


import tempfile
import unittest

from diffusers import (
    DDIMScheduler,
    DDPMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    PNDMScheduler,
    logging,
)
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.utils.testing_utils import CaptureLogger







class      __lowercase  (__SCREAMING_SNAKE_CASE   ):





							"""simple docstring"""







							_UpperCAmelCase     =		"""config.json"""






							@register_to_config
							def __init__(	self       ,		lowerCAmelCase__=2       ,		lowerCAmelCase__=5       ,		lowerCAmelCase__=(2, 5)       ,		lowerCAmelCase__="for diffusion"       ,		lowerCAmelCase__=[1, 3]       ,		):





										"""simple docstring"""




										pass







class      __lowercase  (__SCREAMING_SNAKE_CASE   ):





							"""simple docstring"""







							_UpperCAmelCase     =		"""config.json"""






							@register_to_config
							def __init__(	self       ,		lowerCAmelCase__=2       ,		lowerCAmelCase__=5       ,		lowerCAmelCase__=(2, 5)       ,		lowerCAmelCase__="for diffusion"       ,		lowerCAmelCase__=[1, 3]       ,		):





										"""simple docstring"""




										pass







class      __lowercase  (__SCREAMING_SNAKE_CASE   ):





							"""simple docstring"""







							_UpperCAmelCase     =		"""config.json"""






							@register_to_config
							def __init__(	self       ,		lowerCAmelCase__=2       ,		lowerCAmelCase__=5       ,		lowerCAmelCase__=(2, 5)       ,		lowerCAmelCase__="for diffusion"       ,		lowerCAmelCase__=[1, 3]       ,		lowerCAmelCase__=[1, 3]       ,		):





										"""simple docstring"""




										pass







class      __lowercase  (__SCREAMING_SNAKE_CASE   ):





							"""simple docstring"""







							_UpperCAmelCase     =		"""config.json"""






							@register_to_config
							def __init__(	self       ,		lowerCAmelCase__=2       ,		lowerCAmelCase__=5       ,		lowerCAmelCase__=(2, 5)       ,		lowerCAmelCase__="for diffusion"       ,		lowerCAmelCase__=[1, 5]       ,		lowerCAmelCase__=[5, 4]       ,		):





										"""simple docstring"""




										pass







class      __lowercase  (unittest.TestCase   ):





							"""simple docstring"""







							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										with self.assertRaises(lowerCAmelCase__    ):
													ConfigMixin.load_config('dummy_path'    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			str       						= SampleObject()
										SCREAMING_SNAKE_CASE_						:			int       						= obj.config
										assert config["a"] == 2
										assert config["b"] == 5
										assert config["c"] == (2, 5)
										assert config["d"] == "for diffusion"
										assert config["e"] == [1, 3]

										# init ignore private arguments
										SCREAMING_SNAKE_CASE_						:			Any       						= SampleObject(_name_or_path='lalala'    )
										SCREAMING_SNAKE_CASE_						:			Any       						= obj.config
										assert config["a"] == 2
										assert config["b"] == 5
										assert config["c"] == (2, 5)
										assert config["d"] == "for diffusion"
										assert config["e"] == [1, 3]

										# can override default
										SCREAMING_SNAKE_CASE_						:			Any       						= SampleObject(c=6    )
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= obj.config
										assert config["a"] == 2
										assert config["b"] == 5
										assert config["c"] == 6
										assert config["d"] == "for diffusion"
										assert config["e"] == [1, 3]

										# can use positional arguments.
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= SampleObject(1       ,		c=6    )
										SCREAMING_SNAKE_CASE_						:			Any       						= obj.config
										assert config["a"] == 1
										assert config["b"] == 5
										assert config["c"] == 6
										assert config["d"] == "for diffusion"
										assert config["e"] == [1, 3]






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			str       						= SampleObject()
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= obj.config

										assert config["a"] == 2
										assert config["b"] == 5
										assert config["c"] == (2, 5)
										assert config["d"] == "for diffusion"
										assert config["e"] == [1, 3]

										with tempfile.TemporaryDirectory() as tmpdirname:
													obj.save_config(lowerCAmelCase__    )
													SCREAMING_SNAKE_CASE_						:			Optional[int]       						= SampleObject.from_config(SampleObject.load_config(lowerCAmelCase__    )    )
													SCREAMING_SNAKE_CASE_						:			Dict       						= new_obj.config

										# unfreeze configs
										SCREAMING_SNAKE_CASE_						:			Tuple       						= dict(lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= dict(lowerCAmelCase__    )

										assert config.pop('c'    ) == (2, 5)  # instantiated as tuple
										assert new_config.pop('c'    ) == [2, 5]  # saved & loaded as list because of json
										config.pop('_use_default_values'    )
										assert config == new_config






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Tuple       						= logging.get_logger('diffusers.configuration_utils'    )
										# 30 for warning
										logger.setLevel(3_0    )

										with CaptureLogger(lowerCAmelCase__    ) as cap_logger:
													SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= DDIMScheduler.from_pretrained(
													    'hf-internal-testing/tiny-stable-diffusion-torch'       ,		subfolder='scheduler'    )

										assert ddim.__class__ == DDIMScheduler
										# no warning should be thrown
										assert cap_logger.out == ""






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			List[str]       						= logging.get_logger('diffusers.configuration_utils'    )
										# 30 for warning
										logger.setLevel(3_0    )

										with CaptureLogger(lowerCAmelCase__    ) as cap_logger:
													SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= EulerDiscreteScheduler.from_pretrained(
													    'hf-internal-testing/tiny-stable-diffusion-torch'       ,		subfolder='scheduler'    )

										assert euler.__class__ == EulerDiscreteScheduler
										# no warning should be thrown
										assert cap_logger.out == ""






