


# 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		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							List[Any]					  =  "naver-clova-ix/donut-base-finetuned-docvqa"
							__UpperCAmelCase   :							str					  =  (
							    "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   :							Union[str, Any]					  =  "document_qa"
							__UpperCAmelCase   :							str					  =  AutoProcessor
							__UpperCAmelCase   :							str					  =  VisionEncoderDecoderModel

							__UpperCAmelCase   :							str					  =  ["image", "text"]
							__UpperCAmelCase   :							Optional[int]					  =  ["text"]

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

														super().__init__(*lowerCamelCase   ,     **lowerCamelCase    )

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

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

							def    __snake_case (							self	:  int   ,     lowerCamelCase	:  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=lowerCamelCase   ,     pad_token_id=self.pre_processor.tokenizer.pad_token_id   ,     eos_token_id=self.pre_processor.tokenizer.eos_token_id   ,     use_cache=lowerCamelCase   ,     num_beams=1   ,     bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]]   ,     return_dict_in_generate=lowerCamelCase   ,     ).sequences






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

														return sequence["answer"]





from __future__ import annotations

_snake_case					:		Union[str, Any]      =    []







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							for i in range(len(__lowerCamelCase     )     ):
														if board[row][i] == 1:
																					return False
							for i in range(len(__lowerCamelCase     )     ):
														if board[i][column] == 1:
																					return False
							for i, j in zip(range(__lowerCamelCase							,     -1							,     -1     )							,     range(__lowerCamelCase							,     -1							,     -1     )     ):
														if board[i][j] == 1:
																					return False
							for i, j in zip(range(__lowerCamelCase							,     -1							,     -1     )							,     range(__lowerCamelCase							,     len(__lowerCamelCase     )     )     ):
														if board[i][j] == 1:
																					return False
							return True







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							if row >= len(__lowerCamelCase     ):
														solution.append(__lowerCamelCase     )
														printboard(__lowerCamelCase     )
														print()
														return True
							for i in range(len(__lowerCamelCase     )     ):
														if is_safe(__lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
																					__snake_case		:      Optional[Any]										=       1
																					solve(__lowerCamelCase							,     row + 1     )
																					__snake_case		:      Union[str, Any]										=       0
							return False







def     lowerCAmelCase_					( __lowerCamelCase     ):
							for i in range(len(__lowerCamelCase     )     ):
														for j in range(len(__lowerCamelCase     )     ):
																					if board[i][j] == 1:
																												print("Q"							,     end=" "     )
																					else:
																												print("."							,     end=" "     )
														print()


# n=int(input("The no. of queens"))
_snake_case					:		List[str]      =    8
_snake_case					:		Optional[int]      =    [[0 for i in range(n)] for j in range(n)]
solve(board, 0)

print("The total no. of solutions are :", len(solution))

