



'''simple docstring'''


import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter

import requests







def 				__lowerCamelCase				(   A__    ,						A__=None    )       ->	Any:






   """simple docstring"""
   UpperCamelCase					=  None
   if token is not None:
      UpperCamelCase					=  {'Accept': 'application/vnd.github+json', 'Authorization': F"""Bearer {token}"""}

   UpperCamelCase					=  F"""https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"""
   UpperCamelCase					=  requests.get(A__    ,						headers=A__    ).json()
   UpperCamelCase					=  {}

   try:
      job_links.update({job['name']: job['html_url'] for job in result['jobs']}    )
      UpperCamelCase					=  math.ceil((result['total_count'] - 100) / 100    )

      for i in range(A__    ):
         UpperCamelCase					=  requests.get(url + F"""&page={i + 2}"""    ,						headers=A__    ).json()
         job_links.update({job['name']: job['html_url'] for job in result['jobs']}    )

      return job_links
   except Exception:
      print(F"""Unknown error, could not fetch links:\n{traceback.format_exc()}"""    )

   return {}







def 				__lowerCamelCase				(   A__    ,						A__=None    )       ->	Optional[int]:






   """simple docstring"""
   UpperCamelCase					=  None
   if token is not None:
      UpperCamelCase					=  {'Accept': 'application/vnd.github+json', 'Authorization': F"""Bearer {token}"""}

   UpperCamelCase					=  F"""https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100"""
   UpperCamelCase					=  requests.get(A__    ,						headers=A__    ).json()
   UpperCamelCase					=  {}

   try:
      artifacts.update({artifact['name']: artifact['archive_download_url'] for artifact in result['artifacts']}    )
      UpperCamelCase					=  math.ceil((result['total_count'] - 100) / 100    )

      for i in range(A__    ):
         UpperCamelCase					=  requests.get(url + F"""&page={i + 2}"""    ,						headers=A__    ).json()
         artifacts.update({artifact['name']: artifact['archive_download_url'] for artifact in result['artifacts']}    )

      return artifacts
   except Exception:
      print(F"""Unknown error, could not fetch links:\n{traceback.format_exc()}"""    )

   return {}







def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    ,						A__    )       ->	Any:






   """simple docstring"""
   UpperCamelCase					=  None
   if token is not None:
      UpperCamelCase					=  {'Accept': 'application/vnd.github+json', 'Authorization': F"""Bearer {token}"""}

   UpperCamelCase					=  requests.get(A__    ,						headers=A__    ,						allow_redirects=A__    )
   UpperCamelCase					=  result.headers['Location']
   UpperCamelCase					=  requests.get(A__    ,						allow_redirects=A__    )
   UpperCamelCase					=  os.path.join(A__    ,						F"""{artifact_name}.zip"""    )
   with open(A__    ,						'wb'    ) as fp:
      fp.write(response.content    )







def 				__lowerCamelCase				(   A__    ,						A__=None    )       ->	str:






   """simple docstring"""
   UpperCamelCase					=  []
   UpperCamelCase					=  []
   UpperCamelCase					=  None

   with zipfile.ZipFile(A__    ) as z:
      for filename in z.namelist():
         if not os.path.isdir(A__    ):
            # read the file
            if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
               with z.open(A__    ) as f:
                  for line in f:
                     UpperCamelCase					=  line.decode('UTF-8'    ).strip()
                     if filename == "failures_line.txt":
                        try:
                           # `error_line` is the place where `error` occurs
                           UpperCamelCase					=  line[: line.index(': '    )]
                           UpperCamelCase					=  line[line.index(': '    ) + len(': '    ) :]
                           errors.append([error_line, error]    )
                        except Exception:
                           # skip un-related lines
                           pass
                     elif filename == "summary_short.txt" and line.startswith('FAILED '    ):
                        # `test` is the test method that failed
                        UpperCamelCase					=  line[len('FAILED '    ) :]
                        failed_tests.append(A__    )
                     elif filename == "job_name.txt":
                        UpperCamelCase					=  line

   if len(A__    ) != len(A__    ):
      raise ValueError(
          F"""`errors` and `failed_tests` should have the same number of elements. Got {len(A__    )} for `errors` """
          F"""and {len(A__    )} for `failed_tests` instead. The test reports in {artifact_zip_path} have some"""
          ' problem.'    )

   UpperCamelCase					=  None
   if job_name and job_links:
      UpperCamelCase					=  job_links.get(A__    ,						A__    )

   # A list with elements of the form (line of error, error, failed test)
   UpperCamelCase					=  [x + [y] + [job_link] for x, y in zip(A__    ,						A__    )]

   return result







def 				__lowerCamelCase				(   A__    ,						A__=None    )       ->	Tuple:






   """simple docstring"""
   UpperCamelCase					=  []