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= logging.get_logger('diffusers.configuration_utils'    )
										# 30 for warning
										logger.setLevel(3_0    )

										with CaptureLogger(lowerCAmelCase__    ) as cap_logger:
													SCREAMING_SNAKE_CASE_						:			Dict       						= EulerAncestralDiscreteScheduler.from_pretrained(
													    'hf-internal-testing/tiny-stable-diffusion-torch'       ,		subfolder='scheduler'    )

										assert euler.__class__ == EulerAncestralDiscreteScheduler
										# no warning should be thrown
										assert cap_logger.out == ""






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			List[str]       						= logging.get_logger('diffusers.configuration_utils'    )
										# 30 for warning
										logger.setLevel(3_0    )

										with CaptureLogger(lowerCAmelCase__    ) as cap_logger:
													SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= PNDMScheduler.from_pretrained(
													    'hf-internal-testing/tiny-stable-diffusion-torch'       ,		subfolder='scheduler'    )

										assert pndm.__class__ == PNDMScheduler
										# no warning should be thrown
										assert cap_logger.out == ""






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= logging.get_logger('diffusers.configuration_utils'    )
										# 30 for warning
										logger.setLevel(3_0    )

										with CaptureLogger(lowerCAmelCase__    ) as cap_logger:
													SCREAMING_SNAKE_CASE_						:			Any       						= DDPMScheduler.from_pretrained(
													    'hf-internal-testing/tiny-stable-diffusion-torch'       ,		subfolder='scheduler'       ,		prediction_type='sample'       ,		beta_end=8       ,		)

										with CaptureLogger(lowerCAmelCase__    ) as cap_logger_a:
													SCREAMING_SNAKE_CASE_						:			Optional[int]       						= DDPMScheduler.from_pretrained('google/ddpm-celebahq-256'       ,		beta_start=8_8    )

										assert ddpm.__class__ == DDPMScheduler
										assert ddpm.config.prediction_type == "sample"
										assert ddpm.config.beta_end == 8
										assert ddpm_a.config.beta_start == 8_8

										# no warning should be thrown
										assert cap_logger.out == ""
										assert cap_logger_a.out == ""






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= logging.get_logger('diffusers.configuration_utils'    )
										# 30 for warning
										logger.setLevel(3_0    )

										with CaptureLogger(lowerCAmelCase__    ) as cap_logger:
													SCREAMING_SNAKE_CASE_						:			Tuple       						= DPMSolverMultistepScheduler.from_pretrained(
													    'hf-internal-testing/tiny-stable-diffusion-torch'       ,		subfolder='scheduler'    )

										assert dpm.__class__ == DPMSolverMultistepScheduler
										# no warning should be thrown
										assert cap_logger.out == ""






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			int       						= SampleObject()

										SCREAMING_SNAKE_CASE_						:			Tuple       						= {k: v for k, v in config.config.items() if not k.startswith('_'    )}

										# make sure that default config has all keys in `_use_default_values`
										assert set(config_dict.keys()    ) == set(config.config._use_default_values    )

										with tempfile.TemporaryDirectory() as tmpdirname:
													config.save_config(lowerCAmelCase__    )

													# now loading it with SampleObject2 should put f into `_use_default_values`
													SCREAMING_SNAKE_CASE_						:			Any       						= SampleObjecta.from_config(lowerCAmelCase__    )

													assert "f" in config._use_default_values
													assert config.f == [1, 3]

										# now loading the config, should **NOT** use [1, 3] for `f`, but the default [1, 4] value
										# **BECAUSE** it is part of `config._use_default_values`
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= SampleObjecta.from_config(config.config    )
										assert new_config.f == [5, 4]

										config.config._use_default_values.pop()
										SCREAMING_SNAKE_CASE_						:			str       						= SampleObjecta.from_config(config.config    )
										assert new_config_a.f == [1, 3]

										# Nevertheless "e" should still be correctly loaded to [1, 3] from SampleObject2 instead of defaulting to [1, 5]
										assert new_config_a.e == [1, 3]









# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.

import os
import sys

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow

from .utils import load_json







class      __lowercase  (__SCREAMING_SNAKE_CASE   ):





							"""simple docstring"""







							@classmethod
							def  UpperCamelCase__			(	cls    ):





										"""simple docstring"""




										return cls






							@slow
							@require_torch_gpu
							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			List[str]       						= self.get_auto_remove_tmp_dir()
										SCREAMING_SNAKE_CASE_						:			Tuple       						= F'''
            --model_name Helsinki-NLP/opus-mt-en-ro
            --save_dir {output_dir}
            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro
            --num_beams 2
            --task translation
        '''.split()

										# we want this test to run even if there is only one GPU, but if there are more we use them all
										SCREAMING_SNAKE_CASE_						:			Dict       						= get_gpu_count()
										SCREAMING_SNAKE_CASE_						:			int       						= F'''
            -m torch.distributed.launch
            --nproc_per_node={n_gpu}
            {self.test_file_dir}/run_distributed_eval.py
        '''.split()
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= [sys.executable] + distributed_args + args
										execute_subprocess_async(lowerCAmelCase__       ,		env=self.get_env()    )

										SCREAMING_SNAKE_CASE_						:			List[str]       						= os.path.join(lowerCAmelCase__       ,		'test_bleu.json'    )
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= load_json(lowerCAmelCase__    )
										# print(metrics)
										self.assertGreaterEqual(metrics['bleu']       ,		2_5    )





