"""simple docstring"""
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class      lowerCAmelCase_			(	lowerCAmelCase    ):



				"""simple docstring"""
				_lowerCAmelCase   :			int	     =  42
				_lowerCAmelCase   :			str	     =  None





def       lowerCAmelCase__			(	_UpperCamelCase				: Dict       ,	_UpperCamelCase				: List[str]=0.9_99       ,	_UpperCamelCase				: Optional[Any]="cosine"       ,	)    -> Union[str, Any]:





						"""simple docstring"""






						if alpha_transform_type == "cosine":

												def alpha_bar_fn(_UpperCamelCase				: Optional[int]    ):
																		return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2    ) ** 2

						elif alpha_transform_type == "exp":

												def alpha_bar_fn(_UpperCamelCase				: Any    ):
																		return math.exp(t * -12.0    )

						else:
												raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}"""    )

						snake_case	    =	[]
						for i in range(_snake_case    ):
												snake_case	    =	i / num_diffusion_timesteps
												snake_case	    =	(i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar_fn(_snake_case    ) / alpha_bar_fn(_snake_case    )       ,	_snake_case    )    )
						return torch.tensor(_snake_case       ,	dtype=torch.floataa    )



class      lowerCAmelCase_			(	lowerCAmelCase   ,			lowerCAmelCase    ):



				"""simple docstring"""

				@register_to_config
				def __init__( self						,			lowerCAmelCase = 10_00						,			lowerCAmelCase = "fixed_small_log"						,			lowerCAmelCase = True						,			lowerCAmelCase = 1.0						,			lowerCAmelCase = "epsilon"						,			lowerCAmelCase = "squaredcos_cap_v2"						,			):






										"""simple docstring"""

										if beta_schedule != "squaredcos_cap_v2":
																raise ValueError('UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''			)

										snake_case	    =	betas_for_alpha_bar(_a			)

										snake_case	    =	1.0 - self.betas
										snake_case	    =	torch.cumprod(self.alphas						,			dim=0			)
										snake_case	    =	torch.tensor(1.0			)

										# standard deviation of the initial noise distribution
										snake_case	    =	1.0

										# setable values
										snake_case	    =	None
										snake_case	    =	torch.from_numpy(np.arange(0						,			_a			)[::-1].copy()			)

										snake_case	    =	variance_type

				def 						snake_case       ( self						,			lowerCAmelCase						,			lowerCAmelCase = None			):






										"""simple docstring"""

										return sample

				def 						snake_case       ( self						,			lowerCAmelCase						,			lowerCAmelCase = None			):






										"""simple docstring"""

										snake_case	    =	num_inference_steps
										snake_case	    =	(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
										snake_case	    =	(np.arange(0						,			_a			) * step_ratio).round()[::-1].copy().astype(np.intaa			)
										snake_case	    =	torch.from_numpy(_a			).to(_a			)

				def 						snake_case       ( self						,			lowerCAmelCase						,			lowerCAmelCase=None						,			lowerCAmelCase=None						,			lowerCAmelCase=None			):






										"""simple docstring"""

										if prev_timestep is None:
																snake_case	    =	t - 1

										snake_case	    =	self.alphas_cumprod[t]
										snake_case	    =	self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
										snake_case	    =	1 - alpha_prod_t
										snake_case	    =	1 - alpha_prod_t_prev

										if prev_timestep == t - 1:
																snake_case	    =	self.betas[t]
										else:
																snake_case	    =	1 - alpha_prod_t / alpha_prod_t_prev

										# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
										# and sample from it to get previous sample
										# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
										snake_case	    =	beta_prod_t_prev / beta_prod_t * beta

										if variance_type is None:
																snake_case	    =	self.config.variance_type

										# hacks - were probably added for training stability
										if variance_type == "fixed_small_log":
																snake_case	    =	torch.log(torch.clamp(_a						,			min=1E-20			)			)
																snake_case	    =	torch.exp(0.5 * variance			)
										elif variance_type == "learned_range":
																# NOTE difference with DDPM scheduler
																snake_case	    =	variance.log()
																snake_case	    =	beta.log()

																snake_case	    =	(predicted_variance + 1) / 2
																snake_case	    =	frac * max_log + (1 - frac) * min_log

										return variance

				def 						snake_case       ( self						,			lowerCAmelCase						,			lowerCAmelCase						,			lowerCAmelCase						,			lowerCAmelCase = None						,			lowerCAmelCase=None						,			lowerCAmelCase = True						,			):






