


"""simple docstring"""






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


lowercase  		=			get_logger(__name__)



def 			A__		(					_UpperCAmelCase    :      Optional[int]	)							->	Dict:

	'''simple docstring'''


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






'''simple docstring'''

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class 		SCREAMING_SNAKE_CASE_					(_UpperCAmelCase      ):



			"""simple docstring"""






			__magic_name__ :				Dict   							=       ["""image_processor""", """tokenizer"""]
			__magic_name__ :				Union[str, Any]   							=       """VideoMAEImageProcessor"""
			__magic_name__ :				List[str]   							=       ("""CLIPTokenizer""", """CLIPTokenizerFast""")



			def __init__(							self				,			snake_case_=None				,			snake_case_=None				,			**snake_case_					):
									snake_case :       int				=    None
									if "feature_extractor" in kwargs:
															warnings.warn(
															    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
															    " instead."				,			snake_case_				,			)
															snake_case :       Optional[int]				=    kwargs.pop("feature_extractor"					)

									snake_case :       Union[str, Any]				=    image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
															raise ValueError("You need to specify an `image_processor`."					)
									if tokenizer is None:
															raise ValueError("You need to specify a `tokenizer`."					)

									super().__init__(snake_case_				,			snake_case_					)
									snake_case :       List[Any]				=    self.image_processor



			def __call__(							self				,			snake_case_=None				,			snake_case_=None				,			snake_case_=None				,			**snake_case_					):

									if text is None and videos is None:
															raise ValueError("You have to specify either text or videos. Both cannot be none."					)

									if text is not None:
															snake_case :       List[Any]				=    self.tokenizer(snake_case_				,			return_tensors=snake_case_				,			**snake_case_					)

									if videos is not None:
															snake_case :       List[Any]				=    self.image_processor(snake_case_				,			return_tensors=snake_case_				,			**snake_case_					)

									if text is not None and videos is not None:
															snake_case :       Tuple				=    image_features.pixel_values
															return encoding
									elif text is not None:
															return encoding
									else:
															return BatchEncoding(data=dict(**snake_case_					)				,			tensor_type=snake_case_					)



			def   lowerCamelCase (							self				,			*snake_case_				,			**snake_case_					):
									return self.tokenizer.batch_decode(*snake_case_				,			**snake_case_					)



			def   lowerCamelCase (							self				,			*snake_case_				,			**snake_case_					):
									return self.tokenizer.decode(*snake_case_				,			**snake_case_					)



			@property
			def   lowerCamelCase (							self					):
									return ["input_ids", "attention_mask", "position_ids", "pixel_values"]



			@property
			def   lowerCamelCase (							self					):
									warnings.warn(
									    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."				,			snake_case_				,			)
									return self.image_processor_class






			@property
			def   lowerCamelCase (							self					):
									warnings.warn(
									    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."				,			snake_case_				,			)
									return self.image_processor





