






"""simple docstring"""

import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


lowercase__      =				logging.get_logger(__name__)


@dataclass
class 	__lowerCamelCase       :





		'''simple docstring'''






		a_			:       int      =							42
		a_			:       Optional[Any]      =							42
		a_			:       int      =							None
		a_			:       List[str]      =							None





		def      lowerCamelCase (     self							:			str			):
				return json.dumps(dataclasses.asdict(self			)		,    indent=2			) + "\n"







@dataclass(frozen=UpperCAmelCase_    )
class 	__lowerCamelCase       :





		'''simple docstring'''






		a_			:       Union[str, Any]      =							42
		a_			:       List[str]      =							None
		a_			:       Optional[Any]      =							None
		a_			:       Union[str, Any]      =							None





		def      lowerCamelCase (     self							:			Optional[Any]			):
				return json.dumps(dataclasses.asdict(self			)			) + "\n"







class 	__lowerCamelCase       :





		'''simple docstring'''






		def      lowerCamelCase (     self							:			Optional[Any]		,    a_							:			int			):
				raise NotImplementedError()





		def      lowerCamelCase (     self							:			Any		,    a_							:			List[str]			):
				raise NotImplementedError()





		def      lowerCamelCase (     self							:			Tuple		,    a_							:			List[Any]			):
				raise NotImplementedError()





		def      lowerCamelCase (     self							:			List[Any]		,    a_							:			Union[str, Any]			):
				raise NotImplementedError()





		def      lowerCamelCase (     self							:			int			):
				raise NotImplementedError()





		def      lowerCamelCase (     self							:			str		,    a_							:			Tuple			):
				if len(self.get_labels()			) > 1:
						lowerCAmelCase_     :							Optional[Any]             =    self.get_labels()[int(example.label			)]
				return example





		@classmethod
		def      lowerCamelCase (     cls							:			Union[str, Any]		,    a_							:			List[str]		,    a_							:			List[Any]=None			):
				with open(a_		,    "r"		,    encoding="utf-8-sig"			) as f:
						return list(csv.reader(a_		,    delimiter="\t"		,    quotechar=a_			)			)







class 	__lowerCamelCase       (				UpperCAmelCase_    ):





		'''simple docstring'''






		def __init__(     self							:			Union[str, Any]		,    a_							:			Union[str, Any]=None		,    a_							:			Dict=None		,    a_							:			List[Any]="classification"		,    a_							:			Optional[Any]=False			):
				lowerCAmelCase_     :							List[Any]             =    [] if labels is None else labels
				lowerCAmelCase_     :							Dict             =    [] if examples is None else examples
				lowerCAmelCase_     :							Optional[Any]             =    mode
				lowerCAmelCase_     :							str             =    verbose





		def __len__(     self							:			Optional[Any]			):
				return len(self.examples			)





		def __getitem__(     self							:			Union[str, Any]		,    a_							:			Optional[Any]			):
				if isinstance(a_		,    a_			):
						return SingleSentenceClassificationProcessor(labels=self.labels		,    examples=self.examples[idx]			)
				return self.examples[idx]





		@classmethod
		def      lowerCamelCase (     cls							:			Any		,    a_							:			Any		,    a_							:			Tuple=""		,    a_							:			List[str]=0		,    a_							:			Optional[int]=1		,    a_							:			Dict=None		,    a_							:			Dict=False		,    **a_							:			str			):
				lowerCAmelCase_     :							Optional[Any]             =    cls(**a_			)
				processor.add_examples_from_csv(
				    a_		,    split_name=a_		,    column_label=a_		,    column_text=a_		,    column_id=a_		,    skip_first_row=a_		,    overwrite_labels=a_		,    overwrite_examples=a_		,    )
				return processor