										"""simple docstring"""

										snake_case	    =	timestep

										if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
																snake_case	    =	torch.split(_a						,			sample.shape[1]						,			dim=1			)
										else:
																snake_case	    =	None

										# 1. compute alphas, betas
										if prev_timestep is None:
																snake_case	    =	t - 1

										snake_case	    =	self.alphas_cumprod[t]
										snake_case	    =	self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
										snake_case	    =	1 - alpha_prod_t
										snake_case	    =	1 - alpha_prod_t_prev

										if prev_timestep == t - 1:
																snake_case	    =	self.betas[t]
																snake_case	    =	self.alphas[t]
										else:
																snake_case	    =	1 - alpha_prod_t / alpha_prod_t_prev
																snake_case	    =	1 - beta

										# 2. compute predicted original sample from predicted noise also called
										# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
										if self.config.prediction_type == "epsilon":
																snake_case	    =	(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
										elif self.config.prediction_type == "sample":
																snake_case	    =	model_output
										else:
																raise ValueError(
																    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"""
																    ' for the UnCLIPScheduler.'			)

										# 3. Clip "predicted x_0"
										if self.config.clip_sample:
																snake_case	    =	torch.clamp(
																    _a						,			-self.config.clip_sample_range						,			self.config.clip_sample_range			)

										# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
										# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
										snake_case	    =	(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
										snake_case	    =	alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

										# 5. Compute predicted previous sample µ_t
										# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
										snake_case	    =	pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

										# 6. Add noise
										snake_case	    =	0
										if t > 0:
																snake_case	    =	randn_tensor(
																    model_output.shape						,			dtype=model_output.dtype						,			generator=_a						,			device=model_output.device			)

																snake_case	    =	self._get_variance(
																    _a						,			predicted_variance=_a						,			prev_timestep=_a						,			)

																if self.variance_type == "fixed_small_log":
																						snake_case	    =	variance
																elif self.variance_type == "learned_range":
																						snake_case	    =	(0.5 * variance).exp()
																else:
																						raise ValueError(
																						    F"""variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"""
																						    ' for the UnCLIPScheduler.'			)

																snake_case	    =	variance * variance_noise

										snake_case	    =	pred_prev_sample + variance

										if not return_dict:
																return (pred_prev_sample,)

										return UnCLIPSchedulerOutput(prev_sample=_a						,			pred_original_sample=_a			)

				def 						snake_case       ( self						,			lowerCAmelCase						,			lowerCAmelCase						,			lowerCAmelCase						,			):






										"""simple docstring"""

										snake_case	    =	self.alphas_cumprod.to(device=original_samples.device						,			dtype=original_samples.dtype			)
										snake_case	    =	timesteps.to(original_samples.device			)

										snake_case	    =	alphas_cumprod[timesteps] ** 0.5
										snake_case	    =	sqrt_alpha_prod.flatten()
										while len(sqrt_alpha_prod.shape			) < len(original_samples.shape			):
																snake_case	    =	sqrt_alpha_prod.unsqueeze(-1			)

										snake_case	    =	(1 - alphas_cumprod[timesteps]) ** 0.5
										snake_case	    =	sqrt_one_minus_alpha_prod.flatten()
										while len(sqrt_one_minus_alpha_prod.shape			) < len(original_samples.shape			):
																snake_case	    =	sqrt_one_minus_alpha_prod.unsqueeze(-1			)

										snake_case	    =	sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
										return noisy_samples




import importlib
import os

import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry

from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem

from .utils import require_lza, require_zstandard






def   lowerCAmelCase_						(					_snake_case      :  List[Any]   )				->		List[Any]:







	'''simple docstring'''


	assert "mock" in _fsspec_registry
	assert "bz2" in _fsspec_registry






def   lowerCAmelCase_						(					)				->		Tuple:







	'''simple docstring'''


	assert "mock" not in _fsspec_registry
	assert "bz2" in _fsspec_registry






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







	'''simple docstring'''


	__magic_name__		:		Dict	   =     "mock-s3-bucket"
	__magic_name__		:		Any	   =     F'''s3://{mock_bucket}'''
	__magic_name__		:		str	   =     extract_path_from_uri(_snake_case   )
	assert dataset_path.startswith("s3://"   ) is False

	__magic_name__		:		Tuple	   =     "./local/path"
	__magic_name__		:		Optional[Any]	   =     extract_path_from_uri(_snake_case   )
	assert dataset_path == new_dataset_path






def   lowerCAmelCase_						(					_snake_case      :  List[str]   )				->		Optional[Any]:







