


"""simple docstring"""


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					(     _UpperCamelCase):
		__UpperCamelCase							=	'mt5'
		__UpperCamelCase							=	['past_key_values']







		def __init__(    self					:				List[str]			,      a__					:				str=25_01_12			,      a__					:				Optional[int]=5_12			,      a__					:				Union[str, Any]=64			,      a__					:				Union[str, Any]=10_24			,      a__					:				Tuple=8			,      a__					:				Union[str, Any]=None			,      a__					:				str=6			,      a__					:				Any=32			,      a__					:				Optional[Any]=1_28			,      a__					:				List[str]=0.1			,      a__					:				Dict=1E-6			,      a__					:				Any=1.0			,      a__					:				Tuple="gated-gelu"			,      a__					:				Optional[Any]=True			,      a__					:				Union[str, Any]=True			,      a__					:				Union[str, Any]="T5Tokenizer"			,      a__					:				Tuple=False			,      a__					:				List[str]=0			,      a__					:				Tuple=1			,      a__					:				Dict=0			,      **a__					:				Optional[Any]			,      )				->  Any:





						'''simple docstring'''







						super().__init__(
						    is_encoder_decoder=a__			,      tokenizer_class=a__			,      tie_word_embeddings=a__			,      pad_token_id=a__			,      eos_token_id=a__			,      decoder_start_token_id=a__			,      **a__			,      )
						_A         =	vocab_size
						_A         =	d_model
						_A         =	d_kv
						_A         =	d_ff
						_A         =	num_layers
						_A         =	(
						    num_decoder_layers if num_decoder_layers is not None else self.num_layers
						)  # default = symmetry
						_A         =	num_heads
						_A         =	relative_attention_num_buckets
						_A         =	relative_attention_max_distance
						_A         =	dropout_rate
						_A         =	layer_norm_epsilon
						_A         =	initializer_factor
						_A         =	feed_forward_proj
						_A         =	use_cache

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

						if len(a__						) > 1 and act_info[0] != "gated" or len(a__						) > 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":
										_A         =	"gelu_new"







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





						'''simple docstring'''







						return self.d_model







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





						'''simple docstring'''







						return self.num_heads







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





						'''simple docstring'''







						return self.num_layers





class snake_case					(     _UpperCamelCase):







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





						'''simple docstring'''







						_A         =	{
						    "input_ids": {0: "batch", 1: "encoder_sequence"},
						    "attention_mask": {0: "batch", 1: "encoder_sequence"},
						}
						if self.use_past:
										_A         =	"past_encoder_sequence + sequence"
										_A         =	{0: "batch"}
										_A         =	{0: "batch", 1: "past_decoder_sequence + sequence"}
						else:
										_A         =	{0: "batch", 1: "decoder_sequence"}
										_A         =	{0: "batch", 1: "decoder_sequence"}

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

						return common_inputs







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





						'''simple docstring'''







						return 13







		@property
		def        a_       (    self					:				List[str]						)				->  float:





						'''simple docstring'''







						return 5E-4



"""simple docstring"""


import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_reformer import ReformerTokenizer
else:
		a_											=			None


a_											=			logging.get_logger(__name__)


a_											=			"▁"

a_											=			{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

a_											=			{
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    },
    "tokenizer_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json"
        )
    },
}

a_											=			{
    "google/reformer-crime-and-punishment": 52_42_88,
}
class snake_case					(     _UpperCamelCase):
		__UpperCamelCase							=	VOCAB_FILES_NAMES
		__UpperCamelCase							=	PRETRAINED_VOCAB_FILES_MAP
		__UpperCamelCase							=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__UpperCamelCase							=	['input_ids', 'attention_mask']
		__UpperCamelCase							=	ReformerTokenizer







		def __init__(    self					:				Dict			,      a__					:				List[Any]=None			,      a__					:				int=None			,      a__					:				int="</s>"			,      a__					:				List[Any]="<unk>"			,      a__					:				Optional[Any]=[]			,      **a__					:				Tuple			,      )				->  Tuple:





						'''simple docstring'''







						super().__init__(
						    a__			,      tokenizer_file=a__			,      eos_token=a__			,      unk_token=a__			,      additional_special_tokens=a__			,      **a__			,      )

						_A         =	vocab_file
						_A         =	False if not self.vocab_file else True







		def        a_       (    self					:				Any			,      a__					:				str			,      a__					:				Optional[str] = None						)				->  Tuple[str]:





						'''simple docstring'''







						if not self.can_save_slow_tokenizer:
										raise ValueError(
										    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
										    "tokenizer."						)

						if not os.path.isdir(a__						):
										logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""						)
										return
						_A         =	os.path.join(
						    a__			,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

						if os.path.abspath(self.vocab_file						) != os.path.abspath(a__						):
										copyfile(self.vocab_file			,      a__						)

						return (out_vocab_file,)