


"""simple docstring"""






from typing import List

import datasets
from datasets.tasks import ImageClassification

from ..folder_based_builder import folder_based_builder


lowercase  		=			datasets.utils.logging.get_logger(__name__)


class 			SCREAMING_SNAKE_CASE_  (   folder_based_builder.FolderBasedBuilderConfig):




							'''simple docstring'''





							__magic_name__					:     bool				  =  None
							__magic_name__					:     bool				  =  None


class 			SCREAMING_SNAKE_CASE_  (   folder_based_builder.FolderBasedBuilder):




							'''simple docstring'''





							__magic_name__					:     Optional[int]				  =  datasets.Image()
							__magic_name__					:     List[Any]				  =  '''image'''
							__magic_name__					:     Union[str, Any]				  =  ImageFolderConfig
							__magic_name__					:     List[str]  # definition at the bottom of the script
							__magic_name__					:     str				  =  ImageClassification(image_column='''image'''	,      label_column='''label''')


lowercase  		=			[
    """.blp""",
    """.bmp""",
    """.dib""",
    """.bufr""",
    """.cur""",
    """.pcx""",
    """.dcx""",
    """.dds""",
    """.ps""",
    """.eps""",
    """.fit""",
    """.fits""",
    """.fli""",
    """.flc""",
    """.ftc""",
    """.ftu""",
    """.gbr""",
    """.gif""",
    """.grib""",
    """.h5""",
    """.hdf""",
    """.png""",
    """.apng""",
    """.jp2""",
    """.j2k""",
    """.jpc""",
    """.jpf""",
    """.jpx""",
    """.j2c""",
    """.icns""",
    """.ico""",
    """.im""",
    """.iim""",
    """.tif""",
    """.tiff""",
    """.jfif""",
    """.jpe""",
    """.jpg""",
    """.jpeg""",
    """.mpg""",
    """.mpeg""",
    """.msp""",
    """.pcd""",
    """.pxr""",
    """.pbm""",
    """.pgm""",
    """.ppm""",
    """.pnm""",
    """.psd""",
    """.bw""",
    """.rgb""",
    """.rgba""",
    """.sgi""",
    """.ras""",
    """.tga""",
    """.icb""",
    """.vda""",
    """.vst""",
    """.webp""",
    """.wmf""",
    """.emf""",
    """.xbm""",
    """.xpm""",
]
lowercase  		=			IMAGE_EXTENSIONS









"""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.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool


lowercase  		=			"""Here is a text containing a lot of information: '''{text}'''.

Can you answer this question about the text: '{question}'"""


class 			SCREAMING_SNAKE_CASE_  (   _lowercase):




							'''simple docstring'''





							__magic_name__					:     List[Any]				  =  '''google/flan-t5-base'''
							__magic_name__					:     Dict				  =  (
							    '''This is a tool that answers questions related to a text. It takes two arguments named `text`, which is the '''
							    '''text where to find the answer, and `question`, which is the question, and returns the answer to the question.'''
							)
							__magic_name__					:     Union[str, Any]				  =  '''text_qa'''
							__magic_name__					:     Union[str, Any]				  =  AutoTokenizer
							__magic_name__					:     List[Any]				  =  AutoModelForSeqaSeqLM

							__magic_name__					:     Tuple				  =  ['''text''', '''text''']
							__magic_name__					:     Tuple				  =  ['''text''']




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__)   ->	Optional[int]:







								'''simple docstring'''




								snake_case__  :						str		=      QA_PROMPT.format(text=lowerCamelCase__	,					question=lowerCamelCase__)
								return self.pre_processor(lowerCamelCase__	,					return_tensors="pt")




							def        UpperCAmelCase (						self	,					lowerCamelCase__)   ->	Optional[int]:







								'''simple docstring'''




								snake_case__  :						Tuple		=      self.model.generate(**lowerCamelCase__)

								snake_case__,					snake_case__  :						Dict		=      inputs["input_ids"].shape
								snake_case__  :						List[Any]		=      output_ids.shape[0]

								return output_ids.reshape(lowerCamelCase__	,					out_b // in_b	,					*output_ids.shape[1:])[0][0]







							def        UpperCAmelCase (						self	,					lowerCamelCase__)   ->	List[Any]:







								'''simple docstring'''




								return self.pre_processor.decode(lowerCamelCase__	,					skip_special_tokens=lowerCamelCase__	,					clean_up_tokenization_spaces=lowerCamelCase__)





