

"""simple docstring"""




from typing import Mapping

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


A_				:      List[Any]           =			logging.get_logger(__name__)

A_				:      List[str]           =			{
    "google/umt5-small": "https://huggingface.co/google/umt5-small/resolve/main/config.json",
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}





class  lowerCamelCase    (A__      ):
			lowerCamelCase__ :		Tuple       		=				'umt5'
			lowerCamelCase__ :		List[str]       		=				['past_key_values']

			def __init__(       self					:    Any     ,					__UpperCAmelCase					:    Dict=2_5_0_1_1_2     ,					__UpperCAmelCase					:    Tuple=5_1_2     ,					__UpperCAmelCase					:    Optional[Any]=6_4     ,					__UpperCAmelCase					:    List[Any]=1_0_2_4     ,					__UpperCAmelCase					:    Optional[Any]=8     ,					__UpperCAmelCase					:    List[Any]=None     ,					__UpperCAmelCase					:    Any=6     ,					__UpperCAmelCase					:    Any=3_2     ,					__UpperCAmelCase					:    Any=1_2_8     ,					__UpperCAmelCase					:    List[str]=0.1     ,					__UpperCAmelCase					:    Union[str, Any]=1e-6     ,					__UpperCAmelCase					:    Optional[int]=1.0     ,					__UpperCAmelCase					:    Optional[int]="gated-gelu"     ,					__UpperCAmelCase					:    List[Any]=True     ,					__UpperCAmelCase					:    int=True     ,					__UpperCAmelCase					:    List[str]="T5Tokenizer"     ,					__UpperCAmelCase					:    int=True     ,					__UpperCAmelCase					:    Optional[Any]=0     ,					__UpperCAmelCase					:    Optional[int]=1     ,					__UpperCAmelCase					:    List[str]=0     ,					**__UpperCAmelCase					:    str     ,					) ->     Dict:
						super().__init__(
						    is_encoder_decoder=__UpperCAmelCase     ,					tokenizer_class=__UpperCAmelCase     ,					tie_word_embeddings=__UpperCAmelCase     ,					pad_token_id=__UpperCAmelCase     ,					eos_token_id=__UpperCAmelCase     ,					decoder_start_token_id=__UpperCAmelCase     ,					**__UpperCAmelCase     ,					)
						SCREAMING_SNAKE_CASE__										=   vocab_size
						SCREAMING_SNAKE_CASE__										=   d_model
						SCREAMING_SNAKE_CASE__										=   d_kv
						SCREAMING_SNAKE_CASE__										=   d_ff
						SCREAMING_SNAKE_CASE__										=   num_layers
						SCREAMING_SNAKE_CASE__										=   (
						    num_decoder_layers if num_decoder_layers is not None else self.num_layers
						)  # default = symmetry
						SCREAMING_SNAKE_CASE__										=   num_heads
						SCREAMING_SNAKE_CASE__										=   relative_attention_num_buckets
						SCREAMING_SNAKE_CASE__										=   relative_attention_max_distance
						SCREAMING_SNAKE_CASE__										=   dropout_rate
						SCREAMING_SNAKE_CASE__										=   layer_norm_epsilon
						SCREAMING_SNAKE_CASE__										=   initializer_factor
						SCREAMING_SNAKE_CASE__										=   feed_forward_proj
						SCREAMING_SNAKE_CASE__										=   use_cache

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

						if len(__UpperCAmelCase							) > 1 and act_info[0] != "gated" or len(__UpperCAmelCase							) > 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'"""							)

						if feed_forward_proj == "gated-gelu":
									SCREAMING_SNAKE_CASE__										=   """gelu_new"""

			@property
			def        SCREAMING_SNAKE_CASE					(       self					:    str							) ->     str:
						return self.d_model

			@property
			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[Any]							) ->     int:
						return self.num_heads





			@property
			def        SCREAMING_SNAKE_CASE					(       self					:    List[Any]							) ->     str:
						return self.num_layers





class  lowerCamelCase    (A__      ):

			@property
			# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
			def        SCREAMING_SNAKE_CASE					(       self					:    List[Any]							) ->     Mapping[str, Mapping[int, str]]:
						SCREAMING_SNAKE_CASE__										=   {
						    """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
						    """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
						}
						if self.use_past:
									SCREAMING_SNAKE_CASE__										=   """past_encoder_sequence + sequence"""
									SCREAMING_SNAKE_CASE__										=   {0: """batch"""}
									SCREAMING_SNAKE_CASE__										=   {0: """batch""", 1: """past_decoder_sequence + sequence"""}
						else:
									SCREAMING_SNAKE_CASE__										=   {0: """batch""", 1: """decoder_sequence"""}
									SCREAMING_SNAKE_CASE__										=   {0: """batch""", 1: """decoder_sequence"""}

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

						return common_inputs

			@property
			# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[int]							) ->     int:
						return 1_3





			@property
			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     float:
						return 5e-4





"""simple docstring"""




A_				:      Any           =			"0.21.0"

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
						from .utils import rich