		@classmethod
		def      lowerCamelCase (     cls							:			Tuple		,    a_							:			Tuple		,    a_							:			Dict=None		,    **a_							:			int			):
				lowerCAmelCase_     :							Optional[Any]             =    cls(**a_			)
				processor.add_examples(a_		,    labels=a_			)
				return processor





		def      lowerCamelCase (     self							:			List[str]		,    a_							:			Union[str, Any]		,    a_							:			Optional[Any]=""		,    a_							:			str=0		,    a_							:			Union[str, Any]=1		,    a_							:			Optional[int]=None		,    a_							:			Union[str, Any]=False		,    a_							:			Union[str, Any]=False		,    a_							:			Union[str, Any]=False		,    ):
				lowerCAmelCase_     :							Optional[Any]             =    self._read_tsv(a_			)
				if skip_first_row:
						lowerCAmelCase_     :							List[str]             =    lines[1:]
				lowerCAmelCase_     :							List[str]             =    []
				lowerCAmelCase_     :							Optional[Any]             =    []
				lowerCAmelCase_     :							Optional[int]             =    []
				for i, line in enumerate(a_			):
						texts.append(line[column_text]			)
						labels.append(line[column_label]			)
						if column_id is not None:
								ids.append(line[column_id]			)
						else:
								lowerCAmelCase_     :							Optional[int]             =    f'''{split_name}-{i}''' if split_name else str(a_			)
								ids.append(a_			)

				return self.add_examples(
				    a_		,    a_		,    a_		,    overwrite_labels=a_		,    overwrite_examples=a_			)





		def      lowerCamelCase (     self							:			Optional[Any]		,    a_							:			int		,    a_							:			Optional[int]=None		,    a_							:			Any=None		,    a_							:			str=False		,    a_							:			Any=False			):
				if labels is not None and len(a_			) != len(a_			):
						raise ValueError(
						    f'''Text and labels have mismatched lengths {len(a_			)} and {len(a_			)}'''			)
				if ids is not None and len(a_			) != len(a_			):
						raise ValueError(f'''Text and ids have mismatched lengths {len(a_			)} and {len(a_			)}'''			)
				if ids is None:
						lowerCAmelCase_     :							int             =    [None] * len(a_			)
				if labels is None:
						lowerCAmelCase_     :							List[str]             =    [None] * len(a_			)
				lowerCAmelCase_     :							Tuple             =    []
				lowerCAmelCase_     :							Optional[Any]             =    set()
				for text_or_text_and_label, label, guid in zip(a_		,    a_		,    a_			):
						if isinstance(a_		,    (tuple, list)			) and label is None:
								lowerCAmelCase_       ,			lowerCAmelCase_     :							Any             =    text_or_text_and_label
						else:
								lowerCAmelCase_     :							Union[str, Any]             =    text_or_text_and_label
						added_labels.add(a_			)
						examples.append(InputExample(guid=a_		,    text_a=a_		,    text_b=a_		,    label=a_			)			)

				# Update examples
				if overwrite_examples:
						lowerCAmelCase_     :							Union[str, Any]             =    examples
				else:
						self.examples.extend(a_			)

				# Update labels
				if overwrite_labels:
						lowerCAmelCase_     :							List[Any]             =    list(a_			)
				else:
						lowerCAmelCase_     :							List[str]             =    list(set(self.labels			).union(a_			)			)

				return self.examples





		def      lowerCamelCase (     self							:			List[Any]		,    a_							:			Union[str, Any]		,    a_							:			str=None		,    a_							:			Union[str, Any]=False		,    a_							:			List[str]=0		,    a_							:			Optional[Any]=True		,    a_							:			Tuple=None		,    ):
				if max_length is None:
						lowerCAmelCase_     :							List[str]             =    tokenizer.max_len

				lowerCAmelCase_     :							List[Any]             =    {label: i for i, label in enumerate(self.labels			)}

				lowerCAmelCase_     :							List[Any]             =    []
				for ex_index, example in enumerate(self.examples			):
						if ex_index % 1_00_00 == 0:
								logger.info(f'''Tokenizing example {ex_index}'''			)

						lowerCAmelCase_     :							List[Any]             =    tokenizer.encode(
						    example.text_a		,    add_special_tokens=a_		,    max_length=min(a_		,    tokenizer.max_len			)		,    )
						all_input_ids.append(a_			)

