



"""simple docstring"""







from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


A		=						logging.get_logger(__name__)

A		=						{
    'google/umt5-small': 'https://huggingface.co/google/umt5-small/resolve/main/config.json',
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}

class       _a  ( SCREAMING_SNAKE_CASE__):
			__magic_name__     					=     """umt5"""
			__magic_name__     					=     ["""past_key_values"""]



			def __init__(			self							:				int   ,	_lowercase							:				int=250112   ,	_lowercase							:				Dict=512   ,	_lowercase							:				List[Any]=64   ,	_lowercase							:				List[Any]=1024   ,	_lowercase							:				Tuple=8   ,	_lowercase							:				Union[str, Any]=None   ,	_lowercase							:				int=6   ,	_lowercase							:				List[str]=32   ,	_lowercase							:				str=128   ,	_lowercase							:				List[Any]=0.1   ,	_lowercase							:				Tuple=1E-6   ,	_lowercase							:				Optional[Any]=1.0   ,	_lowercase							:				str="gated-gelu"   ,	_lowercase							:				Dict=True   ,	_lowercase							:				Optional[int]=True   ,	_lowercase							:				List[str]="T5Tokenizer"   ,	_lowercase							:				str=True   ,	_lowercase							:				List[str]=0   ,	_lowercase							:				Union[str, Any]=1   ,	_lowercase							:				Any=0   ,	**_lowercase							:				List[Any]   ,	)							->					Tuple:
									super().__init__(
									    is_encoder_decoder=_lowercase   ,	tokenizer_class=_lowercase   ,	tie_word_embeddings=_lowercase   ,	pad_token_id=_lowercase   ,	eos_token_id=_lowercase   ,	decoder_start_token_id=_lowercase   ,	**_lowercase   ,	)
									snake_case   :			Optional[Any]						    = vocab_size
									snake_case   :			int						    = d_model
									snake_case   :			int						    = d_kv
									snake_case   :			str						    = d_ff
									snake_case   :			Dict						    = num_layers
									snake_case   :			Dict						    = (
									    num_decoder_layers if num_decoder_layers is not None else self.num_layers
									)  # default = symmetry
									snake_case   :			Tuple						    = num_heads
									snake_case   :			Tuple						    = relative_attention_num_buckets
									snake_case   :			str						    = relative_attention_max_distance
									snake_case   :			Dict						    = dropout_rate
									snake_case   :			List[str]						    = layer_norm_epsilon
									snake_case   :			Union[str, Any]						    = initializer_factor
									snake_case   :			Optional[int]						    = feed_forward_proj
									snake_case   :			int						    = use_cache

									snake_case   :			Optional[Any]						    = self.feed_forward_proj.split("-"		)
									snake_case   :			Any						    = act_info[-1]
									snake_case   :			Tuple						    = act_info[0] == "gated"

									if len(_lowercase		) > 1 and act_info[0] != "gated" or len(_lowercase		) > 2:
															raise ValueError(
															    F'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
															    "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
															    "'gated-gelu' or 'relu'"		)

									if feed_forward_proj == "gated-gelu":
															snake_case   :			List[Any]						    = "gelu_new"



			@property
			def 				__lowercase					(			self							:				Dict		)							->					Dict:
									return self.d_model



			@property
			def 				__lowercase					(			self							:				str		)							->					Optional[Any]:
									return self.num_heads



			@property
			def 				__lowercase					(			self							:				Union[str, Any]		)							->					List[str]:
									return self.num_layers



class       _a  ( SCREAMING_SNAKE_CASE__):



			@property
			# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
			def 				__lowercase					(			self							:				Any		)							->					Mapping[str, Mapping[int, str]]:
									snake_case   :			Any						    = {
									    "input_ids": {0: "batch", 1: "encoder_sequence"},
									    "attention_mask": {0: "batch", 1: "encoder_sequence"},
									}
									if self.use_past:
															snake_case   :			Dict						    = "past_encoder_sequence + sequence"
															snake_case   :			Tuple						    = {0: "batch"}
															snake_case   :			int						    = {0: "batch", 1: "past_decoder_sequence + sequence"}
									else:
															snake_case   :			Tuple						    = {0: "batch", 1: "decoder_sequence"}
															snake_case   :			Any						    = {0: "batch", 1: "decoder_sequence"}

									if self.use_past:
															self.fill_with_past_key_values_(_lowercase   ,	direction="inputs"		)

									return common_inputs



			@property
			# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
			def 				__lowercase					(			self							:				List[str]		)							->					int:
									return 13



			@property
			def 				__lowercase					(			self							:				int		)							->					float:
									return 5E-4








"""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


A		=						logging.get_logger(__name__)

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Optional[int]     ,		lowerCamelCase_:							List[Any] ):
						"""simple docstring"""






						snake_case   :			Optional[Any]						    = set()
						snake_case   :			Optional[int]						    = []

						def parse_line(lowerCamelCase_:							Union[str, Any] ):
												for line in fp:
																		if isinstance(lowerCamelCase_     ,		lowerCamelCase_ ):
																								snake_case   :			Optional[Any]						    = 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:
																														snake_case   :			str						    = "\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:
																								snake_case   :			Dict						    = line.strip()
																								buffer.append(lowerCamelCase_ )

						if from_gh:
												for filename in os.listdir(lowerCamelCase_ ):
																		snake_case   :			Tuple						    = 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  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Union[str, Any]     ,		lowerCamelCase_:							Any ):
						"""simple docstring"""






						snake_case   :			Dict						    = set()

						snake_case   :			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  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Dict ):
									"""simple docstring"""






									return values.split("," )

			A		=						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.',
			)

			A		=						parser.parse_args()

			A		=						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
						A		=						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('=' * 8_0)
									download_artifact(name, url, args.output_dir, args.token)
									# Be gentle to GitHub
									time.sleep(1)

    # extract warnings from artifacts
			A		=						extract_warnings(args.output_dir, args.targets)
			A		=						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)



