
from typing import Mapping

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


_lowerCamelCase :       int     					=    logging.get_logger(__name__)




class      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase      ):





	'''simple docstring'''



	_UpperCAmelCase :	List[Any]					     =      "mt5"
	_UpperCAmelCase :	List[str]					     =      ["past_key_values"]



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


				'''simple docstring'''


				super().__init__(
				    is_encoder_decoder=lowercase    ,						tokenizer_class=lowercase    ,						tie_word_embeddings=lowercase    ,						pad_token_id=lowercase    ,						eos_token_id=lowercase    ,						decoder_start_token_id=lowercase    ,						**lowercase    ,						)
				_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(lowercase			) > 1 and act_info[0] != "gated" or len(lowercase			) > 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":
							_snake_case						=    'gelu_new'



	@property
	def 				A							(						self			:					Any			):


				'''simple docstring'''


				return self.d_model



	@property
	def 				A							(						self			:					List[Any]			):


				'''simple docstring'''


				return self.num_heads





	@property
	def 				A							(						self			:					Dict			):


				'''simple docstring'''


				return self.num_layers




class      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase      ):





	'''simple docstring'''



	@property
	# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
	def 				A							(						self			:					Any			):


				'''simple docstring'''


				_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_(lowercase    ,						direction='inputs'			)

				return common_inputs



	@property
	# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
	def 				A							(						self			:					str			):


				'''simple docstring'''


				return 13





	@property
	def 				A							(						self			:					Any			):


				'''simple docstring'''


				return 5E-4

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


_lowerCamelCase :       Optional[int]     					=    {}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_lowerCamelCase :       List[str]     					=    ['''MBart50Tokenizer''']

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_lowerCamelCase :       Optional[int]     					=    ['''MBart50TokenizerFast''']


if TYPE_CHECKING:
			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_mbartaa import MBartaaTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_mbartaa_fast import MBartaaTokenizerFast

else:
			import sys

			_lowerCamelCase :       int     					=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)