

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       SCREAMING_SNAKE_CASE__					(__snake_case     ):
    __lowerCamelCase						:       torch.FloatTensor
    __lowerCamelCase						:       Optional[torch.FloatTensor]														=     None



def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    List[str]	,     SCREAMING_SNAKE_CASE_		:    Any=0.999	,     SCREAMING_SNAKE_CASE_		:    Union[str, Any]="cosine"	,     ):



     '''simple docstring'''





     if alpha_transform_type == "cosine":

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

     elif alpha_transform_type == "exp":

          def alpha_bar_fn(SCREAMING_SNAKE_CASE_		:    Union[str, Any]			):
               return math.exp(t * -12.0			)

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

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


class       SCREAMING_SNAKE_CASE__					(__snake_case		,       __snake_case     ):





    @register_to_config
    def __init__(      self						,   a = 1000						,   a = "fixed_small_log"						,   a = True						,   a = 1.0						,   a = "epsilon"						,   a = "squaredcos_cap_v2"						,   ):
         if beta_schedule != "squaredcos_cap_v2":
              raise ValueError('UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\'')

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

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

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

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

         lowercase__    :   Union[str, Any]      =       variance_type





    def       snake_case_				(      self						,   a						,   a = None):
         return sample





    def       snake_case_				(      self						,   a						,   a = None):
         lowercase__    :   int      =       num_inference_steps
         lowercase__    :   Union[str, Any]      =       (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
         lowercase__    :   str      =       (np.arange(0						,   a) * step_ratio).round()[::-1].copy().astype(np.intaa)
         lowercase__    :   int      =       torch.from_numpy(a).to(a)





    def       snake_case_				(      self						,   a						,   a=None						,   a=None						,   a=None):
         if prev_timestep is None:
              lowercase__    :   Optional[int]      =       t - 1

         lowercase__    :   Tuple      =       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__    :   str      =       1 - alpha_prod_t_prev

         if prev_timestep == t - 1:
              lowercase__    :   Any      =       self.betas[t]
         else:
              lowercase__    :   Union[str, Any]      =       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__    :   Optional[int]      =       beta_prod_t_prev / beta_prod_t * beta

         if variance_type is None:
              lowercase__    :   Tuple      =       self.config.variance_type

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

              lowercase__    :   int      =       (predicted_variance + 1) / 2
              lowercase__    :   List[Any]      =       frac * max_log + (1 - frac) * min_log

         return variance





    def       snake_case_				(      self						,   a						,   a						,   a						,   a = None						,   a=None						,   a = True						,   ):
         lowercase__    :   int      =       timestep

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

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

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

         if prev_timestep == t - 1:
              lowercase__    :   Optional[Any]      =       self.betas[t]
              lowercase__    :   Dict      =       self.alphas[t]
         else:
              lowercase__    :   str      =       1 - alpha_prod_t / alpha_prod_t_prev
              lowercase__    :   str      =       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__    :   List[Any]      =       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__    :   Optional[int]      =       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
         lowercase__    :   List[Any]      =       (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
         lowercase__    :   Union[str, Any]      =       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__    :   int      =       pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

         # 6. Add noise
         lowercase__    :   Dict      =       0
         if t > 0:
              lowercase__    :   str      =       randn_tensor(
                  model_output.shape						,   dtype=model_output.dtype						,   generator=a						,   device=model_output.device)

              lowercase__    :   str      =       self._get_variance(
                  a						,   predicted_variance=a						,   prev_timestep=a						,   )

              if self.variance_type == "fixed_small_log":
                   lowercase__    :   Optional[Any]      =       variance
              elif self.variance_type == "learned_range":
                   lowercase__    :   Optional[Any]      =       (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__    :   Any      =       variance * variance_noise

         lowercase__    :   Any      =       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						,   a						,   a						,   a						,   ):
         # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
         lowercase__    :   Dict      =       self.alphas_cumprod.to(device=original_samples.device						,   dtype=original_samples.dtype)
         lowercase__    :   str      =       timesteps.to(original_samples.device)

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

         lowercase__    :   Union[str, Any]      =       sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
         return noisy_samples




import argparse
import json
import os
import time
import zipfile

from get_ci_error_statistics import download_artifact, get_artifacts_links

from transformers import logging


snake_case_			  = logging.get_logger(__name__)

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Tuple	,     SCREAMING_SNAKE_CASE_		:    Union[str, Any]			):



     '''simple docstring'''





     lowercase__    :   List[str]      =       set()
     lowercase__    :   List[str]      =       []

     def parse_line(SCREAMING_SNAKE_CASE_		:    Union[str, Any]			):
          for line in fp:
               if isinstance(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_			):
                    lowercase__    :   Optional[int]      =       line.decode('UTF-8'			)
               if "warnings summary (final)" in line:
                    continue
               # This means we are outside the body of a warning
               elif not line.startswith(' '			):
                    # process a single warning and move it to `selected_warnings`.
                    if len(SCREAMING_SNAKE_CASE_			) > 0:
                         lowercase__    :   Optional[Any]      =       '\n'.join(SCREAMING_SNAKE_CASE_			)
                         # Only keep the warnings specified in `targets`
                         if any(f""": {x}: """ in warning for x in targets			):
                              selected_warnings.add(SCREAMING_SNAKE_CASE_			)
                         buffer.clear()
                    continue
               else:
                    lowercase__    :   Optional[Any]      =       line.strip()
                    buffer.append(SCREAMING_SNAKE_CASE_			)

     if from_gh:
          for filename in os.listdir(SCREAMING_SNAKE_CASE_			):
               lowercase__    :   int      =       os.path.join(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_			)
               if not os.path.isdir(SCREAMING_SNAKE_CASE_			):
                    # read the file
                    if filename != "warnings.txt":
                         continue
                    with open(SCREAMING_SNAKE_CASE_			) as fp:
                         parse_line(SCREAMING_SNAKE_CASE_			)
     else:
          try:
               with zipfile.ZipFile(SCREAMING_SNAKE_CASE_			) as z:
                    for filename in z.namelist():
                         if not os.path.isdir(SCREAMING_SNAKE_CASE_			):
                              # read the file
                              if filename != "warnings.txt":
                                   continue
                              with z.open(SCREAMING_SNAKE_CASE_			) as fp:
                                   parse_line(SCREAMING_SNAKE_CASE_			)
          except Exception:
               logger.warning(
                   f"""{artifact_path} is either an invalid zip file or something else wrong. This file is skipped."""			)

     return selected_warnings

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    List[Any]	,     SCREAMING_SNAKE_CASE_		:    Optional[Any]			):



     '''simple docstring'''





     lowercase__    :   Optional[Any]      =       set()

     lowercase__    :   List[str]      =       [os.path.join(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_			) for p in os.listdir(SCREAMING_SNAKE_CASE_			) if (p.endswith('.zip'			) or from_gh)]
     for p in paths:
          selected_warnings.update(extract_warnings_from_single_artifact(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_			)			)

     return selected_warnings


if __name__ == "__main__":

       def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Any			):



            '''simple docstring'''





            return values.split(','			)

       snake_case_			  = argparse.ArgumentParser()
       # Required parameters
       parser.add_argument('''--workflow_run_id''', type=str, required=True, help='''A GitHub Actions workflow run id.''')
       parser.add_argument(
           '''--output_dir''',
           type=str,
           required=True,
           help='''Where to store the downloaded artifacts and other result files.''',
       )
       parser.add_argument('''--token''', default=None, type=str, help='''A token that has actions:read permission.''')
       # optional parameters
       parser.add_argument(
           '''--targets''',
           default='''DeprecationWarning,UserWarning,FutureWarning''',
           type=list_str,
           help='''Comma-separated list of target warning(s) which we want to extract.''',
       )
       parser.add_argument(
           '''--from_gh''',
           action='''store_true''',
           help='''If running from a GitHub action workflow and collecting warnings from its artifacts.''',
       )

       snake_case_			  = parser.parse_args()

       snake_case_			  = args.from_gh
       if from_gh:
              # The artifacts have to be downloaded using `actions/download-artifact@v3`
              pass
       else:
              os.makedirs(args.output_dir, exist_ok=True)

              # get download links
              snake_case_			  = get_artifacts_links(args.workflow_run_id, token=args.token)
              with open(os.path.join(args.output_dir, '''artifacts.json'''), '''w''', encoding='''UTF-8''') as fp:
                     json.dump(artifacts, fp, ensure_ascii=False, indent=4)

              # download artifacts
              for idx, (name, url) in enumerate(artifacts.items()):
                     print(name)
                     print(url)
                     print('''=''' * 80)
                     download_artifact(name, url, args.output_dir, args.token)
                     # Be gentle to GitHub
                     time.sleep(1)

    # extract warnings from artifacts
       snake_case_			  = extract_warnings(args.output_dir, args.targets)
       snake_case_			  = sorted(selected_warnings)
       with open(os.path.join(args.output_dir, '''selected_warnings.json'''), '''w''', encoding='''UTF-8''') as fp:
              json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)

