


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

import requests



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_=None					)       ->		Optional[Any]:
    _lowercase			:				Dict 						=						None
    if token is not None:
        _lowercase			:				int 						=						{'Accept': 'application/vnd.github+json', 'Authorization': F'''Bearer {token}'''}

    _lowercase			:				Dict 						=						F'''https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'''
    _lowercase			:				Optional[Any] 						=						requests.get(lowerCamelCase_				,					headers=lowerCamelCase_					).json()
    _lowercase			:				str 						=						{}

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

        for i in range(lowerCamelCase_					):
            _lowercase			:				List[str] 						=						requests.get(url + F'''&page={i + 2}'''				,					headers=lowerCamelCase_					).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 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_=None					)       ->		int:
    _lowercase			:				Optional[int] 						=						None
    if token is not None:
        _lowercase			:				Union[str, Any] 						=						{'Accept': 'application/vnd.github+json', 'Authorization': F'''Bearer {token}'''}

    _lowercase			:				Dict 						=						F'''https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100'''
    _lowercase			:				List[Any] 						=						requests.get(lowerCamelCase_				,					headers=lowerCamelCase_					).json()
    _lowercase			:				Any 						=						{}

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

        for i in range(lowerCamelCase_					):
            _lowercase			:				Union[str, Any] 						=						requests.get(url + F'''&page={i + 2}'''				,					headers=lowerCamelCase_					).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 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		Optional[int]:
    _lowercase			:				List[Any] 						=						None
    if token is not None:
        _lowercase			:				str 						=						{'Accept': 'application/vnd.github+json', 'Authorization': F'''Bearer {token}'''}

    _lowercase			:				Dict 						=						requests.get(lowerCamelCase_				,					headers=lowerCamelCase_				,					allow_redirects=lowerCamelCase_					)
    _lowercase			:				Union[str, Any] 						=						result.headers['Location']
    _lowercase			:				Dict 						=						requests.get(lowerCamelCase_				,					allow_redirects=lowerCamelCase_					)
    _lowercase			:				Tuple 						=						os.path.join(lowerCamelCase_				,					F'''{artifact_name}.zip'''					)
    with open(lowerCamelCase_				,					'wb'					) as fp:
        fp.write(response.content					)



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_=None					)       ->		List[str]:
    _lowercase			:				List[str] 						=						[]
    _lowercase			:				List[Any] 						=						[]
    _lowercase			:				Optional[Any] 						=						None

    with zipfile.ZipFile(lowerCamelCase_					) as z:
        for filename in z.namelist():
            if not os.path.isdir(lowerCamelCase_					):
                # read the file
                if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
                    with z.open(lowerCamelCase_					) as f:
                        for line in f:
                            _lowercase			:				str 						=						line.decode('UTF-8'					).strip()
                            if filename == "failures_line.txt":
                                try:
                                    # `error_line` is the place where `error` occurs
                                    _lowercase			:				Dict 						=						line[: line.index(': '					)]
                                    _lowercase			:				Union[str, Any] 						=						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
                                _lowercase			:				Union[str, Any] 						=						line[len('FAILED '					) :]
                                failed_tests.append(lowerCamelCase_					)
                            elif filename == "job_name.txt":
                                _lowercase			:				List[Any] 						=						line

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

    _lowercase			:				Dict 						=						None
    if job_name and job_links:
        _lowercase			:				Tuple 						=						job_links.get(lowerCamelCase_				,					lowerCamelCase_					)

    # A list with elements of the form (line of error, error, failed test)
    _lowercase			:				List[str] 						=						[x + [y] + [job_link] for x, y in zip(lowerCamelCase_				,					lowerCamelCase_					)]

    return result



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_=None					)       ->		str:
    _lowercase			:				Optional[int] 						=						[]

    _lowercase			:				Union[str, Any] 						=						[os.path.join(lowerCamelCase_				,					lowerCamelCase_					) for p in os.listdir(lowerCamelCase_					) if p.endswith('.zip'					)]
    for p in paths:
        errors.extend(get_errors_from_single_artifact(lowerCamelCase_				,					job_links=lowerCamelCase_					)					)

    return errors



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_=None					)       ->		List[Any]:
    _lowercase			:				int 						=						Counter()
    counter.update([x[1] for x in logs]					)
    _lowercase			:				Dict 						=						counter.most_common()
    _lowercase			:				Dict 						=						{}
    for error, count in counts:
        if error_filter is None or error not in error_filter:
            _lowercase			:				Dict 						=						{'count': count, 'failed_tests': [(x[2], x[0]) for x in logs if x[1] == error]}

