






"""simple docstring"""


import gc
import random
import tempfile
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu






class 						snake_case  (  unittest.TestCase	):





    def     lowerCamelCase__				(							self     : str      ):


      '''simple docstring'''




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





    @property
    def     lowerCamelCase__				(							self     : str      ):


      '''simple docstring'''




      a					:      str    =       1
      a					:      List[Any]    =       3
      a					:      int    =       (3_2, 3_2)

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





    @property
    def     lowerCamelCase__				(							self     : str      ):


      '''simple docstring'''




      torch.manual_seed(0      )
      a					:      Optional[Any]    =       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')    ,		cross_attention_dim=3_2    ,		)
      return model





    @property
    def     lowerCamelCase__				(							self     : int      ):


      '''simple docstring'''




      torch.manual_seed(0      )
      a					:      Dict    =       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    ,		)
      return model





    @property
    def     lowerCamelCase__				(							self     : List[Any]      ):


      '''simple docstring'''




      torch.manual_seed(0      )
      a					:      List[Any]    =       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    ,		)
      return CLIPTextModel(A      )





    @property
    def     lowerCamelCase__				(							self     : List[Any]      ):


      '''simple docstring'''




      def extract(*A     : Union[str, Any]    ,		**A     : Optional[int]      ):






        class 						snake_case  :





            def __init__(							self     : List[str]      ):


              '''simple docstring'''




              a					:      Union[str, Any]    =       torch.ones([0]      )





            def     lowerCamelCase__				(							self     : List[Any]    ,		A     : str      ):


              '''simple docstring'''




              self.pixel_values.to(A      )
              return self




        return Out()

      return extract





    def     lowerCamelCase__				(							self     : Any      ):


      '''simple docstring'''




      a					:      Union[str, Any]    =       'cpu'  # ensure determinism for the device-dependent torch.Generator
      a					:      Any    =       self.dummy_cond_unet
      a					:      Union[str, Any]    =       DDIMScheduler(
          beta_start=0.0_00_85    ,		beta_end=0.0_12    ,		beta_schedule='scaled_linear'    ,		clip_sample=A    ,		set_alpha_to_one=A    ,		)

      a					:      List[Any]    =       self.dummy_vae
      a					:      Tuple    =       self.dummy_text_encoder
      a					:      Union[str, Any]    =       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

      # make sure here that pndm scheduler skips prk
      a					:      str    =       StableDiffusionPipeline(
          unet=A    ,		scheduler=A    ,		vae=A    ,		text_encoder=A    ,		tokenizer=A    ,		safety_checker=A    ,		feature_extractor=self.dummy_extractor    ,		)
      a					:      List[Any]    =       sd_pipe.to(A      )
      sd_pipe.set_progress_bar_config(disable=A      )

      a					:      List[str]    =       'A painting of a squirrel eating a burger'

      a					:      Optional[int]    =       torch.Generator(device=A      ).manual_seed(0      )
      a					:      Optional[Any]    =       sd_pipe([prompt]    ,		generator=A    ,		guidance_scale=6.0    ,		num_inference_steps=2    ,		output_type='np'      )
      a					:      Optional[int]    =       output.images

      a					:      List[Any]    =       torch.Generator(device=A      ).manual_seed(0      )
      a					:      int    =       sd_pipe(
          [prompt]    ,		generator=A    ,		guidance_scale=6.0    ,		num_inference_steps=2    ,		output_type='np'    ,		return_dict=A    ,		)[0]

      a					:      Union[str, Any]    =       image[0, -3:, -3:, -1]
      a					:      int    =       image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 6_4, 6_4, 3)
      a					:      List[str]    =       np.array([0.57_56, 0.61_18, 0.50_05, 0.50_41, 0.54_71, 0.47_26, 0.49_76, 0.48_65, 0.48_64]      )

      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     lowerCamelCase__				(							self     : Union[str, Any]      ):