				lowerCAmelCase_     :							Dict             =    max(len(a_			) for input_ids in all_input_ids			)

				lowerCAmelCase_     :							Optional[Any]             =    []
				for ex_index, (input_ids, example) in enumerate(zip(a_		,    self.examples			)			):
						if ex_index % 1_00_00 == 0:
								logger.info(f'''Writing example {ex_index}/{len(self.examples			)}'''			)
						# The mask has 1 for real tokens and 0 for padding tokens. Only real
						# tokens are attended to.
						lowerCAmelCase_     :							Any             =    [1 if mask_padding_with_zero else 0] * len(a_			)

						# Zero-pad up to the sequence length.
						lowerCAmelCase_     :							int             =    batch_length - len(a_			)
						if pad_on_left:
								lowerCAmelCase_     :							List[Any]             =    ([pad_token] * padding_length) + input_ids
								lowerCAmelCase_     :							Dict             =    ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
						else:
								lowerCAmelCase_     :							Any             =    input_ids + ([pad_token] * padding_length)
								lowerCAmelCase_     :							Optional[int]             =    attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

						if len(a_			) != batch_length:
								raise ValueError(f'''Error with input length {len(a_			)} vs {batch_length}'''			)
						if len(a_			) != batch_length:
								raise ValueError(f'''Error with input length {len(a_			)} vs {batch_length}'''			)

						if self.mode == "classification":
								lowerCAmelCase_     :							List[str]             =    label_map[example.label]
						elif self.mode == "regression":
								lowerCAmelCase_     :							Any             =    float(example.label			)
						else:
								raise ValueError(self.mode			)

						if ex_index < 5 and self.verbose:
								logger.info("*** Example ***"			)
								logger.info(f'''guid: {example.guid}'''			)
								logger.info(f'''input_ids: {" ".join([str(a_			) for x in input_ids]			)}'''			)
								logger.info(f'''attention_mask: {" ".join([str(a_			) for x in attention_mask]			)}'''			)
								logger.info(f'''label: {example.label} (id = {label})'''			)

						features.append(InputFeatures(input_ids=a_		,    attention_mask=a_		,    label=a_			)			)

				if return_tensors is None:
						return features
				elif return_tensors == "tf":
						if not is_tf_available():
								raise RuntimeError("return_tensors set to 'tf' but TensorFlow 2.0 can't be imported"			)
						import tensorflow as tf

						def gen():
								for ex in features:
										yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

						lowerCAmelCase_     :							Tuple             =    tf.data.Dataset.from_generator(
						    a_		,    ({"input_ids": tf.intaa, "attention_mask": tf.intaa}, tf.intaa)		,    ({"input_ids": tf.TensorShape([None]			), "attention_mask": tf.TensorShape([None]			)}, tf.TensorShape([]			))		,    )
						return dataset
				elif return_tensors == "pt":
						if not is_torch_available():
								raise RuntimeError("return_tensors set to 'pt' but PyTorch can't be imported"			)
						import torch
						from torch.utils.data import TensorDataset

						lowerCAmelCase_     :							List[str]             =    torch.tensor([f.input_ids for f in features]		,    dtype=torch.long			)
						lowerCAmelCase_     :							Optional[Any]             =    torch.tensor([f.attention_mask for f in features]		,    dtype=torch.long			)
						if self.mode == "classification":
								lowerCAmelCase_     :							int             =    torch.tensor([f.label for f in features]		,    dtype=torch.long			)
						elif self.mode == "regression":
								lowerCAmelCase_     :							Union[str, Any]             =    torch.tensor([f.label for f in features]		,    dtype=torch.float			)

						lowerCAmelCase_     :							Optional[Any]             =    TensorDataset(a_		,    a_		,    a_			)
						return dataset
				else:
						raise ValueError("return_tensors should be one of 'tf' or 'pt'"			)










"""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       (				A__    ):





