



"""simple docstring"""


import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

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 transformers import OneFormerImageProcessor
								from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
								from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
				from PIL import Image

def 			_lowerCAmelCase				(       lowercase_		,     lowercase_="shi-labs/oneformer_demo"						):
						with open(hf_hub_download(lowercase_		,     lowercase_		,     repo_type='dataset'						)		,     'r'						) as f:
												UpperCAmelCase					=			json.load(lowercase_						)
						UpperCAmelCase					=			{}
						UpperCAmelCase					=			[]
						UpperCAmelCase					=			[]
						for key, info in class_info.items():
												UpperCAmelCase					=			info['name']
												class_names.append(info['name']						)
												if info["isthing"]:
																		thing_ids.append(int(lowercase_						)						)
						UpperCAmelCase					=			thing_ids
						UpperCAmelCase					=			class_names
						return metadata







class 				A_      (					unittest.TestCase    ):



						"""simple docstring"""






						def __init__( self			:Optional[int]       ,  lowercase_			:str       ,  lowercase_			:Any=7       ,  lowercase_			:Tuple=3       ,  lowercase_			:Optional[int]=30       ,  lowercase_			:List[str]=4_00       ,  lowercase_			:Optional[int]=None       ,  lowercase_			:List[Any]=True       ,  lowercase_			:int=True       ,  lowercase_			:List[str]=[0.5, 0.5, 0.5]       ,  lowercase_			:List[str]=[0.5, 0.5, 0.5]       ,  lowercase_			:Union[str, Any]=10       ,  lowercase_			:Optional[int]=False       ,  lowercase_			:int=2_55       ,  lowercase_			:List[Any]="shi-labs/oneformer_demo"       ,  lowercase_			:Dict="ade20k_panoptic.json"       ,  lowercase_			:str=10       ,  )    ->      List[Any]:
												UpperCAmelCase					=			parent
												UpperCAmelCase					=			batch_size
												UpperCAmelCase					=			num_channels
												UpperCAmelCase					=			min_resolution
												UpperCAmelCase					=			max_resolution
												UpperCAmelCase					=			do_resize
												UpperCAmelCase					=			{'shortest_edge': 32, 'longest_edge': 13_33} if size is None else size
												UpperCAmelCase					=			do_normalize
												UpperCAmelCase					=			image_mean
												UpperCAmelCase					=			image_std
												UpperCAmelCase					=			class_info_file
												UpperCAmelCase					=			prepare_metadata(lowercase_       ,  lowercase_ )
												UpperCAmelCase					=			num_text
												UpperCAmelCase					=			repo_path

												# for the post_process_functions
												UpperCAmelCase					=			2
												UpperCAmelCase					=			10
												UpperCAmelCase					=			10
												UpperCAmelCase					=			3
												UpperCAmelCase					=			4
												UpperCAmelCase					=			num_labels
												UpperCAmelCase					=			do_reduce_labels
												UpperCAmelCase					=			ignore_index






						def      UpperCAmelCase__					( self			:str )    ->      Union[str, Any]:
												return {
												    "do_resize": self.do_resize,
												    "size": self.size,
												    "do_normalize": self.do_normalize,
												    "image_mean": self.image_mean,
												    "image_std": self.image_std,
												    "num_labels": self.num_labels,
												    "do_reduce_labels": self.do_reduce_labels,
												    "ignore_index": self.ignore_index,
												    "class_info_file": self.class_info_file,
												    "metadata": self.metadata,
												    "num_text": self.num_text,
												}






						def      UpperCAmelCase__					( self			:Any       ,  lowercase_			:Tuple       ,  lowercase_			:str=False )    ->      str:
												if not batched:
																		UpperCAmelCase					=			image_inputs[0]
																		if isinstance(lowercase_       ,  Image.Image ):
																								UpperCAmelCase				,   UpperCAmelCase					=			image.size
																		else:
																								UpperCAmelCase				,   UpperCAmelCase					=			image.shape[1], image.shape[2]
																		if w < h:
																								UpperCAmelCase					=			int(self.size['shortest_edge'] * h / w )
																								UpperCAmelCase					=			self.size['shortest_edge']
																		elif w > h:
																								UpperCAmelCase					=			self.size['shortest_edge']
																								UpperCAmelCase					=			int(self.size['shortest_edge'] * w / h )
																		else:
																								UpperCAmelCase					=			self.size['shortest_edge']
																								UpperCAmelCase					=			self.size['shortest_edge']

												else:
																		UpperCAmelCase					=			[]
																		for image in image_inputs:
																								UpperCAmelCase				,   UpperCAmelCase					=			self.get_expected_values([image] )
																								expected_values.append((expected_height, expected_width) )
																		UpperCAmelCase					=			max(lowercase_       ,  key=lambda lowercase_ : item[0] )[0]
																		UpperCAmelCase					=			max(lowercase_       ,  key=lambda lowercase_ : item[1] )[1]

												return expected_height, expected_width






						def      UpperCAmelCase__					( self			:Optional[Any] )    ->      Optional[Any]:
												return OneFormerForUniversalSegmentationOutput(
												    # +1 for null class
												    class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1) )       ,  masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width) )       ,  )







@require_torch
@require_vision
class 				A_      (					SCREAMING_SNAKE_CASE_     ,       unittest.TestCase    ):



						"""simple docstring"""
						__UpperCamelCase		    =							OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
						# only for test_image_processing_common.test_image_proc_to_json_string
						__UpperCamelCase		    =							image_processing_class






						def      UpperCAmelCase__					( self			:Union[str, Any] )    ->      Dict:
												UpperCAmelCase					=			OneFormerImageProcessorTester(self )






						@property
						def      UpperCAmelCase__					( self			:Union[str, Any] )    ->      Any:
												return self.image_processing_tester.prepare_image_processor_dict()






						def      UpperCAmelCase__					( self			:Any )    ->      Union[str, Any]:
												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' ) )
												self.assertTrue(hasattr(lowercase_       ,  'ignore_index' ) )
												self.assertTrue(hasattr(lowercase_       ,  'class_info_file' ) )
												self.assertTrue(hasattr(lowercase_       ,  'num_text' ) )
												self.assertTrue(hasattr(lowercase_       ,  'repo_path' ) )
												self.assertTrue(hasattr(lowercase_       ,  'metadata' ) )
												self.assertTrue(hasattr(lowercase_       ,  'do_reduce_labels' ) )






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






						def      UpperCAmelCase__					( self			:List[Any] )    ->      Optional[Any]:
												# Initialize image_processor
												UpperCAmelCase					=			self.image_processing_class(**self.image_processor_dict )
												# create random PIL images
												UpperCAmelCase					=			prepare_image_inputs(self.image_processing_tester       ,  equal_resolution=lowercase_ )
												for image in image_inputs:
																		self.assertIsInstance(lowercase_       ,  Image.Image )

												# Test not batched input
												UpperCAmelCase					=			image_processor(image_inputs[0]       ,  ['semantic']       ,  return_tensors='pt' ).pixel_values

												UpperCAmelCase				,   UpperCAmelCase					=			self.image_processing_tester.get_expected_values(lowercase_ )

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

												# Test batched
												UpperCAmelCase				,   UpperCAmelCase					=			self.image_processing_tester.get_expected_values(lowercase_       ,  batched=lowercase_ )

												UpperCAmelCase					=			image_processor(
												    lowercase_       ,  ['semantic'] * len(lowercase_ )       ,  return_tensors='pt' ).pixel_values
												self.assertEqual(
												    encoded_images.shape       ,  (
												        self.image_processing_tester.batch_size,
												        self.image_processing_tester.num_channels,
												        expected_height,
												        expected_width,
												    )       ,  )






						def      UpperCAmelCase__					( self			:Tuple )    ->      List[str]:
												# Initialize image_processor
												UpperCAmelCase					=			self.image_processing_class(**self.image_processor_dict )
												# create random numpy tensors
												UpperCAmelCase					=			prepare_image_inputs(self.image_processing_tester       ,  equal_resolution=lowercase_       ,  numpify=lowercase_ )
												for image in image_inputs:
																		self.assertIsInstance(lowercase_       ,  np.ndarray )

												# Test not batched input
												UpperCAmelCase					=			image_processor(image_inputs[0]       ,  ['semantic']       ,  return_tensors='pt' ).pixel_values

												UpperCAmelCase				,   UpperCAmelCase					=			self.image_processing_tester.get_expected_values(lowercase_ )

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

												# Test batched
												UpperCAmelCase				,   UpperCAmelCase					=			self.image_processing_tester.get_expected_values(lowercase_       ,  batched=lowercase_ )

												UpperCAmelCase					=			image_processor(
												    lowercase_       ,  ['semantic'] * len(lowercase_ )       ,  return_tensors='pt' ).pixel_values
												self.assertEqual(
												    encoded_images.shape       ,  (
												        self.image_processing_tester.batch_size,
												        self.image_processing_tester.num_channels,
												        expected_height,
												        expected_width,
												    )       ,  )






						def      UpperCAmelCase__					( self			:Optional[Any] )    ->      Any:
												# Initialize image_processor
												UpperCAmelCase					=			self.image_processing_class(**self.image_processor_dict )
												# create random PyTorch tensors
												UpperCAmelCase					=			prepare_image_inputs(self.image_processing_tester       ,  equal_resolution=lowercase_       ,  torchify=lowercase_ )
												for image in image_inputs:
																		self.assertIsInstance(lowercase_       ,  torch.Tensor )

