




"""simple docstring"""

from typing import TYPE_CHECKING

from ..utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available


__A 		=			{
    "configuration_utils": ["GenerationConfig"],
    "streamers": ["TextIteratorStreamer", "TextStreamer"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__A 		=			[
							    "Constraint",
							    "ConstraintListState",
							    "DisjunctiveConstraint",
							    "PhrasalConstraint",
							]
							__A 		=			[
							    "BeamHypotheses",
							    "BeamScorer",
							    "BeamSearchScorer",
							    "ConstrainedBeamSearchScorer",
							]
							__A 		=			[
							    "EpsilonLogitsWarper",
							    "EtaLogitsWarper",
							    "ForcedBOSTokenLogitsProcessor",
							    "ForcedEOSTokenLogitsProcessor",
							    "HammingDiversityLogitsProcessor",
							    "InfNanRemoveLogitsProcessor",
							    "LogitsProcessor",
							    "LogitsProcessorList",
							    "LogitsWarper",
							    "MinLengthLogitsProcessor",
							    "MinNewTokensLengthLogitsProcessor",
							    "NoBadWordsLogitsProcessor",
							    "NoRepeatNGramLogitsProcessor",
							    "PrefixConstrainedLogitsProcessor",
							    "RepetitionPenaltyLogitsProcessor",
							    "SequenceBiasLogitsProcessor",
							    "EncoderRepetitionPenaltyLogitsProcessor",
							    "TemperatureLogitsWarper",
							    "TopKLogitsWarper",
							    "TopPLogitsWarper",
							    "TypicalLogitsWarper",
							    "EncoderNoRepeatNGramLogitsProcessor",
							    "ExponentialDecayLengthPenalty",
							    "LogitNormalization",
							]
							__A 		=			[
							    "MaxNewTokensCriteria",
							    "MaxLengthCriteria",
							    "MaxTimeCriteria",
							    "StoppingCriteria",
							    "StoppingCriteriaList",
							    "validate_stopping_criteria",
							]
							__A 		=			[
							    "GenerationMixin",
							    "top_k_top_p_filtering",
							    "GreedySearchEncoderDecoderOutput",
							    "GreedySearchDecoderOnlyOutput",
							    "SampleEncoderDecoderOutput",
							    "SampleDecoderOnlyOutput",
							    "BeamSearchEncoderDecoderOutput",
							    "BeamSearchDecoderOnlyOutput",
							    "BeamSampleEncoderDecoderOutput",
							    "BeamSampleDecoderOnlyOutput",
							    "ContrastiveSearchEncoderDecoderOutput",
							    "ContrastiveSearchDecoderOnlyOutput",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__A 		=			[
							    "TFForcedBOSTokenLogitsProcessor",
							    "TFForcedEOSTokenLogitsProcessor",
							    "TFLogitsProcessor",
							    "TFLogitsProcessorList",
							    "TFLogitsWarper",
							    "TFMinLengthLogitsProcessor",
							    "TFNoBadWordsLogitsProcessor",
							    "TFNoRepeatNGramLogitsProcessor",
							    "TFRepetitionPenaltyLogitsProcessor",
							    "TFTemperatureLogitsWarper",
							    "TFTopKLogitsWarper",
							    "TFTopPLogitsWarper",
							    "TFForceTokensLogitsProcessor",
							    "TFSuppressTokensAtBeginLogitsProcessor",
							    "TFSuppressTokensLogitsProcessor",
							]
							__A 		=			[
							    "TFGenerationMixin",
							    "tf_top_k_top_p_filtering",
							    "TFGreedySearchDecoderOnlyOutput",
							    "TFGreedySearchEncoderDecoderOutput",
							    "TFSampleEncoderDecoderOutput",
							    "TFSampleDecoderOnlyOutput",
							    "TFBeamSearchEncoderDecoderOutput",
							    "TFBeamSearchDecoderOnlyOutput",
							    "TFBeamSampleEncoderDecoderOutput",
							    "TFBeamSampleDecoderOnlyOutput",
							    "TFContrastiveSearchEncoderDecoderOutput",
							    "TFContrastiveSearchDecoderOnlyOutput",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__A 		=			[
							    "FlaxForcedBOSTokenLogitsProcessor",
							    "FlaxForcedEOSTokenLogitsProcessor",
							    "FlaxLogitsProcessor",
							    "FlaxLogitsProcessorList",
							    "FlaxLogitsWarper",
							    "FlaxMinLengthLogitsProcessor",
							    "FlaxTemperatureLogitsWarper",
							    "FlaxTopKLogitsWarper",
							    "FlaxTopPLogitsWarper",
							]
							__A 		=			[
							    "FlaxGenerationMixin",
							    "FlaxGreedySearchOutput",
							    "FlaxSampleOutput",
							    "FlaxBeamSearchOutput",
							]

