"""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_   (  __UpperCamelCase      ):
					UpperCamelCase_			:						List[Any]      =     "naver-clova-ix/donut-base-finetuned-docvqa"
					UpperCamelCase_			:						Dict      =     (
					    "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_			:						Tuple      =     "document_qa"
					UpperCamelCase_			:						List[str]      =     AutoProcessor
					UpperCamelCase_			:						Optional[Any]      =     VisionEncoderDecoderModel

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




					def __init__(						self    :  Union[str, Any]	,    *snake_case__    :  Any	,    **snake_case__    :  Any  ):
							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    :  Dict	,    snake_case__    :  "Image"	,    snake_case__    :  str  ):
							lowerCAmelCase__   = """<s_docvqa><s_question>{user_input}</s_question><s_answer>"""
							lowerCAmelCase__   = task_prompt.replace("""{user_input}"""	,    snake_case__  )
							lowerCAmelCase__   = self.pre_processor.tokenizer(
							    snake_case__	,    add_special_tokens=snake_case__	,    return_tensors="""pt"""  ).input_ids
							lowerCAmelCase__   = 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    :  Tuple	,    snake_case__    :  Tuple  ):
							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    :  Dict	,    snake_case__    :  Tuple  ):
							lowerCAmelCase__   = self.pre_processor.batch_decode(snake_case__  )[0]
							lowerCAmelCase__   = sequence.replace(self.pre_processor.tokenizer.eos_token	,    """"""  )
							lowerCAmelCase__   = sequence.replace(self.pre_processor.tokenizer.pad_token	,    """"""  )
							lowerCAmelCase__   = re.sub(R"""<.*?>"""	,    """"""	,    snake_case__	,    count=1  ).strip()  # remove first task start token
							lowerCAmelCase__   = self.pre_processor.tokenajson(snake_case__  )

							return sequence["answer"]





"""simple docstring"""




import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class    a_   (  __UpperCamelCase			,		unittest.TestCase      ):
					UpperCamelCase_			:						Optional[int]      =     MgpstrTokenizer
					UpperCamelCase_			:						Optional[int]      =     False
					UpperCamelCase_			:						Tuple      =     {}
					UpperCamelCase_			:						List[str]      =     False




					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict  ):
							super().setUp()

							# fmt: off
							lowerCAmelCase__   = ["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""]
							# fmt: on
							lowerCAmelCase__   = dict(zip(snake_case__	,    range(len(snake_case__  )  )  )  )

							lowerCAmelCase__   = os.path.join(self.tmpdirname	,    VOCAB_FILES_NAMES["""vocab_file"""]  )
							with open(self.vocab_file	,    """w"""	,    encoding="""utf-8"""  ) as fp:
									fp.write(json.dumps(snake_case__  ) + """\n"""  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  int	,    **snake_case__    :  Tuple  ):
							return MgpstrTokenizer.from_pretrained(self.tmpdirname	,    **snake_case__  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[str]	,    snake_case__    :  Dict  ):
							lowerCAmelCase__   = """tester"""
							lowerCAmelCase__   = """tester"""
							return input_text, output_text




					@unittest.skip("""MGP-STR always lower cases letters."""  )
					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[int]  ):
							pass




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[int]  ):
							lowerCAmelCase__   = self.get_tokenizers(do_lower_case=snake_case__  )
							for tokenizer in tokenizers:
									with self.subTest(F"""{tokenizer.__class__.__name__}"""  ):
											lowerCAmelCase__   = """[SPECIAL_TOKEN]"""

											tokenizer.add_special_tokens({"""cls_token""": special_token}  )
											lowerCAmelCase__   = tokenizer.encode([special_token]	,    add_special_tokens=snake_case__  )
											self.assertEqual(len(snake_case__  )	,    1  )

											lowerCAmelCase__   = tokenizer.decode(snake_case__	,    skip_special_tokens=snake_case__  )
											self.assertTrue(special_token not in decoded  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[str]  ):
							lowerCAmelCase__   = self.get_tokenizers()
							for tokenizer in tokenizers:
									with self.subTest(F"""{tokenizer.__class__.__name__}"""  ):
											lowerCAmelCase__ ,       lowerCAmelCase__   = self.get_input_output_texts(snake_case__  )

											lowerCAmelCase__   = tokenizer.tokenize(snake_case__  )
											lowerCAmelCase__   = tokenizer.convert_tokens_to_ids(snake_case__  )
											lowerCAmelCase__   = tokenizer.encode(snake_case__	,    add_special_tokens=snake_case__  )
											self.assertListEqual(snake_case__	,    snake_case__  )

											lowerCAmelCase__   = tokenizer.convert_ids_to_tokens(snake_case__  )
											self.assertNotEqual(len(snake_case__  )	,    0  )
											lowerCAmelCase__   = tokenizer.decode(snake_case__  )
											self.assertIsInstance(snake_case__	,    snake_case__  )

											self.assertEqual(text_a.replace(""" """	,    """"""  )	,    snake_case__  )




					@unittest.skip("""MGP-STR tokenizer only handles one sequence."""  )
					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[Any]  ):
							pass




					@unittest.skip("""inputs cannot be pretokenized in MgpstrTokenizer"""  )
					def     _SCREAMING_SNAKE_CASE    (						self    :  List[Any]  ):
							pass




