
"""simple docstring"""







import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest




class 			A__	(     _lowerCamelCase):
		A_						:	Union[str, Any]           =     (UnCLIPScheduler,)

		def 							__lowerCamelCase							(				self	,		**_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						List[str]						      =				{
			    'num_train_timesteps': 10_00,
			    'variance_type': 'fixed_small_log',
			    'clip_sample': True,
			    'clip_sample_range': 1.0,
			    'prediction_type': 'epsilon',
			}

			config.update(**_SCREAMING_SNAKE_CASE			)
			return config

		def 							__lowerCamelCase							(				self			):
			for timesteps in [1, 5, 1_00, 10_00]:
				self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self			):
			for variance in ["fixed_small_log", "learned_range"]:
				self.check_over_configs(variance_type=_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self			):
			for clip_sample in [True, False]:
				self.check_over_configs(clip_sample=_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self			):
			for clip_sample_range in [1, 5, 10, 20]:
				self.check_over_configs(clip_sample_range=_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self			):
			for prediction_type in ["epsilon", "sample"]:
				self.check_over_configs(prediction_type=_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self			):
			for time_step in [0, 5_00, 9_99]:
				for prev_timestep in [None, 5, 1_00, 2_50, 5_00, 7_50]:
					if prev_timestep is not None and prev_timestep >= time_step:
						continue

					self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE	,		prev_timestep=_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						List[Any]						      =				self.scheduler_classes[0]
			__lowerCAmelCase      :						Any						      =				self.get_scheduler_config(variance_type='fixed_small_log'			)
			__lowerCAmelCase      :						List[Any]						      =				scheduler_class(**_SCREAMING_SNAKE_CASE			)

			assert torch.sum(torch.abs(scheduler._get_variance(0			) - 1.00_00E-10			)			) < 1E-5
			assert torch.sum(torch.abs(scheduler._get_variance(4_87			) - 0.054_9625			)			) < 1E-5
			assert torch.sum(torch.abs(scheduler._get_variance(9_99			) - 0.999_4987			)			) < 1E-5

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						str						      =				self.scheduler_classes[0]
			__lowerCAmelCase      :						Tuple						      =				self.get_scheduler_config(variance_type='learned_range'			)
			__lowerCAmelCase      :						Dict						      =				scheduler_class(**_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						Tuple						      =				0.5

			assert scheduler._get_variance(1	,		predicted_variance=_SCREAMING_SNAKE_CASE			) - -10.171_2790 < 1E-5
			assert scheduler._get_variance(4_87	,		predicted_variance=_SCREAMING_SNAKE_CASE			) - -5.799_8052 < 1E-5
			assert scheduler._get_variance(9_99	,		predicted_variance=_SCREAMING_SNAKE_CASE			) - -0.001_0011 < 1E-5

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Dict						      =				self.scheduler_classes[0]
			__lowerCAmelCase      :						List[str]						      =				self.get_scheduler_config()
			__lowerCAmelCase      :						Any						      =				scheduler_class(**_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						Union[str, Any]						      =				scheduler.timesteps

			__lowerCAmelCase      :						List[str]						      =				self.dummy_model()
			__lowerCAmelCase      :						Tuple						      =				self.dummy_sample_deter
			__lowerCAmelCase      :						int						      =				torch.manual_seed(0			)

			for i, t in enumerate(_SCREAMING_SNAKE_CASE			):
				# 1. predict noise residual
				__lowerCAmelCase      :						Optional[int]						      =				model(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)

				# 2. predict previous mean of sample x_t-1
				__lowerCAmelCase      :						Optional[int]						      =				scheduler.step(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		generator=_SCREAMING_SNAKE_CASE			).prev_sample

				__lowerCAmelCase      :						int						      =				pred_prev_sample

			__lowerCAmelCase      :						List[str]						      =				torch.sum(torch.abs(_SCREAMING_SNAKE_CASE			)			)
			__lowerCAmelCase      :						str						      =				torch.mean(torch.abs(_SCREAMING_SNAKE_CASE			)			)

			assert abs(result_sum.item() - 252.268_2495			) < 1E-2
			assert abs(result_mean.item() - 0.328_4743			) < 1E-3

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Any						      =				self.scheduler_classes[0]
			__lowerCAmelCase      :						Tuple						      =				self.get_scheduler_config()
			__lowerCAmelCase      :						Optional[Any]						      =				scheduler_class(**_SCREAMING_SNAKE_CASE			)

			scheduler.set_timesteps(25			)

