






'''simple docstring'''







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       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	"""ZinengTang/tvlt-base"""
								lowerCamelCase__								=	tempfile.mkdtemp()






		def       UpperCamelCase_       (   self ,**_lowerCAmelCase							):
								return TvltImageProcessor.from_pretrained(self.checkpoint ,**_lowerCAmelCase							)






		def       UpperCamelCase_       (   self ,**_lowerCAmelCase							):
								return TvltFeatureExtractor.from_pretrained(self.checkpoint ,**_lowerCAmelCase							)






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






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	self.get_image_processor()
								lowerCamelCase__								=	self.get_feature_extractor()

								lowerCamelCase__								=	TvltProcessor(image_processor=_lowerCAmelCase ,feature_extractor=_lowerCAmelCase							)
								processor.save_pretrained(self.tmpdirname							)
								lowerCamelCase__								=	TvltProcessor.from_pretrained(self.tmpdirname							)

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






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	self.get_image_processor()
								lowerCamelCase__								=	self.get_feature_extractor()

								lowerCamelCase__								=	TvltProcessor(image_processor=_lowerCAmelCase ,feature_extractor=_lowerCAmelCase							)

								lowerCamelCase__								=	np.ones([1_20_00]							)

								lowerCamelCase__								=	feature_extractor(_lowerCAmelCase ,return_tensors="""np"""							)
								lowerCamelCase__								=	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       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	self.get_image_processor()
								lowerCamelCase__								=	self.get_feature_extractor()

								lowerCamelCase__								=	TvltProcessor(image_processor=_lowerCAmelCase ,feature_extractor=_lowerCAmelCase							)

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

								lowerCamelCase__								=	image_processor(_lowerCAmelCase ,return_tensors="""np"""							)
								lowerCamelCase__								=	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       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	self.get_image_processor()
								lowerCamelCase__								=	self.get_feature_extractor()

								lowerCamelCase__								=	TvltProcessor(image_processor=_lowerCAmelCase ,feature_extractor=_lowerCAmelCase							)

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

								lowerCamelCase__								=	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       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	self.get_image_processor()
								lowerCamelCase__								=	self.get_feature_extractor()

								lowerCamelCase__								=	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""" ,)



from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
)


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_pt_objects import *  # noqa F403
else:
   from .scheduling_consistency_models import CMStochasticIterativeScheduler
   from .scheduling_ddim import DDIMScheduler
   from .scheduling_ddim_inverse import DDIMInverseScheduler
   from .scheduling_ddim_parallel import DDIMParallelScheduler
   from .scheduling_ddpm import DDPMScheduler
   from .scheduling_ddpm_parallel import DDPMParallelScheduler
   from .scheduling_deis_multistep import DEISMultistepScheduler
   from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
   from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
   from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
   from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
   from .scheduling_euler_discrete import EulerDiscreteScheduler
   from .scheduling_heun_discrete import HeunDiscreteScheduler
   from .scheduling_ipndm import IPNDMScheduler
   from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
   from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
   from .scheduling_karras_ve import KarrasVeScheduler
   from .scheduling_pndm import PNDMScheduler
   from .scheduling_repaint import RePaintScheduler
   from .scheduling_sde_ve import ScoreSdeVeScheduler
   from .scheduling_sde_vp import ScoreSdeVpScheduler
   from .scheduling_unclip import UnCLIPScheduler
   from .scheduling_unipc_multistep import UniPCMultistepScheduler
   from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
   from .scheduling_vq_diffusion import VQDiffusionScheduler

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_flax_objects import *  # noqa F403
else:
   from .scheduling_ddim_flax import FlaxDDIMScheduler
   from .scheduling_ddpm_flax import FlaxDDPMScheduler
   from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
   from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
   from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
   from .scheduling_pndm_flax import FlaxPNDMScheduler
   from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
   from .scheduling_utils_flax import (
       FlaxKarrasDiffusionSchedulers,
       FlaxSchedulerMixin,
       FlaxSchedulerOutput,
       broadcast_to_shape_from_left,
   )


try:
   if not (is_torch_available() and is_scipy_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
   from .scheduling_lms_discrete import LMSDiscreteScheduler

try:
   if not (is_torch_available() and is_torchsde_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
   from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler





