






"""simple docstring"""


import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


_UpperCamelCase    :   Union[str, Any]   			=						False






class 						snake_case  (  unittest.TestCase	):
    pass






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





    def     lowerCamelCase__				(							self     : Optional[int]      ):


      '''simple docstring'''




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





    def     lowerCamelCase__				(							self     : int      ):


      '''simple docstring'''




      a					:      List[Any]    =       VersatileDiffusionTextToImagePipeline.from_pretrained('shi-labs/versatile-diffusion'      )
      # remove text_unet
      pipe.remove_unused_weights()
      pipe.to(A      )
      pipe.set_progress_bar_config(disable=A      )

      a					:      Dict    =       'A painting of a squirrel eating a burger '
      a					:      List[str]    =       torch.manual_seed(0      )
      a					:      Optional[Any]    =       pipe(
          prompt=A    ,		generator=A    ,		guidance_scale=7.5    ,		num_inference_steps=2    ,		output_type='numpy'      ).images

      with tempfile.TemporaryDirectory() as tmpdirname:
        pipe.save_pretrained(A      )
        a					:      List[str]    =       VersatileDiffusionTextToImagePipeline.from_pretrained(A      )
      pipe.to(A      )
      pipe.set_progress_bar_config(disable=A      )

      a					:      Dict    =       generator.manual_seed(0      )
      a					:      Tuple    =       pipe(
          prompt=A    ,		generator=A    ,		guidance_scale=7.5    ,		num_inference_steps=2    ,		output_type='numpy'      ).images

      assert np.abs(image - new_image      ).sum() < 1E-5, "Models don't have the same forward pass"





    def     lowerCamelCase__				(							self     : Tuple      ):


      '''simple docstring'''




      a					:      Optional[Any]    =       VersatileDiffusionTextToImagePipeline.from_pretrained(
          'shi-labs/versatile-diffusion'    ,		torch_dtype=torch.floataa      )
      pipe.to(A      )
      pipe.set_progress_bar_config(disable=A      )

      a					:      int    =       'A painting of a squirrel eating a burger '
      a					:      Any    =       torch.manual_seed(0      )
      a					:      Optional[Any]    =       pipe(
          prompt=A    ,		generator=A    ,		guidance_scale=7.5    ,		num_inference_steps=5_0    ,		output_type='numpy'      ).images

      a					:      Optional[Any]    =       image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

      assert image.shape == (1, 5_1_2, 5_1_2, 3)
      a					:      List[str]    =       np.array([0.33_67, 0.31_69, 0.26_56, 0.38_70, 0.47_90, 0.37_96, 0.40_09, 0.48_78, 0.47_78]      )

      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




