






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	:							int				=logging.getLogger()







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase			) ->	Any:
     UpperCamelCase__			:   int       				=  "\n".join(__lowerCAmelCase			)
     Path(__lowerCAmelCase			).open("w"			).writelines(__lowerCAmelCase			)


lowerCamelCase	:							int				='''patrickvonplaten/t5-tiny-random'''
lowerCamelCase	:							Dict				='''sshleifer/bart-tiny-random'''
lowerCamelCase	:							List[str]				='''sshleifer/tiny-mbart'''

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





class 			__a  (    A__       ):







 def 					__lowercase					(		self					:   str							,   SCREAMING_SNAKE_CASE					:   Tuple						):


      '''simple docstring'''






      UpperCamelCase__			:   Union[str, Any]       				=  Path(self.get_auto_remove_tmp_dir()						) / "utest_input.source"
      UpperCamelCase__			:   List[Any]       				=  input_file_name.parent / "utest_output.txt"
      assert not output_file_name.exists()
      UpperCamelCase__			:   Optional[int]       				=  [" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
      _dump_articles(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Tuple       				=  str(Path(self.get_auto_remove_tmp_dir()						) / "scores.json"						)
      UpperCamelCase__			:   int       				=  "translation_en_to_de" if model == T5_TINY else "summarization"
      UpperCamelCase__			:   Optional[Any]       				=  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(SCREAMING_SNAKE_CASE							,   "argv"							,   SCREAMING_SNAKE_CASE						):
           run_generate()
           assert Path(SCREAMING_SNAKE_CASE						).exists()
           # os.remove(Path(output_file_name))







 def 					__lowercase					(		self					:   Optional[Any]						):


      '''simple docstring'''






      self.run_eval_tester(SCREAMING_SNAKE_CASE						)







 @parameterized.expand([BART_TINY, MBART_TINY]						)
 @slow
 def 					__lowercase					(		self					:   int							,   SCREAMING_SNAKE_CASE					:   List[str]						):


      '''simple docstring'''






      self.run_eval_tester(SCREAMING_SNAKE_CASE						)







 @parameterized.expand([T5_TINY, MBART_TINY]						)
 @slow
 def 					__lowercase					(		self					:   Any							,   SCREAMING_SNAKE_CASE					:   List[str]						):


      '''simple docstring'''






      UpperCamelCase__			:   str       				=  Path(self.get_auto_remove_tmp_dir()						) / "utest_input.source"
      UpperCamelCase__			:   str       				=  input_file_name.parent / "utest_output.txt"
      assert not output_file_name.exists()

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







# 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 torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool





class 			__a  (    A__       ):
 _lowerCAmelCase       :       str        = '''facebook/bart-large-mnli'''
 _lowerCAmelCase       :       Tuple        = (
     '''This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which '''
     '''should be the text to classify, and `labels`, which should be the list of labels to use for classification. '''
     '''It returns the most likely label in the list of provided `labels` for the input text.'''
 )
 _lowerCAmelCase       :       Any        = '''text_classifier'''
 _lowerCAmelCase       :       int        = AutoTokenizer
 _lowerCAmelCase       :       Union[str, Any]        = AutoModelForSequenceClassification

 _lowerCAmelCase       :       Union[str, Any]        = ['''text''', ['''text''']]
 _lowerCAmelCase       :       Dict        = ['''text''']







 def 					__lowercase					(		self					:   int						):


      '''simple docstring'''






      super().setup()
      UpperCamelCase__			:   Dict       				=  self.model.config
      UpperCamelCase__			:   Union[str, Any]       				=  -1
      for idx, label in config.idalabel.items():
           if label.lower().startswith("entail"						):
                UpperCamelCase__			:   List[str]       				=  int(SCREAMING_SNAKE_CASE						)
      if self.entailment_id == -1:
           raise ValueError("Could not determine the entailment ID from the model config, please pass it at init."						)







 def 					__lowercase					(		self					:   Tuple							,   SCREAMING_SNAKE_CASE					:   Tuple							,   SCREAMING_SNAKE_CASE					:   List[Any]						):


      '''simple docstring'''






      UpperCamelCase__			:   Any       				=  labels
      return self.pre_processor(
          [text] * len(SCREAMING_SNAKE_CASE						)							,   [F'This example is {label}' for label in labels]							,   return_tensors="pt"							,   padding="max_length"							,   )







 def 					__lowercase					(		self					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   Dict						):


      '''simple docstring'''






      UpperCamelCase__			:   List[Any]       				=  outputs.logits
      UpperCamelCase__			:   Any       				=  torch.argmax(logits[:, 2]						).item()
      return self._labels[label_id]