

'''simple docstring'''



import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 ConditionalDetrImageProcessor



class lowerCamelCase					(    unittest.TestCase		):



			'''simple docstring'''






			def __init__( self			:		Union[str, Any]						,     lowerCAmelCase_			:		Optional[Any]						,     lowerCAmelCase_			:		Optional[int]=7						,     lowerCAmelCase_			:		Optional[int]=3						,     lowerCAmelCase_			:		Any=30						,     lowerCAmelCase_			:		Any=4_00						,     lowerCAmelCase_			:		Optional[Any]=True						,     lowerCAmelCase_			:		Dict=None						,     lowerCAmelCase_			:		Union[str, Any]=True						,     lowerCAmelCase_			:		Tuple=[0.5, 0.5, 0.5]						,     lowerCAmelCase_			:		int=[0.5, 0.5, 0.5]						,     lowerCAmelCase_			:		Optional[int]=True						,     lowerCAmelCase_			:		Optional[int]=1 / 2_55						,     lowerCAmelCase_			:		Dict=True						,     )     ->						Optional[Any]:





									'''simple docstring'''
									# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
									A__	:  Union[str, Any]           =size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 13_33}
									A__	:  str           =parent
									A__	:  List[str]           =batch_size
									A__	:  Union[str, Any]           =num_channels
									A__	:  str           =min_resolution
									A__	:  Any           =max_resolution
									A__	:  Any           =do_resize
									A__	:  Dict           =size
									A__	:  Any           =do_normalize
									A__	:  str           =image_mean
									A__	:  Optional[Any]           =image_std
									A__	:  Tuple           =do_rescale
									A__	:  List[str]           =rescale_factor
									A__	:  Dict           =do_pad


			def lowercase__		( self			:		List[Any]					)     ->						Optional[Any]:





									'''simple docstring'''
									return {
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_normalize": self.do_normalize,
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_rescale": self.do_rescale,
									    "rescale_factor": self.rescale_factor,
									    "do_pad": self.do_pad,
									}



			def lowercase__		( self			:		Optional[Any]						,     lowerCAmelCase_			:		Union[str, Any]						,     lowerCAmelCase_			:		Tuple=False					)     ->						Dict:





									'''simple docstring'''
									if not batched:
															A__	:  Tuple           =image_inputs[0]
															if isinstance(__lowercase						,     Image.Image					):
																					A__	:  int           =image.size
															else:
																					A__	:  str           =image.shape[1], image.shape[2]
															if w < h:
																					A__	:  Tuple           =int(self.size["""shortest_edge"""] * h / w					)
																					A__	:  Optional[Any]           =self.size['''shortest_edge''']
															elif w > h:
																					A__	:  List[Any]           =self.size['''shortest_edge''']
																					A__	:  Optional[Any]           =int(self.size["""shortest_edge"""] * w / h					)
															else:
																					A__	:  Any           =self.size['''shortest_edge''']
																					A__	:  int           =self.size['''shortest_edge''']

									else:
															A__	:  str           =[]
															for image in image_inputs:
																					A__	:  Dict           =self.get_expected_values([image]					)
																					expected_values.append((expected_height, expected_width)					)
															A__	:  List[Any]           =max(__lowercase						,     key=lambda lowerCAmelCase_					: item[0]					)[0]
															A__	:  Optional[int]           =max(__lowercase						,     key=lambda lowerCAmelCase_					: item[1]					)[1]

									return expected_height, expected_width



@require_torch
@require_vision
class lowerCamelCase					(    snake_case__			,		unittest.TestCase		):



			'''simple docstring'''






			__snake_case             =			ConditionalDetrImageProcessor if is_vision_available() else None


			def lowercase__		( self			:		List[str]					)     ->						str:





									'''simple docstring'''
									A__	:  Dict           =ConditionalDetrImageProcessingTester(self					)


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





									'''simple docstring'''
									return self.image_processor_tester.prepare_image_processor_dict()


			def lowercase__		( self			:		Tuple					)     ->						List[Any]:





									'''simple docstring'''
									A__	:  List[str]           =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 lowercase__		( self			:		Optional[int]					)     ->						Any:





									'''simple docstring'''
									A__	:  str           =self.image_processing_class.from_dict(self.image_processor_dict					)
									self.assertEqual(image_processor.size						,     {"""shortest_edge""": 18, """longest_edge""": 13_33}					)
									self.assertEqual(image_processor.do_pad						,     __lowercase					)

