

from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


__UpperCAmelCase								=			logging.get_logger(__name__)

__UpperCAmelCase								=			{
    """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json""",
    # See all Marian models at https://huggingface.co/models?filter=marian
}



class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  lowerCamelCase   :      Tuple										="marian"
  lowerCamelCase   :      int										=["past_key_values"]
  lowerCamelCase   :      List[str]										={"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}







  def __init__(      self			:   Optional[int]	,							lowerCAmelCase			:   Optional[int]=5_81_01	,							lowerCAmelCase			:   Optional[int]=None	,							lowerCAmelCase			:   Dict=10_24	,							lowerCAmelCase			:   str=12	,							lowerCAmelCase			:   str=40_96	,							lowerCAmelCase			:   Optional[int]=16	,							lowerCAmelCase			:   str=12	,							lowerCAmelCase			:   List[str]=40_96	,							lowerCAmelCase			:   Union[str, Any]=16	,							lowerCAmelCase			:   Optional[Any]=0.0	,							lowerCAmelCase			:   Optional[Any]=0.0	,							lowerCAmelCase			:   Optional[Any]=True	,							lowerCAmelCase			:   Tuple=True	,							lowerCAmelCase			:   int="gelu"	,							lowerCAmelCase			:   int=10_24	,							lowerCAmelCase			:   Union[str, Any]=0.1	,							lowerCAmelCase			:   Optional[Any]=0.0	,							lowerCAmelCase			:   Union[str, Any]=0.0	,							lowerCAmelCase			:   List[Any]=0.02	,							lowerCAmelCase			:   List[str]=5_81_00	,							lowerCAmelCase			:   Optional[Any]=False	,							lowerCAmelCase			:   Union[str, Any]=5_81_00	,							lowerCAmelCase			:   Optional[Any]=0	,							lowerCAmelCase			:   int=0	,							lowerCAmelCase			:   Optional[int]=True	,							**lowerCAmelCase			:   List[str]	,							)							->     Optional[int]:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						vocab_size
     __lowerCAmelCase  :      Union[str, Any]    =						decoder_vocab_size or vocab_size
     __lowerCAmelCase  :      List[str]    =						max_position_embeddings
     __lowerCAmelCase  :      Union[str, Any]    =						d_model
     __lowerCAmelCase  :      Union[str, Any]    =						encoder_ffn_dim
     __lowerCAmelCase  :      Tuple    =						encoder_layers
     __lowerCAmelCase  :      int    =						encoder_attention_heads
     __lowerCAmelCase  :      Optional[int]    =						decoder_ffn_dim
     __lowerCAmelCase  :      Optional[Any]    =						decoder_layers
     __lowerCAmelCase  :      Tuple    =						decoder_attention_heads
     __lowerCAmelCase  :      str    =						dropout
     __lowerCAmelCase  :      List[str]    =						attention_dropout
     __lowerCAmelCase  :      Dict    =						activation_dropout
     __lowerCAmelCase  :      Union[str, Any]    =						activation_function
     __lowerCAmelCase  :      List[Any]    =						init_std
     __lowerCAmelCase  :      int    =						encoder_layerdrop
     __lowerCAmelCase  :      Optional[Any]    =						decoder_layerdrop
     __lowerCAmelCase  :      int    =						use_cache
     __lowerCAmelCase  :      List[str]    =						encoder_layers
     __lowerCAmelCase  :      Union[str, Any]    =						scale_embedding  # scale factor will be sqrt(d_model) if True
     __lowerCAmelCase  :      Dict    =						share_encoder_decoder_embeddings
     super().__init__(
         pad_token_id=lowerCAmelCase	,							eos_token_id=lowerCAmelCase	,							is_encoder_decoder=lowerCAmelCase	,							decoder_start_token_id=lowerCAmelCase	,							forced_eos_token_id=lowerCAmelCase	,							**lowerCAmelCase	,							)






class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  @property
  # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]   )							->     Mapping[str, Mapping[int, str]]:






     """simple docstring"""







     if self.task in ["default", "seq2seq-lm"]:
        __lowerCAmelCase  :      List[Any]    =						OrderedDict(
            [
                ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
            ]   )

        if self.use_past:
           __lowerCAmelCase  :      List[str]    =						{0: """batch"""}
           __lowerCAmelCase  :      Optional[Any]    =						{0: """batch""", 1: """past_decoder_sequence + sequence"""}
        else:
           __lowerCAmelCase  :      Dict    =						{0: """batch""", 1: """decoder_sequence"""}
           __lowerCAmelCase  :      str    =						{0: """batch""", 1: """decoder_sequence"""}

        if self.use_past:
           self.fill_with_past_key_values_(lowerCAmelCase	,							direction="""inputs"""   )
     elif self.task == "causal-lm":
        # TODO: figure this case out.
        __lowerCAmelCase  :      Union[str, Any]    =						OrderedDict(
            [
                ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
            ]   )
        if self.use_past:
           __lowerCAmelCase   ,__lowerCAmelCase  :      str    =						self.num_layers
           for i in range(lowerCAmelCase   ):
              __lowerCAmelCase  :      str    =						{0: """batch""", 2: """past_sequence + sequence"""}
              __lowerCAmelCase  :      List[Any]    =						{0: """batch""", 2: """past_sequence + sequence"""}
     else:
        __lowerCAmelCase  :      Dict    =						OrderedDict(
            [
                ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                ("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
                ("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
            ]   )

     return common_inputs







  @property
  # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]   )							->     Mapping[str, Mapping[int, str]]:






     """simple docstring"""







     if self.task in ["default", "seq2seq-lm"]:
        __lowerCAmelCase  :      str    =						super().outputs
     else:
        __lowerCAmelCase  :      int    =						super(lowerCAmelCase	,							self   ).outputs
        if self.use_past:
           __lowerCAmelCase   ,__lowerCAmelCase  :      int    =						self.num_layers
           for i in range(lowerCAmelCase   ):
              __lowerCAmelCase  :      Dict    =						{0: """batch""", 2: """past_sequence + sequence"""}
              __lowerCAmelCase  :      Optional[Any]    =						{0: """batch""", 2: """past_sequence + sequence"""}
     return common_outputs







  def 	SCREAMING_SNAKE_CASE   (      self			:   Any	,							lowerCAmelCase			:   PreTrainedTokenizer	,							lowerCAmelCase			:   int = -1	,							lowerCAmelCase			:   int = -1	,							lowerCAmelCase			:   bool = False	,							lowerCAmelCase			:   Optional[TensorType] = None	,							)							->     Mapping[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Tuple    =						self._generate_dummy_inputs_for_encoder_and_decoder(
         lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase   )

     # Generate decoder inputs
     __lowerCAmelCase  :      List[Any]    =						seq_length if not self.use_past else 1
     __lowerCAmelCase  :      Union[str, Any]    =						self._generate_dummy_inputs_for_encoder_and_decoder(
         lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase   )
     __lowerCAmelCase  :      List[Any]    =						{f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
     __lowerCAmelCase  :      Tuple    =						dict(**lowerCAmelCase	,							**lowerCAmelCase   )

     if self.use_past:
        if not is_torch_available():
           raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""   )
        else:
           import torch
        __lowerCAmelCase   ,__lowerCAmelCase  :      List[str]    =						common_inputs["""input_ids"""].shape
        __lowerCAmelCase  :      Any    =						common_inputs["""decoder_input_ids"""].shape[1]
        __lowerCAmelCase   ,__lowerCAmelCase  :      Optional[int]    =						self.num_attention_heads
        __lowerCAmelCase  :      Tuple    =						(
            batch,
            num_encoder_attention_heads,
            encoder_seq_length,
            self._config.hidden_size // num_encoder_attention_heads,
        )
        __lowerCAmelCase  :      Tuple    =						decoder_seq_length + 3
        __lowerCAmelCase  :      Union[str, Any]    =						(
            batch,
            num_decoder_attention_heads,
            decoder_past_length,
            self._config.hidden_size // num_decoder_attention_heads,
        )

