




'''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
class      lowerCamelCase_							(       UpperCAmelCase_							):




					'''simple docstring'''






					lowercase__    = 42
					lowercase__    = 42






def 		_lowercase		(	lowercase_						,							lowercase_=0.999						,							lowercase_="cosine"						,							):



		'''simple docstring'''







		if alpha_transform_type == "cosine":

				def alpha_bar_fn(lowercase_  ):
						return math.cos((t + 0.008) / 1.008 * math.pi / 2  ) ** 2

		elif alpha_transform_type == "exp":

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

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

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





class      lowerCamelCase_							(       UpperCAmelCase_,       UpperCAmelCase_							):




					'''simple docstring'''






					lowercase__    = 1






					@register_to_config
					def __init__(    self,  __UpperCamelCase = 1_000,  __UpperCamelCase = 0.00_01,  __UpperCamelCase = 0.02,  __UpperCamelCase = "linear",  __UpperCamelCase = 0.0,  __UpperCamelCase = None,  __UpperCamelCase = True,  ):
							if trained_betas is not None:
									_snake_case	       =       torch.from_numpy(__UpperCamelCase )
							elif beta_schedule == "linear":
									_snake_case	       =       torch.linspace(__UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  dtype=torch.floataa )
							elif beta_schedule == "scaled_linear":
									# this schedule is very specific to the latent diffusion model.
									_snake_case	       =       (
									    torch.linspace(beta_start**0.5,  beta_end**0.5,  __UpperCamelCase,  dtype=torch.floataa ) ** 2
									)
							elif beta_schedule == "squaredcos_cap_v2":
									# Glide cosine schedule
									_snake_case	       =       betas_for_alpha_bar(__UpperCamelCase )
							elif beta_schedule == "sigmoid":
									# GeoDiff sigmoid schedule
									_snake_case	       =       torch.linspace(-6,  6,  __UpperCamelCase )
									_snake_case	       =       torch.sigmoid(__UpperCamelCase ) * (beta_end - beta_start) + beta_start
							else:
									raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}''' )

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

							_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,  __UpperCamelCase )[::-1].copy() )

							_snake_case	       =       eta






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  __UpperCamelCase = None ):
							return sample






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  __UpperCamelCase = 10,  __UpperCamelCase = 10,  __UpperCamelCase = None,  ):
							_snake_case	       =       min(self.config.num_train_timesteps,  __UpperCamelCase )
							_snake_case	       =       num_inference_steps

							_snake_case	       =       []

							_snake_case	       =       {}
							for j in range(0,  num_inference_steps - jump_length,  __UpperCamelCase ):
									_snake_case	       =       jump_n_sample - 1

							_snake_case	       =       num_inference_steps
							while t >= 1:
									_snake_case	       =       t - 1
									timesteps.append(__UpperCamelCase )

									if jumps.get(__UpperCamelCase,  0 ) > 0:
											_snake_case	       =       jumps[t] - 1
											for _ in range(__UpperCamelCase ):
													_snake_case	       =       t + 1
													timesteps.append(__UpperCamelCase )

							_snake_case	       =       np.array(__UpperCamelCase ) * (self.config.num_train_timesteps // self.num_inference_steps)
							_snake_case	       =       torch.from_numpy(__UpperCamelCase ).to(__UpperCamelCase )






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase ):
							_snake_case	       =       t - self.config.num_train_timesteps // self.num_inference_steps

							_snake_case	       =       self.alphas_cumprod[t]
							_snake_case	       =       self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
							_snake_case	       =       1 - alpha_prod_t
							_snake_case	       =       1 - 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
							# Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
							# without eta.
							# variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
							_snake_case	       =       (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

							return variance






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase = None,  __UpperCamelCase = True,  ):
							_snake_case	       =       timestep
							_snake_case	       =       timestep - self.config.num_train_timesteps // self.num_inference_steps

							# 1. compute alphas, betas
							_snake_case	       =       self.alphas_cumprod[t]
							_snake_case	       =       self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
							_snake_case	       =       1 - alpha_prod_t

							# 2. compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
							_snake_case	       =       (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

							# 3. Clip "predicted x_0"
							if self.config.clip_sample:
									_snake_case	       =       torch.clamp(__UpperCamelCase,  -1,  1 )

							# We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
							# substitute formula (7) in the algorithm coming from DDPM paper
							# (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
							# DDIM schedule gives the same results as DDPM with eta = 1.0
							# Noise is being reused in 7. and 8., but no impact on quality has
							# been observed.

