



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 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Tuple    =    set()
							_lowerCAmelCase     :							int    =    []

							def parse_line(_lowerCamelCase  ):
														for line in fp:
																					if isinstance(_lowerCamelCase				, _lowerCamelCase  ):
																												_lowerCAmelCase     :							List[str]    =    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(_lowerCamelCase  ) > 0:
																																			_lowerCAmelCase     :							Any    =    "\n".join(_lowerCamelCase  )
																																			# Only keep the warnings specified in `targets`
																																			if any(F": {x}: " in warning for x in targets  ):
																																										selected_warnings.add(_lowerCamelCase  )
																																			buffer.clear()
																												continue
																					else:
																												_lowerCAmelCase     :							Tuple    =    line.strip()
																												buffer.append(_lowerCamelCase  )

							if from_gh:
														for filename in os.listdir(_lowerCamelCase  ):
																					_lowerCAmelCase     :							Dict    =    os.path.join(_lowerCamelCase				, _lowerCamelCase  )
																					if not os.path.isdir(_lowerCamelCase  ):
																												# read the file
																												if filename != "warnings.txt":
																																			continue
																												with open(_lowerCamelCase  ) as fp:
																																			parse_line(_lowerCamelCase  )
							else:
														try:
																					with zipfile.ZipFile(_lowerCamelCase  ) as z:
																												for filename in z.namelist():
																																			if not os.path.isdir(_lowerCamelCase  ):
																																										# read the file
																																										if filename != "warnings.txt":
																																																	continue
																																										with z.open(_lowerCamelCase  ) as fp:
																																																	parse_line(_lowerCamelCase  )
														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 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Optional[Any]    =    set()

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

							return selected_warnings


if __name__ == "__main__":






						def 				A				(					_lowerCamelCase  ):





													'''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)





from __future__ import absolute_import, division, print_function, unicode_literals

from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
    ROBERTA_INPUTS_DOCSTRING,
    ROBERTA_START_DOCSTRING,
    RobertaEmbeddings,
)

from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy



@add_start_docstrings(
    'The RoBERTa Model transformer with early exiting (DeeRoBERTa). '   ,					a   ,					)
class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		RobertaConfig
							lowerCamelCase__   			=		'roberta'







							def __init__(   self,			__a):

														'''simple docstring'''

														super().__init__(__a)

														_lowerCAmelCase     :							Optional[Any]    =    RobertaEmbeddings(__a)
														self.init_weights()



@add_start_docstrings(
    'RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,\n    also takes care of multi-layer training. '   ,					a   ,					)
class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		RobertaConfig
							lowerCamelCase__   			=		'roberta'







							def __init__(   self,			__a):

														'''simple docstring'''

														super().__init__(__a)
														_lowerCAmelCase     :							Optional[int]    =    config.num_labels
														_lowerCAmelCase     :							Optional[int]    =    config.num_hidden_layers

														_lowerCAmelCase     :							Optional[int]    =    DeeRobertaModel(__a)
														_lowerCAmelCase     :							Union[str, Any]    =    nn.Dropout(config.hidden_dropout_prob)
														_lowerCAmelCase     :							List[str]    =    nn.Linear(config.hidden_size,			self.config.num_labels)







							@add_start_docstrings_to_model_forward(__a)
							def    snake_case__      (   self,			__a=None,			__a=None,			__a=None,			__a=None,			__a=None,			__a=None,			__a=None,			__a=-1,			__a=False,			):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    self.num_layers
														try:
																					_lowerCAmelCase     :							List[Any]    =    self.roberta(
																					    __a,			attention_mask=__a,			token_type_ids=__a,			position_ids=__a,			head_mask=__a,			inputs_embeds=__a,			)

																					_lowerCAmelCase     :							List[Any]    =    outputs[1]

																					_lowerCAmelCase     :							Dict    =    self.dropout(__a)
																					_lowerCAmelCase     :							Dict    =    self.classifier(__a)
																					_lowerCAmelCase     :							Optional[Any]    =    (logits,) + outputs[2:]  # add hidden states and attention if they are here
														except HighwayException as e:
																					_lowerCAmelCase     :							Tuple    =    e.message
																					_lowerCAmelCase     :							Union[str, Any]    =    e.exit_layer
																					_lowerCAmelCase     :							List[Any]    =    outputs[0]

														if not self.training:
																					_lowerCAmelCase     :							int    =    entropy(__a)
																					_lowerCAmelCase     :							List[Any]    =    []
																					_lowerCAmelCase     :							str    =    []
														if labels is not None:
																					if self.num_labels == 1:
																												#  We are doing regression
																												_lowerCAmelCase     :							Optional[Any]    =    MSELoss()
																												_lowerCAmelCase     :							int    =    loss_fct(logits.view(-1),			labels.view(-1))
																					else:
																												_lowerCAmelCase     :							Optional[Any]    =    CrossEntropyLoss()
																												_lowerCAmelCase     :							Optional[Any]    =    loss_fct(logits.view(-1,			self.num_labels),			labels.view(-1))

																					# work with highway exits
																					_lowerCAmelCase     :							Optional[int]    =    []
																					for highway_exit in outputs[-1]:
																												_lowerCAmelCase     :							Any    =    highway_exit[0]
																												if not self.training:
																																			highway_logits_all.append(__a)
																																			highway_entropy.append(highway_exit[2])
																												if self.num_labels == 1:
																																			#  We are doing regression
																																			_lowerCAmelCase     :							List[str]    =    MSELoss()
																																			_lowerCAmelCase     :							List[Any]    =    loss_fct(highway_logits.view(-1),			labels.view(-1))
																												else:
																																			_lowerCAmelCase     :							Dict    =    CrossEntropyLoss()
																																			_lowerCAmelCase     :							Optional[Any]    =    loss_fct(highway_logits.view(-1,			self.num_labels),			labels.view(-1))
																												highway_losses.append(__a)

																					if train_highway:
																												_lowerCAmelCase     :							int    =    (sum(highway_losses[:-1]),) + outputs
																												# exclude the final highway, of course
																					else:
																												_lowerCAmelCase     :							Any    =    (loss,) + outputs
														if not self.training:
																					_lowerCAmelCase     :							Optional[Any]    =    outputs + ((original_entropy, highway_entropy), exit_layer)
																					if output_layer >= 0:
																												_lowerCAmelCase     :							Optional[Any]    =    (
																												    (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
																												)  # use the highway of the last layer

														return outputs  # (loss), logits, (hidden_states), (attentions), entropy