if TYPE_CHECKING:
							from .configuration_utils import GenerationConfig
							from .streamers import TextIteratorStreamer, TextStreamer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint
														from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
														from .logits_process import (
														    EncoderNoRepeatNGramLogitsProcessor,
														    EncoderRepetitionPenaltyLogitsProcessor,
														    EpsilonLogitsWarper,
														    EtaLogitsWarper,
														    ExponentialDecayLengthPenalty,
														    ForcedBOSTokenLogitsProcessor,
														    ForcedEOSTokenLogitsProcessor,
														    HammingDiversityLogitsProcessor,
														    InfNanRemoveLogitsProcessor,
														    LogitNormalization,
														    LogitsProcessor,
														    LogitsProcessorList,
														    LogitsWarper,
														    MinLengthLogitsProcessor,
														    MinNewTokensLengthLogitsProcessor,
														    NoBadWordsLogitsProcessor,
														    NoRepeatNGramLogitsProcessor,
														    PrefixConstrainedLogitsProcessor,
														    RepetitionPenaltyLogitsProcessor,
														    SequenceBiasLogitsProcessor,
														    TemperatureLogitsWarper,
														    TopKLogitsWarper,
														    TopPLogitsWarper,
														    TypicalLogitsWarper,
														)
														from .stopping_criteria import (
														    MaxLengthCriteria,
														    MaxNewTokensCriteria,
														    MaxTimeCriteria,
														    StoppingCriteria,
														    StoppingCriteriaList,
														    validate_stopping_criteria,
														)
														from .utils import (
														    BeamSampleDecoderOnlyOutput,
														    BeamSampleEncoderDecoderOutput,
														    BeamSearchDecoderOnlyOutput,
														    BeamSearchEncoderDecoderOutput,
														    ContrastiveSearchDecoderOnlyOutput,
														    ContrastiveSearchEncoderDecoderOutput,
														    GenerationMixin,
														    GreedySearchDecoderOnlyOutput,
														    GreedySearchEncoderDecoderOutput,
														    SampleDecoderOnlyOutput,
														    SampleEncoderDecoderOutput,
														    top_k_top_p_filtering,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tf_logits_process import (
														    TFForcedBOSTokenLogitsProcessor,
														    TFForcedEOSTokenLogitsProcessor,
														    TFForceTokensLogitsProcessor,
														    TFLogitsProcessor,
														    TFLogitsProcessorList,
														    TFLogitsWarper,
														    TFMinLengthLogitsProcessor,
														    TFNoBadWordsLogitsProcessor,
														    TFNoRepeatNGramLogitsProcessor,
														    TFRepetitionPenaltyLogitsProcessor,
														    TFSuppressTokensAtBeginLogitsProcessor,
														    TFSuppressTokensLogitsProcessor,
														    TFTemperatureLogitsWarper,
														    TFTopKLogitsWarper,
														    TFTopPLogitsWarper,
														)
														from .tf_utils import (
														    TFBeamSampleDecoderOnlyOutput,
														    TFBeamSampleEncoderDecoderOutput,
														    TFBeamSearchDecoderOnlyOutput,
														    TFBeamSearchEncoderDecoderOutput,
														    TFContrastiveSearchDecoderOnlyOutput,
														    TFContrastiveSearchEncoderDecoderOutput,
														    TFGenerationMixin,
														    TFGreedySearchDecoderOnlyOutput,
														    TFGreedySearchEncoderDecoderOutput,
														    TFSampleDecoderOnlyOutput,
														    TFSampleEncoderDecoderOutput,
														    tf_top_k_top_p_filtering,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .flax_logits_process import (
														    FlaxForcedBOSTokenLogitsProcessor,
														    FlaxForcedEOSTokenLogitsProcessor,
														    FlaxLogitsProcessor,
														    FlaxLogitsProcessorList,
														    FlaxLogitsWarper,
														    FlaxMinLengthLogitsProcessor,
														    FlaxTemperatureLogitsWarper,
														    FlaxTopKLogitsWarper,
														    FlaxTopPLogitsWarper,
														)
														from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput
else:
							import sys

							__A 		=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)







"""simple docstring"""

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 			lowerCamelCase__    (  lowerCamelCase_			):
			a__ :      int								=     """config.json"""