    _lowercase			:				Union[str, Any] 						=						dict(sorted(r.items()				,					key=lambda lowerCamelCase_					: item[1]["count"]				,					reverse=lowerCamelCase_					)					)
    return r



def 							UpperCamelCase_( lowerCamelCase_					)       ->		Dict:
    _lowercase			:				Optional[int] 						=						test.split('::'					)[0]
    if test.startswith('tests/models/'					):
        _lowercase			:				Tuple 						=						test.split('/'					)[2]
    else:
        _lowercase			:				str 						=						None

    return test



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_=None					)       ->		Optional[Any]:
    _lowercase			:				Dict 						=						[(x[0], x[1], get_model(x[2]					)) for x in logs]
    _lowercase			:				Dict 						=						[x for x in logs if x[2] is not None]
    _lowercase			:				Optional[int] 						=						{x[2] for x in logs}

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

    _lowercase			:				Optional[Any] 						=						dict(sorted(r.items()				,					key=lambda lowerCamelCase_					: item[1]["count"]				,					reverse=lowerCamelCase_					)					)
    return r



def 							UpperCamelCase_( lowerCamelCase_					)       ->		Any:
    _lowercase			:				int 						=						'| no. | error | status |'
    _lowercase			:				int 						=						'|-:|:-|:-|'
    _lowercase			:				Union[str, Any] 						=						[header, sep]
    for error in reduced_by_error:
        _lowercase			:				str 						=						reduced_by_error[error]['count']
        _lowercase			:				Dict 						=						F'''| {count} | {error[:100]} |  |'''
        lines.append(lowerCamelCase_					)

    return "\n".join(lowerCamelCase_					)



def 							UpperCamelCase_( lowerCamelCase_					)       ->		Dict:
    _lowercase			:				str 						=						'| model | no. of errors | major error | count |'
    _lowercase			:				Tuple 						=						'|-:|-:|-:|-:|'
    _lowercase			:				List[Any] 						=						[header, sep]
    for model in reduced_by_model:
        _lowercase			:				str 						=						reduced_by_model[model]['count']
        _lowercase						,							_lowercase			:				Dict 						=						list(reduced_by_model[model]['errors'].items()					)[0]
        _lowercase			:				Dict 						=						F'''| {model} | {count} | {error[:60]} | {_count} |'''
        lines.append(lowerCamelCase_					)

    return "\n".join(lowerCamelCase_					)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE			: Tuple				   =					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.")
 SCREAMING_SNAKE_CASE			: str				   =					parser.parse_args()

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

 SCREAMING_SNAKE_CASE			: Tuple				   =					get_job_links(args.workflow_run_id, token=args.token)
 SCREAMING_SNAKE_CASE			: Dict				   =					{}
 # 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:
    SCREAMING_SNAKE_CASE			: Union[str, Any]				   =					k.find(" / ")
    SCREAMING_SNAKE_CASE			: Union[str, Any]				   =					k[index + len(" / ") :]
   SCREAMING_SNAKE_CASE			: 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)

 SCREAMING_SNAKE_CASE			: Tuple				   =					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)

 SCREAMING_SNAKE_CASE			: Any				   =					get_all_errors(args.output_dir, job_links=job_links)

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

 # print the top 30 most common test errors
 SCREAMING_SNAKE_CASE			: 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)

 SCREAMING_SNAKE_CASE			: Optional[Any]				   =					reduce_by_error(errors)
 SCREAMING_SNAKE_CASE			: Tuple				   =					reduce_by_model(errors)

 SCREAMING_SNAKE_CASE			: Dict				   =					make_github_table(reduced_by_error)
 SCREAMING_SNAKE_CASE			: List[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)






import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


SCREAMING_SNAKE_CASE			: str				   =					"scheduler_config.json"






class _lowerCamelCase(		_a  ):
       lowercase_					:      Any       					=     1
       lowercase_					:      Dict       					=     2
       lowercase_					:      Union[str, Any]       					=     3
       lowercase_					:      Tuple       					=     4
       lowercase_					:      Optional[Any]       					=     5






@dataclass
class _lowerCamelCase(		_a  ):
       lowercase_					:      jnp.ndarray






class _lowerCamelCase:
       lowercase_					:      Union[str, Any]       					=     SCHEDULER_CONFIG_NAME
       lowercase_					:      str       					=     ["""dtype"""]
       lowercase_					:      Dict       					=     []
       lowercase_					:      int       					=     True
       @classmethod
       def      UpperCamelCase	(   cls, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase=False, **lowerCamelCase, )			->  Optional[int]:




           """simple docstring"""

           _lowercase						,							_lowercase			:				Optional[int] 						=						cls.load_config(
               pretrained_model_name_or_path=lowerCamelCase, subfolder=lowerCamelCase, return_unused_kwargs=lowerCamelCase, **lowerCamelCase, )
           _lowercase						,							_lowercase			:				Tuple 						=						cls.from_config(lowerCamelCase, return_unused_kwargs=lowerCamelCase, **lowerCamelCase)

           if hasattr(lowerCamelCase, 'create_state') and getattr(lowerCamelCase, 'has_state', lowerCamelCase):
               _lowercase			:				List[Any] 						=						scheduler.create_state()

           if return_unused_kwargs:
               return scheduler, state, unused_kwargs

           return scheduler, state
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase = False, **lowerCamelCase)			->  Any:




