






import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
	from .tokenization_fnet import FNetTokenizer
else:
	lowerCamelCase       :       Optional[Any]        =				None

lowerCamelCase       :       str        =				logging.get_logger(__name__)
lowerCamelCase       :       Union[str, Any]        =				{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCamelCase       :       Tuple        =				{
    '''vocab_file''': {
        '''google/fnet-base''': '''https://huggingface.co/google/fnet-base/resolve/main/spiece.model''',
        '''google/fnet-large''': '''https://huggingface.co/google/fnet-large/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''google/fnet-base''': '''https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json''',
        '''google/fnet-large''': '''https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json''',
    },
}

lowerCamelCase       :       Union[str, Any]        =				{
    '''google/fnet-base''': 5_12,
    '''google/fnet-large''': 5_12,
}

lowerCamelCase       :       Optional[Any]        =				'''▁'''

class 						lowerCAmelCase   (       __a	):

					'''simple docstring'''

					_A  :	Any     =					VOCAB_FILES_NAMES
					_A  :	str     =					PRETRAINED_VOCAB_FILES_MAP
					_A  :	Optional[Any]     =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_A  :	Any     =					['''input_ids''', '''token_type_ids''']
					_A  :	Dict     =					FNetTokenizer



					def __init__(     self :		Optional[int]						,   __a :		Optional[Any]=None						,   __a :		int=None						,   __a :		Optional[int]=False						,   __a :		List[str]=True						,   __a :		str=True						,   __a :		List[str]="<unk>"						,   __a :		List[str]="[SEP]"						,   __a :		str="<pad>"						,   __a :		Tuple="[CLS]"						,   __a :		Any="[MASK]"						,   **__a :		Tuple						,   )	->     List[str]:


												"""simple docstring"""

												__lowercase					:							List[str]			=   (
												    AddedToken(__a						,   lstrip=__a						,   rstrip=__a						,   normalized=__a							)
												    if isinstance(__a						,   __a							)
												    else mask_token
												)

												super().__init__(
												    __a						,   tokenizer_file=__a						,   do_lower_case=__a						,   remove_space=__a						,   keep_accents=__a						,   unk_token=__a						,   sep_token=__a						,   pad_token=__a						,   cls_token=__a						,   mask_token=__a						,   **__a						,   )

												__lowercase					:							Any			=   do_lower_case
												__lowercase					:							List[str]			=   remove_space
												__lowercase					:							str			=   keep_accents
												__lowercase					:							str			=   vocab_file
												__lowercase					:							Any			=   False if not self.vocab_file else True



					def 					lowerCAmelCase (     self :		Tuple						,   __a :		List[int]						,   __a :		Optional[List[int]] = None							)	->     List[int]:


												"""simple docstring"""

												__lowercase					:							int			=   [self.sep_token_id]
												__lowercase					:							Dict			=   [self.cls_token_id]
												if token_ids_a is None:
																			return cls + token_ids_a + sep
												return cls + token_ids_a + sep + token_ids_a + sep



					def 					lowerCAmelCase (     self :		List[str]						,   __a :		List[int]						,   __a :		Optional[List[int]] = None							)	->     List[int]:


												"""simple docstring"""

												__lowercase					:							List[str]			=   [self.sep_token_id]
												__lowercase					:							Union[str, Any]			=   [self.cls_token_id]

												if token_ids_a is None:
																			return len(cls + token_ids_a + sep							) * [0]
												return len(cls + token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1]



					def 					lowerCAmelCase (     self :		Dict						,   __a :		str						,   __a :		Optional[str] = None							)	->     Tuple[str]:


												"""simple docstring"""

												if not os.path.isdir(__a							):
																			logger.error(F"Vocabulary path ({save_directory}) should be a directory"							)
																			return
												__lowercase					:							Dict			=   os.path.join(
												    __a						,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)

												if os.path.abspath(self.vocab_file							) != os.path.abspath(__a							):
																			copyfile(self.vocab_file						,   __a							)

												return (out_vocab_file,)







# 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 						lowerCAmelCase   (       __a	):

					'''simple docstring'''

					_A  :	List[str]     =					'''naver-clova-ix/donut-base-finetuned-docvqa'''
					_A  :	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.'''
					)
					_A  :	Tuple     =					'''document_qa'''
					_A  :	Dict     =					AutoProcessor
					_A  :	Tuple     =					VisionEncoderDecoderModel

					_A  :	Optional[int]     =					['''image''', '''text''']
					_A  :	Optional[int]     =					['''text''']



					def __init__(     self :		Any						,   *__a :		List[str]						,   **__a :		Any							)	->     Optional[Any]:


												"""simple docstring"""

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

												super().__init__(*__a						,   **__a							)



					def 					lowerCAmelCase (     self :		List[Any]						,   __a :		"Image"						,   __a :		str							)	->     List[str]:


												"""simple docstring"""

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

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



					def 					lowerCAmelCase (     self :		Optional[int]						,   __a :		int							)	->     int:


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



					def 					lowerCAmelCase (     self :		Union[str, Any]						,   __a :		Union[str, Any]							)	->     Union[str, Any]:


												"""simple docstring"""

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

												return sequence["answer"]