
"""simple docstring"""

# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re

from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool


if is_vision_available():
   from PIL import Image
class   _UpperCAmelCase				(		UpperCAmelCase__				):
      __SCREAMING_SNAKE_CASE :				Optional[int]				   =						"naver-clova-ix/donut-base-finetuned-docvqa"
      __SCREAMING_SNAKE_CASE :				int				   =						(
          "This is a tool that answers a question about an document (pdf). It takes an input named `document` which "
          "should be the document containing the information, as well as a `question` that is the question about the "
          "document. It returns a text that contains the answer to the question."
      )
      __SCREAMING_SNAKE_CASE :				Optional[Any]				   =						"document_qa"
      __SCREAMING_SNAKE_CASE :				Dict				   =						AutoProcessor
      __SCREAMING_SNAKE_CASE :				int				   =						VisionEncoderDecoderModel

      __SCREAMING_SNAKE_CASE :				Optional[Any]				   =						["image", "text"]
      __SCREAMING_SNAKE_CASE :				Any				   =						["text"]




      def __init__(				self ,							*lowercase_ ,							**lowercase_				)    ->       List[str]:

          if not is_vision_available():
              raise ValueError('Pillow must be installed to use the DocumentQuestionAnsweringTool.'				)

          super().__init__(*UpperCAmelCase__ ,							**UpperCAmelCase__				)




      def      a_   (				self ,							lowercase_ ,							lowercase_				)    ->       Optional[int]:
          UpperCAmelCase          =       '<s_docvqa><s_question>{user_input}</s_question><s_answer>'
          UpperCAmelCase          =       task_prompt.replace('{user_input}' ,							UpperCAmelCase__				)
          UpperCAmelCase          =       self.pre_processor.tokenizer(
              UpperCAmelCase__ ,							add_special_tokens=UpperCAmelCase__ ,							return_tensors='pt'				).input_ids
          UpperCAmelCase          =       self.pre_processor(UpperCAmelCase__ ,							return_tensors='pt'				).pixel_values

          return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}




      def      a_   (				self ,							lowercase_				)    ->       Any:
          return self.model.generate(
              inputs['pixel_values'].to(self.device				) ,							decoder_input_ids=inputs['decoder_input_ids'].to(self.device				) ,							max_length=self.model.decoder.config.max_position_embeddings ,							early_stopping=UpperCAmelCase__ ,							pad_token_id=self.pre_processor.tokenizer.pad_token_id ,							eos_token_id=self.pre_processor.tokenizer.eos_token_id ,							use_cache=UpperCAmelCase__ ,							num_beams=1 ,							bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]] ,							return_dict_in_generate=UpperCAmelCase__ ,							).sequences




      def      a_   (				self ,							lowercase_				)    ->       Dict:
          UpperCAmelCase          =       self.pre_processor.batch_decode(UpperCAmelCase__				)[0]
          UpperCAmelCase          =       sequence.replace(self.pre_processor.tokenizer.eos_token ,							''				)
          UpperCAmelCase          =       sequence.replace(self.pre_processor.tokenizer.pad_token ,							''				)
          UpperCAmelCase          =       re.sub(R'<.*?>' ,							'' ,							UpperCAmelCase__ ,							count=1				).strip()  # remove first task start token
          UpperCAmelCase          =       self.pre_processor.tokenajson(UpperCAmelCase__				)

          return sequence["answer"]









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


_lowerCamelCase      :						int							=  logging.get_logger(__name__)







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_		)  ->			Dict:



       """simple docstring"""

       A__ 					=       set()
       A__ 					=       []

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

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



       """simple docstring"""

       A__ 					=       set()

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

       return selected_warnings


if __name__ == "__main__":







  def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			int:



         """simple docstring"""

         return values.split(''','''		)

  _lowerCamelCase      :						int							=  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.""",
  )

  _lowerCamelCase      :						List[Any]							=  parser.parse_args()

  _lowerCamelCase      :						List[str]							=  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
    _lowerCamelCase      :						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
  _lowerCamelCase      :						Any							=  extract_warnings(args.output_dir, args.targets)
  _lowerCamelCase      :						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)


