



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









import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
							import optax
							from flax.training.common_utils import onehot

							from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
							from transformers.models.ta.modeling_flax_ta import shift_tokens_right


@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class      __lowerCAmelCase     (		unittest.TestCase      ):
					"""simple docstring"""






					@slow
					def     _a							(     self					:       int					):






								"""simple docstring"""


								A__       =    FlaxMTaForConditionalGeneration.from_pretrained('google/mt5-small'					)
								A__       =    AutoTokenizer.from_pretrained('google/mt5-small'					)

								A__       =    tokenizer('Hello there'				,					return_tensors='np'					).input_ids
								A__       =    tokenizer('Hi I am'				,					return_tensors='np'					).input_ids

								A__       =    shift_tokens_right(_snake_case				,					model.config.pad_token_id				,					model.config.decoder_start_token_id					)

								A__       =    model(_snake_case				,					decoder_input_ids=_snake_case					).logits
								A__       =    optax.softmax_cross_entropy(_snake_case				,					onehot(_snake_case				,					logits.shape[-1]					)					).mean()

								A__       =    -(labels.shape[-1] * loss.item())

								A__       =    -84.9127
								self.assertTrue(abs(mtf_score - EXPECTED_SCORE					) < 1E-4					)