	'''simple docstring'''


	__magic_name__		:		str	   =     is_remote_filesystem(_snake_case   )
	assert is_remote is True

	__magic_name__		:		Optional[int]	   =     fsspec.filesystem("file"   )

	__magic_name__		:		int	   =     is_remote_filesystem(_snake_case   )
	assert is_remote is False






@pytest.mark.parametrize("compression_fs_class"      ,   _snake_case   )
def   lowerCAmelCase_						(					_snake_case      :  Optional[int]      ,   _snake_case      :  Optional[Any]      ,   _snake_case      :  int      ,   _snake_case      :  Tuple      ,   _snake_case      :  Any      ,   _snake_case      :  Union[str, Any]      ,   _snake_case      :  Any   )				->		int:







	'''simple docstring'''


	__magic_name__		:		Any	   =     {"gzip": gz_file, "xz": xz_file, "zstd": zstd_file, "bz2": bza_file, "lz4": lza_file}
	__magic_name__		:		str	   =     input_paths[compression_fs_class.protocol]
	if input_path is None:
		__magic_name__		:		Dict	   =     F'''for \'{compression_fs_class.protocol}\' compression protocol, '''
		if compression_fs_class.protocol == "lz4":
			reason += require_lza.kwargs["reason"]
		elif compression_fs_class.protocol == "zstd":
			reason += require_zstandard.kwargs["reason"]
		pytest.skip(_snake_case   )
	__magic_name__		:		str	   =     fsspec.filesystem(compression_fs_class.protocol      ,   fo=_snake_case   )
	assert isinstance(_snake_case      ,   _snake_case   )
	__magic_name__		:		int	   =     os.path.basename(_snake_case   )
	__magic_name__		:		Optional[int]	   =     expected_filename[: expected_filename.rindex("."   )]
	assert fs.glob("*"   ) == [expected_filename]
	with fs.open(_snake_case      ,   "r"      ,   encoding="utf-8"   ) as f, open(_snake_case      ,   encoding="utf-8"   ) as expected_file:
		assert f.read() == expected_file.read()






@pytest.mark.parametrize("protocol"      ,   ["zip", "gzip"]   )
def   lowerCAmelCase_						(					_snake_case      :  List[Any]      ,   _snake_case      :  Optional[Any]      ,   _snake_case      :  Optional[Any]   )				->		str:







	'''simple docstring'''


	__magic_name__		:		int	   =     {"zip": zip_jsonl_path, "gzip": jsonl_gz_path}
	__magic_name__		:		int	   =     compressed_file_paths[protocol]
	__magic_name__		:		Tuple	   =     "dataset.jsonl"
	__magic_name__		:		List[str]	   =     F'''{protocol}://{member_file_path}::{compressed_file_path}'''
	__magic_name__   ,    *__magic_name__		:		Optional[Any]	   =     fsspec.get_fs_token_paths(_snake_case   )
	assert fs.isfile(_snake_case   )
	assert not fs.isfile("non_existing_" + member_file_path   )






@pytest.mark.integration
def   lowerCAmelCase_						(					_snake_case      :  Union[str, Any]      ,   _snake_case      :  Dict      ,   _snake_case      :  List[str]      ,   _snake_case      :  Tuple   )				->		str:







	'''simple docstring'''


	__magic_name__		:		int	   =     hf_api.dataset_info(_snake_case      ,   token=_snake_case   )
	__magic_name__		:		Optional[Any]	   =     HfFileSystem(repo_info=_snake_case      ,   token=_snake_case   )
	assert sorted(hffs.glob("*"   )   ) == [".gitattributes", "data"]
	assert hffs.isdir("data"   )
	assert hffs.isfile(".gitattributes"   ) and hffs.isfile("data/text_data.txt"   )
	with open(_snake_case   ) as f:
		assert hffs.open("data/text_data.txt"      ,   "r"   ).read() == f.read()






def   lowerCAmelCase_						(					)				->		Optional[int]:







	'''simple docstring'''


	__magic_name__		:		Optional[Any]	   =     "bz2"

	# Import module
	import datasets.filesystems

	# Overwrite protocol and reload
	register_implementation(_snake_case      ,   _snake_case      ,   clobber=_snake_case   )
	with pytest.warns(_snake_case   ) as warning_info:
		importlib.reload(datasets.filesystems   )

	assert len(_snake_case   ) == 1
	assert (
	    str(warning_info[0].message   )
	    == F'''A filesystem protocol was already set for {protocol} and will be overwritten.'''
	)
