

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


snake_case_    						=				get_logger(__name__)

def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					int    ):



     '''simple docstring'''





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




from typing import TYPE_CHECKING

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


snake_case_    						=				{'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case_    						=				[
           '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''GPTJForCausalLM''',
           '''GPTJForQuestionAnswering''',
           '''GPTJForSequenceClassification''',
           '''GPTJModel''',
           '''GPTJPreTrainedModel''',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case_    						=				[
           '''TFGPTJForCausalLM''',
           '''TFGPTJForQuestionAnswering''',
           '''TFGPTJForSequenceClassification''',
           '''TFGPTJModel''',
           '''TFGPTJPreTrainedModel''',
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case_    						=				[
           '''FlaxGPTJForCausalLM''',
           '''FlaxGPTJModel''',
           '''FlaxGPTJPreTrainedModel''',
       ]


if TYPE_CHECKING:
       from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_gptj import (
                  GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GPTJForCausalLM,
                  GPTJForQuestionAnswering,
                  GPTJForSequenceClassification,
                  GPTJModel,
                  GPTJPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_gptj import (
                  TFGPTJForCausalLM,
                  TFGPTJForQuestionAnswering,
                  TFGPTJForSequenceClassification,
                  TFGPTJModel,
                  TFGPTJPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
       import sys

       snake_case_    						=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

