


import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class 					SCREAMING_SNAKE_CASE__    (							unittest.TestCase     ):
      @property
      def 			SCREAMING_SNAKE_CASE				(			self							:			Optional[Any]	)						->     Dict:
             torch.manual_seed(0	)
             a_   :						Dict				=    UNetaDModel(
                 block_out_channels=(3_2, 6_4)	,				layers_per_block=2	,				sample_size=3_2	,				in_channels=3	,				out_channels=3	,				down_block_types=('DownBlock2D', 'AttnDownBlock2D')	,				up_block_types=('AttnUpBlock2D', 'UpBlock2D')	,				)
             return model

      def 			SCREAMING_SNAKE_CASE				(			self							:			str	)						->     Any:
             a_   :						Dict				=    self.dummy_uncond_unet
             a_   :						str				=    KarrasVeScheduler()

             a_   :						str				=    KarrasVePipeline(unet=_UpperCamelCase	,				scheduler=_UpperCamelCase	)
             pipe.to(_UpperCamelCase	)
             pipe.set_progress_bar_config(disable=_UpperCamelCase	)

             a_   :						int				=    torch.manual_seed(0	)
             a_   :						Dict				=    pipe(num_inference_steps=2	,				generator=_UpperCamelCase	,				output_type='numpy'	).images

             a_   :						Union[str, Any]				=    torch.manual_seed(0	)
             a_   :						Tuple				=    pipe(num_inference_steps=2	,				generator=_UpperCamelCase	,				output_type='numpy'	,				return_dict=_UpperCamelCase	)[0]

             a_   :						List[str]				=    image[0, -3:, -3:, -1]
             a_   :						Any				=    image_from_tuple[0, -3:, -3:, -1]

             assert image.shape == (1, 3_2, 3_2, 3)
             a_   :						Optional[int]				=    np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]	)

             assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2
             assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1E-2
@slow
@require_torch
class 					SCREAMING_SNAKE_CASE__    (							unittest.TestCase     ):
      def 			SCREAMING_SNAKE_CASE				(			self							:			List[str]	)						->     Optional[Any]:
             a_   :						Optional[int]				=    'google/ncsnpp-celebahq-256'
             a_   :						Dict				=    UNetaDModel.from_pretrained(_UpperCamelCase	)
             a_   :						List[str]				=    KarrasVeScheduler()

             a_   :						str				=    KarrasVePipeline(unet=_UpperCamelCase	,				scheduler=_UpperCamelCase	)
             pipe.to(_UpperCamelCase	)
             pipe.set_progress_bar_config(disable=_UpperCamelCase	)

             a_   :						Optional[int]				=    torch.manual_seed(0	)
             a_   :						List[Any]				=    pipe(num_inference_steps=2_0	,				generator=_UpperCamelCase	,				output_type='numpy'	).images

             a_   :						Union[str, Any]				=    image[0, -3:, -3:, -1]
             assert image.shape == (1, 2_5_6, 2_5_6, 3)
             a_   :						Any				=    np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]	)

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






'''simple docstring'''
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import doctest
import sys
import warnings
from os.path import abspath, dirname, join

import _pytest

from transformers.testing_utils import HfDoctestModule, HfDocTestParser


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
lowerCAmelCase						=					abspath(join(dirname(__file__), """src"""))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="""ignore""", category=FutureWarning)

def 					lowerCamelCase_ ( __UpperCamelCase					:       Optional[Any]		)					->	str:







     """simple docstring"""
     config.addinivalue_line(
         'markers'							,    'is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested'		)
     config.addinivalue_line(
         'markers'							,    'is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested'		)
     config.addinivalue_line('markers'							,    'is_pipeline_test: mark test to run only when pipelines are tested'		)
     config.addinivalue_line('markers'							,    'is_staging_test: mark test to run only in the staging environment'		)
     config.addinivalue_line('markers'							,    'accelerate_tests: mark test that require accelerate'		)
     config.addinivalue_line('markers'							,    'tool_tests: mark the tool tests that are run on their specific schedule'		)

def 					lowerCamelCase_ ( __UpperCamelCase					:       Tuple		)					->	List[str]:







     """simple docstring"""
     from transformers.testing_utils import pytest_addoption_shared

     pytest_addoption_shared(__UpperCamelCase		)

def 					lowerCamelCase_ ( __UpperCamelCase					:       Union[str, Any]		)					->	str:







     """simple docstring"""
     from transformers.testing_utils import pytest_terminal_summary_main

     _A				=							terminalreporter.config.getoption('--make-reports'		)
     if make_reports:
          pytest_terminal_summary_main(__UpperCamelCase							,    id=__UpperCamelCase		)

def 					lowerCamelCase_ ( __UpperCamelCase					:       str							,    __UpperCamelCase					:       List[str]		)					->	Optional[int]:







     """simple docstring"""
     # If no tests are collected, pytest exists with code 5, which makes the CI fail.
     if exitstatus == 5:
          _A				=							0


# Doctest custom flag to ignore output.
lowerCAmelCase						=					doctest.register_optionflag("""IGNORE_RESULT""")

lowerCAmelCase						=					doctest.OutputChecker






class        lowerCAmelCase_  (     UpperCAmelCase	):






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase   )->      Tuple:
            if IGNORE_RESULT & optionflags:
                 return True
            return OutputChecker.check_output(self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase   )






lowerCAmelCase						=					CustomOutputChecker
lowerCAmelCase						=					HfDoctestModule
lowerCAmelCase						=					HfDocTestParser





