


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 ViTImageProcessor




class 			lowercase_				(unittest.TestCase      ):





				def __init__(							self					,     lowercase_					,     lowercase_=7					,     lowercase_=3					,     lowercase_=18					,     lowercase_=30					,     lowercase_=400					,     lowercase_=True					,     lowercase_=None					,     lowercase_=True					,     lowercase_=[0.5, 0.5, 0.5]					,     lowercase_=[0.5, 0.5, 0.5]					,     )     ->   Union[str, Any]:
											a__     						=size if size is not None else {'height': 18, 'width': 18}
											a__     						=parent
											a__     						=batch_size
											a__     						=num_channels
											a__     						=image_size
											a__     						=min_resolution
											a__     						=max_resolution
											a__     						=do_resize
											a__     						=size
											a__     						=do_normalize
											a__     						=image_mean
											a__     						=image_std





				def 	__UpperCamelCase						(							self)     ->   List[Any]:
											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 			lowercase_				(lowercase__			,				unittest.TestCase      ):
				snake_case  						=ViTImageProcessor if is_vision_available() else None





				def 	__UpperCamelCase						(							self)     ->   Optional[int]:
											a__     						=ViTImageProcessingTester(self)





				@property
				def 	__UpperCamelCase						(							self)     ->   List[str]:
											return self.image_processor_tester.prepare_image_processor_dict()





				def 	__UpperCamelCase						(							self)     ->   List[Any]:
											a__     						=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 	__UpperCamelCase						(							self)     ->   Optional[int]:
											a__     						=self.image_processing_class.from_dict(self.image_processor_dict)
											self.assertEqual(image_processor.size					,     {'height': 18, 'width': 18})

											a__     						=self.image_processing_class.from_dict(self.image_processor_dict					,     size=42)
											self.assertEqual(image_processor.size					,     {'height': 42, 'width': 42})





				def 	__UpperCamelCase						(							self)     ->   Optional[Any]:
											pass





				def 	__UpperCamelCase						(							self)     ->   int:
											# Initialize image_processing
											a__     						=self.image_processing_class(**self.image_processor_dict)
											# create random PIL images
											a__     						=prepare_image_inputs(self.image_processor_tester					,     equal_resolution=lowercase_)
											for image in image_inputs:
																		self.assertIsInstance(lowercase_					,     Image.Image)

											# Test not batched input
											a__     						=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
											a__     						=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 	__UpperCamelCase						(							self)     ->   List[str]:
											# Initialize image_processing
											a__     						=self.image_processing_class(**self.image_processor_dict)
											# create random numpy tensors
											a__     						=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
											a__     						=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
											a__     						=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 	__UpperCamelCase						(							self)     ->   List[Any]:
											# Initialize image_processing
											a__     						=self.image_processing_class(**self.image_processor_dict)
											# create random PyTorch tensors
											a__     						=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
											a__     						=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
											a__     						=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'],
											    )					,     )





# 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:	Any      							=					'Here is a text containing a lot of information: \'\'\'{text}\'\'\'.\n\nCan you answer this question about the text: \'{question}\''




class 			lowercase_				(lowercase__      ):
				snake_case  						='google/flan-t5-base'
				snake_case  						=(
				    '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.'
				)
				snake_case  						='text_qa'
				snake_case  						=AutoTokenizer
				snake_case  						=AutoModelForSeqaSeqLM

				snake_case  						=['text', 'text']
				snake_case  						=['text']





				def 	__UpperCamelCase						(							self					,     lowercase_					,     lowercase_)     ->   Optional[int]:
											a__     						=QA_PROMPT.format(text=lowercase_					,     question=lowercase_)
											return self.pre_processor(lowercase_					,     return_tensors='pt')





				def 	__UpperCamelCase						(							self					,     lowercase_)     ->   Dict:
											a__     						=self.model.generate(**lowercase_)

											a__						,    a__     						=inputs['input_ids'].shape
											a__     						=output_ids.shape[0]

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





				def 	__UpperCamelCase						(							self					,     lowercase_)     ->   Union[str, Any]:
											return self.pre_processor.decode(lowercase_					,     skip_special_tokens=lowercase_					,     clean_up_tokenization_spaces=lowercase_)