									A__	:  Tuple           =self.image_processing_class.from_dict(
									    self.image_processor_dict						,     size=42						,     max_size=84						,     pad_and_return_pixel_mask=__lowercase					)
									self.assertEqual(image_processor.size						,     {"""shortest_edge""": 42, """longest_edge""": 84}					)
									self.assertEqual(image_processor.do_pad						,     __lowercase					)


			def lowercase__		( self			:		List[Any]					)     ->						Any:





									'''simple docstring'''
									pass


			def lowercase__		( self			:		List[Any]					)     ->						List[str]:





									'''simple docstring'''
									# Initialize image_processing
									A__	:  Dict           =self.image_processing_class(**self.image_processor_dict					)
									# create random PIL images
									A__	:  List[Any]           =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__	:  Tuple           =image_processing(image_inputs[0]						,     return_tensors="""pt"""					).pixel_values

									A__	:  Optional[int]           =self.image_processor_tester.get_expected_values(__lowercase					)

									self.assertEqual(
									    encoded_images.shape						,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)						,     )

									# Test batched
									A__	:  Any           =self.image_processor_tester.get_expected_values(__lowercase						,     batched=__lowercase					)

									A__	:  Dict           =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,
									        expected_height,
									        expected_width,
									    )						,     )


			def lowercase__		( self			:		Dict					)     ->						Any:





									'''simple docstring'''
									# Initialize image_processing
									A__	:  str           =self.image_processing_class(**self.image_processor_dict					)
									# create random numpy tensors
									A__	:  Union[str, Any]           =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__	:  List[str]           =image_processing(image_inputs[0]						,     return_tensors="""pt"""					).pixel_values

									A__	:  str           =self.image_processor_tester.get_expected_values(__lowercase					)

									self.assertEqual(
									    encoded_images.shape						,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)						,     )

									# Test batched
									A__	:  Optional[Any]           =image_processing(__lowercase						,     return_tensors="""pt"""					).pixel_values

									A__	:  List[Any]           =self.image_processor_tester.get_expected_values(__lowercase						,     batched=__lowercase					)

									self.assertEqual(
									    encoded_images.shape						,     (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )						,     )


			def lowercase__		( self			:		Optional[int]					)     ->						Optional[int]:





									'''simple docstring'''
									# Initialize image_processing
									A__	:  List[str]           =self.image_processing_class(**self.image_processor_dict					)
									# create random PyTorch tensors
									A__	:  Union[str, Any]           =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__	:  Optional[int]           =image_processing(image_inputs[0]						,     return_tensors="""pt"""					).pixel_values

									A__	:  Dict           =self.image_processor_tester.get_expected_values(__lowercase					)

									self.assertEqual(
									    encoded_images.shape						,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)						,     )

									# Test batched
									A__	:  Union[str, Any]           =image_processing(__lowercase						,     return_tensors="""pt"""					).pixel_values

									A__	:  int           =self.image_processor_tester.get_expected_values(__lowercase						,     batched=__lowercase					)

									self.assertEqual(
									    encoded_images.shape						,     (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )						,     )


			@slow
			def lowercase__		( self			:		Dict					)     ->						Union[str, Any]:





									'''simple docstring'''
									# prepare image and target
									A__	:  List[Any]           =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
									with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""						,     """r"""					) as f:
															A__	:  int           =json.loads(f.read()					)

									A__	:  List[Any]           ={'''image_id''': 3_97_69, '''annotations''': target}

									# encode them
									A__	:  Dict           =ConditionalDetrImageProcessor.from_pretrained("""microsoft/conditional-detr-resnet-50"""					)
									A__	:  List[str]           =image_processing(images=__lowercase						,     annotations=__lowercase						,     return_tensors="""pt"""					)

									# verify pixel values
									A__	:  Dict           =torch.Size([1, 3, 8_00, 10_66]					)
									self.assertEqual(encoding["""pixel_values"""].shape						,     __lowercase					)

									A__	:  Optional[int]           =torch.tensor([0.2796, 0.3138, 0.3481]					)
									self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]						,     __lowercase						,     atol=1e-4					)					)