   UpperCamelCase					=  [os.path.join(A__    ,						A__    ) for p in os.listdir(A__    ) if p.endswith('.zip'    )]
   for p in paths:
      errors.extend(get_errors_from_single_artifact(A__    ,						job_links=A__    )    )

   return errors







def 				__lowerCamelCase				(   A__    ,						A__=None    )       ->	Tuple:






   """simple docstring"""
   UpperCamelCase					=  Counter()
   counter.update([x[1] for x in logs]    )
   UpperCamelCase					=  counter.most_common()
   UpperCamelCase					=  {}
   for error, count in counts:
      if error_filter is None or error not in error_filter:
         UpperCamelCase					=  {'count': count, 'failed_tests': [(x[2], x[0]) for x in logs if x[1] == error]}

   UpperCamelCase					=  dict(sorted(r.items()    ,						key=lambda A__    : item[1]["count"]    ,						reverse=A__    )    )
   return r







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






   """simple docstring"""
   UpperCamelCase					=  test.split('::'    )[0]
   if test.startswith('tests/models/'    ):
      UpperCamelCase					=  test.split('/'    )[2]
   else:
      UpperCamelCase					=  None

   return test







def 				__lowerCamelCase				(   A__    ,						A__=None    )       ->	List[Any]:






   """simple docstring"""
   UpperCamelCase					=  [(x[0], x[1], get_model(x[2]    )) for x in logs]
   UpperCamelCase					=  [x for x in logs if x[2] is not None]
   UpperCamelCase					=  {x[2] for x in logs}

   UpperCamelCase					=  {}
   for test in tests:
      UpperCamelCase					=  Counter()
      # count by errors in `test`
      counter.update([x[1] for x in logs if x[2] == test]    )
      UpperCamelCase					=  counter.most_common()
      UpperCamelCase					=  {error: count for error, count in counts if (error_filter is None or error not in error_filter)}
      UpperCamelCase					=  sum(error_counts.values()    )
      if n_errors > 0:
         UpperCamelCase					=  {'count': n_errors, 'errors': error_counts}

   UpperCamelCase					=  dict(sorted(r.items()    ,						key=lambda A__    : item[1]["count"]    ,						reverse=A__    )    )
   return r







def 				__lowerCamelCase				(   A__    )       ->	Tuple:






   """simple docstring"""
   UpperCamelCase					=  '| no. | error | status |'
   UpperCamelCase					=  '|-:|:-|:-|'
   UpperCamelCase					=  [header, sep]
   for error in reduced_by_error:
      UpperCamelCase					=  reduced_by_error[error]['count']
      UpperCamelCase					=  F"""| {count} | {error[:100]} |  |"""
      lines.append(A__    )

   return "\n".join(A__    )







def 				__lowerCamelCase				(   A__    )       ->	str:






   """simple docstring"""
   UpperCamelCase					=  '| model | no. of errors | major error | count |'
   UpperCamelCase					=  '|-:|-:|-:|-:|'
   UpperCamelCase					=  [header, sep]
   for model in reduced_by_model:
      UpperCamelCase					=  reduced_by_model[model]['count']
      UpperCamelCase				,      UpperCamelCase					=  list(reduced_by_model[model]['errors'].items()    )[0]
      UpperCamelCase					=  F"""| {model} | {count} | {error[:60]} | {_count} |"""
      lines.append(A__    )

   return "\n".join(A__    )


if __name__ == "__main__":
   _lowerCamelCase	:       Any      	=	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.")
   _lowerCamelCase	:       List[str]      	=	parser.parse_args()

   os.makedirs(args.output_dir, exist_ok=True)

   _lowerCamelCase	:       int      	=	get_job_links(args.workflow_run_id, token=args.token)
   _lowerCamelCase	:       int      	=	{}
   # To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee.
   # For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`.
   if _job_links:
      for k, v in _job_links.items():
         # This is how GitHub actions combine job names.
         if " / " in k:
            _lowerCamelCase	:       Optional[int]      	=	k.find(" / ")
            _lowerCamelCase	:       int      	=	k[index + len(" / ") :]
         _lowerCamelCase	:       int      	=	v
   with open(os.path.join(args.output_dir, "job_links.json"), "w", encoding="UTF-8") as fp:
      json.dump(job_links, fp, ensure_ascii=False, indent=4)

   _lowerCamelCase	:       str      	=	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)

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

   _lowerCamelCase	:       Union[str, Any]      	=	get_all_errors(args.output_dir, job_links=job_links)

   # `e[1]` is the error
   _lowerCamelCase	:       Optional[Any]      	=	Counter()
   counter.update([e[1] for e in errors])

   # print the top 30 most common test errors
   _lowerCamelCase	:       Optional[Any]      	=	counter.most_common(30)
   for item in most_common:
      print(item)

   with open(os.path.join(args.output_dir, "errors.json"), "w", encoding="UTF-8") as fp:
      json.dump(errors, fp, ensure_ascii=False, indent=4)

