






from ...processing_utils import ProcessorMixin



class 				__UpperCAmelCase		(_UpperCAmelCase   ):
			__snake_case    :				Any									=							"SpeechT5FeatureExtractor"
			__snake_case    :				Any									=							"SpeechT5Tokenizer"







			def __init__(     self:  int				,     UpperCAmelCase_:  Union[str, Any]				,     UpperCAmelCase_:  Optional[Any]					):







									'''simple docstring'''




									super().__init__(UpperCAmelCase_				,     UpperCAmelCase_					)







			def __call__(     self:  Any				,     *UpperCAmelCase_:  int				,     **UpperCAmelCase_:  Any					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  kwargs.pop("""audio"""				,     UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  kwargs.pop("""text"""				,     UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  kwargs.pop("""text_target"""				,     UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  kwargs.pop("""audio_target"""				,     UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  kwargs.pop("""sampling_rate"""				,     UpperCAmelCase_					)

									if audio is not None and text is not None:
															raise ValueError(
															    """Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?"""					)
									if audio_target is not None and text_target is not None:
															raise ValueError(
															    """Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?"""					)
									if audio is None and audio_target is None and text is None and text_target is None:
															raise ValueError(
															    """You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process."""					)

									if audio is not None:
															_SCREAMING_SNAKE_CASE     				=  self.feature_extractor(UpperCAmelCase_				,     *UpperCAmelCase_				,     sampling_rate=UpperCAmelCase_				,     **UpperCAmelCase_					)
									elif text is not None:
															_SCREAMING_SNAKE_CASE     				=  self.tokenizer(UpperCAmelCase_				,     **UpperCAmelCase_					)
									else:
															_SCREAMING_SNAKE_CASE     				=  None

									if audio_target is not None:
															_SCREAMING_SNAKE_CASE     				=  self.feature_extractor(audio_target=UpperCAmelCase_				,     *UpperCAmelCase_				,     sampling_rate=UpperCAmelCase_				,     **UpperCAmelCase_					)
															_SCREAMING_SNAKE_CASE     				=  targets["""input_values"""]
									elif text_target is not None:
															_SCREAMING_SNAKE_CASE     				=  self.tokenizer(UpperCAmelCase_				,     **UpperCAmelCase_					)
															_SCREAMING_SNAKE_CASE     				=  targets["""input_ids"""]
									else:
															_SCREAMING_SNAKE_CASE     				=  None

									if inputs is None:
															return targets

									if targets is not None:
															_SCREAMING_SNAKE_CASE     				=  labels

															_SCREAMING_SNAKE_CASE     				=  targets.get("""attention_mask"""					)
															if decoder_attention_mask is not None:
																					_SCREAMING_SNAKE_CASE     				=  decoder_attention_mask

									return inputs







			def 							UpperCamelCase				(     self:  List[str]				,     *UpperCAmelCase_:  Optional[int]				,     **UpperCAmelCase_:  Union[str, Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  kwargs.pop("""input_values"""				,     UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  kwargs.pop("""input_ids"""				,     UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  kwargs.pop("""labels"""				,     UpperCAmelCase_					)

									if input_values is not None and input_ids is not None:
															raise ValueError("""Cannot process both `input_values` and `input_ids` inputs."""					)
									if input_values is None and input_ids is None and labels is None:
															raise ValueError(
															    """You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded."""					)

									if input_values is not None:
															_SCREAMING_SNAKE_CASE     				=  self.feature_extractor.pad(UpperCAmelCase_				,     *UpperCAmelCase_				,     **UpperCAmelCase_					)
									elif input_ids is not None:
															_SCREAMING_SNAKE_CASE     				=  self.tokenizer.pad(UpperCAmelCase_				,     **UpperCAmelCase_					)
									else:
															_SCREAMING_SNAKE_CASE     				=  None

