

'''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)
lowerCamelCase :      List[Any]					 =     logging.getLogger()


def        _lowerCAmelCase					(						_UpperCamelCase						:     Path							, _UpperCamelCase						:     list				)      ->			Any:
     """simple docstring"""




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


lowerCamelCase :      Tuple					 =     "patrickvonplaten/t5-tiny-random"
lowerCamelCase :      Tuple					 =     "sshleifer/bart-tiny-random"
lowerCamelCase :      List[Any]					 =     "sshleifer/tiny-mbart"

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







class 							A__    (       A__  ):





  def 						A  (		self      :	Any							,					_a      :	Dict )	->    Dict:





       '''simple docstring'''






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

       _SCREAMING_SNAKE_CASE							    =str(Path(self.get_auto_remove_tmp_dir() ) / 'scores.json' )
       _SCREAMING_SNAKE_CASE							    ='translation_en_to_de' if model == T5_TINY else 'summarization'
       _SCREAMING_SNAKE_CASE							    =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(_a							,					'argv'							,					_a ):
            run_generate()
            assert Path(_a ).exists()
            # os.remove(Path(output_file_name))





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





       '''simple docstring'''






       self.run_eval_tester(_a )





  @parameterized.expand([BART_TINY, MBART_TINY] )
  @slow
  def 						A  (		self      :	Optional[Any]							,					_a      :	Tuple )	->    List[str]:





       '''simple docstring'''






       self.run_eval_tester(_a )





  @parameterized.expand([T5_TINY, MBART_TINY] )
  @slow
  def 						A  (		self      :	Dict							,					_a      :	Dict )	->    Union[str, Any]:





       '''simple docstring'''






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

       _SCREAMING_SNAKE_CASE							    ={
           '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!',
           ],
       }

       _SCREAMING_SNAKE_CASE							    =Path(self.get_auto_remove_tmp_dir() )
       _SCREAMING_SNAKE_CASE							    =str(tmp_dir / 'scores.json' )
       _SCREAMING_SNAKE_CASE							    =str(tmp_dir / 'val.target' )
       _dump_articles(_a							,					text['en'] )
       _dump_articles(_a							,					text['de'] )
       _SCREAMING_SNAKE_CASE							    ='translation_en_to_de' if model == T5_TINY else 'summarization'
       _SCREAMING_SNAKE_CASE							    =f"\n            run_eval_search.py\n            {model}\n            {str(_a )}\n            {str(_a )}\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(_a							,					'argv'							,					_a ):
            with CaptureStdout() as cs:
                 run_search()
            _SCREAMING_SNAKE_CASE							    =[' num_beams | length_penalty', model, 'Best score args']
            _SCREAMING_SNAKE_CASE							    =['Info']
            if "translation" in task:
                 expected_strings.append('bleu' )
            else:
                 expected_strings.extend(_a )
            for w in expected_strings:
                 assert w in cs.out
            for w in un_expected_strings:
                 assert w not in cs.out
            assert Path(_a ).exists()
            os.remove(Path(_a ) )



'''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 							A__    (       A__  ):
  A__		 =				(
      '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.'
  )
  A__		 =				'CIDAS/clipseg-rd64-refined'
  A__		 =				'image_segmenter'
  A__		 =				CLIPSegForImageSegmentation

  A__		 =				['image', 'text']
  A__		 =				['image']





  def __init__(		self      :	Any							,					*_a      :	Dict							,					**_a      :	str )	->    Any:





       '''simple docstring'''






       requires_backends(self							,					['vision'] )
       super().__init__(*_a							,					**_a )





  def 						A  (		self      :	int							,					_a      :	"Image"							,					_a      :	str )	->    Optional[Any]:





       '''simple docstring'''






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





  def 						A  (		self      :	Dict							,					_a      :	Dict )	->    str:





       '''simple docstring'''






       with torch.no_grad():
            _SCREAMING_SNAKE_CASE							    =self.model(**_a ).logits
       return logits





  def 						A  (		self      :	Any							,					_a      :	str )	->    Union[str, Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =outputs.cpu().detach().numpy()
       _SCREAMING_SNAKE_CASE							    =0
       _SCREAMING_SNAKE_CASE							    =1
       return Image.fromarray((array * 255).astype(np.uinta ) )
