






from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch
@require_torch
@require_tf
class        a       (					lowercase__  ):



      """simple docstring"""


      def       UpperCAmelCase ( self   :     Tuple   )				-> Union[str, Any]:
        __UpperCAmelCase				:      Tuple	 =    SMALL_MODEL_IDENTIFIER
        __UpperCAmelCase				:      List[Any]	 =    """pt"""
        __UpperCAmelCase				:      List[Any]	 =    """tf"""
      def       UpperCAmelCase ( self   :     Tuple						, __lowercase   :     List[str]   )				-> Union[str, Any]:
        __UpperCAmelCase				:      Dict	 =    AutoModel.from_pretrained(self.test_model   )
        model_pt.save_pretrained(__lowercase   )
      def       UpperCAmelCase ( self   :     Any						, __lowercase   :     Optional[int]   )				-> Optional[Any]:
        __UpperCAmelCase				:      Any	 =    TFAutoModel.from_pretrained(self.test_model						, from_pt=__lowercase   )
        model_tf.save_pretrained(__lowercase   )
      def       UpperCAmelCase ( self   :     Tuple   )				-> Tuple:
        __UpperCAmelCase				:      Optional[int]	 =    """mock_framework"""

        # Framework provided - return whatever the user provides
        __UpperCAmelCase				:      int	 =    FeaturesManager.determine_framework(self.test_model						, __lowercase   )
        self.assertEqual(__lowercase						, __lowercase   )

        # Local checkpoint and framework provided - return provided framework
        # PyTorch checkpoint
        with TemporaryDirectory() as local_pt_ckpt:
          self._setup_pt_ckpt(__lowercase   )
          __UpperCAmelCase				:      List[str]	 =    FeaturesManager.determine_framework(__lowercase						, __lowercase   )
          self.assertEqual(__lowercase						, __lowercase   )

        # TensorFlow checkpoint
        with TemporaryDirectory() as local_tf_ckpt:
          self._setup_tf_ckpt(__lowercase   )
          __UpperCAmelCase				:      List[str]	 =    FeaturesManager.determine_framework(__lowercase						, __lowercase   )
          self.assertEqual(__lowercase						, __lowercase   )
      def       UpperCAmelCase ( self   :     List[str]   )				-> int:
        # PyTorch checkpoint
        with TemporaryDirectory() as local_pt_ckpt:
          self._setup_pt_ckpt(__lowercase   )
          __UpperCAmelCase				:      Optional[Any]	 =    FeaturesManager.determine_framework(__lowercase   )
          self.assertEqual(__lowercase						, self.framework_pt   )

        # TensorFlow checkpoint
        with TemporaryDirectory() as local_tf_ckpt:
          self._setup_tf_ckpt(__lowercase   )
          __UpperCAmelCase				:      str	 =    FeaturesManager.determine_framework(__lowercase   )
          self.assertEqual(__lowercase						, self.framework_tf   )

        # Invalid local checkpoint
        with TemporaryDirectory() as local_invalid_ckpt:
          with self.assertRaises(__lowercase   ):
            __UpperCAmelCase				:      Any	 =    FeaturesManager.determine_framework(__lowercase   )




      def       UpperCAmelCase ( self   :     Optional[Any]   )				-> Optional[int]:
        __UpperCAmelCase				:      Dict	 =    MagicMock(return_value=__lowercase   )
        with patch("""transformers.onnx.features.is_tf_available"""						, __lowercase   ):
          __UpperCAmelCase				:      List[str]	 =    FeaturesManager.determine_framework(self.test_model   )
          self.assertEqual(__lowercase						, self.framework_pt   )

        # PyTorch not in environment -> use TensorFlow
        __UpperCAmelCase				:      Dict	 =    MagicMock(return_value=__lowercase   )
        with patch("""transformers.onnx.features.is_torch_available"""						, __lowercase   ):
          __UpperCAmelCase				:      Tuple	 =    FeaturesManager.determine_framework(self.test_model   )
          self.assertEqual(__lowercase						, self.framework_tf   )

