import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal


__a:			Any	       =       datasets.utils.logging.get_logger(__name__)

__a:			Tuple	       =       ["""names""", """prefix"""]
__a:			Dict	       =       ["""warn_bad_lines""", """error_bad_lines""", """mangle_dupe_cols"""]
__a:			Optional[Any]	       =       ["""encoding_errors""", """on_bad_lines"""]
__a:			Union[str, Any]	       =       ["""date_format"""]



@dataclass
class       UpperCAmelCase  (						datasets.BuilderConfig ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   ","
       SCREAMING_SNAKE_CASE 		=   None
       SCREAMING_SNAKE_CASE 		=   "infer"
       SCREAMING_SNAKE_CASE 		=   None
       SCREAMING_SNAKE_CASE 		=   None
       SCREAMING_SNAKE_CASE 		=   None
       SCREAMING_SNAKE_CASE 		=   None
       SCREAMING_SNAKE_CASE 		=   None
       SCREAMING_SNAKE_CASE 		=   True
       SCREAMING_SNAKE_CASE 		=   None
       SCREAMING_SNAKE_CASE 		=   None
       SCREAMING_SNAKE_CASE 		=   None
       SCREAMING_SNAKE_CASE 		=   None
       SCREAMING_SNAKE_CASE 		=   False
       SCREAMING_SNAKE_CASE 		=   None
       SCREAMING_SNAKE_CASE 		=   None
       SCREAMING_SNAKE_CASE 		=   None
       SCREAMING_SNAKE_CASE 		=   True
       SCREAMING_SNAKE_CASE 		=   True
       SCREAMING_SNAKE_CASE 		=   False
       SCREAMING_SNAKE_CASE 		=   True
       SCREAMING_SNAKE_CASE 		=   None
       SCREAMING_SNAKE_CASE 		=   "."
       SCREAMING_SNAKE_CASE 		=   None
       SCREAMING_SNAKE_CASE 		=   "\""
       SCREAMING_SNAKE_CASE 		=   0
       SCREAMING_SNAKE_CASE 		=   None
       SCREAMING_SNAKE_CASE 		=   None
       SCREAMING_SNAKE_CASE 		=   None
       SCREAMING_SNAKE_CASE 		=   None
       SCREAMING_SNAKE_CASE 		=   True
       SCREAMING_SNAKE_CASE 		=   True
       SCREAMING_SNAKE_CASE 		=   0
       SCREAMING_SNAKE_CASE 		=   True
       SCREAMING_SNAKE_CASE 		=   False
       SCREAMING_SNAKE_CASE 		=   None
       SCREAMING_SNAKE_CASE 		=   1_0_0_0_0
       SCREAMING_SNAKE_CASE 		=   None
       SCREAMING_SNAKE_CASE 		=   "strict"
       SCREAMING_SNAKE_CASE 		=   "error"
       SCREAMING_SNAKE_CASE 		=   None

       def _lowerCAmelCase(						self			)  ->  List[Any]:
        if self.delimiter is not None:
         lowercase__    :   int						= self.delimiter
        if self.column_names is not None:
         lowercase__    :   str						= self.column_names


       @property
       def _lowerCAmelCase(						self			)  ->  Union[str, Any]:
        lowercase__    :   Optional[int]						= {
            '''sep''': self.sep,
            '''header''': self.header,
            '''names''': self.names,
            '''index_col''': self.index_col,
            '''usecols''': self.usecols,
            '''prefix''': self.prefix,
            '''mangle_dupe_cols''': self.mangle_dupe_cols,
            '''engine''': self.engine,
            '''converters''': self.converters,
            '''true_values''': self.true_values,
            '''false_values''': self.false_values,
            '''skipinitialspace''': self.skipinitialspace,
            '''skiprows''': self.skiprows,
            '''nrows''': self.nrows,
            '''na_values''': self.na_values,
            '''keep_default_na''': self.keep_default_na,
            '''na_filter''': self.na_filter,
            '''verbose''': self.verbose,
            '''skip_blank_lines''': self.skip_blank_lines,
            '''thousands''': self.thousands,
            '''decimal''': self.decimal,
            '''lineterminator''': self.lineterminator,
            '''quotechar''': self.quotechar,
            '''quoting''': self.quoting,
            '''escapechar''': self.escapechar,
            '''comment''': self.comment,
            '''encoding''': self.encoding,
            '''dialect''': self.dialect,
            '''error_bad_lines''': self.error_bad_lines,
            '''warn_bad_lines''': self.warn_bad_lines,
            '''skipfooter''': self.skipfooter,
            '''doublequote''': self.doublequote,
            '''memory_map''': self.memory_map,
            '''float_precision''': self.float_precision,
            '''chunksize''': self.chunksize,
            '''encoding_errors''': self.encoding_errors,
            '''on_bad_lines''': self.on_bad_lines,
            '''date_format''': self.date_format,
        }

