





import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
				from .testing_utils import (
				    floats_tensor,
				    load_hf_numpy,
				    load_image,
				    load_numpy,
				    load_pt,
				    nightly,
				    parse_flag_from_env,
				    print_tensor_test,
				    require_torch_a,
				    require_torch_gpu,
				    skip_mps,
				    slow,
				    torch_all_close,
				    torch_device,
				)
				from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


__A    :   Optional[int]          =       get_logger(__name__)





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    Optional[int]:







			'''simple docstring'''

			if version.parse(UpperCamelCase__			) < version.parse(UpperCamelCase__			):
						if "dev" in min_version:
									UpperCAmelCase       		=    (
									    '''This example requires a source install from HuggingFace diffusers (see '''
									    '''`https://huggingface.co/docs/diffusers/installation#install-from-source`),'''
									)
						else:
									UpperCAmelCase       		=    F"""This example requires a minimum version of {min_version},"""
						error_message += F""" but the version found is {__version__}.\n"""
						raise ImportError(UpperCamelCase__			)










import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


__A    :   Optional[int]          =       logging.get_logger(__name__)

class      A_	(a_      ):
	UpperCAmelCase__ =      '''speech-encoder-decoder'''
	UpperCAmelCase__ =      True
	def __init__(   self							, **_A		):






				'''simple docstring'''







				super().__init__(**_A		)
				if "encoder" not in kwargs or "decoder" not in kwargs:
							raise ValueError(
							    F"""A configuraton of type {self.model_type} cannot be instantiated because not both `encoder` and"""
							    F""" `decoder` sub-configurations are passed, but only {kwargs}"""		)

				UpperCAmelCase       		=    kwargs.pop('''encoder'''		)
				UpperCAmelCase       		=    encoder_config.pop('''model_type'''		)
				UpperCAmelCase       		=    kwargs.pop('''decoder'''		)
				UpperCAmelCase       		=    decoder_config.pop('''model_type'''		)

				UpperCAmelCase       		=    AutoConfig.for_model(_A							, **_A		)
				UpperCAmelCase       		=    AutoConfig.for_model(_A							, **_A		)
				UpperCAmelCase       		=    True
	@classmethod
	def 					_lowercase    (   cls							, _A							, _A							, **_A		):






				'''simple docstring'''







				logger.info('''Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''		)
				UpperCAmelCase       		=    True
				UpperCAmelCase       		=    True

				return cls(encoder=encoder_config.to_dict()							, decoder=decoder_config.to_dict()							, **_A		)







	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    copy.deepcopy(self.__dict__		)
				UpperCAmelCase       		=    self.encoder.to_dict()
				UpperCAmelCase       		=    self.decoder.to_dict()
				UpperCAmelCase       		=    self.__class__.model_type
				return output



