
'''simple docstring'''







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


lowercase    :							str      =					logging.get_logger(__name__)







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	Union[str, Any]:
	_snake_case   						=		set()
	_snake_case   						=		[]

	def parse_line(__A	):
		for line in fp:
			if isinstance(__A			,				__A	):
				_snake_case   						=		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(__A	) > 0:
					_snake_case   						=		'\n'.join(__A	)
					# Only keep the warnings specified in `targets`
					if any(F': {x}: ' in warning for x in targets	):
						selected_warnings.add(__A	)
					buffer.clear()
				continue
			else:
				_snake_case   						=		line.strip()
				buffer.append(__A	)

	if from_gh:
		for filename in os.listdir(__A	):
			_snake_case   						=		os.path.join(__A			,				__A	)
			if not os.path.isdir(__A	):
				# read the file
				if filename != "warnings.txt":
					continue
				with open(__A	) as fp:
					parse_line(__A	)
	else:
		try:
			with zipfile.ZipFile(__A	) as z:
				for filename in z.namelist():
					if not os.path.isdir(__A	):
						# read the file
						if filename != "warnings.txt":
							continue
						with z.open(__A	) as fp:
							parse_line(__A	)
		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 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	Optional[int]:
	_snake_case   						=		set()

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

	return selected_warnings


if __name__ == "__main__":







					def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	List[str]:
						return values.split(','	)

					lowercase    :							Union[str, 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.")
					# 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.",
					)

					lowercase    :							Tuple      =					parser.parse_args()

					lowercase    :							Optional[int]      =					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
										lowercase    :							List[Any]      =					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
					lowercase    :							Optional[Any]      =					extract_warnings(args.output_dir, args.targets)
					lowercase    :							Tuple      =					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)



'''simple docstring'''







import tensorflow as tf

from ...tf_utils import shape_list


class 						__UpperCAmelCase      (					tf.keras.layers.Layer						):


							def __init__(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_=1    ,   lowerCAmelCase_=False    ,   **lowerCAmelCase_  ):





								"""simple docstring"""






								super().__init__(**lowerCAmelCase_  )

								_snake_case   						=		vocab_size
								_snake_case   						=		d_embed
								_snake_case   						=		d_proj

								_snake_case   						=		cutoffs + [vocab_size]
								_snake_case   						=		[0] + self.cutoffs
								_snake_case   						=		div_val

								_snake_case   						=		self.cutoffs[0]
								_snake_case   						=		len(self.cutoffs  ) - 1
								_snake_case   						=		self.shortlist_size + self.n_clusters
								_snake_case   						=		keep_order

								_snake_case   						=		[]
								_snake_case   						=		[]


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								if self.n_clusters > 0:
									_snake_case   						=		self.add_weight(
									    shape=(self.n_clusters, self.d_embed)    ,   initializer='zeros'    ,   trainable=lowerCAmelCase_    ,   name='cluster_weight'  )
									_snake_case   						=		self.add_weight(
									    shape=(self.n_clusters,)    ,   initializer='zeros'    ,   trainable=lowerCAmelCase_    ,   name='cluster_bias'  )

								if self.div_val == 1:
									for i in range(len(self.cutoffs  )  ):
										if self.d_proj != self.d_embed:
											_snake_case   						=		self.add_weight(
											    shape=(self.d_embed, self.d_proj)    ,   initializer='zeros'    ,   trainable=lowerCAmelCase_    ,   name=F'out_projs_._{i}'    ,   )
											self.out_projs.append(lowerCAmelCase_  )
										else:
											self.out_projs.append(lowerCAmelCase_  )
										_snake_case   						=		self.add_weight(
										    shape=(self.vocab_size, self.d_embed)    ,   initializer='zeros'    ,   trainable=lowerCAmelCase_    ,   name=F'out_layers_._{i}_._weight'    ,   )
										_snake_case   						=		self.add_weight(
										    shape=(self.vocab_size,)    ,   initializer='zeros'    ,   trainable=lowerCAmelCase_    ,   name=F'out_layers_._{i}_._bias'    ,   )
										self.out_layers.append((weight, bias)  )
								else:
									for i in range(len(self.cutoffs  )  ):
										_snake_case						, _snake_case   						=		self.cutoff_ends[i], self.cutoff_ends[i + 1]
										_snake_case   						=		self.d_embed // (self.div_val**i)

										_snake_case   						=		self.add_weight(
										    shape=(d_emb_i, self.d_proj)    ,   initializer='zeros'    ,   trainable=lowerCAmelCase_    ,   name=F'out_projs_._{i}'  )
										self.out_projs.append(lowerCAmelCase_  )
										_snake_case   						=		self.add_weight(
										    shape=(r_idx - l_idx, d_emb_i)    ,   initializer='zeros'    ,   trainable=lowerCAmelCase_    ,   name=F'out_layers_._{i}_._weight'    ,   )
										_snake_case   						=		self.add_weight(
										    shape=(r_idx - l_idx,)    ,   initializer='zeros'    ,   trainable=lowerCAmelCase_    ,   name=F'out_layers_._{i}_._bias'    ,   )
										self.out_layers.append((weight, bias)  )
								super().build(lowerCAmelCase_  )


