



import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModel,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

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


enable_full_determinism()



class  __lowerCAmelCase     (	lowerCAmelCase__  ,	lowerCAmelCase__  ,	lowerCAmelCase__  ,	unittest.TestCase   ):
 lowerCAmelCase__       					=  StableUnCLIPImgaImgPipeline
 lowerCAmelCase__       					=  TEXT_GUIDED_IMAGE_VARIATION_PARAMS
 lowerCAmelCase__       					=  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
 lowerCAmelCase__       					=  frozenset(
     []   )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 lowerCAmelCase__       					=  frozenset([]   )




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    32
    __lowerCamelCase													=    embedder_hidden_size

    # image encoding components

    __lowerCamelCase													=    CLIPImageProcessor(crop_size=32	,				size=32				)

    torch.manual_seed(0				)
    __lowerCamelCase													=    CLIPVisionModelWithProjection(
        CLIPVisionConfig(
            hidden_size=__UpperCAmelCase	,				projection_dim=__UpperCAmelCase	,				num_hidden_layers=5	,				num_attention_heads=4	,				image_size=32	,				intermediate_size=37	,				patch_size=1	,				)				)

    # regular denoising components

    torch.manual_seed(0				)
    __lowerCamelCase													=    StableUnCLIPImageNormalizer(embedding_dim=__UpperCAmelCase				)
    __lowerCamelCase													=    DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''				)

    torch.manual_seed(0				)
    __lowerCamelCase													=    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

    torch.manual_seed(0				)
    __lowerCamelCase													=    CLIPTextModel(
        CLIPTextConfig(
            bos_token_id=0	,				eos_token_id=2	,				hidden_size=__UpperCAmelCase	,				projection_dim=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	,				)				)

    torch.manual_seed(0				)
    __lowerCamelCase													=    UNetaDConditionModel(
        sample_size=32	,				in_channels=4	,				out_channels=4	,				down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''')	,				up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''')	,				block_out_channels=(32, 64)	,				attention_head_dim=(2, 4)	,				class_embed_type='''projection'''	,				projection_class_embeddings_input_dim=embedder_projection_dim * 2	,				cross_attention_dim=__UpperCAmelCase	,				layers_per_block=1	,				upcast_attention=__UpperCAmelCase	,				use_linear_projection=__UpperCAmelCase	,				)

    torch.manual_seed(0				)
    __lowerCamelCase													=    DDIMScheduler(
        beta_schedule='''scaled_linear'''	,				beta_start=0.00_085	,				beta_end=0.012	,				prediction_type='''v_prediction'''	,				set_alpha_to_one=__UpperCAmelCase	,				steps_offset=1	,				)

    torch.manual_seed(0				)
    __lowerCamelCase													=    AutoencoderKL()

    __lowerCamelCase													=    {
        # image encoding components
        '''feature_extractor''': feature_extractor,
        '''image_encoder''': image_encoder.eval(),
        # image noising components
        '''image_normalizer''': image_normalizer.eval(),
        '''image_noising_scheduler''': image_noising_scheduler,
        # regular denoising components
        '''tokenizer''': tokenizer,
        '''text_encoder''': text_encoder.eval(),
        '''unet''': unet.eval(),
        '''scheduler''': scheduler,
        '''vae''': vae.eval(),
    }

    return components




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase=0	,				__UpperCAmelCase=True				):



    '''simple docstring'''
    if str(__UpperCAmelCase				).startswith('''mps'''				):
       __lowerCamelCase													=    torch.manual_seed(__UpperCAmelCase				)
    else:
       __lowerCamelCase													=    torch.Generator(device=__UpperCAmelCase				).manual_seed(__UpperCAmelCase				)

    __lowerCamelCase													=    floats_tensor((1, 3, 32, 32)	,				rng=random.Random(__UpperCAmelCase				)				).to(__UpperCAmelCase				)

    if pil_image:
       __lowerCamelCase													=    input_image * 0.5 + 0.5
       __lowerCamelCase													=    input_image.clamp(0	,				1				)
       __lowerCamelCase													=    input_image.cpu().permute(0	,				2	,				3	,				1				).float().numpy()
       __lowerCamelCase													=    DiffusionPipeline.numpy_to_pil(__UpperCAmelCase				)[0]

    return {
        "prompt": "An anime racoon running a marathon",
        "image": input_image,
        "generator": generator,
        "num_inference_steps": 2,
        "output_type": "np",
    }




 @skip_mps
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
    __lowerCamelCase													=    self.get_dummy_components()
    __lowerCamelCase													=    StableUnCLIPImgaImgPipeline(**__UpperCAmelCase				)
    __lowerCamelCase													=    sd_pipe.to(__UpperCAmelCase				)
    sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

    __lowerCamelCase													=    self.get_dummy_inputs(__UpperCAmelCase				)
    inputs.update({'''image_embeds''': None}				)
    __lowerCamelCase													=    sd_pipe(**__UpperCAmelCase				).images
    __lowerCamelCase													=    image[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    __lowerCamelCase													=    np.array([0.3_872, 0.7_224, 0.5_601, 0.4_741, 0.6_872, 0.5_814, 0.4_636, 0.3_867, 0.5_078]				)

    assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-3




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    torch_device in ['''cpu''', '''mps''']

    self._test_attention_slicing_forward_pass(test_max_difference=__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    torch_device in ['''cpu''', '''mps''']

    self._test_inference_batch_single_identical(test_max_difference=__UpperCAmelCase				)




 @unittest.skipIf(
     torch_device != '''cuda''' or not is_xformers_available()	,				reason='''XFormers attention is only available with CUDA and `xformers` installed'''	,				)
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    self._test_xformers_attention_forwardGenerator_pass(test_max_difference=__UpperCAmelCase				)