           """simple docstring"""

           self.save_config(save_directory=lowerCamelCase, push_to_hub=lowerCamelCase, **lowerCamelCase)
       @property
       def      UpperCamelCase	(   self)			->  Union[str, Any]:




           """simple docstring"""

           return self._get_compatibles()




       @classmethod
       def      UpperCamelCase	(   cls)			->  Any:




           """simple docstring"""

           _lowercase			:				Any 						=						list(set([cls.__name__] + cls._compatibles))
           _lowercase			:				Dict 						=						importlib.import_module(__name__.split('.')[0])
           _lowercase			:				Any 						=						[
               getattr(lowerCamelCase, lowerCamelCase) for c in compatible_classes_str if hasattr(lowerCamelCase, lowerCamelCase)
           ]
           return compatible_classes



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_					)       ->		jnp.ndarray:
    assert len(lowerCamelCase_					) >= x.ndim
    return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowerCamelCase_					) - x.ndim)					)				,					lowerCamelCase_					)



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_=0.9_99				,					lowerCamelCase_=jnp.floataa					)       ->		jnp.ndarray:

    def alpha_bar(lowerCamelCase_					):
        return math.cos((time_step + 0.0_08) / 1.0_08 * math.pi / 2					) ** 2

    _lowercase			:				List[Any] 						=						[]
    for i in range(lowerCamelCase_					):
        _lowercase			:				Any 						=						i / num_diffusion_timesteps
        _lowercase			:				Any 						=						(i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar(lowerCamelCase_					) / alpha_bar(lowerCamelCase_					)				,					lowerCamelCase_					)					)
    return jnp.array(lowerCamelCase_				,					dtype=lowerCamelCase_					)






@flax.struct.dataclass
class _lowerCamelCase:
       lowercase_					:      jnp.ndarray
       lowercase_					:      jnp.ndarray
       lowercase_					:      jnp.ndarray
       @classmethod
       def      UpperCamelCase	(   cls, lowerCamelCase)			->  str:




           """simple docstring"""

           _lowercase			:				int 						=						scheduler.config

           if config.trained_betas is not None:
               _lowercase			:				str 						=						jnp.asarray(config.trained_betas, dtype=scheduler.dtype)
           elif config.beta_schedule == "linear":
               _lowercase			:				List[Any] 						=						jnp.linspace(config.beta_start, config.beta_end, config.num_train_timesteps, dtype=scheduler.dtype)
           elif config.beta_schedule == "scaled_linear":
               # this schedule is very specific to the latent diffusion model.
               _lowercase			:				Dict 						=						(
                   jnp.linspace(
                       config.beta_start**0.5, config.beta_end**0.5, config.num_train_timesteps, dtype=scheduler.dtype)
                   ** 2
               )
           elif config.beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               _lowercase			:				Optional[int] 						=						betas_for_alpha_bar(config.num_train_timesteps, dtype=scheduler.dtype)
           else:
               raise NotImplementedError(
                   F'''beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}''')

           _lowercase			:				List[str] 						=						1.0 - betas

           _lowercase			:				Union[str, Any] 						=						jnp.cumprod(lowerCamelCase, axis=0)

           return cls(
               alphas=lowerCamelCase, betas=lowerCamelCase, alphas_cumprod=lowerCamelCase, )



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		int:
    _lowercase			:				str 						=						state.alphas_cumprod

    _lowercase			:				str 						=						alphas_cumprod[timesteps] ** 0.5
    _lowercase			:				Optional[Any] 						=						sqrt_alpha_prod.flatten()
    _lowercase			:				Tuple 						=						broadcast_to_shape_from_left(lowerCamelCase_				,					original_samples.shape					)

    _lowercase			:				Union[str, Any] 						=						(1 - alphas_cumprod[timesteps]) ** 0.5
    _lowercase			:				Optional[Any] 						=						sqrt_one_minus_alpha_prod.flatten()
    _lowercase			:				int 						=						broadcast_to_shape_from_left(lowerCamelCase_				,					original_samples.shape					)

    return sqrt_alpha_prod, sqrt_one_minus_alpha_prod



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		List[str]:
    _lowercase						,							_lowercase			:				Optional[int] 						=						get_sqrt_alpha_prod(lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)
    _lowercase			:				str 						=						sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
    return noisy_samples



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		Tuple:
    _lowercase						,							_lowercase			:				Tuple 						=						get_sqrt_alpha_prod(lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)
    _lowercase			:				List[Any] 						=						sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
    return velocity