        # some kwargs must not be passed if they don't have a default value
        # some others are deprecated and we can also not pass them if they are the default value
        for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
         if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig()     ,       __lowerCAmelCase			):
          del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 2.0 new arguments
        if not (datasets.config.PANDAS_VERSION.major >= 2):
         for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
          del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 1.3 new arguments
        if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
         for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
          del pd_read_csv_kwargs[pd_read_csv_parameter]

        return pd_read_csv_kwargs



class       UpperCAmelCase  (						datasets.ArrowBasedBuilder ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   CsvConfig

       def _lowerCAmelCase(						self			)  ->  Union[str, Any]:
        return datasets.DatasetInfo(features=self.config.features			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  List[Any]:
        if not self.config.data_files:
         raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}"""			)
        lowercase__    :   Optional[int]						= dl_manager.download_and_extract(self.config.data_files			)
        if isinstance(__lowerCAmelCase     ,       (str, list, tuple)			):
         lowercase__    :   int						= data_files
         if isinstance(__lowerCAmelCase     ,       __lowerCAmelCase			):
          lowercase__    :   Any						= [files]
         lowercase__    :   Optional[Any]						= [dl_manager.iter_files(__lowerCAmelCase			) for file in files]
         return [datasets.SplitGenerator(name=datasets.Split.TRAIN     ,       gen_kwargs={'''files''': files}			)]
        lowercase__    :   Union[str, Any]						= []
        for split_name, files in data_files.items():
         if isinstance(__lowerCAmelCase     ,       __lowerCAmelCase			):
          lowercase__    :   Optional[int]						= [files]
         lowercase__    :   List[str]						= [dl_manager.iter_files(__lowerCAmelCase			) for file in files]
         splits.append(datasets.SplitGenerator(name=__lowerCAmelCase     ,       gen_kwargs={'''files''': files}			)			)
        return splits

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  pa.Table:
        if self.config.features is not None:
         lowercase__    :   str						= self.config.features.arrow_schema
         if all(not require_storage_cast(__lowerCAmelCase			) for feature in self.config.features.values()			):
          # cheaper cast
          lowercase__    :   Optional[Any]						= pa.Table.from_arrays([pa_table[field.name] for field in schema]     ,       schema=__lowerCAmelCase			)
         else:
          # more expensive cast; allows str <-> int/float or str to Audio for example
          lowercase__    :   Union[str, Any]						= table_cast(__lowerCAmelCase     ,       __lowerCAmelCase			)
        return pa_table


       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  Optional[Any]:
        lowercase__    :   Tuple						= self.config.features.arrow_schema if self.config.features else None
        # dtype allows reading an int column as str
        lowercase__    :   str						= (
            {
                name: dtype.to_pandas_dtype() if not require_storage_cast(__lowerCAmelCase			) else object
                for name, dtype, feature in zip(schema.names     ,       schema.types     ,       self.config.features.values()			)
            }
            if schema is not None
            else None
        )
        for file_idx, file in enumerate(itertools.chain.from_iterable(__lowerCAmelCase			)			):
         lowercase__    :   Optional[Any]						= pd.read_csv(__lowerCAmelCase     ,       iterator=__lowerCAmelCase     ,       dtype=__lowerCAmelCase     ,       **self.config.pd_read_csv_kwargs			)
         try:
          for batch_idx, df in enumerate(__lowerCAmelCase			):
           lowercase__    :   List[str]						= pa.Table.from_pandas(__lowerCAmelCase			)
           # Uncomment for debugging (will print the Arrow table size and elements)
           # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
           # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
           yield (file_idx, batch_idx), self._cast_table(__lowerCAmelCase			)
         except ValueError as e:
          logger.error(F"""Failed to read file '{file}' with error {type(__lowerCAmelCase			)}: {e}"""			)
          raise


'''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       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   42
       SCREAMING_SNAKE_CASE 		=   None


def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase=0.9_9_9		,					UpperCAmelCase="cosine"		,					):
 if alpha_transform_type == "cosine":

  def alpha_bar_fn(UpperCAmelCase					):
   return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2					) ** 2

 elif alpha_transform_type == "exp":

  def alpha_bar_fn(UpperCAmelCase					):
   return math.exp(t * -1_2.0					)

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

 lowercase__    :   str						= []
 for i in range(UpperCAmelCase					):
  lowercase__    :   int						= i / num_diffusion_timesteps
  lowercase__    :   Tuple						= (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       UpperCAmelCase  (						a__   ,						a__ ):





       '''simple docstring'''



       @register_to_config
       def __init__(						self     ,       __lowerCAmelCase = 1000     ,       __lowerCAmelCase = "fixed_small_log"     ,       __lowerCAmelCase = True     ,       __lowerCAmelCase = 1.0     ,       __lowerCAmelCase = "epsilon"     ,       __lowerCAmelCase = "squaredcos_cap_v2"     ,       )  ->  Optional[int]:
        if beta_schedule != "squaredcos_cap_v2":
         raise ValueError('''UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''''			)

        lowercase__    :   Union[str, Any]						= betas_for_alpha_bar(__lowerCAmelCase			)