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




 def        lowerCamelCase      (						self				):



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




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'''				)

    __lowerCamelCase													=    load_numpy(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy'''				)

    __lowerCamelCase													=    StableUnCLIPImgaImgPipeline.from_pretrained(
        '''fusing/stable-unclip-2-1-l-img2img'''	,				torch_dtype=torch.floataa				)
    pipe.to(__UpperCAmelCase				)
    pipe.set_progress_bar_config(disable=__UpperCAmelCase				)
    # stable unclip will oom when integration tests are run on a V100,
    # so turn on memory savings
    pipe.enable_attention_slicing()
    pipe.enable_sequential_cpu_offload()

    __lowerCamelCase													=    torch.Generator(device='''cpu'''				).manual_seed(0				)
    __lowerCamelCase													=    pipe(__UpperCAmelCase	,				'''anime turle'''	,				generator=__UpperCAmelCase	,				output_type='''np'''				)

    __lowerCamelCase													=    output.images[0]

    assert image.shape == (768, 768, 3)

    assert_mean_pixel_difference(__UpperCAmelCase	,				__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'''				)

    __lowerCamelCase													=    load_numpy(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy'''				)

    __lowerCamelCase													=    StableUnCLIPImgaImgPipeline.from_pretrained(
        '''fusing/stable-unclip-2-1-h-img2img'''	,				torch_dtype=torch.floataa				)
    pipe.to(__UpperCAmelCase				)
    pipe.set_progress_bar_config(disable=__UpperCAmelCase				)
    # stable unclip will oom when integration tests are run on a V100,
    # so turn on memory savings
    pipe.enable_attention_slicing()
    pipe.enable_sequential_cpu_offload()

    __lowerCamelCase													=    torch.Generator(device='''cpu'''				).manual_seed(0				)
    __lowerCamelCase													=    pipe(__UpperCAmelCase	,				'''anime turle'''	,				generator=__UpperCAmelCase	,				output_type='''np'''				)

    __lowerCamelCase													=    output.images[0]

    assert image.shape == (768, 768, 3)

    assert_mean_pixel_difference(__UpperCAmelCase	,				__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'''				)

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

    __lowerCamelCase													=    StableUnCLIPImgaImgPipeline.from_pretrained(
        '''fusing/stable-unclip-2-1-h-img2img'''	,				torch_dtype=torch.floataa				)
    __lowerCamelCase													=    pipe.to(__UpperCAmelCase				)
    pipe.set_progress_bar_config(disable=__UpperCAmelCase				)
    pipe.enable_attention_slicing()
    pipe.enable_sequential_cpu_offload()

    __lowerCamelCase													=    pipe(
        __UpperCAmelCase	,				'''anime turtle'''	,				num_inference_steps=2	,				output_type='''np'''	,				)

    __lowerCamelCase													=    torch.cuda.max_memory_allocated()
    # make sure that less than 7 GB is allocated
    assert mem_bytes < 7 * 10**9











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     (	lowerCAmelCase__   ):




 def __init__(						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase=1024	,				__UpperCAmelCase=1024	,				__UpperCAmelCase=3.6				):



    '''simple docstring'''
    __lowerCamelCase													=    tokenizer
    __lowerCamelCase													=    tokenizer.bos_token_id
    __lowerCamelCase													=    dataset
    __lowerCamelCase													=    seq_length
    __lowerCamelCase													=    seq_length * chars_per_token * num_of_sequences




 def __iter__(						self				):



    '''simple docstring'''
    __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(__UpperCAmelCase				)['''content''']				)
             buffer_len += len(buffer[-1]				)
          except StopIteration:
             __lowerCamelCase													=    False
             break
       __lowerCamelCase													=    tokenizer(__UpperCAmelCase	,				truncation=__UpperCAmelCase				)['''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(__UpperCAmelCase				)	,				self.seq_length				):
          __lowerCamelCase													=    all_token_ids[i : i + self.seq_length]
          if len(__UpperCAmelCase				) == self.seq_length:
             yield torch.tensor(__UpperCAmelCase				)





def 		a__			(		_UpperCamelCase			: List[Any]							):
   __lowerCamelCase													=    {'''streaming''': True}
   __lowerCamelCase													=    load_dataset(args.dataset_name ,split='''train''' ,**_UpperCamelCase							)
   __lowerCamelCase													=    ConstantLengthDataset(_UpperCamelCase ,_UpperCamelCase ,seq_length=args.seq_length							)
   __lowerCamelCase													=    DataLoader(_UpperCamelCase ,batch_size=args.batch_size							)
   return eval_dataloader





def 		a__			(		_UpperCamelCase			: str							):
   model.eval()
   __lowerCamelCase													=    []
   for step, batch in enumerate(_UpperCamelCase							):
      with torch.no_grad():
         __lowerCamelCase													=    model(_UpperCamelCase ,labels=_UpperCamelCase							)
      __lowerCamelCase													=    outputs.loss.repeat(args.batch_size							)
      losses.append(accelerator.gather(_UpperCamelCase							)							)

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


# Setup Accelerator
a_			=					Accelerator()

# Parse configuration
a_			=					HfArgumentParser(EvaluationArguments)
a_			=					parser.parse_args()
set_seed(args.seed)

# Logging
a_			=					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
a_			=					AutoModelForCausalLM.from_pretrained(args.model_ckpt)
a_			=					AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
a_			=					create_dataloader(args)

# Prepare everything with our `accelerator`.
a_		,						a_			=					accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info("""Evaluating and saving model after training""")
a_		,						a_			=					evaluate(args)





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






