
"""simple docstring"""





import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
_a														=		logging.getLogger()



def 				lowerCamelCase__		(      __snake_case,					__snake_case     )							->		List[str]:


					"""simple docstring"""





					_UpperCamelCase										=  '''\n'''.join(__UpperCAmelCase     )
					Path(__UpperCAmelCase     ).open('''w'''     ).writelines(__UpperCAmelCase     )


_a														=		'patrickvonplaten/t5-tiny-random'
_a														=		'sshleifer/bart-tiny-random'
_a														=		'sshleifer/tiny-mbart'

_a														=		logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks






class    _UpperCAmelCase(   _lowerCamelCase     ):



		def 						UpperCAmelCase							( self    ,		__a)	->	List[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  Path(self.get_auto_remove_tmp_dir()) / '''utest_input.source'''
							_UpperCamelCase										=  input_file_name.parent / '''utest_output.txt'''
							assert not output_file_name.exists()
							_UpperCamelCase										=  [''' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.''']
							_dump_articles(lowercase_    ,		lowercase_)

							_UpperCamelCase										=  str(Path(self.get_auto_remove_tmp_dir()) / '''scores.json''')
							_UpperCamelCase										=  '''translation_en_to_de''' if model == T5_TINY else '''summarization'''
							_UpperCamelCase										=  F'''\n            run_eval_search.py\n            {model}\n            {input_file_name}\n            {output_file_name}\n            --score_path {score_path}\n            --task {task}\n            --num_beams 2\n            --length_penalty 2.0\n            '''.split()

							with patch.object(lowercase_    ,		'''argv'''    ,		lowercase_):
												run_generate()
												assert Path(lowercase_).exists()
												# os.remove(Path(output_file_name))



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






							'''simple docstring'''
							self.run_eval_tester(lowercase_)



		@parameterized.expand([BART_TINY, MBART_TINY])
		@slow
		def 						UpperCAmelCase							( self    ,		__a)	->	int:






							'''simple docstring'''
							self.run_eval_tester(lowercase_)




		@parameterized.expand([T5_TINY, MBART_TINY])
		@slow
		def 						UpperCAmelCase							( self    ,		__a)	->	int:






							'''simple docstring'''
							_UpperCamelCase										=  Path(self.get_auto_remove_tmp_dir()) / '''utest_input.source'''
							_UpperCamelCase										=  input_file_name.parent / '''utest_output.txt'''
							assert not output_file_name.exists()

							_UpperCamelCase										=  {
							    '''en''': ['''Machine learning is great, isn\'t it?''', '''I like to eat bananas''', '''Tomorrow is another great day!'''],
							    '''de''': [
							        '''Maschinelles Lernen ist großartig, oder?''',
							        '''Ich esse gerne Bananen''',
							        '''Morgen ist wieder ein toller Tag!''',
							    ],
							}

							_UpperCamelCase										=  Path(self.get_auto_remove_tmp_dir())
							_UpperCamelCase										=  str(tmp_dir / '''scores.json''')
							_UpperCamelCase										=  str(tmp_dir / '''val.target''')
							_dump_articles(lowercase_    ,		text['''en'''])
							_dump_articles(lowercase_    ,		text['''de'''])
							_UpperCamelCase										=  '''translation_en_to_de''' if model == T5_TINY else '''summarization'''
							_UpperCamelCase										=  F'''\n            run_eval_search.py\n            {model}\n            {str(lowercase_)}\n            {str(lowercase_)}\n            --score_path {score_path}\n            --reference_path {reference_path}\n            --task {task}\n            '''.split()
							testargs.extend(['''--search''', '''num_beams=1:2 length_penalty=0.9:1.0'''])

							with patch.object(lowercase_    ,		'''argv'''    ,		lowercase_):
												with CaptureStdout() as cs:
																	run_search()
												_UpperCamelCase										=  [''' num_beams | length_penalty''', model, '''Best score args''']
												_UpperCamelCase										=  ['''Info''']
												if "translation" in task:
																	expected_strings.append('''bleu''')
												else:
																	expected_strings.extend(lowercase_)
												for w in expected_strings:
																	assert w in cs.out
												for w in un_expected_strings:
																	assert w not in cs.out
												assert Path(lowercase_).exists()
												os.remove(Path(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.
import numpy as np
import torch

from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool


if is_vision_available():
				from PIL import Image






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				(
		    'This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image.'
		    'It takes two arguments named `image` which should be the original image, and `label` which should be a text '
		    'describing the elements what should be identified in the segmentation mask. The tool returns the mask.'
		)
		lowercase__      =				'CIDAS/clipseg-rd64-refined'
		lowercase__      =				'image_segmenter'
		lowercase__      =				CLIPSegForImageSegmentation

		lowercase__      =				['image', 'text']
		lowercase__      =				['image']



		def __init__( self    ,		*__a    ,		**__a)	->	Any:






							'''simple docstring'''
							requires_backends(self    ,		['''vision'''])
							super().__init__(*__a    ,		**__a)



		def 						UpperCAmelCase							( self    ,		__a    ,		__a)	->	Dict:






							'''simple docstring'''
							return self.pre_processor(text=[label]    ,		images=[image]    ,		padding=__a    ,		return_tensors='''pt''')



		def 						UpperCAmelCase							( self    ,		__a)	->	Tuple:






							'''simple docstring'''
							with torch.no_grad():
												_UpperCamelCase										=  self.model(**__a).logits
							return logits




		def 						UpperCAmelCase							( self    ,		__a)	->	Any:






							'''simple docstring'''
							_UpperCamelCase										=  outputs.cpu().detach().numpy()
							_UpperCamelCase										=  0
							_UpperCamelCase										=  1
							return Image.fromarray((array * 2_55).astype(np.uinta))