        __lowerCAmelCase  :      List[str]    =						torch.cat(
            [common_inputs["""decoder_attention_mask"""], torch.ones(lowerCAmelCase	,							lowerCAmelCase   )]	,							dim=1   )

        __lowerCAmelCase  :      Dict    =						[]
        # If the number of encoder and decoder layers are present in the model configuration, both are considered
        __lowerCAmelCase   ,__lowerCAmelCase  :      List[str]    =						self.num_layers
        __lowerCAmelCase  :      str    =						min(lowerCAmelCase	,							lowerCAmelCase   )
        __lowerCAmelCase  :      List[str]    =						max(lowerCAmelCase	,							lowerCAmelCase   ) - min_num_layers
        __lowerCAmelCase  :      Optional[Any]    =						"""encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

        for _ in range(lowerCAmelCase   ):
           common_inputs["past_key_values"].append(
               (
                   torch.zeros(lowerCAmelCase   ),
                   torch.zeros(lowerCAmelCase   ),
                   torch.zeros(lowerCAmelCase   ),
                   torch.zeros(lowerCAmelCase   ),
               )   )
        # TODO: test this.
        __lowerCAmelCase  :      Dict    =						encoder_shape if remaining_side_name == """encoder""" else decoder_shape
        for _ in range(lowerCAmelCase	,							lowerCAmelCase   ):
           common_inputs["past_key_values"].append((torch.zeros(lowerCAmelCase   ), torch.zeros(lowerCAmelCase   ))   )
     return common_inputs







  def 	SCREAMING_SNAKE_CASE   (      self			:   Any	,							lowerCAmelCase			:   PreTrainedTokenizer	,							lowerCAmelCase			:   int = -1	,							lowerCAmelCase			:   int = -1	,							lowerCAmelCase			:   bool = False	,							lowerCAmelCase			:   Optional[TensorType] = None	,							)							->     Mapping[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :      str    =						self._generate_dummy_inputs_for_encoder_and_decoder(
         lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase   )

     if self.use_past:
        if not is_torch_available():
           raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""   )
        else:
           import torch
        __lowerCAmelCase   ,__lowerCAmelCase  :      Dict    =						common_inputs["""input_ids"""].shape
        # Not using the same length for past_key_values
        __lowerCAmelCase  :      int    =						seqlen + 2
        __lowerCAmelCase   ,__lowerCAmelCase  :      int    =						self.num_layers
        __lowerCAmelCase   ,__lowerCAmelCase  :      Dict    =						self.num_attention_heads
        __lowerCAmelCase  :      Dict    =						(
            batch,
            num_encoder_attention_heads,
            past_key_values_length,
            self._config.hidden_size // num_encoder_attention_heads,
        )

        __lowerCAmelCase  :      Dict    =						common_inputs["""attention_mask"""].dtype
        __lowerCAmelCase  :      List[Any]    =						torch.cat(
            [common_inputs["""attention_mask"""], torch.ones(lowerCAmelCase	,							lowerCAmelCase	,							dtype=lowerCAmelCase   )]	,							dim=1   )
        __lowerCAmelCase  :      str    =						[
            (torch.zeros(lowerCAmelCase   ), torch.zeros(lowerCAmelCase   )) for _ in range(lowerCAmelCase   )
        ]
     return common_inputs







