummy_inputs(_lowercase   )
									_lowerCamelCase        =				3 * ["""this is a negative prompt"""]
									_lowerCamelCase        =				negative_prompt
									_lowerCamelCase        =				3 * [inputs["""prompt"""]]

									_lowerCamelCase        =				sd_pipe(**_lowercase   )
									_lowerCamelCase        =				output.images[0, -3:, -3:, -1]

									# forward with prompt embeds
									_lowerCamelCase        =				self.get_dummy_inputs(_lowercase   )
									_lowerCamelCase        =				3 * ["""this is a negative prompt"""]
									_lowerCamelCase        =				3 * [inputs.pop('''prompt'''   )]

									(
									    _lowerCamelCase     
									)        =				sd_pipe.encode_prompt(_lowercase				,       negative_prompt=_lowercase   )

									_lowerCamelCase        =				sd_pipe(
									    **_lowercase				,       prompt_embeds=_lowercase				,       negative_prompt_embeds=_lowercase				,       pooled_prompt_embeds=_lowercase				,       negative_pooled_prompt_embeds=_lowercase				,       )
									_lowerCamelCase        =				output.images[0, -3:, -3:, -1]

									# make sure that it's equal
									assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()   ).max() < 1e-4


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





					'''simple docstring'''



					def 			snake_case__      (    self   ):
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__="cpu"				,       lowerCamelCase__=torch.floataa				,       lowerCamelCase__=0   ):
									_lowerCamelCase        =				torch.Generator(device=_lowercase   ).manual_seed(_lowercase   )
									_lowerCamelCase        =				np.random.RandomState(_lowercase   ).standard_normal((1, 4, 6_4, 6_4)   )
									_lowerCamelCase        =				torch.from_numpy(_lowercase   ).to(device=_lowercase				,       dtype=_lowercase   )
									_lowerCamelCase        =				{
									    """prompt""": """a photograph of an astronaut riding a horse""",
									    """latents""": latents,
									    """generator""": generator,
									    """num_inference_steps""": 3,
									    """guidance_scale""": 7.5,
									    """output_type""": """numpy""",
									}
									return inputs





					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base'''   )
									pipe.to(_lowercase   )
									pipe.set_progress_bar_config(disable=_lowercase   )

									_lowerCamelCase        =				self.get_inputs(_lowercase   )
									_lowerCamelCase        =				pipe(**_lowercase   ).images
									_lowerCamelCase        =				image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 5_1_2, 5_1_2, 3)
									_lowerCamelCase        =				np.array([0.4_9_4_9_3, 0.4_7_8_9_6, 0.4_0_7_9_8, 0.5_4_2_1_4, 0.5_3_2_1_2, 0.4_8_2_0_2, 0.4_7_6_5_6, 0.4_6_3_2_9, 0.4_8_5_0_6]   )
									assert np.abs(image_slice - expected_slice   ).max() < 7e-3





"""simple docstring"""
import logging

import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader

from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed


class 							lowerCamelCase_(   A__    ):





					'''simple docstring'''



					def __init__(    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__=1_0_2_4				,       lowerCamelCase__=1_0_2_4				,       lowerCamelCase__=3.6   ):
									_lowerCamelCase        =				tokenizer
									_lowerCamelCase        =				tokenizer.bos_token_id
									_lowerCamelCase        =				dataset
									_lowerCamelCase        =				seq_length
									_lowerCamelCase        =				seq_length * chars_per_token * num_of_sequences





					def __iter__(    self   ):
									_lowerCamelCase        =				iter(self.dataset   )
									_lowerCamelCase        =				True
									while more_examples:
													_lowerCamelCase  ,     _lowerCamelCase        =				[], 0
													while True:
																	if buffer_len >= self.input_characters:
																					break
																	try:
																					buffer.append(next(lowerCamelCase__   )['''content''']   )
																					buffer_len += len(buffer[-1]   )
																	except StopIteration:
																					_lowerCamelCase        =				False
																					break
													_lowerCamelCase        =				tokenizer(lowerCamelCase__				,       truncation=lowerCamelCase__   )['''input_ids''']
													_lowerCamelCase        =				[]
													for tokenized_input in tokenized_inputs:
																	all_token_ids.extend(tokenized_input + [self.concat_token_id]   )
													for i in range(0				,       len(lowerCamelCase__   )				,       self.seq_length   ):
																	_lowerCamelCase        =				all_token_ids[i : i + self.seq_length]
																	if len(lowerCamelCase__   ) == self.seq_length:
																					yield torch.tensor(lowerCamelCase__   )



def        lowerCAmelCase_(   lowercase_     :							Any							)	->       Optional[Any]:
				_lowerCamelCase        =				{'''streaming''': True}
				_lowerCamelCase        =				load_dataset(args.dataset_name						,   split='''train'''						,   **lowercase_							)
				_lowerCamelCase        =				ConstantLengthDataset(lowercase_						,   lowercase_						,   seq_length=args.seq_length							)
				_lowerCamelCase        =				DataLoader(lowercase_						,   batch_size=args.batch_size							)
				return eval_dataloader



def        lowerCAmelCase_(   lowercase_     :							Tuple							)	->       str:
				model.eval()
				_lowerCamelCase        =				[]
				for step, batch in enumerate(lowercase_							):
								with torch.no_grad():
												_lowerCamelCase        =				model(lowercase_						,   labels=lowercase_							)
								_lowerCamelCase        =				outputs.loss.repeat(args.batch_size							)
								losses.append(accelerator.gather(lowercase_							)							)

								if args.max_eval_steps > 0 and step >= args.max_eval_steps:
												break
				_lowerCamelCase        =				torch.mean(torch.cat(lowercase_							)							)
				try:
								_lowerCamelCase        =				torch.exp(lowercase_							)
				except OverflowError:
								_lowerCamelCase        =				float('''inf'''							)
				return loss.item(), perplexity.item()


# Setup Accelerator
__SCREAMING_SNAKE_CASE  :						Dict    							=      Accelerator()

# Parse configuration
__SCREAMING_SNAKE_CASE  :						Tuple    							=      HfArgumentParser(EvaluationArguments)
__SCREAMING_SNAKE_CASE  :						Optional[int]    							=      parser.parse_args()
set_seed(args.seed)

# Logging
__SCREAMING_SNAKE_CASE  :						Optional[Any]    							=      logging.getLogger(__name__)
logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)

# Load model and tokenizer
__SCREAMING_SNAKE_CASE  :						str    							=      AutoModelForCausalLM.from_pretrained(args.model_ckpt)
__SCREAMING_SNAKE_CASE  :						Union[str, Any]    							=      AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
__SCREAMING_SNAKE_CASE  :						str    							=      create_dataloader(args)

# Prepare everything with our `accelerator`.
__SCREAMING_SNAKE_CASE							,					__SCREAMING_SNAKE_CASE  :						Any    							=      accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info('''Evaluating and saving model after training''')
__SCREAMING_SNAKE_CASE							,					__SCREAMING_SNAKE_CASE  :						List[str]    							=      evaluate(args)




logger.info(F"""loss/eval: {eval_loss}, perplexity: {perplexity}""")