							# 5. Add noise
							_snake_case	       =       model_output.device
							_snake_case	       =       randn_tensor(model_output.shape,  generator=__UpperCamelCase,  device=__UpperCamelCase,  dtype=model_output.dtype )
							_snake_case	       =       self.eta * self._get_variance(__UpperCamelCase ) ** 0.5

							_snake_case	       =       0
							if t > 0 and self.eta > 0:
									_snake_case	       =       std_dev_t * noise

							# 6. compute "direction pointing to x_t" of formula (12)
							# from https://arxiv.org/pdf/2010.02502.pdf
							_snake_case	       =       (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

							# 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							_snake_case	       =       alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

							# 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
							_snake_case	       =       (alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

							# 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
							_snake_case	       =       mask * prev_known_part + (1.0 - mask) * prev_unknown_part

							if not return_dict:
									return (
									    pred_prev_sample,
									    pred_original_sample,
									)

							return RePaintSchedulerOutput(prev_sample=__UpperCamelCase,  pred_original_sample=__UpperCamelCase )






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase=None ):
							_snake_case	       =       self.config.num_train_timesteps // self.num_inference_steps

							for i in range(__UpperCamelCase ):
									_snake_case	       =       self.betas[timestep + i]
									if sample.device.type == "mps":
											# randn does not work reproducibly on mps
											_snake_case	       =       randn_tensor(sample.shape,  dtype=sample.dtype,  generator=__UpperCamelCase )
											_snake_case	       =       noise.to(sample.device )
									else:
											_snake_case	       =       randn_tensor(sample.shape,  generator=__UpperCamelCase,  device=sample.device,  dtype=sample.dtype )

									# 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
									_snake_case	       =       (1 - beta) ** 0.5 * sample + beta**0.5 * noise

							return sample






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  ):
							raise NotImplementedError("Use `DDPMScheduler.add_noise()` to train for sampling with RePaint." )






					def __len__(    self ):
							return self.config.num_train_timesteps












import os
import tempfile
from pathlib import Path
from unittest import TestCase

import pyarrow as pa
import pytest

from datasets.arrow_dataset import Dataset
from datasets.arrow_reader import ArrowReader, BaseReader, FileInstructions, ReadInstruction, make_file_instructions
from datasets.info import DatasetInfo
from datasets.splits import NamedSplit, Split, SplitDict, SplitInfo

from .utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases





class 	lowerCAmelCase__(       __lowercase			):






       '''simple docstring'''


       def 			UpperCamelCase_	(					self			,  __lowerCamelCase			,  __lowerCamelCase=False							)	->   List[Any]:
            _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							(
                filename_skip_take["filename"],
                filename_skip_take["skip"] if "skip" in filename_skip_take else None,
                filename_skip_take["take"] if "take" in filename_skip_take else None,
            )
            open(os.path.join(__lowerCamelCase							)			,  "wb"							).close()
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							pa.Table.from_pydict({"filename": [Path(__lowerCamelCase							).name] * 1_0_0}							)
            if take == -1:
                 _SCREAMING_SNAKE_CASE				:		int  			=							len(__lowerCamelCase							) - skip
            if skip is not None and take is not None:
                 _SCREAMING_SNAKE_CASE				:		List[str]  			=							pa_table.slice(__lowerCamelCase			,  __lowerCamelCase							)
            return pa_table





class 	lowerCAmelCase__(       __lowercase			):






       '''simple docstring'''


       def 			UpperCamelCase_	(					self							)	->   List[Any]:
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							"my_name"
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							SplitInfo(name="train"			,  num_examples=1_0_0							)
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							SplitInfo(name="test"			,  num_examples=1_0_0							)
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							[train_info, test_info]
            _SCREAMING_SNAKE_CASE				:		str  			=							SplitDict()
            split_dict.add(__lowerCamelCase							)
            split_dict.add(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE				:		str  			=							DatasetInfo(splits=__lowerCamelCase							)

            with tempfile.TemporaryDirectory() as tmp_dir:
                 _SCREAMING_SNAKE_CASE				:		Tuple  			=							ReaderTest(__lowerCamelCase			,  __lowerCamelCase							)

                 _SCREAMING_SNAKE_CASE				:		Dict  			=							"test[:33%]"
                 _SCREAMING_SNAKE_CASE				:		Tuple  			=							Dataset(**reader.read(__lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase							)							)
                 self.assertEqual(dset["filename"][0]			,  F"""{name}-test"""							)
                 self.assertEqual(dset.num_rows			,  3_3							)
                 self.assertEqual(dset.num_columns			,  1							)