      '''simple docstring'''




      a					:      Any    =       'cpu'  # ensure determinism for the device-dependent torch.Generator
      a					:      List[str]    =       self.dummy_cond_unet
      a					:      int    =       PNDMScheduler(skip_prk_steps=A      )
      a					:      Optional[Any]    =       self.dummy_vae
      a					:      str    =       self.dummy_text_encoder
      a					:      List[str]    =       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

      # make sure here that pndm scheduler skips prk
      a					:      int    =       StableDiffusionPipeline(
          unet=A    ,		scheduler=A    ,		vae=A    ,		text_encoder=A    ,		tokenizer=A    ,		safety_checker=A    ,		feature_extractor=self.dummy_extractor    ,		)
      a					:      str    =       sd_pipe.to(A      )
      sd_pipe.set_progress_bar_config(disable=A      )

      a					:      Union[str, Any]    =       'A painting of a squirrel eating a burger'
      a					:      List[str]    =       torch.Generator(device=A      ).manual_seed(0      )
      a					:      List[str]    =       sd_pipe([prompt]    ,		generator=A    ,		guidance_scale=6.0    ,		num_inference_steps=2    ,		output_type='np'      )

      a					:      List[Any]    =       output.images

      a					:      Optional[Any]    =       torch.Generator(device=A      ).manual_seed(0      )
      a					:      int    =       sd_pipe(
          [prompt]    ,		generator=A    ,		guidance_scale=6.0    ,		num_inference_steps=2    ,		output_type='np'    ,		return_dict=A    ,		)[0]

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

      assert image.shape == (1, 6_4, 6_4, 3)
      a					:      List[Any]    =       np.array([0.51_25, 0.57_16, 0.48_28, 0.50_60, 0.56_50, 0.47_68, 0.51_85, 0.48_95, 0.49_93]      )

      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     lowerCamelCase__				(							self     : List[Any]      ):


      '''simple docstring'''




      a					:      str    =       StableDiffusionPipeline.from_pretrained(
          'hf-internal-testing/tiny-stable-diffusion-lms-pipe'    ,		safety_checker=A      )
      assert isinstance(A    ,		A      )
      assert isinstance(pipe.scheduler    ,		A      )
      assert pipe.safety_checker is None

      a					:      Any    =       pipe('example prompt'    ,		num_inference_steps=2      ).images[0]
      assert image is not None

      # check that there's no error when saving a pipeline with one of the models being None
      with tempfile.TemporaryDirectory() as tmpdirname:
        pipe.save_pretrained(A      )
        a					:      List[Any]    =       StableDiffusionPipeline.from_pretrained(A      )

      # sanity check that the pipeline still works
      assert pipe.safety_checker is None
      a					:      str    =       pipe('example prompt'    ,		num_inference_steps=2      ).images[0]
      assert image is not None





    @unittest.skipIf(torch_device != 'cuda'    ,		'This test requires a GPU'      )
    def     lowerCamelCase__				(							self     : str      ):


      '''simple docstring'''




      a					:      str    =       self.dummy_cond_unet
      a					:      Optional[Any]    =       PNDMScheduler(skip_prk_steps=A      )
      a					:      Tuple    =       self.dummy_vae
      a					:      List[str]    =       self.dummy_text_encoder
      a					:      Optional[int]    =       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

      # put models in fp16
      a					:      int    =       unet.half()
      a					:      Tuple    =       vae.half()
      a					:      Any    =       bert.half()

      # make sure here that pndm scheduler skips prk
      a					:      Any    =       StableDiffusionPipeline(
          unet=A    ,		scheduler=A    ,		vae=A    ,		text_encoder=A    ,		tokenizer=A    ,		safety_checker=A    ,		feature_extractor=self.dummy_extractor    ,		)
      a					:      Optional[int]    =       sd_pipe.to(A      )
      sd_pipe.set_progress_bar_config(disable=A      )

      a					:      Tuple    =       'A painting of a squirrel eating a burger'
      a					:      Optional[int]    =       sd_pipe([prompt]    ,		num_inference_steps=2    ,		output_type='np'      ).images

      assert image.shape == (1, 6_4, 6_4, 3)