									# verify area
									A__	:  int           =torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]					)
									self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]						,     __lowercase					)					)
									# verify boxes
									A__	:  List[Any]           =torch.Size([6, 4]					)
									self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape						,     __lowercase					)
									A__	:  str           =torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]					)
									self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]						,     __lowercase						,     atol=1e-3					)					)
									# verify image_id
									A__	:  Tuple           =torch.tensor([3_97_69]					)
									self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]						,     __lowercase					)					)
									# verify is_crowd
									A__	:  Dict           =torch.tensor([0, 0, 0, 0, 0, 0]					)
									self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]						,     __lowercase					)					)
									# verify class_labels
									A__	:  List[str]           =torch.tensor([75, 75, 63, 65, 17, 17]					)
									self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]						,     __lowercase					)					)
									# verify orig_size
									A__	:  List[Any]           =torch.tensor([4_80, 6_40]					)
									self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]						,     __lowercase					)					)
									# verify size
									A__	:  Any           =torch.tensor([8_00, 10_66]					)
									self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]						,     __lowercase					)					)



			@slow
			def lowercase__		( self			:		str					)     ->						Any:





									'''simple docstring'''
									# prepare image, target and masks_path
									A__	:  Union[str, Any]           =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
									with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""						,     """r"""					) as f:
															A__	:  List[Any]           =json.loads(f.read()					)

									A__	:  Any           ={'''file_name''': '''000000039769.png''', '''image_id''': 3_97_69, '''segments_info''': target}

									A__	:  List[str]           =pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""					)

									# encode them
									A__	:  int           =ConditionalDetrImageProcessor(format="""coco_panoptic"""					)
									A__	:  Optional[int]           =image_processing(images=__lowercase						,     annotations=__lowercase						,     masks_path=__lowercase						,     return_tensors="""pt"""					)

									# verify pixel values
									A__	:  Optional[Any]           =torch.Size([1, 3, 8_00, 10_66]					)
									self.assertEqual(encoding["""pixel_values"""].shape						,     __lowercase					)

									A__	:  Dict           =torch.tensor([0.2796, 0.3138, 0.3481]					)
									self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]						,     __lowercase						,     atol=1e-4					)					)

									# verify area
									A__	:  int           =torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]					)
									self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]						,     __lowercase					)					)
									# verify boxes
									A__	:  Any           =torch.Size([6, 4]					)
									self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape						,     __lowercase					)
									A__	:  int           =torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]					)
									self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]						,     __lowercase						,     atol=1e-3					)					)
									# verify image_id
									A__	:  List[Any]           =torch.tensor([3_97_69]					)
									self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]						,     __lowercase					)					)
									# verify is_crowd
									A__	:  Tuple           =torch.tensor([0, 0, 0, 0, 0, 0]					)
									self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]						,     __lowercase					)					)
									# verify class_labels
									A__	:  Any           =torch.tensor([17, 17, 63, 75, 75, 93]					)
									self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]						,     __lowercase					)					)
									# verify masks
									A__	:  Optional[Any]           =82_28_73
									self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()						,     __lowercase					)
									# verify orig_size
									A__	:  List[str]           =torch.tensor([4_80, 6_40]					)
									self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]						,     __lowercase					)					)
									# verify size
									A__	:  Any           =torch.tensor([8_00, 10_66]					)
									self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]						,     __lowercase					)					)








"""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
class 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""
				__snake_case :							int									=		"""philschmid/bart-large-cnn-samsum"""
				__snake_case :							Optional[int]									=		(
				    """This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, """
				    """and returns a summary of the text."""
				)
				__snake_case :							Dict									=		"""summarizer"""
				__snake_case :							str									=		AutoTokenizer
				__snake_case :							List[str]									=		AutoModelForSeqaSeqLM

				__snake_case :							List[Any]									=		["""text"""]
				__snake_case :							Dict									=		["""text"""]




				def        __lowercase	(  self	:int    ,     __lowercase	:List[Any]      ):
											return self.pre_processor(__lowercase    ,     return_tensors='''pt'''    ,     truncation=__lowercase      )




				def        __lowercase	(  self	:Optional[int]    ,     __lowercase	:Optional[int]      ):
											return self.model.generate(**__lowercase      )[0]




				def        __lowercase	(  self	:List[Any]    ,     __lowercase	:str      ):
											return self.pre_processor.decode(__lowercase    ,     skip_special_tokens=__lowercase    ,     clean_up_tokenization_spaces=__lowercase      )