												# Test not batched input
												UpperCAmelCase					=			image_processor(image_inputs[0]       ,  ['semantic']       ,  return_tensors='pt' ).pixel_values

												UpperCAmelCase				,   UpperCAmelCase					=			self.image_processing_tester.get_expected_values(lowercase_ )

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

												# Test batched
												UpperCAmelCase				,   UpperCAmelCase					=			self.image_processing_tester.get_expected_values(lowercase_       ,  batched=lowercase_ )

												UpperCAmelCase					=			image_processor(
												    lowercase_       ,  ['semantic'] * len(lowercase_ )       ,  return_tensors='pt' ).pixel_values
												self.assertEqual(
												    encoded_images.shape       ,  (
												        self.image_processing_tester.batch_size,
												        self.image_processing_tester.num_channels,
												        expected_height,
												        expected_width,
												    )       ,  )






						def      UpperCAmelCase__					( self			:Any       ,  lowercase_			:List[Any]=False       ,  lowercase_			:int=False       ,  lowercase_			:List[Any]="np" )    ->      List[Any]:
												UpperCAmelCase					=			self.image_processing_class(**self.image_processor_dict )
												# prepare image and target
												UpperCAmelCase					=			self.image_processing_tester.num_labels
												UpperCAmelCase					=			None
												UpperCAmelCase					=			None
												UpperCAmelCase					=			prepare_image_inputs(self.image_processing_tester       ,  equal_resolution=lowercase_ )
												if with_segmentation_maps:
																		UpperCAmelCase					=			num_labels
																		if is_instance_map:
																								UpperCAmelCase					=			list(range(lowercase_ ) ) * 2
																								UpperCAmelCase					=			dict(enumerate(lowercase_ ) )
																		UpperCAmelCase					=			[
																		    np.random.randint(0       ,  high * 2       ,  (img.size[1], img.size[0]) ).astype(np.uinta ) for img in image_inputs
																		]
																		if segmentation_type == "pil":
																								UpperCAmelCase					=			[Image.fromarray(lowercase_ ) for annotation in annotations]

												UpperCAmelCase					=			image_processor(
												    lowercase_       ,  ['semantic'] * len(lowercase_ )       ,  lowercase_       ,  return_tensors='pt'       ,  instance_id_to_semantic_id=lowercase_       ,  pad_and_return_pixel_mask=lowercase_       ,  )

												return inputs






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






						def      UpperCAmelCase__					( self			:Optional[Any] )    ->      Dict:
												def common(lowercase_			:Any=False       ,  lowercase_			:Optional[Any]=None ):
																		UpperCAmelCase					=			self.comm_get_image_processor_inputs(
																		    with_segmentation_maps=lowercase_       ,  is_instance_map=lowercase_       ,  segmentation_type=lowercase_ )

																		UpperCAmelCase					=			inputs['mask_labels']
																		UpperCAmelCase					=			inputs['class_labels']
																		UpperCAmelCase					=			inputs['pixel_values']
																		UpperCAmelCase					=			inputs['text_inputs']

																		# check the batch_size
																		for mask_label, class_label, text_input in zip(lowercase_       ,  lowercase_       ,  lowercase_ ):
																								self.assertEqual(mask_label.shape[0]       ,  class_label.shape[0] )
																								# this ensure padding has happened
																								self.assertEqual(mask_label.shape[1:]       ,  pixel_values.shape[2:] )
																								self.assertEqual(len(lowercase_ )       ,  self.image_processing_tester.num_text )

												common()
												common(is_instance_map=lowercase_ )
												common(is_instance_map=lowercase_       ,  segmentation_type='pil' )
												common(is_instance_map=lowercase_       ,  segmentation_type='pil' )






						def      UpperCAmelCase__					( self			:int )    ->      Dict:
												UpperCAmelCase					=			np.zeros((20, 50) )
												UpperCAmelCase					=			1
												UpperCAmelCase					=			1
												UpperCAmelCase					=			1