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple	,							lowerCAmelCase			:   PreTrainedTokenizer	,							lowerCAmelCase			:   int = -1	,							lowerCAmelCase			:   int = -1	,							lowerCAmelCase			:   bool = False	,							lowerCAmelCase			:   Optional[TensorType] = None	,							)							->     Mapping[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						compute_effective_axis_dimension(
         lowerCAmelCase	,							fixed_dimension=OnnxConfig.default_fixed_batch	,							num_token_to_add=0   )

     # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
     __lowerCAmelCase  :      List[Any]    =						tokenizer.num_special_tokens_to_add(lowerCAmelCase   )
     __lowerCAmelCase  :      str    =						compute_effective_axis_dimension(
         lowerCAmelCase	,							fixed_dimension=OnnxConfig.default_fixed_sequence	,							num_token_to_add=lowerCAmelCase   )

     # Generate dummy inputs according to compute batch and sequence
     __lowerCAmelCase  :      int    =						[""" """.join([tokenizer.unk_token]   ) * seq_length] * batch_size
     __lowerCAmelCase  :      Dict    =						dict(tokenizer(lowerCAmelCase	,							return_tensors=lowerCAmelCase   )   )
     return common_inputs







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]	,							lowerCAmelCase			:   PreTrainedTokenizer	,							lowerCAmelCase			:   int = -1	,							lowerCAmelCase			:   int = -1	,							lowerCAmelCase			:   bool = False	,							lowerCAmelCase			:   Optional[TensorType] = None	,							)							->     Mapping[str, Any]:






     """simple docstring"""







     if self.task in ["default", "seq2seq-lm"]:
        __lowerCAmelCase  :      List[str]    =						self._generate_dummy_inputs_for_default_and_seqaseq_lm(
            lowerCAmelCase	,							batch_size=lowerCAmelCase	,							seq_length=lowerCAmelCase	,							is_pair=lowerCAmelCase	,							framework=lowerCAmelCase   )

     else:
        __lowerCAmelCase  :      Optional[Any]    =						self._generate_dummy_inputs_for_causal_lm(
            lowerCAmelCase	,							batch_size=lowerCAmelCase	,							seq_length=lowerCAmelCase	,							is_pair=lowerCAmelCase	,							framework=lowerCAmelCase   )

     return common_inputs







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   Dict	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   str   )							->     Optional[Any]:






     """simple docstring"""







     if self.task in ["default", "seq2seq-lm"]:
        __lowerCAmelCase  :      Union[str, Any]    =						super()._flatten_past_key_values_(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase   )
     else:
        __lowerCAmelCase  :      List[Any]    =						super(lowerCAmelCase	,							self   )._flatten_past_key_values_(
            lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase   )







  @property
  def 	SCREAMING_SNAKE_CASE   (      self			:   str   )							->     float:






     """simple docstring"""







     return 1e-4





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								=			logging.get_logger(__name__)





def      snake_case_				(__A					:						List[str] ,       __A					:						str					)      ->     int:
   __lowerCAmelCase  :      str    =						set()
   __lowerCAmelCase  :      int    =						[]

   def parse_line(__A					:						List[Any]					):
      for line in fp:
         if isinstance(__A ,       __A					):
            __lowerCAmelCase  :      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(__A					) > 0:
               __lowerCAmelCase  :      Tuple    =						"""\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:
            __lowerCAmelCase  :      Optional[int]    =						line.strip()
            buffer.append(__A					)

   if from_gh:
      for filename in os.listdir(__A					):
         __lowerCAmelCase  :      Optional[Any]    =						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      snake_case_				(__A					:						Dict ,       __A					:						Union[str, Any]					)      ->     Dict:
   __lowerCAmelCase  :      Any    =						set()

   __lowerCAmelCase  :      Optional[int]    =						[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      snake_case_				(__A					:						int					)      ->     Tuple:
      return values.split(""","""					)

   __UpperCAmelCase								=			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								=			parser.parse_args()

   __UpperCAmelCase								=			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								=			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								=			extract_warnings(args.output_dir, args.targets)
   __UpperCAmelCase								=			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)