        # Both in environment -> use PyTorch
        __UpperCAmelCase				:      List[str]	 =    MagicMock(return_value=__lowercase   )
        __UpperCAmelCase				:      int	 =    MagicMock(return_value=__lowercase   )
        with patch("""transformers.onnx.features.is_tf_available"""						, __lowercase   ), patch(
            """transformers.onnx.features.is_torch_available"""						, __lowercase   ):
          __UpperCAmelCase				:      Optional[Any]	 =    FeaturesManager.determine_framework(self.test_model   )
          self.assertEqual(__lowercase						, self.framework_pt   )

        # Both not in environment -> raise error
        __UpperCAmelCase				:      Optional[int]	 =    MagicMock(return_value=__lowercase   )
        __UpperCAmelCase				:      Tuple	 =    MagicMock(return_value=__lowercase   )
        with patch("""transformers.onnx.features.is_tf_available"""						, __lowercase   ), patch(
            """transformers.onnx.features.is_torch_available"""						, __lowercase   ):
          with self.assertRaises(__lowercase   ):
            __UpperCAmelCase				:      Tuple	 =    FeaturesManager.determine_framework(self.test_model   )





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 							UpperCAmelCase_						(     unittest.TestCase  ):
							"""simple docstring"""







							def 							__lowercase							(     self			)       ->       int:
												_a      :       Dict   =						'''ZinengTang/tvlt-base'''
												_a      :       List[str]   =						tempfile.mkdtemp()


							def 							__lowercase							(     self			,				**_a			)       ->       int:
												return TvltImageProcessor.from_pretrained(self.checkpoint			,				**_a			)


							def 							__lowercase							(     self			,				**_a			)       ->       List[Any]:
												return TvltFeatureExtractor.from_pretrained(self.checkpoint			,				**_a			)


							def 							__lowercase							(     self			)       ->       Optional[int]:
												shutil.rmtree(self.tmpdirname			)


							def 							__lowercase							(     self			)       ->       Dict:
												_a      :       Union[str, Any]   =						self.get_image_processor()
												_a      :       Dict   =						self.get_feature_extractor()

												_a      :       Optional[int]   =						TvltProcessor(image_processor=_a			,				feature_extractor=_a			)
												processor.save_pretrained(self.tmpdirname			)
												_a      :       Any   =						TvltProcessor.from_pretrained(self.tmpdirname			)

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


							def 							__lowercase							(     self			)       ->       Any:
												_a      :       Optional[Any]   =						self.get_image_processor()
												_a      :       Dict   =						self.get_feature_extractor()

												_a      :       Dict   =						TvltProcessor(image_processor=_a			,				feature_extractor=_a			)

												_a      :       Union[str, Any]   =						np.ones([1_2_0_0_0]			)

												_a      :       Dict   =						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 							__lowercase							(     self			)       ->       int:
												_a      :       Optional[Any]   =						self.get_image_processor()
												_a      :       Union[str, Any]   =						self.get_feature_extractor()

												_a      :       Optional[Any]   =						TvltProcessor(image_processor=_a			,				feature_extractor=_a			)

												_a      :       List[Any]   =						np.ones([3, 2_2_4, 2_2_4]			)

												_a      :       int   =						image_processor(_a			,				return_tensors='''np'''			)
												_a      :       Optional[int]   =						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 							__lowercase							(     self			)       ->       Union[str, Any]:
												_a      :       int   =						self.get_image_processor()
												_a      :       Union[str, Any]   =						self.get_feature_extractor()

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

												_a      :       List[str]   =						np.ones([1_2_0_0_0]			)
												_a      :       Optional[int]   =						np.ones([3, 2_2_4, 2_2_4]			)

												_a      :       int   =						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 							__lowercase							(     self			)       ->       Union[str, Any]:
												_a      :       str   =						self.get_image_processor()
												_a      :       Union[str, Any]   =						self.get_feature_extractor()

												_a      :       Dict   =						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'''			,				)