												UpperCAmelCase					=			binary_mask_to_rle(lowercase_ )
												self.assertEqual(len(lowercase_ )       ,  4 )
												self.assertEqual(rle[0]       ,  21 )
												self.assertEqual(rle[1]       ,  45 )






						def      UpperCAmelCase__					( self			:Any )    ->      Dict:
												UpperCAmelCase					=			self.image_processing_class(
												    num_labels=self.image_processing_tester.num_classes       ,  max_seq_length=77       ,  task_seq_length=77       ,  class_info_file='ade20k_panoptic.json'       ,  num_text=self.image_processing_tester.num_text       ,  repo_path='shi-labs/oneformer_demo'       ,  )
												UpperCAmelCase					=			self.image_processing_tester.get_fake_oneformer_outputs()

												UpperCAmelCase					=			fature_extractor.post_process_semantic_segmentation(lowercase_ )

												self.assertEqual(len(lowercase_ )       ,  self.image_processing_tester.batch_size )
												self.assertEqual(
												    segmentation[0].shape       ,  (
												        self.image_processing_tester.height,
												        self.image_processing_tester.width,
												    )       ,  )

												UpperCAmelCase					=			[(1, 4) for i in range(self.image_processing_tester.batch_size )]
												UpperCAmelCase					=			fature_extractor.post_process_semantic_segmentation(lowercase_       ,  target_sizes=lowercase_ )

												self.assertEqual(segmentation[0].shape       ,  target_sizes[0] )






						def      UpperCAmelCase__					( self			:Any )    ->      List[str]:
												UpperCAmelCase					=			self.image_processing_class(
												    num_labels=self.image_processing_tester.num_classes       ,  max_seq_length=77       ,  task_seq_length=77       ,  class_info_file='ade20k_panoptic.json'       ,  num_text=self.image_processing_tester.num_text       ,  repo_path='shi-labs/oneformer_demo'       ,  )
												UpperCAmelCase					=			self.image_processing_tester.get_fake_oneformer_outputs()
												UpperCAmelCase					=			image_processor.post_process_instance_segmentation(lowercase_       ,  threshold=0 )

												self.assertTrue(len(lowercase_ ) == self.image_processing_tester.batch_size )
												for el in segmentation:
																		self.assertTrue('segmentation' in el )
																		self.assertTrue('segments_info' in el )
																		self.assertEqual(type(el['segments_info'] )       ,  lowercase_ )
																		self.assertEqual(
																		    el['segmentation'].shape       ,  (self.image_processing_tester.height, self.image_processing_tester.width) )






						def      UpperCAmelCase__					( self			:Tuple )    ->      Optional[Any]:
												UpperCAmelCase					=			self.image_processing_class(
												    num_labels=self.image_processing_tester.num_classes       ,  max_seq_length=77       ,  task_seq_length=77       ,  class_info_file='ade20k_panoptic.json'       ,  num_text=self.image_processing_tester.num_text       ,  repo_path='shi-labs/oneformer_demo'       ,  )
												UpperCAmelCase					=			self.image_processing_tester.get_fake_oneformer_outputs()
												UpperCAmelCase					=			image_processor.post_process_panoptic_segmentation(lowercase_       ,  threshold=0 )

												self.assertTrue(len(lowercase_ ) == self.image_processing_tester.batch_size )
												for el in segmentation:
																		self.assertTrue('segmentation' in el )
																		self.assertTrue('segments_info' in el )
																		self.assertEqual(type(el['segments_info'] )       ,  lowercase_ )
																		self.assertEqual(
																		    el['segmentation'].shape       ,  (self.image_processing_tester.height, self.image_processing_tester.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







class 				A_      (					SCREAMING_SNAKE_CASE_    ):



						"""simple docstring"""
						__UpperCamelCase		    =							"""philschmid/bart-large-cnn-samsum"""
						__UpperCamelCase		    =							(
						    """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."""
						)
						__UpperCamelCase		    =							"""summarizer"""
						__UpperCamelCase		    =							AutoTokenizer
						__UpperCamelCase		    =							AutoModelForSeqaSeqLM

						__UpperCamelCase		    =							["""text"""]
						__UpperCamelCase		    =							["""text"""]






						def      UpperCAmelCase__					( self			:int       ,  lowercase_			:Tuple )    ->      int:
												return self.pre_processor(lowercase_       ,  return_tensors='pt'       ,  truncation=lowercase_ )






						def      UpperCAmelCase__					( self			:Dict       ,  lowercase_			:Dict )    ->      Optional[int]:
												return self.model.generate(**lowercase_ )[0]






						def      UpperCAmelCase__					( self			:Dict       ,  lowercase_			:Tuple )    ->      List[str]:
												return self.pre_processor.decode(lowercase_       ,  skip_special_tokens=lowercase_       ,  clean_up_tokenization_spaces=lowercase_ )