   _lowerCamelCase	:       Optional[int]      	=	reduce_by_error(errors)
   _lowerCamelCase	:       Tuple      	=	reduce_by_model(errors)

   _lowerCamelCase	:       Optional[int]      	=	make_github_table(reduced_by_error)
   _lowerCamelCase	:       Union[str, Any]      	=	make_github_table_per_model(reduced_by_model)

   with open(os.path.join(args.output_dir, "reduced_by_error.txt"), "w", encoding="UTF-8") as fp:
      fp.write(sa)
   with open(os.path.join(args.output_dir, "reduced_by_model.txt"), "w", encoding="UTF-8") as fp:
      fp.write(sa)






'''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  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  42
    _SCREAMING_SNAKE_CASE	  =  None







def 				__lowerCamelCase				(   A__    ,						A__=0.999    ,						A__="cosine"    ,						)       ->	Tuple:






   """simple docstring"""
   if alpha_transform_type == "cosine":

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

   elif alpha_transform_type == "exp":

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

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

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

class  SCREAMING_SNAKE_CASE (  _a   ,						_a		):






    """simple docstring"""





    @register_to_config
    def __init__(  self      :					List[str]  ,						UpperCamelCase__      :					int = 1_0_0_0  ,						UpperCamelCase__      :					str = "fixed_small_log"  ,						UpperCamelCase__      :					bool = True  ,						UpperCamelCase__      :					Optional[float] = 1.0  ,						UpperCamelCase__      :					str = "epsilon"  ,						UpperCamelCase__      :					str = "squaredcos_cap_v2"  ,						):


       """simple docstring"""







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

       UpperCamelCase					=  betas_for_alpha_bar(UpperCamelCase__ )

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

       # standard deviation of the initial noise distribution
       UpperCamelCase					=  1.0

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

       UpperCamelCase					=  variance_type






    def  A       (  self      :					Dict  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					Optional[int] = None ):


       """simple docstring"""







       return sample






    def  A       (  self      :					List[str]  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					Union[str, torch.device] = None ):


       """simple docstring"""







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






    def  A       (  self      :					Dict  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					Union[str, Any]=None  ,						UpperCamelCase__      :					Optional[int]=None  ,						UpperCamelCase__      :					Tuple=None ):


       """simple docstring"""







       if prev_timestep is None:
          UpperCamelCase					=  t - 1

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

       if prev_timestep == t - 1:
          UpperCamelCase					=  self.betas[t]
       else:
          UpperCamelCase					=  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
       UpperCamelCase					=  beta_prod_t_prev / beta_prod_t * beta

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

       # hacks - were probably added for training stability
       if variance_type == "fixed_small_log":
          UpperCamelCase					=  torch.log(torch.clamp(UpperCamelCase__  ,						min=1E-2_0 ) )
          UpperCamelCase					=  torch.exp(0.5 * variance )
       elif variance_type == "learned_range":
          # NOTE difference with DDPM scheduler
          UpperCamelCase					=  variance.log()
          UpperCamelCase					=  beta.log()

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

       return variance






    def  A       (  self      :					int  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					Optional[int] = None  ,						UpperCamelCase__      :					str=None  ,						UpperCamelCase__      :					bool = True  ,						):


       """simple docstring"""







       UpperCamelCase					=  timestep

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

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

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

       if prev_timestep == t - 1:
          UpperCamelCase					=  self.betas[t]
          UpperCamelCase					=  self.alphas[t]
       else:
          UpperCamelCase					=  1 - alpha_prod_t / alpha_prod_t_prev
          UpperCamelCase					=  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":
          UpperCamelCase					=  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
       elif self.config.prediction_type == "sample":
          UpperCamelCase					=  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:
          UpperCamelCase					=  torch.clamp(
              UpperCamelCase__  ,						-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
       UpperCamelCase					=  (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
       UpperCamelCase					=  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
       UpperCamelCase					=  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

          UpperCamelCase					=  self._get_variance(
              UpperCamelCase__  ,						predicted_variance=UpperCamelCase__  ,						prev_timestep=UpperCamelCase__  ,						)

          if self.variance_type == "fixed_small_log":
             UpperCamelCase					=  variance
          elif self.variance_type == "learned_range":
             UpperCamelCase					=  (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.' )

          UpperCamelCase					=  variance * variance_noise

       UpperCamelCase					=  pred_prev_sample + variance

       if not return_dict:
          return (pred_prev_sample,)

       return UnCLIPSchedulerOutput(prev_sample=UpperCamelCase__  ,						pred_original_sample=UpperCamelCase__ )






    def  A       (  self      :					int  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					torch.IntTensor  ,						):


       """simple docstring"""







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

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

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

       UpperCamelCase					=  sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
       return noisy_samples

