
"""simple docstring"""






from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


_lowercase							 =							logging.get_logger(__name__)

_lowercase							 =							{
    '''google/long-t5-local-base''': '''https://huggingface.co/google/long-t5-local-base/blob/main/config.json''',
    '''google/long-t5-local-large''': '''https://huggingface.co/google/long-t5-local-large/blob/main/config.json''',
    '''google/long-t5-tglobal-base''': '''https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json''',
    '''google/long-t5-tglobal-large''': '''https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json''',
}

class 				__a    (   __a					):



							'''simple docstring'''







							_lowerCamelCase				: Tuple					      =      """longt5"""
							_lowerCamelCase				: Dict					      =      ["""past_key_values"""]
							_lowerCamelCase				: List[str]					      =      {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}
							def __init__(    self     ,						_lowerCamelCase=32_128     ,						_lowerCamelCase=512     ,						_lowerCamelCase=64     ,						_lowerCamelCase=2_048     ,						_lowerCamelCase=6     ,						_lowerCamelCase=None     ,						_lowerCamelCase=8     ,						_lowerCamelCase=127     ,						_lowerCamelCase=16     ,						_lowerCamelCase=32     ,						_lowerCamelCase=128     ,						_lowerCamelCase=0.1     ,						_lowerCamelCase=1e-6     ,						_lowerCamelCase=1.0     ,						_lowerCamelCase="relu"     ,						_lowerCamelCase=True     ,						_lowerCamelCase="local"     ,						_lowerCamelCase=True     ,						_lowerCamelCase=0     ,						_lowerCamelCase=1     ,						**_lowerCamelCase     ,						) ->       str:




										'''simple docstring'''
										__lowercase						      =					vocab_size
										__lowercase						      =					d_model
										__lowercase						      =					d_kv
										__lowercase						      =					d_ff
										__lowercase						      =					num_layers
										# default = symmetry
										__lowercase						      =					num_decoder_layers if num_decoder_layers is not None else self.num_layers
										__lowercase						      =					num_heads
										__lowercase						      =					local_radius
										__lowercase						      =					global_block_size
										__lowercase						      =					relative_attention_num_buckets
										__lowercase						      =					relative_attention_max_distance
										__lowercase						      =					dropout_rate
										__lowercase						      =					layer_norm_epsilon
										__lowercase						      =					initializer_factor
										__lowercase						      =					feed_forward_proj
										__lowercase						      =					encoder_attention_type
										__lowercase						      =					use_cache

										__lowercase						      =					self.feed_forward_proj.split("-"					)
										__lowercase						      =					act_info[-1]
										__lowercase						      =					act_info[0] == "gated"

										if len(_lowerCamelCase					) > 1 and act_info[0] != "gated" or len(_lowerCamelCase					) > 2:
													raise ValueError(
													    f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
													    "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
													    "'gated-gelu' or 'relu'"					)

										# for backwards compatibility
										if feed_forward_proj == "gated-gelu":
													__lowercase						      =					"gelu_new"

										super().__init__(
										    pad_token_id=_lowerCamelCase     ,						eos_token_id=_lowerCamelCase     ,						is_encoder_decoder=_lowerCamelCase     ,						**_lowerCamelCase     ,						)





class 				__a    (   __a					):



							'''simple docstring'''







							@property
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Mapping[str, Mapping[int, str]]:




										'''simple docstring'''
										__lowercase						      =					{
										    "input_ids": {0: "batch", 1: "encoder_sequence"},
										    "attention_mask": {0: "batch", 1: "encoder_sequence"},
										}
										if self.use_past:
													__lowercase						      =					"past_encoder_sequence + sequence"
													__lowercase						      =					{0: "batch"}
													__lowercase						      =					{0: "batch", 1: "past_decoder_sequence + sequence"}
										else:
													__lowercase						      =					{0: "batch", 1: "decoder_sequence"}
													__lowercase						      =					{0: "batch", 1: "decoder_sequence"}

										if self.use_past:
													self.fill_with_past_key_values_(_lowerCamelCase     ,						direction="inputs"					)

										return common_inputs





							@property
							def      SCREAMING_SNAKE_CASE	(    self					) ->       int:




										'''simple docstring'''
										return 13







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





