

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    _a						(unittest.TestCase    ):


					'''simple docstring'''






					def   __A      (			self      ):
						A__							:   Optional[int]					       =		"""ZinengTang/tvlt-base"""
						A__							:   Dict					       =		tempfile.mkdtemp()






					def   __A      (			self   ,	**A__      ):
						return TvltImageProcessor.from_pretrained(self.checkpoint   ,	**A__      )






					def   __A      (			self   ,	**A__      ):
						return TvltFeatureExtractor.from_pretrained(self.checkpoint   ,	**A__      )






					def   __A      (			self      ):
						shutil.rmtree(self.tmpdirname      )






					def   __A      (			self      ):
						A__							:   Union[str, Any]					       =		self.get_image_processor()
						A__							:   str					       =		self.get_feature_extractor()

						A__							:   str					       =		TvltProcessor(image_processor=A__   ,	feature_extractor=A__      )
						processor.save_pretrained(self.tmpdirname      )
						A__							:   List[Any]					       =		TvltProcessor.from_pretrained(self.tmpdirname      )

						self.assertIsInstance(processor.feature_extractor   ,	A__      )
						self.assertIsInstance(processor.image_processor   ,	A__      )






					def   __A      (			self      ):
						A__							:   int					       =		self.get_image_processor()
						A__							:   Optional[int]					       =		self.get_feature_extractor()

						A__							:   Union[str, Any]					       =		TvltProcessor(image_processor=A__   ,	feature_extractor=A__      )

						A__							:   Dict					       =		np.ones([1_2000]      )

						A__							:   List[str]					       =		feature_extractor(A__   ,	return_tensors="""np"""      )
						A__							:   Tuple					       =		processor(audio=A__   ,	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      ):
						A__							:   Union[str, Any]					       =		self.get_image_processor()
						A__							:   Union[str, Any]					       =		self.get_feature_extractor()

						A__							:   Any					       =		TvltProcessor(image_processor=A__   ,	feature_extractor=A__      )

						A__							:   Optional[Any]					       =		np.ones([3, 224, 224]      )

						A__							:   List[str]					       =		image_processor(A__   ,	return_tensors="""np"""      )
						A__							:   Any					       =		processor(images=A__   ,	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      ):
						A__							:   Union[str, Any]					       =		self.get_image_processor()
						A__							:   List[Any]					       =		self.get_feature_extractor()

						A__							:   Any					       =		TvltProcessor(image_processor=A__   ,	feature_extractor=A__      )

						A__							:   Any					       =		np.ones([1_2000]      )
						A__							:   str					       =		np.ones([3, 224, 224]      )

						A__							:   Tuple					       =		processor(audio=A__   ,	images=A__      )

						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(A__      ):
							processor()






					def   __A      (			self      ):
						A__							:   Dict					       =		self.get_image_processor()
						A__							:   Tuple					       =		self.get_feature_extractor()

						A__							:   Tuple					       =		TvltProcessor(image_processor=A__   ,	feature_extractor=A__      )

						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"""   ,	)






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
	from transformers import AutoModelForSeqaSeqLM, AutoTokenizer



@require_torch
@require_sentencepiece
@require_tokenizers
class    _a						(unittest.TestCase    ):


					'''simple docstring'''






					@slow
					def   __A      (			self      ):
						A__							:   Optional[Any]					       =		AutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""   ,	return_dict=A__      ).to(A__      )
						A__							:   str					       =		AutoTokenizer.from_pretrained("""google/mt5-small"""      )

						A__							:   int					       =		tokenizer("""Hello there"""   ,	return_tensors="""pt"""      ).input_ids
						A__							:   List[Any]					       =		tokenizer("""Hi I am"""   ,	return_tensors="""pt"""      ).input_ids

						A__							:   Union[str, Any]					       =		model(input_ids.to(A__      )   ,	labels=labels.to(A__      )      ).loss
						A__							:   Union[str, Any]					       =		-(labels.shape[-1] * loss.item())

						A__							:   Any					       =		-8_4.9_1_2_7
						self.assertTrue(abs(mtf_score - EXPECTED_SCORE      ) < 1e-4      )