		'''simple docstring'''






		a_			:       torch.FloatTensor
		a_			:       torch.FloatTensor







def 	__lowerCamelCase			(						__UpperCamelCase   , __UpperCamelCase=0.9_99   , __UpperCamelCase="cosine"   , )		->					Any:

		"""simple docstring"""







		if alpha_transform_type == "cosine":

				def alpha_bar_fn(__UpperCamelCase ):
						return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2 ) ** 2

		elif alpha_transform_type == "exp":

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

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

		lowerCAmelCase_     :							List[str]             =    []
		for i in range(__UpperCamelCase ):
				lowerCAmelCase_     :							int             =    i / num_diffusion_timesteps
				lowerCAmelCase_     :							Optional[Any]             =    (i + 1) / num_diffusion_timesteps
				betas.append(min(1 - alpha_bar_fn(__UpperCamelCase ) / alpha_bar_fn(__UpperCamelCase )   , __UpperCamelCase ) )
		return torch.tensor(__UpperCamelCase   , dtype=torch.floataa )



class 	__lowerCamelCase       (				A__				,							A__    ):





		'''simple docstring'''






		a_			:       Tuple      =							1





		@register_to_config
		def __init__(     self							:			Optional[int]		,    a_							:			int = 10_00		,    a_							:			float = 0.0001		,    a_							:			float = 0.02		,    a_							:			str = "linear"		,    a_							:			float = 0.0		,    a_							:			Optional[np.ndarray] = None		,    a_							:			bool = True		,    ):
				if trained_betas is not None:
						lowerCAmelCase_     :							Tuple             =    torch.from_numpy(a_			)
				elif beta_schedule == "linear":
						lowerCAmelCase_     :							int             =    torch.linspace(a_		,    a_		,    a_		,    dtype=torch.floataa			)
				elif beta_schedule == "scaled_linear":
						# this schedule is very specific to the latent diffusion model.
						lowerCAmelCase_     :							Optional[Any]             =    (
						    torch.linspace(beta_start**0.5		,    beta_end**0.5		,    a_		,    dtype=torch.floataa			) ** 2
						)
				elif beta_schedule == "squaredcos_cap_v2":
						# Glide cosine schedule
						lowerCAmelCase_     :							Tuple             =    betas_for_alpha_bar(a_			)
				elif beta_schedule == "sigmoid":
						# GeoDiff sigmoid schedule
						lowerCAmelCase_     :							List[str]             =    torch.linspace(-6		,    6		,    a_			)
						lowerCAmelCase_     :							int             =    torch.sigmoid(a_			) * (beta_end - beta_start) + beta_start
				else:
						raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''			)

				lowerCAmelCase_     :							Union[str, Any]             =    1.0 - self.betas
				lowerCAmelCase_     :							Optional[int]             =    torch.cumprod(self.alphas		,    dim=0			)
				lowerCAmelCase_     :							int             =    torch.tensor(1.0			)

				lowerCAmelCase_     :							int             =    torch.tensor(1.0			)

				# standard deviation of the initial noise distribution
				lowerCAmelCase_     :							List[str]             =    1.0

				# setable values
				lowerCAmelCase_     :							Dict             =    None
				lowerCAmelCase_     :							List[str]             =    torch.from_numpy(np.arange(0		,    a_			)[::-1].copy()			)

				lowerCAmelCase_     :							int             =    eta





		def      lowerCamelCase (     self							:			Optional[Any]		,    a_							:			torch.FloatTensor		,    a_							:			Optional[int] = None			):
				return sample





		def      lowerCamelCase (     self							:			Dict		,    a_							:			int		,    a_							:			int = 10		,    a_							:			int = 10		,    a_							:			Union[str, torch.device] = None		,    ):
				lowerCAmelCase_     :							Union[str, Any]             =    min(self.config.num_train_timesteps		,    a_			)
				lowerCAmelCase_     :							Dict             =    num_inference_steps

				lowerCAmelCase_     :							List[str]             =    []

