


"""simple docstring"""







import argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
 # Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
 # setup instructions, if using on-demand hardware

 # If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
 # If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
 # Throw an error if user passes both BYO and on-demand cluster args
 # Otherwise, use default values
 __lowercase    					=      argparse.ArgumentParser()
 parser.add_argument("""--user""", type=str, default="""ubuntu""")
 parser.add_argument("""--host""", type=str, default="""localhost""")
 parser.add_argument("""--key_path""", type=str, default=None)
 parser.add_argument("""--instance""", type=str, default="""V100:1""")
 parser.add_argument("""--provider""", type=str, default="""cheapest""")
 parser.add_argument("""--use_spot""", type=bool, default=False)
 parser.add_argument("""--example""", type=str, default="""pytorch/text-generation/run_generation.py""")
 __lowercase					,							__lowercase    					=      parser.parse_known_args()
 if args.host != "localhost":
  if args.instance != "V100:1" or args.provider != "cheapest":
   raise ValueError("""Cannot specify both BYO and on-demand cluster args""")
  __lowercase    					=      rh.cluster(
      name="""rh-cluster""", ips=[args.host], ssh_creds={"""ssh_user""": args.user, """ssh_private_key""": args.key_path}
  )
 else:
  __lowercase    					=      rh.cluster(
      name="""rh-cluster""", instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
  )
 __lowercase    					=      args.example.rsplit("""/""", 1)[0]

 # Set up remote environment
 cluster.install_packages(["""pip:./"""])  # Installs transformers from local source
 # Note transformers is copied into the home directory on the remote machine, so we can install from there
 cluster.run([f'''pip install -r transformers/examples/{example_dir}/requirements.txt'''])
 cluster.run(["""pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117"""])

 # Run example. You can bypass the CLI wrapper and paste your own code here.
 cluster.run([f'''python transformers/examples/{args.example} {' '.join(shlex.quote(arg) for arg in unknown)}'''])

 # Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
 # from my_script... import train
 # reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
 # launch_train_gpu = rh.function(fn=train,
 #                                system=gpu,
 #                                reqs=reqs,
 #                                name='train_bert_glue')
 #
 # We can pass in arguments just like we would to a function:
 # launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
 #                  stream_logs=True)










"""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 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase       :							str	=       """naver-clova-ix/donut-base-finetuned-docvqa"""
    UpperCAmelCase       :							Tuple	=       (
        """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."""
    )
    UpperCAmelCase       :							List[str]	=       """document_qa"""
    UpperCAmelCase       :							str	=       AutoProcessor
    UpperCAmelCase       :							Optional[int]	=       VisionEncoderDecoderModel

    UpperCAmelCase       :							int	=       ["""image""", """text"""]
    UpperCAmelCase       :							int	=       ["""text"""]






    def __init__(					self				:    Tuple				, *__UpperCAmelCase				:    Union[str, Any]				, **__UpperCAmelCase				:    Any):
      if not is_vision_available():
        raise ValueError("Pillow must be installed to use the DocumentQuestionAnsweringTool.")

      super().__init__(*__UpperCAmelCase				, **__UpperCAmelCase)






    def 	__snake_case							(					self				:    Tuple				, __UpperCAmelCase				:    "Image"				, __UpperCAmelCase				:    str):
      a						:		Any      							=			"<s_docvqa><s_question>{user_input}</s_question><s_answer>"
      a						:		Union[str, Any]      							=			task_prompt.replace("{user_input}"				, __UpperCAmelCase)
      a						:		Optional[Any]      							=			self.pre_processor.tokenizer(
          __UpperCAmelCase				, add_special_tokens=__UpperCAmelCase				, return_tensors="pt").input_ids
      a						:		Any      							=			self.pre_processor(__UpperCAmelCase				, return_tensors="pt").pixel_values

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






    def 	__snake_case							(					self				:    int				, __UpperCAmelCase				:    int):
      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 	__snake_case							(					self				:    str				, __UpperCAmelCase				:    List[Any]):
      a						:		Union[str, Any]      							=			self.pre_processor.batch_decode(__UpperCAmelCase)[0]
      a						:		Optional[Any]      							=			sequence.replace(self.pre_processor.tokenizer.eos_token				, "")
      a						:		Any      							=			sequence.replace(self.pre_processor.tokenizer.pad_token				, "")
      a						:		Optional[Any]      							=			re.sub(r"<.*?>"				, ""				, __UpperCAmelCase				, count=1).strip()  # remove first task start token
      a						:		List[str]      							=			self.pre_processor.tokenajson(__UpperCAmelCase)

      return sequence["answer"]






