
# 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__							(       __lowercase):




	'''simple docstring'''




	_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	:Any  ,     *a	:Optional[int]  ,     **a	:List[str]		)	->				Optional[Any]:
								if not is_vision_available():
															raise ValueError("Pillow must be installed to use the DocumentQuestionAnsweringTool."		)

								super().__init__(*a  ,     **a		)







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

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







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

								return sequence["answer"]

from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    str)      ->					None:






							'''simple docstring'''





							__UpperCamelCase     ,		__UpperCamelCase			:     Optional[Any]         =      analyze_text(_lowerCamelCase)
							__UpperCamelCase			:     List[str]         =      list(" " + ascii_lowercase)
							# what is our total sum of probabilities.
							__UpperCamelCase			:     Any         =      sum(single_char_strings.values())

							# one length string
							__UpperCamelCase			:     Optional[Any]         =      0
							# for each alpha we go in our dict and if it is in it we calculate entropy
							for ch in my_alphas:
														if ch in single_char_strings:
																					__UpperCamelCase			:     List[Any]         =      single_char_strings[ch]
																					__UpperCamelCase			:     List[str]         =      my_str / all_sum
																					my_fir_sum += prob * math.loga(_lowerCamelCase)  # entropy formula.

    # print entropy
							print(F'{round(-1 * my_fir_sum):.1f}')

							# two len string
							__UpperCamelCase			:     Optional[Any]         =      sum(two_char_strings.values())
							__UpperCamelCase			:     Tuple         =      0
							# for each alpha (two in size) calculate entropy.
							for cha in my_alphas:
														for cha in my_alphas:
																					__UpperCamelCase			:     List[str]         =      cha + cha
																					if sequence in two_char_strings:
																												__UpperCamelCase			:     Optional[Any]         =      two_char_strings[sequence]
																												__UpperCamelCase			:     Any         =      int(_lowerCamelCase) / all_sum
																												my_sec_sum += prob * math.loga(_lowerCamelCase)

    # print second entropy
							print(F'{round(-1 * my_sec_sum):.1f}')

							# print the difference between them
							print(F'{round((-1 * my_sec_sum) - (-1 * my_fir_sum)):.1f}')




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    str)      ->					tuple[dict, dict]:






							'''simple docstring'''





							__UpperCamelCase			:     Tuple         =      Counter()  # type: ignore
							__UpperCamelCase			:     Any         =      Counter()  # type: ignore
							single_char_strings[text[-1]] += 1

							# first case when we have space at start.
							two_char_strings[" " + text[0]] += 1
							for i in range(0		,						len(_lowerCamelCase) - 1):
														single_char_strings[text[i]] += 1
														two_char_strings[text[i : i + 2]] += 1
							return single_char_strings, two_char_strings




def 			_SCREAMING_SNAKE_CASE      (	)      ->					List[str]:






							'''simple docstring'''





							import doctest

							doctest.testmod()
							# text = (
							#     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
							#     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
							#     "jointure saw horrible. He private he on be imagine suppose. Fertile "
							#     "beloved evident through no service elderly is. Blind there if every no so "
							#     "at. Own neglected you preferred way sincerity delivered his attempted. To "
							#     "of message cottage windows do besides against uncivil.  Delightful "
							#     "unreserved impossible few estimating men favourable see entreaties. She "
							#     "propriety immediate was improving. He or entrance humoured likewise "
							#     "moderate. Much nor game son say feel. Fat make met can must form into "
							#     "gate. Me we offending prevailed discovery. "
							# )

							# calculate_prob(text)


if __name__ == "__main__":
		main()