





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


_UpperCAmelCase				:       Optional[int]	   =				logging.get_logger(__name__)


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase )			->     Dict:
    lowerCamelCase__    :      str					 =		set()
    lowerCamelCase__    :      Any					 =		[]

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

    if from_gh:
        for filename in os.listdir(_UpperCAmelCase ):
            lowerCamelCase__    :      Dict					 =		os.path.join(_UpperCAmelCase						,	_UpperCAmelCase )
            if not os.path.isdir(_UpperCAmelCase ):
                # read the file
                if filename != "warnings.txt":
                    continue
                with open(_UpperCAmelCase ) as fp:
                    parse_line(_UpperCAmelCase )
    else:
        try:
            with zipfile.ZipFile(_UpperCAmelCase ) as z:
                for filename in z.namelist():
                    if not os.path.isdir(_UpperCAmelCase ):
                        # read the file
                        if filename != "warnings.txt":
                            continue
                        with z.open(_UpperCAmelCase ) as fp:
                            parse_line(_UpperCAmelCase )
        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      (       _UpperCAmelCase						,	_UpperCAmelCase )			->     Union[str, Any]:
    lowerCamelCase__    :      Tuple					 =		set()

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

    return selected_warnings


if __name__ == "__main__":


    def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     Tuple:
        return values.split(',' )

    _UpperCAmelCase				:       List[str]	   =				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.""",
    )

    _UpperCAmelCase				:       Union[str, Any]	   =				parser.parse_args()

    _UpperCAmelCase				:       Dict	   =				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
        _UpperCAmelCase				:       Union[str, 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
    _UpperCAmelCase				:       Dict	   =				extract_warnings(args.output_dir, args.targets)
    _UpperCAmelCase				:       Optional[Any]	   =				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 ....configuration_utils import PretrainedConfig
from ....utils import logging


_UpperCamelCase 			=		logging.get_logger(__name__)

_UpperCamelCase 			=		{
    "CarlCochet/trajectory-transformer-halfcheetah-medium-v2": (
        "https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json"
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}







class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  """trajectory_transformer"""
 _UpperCamelCase			  =  ["""past_key_values"""]
 _UpperCamelCase			  =  {
     """hidden_size""": """n_embd""",
     """num_attention_heads""": """n_head""",
     """num_hidden_layers""": """n_layer""",
 }
 def __init__(							self      ,							A_=100      ,							A_=5      ,							A_=1      ,							A_=1      ,							A_=249      ,							A_=6      ,							A_=17      ,							A_=25      ,							A_=4      ,							A_=4      ,							A_=128      ,							A_=0.1      ,							A_=0.1      ,							A_=0.1      ,							A_=0.0_006      ,							A_=512      ,							A_=0.02      ,							A_=1e-12      ,							A_=1      ,							A_=True      ,							A_=1      ,							A_=5_0256      ,							A_=5_0256      ,							**A_      ,							)					->int:



       '''simple docstring'''





       __lowerCAmelCase					:						Any					=					vocab_size
       __lowerCAmelCase					:						Tuple					=					action_weight
       __lowerCAmelCase					:						Tuple					=					reward_weight
       __lowerCAmelCase					:						Union[str, Any]					=					value_weight
       __lowerCAmelCase					:						List[str]					=					max_position_embeddings
       __lowerCAmelCase					:						str					=					block_size
       __lowerCAmelCase					:						Optional[Any]					=					action_dim
       __lowerCAmelCase					:						Union[str, Any]					=					observation_dim
       __lowerCAmelCase					:						Union[str, Any]					=					transition_dim
       __lowerCAmelCase					:						Dict					=					learning_rate
       __lowerCAmelCase					:						Any					=					n_layer
       __lowerCAmelCase					:						Any					=					n_head
       __lowerCAmelCase					:						Optional[int]					=					n_embd
       __lowerCAmelCase					:						str					=					embd_pdrop
       __lowerCAmelCase					:						Dict					=					attn_pdrop
       __lowerCAmelCase					:						Optional[int]					=					resid_pdrop
       __lowerCAmelCase					:						Union[str, Any]					=					initializer_range
       __lowerCAmelCase					:						Optional[int]					=					layer_norm_eps
       __lowerCAmelCase					:						Any					=					kaiming_initializer_range
       __lowerCAmelCase					:						List[str]					=					use_cache
       super().__init__(pad_token_id=A_      ,							bos_token_id=A_      ,							eos_token_id=A_      ,							**A_					)


