






from typing import Mapping

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


lowercase  :							Any			  =    logging.get_logger(__name__)

lowercase  :							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 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					):


						'''simple docstring'''




						A     :   Union[str, Any]						=					'umt5'
						A     :   int						=					['past_key_values']






						def __init__(	self		,				_SCREAMING_SNAKE_CASE=25_0112		,				_SCREAMING_SNAKE_CASE=512		,				_SCREAMING_SNAKE_CASE=64		,				_SCREAMING_SNAKE_CASE=1024		,				_SCREAMING_SNAKE_CASE=8		,				_SCREAMING_SNAKE_CASE=None		,				_SCREAMING_SNAKE_CASE=6		,				_SCREAMING_SNAKE_CASE=32		,				_SCREAMING_SNAKE_CASE=128		,				_SCREAMING_SNAKE_CASE=0.1		,				_SCREAMING_SNAKE_CASE=1e-6		,				_SCREAMING_SNAKE_CASE=1.0		,				_SCREAMING_SNAKE_CASE="gated-gelu"		,				_SCREAMING_SNAKE_CASE=True		,				_SCREAMING_SNAKE_CASE=True		,				_SCREAMING_SNAKE_CASE="T5Tokenizer"		,				_SCREAMING_SNAKE_CASE=True		,				_SCREAMING_SNAKE_CASE=0		,				_SCREAMING_SNAKE_CASE=1		,				_SCREAMING_SNAKE_CASE=0		,				**_SCREAMING_SNAKE_CASE		,				) ->				List[str]:
								super().__init__(
								    is_encoder_decoder=_SCREAMING_SNAKE_CASE		,				tokenizer_class=_SCREAMING_SNAKE_CASE		,				tie_word_embeddings=_SCREAMING_SNAKE_CASE		,				pad_token_id=_SCREAMING_SNAKE_CASE		,				eos_token_id=_SCREAMING_SNAKE_CASE		,				decoder_start_token_id=_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE		,				)
								snake_case_				:							Dict						 =       vocab_size
								snake_case_				:							str						 =       d_model
								snake_case_				:							Optional[int]						 =       d_kv
								snake_case_				:							Dict						 =       d_ff
								snake_case_				:							Dict						 =       num_layers
								snake_case_				:							str						 =       (
								    num_decoder_layers if num_decoder_layers is not None else self.num_layers
								)  # default = symmetry
								snake_case_				:							Optional[int]						 =       num_heads
								snake_case_				:							List[str]						 =       relative_attention_num_buckets
								snake_case_				:							Any						 =       relative_attention_max_distance
								snake_case_				:							List[Any]						 =       dropout_rate
								snake_case_				:							Optional[Any]						 =       layer_norm_epsilon
								snake_case_				:							Union[str, Any]						 =       initializer_factor
								snake_case_				:							Union[str, Any]						 =       feed_forward_proj
								snake_case_				:							List[Any]						 =       use_cache

								snake_case_				:							int						 =       self.feed_forward_proj.split("-"				)
								snake_case_				:							Optional[int]						 =       act_info[-1]
								snake_case_				:							Union[str, Any]						 =       act_info[0] == "gated"

								if len(_SCREAMING_SNAKE_CASE				) > 1 and act_info[0] != "gated" or len(_SCREAMING_SNAKE_CASE				) > 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_				:							List[Any]						 =       "gelu_new"






						@property
						def       _lowerCAmelCase       (	self				) ->				List[Any]:
								return self.d_model






						@property
						def       _lowerCAmelCase       (	self				) ->				List[str]:
								return self.num_heads






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




class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					):


						'''simple docstring'''




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

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

								return common_inputs






						@property
						# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
						def       _lowerCAmelCase       (	self				) ->				int:
								return 13






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








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase  :							Union[str, Any]			  =    {'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							Optional[int]			  =    ['''XGLMTokenizer''']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							Tuple			  =    ['''XGLMTokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							str			  =    [
							    '''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''XGLMForCausalLM''',
							    '''XGLMModel''',
							    '''XGLMPreTrainedModel''',
							]


try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							str			  =    [
							    '''FlaxXGLMForCausalLM''',
							    '''FlaxXGLMModel''',
							    '''FlaxXGLMPreTrainedModel''',
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							Tuple			  =    [
							    '''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFXGLMForCausalLM''',
							    '''TFXGLMModel''',
							    '''TFXGLMPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm import XGLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm_fast import XGLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_xglm import (
														    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFXGLMForCausalLM,
														    TFXGLMModel,
														    TFXGLMPreTrainedModel,
														)


else:
							import sys

							lowercase  :							Dict			  =    _LazyModule(__name__, globals()['''__file__'''], _import_structure)