@nightly
@require_torch_gpu
class 						snake_case  (  unittest.TestCase	):





    def     lowerCamelCase__				(							self     : Optional[Any]      ):


      '''simple docstring'''




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





    def     lowerCamelCase__				(							self     : Optional[Any]      ):


      '''simple docstring'''




      a					:      Optional[Any]    =       StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'    ,		safety_checker=A      )
      a					:      str    =       LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config      )
      a					:      Any    =       sd_pipe.to(A      )
      sd_pipe.set_progress_bar_config(disable=A      )

      a					:      Union[str, Any]    =       (
          'portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle'
          ' coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with'
          ' anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and'
          ' children from bahnhof zoo, detailed '
      )
      a					:      Tuple    =       4_0_0_3_6_6_0_3_4_6
      a					:      Tuple    =       7

      # without safety guidance (sld_guidance_scale = 0)
      a					:      Any    =       torch.manual_seed(A      )
      a					:      Dict    =       sd_pipe(
          [prompt]    ,		generator=A    ,		guidance_scale=A    ,		num_inference_steps=5_0    ,		output_type='np'    ,		width=5_1_2    ,		height=5_1_2    ,		sld_guidance_scale=0    ,		)

      a					:      Optional[int]    =       output.images
      a					:      Optional[int]    =       image[0, -3:, -3:, -1]
      a					:      Dict    =       [0.22_78, 0.22_31, 0.22_49, 0.23_33, 0.23_03, 0.18_85, 0.22_73, 0.21_44, 0.21_76]

      assert image.shape == (1, 5_1_2, 5_1_2, 3)

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

      # without safety guidance (strong configuration)
      a					:      Union[str, Any]    =       torch.manual_seed(A      )
      a					:      Optional[int]    =       sd_pipe(
          [prompt]    ,		generator=A    ,		guidance_scale=A    ,		num_inference_steps=5_0    ,		output_type='np'    ,		width=5_1_2    ,		height=5_1_2    ,		sld_guidance_scale=2_0_0_0    ,		sld_warmup_steps=7    ,		sld_threshold=0.0_25    ,		sld_momentum_scale=0.5    ,		sld_mom_beta=0.7    ,		)

      a					:      Any    =       output.images
      a					:      Optional[int]    =       image[0, -3:, -3:, -1]
      a					:      Union[str, Any]    =       [0.23_83, 0.22_76, 0.2_36, 0.21_92, 0.21_86, 0.20_53, 0.19_71, 0.19_01, 0.17_19]

      assert image.shape == (1, 5_1_2, 5_1_2, 3)

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





    def     lowerCamelCase__				(							self     : List[Any]      ):


      '''simple docstring'''




      a					:      Optional[Any]    =       StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'    ,		safety_checker=A      )
      a					:      str    =       LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config      )
      a					:      Dict    =       sd_pipe.to(A      )
      sd_pipe.set_progress_bar_config(disable=A      )

      a					:      Any    =       'padme amidala taking a bath artwork, safe for work, no nudity'
      a					:      Optional[Any]    =       2_7_3_4_9_7_1_7_5_5
      a					:      Optional[int]    =       7

      a					:      Dict    =       torch.manual_seed(A      )
      a					:      Any    =       sd_pipe(
          [prompt]    ,		generator=A    ,		guidance_scale=A    ,		num_inference_steps=5_0    ,		output_type='np'    ,		width=5_1_2    ,		height=5_1_2    ,		sld_guidance_scale=0    ,		)

      a					:      Any    =       output.images
      a					:      Union[str, Any]    =       image[0, -3:, -3:, -1]
      a					:      Union[str, Any]    =       [0.35_02, 0.36_22, 0.33_96, 0.36_42, 0.34_78, 0.33_18, 0.35, 0.33_48, 0.32_97]

      assert image.shape == (1, 5_1_2, 5_1_2, 3)

