


from typing import Mapping

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


A_														=      logging.get_logger(__name__)

class 						snake_case			(       lowerCAmelCase__	):
			'''simple docstring'''

			UpperCAmelCase       :       Any       = """mt5"""
			UpperCAmelCase       :       Optional[int]       = ["""past_key_values"""]

			def __init__(	self     :     Optional[int]							,							lowerCAmelCase_     :     List[str]=250_112							,							lowerCAmelCase_     :     Dict=512							,							lowerCAmelCase_     :     List[str]=64							,							lowerCAmelCase_     :     Any=1_024							,							lowerCAmelCase_     :     Optional[int]=8							,							lowerCAmelCase_     :     Dict=None							,							lowerCAmelCase_     :     Dict=6							,							lowerCAmelCase_     :     Any=32							,							lowerCAmelCase_     :     Dict=128							,							lowerCAmelCase_     :     Tuple=0.1							,							lowerCAmelCase_     :     str=1e-6							,							lowerCAmelCase_     :     List[str]=1.0							,							lowerCAmelCase_     :     Optional[int]="gated-gelu"							,							lowerCAmelCase_     :     Union[str, Any]=True							,							lowerCAmelCase_     :     List[Any]=True							,							lowerCAmelCase_     :     str="T5Tokenizer"							,							lowerCAmelCase_     :     str=False							,							lowerCAmelCase_     :     List[str]=0							,							lowerCAmelCase_     :     Optional[int]=1							,							lowerCAmelCase_     :     List[str]=0							,							**lowerCAmelCase_     :     List[str]							,							)					->					Optional[int]:




								"""simple docstring"""






								super().__init__(
								    is_encoder_decoder=lowerCAmelCase_							,							tokenizer_class=lowerCAmelCase_							,							tie_word_embeddings=lowerCAmelCase_							,							pad_token_id=lowerCAmelCase_							,							eos_token_id=lowerCAmelCase_							,							decoder_start_token_id=lowerCAmelCase_							,							**lowerCAmelCase_							,							)
								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(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":
													SCREAMING_SNAKE_CASE_	      =       '''gelu_new'''

			@property
			def _lowercase (	self     :     List[str]					)					->					List[Any]:




								"""simple docstring"""






								return self.d_model

			@property
			def _lowercase (	self     :     Union[str, Any]					)					->					Tuple:




								"""simple docstring"""






								return self.num_heads




			@property
			def _lowercase (	self     :     str					)					->					str:




								"""simple docstring"""






								return self.num_layers

class 						snake_case			(       lowerCAmelCase__	):
			'''simple docstring'''

			@property
			# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
			def _lowercase (	self     :     str					)					->					Mapping[str, Mapping[int, str]]:




								"""simple docstring"""






								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_(lowerCAmelCase_							,							direction='''inputs'''					)

								return common_inputs

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




								"""simple docstring"""






								return 13




			@property
			def _lowercase (	self     :     int					)					->					float:




								"""simple docstring"""






								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,
)


A_														=      {
    "configuration_marian": ["MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "MarianConfig", "MarianOnnxConfig"],
}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A_														=      ["MarianTokenizer"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A_														=      [
		    "MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "MarianForCausalLM",
		    "MarianModel",
		    "MarianMTModel",
		    "MarianPreTrainedModel",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A_														=      ["TFMarianModel", "TFMarianMTModel", "TFMarianPreTrainedModel"]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A_														=      ["FlaxMarianModel", "FlaxMarianMTModel", "FlaxMarianPreTrainedModel"]

if TYPE_CHECKING:
		from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_marian import MarianTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_marian import (
				    MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MarianForCausalLM,
				    MarianModel,
				    MarianMTModel,
				    MarianPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
		import sys

		A_														=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


