


'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A :  int    							=				{'configuration_opt': ['OPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OPTConfig']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A :  Optional[Any]    							=				[
		    'OPT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'OPTForCausalLM',
		    'OPTModel',
		    'OPTPreTrainedModel',
		    'OPTForSequenceClassification',
		    'OPTForQuestionAnswering',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A :  Optional[int]    							=				['TFOPTForCausalLM', 'TFOPTModel', 'TFOPTPreTrainedModel']

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A :  int    							=				[
		    'FlaxOPTForCausalLM',
		    'FlaxOPTModel',
		    'FlaxOPTPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_opt import (
				    OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    OPTForCausalLM,
				    OPTForQuestionAnswering,
				    OPTForSequenceClassification,
				    OPTModel,
				    OPTPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
		import sys

		__A :  int    							=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)







'''simple docstring'''






from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


try:
  if not (is_transformers_available() and is_torch_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
  from .notes_encoder import SpectrogramNotesEncoder
  from .continous_encoder import SpectrogramContEncoder
  from .pipeline_spectrogram_diffusion import (
      SpectrogramContEncoder,
      SpectrogramDiffusionPipeline,
      TaFilmDecoder,
  )

try:
  if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
  from .midi_utils import MidiProcessor