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

      a					:      int    =       torch.manual_seed(A      )
      a					:      List[Any]    =       sd_pipe(
          [prompt]    ,		generator=A    ,		guidance_scale=A    ,		num_inference_steps=5_0    ,		output_type='np'    ,		width=5_1_2    ,		height=5_1_2    ,		sld_guidance_scale=2_0_0_0    ,		sld_warmup_steps=7    ,		sld_threshold=0.0_25    ,		sld_momentum_scale=0.5    ,		sld_mom_beta=0.7    ,		)

      a					:      str    =       output.images
      a					:      Dict    =       image[0, -3:, -3:, -1]
      a					:      Union[str, Any]    =       [0.55_31, 0.52_06, 0.48_95, 0.51_56, 0.51_82, 0.47_51, 0.48_02, 0.48_03, 0.44_43]

      assert image.shape == (1, 5_1_2, 5_1_2, 3)

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





    def     lowerCamelCase__				(							self     : List[str]      ):


      '''simple docstring'''




      a					:      str    =       StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'      )
      a					:      str    =       sd_pipe.to(A      )
      sd_pipe.set_progress_bar_config(disable=A      )

      a					:      List[str]    =       (
          'the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.'
          ' leyendecker'
      )
      a					:      List[Any]    =       1_0_4_4_3_5_5_2_3_4
      a					:      Dict    =       1_2

      a					:      Optional[Any]    =       torch.manual_seed(A      )
      a					:      Optional[Any]    =       sd_pipe(
          [prompt]    ,		generator=A    ,		guidance_scale=A    ,		num_inference_steps=5_0    ,		output_type='np'    ,		width=5_1_2    ,		height=5_1_2    ,		sld_guidance_scale=0    ,		)

      a					:      List[str]    =       output.images
      a					:      Tuple    =       image[0, -3:, -3:, -1]
      a					:      Any    =       np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]      )

      assert image.shape == (1, 5_1_2, 5_1_2, 3)

      assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-7

      a					:      List[Any]    =       torch.manual_seed(A      )
      a					:      Any    =       sd_pipe(
          [prompt]    ,		generator=A    ,		guidance_scale=A    ,		num_inference_steps=5_0    ,		output_type='np'    ,		width=5_1_2    ,		height=5_1_2    ,		sld_guidance_scale=2_0_0_0    ,		sld_warmup_steps=7    ,		sld_threshold=0.0_25    ,		sld_momentum_scale=0.5    ,		sld_mom_beta=0.7    ,		)

      a					:      List[str]    =       output.images
      a					:      Tuple    =       image[0, -3:, -3:, -1]
      a					:      Optional[Any]    =       np.array([0.58_18, 0.62_85, 0.68_35, 0.60_19, 0.6_25, 0.67_54, 0.60_96, 0.63_34, 0.65_61]      )
      assert image.shape == (1, 5_1_2, 5_1_2, 3)

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


_UpperCamelCase    :   Dict   			=						logging.getLogger(__name__)






@dataclass
@add_start_docstrings(TrainingArguments.__doc__	)
class 						snake_case  (  UpperCAmelCase	):
    __magic_name__   					=					field(default=UpperCAmelCase       ,			metadata={'''help''': '''Whether to use SortishSampler or not.'''}	)
    __magic_name__   					=					field(
        default=UpperCAmelCase       ,			metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}	)
    __magic_name__   					=					field(
        default=UpperCAmelCase       ,			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.'''
            )
        }       ,			)
    __magic_name__   					=					field(
        default=UpperCAmelCase       ,			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.'''
            )
        }       ,			)
    __magic_name__   					=					field(
        default=UpperCAmelCase       ,			metadata={
            '''help''': '''Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'''
        }       ,			)





    def     lowerCamelCase__				(							self     : Optional[int]      ):


      '''simple docstring'''




      a					:      List[Any]    =       super().to_dict()
      for k, v in d.items():
        if isinstance(A    ,		A      ):
          a					:      str    =       v.to_dict()
      return d




