


'''simple docstring'''




import gc
import random
import unittest

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

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
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

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





def 	a_					( lowerCamelCase	:     Dict				):
   lowerCAmelCase     =			[tensor.shape for tensor in tensor_list]
   return all(shape == shapes[0] for shape in shapes[1:]				)

class 		UpperCAmelCase_      (  __lowercase       ,	__lowercase       ,	__lowercase       ,	unittest.TestCase		):
     lowerCamelCase				:	Tuple		=     StableDiffusionLatentUpscalePipeline
     lowerCamelCase				:	Union[str, Any]		=     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
         '''height''',
         '''width''',
         '''cross_attention_kwargs''',
         '''negative_prompt_embeds''',
         '''prompt_embeds''',
     }
     lowerCamelCase				:	List[Any]		=     PipelineTesterMixin.required_optional_params - {'''num_images_per_prompt'''}
     lowerCamelCase				:	Tuple		=     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     lowerCamelCase				:	Any		=     frozenset(
         []		)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
     lowerCamelCase				:	List[str]		=     frozenset([]		)

     lowerCamelCase				:	Optional[int]		=     True





     @property
     def 							__UpperCAmelCase      ( self       :      str    )				->  Union[str, Any]:
        lowerCAmelCase     =			1
        lowerCAmelCase     =			4
        lowerCAmelCase     =			(1_6, 1_6)

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





     def 							__UpperCAmelCase      ( self       :      Dict    )				->  List[Any]:
        torch.manual_seed(0    )
        lowerCAmelCase     =			UNetaDConditionModel(
            act_fn='gelu'    ,      attention_head_dim=8    ,      norm_num_groups=UpperCAmelCase__    ,      block_out_channels=[3_2, 3_2, 6_4, 6_4]    ,      time_cond_proj_dim=1_6_0    ,      conv_in_kernel=1    ,      conv_out_kernel=1    ,      cross_attention_dim=3_2    ,      down_block_types=(
                'KDownBlock2D',
                'KCrossAttnDownBlock2D',
                'KCrossAttnDownBlock2D',
                'KCrossAttnDownBlock2D',
            )    ,      in_channels=8    ,      mid_block_type=UpperCAmelCase__    ,      only_cross_attention=UpperCAmelCase__    ,      out_channels=5    ,      resnet_time_scale_shift='scale_shift'    ,      time_embedding_type='fourier'    ,      timestep_post_act='gelu'    ,      up_block_types=('KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KUpBlock2D')    ,      )
        lowerCAmelCase     =			AutoencoderKL(
            block_out_channels=[3_2, 3_2, 6_4, 6_4]    ,      in_channels=3    ,      out_channels=3    ,      down_block_types=[
                'DownEncoderBlock2D',
                'DownEncoderBlock2D',
                'DownEncoderBlock2D',
                'DownEncoderBlock2D',
            ]    ,      up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D']    ,      latent_channels=4    ,      )
        lowerCAmelCase     =			EulerDiscreteScheduler(prediction_type='sample'    )
        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='quick_gelu'    ,      projection_dim=5_1_2    ,      )
        lowerCAmelCase     =			CLIPTextModel(UpperCAmelCase__    )
        lowerCAmelCase     =			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )

        lowerCAmelCase     =			{
            'unet': model.eval(),
            'vae': vae.eval(),
            'scheduler': scheduler,
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
        }

        return components





     def 							__UpperCAmelCase      ( self       :      Union[str, Any]    ,      UpperCAmelCase__       :      Optional[int]    ,      UpperCAmelCase__       :      Optional[Any]=0    )				->  Union[str, Any]:
        if str(UpperCAmelCase__    ).startswith('mps'    ):
           lowerCAmelCase     =			torch.manual_seed(UpperCAmelCase__    )
        else:
           lowerCAmelCase     =			torch.Generator(device=UpperCAmelCase__    ).manual_seed(UpperCAmelCase__    )
        lowerCAmelCase     =			{
            'prompt': 'A painting of a squirrel eating a burger',
            'image': self.dummy_image.cpu(),
            'generator': generator,
            'num_inference_steps': 2,
            'output_type': 'numpy',
        }
        return inputs





     def 							__UpperCAmelCase      ( self       :      List[Any]    )				->  Any:
        lowerCAmelCase     =			'cpu'

        lowerCAmelCase     =			self.get_dummy_components()
        lowerCAmelCase     =			self.pipeline_class(**UpperCAmelCase__    )
        pipe.to(UpperCAmelCase__    )
        pipe.set_progress_bar_config(disable=UpperCAmelCase__    )

        lowerCAmelCase     =			self.get_dummy_inputs(UpperCAmelCase__    )
        lowerCAmelCase     =			pipe(**UpperCAmelCase__    ).images
        lowerCAmelCase     =			image[0, -3:, -3:, -1]

        self.assertEqual(image.shape    ,      (1, 2_5_6, 2_5_6, 3)    )
        lowerCAmelCase     =			np.array(
            [0.47_222_412, 0.41_921_633, 0.44_717_434, 0.46_874_192, 0.42_588_258, 0.46_150_726, 0.4_677_534, 0.45_583_832, 0.48_579_055]    )
        lowerCAmelCase     =			np.abs(image_slice.flatten() - expected_slice    ).max()
        self.assertLessEqual(UpperCAmelCase__    ,      1E-3    )





     def 							__UpperCAmelCase      ( self       :      Union[str, Any]    )				->  Dict:
        super().test_attention_slicing_forward_pass(expected_max_diff=7E-3    )





     def 							__UpperCAmelCase      ( self       :      Optional[int]    )				->  Union[str, Any]:
        super().test_cpu_offload_forward_pass(expected_max_diff=3E-3    )