                 _SCREAMING_SNAKE_CASE				:		List[str]  			=							["train", "test[:33%]"]
                 _SCREAMING_SNAKE_CASE				:		Tuple  			=							[Split.TRAIN, ReadInstruction.from_spec("test[:33%]"							)]
                 for instructions in [instructionsa, instructionsa]:
                      _SCREAMING_SNAKE_CASE				:		List[Any]  			=							[reader.read(__lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase							) for instr in instructions]
                      _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE				:		Any  			=							(Dataset(**__lowerCamelCase							) for dataset_kwargs in datasets_kwargs)
                      self.assertEqual(train_dset["filename"][0]			,  F"""{name}-train"""							)
                      self.assertEqual(train_dset.num_rows			,  1_0_0							)
                      self.assertEqual(train_dset.num_columns			,  1							)
                      self.assertIsInstance(train_dset.split			,  __lowerCamelCase							)
                      self.assertEqual(str(train_dset.split							)			,  "train"							)
                      self.assertEqual(test_dset["filename"][0]			,  F"""{name}-test"""							)
                      self.assertEqual(test_dset.num_rows			,  3_3							)
                      self.assertEqual(test_dset.num_columns			,  1							)
                      self.assertIsInstance(test_dset.split			,  __lowerCamelCase							)
                      self.assertEqual(str(test_dset.split							)			,  "test[:33%]"							)
                      del train_dset, test_dset




       def 			UpperCamelCase_	(					self							)	->   int:
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							"my_name"
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							SplitInfo(name="train"			,  num_examples=1_0_0_0			,  shard_lengths=[1_0_0] * 1_0							)
            _SCREAMING_SNAKE_CASE				:		str  			=							[train_info]
            _SCREAMING_SNAKE_CASE				:		Dict  			=							SplitDict()
            split_dict.add(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							DatasetInfo(splits=__lowerCamelCase							)

            with tempfile.TemporaryDirectory() as tmp_dir:
                 _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							ReaderTest(__lowerCamelCase			,  __lowerCamelCase							)

                 _SCREAMING_SNAKE_CASE				:		int  			=							"train[:33%]"
                 _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							Dataset(**reader.read(__lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase							)							)
                 self.assertEqual(dset["filename"][0]			,  F"""{name}-train-00000-of-00010"""							)
                 self.assertEqual(dset["filename"][-1]			,  F"""{name}-train-00003-of-00010"""							)
                 self.assertEqual(dset.num_rows			,  3_3_0							)
                 self.assertEqual(dset.num_columns			,  1							)







       def 			UpperCamelCase_	(					self							)	->   str:
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							SplitInfo(name="train"			,  num_examples=1_0_0							)
            _SCREAMING_SNAKE_CASE				:		Dict  			=							SplitInfo(name="test"			,  num_examples=1_0_0							)
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							SplitDict()
            split_dict.add(__lowerCamelCase							)
            split_dict.add(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE				:		Any  			=							DatasetInfo(splits=__lowerCamelCase							)

            with tempfile.TemporaryDirectory() as tmp_dir:
                 _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							ReaderTest(__lowerCamelCase			,  __lowerCamelCase							)

                 _SCREAMING_SNAKE_CASE				:		List[Any]  			=							[
                     {"filename": os.path.join(__lowerCamelCase			,  "train"							)},
                     {"filename": os.path.join(__lowerCamelCase			,  "test"							), "skip": 1_0, "take": 1_0},
                 ]
                 _SCREAMING_SNAKE_CASE				:		Any  			=							Dataset(**reader.read_files(__lowerCamelCase			,  original_instructions="train+test[10:20]"							)							)
                 self.assertEqual(dset.num_rows			,  1_1_0							)
                 self.assertEqual(dset.num_columns			,  1							)
                 del dset






@pytest.mark.parametrize("in_memory",				[False, True]		)
def 			lowerCamelCase__	(__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase		):
     _SCREAMING_SNAKE_CASE				:		Dict  			=							arrow_file
     with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
          _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							ArrowReader.read_table(__lowerCamelCase,				in_memory=__lowerCamelCase		)
     assert table.shape == dataset.data.shape
     assert set(table.column_names		) == set(dataset.data.column_names		)
     assert dict(table.to_pydict()		) == dict(dataset.data.to_pydict()		)  # to_pydict returns OrderedDict






