"""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 	UpperCamelCase    (					lowercase		):
				UpperCAmelCase       :   Dict					=	"""naver-clova-ix/donut-base-finetuned-docvqa"""
				UpperCAmelCase       :   List[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."""
				)
				UpperCAmelCase       :   Dict					=	"""document_qa"""
				UpperCAmelCase       :   Any					=	AutoProcessor
				UpperCAmelCase       :   Union[str, Any]					=	VisionEncoderDecoderModel

				UpperCAmelCase       :   Tuple					=	["""image""", """text"""]
				UpperCAmelCase       :   Optional[int]					=	["""text"""]



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

					super().__init__(*_A			,						**_A)



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

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



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

					return sequence["answer"]






"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


_a  :  Any=				{"configuration_speech_encoder_decoder": ["SpeechEncoderDecoderConfig"]}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a  :  Union[str, Any]=				["SpeechEncoderDecoderModel"]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a  :  Tuple=				["FlaxSpeechEncoderDecoderModel"]

if TYPE_CHECKING:
	from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel

else:
	import sys

	_a  :  str=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





