





import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer

class   A							(						tf.keras.layers.Layer    ):



      def __init__(						self:    Optional[Any]		,	_lowerCAmelCase:    Dict[str, int]		,	_lowerCAmelCase:    List[str]		,	_lowerCAmelCase:    int = None		,	_lowerCAmelCase:    int = None							)    ->     List[Any]:
         '''simple docstring'''

         super().__init__()
         UpperCAmelCase_           =pad_token_id
         UpperCAmelCase_           =max_length
         UpperCAmelCase_           =vocab
         UpperCAmelCase_           =merges
         UpperCAmelCase_           =BytePairTokenizer(_lowerCAmelCase		,	_lowerCAmelCase		,	sequence_length=_lowerCAmelCase							)



      @classmethod
      def 						lowerCAmelCase__     (						cls:    Union[str, Any]		,	_lowerCAmelCase:    GPTaTokenizer		,	*_lowerCAmelCase:    List[Any]		,	**_lowerCAmelCase:    Dict							)    ->     List[Any]:
         '''simple docstring'''

         UpperCAmelCase_           =[" ".join(_lowerCAmelCase							) for m in tokenizer.bpe_ranks.keys()]
         UpperCAmelCase_           =tokenizer.get_vocab()
         return cls(_lowerCAmelCase		,	_lowerCAmelCase		,	*_lowerCAmelCase		,	**_lowerCAmelCase							)



      @classmethod
      def 						lowerCAmelCase__     (						cls:    Dict		,	_lowerCAmelCase:    Union[str, os.PathLike]		,	*_lowerCAmelCase:    List[Any]		,	**_lowerCAmelCase:    List[Any]							)    ->     List[Any]:
         '''simple docstring'''

         UpperCAmelCase_           =GPTaTokenizer.from_pretrained(_lowerCAmelCase		,	*_lowerCAmelCase		,	**_lowerCAmelCase							)
         return cls.from_tokenizer(_lowerCAmelCase		,	*_lowerCAmelCase		,	**_lowerCAmelCase							)



      @classmethod
      def 						lowerCAmelCase__     (						cls:    List[str]		,	_lowerCAmelCase:    Optional[int]							)    ->     List[Any]:
         '''simple docstring'''

         return cls(**_lowerCAmelCase							)



      def 						lowerCAmelCase__     (						self:    Dict							)    ->     int:
         '''simple docstring'''

         return {
             "vocab": self.vocab,
             "merges": self.merges,
             "max_length": self.max_length,
             "pad_token_id": self.pad_token_id,
         }






      def 						lowerCAmelCase__     (						self:    Union[str, Any]		,	_lowerCAmelCase:    List[str]		,	_lowerCAmelCase:    int = None							)    ->     Optional[Any]:
         '''simple docstring'''

         UpperCAmelCase_           =self.tf_tokenizer(_lowerCAmelCase							)
         UpperCAmelCase_           =tf.ones_like(_lowerCAmelCase							)

         if self.pad_token_id is not None:
            # pad the tokens up to max length
            UpperCAmelCase_           =max_length if max_length is not None else self.max_length

            if max_length is not None:
               UpperCAmelCase_    ,							UpperCAmelCase_           =pad_model_inputs(
                   _lowerCAmelCase		,	max_seq_length=_lowerCAmelCase		,	pad_value=self.pad_token_id							)

         return {"attention_mask": attention_mask, "input_ids": input_ids}










from typing import Mapping

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


SCREAMING_SNAKE_CASE:     List[Any]									= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE:     Optional[Any]									= {
    '''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    lowercase_		(SCREAMING_SNAKE_CASE__ ):
						lowerCAmelCase__			      ="umt5"
						lowerCAmelCase__			      =["past_key_values"]



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

								"""simple docstring"""

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



						@property
						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								return self.d_model



						@property
						def 	__a					(			self	:   Union[str, Any]  ):

								"""simple docstring"""

								return self.num_heads






						@property
						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								return self.num_layers



class    lowercase_		(SCREAMING_SNAKE_CASE__ ):



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

								"""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_(snake_case__					,							direction='inputs'  )

								return common_inputs



						@property
						# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
						def 	__a					(			self	:   Union[str, Any]  ):

								"""simple docstring"""

								return 13






						@property
						def 	__a					(			self	:   Dict  ):

								"""simple docstring"""

								return 5e-4