									if labels is not None:
															if "input_ids" in labels or (isinstance(UpperCAmelCase_				,     UpperCAmelCase_					) and "input_ids" in labels[0]):
																					_SCREAMING_SNAKE_CASE     				=  self.tokenizer.pad(UpperCAmelCase_				,     **UpperCAmelCase_					)
																					_SCREAMING_SNAKE_CASE     				=  targets["""input_ids"""]
															else:
																					_SCREAMING_SNAKE_CASE     				=  self.feature_extractor.feature_size
																					_SCREAMING_SNAKE_CASE     				=  self.feature_extractor.num_mel_bins
																					_SCREAMING_SNAKE_CASE     				=  self.feature_extractor.pad(UpperCAmelCase_				,     *UpperCAmelCase_				,     **UpperCAmelCase_					)
																					_SCREAMING_SNAKE_CASE     				=  feature_size_hack
																					_SCREAMING_SNAKE_CASE     				=  targets["""input_values"""]
									else:
															_SCREAMING_SNAKE_CASE     				=  None

									if inputs is None:
															return targets

									if targets is not None:
															_SCREAMING_SNAKE_CASE     				=  labels

															_SCREAMING_SNAKE_CASE     				=  targets.get("""attention_mask"""					)
															if decoder_attention_mask is not None:
																					_SCREAMING_SNAKE_CASE     				=  decoder_attention_mask

									return inputs







			def 							UpperCamelCase				(     self:  Optional[int]				,     *UpperCAmelCase_:  Any				,     **UpperCAmelCase_:  List[Any]					):







									'''simple docstring'''




									return self.tokenizer.batch_decode(*UpperCAmelCase_				,     **UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  List[str]				,     *UpperCAmelCase_:  Union[str, Any]				,     **UpperCAmelCase_:  Tuple					):







									'''simple docstring'''




									return self.tokenizer.decode(*UpperCAmelCase_				,     **UpperCAmelCase_					)









# 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		(_UpperCAmelCase   ):
			__snake_case    :				Dict									=							"naver-clova-ix/donut-base-finetuned-docvqa"
			__snake_case    :				Dict									=							(
			    "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."
			)
			__snake_case    :				Dict									=							"document_qa"
			__snake_case    :				Any									=							AutoProcessor
			__snake_case    :				int									=							VisionEncoderDecoderModel

			__snake_case    :				Union[str, Any]									=							["image", "text"]
			__snake_case    :				Optional[int]									=							["text"]







			def __init__(     self:  List[Any]				,     *UpperCAmelCase_:  Optional[int]				,     **UpperCAmelCase_:  List[str]					):







									'''simple docstring'''




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

									super().__init__(*UpperCAmelCase_				,     **UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Optional[int]				,     UpperCAmelCase_:  "Image"				,     UpperCAmelCase_:  str					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  """<s_docvqa><s_question>{user_input}</s_question><s_answer>"""
									_SCREAMING_SNAKE_CASE     				=  task_prompt.replace("""{user_input}"""				,     UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  self.pre_processor.tokenizer(
									    UpperCAmelCase_				,     add_special_tokens=UpperCAmelCase_				,     return_tensors="""pt"""					).input_ids
									_SCREAMING_SNAKE_CASE     				=  self.pre_processor(UpperCAmelCase_				,     return_tensors="""pt"""					).pixel_values

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







			def 							UpperCamelCase				(     self:  Dict				,     UpperCAmelCase_:  str					):







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







			def 							UpperCamelCase				(     self:  Dict				,     UpperCAmelCase_:  Tuple					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.pre_processor.batch_decode(UpperCAmelCase_					)[0]
									_SCREAMING_SNAKE_CASE     				=  sequence.replace(self.pre_processor.tokenizer.eos_token				,     """"""					)
									_SCREAMING_SNAKE_CASE     				=  sequence.replace(self.pre_processor.tokenizer.pad_token				,     """"""					)
									_SCREAMING_SNAKE_CASE     				=  re.sub(R"""<.*?>"""				,     """"""				,     UpperCAmelCase_				,     count=1					).strip()  # remove first task start token
									_SCREAMING_SNAKE_CASE     				=  self.pre_processor.tokenajson(UpperCAmelCase_					)

									return sequence["answer"]

