




'''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 MobileNetVaImageProcessor





class __magic_name__(						unittest.TestCase      ):






						def __init__(   self						:						int					,   __UpperCamelCase						:						List[Any]					,   __UpperCamelCase						:						int=7					,   __UpperCamelCase						:						Optional[Any]=3					,   __UpperCamelCase						:						Dict=1_8					,   __UpperCamelCase						:						Tuple=3_0					,   __UpperCamelCase						:						List[Any]=4_0_0					,   __UpperCamelCase						:						List[str]=True					,   __UpperCamelCase						:						Any=None					,   __UpperCamelCase						:						int=True					,   __UpperCamelCase						:						Optional[int]=None					,   ):



									'''simple docstring'''






									snake_case__          =						size if size is not None else {"""shortest_edge""": 2_0}
									snake_case__          =						crop_size if crop_size is not None else {"""height""": 1_8, """width""": 1_8}
									snake_case__          =						parent
									snake_case__          =						batch_size
									snake_case__          =						num_channels
									snake_case__          =						image_size
									snake_case__          =						min_resolution
									snake_case__          =						max_resolution
									snake_case__          =						do_resize
									snake_case__          =						size
									snake_case__          =						do_center_crop
									snake_case__          =						crop_size







						def       __lowerCAmelCase(   self						:						Dict       ):



									'''simple docstring'''






									return {
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_center_crop": self.do_center_crop,
									    "crop_size": self.crop_size,
									}







@require_torch
@require_vision
class __magic_name__(						__lowerCAmelCase    ,		unittest.TestCase      ):
						UpperCAmelCase_		:					Optional[Any]    =  MobileNetVaImageProcessor if is_vision_available() else None






						def       __lowerCAmelCase(   self						:						Dict       ):



									'''simple docstring'''






									snake_case__          =						MobileNetVaImageProcessingTester(self       )






						@property
						def       __lowerCAmelCase(   self						:						int       ):



									'''simple docstring'''






									return self.image_processor_tester.prepare_image_processor_dict()






						def       __lowerCAmelCase(   self						:						Optional[int]       ):



									'''simple docstring'''






									snake_case__          =						self.image_processing_class(**self.image_processor_dict       )
									self.assertTrue(hasattr(__UpperCamelCase					,   """do_resize"""       )       )
									self.assertTrue(hasattr(__UpperCamelCase					,   """size"""       )       )
									self.assertTrue(hasattr(__UpperCamelCase					,   """do_center_crop"""       )       )
									self.assertTrue(hasattr(__UpperCamelCase					,   """center_crop"""       )       )






						def       __lowerCAmelCase(   self						:						Optional[int]       ):



									'''simple docstring'''






									snake_case__          =						self.image_processing_class.from_dict(self.image_processor_dict       )
									self.assertEqual(image_processor.size					,   {"""shortest_edge""": 2_0}       )
									self.assertEqual(image_processor.crop_size					,   {"""height""": 1_8, """width""": 1_8}       )

									snake_case__          =						self.image_processing_class.from_dict(self.image_processor_dict					,   size=4_2					,   crop_size=8_4       )
									self.assertEqual(image_processor.size					,   {"""shortest_edge""": 4_2}       )
									self.assertEqual(image_processor.crop_size					,   {"""height""": 8_4, """width""": 8_4}       )






						def       __lowerCAmelCase(   self						:						Optional[int]       ):



									'''simple docstring'''






									pass






						def       __lowerCAmelCase(   self						:						Union[str, Any]       ):



									'''simple docstring'''






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

									# Test not batched input
									snake_case__          =						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.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )					,   )

									# Test batched
									snake_case__          =						image_processing(__UpperCamelCase					,   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.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )					,   )






						def       __lowerCAmelCase(   self						:						int       ):



									'''simple docstring'''






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

									# Test not batched input
									snake_case__          =						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.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )					,   )

									# Test batched
									snake_case__          =						image_processing(__UpperCamelCase					,   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.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )					,   )







						def       __lowerCAmelCase(   self						:						Tuple       ):



									'''simple docstring'''






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

									# Test not batched input
									snake_case__          =						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.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )					,   )

									# Test batched
									snake_case__          =						image_processing(__UpperCamelCase					,   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.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )					,   )
"""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


_a	:	Union[str, Any]=					"Here is a text containing a lot of information: '''{text}'''.\n\nCan you answer this question about the text: '{question}'"

class 						UpperCamelCase     (       lowercase		):
				UpperCAmelCase							: str       =						"""google/flan-t5-base"""
				UpperCAmelCase							: 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."""
				)
				UpperCAmelCase							: Dict       =						"""text_qa"""
				UpperCAmelCase							: Dict       =						AutoTokenizer
				UpperCAmelCase							: Optional[Any]       =						AutoModelForSeqaSeqLM

				UpperCAmelCase							: List[str]       =						["""text""", """text"""]
				UpperCAmelCase							: Any       =						["""text"""]



				def    _lowercase	(self							: Tuple      ,    _A							: str      ,    _A							: str)       ->      Tuple:
					__snake_case     :				List[Any] 						=					QA_PROMPT.format(text=_A      ,    question=_A)
					return self.pre_processor(_A      ,    return_tensors='pt')



				def    _lowercase	(self							: Tuple      ,    _A							: Optional[Any])       ->      List[Any]:
					__snake_case     :				List[Any] 						=					self.model.generate(**_A)

					__snake_case						,							__snake_case     :				Any 						=					inputs['input_ids'].shape
					__snake_case     :				Any 						=					output_ids.shape[0]

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






				def    _lowercase	(self							: Optional[int]      ,    _A							: int)       ->      str:
					return self.pre_processor.decode(_A      ,    skip_special_tokens=_A      ,    clean_up_tokenization_spaces=_A)