				lowerCAmelCase_     :							Dict             =    {}
				for j in range(0		,    num_inference_steps - jump_length		,    a_			):
						lowerCAmelCase_     :							Dict             =    jump_n_sample - 1

				lowerCAmelCase_     :							List[Any]             =    num_inference_steps
				while t >= 1:
						lowerCAmelCase_     :							int             =    t - 1
						timesteps.append(a_			)

						if jumps.get(a_		,    0			) > 0:
								lowerCAmelCase_     :							Optional[int]             =    jumps[t] - 1
								for _ in range(a_			):
										lowerCAmelCase_     :							Any             =    t + 1
										timesteps.append(a_			)

				lowerCAmelCase_     :							List[Any]             =    np.array(a_			) * (self.config.num_train_timesteps // self.num_inference_steps)
				lowerCAmelCase_     :							int             =    torch.from_numpy(a_			).to(a_			)





		def      lowerCamelCase (     self							:			Optional[int]		,    a_							:			Optional[Any]			):
				lowerCAmelCase_     :							Optional[Any]             =    t - self.config.num_train_timesteps // self.num_inference_steps

				lowerCAmelCase_     :							List[Any]             =    self.alphas_cumprod[t]
				lowerCAmelCase_     :							Dict             =    self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
				lowerCAmelCase_     :							List[str]             =    1 - alpha_prod_t
				lowerCAmelCase_     :							str             =    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]
				lowerCAmelCase_     :							Dict             =    (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

				return variance





		def      lowerCamelCase (     self							:			Optional[Any]		,    a_							:			torch.FloatTensor		,    a_							:			int		,    a_							:			torch.FloatTensor		,    a_							:			torch.FloatTensor		,    a_							:			torch.FloatTensor		,    a_							:			Optional[torch.Generator] = None		,    a_							:			bool = True		,    ):
				lowerCAmelCase_     :							Any             =    timestep
				lowerCAmelCase_     :							str             =    timestep - self.config.num_train_timesteps // self.num_inference_steps

				# 1. compute alphas, betas
				lowerCAmelCase_     :							Any             =    self.alphas_cumprod[t]
				lowerCAmelCase_     :							List[str]             =    self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
				lowerCAmelCase_     :							Optional[Any]             =    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
				lowerCAmelCase_     :							List[Any]             =    (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

				# 3. Clip "predicted x_0"
				if self.config.clip_sample:
						lowerCAmelCase_     :							Optional[int]             =    torch.clamp(a_		,    -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
				lowerCAmelCase_     :							Optional[Any]             =    model_output.device
				lowerCAmelCase_     :							List[str]             =    randn_tensor(model_output.shape		,    generator=a_		,    device=a_		,    dtype=model_output.dtype			)
				lowerCAmelCase_     :							List[Any]             =    self.eta * self._get_variance(a_			) ** 0.5

				lowerCAmelCase_     :							Optional[int]             =    0
				if t > 0 and self.eta > 0:
						lowerCAmelCase_     :							Optional[int]             =    std_dev_t * noise

				# 6. compute "direction pointing to x_t" of formula (12)
				# from https://arxiv.org/pdf/2010.02502.pdf
				lowerCAmelCase_     :							Union[str, Any]             =    (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
				lowerCAmelCase_     :							Dict             =    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
				lowerCAmelCase_     :							Union[str, Any]             =    (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
				lowerCAmelCase_     :							Tuple             =    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=a_		,    pred_original_sample=a_			)





		def      lowerCamelCase (     self							:			Dict		,    a_							:			Any		,    a_							:			Dict		,    a_							:			Optional[int]=None			):
				lowerCAmelCase_     :							Dict             =    self.config.num_train_timesteps // self.num_inference_steps

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

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

				return sample





		def      lowerCamelCase (     self							:			Any		,    a_							:			torch.FloatTensor		,    a_							:			torch.FloatTensor		,    a_							:			torch.IntTensor		,    ):
				raise NotImplementedError("Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."			)





		def __len__(     self							:			Union[str, Any]			):
				return self.config.num_train_timesteps