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

        # standard deviation of the initial noise distribution
        lowercase__    :   Optional[Any]						= 1.0

        # setable values
        lowercase__    :   Optional[Any]						= None
        lowercase__    :   List[Any]						= torch.from_numpy(np.arange(0     ,       __lowerCAmelCase			)[::-1].copy()			)

        lowercase__    :   Tuple						= variance_type

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None			)  ->  torch.FloatTensor:
        return sample

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None			)  ->  Optional[int]:
        lowercase__    :   List[str]						= num_inference_steps
        lowercase__    :   List[str]						= (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
        lowercase__    :   List[str]						= (np.arange(0     ,       __lowerCAmelCase			) * step_ratio).round()[::-1].copy().astype(np.intaa			)
        lowercase__    :   str						= torch.from_numpy(__lowerCAmelCase			).to(__lowerCAmelCase			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase=None			)  ->  Tuple:
        if prev_timestep is None:
         lowercase__    :   Any						= t - 1

        lowercase__    :   Any						= self.alphas_cumprod[t]
        lowercase__    :   List[Any]						= self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
        lowercase__    :   str						= 1 - alpha_prod_t
        lowercase__    :   int						= 1 - alpha_prod_t_prev

        if prev_timestep == t - 1:
         lowercase__    :   Tuple						= self.betas[t]
        else:
         lowercase__    :   Dict						= 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
        lowercase__    :   Any						= beta_prod_t_prev / beta_prod_t * beta

        if variance_type is None:
         lowercase__    :   Union[str, Any]						= self.config.variance_type

        # hacks - were probably added for training stability
        if variance_type == "fixed_small_log":
         lowercase__    :   int						= torch.log(torch.clamp(__lowerCAmelCase     ,       min=1E-20			)			)
         lowercase__    :   Dict						= torch.exp(0.5 * variance			)
        elif variance_type == "learned_range":
         # NOTE difference with DDPM scheduler
         lowercase__    :   Union[str, Any]						= variance.log()
         lowercase__    :   Optional[int]						= beta.log()

         lowercase__    :   Tuple						= (predicted_variance + 1) / 2
         lowercase__    :   Dict						= frac * max_log + (1 - frac) * min_log

        return variance

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase = True     ,       )  ->  Union[UnCLIPSchedulerOutput, Tuple]:
        lowercase__    :   Tuple						= timestep

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

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

        lowercase__    :   Optional[int]						= self.alphas_cumprod[t]
        lowercase__    :   Optional[int]						= self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
        lowercase__    :   Optional[int]						= 1 - alpha_prod_t
        lowercase__    :   List[Any]						= 1 - alpha_prod_t_prev

        if prev_timestep == t - 1:
         lowercase__    :   Optional[int]						= self.betas[t]
         lowercase__    :   Optional[Any]						= self.alphas[t]
        else:
         lowercase__    :   Any						= 1 - alpha_prod_t / alpha_prod_t_prev
         lowercase__    :   Optional[int]						= 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":
         lowercase__    :   int						= (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
        elif self.config.prediction_type == "sample":
         lowercase__    :   Dict						= 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:
         lowercase__    :   List[Any]						= torch.clamp(
             __lowerCAmelCase     ,       -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
        lowercase__    :   Optional[Any]						= (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
        lowercase__    :   Tuple						= 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
        lowercase__    :   List[Any]						= pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

        # 6. Add noise
        lowercase__    :   List[Any]						= 0
        if t > 0:
         lowercase__    :   Dict						= randn_tensor(
             model_output.shape     ,       dtype=model_output.dtype     ,       generator=__lowerCAmelCase     ,       device=model_output.device			)

         lowercase__    :   Union[str, Any]						= self._get_variance(
             __lowerCAmelCase     ,       predicted_variance=__lowerCAmelCase     ,       prev_timestep=__lowerCAmelCase     ,       )

         if self.variance_type == "fixed_small_log":
          lowercase__    :   List[Any]						= variance
         elif self.variance_type == "learned_range":
          lowercase__    :   int						= (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.'''			)

         lowercase__    :   List[str]						= variance * variance_noise

        lowercase__    :   Union[str, Any]						= pred_prev_sample + variance

        if not return_dict:
         return (pred_prev_sample,)

        return UnCLIPSchedulerOutput(prev_sample=__lowerCAmelCase     ,       pred_original_sample=__lowerCAmelCase			)


       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       )  ->  torch.FloatTensor:
        # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
        lowercase__    :   Tuple						= self.alphas_cumprod.to(device=original_samples.device     ,       dtype=original_samples.dtype			)
        lowercase__    :   str						= timesteps.to(original_samples.device			)

        lowercase__    :   Union[str, Any]						= alphas_cumprod[timesteps] ** 0.5
        lowercase__    :   List[str]						= sqrt_alpha_prod.flatten()
        while len(sqrt_alpha_prod.shape			) < len(original_samples.shape			):
         lowercase__    :   List[str]						= sqrt_alpha_prod.unsqueeze(-1			)

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

        lowercase__    :   Optional[int]						= sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
        return noisy_samples

