






from typing import Mapping

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


__UpperCamelCase       :		str							=    logging.get_logger(__name__)

__UpperCamelCase       :		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 	__SCREAMING_SNAKE_CASE(						snake_case_						):
							_UpperCAmelCase			     =							'''umt5'''
							_UpperCAmelCase			     =							['''past_key_values''']
							def __init__(  self:   Tuple							,						UpperCamelCase:   str=25_01_12							,						UpperCamelCase:   str=5_12							,						UpperCamelCase:   List[Any]=64							,						UpperCamelCase:   Tuple=10_24							,						UpperCamelCase:   Any=8							,						UpperCamelCase:   Any=None							,						UpperCamelCase:   List[Any]=6							,						UpperCamelCase:   str=32							,						UpperCamelCase:   int=1_28							,						UpperCamelCase:   Tuple=0.1							,						UpperCamelCase:   Optional[int]=1e-6							,						UpperCamelCase:   Optional[Any]=1.0							,						UpperCamelCase:   Union[str, Any]="gated-gelu"							,						UpperCamelCase:   Any=True							,						UpperCamelCase:   str=True							,						UpperCamelCase:   Any="T5Tokenizer"							,						UpperCamelCase:   Any=True							,						UpperCamelCase:   str=0							,						UpperCamelCase:   Any=1							,						UpperCamelCase:   int=0							,						**UpperCamelCase:   Any							,						)		->		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__							,						)
											snake_case__						=  vocab_size
											snake_case__						=  d_model
											snake_case__						=  d_kv
											snake_case__						=  d_ff
											snake_case__						=  num_layers
											snake_case__						=  (
											    num_decoder_layers if num_decoder_layers is not None else self.num_layers
											)  # default = symmetry
											snake_case__						=  num_heads
											snake_case__						=  relative_attention_num_buckets
											snake_case__						=  relative_attention_max_distance
											snake_case__						=  dropout_rate
											snake_case__						=  layer_norm_epsilon
											snake_case__						=  initializer_factor
											snake_case__						=  feed_forward_proj
											snake_case__						=  use_cache

											snake_case__						=  self.feed_forward_proj.split('-'    )
											snake_case__						=  act_info[-1]
											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":
															snake_case__						=  "gelu_new"
							@property
							def lowerCAmelCase_			(  self:   Dict    )		->		Any:
											return self.d_model
							@property
							def lowerCAmelCase_			(  self:   Optional[Any]    )		->		str:
											return self.num_heads


							@property
							def lowerCAmelCase_			(  self:   Optional[int]    )		->		Union[str, Any]:
											return self.num_layers


class 	__SCREAMING_SNAKE_CASE(						snake_case_						):
							@property
							# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
							def lowerCAmelCase_			(  self:   Tuple    )		->		Mapping[str, Mapping[int, str]]:
											snake_case__						=  {
											    "input_ids": {0: "batch", 1: "encoder_sequence"},
											    "attention_mask": {0: "batch", 1: "encoder_sequence"},
											}
											if self.use_past:
															snake_case__						=  "past_encoder_sequence + sequence"
															snake_case__						=  {0: "batch"}
															snake_case__						=  {0: "batch", 1: "past_decoder_sequence + sequence"}
											else:
															snake_case__						=  {0: "batch", 1: "decoder_sequence"}
															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 lowerCAmelCase_			(  self:   Optional[Any]    )		->		int:
											return 13


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








"""simple docstring"""



from .imports import is_tqdm_available


if is_tqdm_available():
  from tqdm.auto import tqdm as _tqdm

from ..state import PartialState







def     __lowerCAmelCase		(		lowercase    :    bool = True		,				*lowercase    :    str		,				**lowercase    :    List[Any]					)      ->		List[Any]:


    """simple docstring"""





    if not is_tqdm_available():
        raise ImportError("Accelerate's `tqdm` module requires `tqdm` to be installed. Please run `pip install tqdm`."					)
    snake_case       : Dict    =       False
    if main_process_only:
        snake_case       : int    =       PartialState().local_process_index == 0
    return _tqdm(*lowercase		,				**lowercase		,				disable=lowercase					)





