
"""simple docstring"""
import argparse
from collections import defaultdict

import yaml


lowercase_       :						int     					=	'docs/source/en/_toctree.yml'





def    _lowerCAmelCase    (     lowerCamelCase__							:							Any				)  ->    List[Any]:
  _SCREAMING_SNAKE_CASE   :				Tuple							    = defaultdict(lowerCamelCase__				)
  for doc in model_doc:
    counts[doc["local"]] += 1
  _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = [key for key, value in counts.items() if value > 1]

  _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = []
  for duplicate_key in duplicates:
    _SCREAMING_SNAKE_CASE   :				Dict							    = list({doc["title"] for doc in model_doc if doc["local"] == duplicate_key}				)
    if len(lowerCamelCase__				) > 1:
      raise ValueError(
          f'''{duplicate_key} is present several times in the documentation table of content at '''
          "`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the "
          "others."				)
    # Only add this once
    new_doc.append({"local": duplicate_key, "title": titles[0]}				)

  # Add none duplicate-keys
  new_doc.extend([doc for doc in model_doc if counts[doc["local"]] == 1]				)

  # Sort
  return sorted(lowerCamelCase__,						key=lambda lowerCamelCase__				: s["title"].lower()				)





def    _lowerCAmelCase    (     lowerCamelCase__							:							Optional[int]=False				)  ->    Optional[int]:






  with open(lowerCamelCase__,						encoding="utf-8"				) as f:
    _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = yaml.safe_load(f.read()				)

  # Get to the API doc
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = 0
  while content[api_idx]["title"] != "API":
    api_idx += 1
  _SCREAMING_SNAKE_CASE   :				int							    = content[api_idx]['''sections''']

  # Then to the model doc
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = 0
  while api_doc[model_idx]["title"] != "Models":
    model_idx += 1

  _SCREAMING_SNAKE_CASE   :				Dict							    = api_doc[model_idx]['''sections''']

  _SCREAMING_SNAKE_CASE   :				List[str]							    = [(idx, section) for idx, section in enumerate(lowerCamelCase__				) if '''sections''' in section]
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = False
  for idx, modality_doc in modalities_docs:
    _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = modality_doc['''sections''']
    _SCREAMING_SNAKE_CASE   :				List[Any]							    = clean_model_doc_toc(lowerCamelCase__				)

    if old_modality_doc != new_modality_doc:
      _SCREAMING_SNAKE_CASE   :				Any							    = True
      if overwrite:
        _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = new_modality_doc

  if diff:
    if overwrite:
      _SCREAMING_SNAKE_CASE   :				Dict							    = model_doc
      _SCREAMING_SNAKE_CASE   :				Any							    = api_doc
      with open(lowerCamelCase__,						"w",						encoding="utf-8"				) as f:
        f.write(yaml.dump(lowerCamelCase__,						allow_unicode=lowerCamelCase__				)				)
    else:
      raise ValueError(
          "The model doc part of the table of content is not properly sorted, run `make style` to fix this."				)


if __name__ == "__main__":
      lowercase_       :						List[str]     					=	argparse.ArgumentParser()
      parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
      lowercase_       :						Optional[int]     					=	parser.parse_args()

      check_model_doc(args.fix_and_overwrite)





"""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      (							__SCREAMING_SNAKE_CASE  ):
     A__      =			"""naver-clova-ix/donut-base-finetuned-docvqa"""
     A__      =			(
         """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."""
     )
     A__      =			"""document_qa"""
     A__      =			AutoProcessor
     A__      =			VisionEncoderDecoderModel

     A__      =			["""image""", """text"""]
     A__      =			["""text"""]
     def __init__(     self  ,     *snake_case__  ,     **snake_case__				):




       """simple docstring"""


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

       super().__init__(*snake_case__  ,     **snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[str]							    = "<s_docvqa><s_question>{user_input}</s_question><s_answer>"
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = task_prompt.replace("{user_input}"  ,     snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Tuple							    = self.pre_processor.tokenizer(
           snake_case__  ,     add_special_tokens=snake_case__  ,     return_tensors="pt"				).input_ids
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = self.pre_processor(snake_case__  ,     return_tensors="pt"				).pixel_values

       return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       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=snake_case__  ,     pad_token_id=self.pre_processor.tokenizer.pad_token_id  ,     eos_token_id=self.pre_processor.tokenizer.eos_token_id  ,     use_cache=snake_case__  ,     num_beams=1  ,     bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]]  ,     return_dict_in_generate=snake_case__  ,     ).sequences




     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				int							    = self.pre_processor.batch_decode(snake_case__				)[0]
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = sequence.replace(self.pre_processor.tokenizer.eos_token  ,     ""				)
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = sequence.replace(self.pre_processor.tokenizer.pad_token  ,     ""				)
       _SCREAMING_SNAKE_CASE   :				Dict							    = re.sub(r"<.*?>"  ,     ""  ,     snake_case__  ,     count=1				).strip()  # remove first task start token
       _SCREAMING_SNAKE_CASE   :				Dict							    = self.pre_processor.tokenajson(snake_case__				)

       return sequence["answer"]