     def 							__UpperCAmelCase      ( self       :      Tuple    )				->  str:
        super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3    )





     def 							__UpperCAmelCase      ( self       :      str    )				->  Union[str, Any]:
        super().test_inference_batch_single_identical(expected_max_diff=7E-3    )





     def 							__UpperCAmelCase      ( self       :      List[Any]    )				->  Dict:
        super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3E-3    )





     def 							__UpperCAmelCase      ( self       :      str    )				->  int:
        super().test_save_load_local(expected_max_difference=3E-3    )





     def 							__UpperCAmelCase      ( self       :      Any    )				->  Optional[Any]:
        super().test_save_load_optional_components(expected_max_difference=3E-3    )





     def 							__UpperCAmelCase      ( self       :      List[Any]    )				->  Tuple:
        lowerCAmelCase     =			[
            'DDIMScheduler',
            'DDPMScheduler',
            'PNDMScheduler',
            'HeunDiscreteScheduler',
            'EulerAncestralDiscreteScheduler',
            'KDPM2DiscreteScheduler',
            'KDPM2AncestralDiscreteScheduler',
            'DPMSolverSDEScheduler',
        ]
        lowerCAmelCase     =			self.get_dummy_components()
        lowerCAmelCase     =			self.pipeline_class(**UpperCAmelCase__    )

        # make sure that PNDM does not need warm-up
        pipe.scheduler.register_to_config(skip_prk_steps=UpperCAmelCase__    )

        pipe.to(UpperCAmelCase__    )
        pipe.set_progress_bar_config(disable=UpperCAmelCase__    )
        lowerCAmelCase     =			self.get_dummy_inputs(UpperCAmelCase__    )
        lowerCAmelCase     =			2

        lowerCAmelCase     =			[]
        for scheduler_enum in KarrasDiffusionSchedulers:
           if scheduler_enum.name in skip_schedulers:
              # no sigma schedulers are not supported
              # no schedulers
              continue

           lowerCAmelCase     =			getattr(UpperCAmelCase__    ,      scheduler_enum.name    )
           lowerCAmelCase     =			scheduler_cls.from_config(pipe.scheduler.config    )
           lowerCAmelCase     =			pipe(**UpperCAmelCase__    )[0]
           outputs.append(UpperCAmelCase__    )

        assert check_same_shape(UpperCAmelCase__    )




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





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





     def 							__UpperCAmelCase      ( self       :      int    )				->  int:
        lowerCAmelCase     =			torch.manual_seed(3_3    )

        lowerCAmelCase     =			StableDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'    ,      torch_dtype=torch.floataa    )
        pipe.to('cuda'    )

        lowerCAmelCase     =			StableDiffusionLatentUpscalePipeline.from_pretrained(
            'stabilityai/sd-x2-latent-upscaler'    ,      torch_dtype=torch.floataa    )
        upscaler.to('cuda'    )

        lowerCAmelCase     =			'a photo of an astronaut high resolution, unreal engine, ultra realistic'

        lowerCAmelCase     =			pipe(UpperCAmelCase__    ,      generator=UpperCAmelCase__    ,      output_type='latent'    ).images

        lowerCAmelCase     =			upscaler(
            prompt=UpperCAmelCase__    ,      image=UpperCAmelCase__    ,      num_inference_steps=2_0    ,      guidance_scale=0    ,      generator=UpperCAmelCase__    ,      output_type='np'    ,      ).images[0]

        lowerCAmelCase     =			load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy'    )
        assert np.abs((expected_image - image).mean()    ) < 5E-2





     def 							__UpperCAmelCase      ( self       :      List[Any]    )				->  Optional[int]:
        lowerCAmelCase     =			torch.manual_seed(3_3    )

        lowerCAmelCase     =			StableDiffusionLatentUpscalePipeline.from_pretrained(
            'stabilityai/sd-x2-latent-upscaler'    ,      torch_dtype=torch.floataa    )
        upscaler.to('cuda'    )

        lowerCAmelCase     =			'the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas'

        lowerCAmelCase     =			load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png'    )

        lowerCAmelCase     =			upscaler(
            prompt=UpperCAmelCase__    ,      image=UpperCAmelCase__    ,      num_inference_steps=2_0    ,      guidance_scale=0    ,      generator=UpperCAmelCase__    ,      output_type='np'    ,      ).images[0]

        lowerCAmelCase     =			load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy'    )
        assert np.abs((expected_image - image).max()    ) < 5E-2









'''simple docstring'''




import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


__snake_case    						=logging.getLogger(__name__)

@dataclass
@add_start_docstrings(TrainingArguments.__doc__		)
class 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	bool		=     field(default=__lowercase       ,	metadata={'''help''': '''Whether to use SortishSampler or not.'''}		)
     lowerCamelCase				:	bool		=     field(
         default=__lowercase       ,	metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}		)
     lowerCamelCase				:	Optional[int]		=     field(
         default=__lowercase       ,	metadata={
             '''help''': (
                 '''The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
                 '''to the `max_length` value of the model configuration.'''
             )
         }       ,	)
     lowerCamelCase				:	Optional[int]		=     field(
         default=__lowercase       ,	metadata={
             '''help''': (
                 '''The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
                 '''to the `num_beams` value of the model configuration.'''
             )
         }       ,	)
     lowerCamelCase				:	Optional[Union[str, Path, GenerationConfig]]		=     field(
         default=__lowercase       ,	metadata={
             '''help''': '''Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'''
         }       ,	)





     def 							__UpperCAmelCase      ( self       :      Dict    )				->  List[str]:
        lowerCAmelCase     =			super().to_dict()
        for k, v in d.items():
           if isinstance(UpperCAmelCase__    ,      UpperCAmelCase__    ):
              lowerCAmelCase     =			v.to_dict()
        return d





