
"""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 							__magic_name__     (		UpperCAmelCase_   ):
     _SCREAMING_SNAKE_CASE       :					List[Any]     							=   "naver-clova-ix/donut-base-finetuned-docvqa"
     _SCREAMING_SNAKE_CASE       :					Optional[Any]     							=   (
         "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       :					Tuple     							=   "document_qa"
     _SCREAMING_SNAKE_CASE       :					str     							=   AutoProcessor
     _SCREAMING_SNAKE_CASE       :					Union[str, Any]     							=   VisionEncoderDecoderModel

     _SCREAMING_SNAKE_CASE       :					int     							=   ["image", "text"]
     _SCREAMING_SNAKE_CASE       :					Any     							=   ["text"]





     def __init__(				self   :							Optional[int]							,						*snake_case_   :							Tuple							,						**snake_case_   :							Tuple    ):


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

            super().__init__(*_snake_case							,						**_snake_case    )





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

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





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

            return sequence["answer"]


"""simple docstring"""







import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     List[Any]:







							"""simple docstring"""


							__snake_case     			=       TapasConfig.from_json_file(SCREAMING_SNAKE_CASE )
							# set absolute/relative position embeddings parameter
							__snake_case     			=       reset_position_index_per_cell

							# set remaining parameters of TapasConfig as well as the model based on the task
							if task == "SQA":
														__snake_case     			=       TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE )
							elif task == "WTQ":
														# run_task_main.py hparams
														__snake_case     			=       4
														__snake_case     			=       True
														# hparam_utils.py hparams
														__snake_case     			=       0.664_694
														__snake_case     			=       0.207_951
														__snake_case     			=       0.121_194
														__snake_case     			=       True
														__snake_case     			=       True
														__snake_case     			=       False
														__snake_case     			=       0.0_352_513

														__snake_case     			=       TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE )
							elif task == "WIKISQL_SUPERVISED":
														# run_task_main.py hparams
														__snake_case     			=       4
														__snake_case     			=       False
														# hparam_utils.py hparams
														__snake_case     			=       36.4_519
														__snake_case     			=       0.903_421
														__snake_case     			=       222.088
														__snake_case     			=       True
														__snake_case     			=       True
														__snake_case     			=       True
														__snake_case     			=       0.763_141

														__snake_case     			=       TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE )
							elif task == "TABFACT":
														__snake_case     			=       TapasForSequenceClassification(config=SCREAMING_SNAKE_CASE )
							elif task == "MLM":
														__snake_case     			=       TapasForMaskedLM(config=SCREAMING_SNAKE_CASE )
							elif task == "INTERMEDIATE_PRETRAINING":
														__snake_case     			=       TapasModel(config=SCREAMING_SNAKE_CASE )
							else:
														raise ValueError(F'''Task {task} not supported.''' )

							print(F'''Building PyTorch model from configuration: {config}''' )
							# Load weights from tf checkpoint
							load_tf_weights_in_tapas(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )

							# Save pytorch-model (weights and configuration)
							print(F'''Save PyTorch model to {pytorch_dump_path}''' )
							model.save_pretrained(SCREAMING_SNAKE_CASE )

							# Save tokenizer files
							print(F'''Save tokenizer files to {pytorch_dump_path}''' )
							__snake_case     			=       TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt"							,    model_max_length=5_12 )
							tokenizer.save_pretrained(SCREAMING_SNAKE_CASE )

							print("Used relative position embeddings:"							,    model.config.reset_position_index_per_cell )


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE	       =      argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--task""", default="""SQA""", type=str, help="""Model task for which to convert a checkpoint. Defaults to SQA."""
		)
		parser.add_argument(
		    """--reset_position_index_per_cell""",
		    default=False,
		    action="""store_true""",
		    help="""Whether to use relative position embeddings or not. Defaults to True.""",
		)
		parser.add_argument(
		    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
		)
		parser.add_argument(
		    """--tapas_config_file""",
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        """The config json file corresponding to the pre-trained TAPAS model. \n"""
		        """This specifies the model architecture."""
		    ),
		)
		parser.add_argument(
		    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
		)
		_SCREAMING_SNAKE_CASE	       =      parser.parse_args()
		convert_tf_checkpoint_to_pytorch(
		    args.task,
		    args.reset_position_index_per_cell,
		    args.tf_checkpoint_path,
		    args.tapas_config_file,
		    args.pytorch_dump_path,
		)