@pytest.mark.parametrize("in_memory",				[False, True]		)
def 			lowerCamelCase__	(__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase		):
     _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							arrow_file
     _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							ArrowReader("",				__lowerCamelCase		)
     with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
          _SCREAMING_SNAKE_CASE				:		Dict  			=							reader.read_files([{"filename": filename}],				in_memory=__lowerCamelCase		)
     assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
     _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							dataset_kwargs["arrow_table"]
     assert table.shape == dataset.data.shape
     assert set(table.column_names		) == set(dataset.data.column_names		)
     assert dict(table.to_pydict()		) == dict(dataset.data.to_pydict()		)  # to_pydict returns OrderedDict






def 			lowerCamelCase__	():
     assert ReadInstruction("train",				to=10,				unit="abs"		).to_spec() == "train[:10]"
     assert ReadInstruction("train",				from_=-80,				to=10,				unit="%"		).to_spec() == "train[-80%:10%]"

     _SCREAMING_SNAKE_CASE				:		str  			=							"train+test"
     assert ReadInstruction.from_spec(__lowerCamelCase		).to_spec() == spec_train_test

     _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							"train[2:10]"
     assert ReadInstruction.from_spec(__lowerCamelCase		).to_spec() == spec_train_abs

     _SCREAMING_SNAKE_CASE				:		List[Any]  			=							"train[15%:-20%]"
     assert ReadInstruction.from_spec(__lowerCamelCase		).to_spec() == spec_train_pct

     _SCREAMING_SNAKE_CASE				:		List[str]  			=							"train[:10%](closest)"
     assert ReadInstruction.from_spec(__lowerCamelCase		).to_spec() == "train[:10%]"

     _SCREAMING_SNAKE_CASE				:		Any  			=							"train[:10%](pct1_dropremainder)"
     assert ReadInstruction.from_spec(__lowerCamelCase		).to_spec() == spec_train_pct_rounding

     _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							"train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)"
     assert ReadInstruction.from_spec(__lowerCamelCase		).to_spec() == spec_train_test_pct_rounding






def 			lowerCamelCase__	():
     _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							"dummy"
     _SCREAMING_SNAKE_CASE				:		List[str]  			=							[SplitInfo(name="train",				num_examples=100		)]
     _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							"train[:33%]"
     _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							"arrow"
     _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							"prefix"

     _SCREAMING_SNAKE_CASE				:		Any  			=							make_file_instructions(__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase		)
     assert isinstance(__lowerCamelCase,				__lowerCamelCase		)
     assert file_instructions.num_examples == 33
     assert file_instructions.file_instructions == [
         {"filename": os.path.join(__lowerCamelCase,				f"""{name}-train.arrow"""		), "skip": 0, "take": 33}
     ]

     _SCREAMING_SNAKE_CASE				:		List[str]  			=							[SplitInfo(name="train",				num_examples=100,				shard_lengths=[10] * 10		)]
     _SCREAMING_SNAKE_CASE				:		Tuple  			=							make_file_instructions(__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase		)
     assert isinstance(__lowerCamelCase,				__lowerCamelCase		)
     assert file_instructions.num_examples == 33
     assert file_instructions.file_instructions == [
         {"filename": os.path.join(__lowerCamelCase,				f"""{name}-train-00000-of-00010.arrow"""		), "skip": 0, "take": -1},
         {"filename": os.path.join(__lowerCamelCase,				f"""{name}-train-00001-of-00010.arrow"""		), "skip": 0, "take": -1},
         {"filename": os.path.join(__lowerCamelCase,				f"""{name}-train-00002-of-00010.arrow"""		), "skip": 0, "take": -1},
         {"filename": os.path.join(__lowerCamelCase,				f"""{name}-train-00003-of-00010.arrow"""		), "skip": 0, "take": 3},
     ]






@pytest.mark.parametrize("name, expected_exception",				[(None, TypeError), ("", ValueError)]		)
def 			lowerCamelCase__	(__lowerCamelCase,				__lowerCamelCase		):
     _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							[SplitInfo(name="train",				num_examples=100		)]
     _SCREAMING_SNAKE_CASE				:		int  			=							"train"
     _SCREAMING_SNAKE_CASE				:		int  			=							"arrow"
     _SCREAMING_SNAKE_CASE				:		Tuple  			=							"prefix_path"
     with pytest.raises(__lowerCamelCase		):
          _SCREAMING_SNAKE_CASE				:		List[str]  			=							make_file_instructions(__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase		)