							@staticmethod
							def  lowerCamelCase				(       lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_=None  ):





								"""simple docstring"""






								_snake_case   						=		x
								if proj is not None:
									_snake_case   						=		tf.einsum('ibd,ed->ibe'    ,   lowerCAmelCase_    ,   lowerCAmelCase_  )
								return tf.einsum('ibd,nd->ibn'    ,   lowerCAmelCase_    ,   lowerCAmelCase_  ) + b


							@staticmethod
							def  lowerCamelCase				(       lowerCAmelCase_    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								_snake_case   						=		shape_list(lowerCAmelCase_  )
								_snake_case   						=		tf.range(lp_size[0]    ,   dtype=target.dtype  )
								_snake_case   						=		tf.stack([r, target]    ,   1  )
								return tf.gather_nd(lowerCAmelCase_    ,   lowerCAmelCase_  )




							def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_=True    ,   lowerCAmelCase_=False  ):





								"""simple docstring"""






								_snake_case   						=		0
								if self.n_clusters == 0:
									_snake_case   						=		self._logit(lowerCAmelCase_    ,   self.out_layers[0][0]    ,   self.out_layers[0][1]    ,   self.out_projs[0]  )
									if target is not None:
										_snake_case   						=		tf.nn.sparse_softmax_cross_entropy_with_logits(labels=lowerCAmelCase_    ,   logits=lowerCAmelCase_  )
									_snake_case   						=		tf.nn.log_softmax(lowerCAmelCase_    ,   axis=-1  )
								else:
									_snake_case   						=		shape_list(lowerCAmelCase_  )
									_snake_case   						=		[]
									_snake_case   						=		tf.zeros(hidden_sizes[:2]  )
									for i in range(len(self.cutoffs  )  ):
										_snake_case						, _snake_case   						=		self.cutoff_ends[i], self.cutoff_ends[i + 1]
										if target is not None:
											_snake_case   						=		(target >= l_idx) & (target < r_idx)
											_snake_case   						=		tf.where(lowerCAmelCase_  )
											_snake_case   						=		tf.boolean_mask(lowerCAmelCase_    ,   lowerCAmelCase_  ) - l_idx

										if self.div_val == 1:
											_snake_case   						=		self.out_layers[0][0][l_idx:r_idx]
											_snake_case   						=		self.out_layers[0][1][l_idx:r_idx]
										else:
											_snake_case   						=		self.out_layers[i][0]
											_snake_case   						=		self.out_layers[i][1]

										if i == 0:
											_snake_case   						=		tf.concat([cur_W, self.cluster_weight]    ,   0  )
											_snake_case   						=		tf.concat([cur_b, self.cluster_bias]    ,   0  )

											_snake_case   						=		self._logit(lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   self.out_projs[0]  )
											_snake_case   						=		tf.nn.log_softmax(lowerCAmelCase_  )
											out.append(head_logprob[..., : self.cutoffs[0]]  )
											if target is not None:
												_snake_case   						=		tf.boolean_mask(lowerCAmelCase_    ,   lowerCAmelCase_  )
												_snake_case   						=		self._gather_logprob(lowerCAmelCase_    ,   lowerCAmelCase_  )
										else:
											_snake_case   						=		self._logit(lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   self.out_projs[i]  )
											_snake_case   						=		tf.nn.log_softmax(lowerCAmelCase_  )
											_snake_case   						=		self.cutoffs[0] + i - 1  # No probability for the head cluster
											_snake_case   						=		head_logprob[..., cluster_prob_idx, None] + tail_logprob
											out.append(lowerCAmelCase_  )
											if target is not None:
												_snake_case   						=		tf.boolean_mask(lowerCAmelCase_    ,   lowerCAmelCase_  )
												_snake_case   						=		tf.boolean_mask(lowerCAmelCase_    ,   lowerCAmelCase_  )
												_snake_case   						=		self._gather_logprob(lowerCAmelCase_    ,   lowerCAmelCase_  )
												cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
										if target is not None:
											loss += tf.scatter_nd(lowerCAmelCase_    ,   -cur_logprob    ,   shape_list(lowerCAmelCase_  )  )
									_snake_case   						=		tf.concat(lowerCAmelCase_    ,   axis=-1  )

								if target is not None:
									if return_mean:
										_snake_case   						=		tf.reduce_mean(lowerCAmelCase_  )
									# Add the training-time loss value to the layer using `self.add_loss()`.
									self.add_loss(lowerCAmelCase_  )

									# Log the loss as a metric (we could log arbitrary metrics,
									# including different metrics for training and inference.
									self.add_metric(lowerCAmelCase_    ,   name=self.name    ,   aggregation='mean' if return_mean else ''  )

								return out