			__lowerCAmelCase      :						int						      =				scheduler.timesteps

			__lowerCAmelCase      :						str						      =				self.dummy_model()
			__lowerCAmelCase      :						str						      =				self.dummy_sample_deter
			__lowerCAmelCase      :						Any						      =				torch.manual_seed(0			)

			for i, t in enumerate(_SCREAMING_SNAKE_CASE			):
				# 1. predict noise residual
				__lowerCAmelCase      :						List[str]						      =				model(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)

				if i + 1 == timesteps.shape[0]:
					__lowerCAmelCase      :						Union[str, Any]						      =				None
				else:
					__lowerCAmelCase      :						Optional[Any]						      =				timesteps[i + 1]

				# 2. predict previous mean of sample x_t-1
				__lowerCAmelCase      :						Union[str, Any]						      =				scheduler.step(
				    _SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		prev_timestep=_SCREAMING_SNAKE_CASE	,		generator=_SCREAMING_SNAKE_CASE			).prev_sample

				__lowerCAmelCase      :						int						      =				pred_prev_sample

			__lowerCAmelCase      :						Tuple						      =				torch.sum(torch.abs(_SCREAMING_SNAKE_CASE			)			)
			__lowerCAmelCase      :						int						      =				torch.mean(torch.abs(_SCREAMING_SNAKE_CASE			)			)

			assert abs(result_sum.item() - 258.204_4983			) < 1E-2
			assert abs(result_mean.item() - 0.336_2038			) < 1E-3

		def 							__lowerCamelCase							(				self			):
			pass







		def 							__lowerCamelCase							(				self			):
			pass

"""simple docstring"""







# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


lowerCamelCase__        =		logging.get_logger(__name__)

lowerCamelCase__        =		{}
lowerCamelCase__        =		{}
lowerCamelCase__        =		{}
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase = None	,  ):
	__lowerCAmelCase      :						Union[str, Any]						      =				aliases if aliases is not None else []
	if format_type in _FORMAT_TYPES:
		logger.warning(
		    F"Overwriting format type '{format_type}' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})"		)
	__lowerCAmelCase      :						str						      =				formatter_cls
	for alias in set(aliases + [format_type]		):
		if alias in _FORMAT_TYPES_ALIASES:
			logger.warning(
			    F"Overwriting format type alias '{alias}' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})"		)
		__lowerCAmelCase      :						Any						      =				format_type
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase = None		):
	__lowerCAmelCase      :						int						      =				aliases if aliases is not None else []
	for alias in set(aliases + [format_type]		):
		__lowerCAmelCase      :						str						      =				unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=["""python"""])
_register_formatter(ArrowFormatter, """arrow""", aliases=["""pa""", """pyarrow"""])
_register_formatter(NumpyFormatter, """numpy""", aliases=["""np"""])
_register_formatter(PandasFormatter, """pandas""", aliases=["""pd"""])
_register_formatter(CustomFormatter, """custom""")

if config.TORCH_AVAILABLE:
							from .torch_formatter import TorchFormatter

							_register_formatter(TorchFormatter, """torch""", aliases=["""pt""", """pytorch"""])
else:
							lowerCamelCase__        =		ValueError("""PyTorch needs to be installed to be able to return PyTorch tensors.""")
							_register_unavailable_formatter(_torch_error, """torch""", aliases=["""pt""", """pytorch"""])

if config.TF_AVAILABLE:
							from .tf_formatter import TFFormatter

							_register_formatter(TFFormatter, """tensorflow""", aliases=["""tf"""])
else:
							lowerCamelCase__        =		ValueError("""Tensorflow needs to be installed to be able to return Tensorflow tensors.""")
							_register_unavailable_formatter(_tf_error, """tensorflow""", aliases=["""tf"""])

if config.JAX_AVAILABLE:
							from .jax_formatter import JaxFormatter

							_register_formatter(JaxFormatter, """jax""", aliases=[])
else:
							lowerCamelCase__        =		ValueError("""JAX needs to be installed to be able to return JAX arrays.""")
							_register_unavailable_formatter(_jax_error, """jax""", aliases=[])
def    __lowerCAmelCase    (_UpperCamelCase		):
	if format_type in _FORMAT_TYPES_ALIASES:
		return _FORMAT_TYPES_ALIASES[format_type]
	else:
		return format_type
def    __lowerCAmelCase    (_UpperCamelCase	,  **_UpperCamelCase		):
	__lowerCAmelCase      :						Any						      =				get_format_type_from_alias(_UpperCamelCase		)
	if format_type in _FORMAT_TYPES:
		return _FORMAT_TYPES[format_type](**_UpperCamelCase		)
	if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
		raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
	else:
		raise ValueError(
		    F"Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None		)}, but got '{format_type}'"		)