



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__     				=	get_logger(__name__)


def 		_UpperCAmelCase    (				snake_case      ):





					"""simple docstring"""

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








# Lint as: python3

import hashlib
from typing import Union


def 		_UpperCAmelCase    (				snake_case      ):





					"""simple docstring"""

					if isinstance(snake_case     ,						snake_case      ):
										# Data already in bytes, returns as it as
										return hash_data
					elif isinstance(snake_case     ,						snake_case      ):
										# We keep the data as it as for it ot be later encoded to UTF-8
										# However replace `\\` with `/` for Windows compatibility
										_lowerCAmelCase      =   hash_data.replace("""\\"""     ,						"""/"""      )
					elif isinstance(snake_case     ,						snake_case      ):
										_lowerCAmelCase      =   str(snake_case      )
					else:
										# If data is not of the required type, raise error
										raise InvalidKeyError(snake_case      )

					return hash_data.encode("""utf-8"""      )





class  __lowerCAmelCase			(					lowerCamelCase__	):




				def __init__(							self						,    _snake_case		):



									"""simple docstring"""





									_lowerCAmelCase      =   """\nFAILURE TO GENERATE DATASET: Invalid key type detected"""
									_lowerCAmelCase      =   F'\nFound Key {hash_data} of type {type(_snake_case		)}'
									_lowerCAmelCase      =   """\nKeys should be either str, int or bytes type"""
									super().__init__(F'{self.prefix}{self.err_msg}{self.suffix}'		)





class  __lowerCAmelCase			(					lowerCamelCase__	):




				def __init__(							self						,    _snake_case						,    _snake_case						,    _snake_case=""		):



									"""simple docstring"""





									_lowerCAmelCase      =   key
									_lowerCAmelCase      =   duplicate_key_indices
									_lowerCAmelCase      =   fix_msg
									_lowerCAmelCase      =   """Found multiple examples generated with the same key"""
									if len(_snake_case		) <= 20:
														_lowerCAmelCase      =   F'\nThe examples at index {", ".join(_snake_case		)} have the key {key}'
									else:
														_lowerCAmelCase      =   F'\nThe examples at index {", ".join(duplicate_key_indices[:20]		)}... ({len(_snake_case		) - 20} more) have the key {key}'
									_lowerCAmelCase      =   """\n""" + fix_msg if fix_msg else """"""
									super().__init__(F'{self.prefix}{self.err_msg}{self.suffix}'		)





class  __lowerCAmelCase			:




				def __init__(							self						,    _snake_case		):



									"""simple docstring"""





									_lowerCAmelCase      =   hashlib.mda(_as_bytes(_snake_case		)		)




				def 							snake_case       (							self						,    _snake_case		):



									"""simple docstring"""





									_lowerCAmelCase      =   self._split_mda.copy()
									_lowerCAmelCase      =   _as_bytes(_snake_case		)
									mda.update(_snake_case		)
									# Convert to integer with hexadecimal conversion
									return int(mda.hexdigest()						,    16		)