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



					"""simple docstring"""







					pass







class 			lowerCamelCase__    (  lowerCamelCase_			):
			a__ :      Dict								=     """config.json"""


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



					"""simple docstring"""







					pass







class 			lowerCamelCase__    (  lowerCamelCase_			):
			a__ :      Union[str, Any]								=     """config.json"""


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



					"""simple docstring"""







					pass







class 			lowerCamelCase__    (  lowerCamelCase_			):
			a__ :      Tuple								=     """config.json"""


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



					"""simple docstring"""







					pass







class 			lowerCamelCase__    (  unittest.TestCase			):


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					with self.assertRaises(SCREAMING_SNAKE_CASE							):
							ConfigMixin.load_config("dummy_path"							)


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Dict								=				SampleObject()
					snake_case		:						Dict								=				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
					snake_case		:						Dict								=				SampleObject(_name_or_path="lalala"							)
					snake_case		:						List[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
					snake_case		:						str								=				SampleObject(c=6							)
					snake_case		:						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.
					snake_case		:						int								=				SampleObject(1				, c=6							)
					snake_case		:						Dict								=				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 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						str								=				SampleObject()
					snake_case		:						List[str]								=				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(SCREAMING_SNAKE_CASE							)
							snake_case		:						Union[str, Any]								=				SampleObject.from_config(SampleObject.load_config(SCREAMING_SNAKE_CASE							)							)
							snake_case		:						Union[str, Any]								=				new_obj.config

					# unfreeze configs
					snake_case		:						List[str]								=				dict(SCREAMING_SNAKE_CASE							)
					snake_case		:						Union[str, Any]								=				dict(SCREAMING_SNAKE_CASE							)

					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 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						List[Any]								=				logging.get_logger("diffusers.configuration_utils"							)
					# 30 for warning
					logger.setLevel(30							)

					with CaptureLogger(SCREAMING_SNAKE_CASE							) as cap_logger:
							snake_case		:						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 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						int								=				logging.get_logger("diffusers.configuration_utils"							)
					# 30 for warning
					logger.setLevel(30							)

					with CaptureLogger(SCREAMING_SNAKE_CASE							) as cap_logger:
							snake_case		:						Optional[int]								=				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 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Any								=				logging.get_logger("diffusers.configuration_utils"							)
					# 30 for warning
					logger.setLevel(30							)

					with CaptureLogger(SCREAMING_SNAKE_CASE							) as cap_logger:
							snake_case		:						Union[str, Any]								=				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 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Union[str, Any]								=				logging.get_logger("diffusers.configuration_utils"							)
					# 30 for warning
					logger.setLevel(30							)

					with CaptureLogger(SCREAMING_SNAKE_CASE							) as cap_logger:
							snake_case		:						List[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 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						str								=				logging.get_logger("diffusers.configuration_utils"							)
					# 30 for warning
					logger.setLevel(30							)

					with CaptureLogger(SCREAMING_SNAKE_CASE							) as cap_logger:
							snake_case		:						List[Any]								=				DDPMScheduler.from_pretrained(
							    "hf-internal-testing/tiny-stable-diffusion-torch"				, subfolder="scheduler"				, prediction_type="sample"				, beta_end=8				, )

					with CaptureLogger(SCREAMING_SNAKE_CASE							) as cap_logger_a:
							snake_case		:						Optional[int]								=				DDPMScheduler.from_pretrained("google/ddpm-celebahq-256"				, beta_start=88							)

					assert ddpm.__class__ == DDPMScheduler
					assert ddpm.config.prediction_type == "sample"
					assert ddpm.config.beta_end == 8
					assert ddpm_a.config.beta_start == 88

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


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Dict								=				logging.get_logger("diffusers.configuration_utils"							)
					# 30 for warning
					logger.setLevel(30							)

					with CaptureLogger(SCREAMING_SNAKE_CASE							) as cap_logger:
							snake_case		:						Union[str, Any]								=				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 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Optional[Any]								=				SampleObject()

					snake_case		:						str								=				{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(SCREAMING_SNAKE_CASE							)

							# now loading it with SampleObject2 should put f into `_use_default_values`
							snake_case		:						Optional[int]								=				SampleObjecta.from_config(SCREAMING_SNAKE_CASE							)

							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`
					snake_case		:						Tuple								=				SampleObjecta.from_config(config.config							)
					assert new_config.f == [5, 4]

					config.config._use_default_values.pop()
					snake_case		:						List[Any]								=				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]

