




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)
UpperCAmelCase_		:					int          =  logging.getLogger()




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase ):
				__magic_name__						:						str										="""\n""".join(__lowerCAmelCase )
				Path(__lowerCAmelCase ).open("""w""" ).writelines(__lowerCAmelCase )


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

UpperCAmelCase_		:					Optional[int]          =  logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks




class 		__A   (      __UpperCamelCase      ):

						def      A__    (		self :Dict     ,			__snake_case :Union[str, Any]			):
										'''simple docstring'''

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

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

						def      A__    (		self :Any			):
										'''simple docstring'''

										self.run_eval_tester(__snake_case			)

						@parameterized.expand([BART_TINY, MBART_TINY]			)
						@slow
						def      A__    (		self :Dict     ,			__snake_case :Optional[int]			):
										'''simple docstring'''

										self.run_eval_tester(__snake_case			)


						@parameterized.expand([T5_TINY, MBART_TINY]			)
						@slow
						def      A__    (		self :int     ,			__snake_case :int			):
										'''simple docstring'''

										__magic_name__						:						str										=Path(self.get_auto_remove_tmp_dir()			) / """utest_input.source"""
										__magic_name__						:						Optional[int]										=input_file_name.parent / """utest_output.txt"""
										assert not output_file_name.exists()

										__magic_name__						:						int										={
										    """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!""",
										    ],
										}

										__magic_name__						:						List[str]										=Path(self.get_auto_remove_tmp_dir()			)
										__magic_name__						:						Optional[Any]										=str(tmp_dir / """scores.json"""			)
										__magic_name__						:						List[str]										=str(tmp_dir / """val.target"""			)
										_dump_articles(__snake_case     ,			text["""en"""]			)
										_dump_articles(__snake_case     ,			text["""de"""]			)
										__magic_name__						:						Tuple										="""translation_en_to_de""" if model == T5_TINY else """summarization"""
										__magic_name__						:						Optional[Any]										=f"\n            run_eval_search.py\n            {model}\n            {str(__snake_case			)}\n            {str(__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(__snake_case     ,			"""argv"""     ,			__snake_case			):
														with CaptureStdout() as cs:
																		run_search()
														__magic_name__						:						Dict										=[""" num_beams | length_penalty""", model, """Best score args"""]
														__magic_name__						:						Dict										=["""Info"""]
														if "translation" in task:
																		expected_strings.append("""bleu"""			)
														else:
																		expected_strings.extend(__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(__snake_case			).exists()
														os.remove(Path(__snake_case			)			)






import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
     from transformers import TvltImageProcessor

if is_speech_available():
     from transformers import TvltFeatureExtractor

from transformers import TvltProcessor




@require_torch
class        _SCREAMING_SNAKE_CASE	(   unittest.TestCase			):
      def A_      (      self			):
          snake_case__    =					"ZinengTang/tvlt-base"
          snake_case__    =					tempfile.mkdtemp()
      def A_      (      self		,						**lowerCamelCase			):
          return TvltImageProcessor.from_pretrained(self.checkpoint		,						**lowerCamelCase			)
      def A_      (      self		,						**lowerCamelCase			):
          return TvltFeatureExtractor.from_pretrained(self.checkpoint		,						**lowerCamelCase			)
      def A_      (      self			):
          shutil.rmtree(self.tmpdirname			)
      def A_      (      self			):
          snake_case__    =					self.get_image_processor()
          snake_case__    =					self.get_feature_extractor()

          snake_case__    =					TvltProcessor(image_processor=lowerCamelCase		,						feature_extractor=lowerCamelCase			)
          processor.save_pretrained(self.tmpdirname			)
          snake_case__    =					TvltProcessor.from_pretrained(self.tmpdirname			)

          self.assertIsInstance(processor.feature_extractor		,						lowerCamelCase			)
          self.assertIsInstance(processor.image_processor		,						lowerCamelCase			)
      def A_      (      self			):
          snake_case__    =					self.get_image_processor()
          snake_case__    =					self.get_feature_extractor()

          snake_case__    =					TvltProcessor(image_processor=lowerCamelCase		,						feature_extractor=lowerCamelCase			)

          snake_case__    =					np.ones([1_20_00]			)

          snake_case__    =					feature_extractor(lowerCamelCase		,						return_tensors="np"			)
          snake_case__    =					processor(audio=lowerCamelCase		,						return_tensors="np"			)

          for key in audio_dict.keys():
              self.assertAlmostEqual(audio_dict[key].sum()		,						input_processor[key].sum()		,						delta=1e-2			)
      def A_      (      self			):
          snake_case__    =					self.get_image_processor()
          snake_case__    =					self.get_feature_extractor()

          snake_case__    =					TvltProcessor(image_processor=lowerCamelCase		,						feature_extractor=lowerCamelCase			)

          snake_case__    =					np.ones([3, 2_24, 2_24]			)

          snake_case__    =					image_processor(lowerCamelCase		,						return_tensors="np"			)
          snake_case__    =					processor(images=lowerCamelCase		,						return_tensors="np"			)

          for key in image_dict.keys():
              self.assertAlmostEqual(image_dict[key].sum()		,						input_processor[key].sum()		,						delta=1e-2			)
      def A_      (      self			):
          snake_case__    =					self.get_image_processor()
          snake_case__    =					self.get_feature_extractor()

          snake_case__    =					TvltProcessor(image_processor=lowerCamelCase		,						feature_extractor=lowerCamelCase			)

          snake_case__    =					np.ones([1_20_00]			)
          snake_case__    =					np.ones([3, 2_24, 2_24]			)

          snake_case__    =					processor(audio=lowerCamelCase		,						images=lowerCamelCase			)

          self.assertListEqual(list(inputs.keys()			)		,						["audio_values", "audio_mask", "pixel_values", "pixel_mask"]			)

          # test if it raises when no input is passed
          with pytest.raises(lowerCamelCase			):
              processor()



      def A_      (      self			):
          snake_case__    =					self.get_image_processor()
          snake_case__    =					self.get_feature_extractor()

          snake_case__    =					TvltProcessor(image_processor=lowerCamelCase		,						feature_extractor=lowerCamelCase			)

          self.assertListEqual(
              processor.model_input_names		,						image_processor.model_input_names + feature_extractor.model_input_names		,						msg="`processor` and `image_processor`+`feature_extractor` model input names do not match"		,						)



