






from typing import TYPE_CHECKING

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


lowerCamelCase       :       int        =				{
    '''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
    '''tokenization_xlm''': ['''XLMTokenizer'''],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase       :       str        =				[
	    '''XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''XLMForMultipleChoice''',
	    '''XLMForQuestionAnswering''',
	    '''XLMForQuestionAnsweringSimple''',
	    '''XLMForSequenceClassification''',
	    '''XLMForTokenClassification''',
	    '''XLMModel''',
	    '''XLMPreTrainedModel''',
	    '''XLMWithLMHeadModel''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase       :       Union[str, Any]        =				[
	    '''TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFXLMForMultipleChoice''',
	    '''TFXLMForQuestionAnsweringSimple''',
	    '''TFXLMForSequenceClassification''',
	    '''TFXLMForTokenClassification''',
	    '''TFXLMMainLayer''',
	    '''TFXLMModel''',
	    '''TFXLMPreTrainedModel''',
	    '''TFXLMWithLMHeadModel''',
	]


if TYPE_CHECKING:
	from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
	from .tokenization_xlm import XLMTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xlm import (
		    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XLMForMultipleChoice,
		    XLMForQuestionAnswering,
		    XLMForQuestionAnsweringSimple,
		    XLMForSequenceClassification,
		    XLMForTokenClassification,
		    XLMModel,
		    XLMPreTrainedModel,
		    XLMWithLMHeadModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_xlm import (
		    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFXLMForMultipleChoice,
		    TFXLMForQuestionAnsweringSimple,
		    TFXLMForSequenceClassification,
		    TFXLMForTokenClassification,
		    TFXLMMainLayer,
		    TFXLMModel,
		    TFXLMPreTrainedModel,
		    TFXLMWithLMHeadModel,
		)

else:
	import sys

	lowerCamelCase       :       Optional[Any]        =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)







# 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"]