


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


SCREAMING_SNAKE_CASE : Optional[Any]          =    get_logger(__name__)





def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: Optional[Any]      ):






							"""simple docstring"""



							if version.parse(SCREAMING_SNAKE_CASE_      ) < version.parse(SCREAMING_SNAKE_CASE_      ):
														if "dev" in min_version:
																					a_       :   str			=	(
																					    """This example requires a source install from HuggingFace diffusers (see """
																					    """`https://huggingface.co/docs/diffusers/installation#install-from-source`),"""
																					)
														else:
																					a_       :   Any			=	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_      )






import itertools
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa

import datasets
from datasets.table import table_cast


SCREAMING_SNAKE_CASE : int          =    datasets.utils.logging.get_logger(__name__)





@dataclass
class 					snake_case__  ( datasets.BuilderConfig       ):
	UpperCAmelCase   :							Optional[datasets.Features]         = None





class 					snake_case__  ( datasets.ArrowBasedBuilder       ):
	UpperCAmelCase   :							Optional[int]         = ArrowConfig





	def     A				(  self							)		->			str:
								"""simple docstring"""






								return datasets.DatasetInfo(features=self.config.features							)





	def     A				(  self				,    UpperCamelCase_							)		->			str:
								"""simple docstring"""






								if not self.config.data_files:
															raise ValueError(f"""At least one data file must be specified, but got data_files={self.config.data_files}"""							)
								a_       :   List[str]			=	dl_manager.download_and_extract(self.config.data_files							)
								if isinstance(UpperCamelCase_				,    (str, list, tuple)							):
															a_       :   Dict			=	data_files
															if isinstance(UpperCamelCase_				,    UpperCamelCase_							):
																						a_       :   Optional[Any]			=	[files]
															# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
															a_       :   List[Any]			=	[dl_manager.iter_files(UpperCamelCase_							) for file in files]
															return [datasets.SplitGenerator(name=datasets.Split.TRAIN				,    gen_kwargs={"""files""": files}							)]
								a_       :   Any			=	[]
								for split_name, files in data_files.items():
															if isinstance(UpperCamelCase_				,    UpperCamelCase_							):
																						a_       :   Dict			=	[files]
															# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
															a_       :   List[Any]			=	[dl_manager.iter_files(UpperCamelCase_							) for file in files]
															# Infer features is they are stoed in the arrow schema
															if self.info.features is None:
																						for file in itertools.chain.from_iterable(UpperCamelCase_							):
																													with open(UpperCamelCase_				,    """rb"""							) as f:
																																				a_       :   List[str]			=	datasets.Features.from_arrow_schema(pa.ipc.open_stream(UpperCamelCase_							).schema							)
																													break
															splits.append(datasets.SplitGenerator(name=UpperCamelCase_				,    gen_kwargs={"""files""": files}							)							)
								return splits





	def     A				(  self				,    UpperCamelCase_							)		->			pa.Table:
								"""simple docstring"""






								if self.info.features is not None:
															# more expensive cast to support nested features with keys in a different order
															# allows str <-> int/float or str to Audio for example
															a_       :   int			=	table_cast(UpperCamelCase_				,    self.info.features.arrow_schema							)
								return pa_table





	def     A				(  self				,    UpperCamelCase_							)		->			Optional[Any]:
								"""simple docstring"""






								for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCamelCase_							)							):
															with open(UpperCamelCase_				,    """rb"""							) as f:
																						try:
																													for batch_idx, record_batch in enumerate(pa.ipc.open_stream(UpperCamelCase_							)							):
																																				a_       :   Tuple			=	pa.Table.from_batches([record_batch]							)
																																				# Uncomment for debugging (will print the Arrow table size and elements)
																																				# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																																				# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																																				yield f"""{file_idx}_{batch_idx}""", self._cast_table(UpperCamelCase_							)
																						except ValueError as e:
																													logger.error(f"""Failed to read file '{file}' with error {type(UpperCamelCase_							)}: {e}"""							)
																													raise


