






'''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


lowerCamelCase_					:			List[Any]     						=				'''Here is a text containing a lot of information: \'\'\'{text}\'\'\'.

Can you answer this question about the text: \'{question}\''''
class  _SCREAMING_SNAKE_CASE				(		SCREAMING_SNAKE_CASE						):


			'''simple docstring'''





			__a				:				Union[str, Any]         =       "google/flan-t5-base"
			__a				:				Optional[int]         =       (
			    "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."
			)
			__a				:				Union[str, Any]         =       "text_qa"
			__a				:				Union[str, Any]         =       AutoTokenizer
			__a				:				List[str]         =       AutoModelForSeqaSeqLM

			__a				:				int         =       ["text", "text"]
			__a				:				Any         =       ["text"]






			def 					A   ( self				:    List[Any]			,   lowercase				:    str			,   lowercase				:    str      )				->     str:

									'''simple docstring'''







									UpperCamelCase__       =			QA_PROMPT.format(text=lowercase			,   question=lowercase      )
									return self.pre_processor(lowercase			,   return_tensors="""pt"""      )






			def 					A   ( self				:    Tuple			,   lowercase				:    int      )				->     Dict:

									'''simple docstring'''







									UpperCamelCase__       =			self.model.generate(**lowercase      )

									UpperCamelCase__     , UpperCamelCase__       =			inputs["""input_ids"""].shape
									UpperCamelCase__       =			output_ids.shape[0]

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







			def 					A   ( self				:    str			,   lowercase				:    Union[str, Any]      )				->     str:

									'''simple docstring'''







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








'''simple docstring'''


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import EfficientNetImageProcessor
class  _SCREAMING_SNAKE_CASE				(		unittest.TestCase						):


			'''simple docstring'''





			def __init__( self				:    List[str]			,   lowercase				:    Optional[Any]			,   lowercase				:    int=1_3			,   lowercase				:    List[Any]=3			,   lowercase				:    int=1_8			,   lowercase				:    List[Any]=3_0			,   lowercase				:    Optional[int]=4_0_0			,   lowercase				:    Any=True			,   lowercase				:    int=None			,   lowercase				:    Any=True			,   lowercase				:    Optional[int]=[0.5, 0.5, 0.5]			,   lowercase				:    List[Any]=[0.5, 0.5, 0.5]			,   )				->     List[Any]:

									'''simple docstring'''







									UpperCamelCase__       =			size if size is not None else {"""height""": 1_8, """width""": 1_8}
									UpperCamelCase__       =			parent
									UpperCamelCase__       =			batch_size
									UpperCamelCase__       =			num_channels
									UpperCamelCase__       =			image_size
									UpperCamelCase__       =			min_resolution
									UpperCamelCase__       =			max_resolution
									UpperCamelCase__       =			do_resize
									UpperCamelCase__       =			size
									UpperCamelCase__       =			do_normalize
									UpperCamelCase__       =			image_mean
									UpperCamelCase__       =			image_std







			def 					A   ( self				:    Optional[Any]      )				->     Any:

									'''simple docstring'''







									return {
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_normalize": self.do_normalize,
									    "do_resize": self.do_resize,
									    "size": self.size,
									}
@require_torch
@require_vision
class  _SCREAMING_SNAKE_CASE				(		SCREAMING_SNAKE_CASE							,unittest.TestCase						):


			'''simple docstring'''





			__a				:				List[Any]         =       EfficientNetImageProcessor if is_vision_available() else None






			def 					A   ( self				:    Optional[Any]      )				->     str:

									'''simple docstring'''







									UpperCamelCase__       =			EfficientNetImageProcessorTester(self      )






			@property
			def 					A   ( self				:    Optional[int]      )				->     Optional[Any]:

									'''simple docstring'''







									return self.image_processor_tester.prepare_image_processor_dict()






			def 					A   ( self				:    Dict      )				->     str:

									'''simple docstring'''







									UpperCamelCase__       =			self.image_processing_class(**self.image_processor_dict      )
									self.assertTrue(hasattr(lowercase			,   """image_mean"""      )      )
									self.assertTrue(hasattr(lowercase			,   """image_std"""      )      )
									self.assertTrue(hasattr(lowercase			,   """do_normalize"""      )      )
									self.assertTrue(hasattr(lowercase			,   """do_resize"""      )      )
									self.assertTrue(hasattr(lowercase			,   """size"""      )      )






			def 					A   ( self				:    List[str]      )				->     Optional[int]:

									'''simple docstring'''







									UpperCamelCase__       =			self.image_processing_class.from_dict(self.image_processor_dict      )
									self.assertEqual(image_processor.size			,   {"""height""": 1_8, """width""": 1_8}      )

									UpperCamelCase__       =			self.image_processing_class.from_dict(self.image_processor_dict			,   size=4_2      )
									self.assertEqual(image_processor.size			,   {"""height""": 4_2, """width""": 4_2}      )






			def 					A   ( self				:    Union[str, Any]      )				->     str:

									'''simple docstring'''







									UpperCamelCase__       =			self.image_processing_class(**self.image_processor_dict      )
									# create random PIL images
									UpperCamelCase__       =			prepare_image_inputs(self.image_processor_tester			,   equal_resolution=lowercase      )
									for image in image_inputs:
															self.assertIsInstance(lowercase			,   Image.Image      )

									# Test not batched input
									UpperCamelCase__       =			image_processing(image_inputs[0]			,   return_tensors="""pt"""      ).pixel_values
									self.assertEqual(
									    encoded_images.shape			,   (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )			,   )

									# Test batched
									UpperCamelCase__       =			image_processing(lowercase			,   return_tensors="""pt"""      ).pixel_values
									self.assertEqual(
									    encoded_images.shape			,   (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )			,   )






			def 					A   ( self				:    Dict      )				->     Optional[int]:

									'''simple docstring'''







									UpperCamelCase__       =			self.image_processing_class(**self.image_processor_dict      )
									# create random numpy tensors
									UpperCamelCase__       =			prepare_image_inputs(self.image_processor_tester			,   equal_resolution=lowercase			,   numpify=lowercase      )
									for image in image_inputs:
															self.assertIsInstance(lowercase			,   np.ndarray      )

									# Test not batched input
									UpperCamelCase__       =			image_processing(image_inputs[0]			,   return_tensors="""pt"""      ).pixel_values
									self.assertEqual(
									    encoded_images.shape			,   (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )			,   )

									# Test batched
									UpperCamelCase__       =			image_processing(lowercase			,   return_tensors="""pt"""      ).pixel_values
									self.assertEqual(
									    encoded_images.shape			,   (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )			,   )







			def 					A   ( self				:    Optional[int]      )				->     List[str]:

									'''simple docstring'''







									UpperCamelCase__       =			self.image_processing_class(**self.image_processor_dict      )
									# create random PyTorch tensors
									UpperCamelCase__       =			prepare_image_inputs(self.image_processor_tester			,   equal_resolution=lowercase			,   torchify=lowercase      )
									for image in image_inputs:
															self.assertIsInstance(lowercase			,   torch.Tensor      )

									# Test not batched input
									UpperCamelCase__       =			image_processing(image_inputs[0]			,   return_tensors="""pt"""      ).pixel_values
									self.assertEqual(
									    encoded_images.shape			,   (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )			,   )

									# Test batched
									UpperCamelCase__       =			image_processing(lowercase			,   return_tensors="""pt"""      ).pixel_values
									self.assertEqual(
									    encoded_images.shape			,   (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )			,   )
