


"""simple docstring"""


import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional

import torch

from ..utils import add_start_docstrings, logging


__lowerCamelCase   :int						     =		logging.get_logger(__name__)


__lowerCamelCase   :List[str]						     =		R'\n    Args:\n        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary.\n\n            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n            [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):\n            Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax\n            or scores for each vocabulary token after SoftMax.\n        kwargs (`Dict[str, Any]`, *optional*):\n            Additional stopping criteria specific kwargs.\n\n    Return:\n        `bool`. `False` indicates we should continue, `True` indicates we should stop.\n\n'


class       A__						(    __SCREAMING_SNAKE_CASE):


     """simple docstring"""

     @add_start_docstrings(_SCREAMING_SNAKE_CASE						)
     def __call__(						self:    Dict  ,		__a:    torch.LongTensor  ,		__a:    torch.FloatTensor  ,		**__a:    Dict						)->     bool:
           raise NotImplementedError("""StoppingCriteria needs to be subclassed"""						)


class       A__						(    __SCREAMING_SNAKE_CASE):


     """simple docstring"""

     def __init__(						self:    int  ,		__a:    int  ,		__a:    Optional[int] = None						)->     int:
           lowerCamelCase       :       Any   =						max_length
           lowerCamelCase       :       Optional[int]   =						max_position_embeddings







     @add_start_docstrings(_SCREAMING_SNAKE_CASE						)
     def __call__(						self:    Any  ,		__a:    torch.LongTensor  ,		__a:    torch.FloatTensor  ,		**__a:    List[Any]						)->     bool:
           lowerCamelCase       :       List[Any]   =						input_ids.shape[-1]
           lowerCamelCase       :       str   =						cur_len >= self.max_length
           if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
                 logger.warning_once(
                     """This is a friendly reminder - the current text generation call will exceed the model's predefined """
                     f'maximum length ({self.max_position_embeddings}). Depending on the model, you may observe '
                     """exceptions, performance degradation, or nothing at all."""						)
           return is_done


class       A__						(    __SCREAMING_SNAKE_CASE):


     """simple docstring"""

     def __init__(						self:    Tuple  ,		__a:    int  ,		__a:    int						)->     Optional[Any]:
           warnings.warn(
               """The class `MaxNewTokensCriteria` is deprecated. """
               f'Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` '
               """with `max_length = start_length + max_new_tokens` instead."""  ,		_SCREAMING_SNAKE_CASE  ,		)
           lowerCamelCase       :       int   =						start_length
           lowerCamelCase       :       Optional[int]   =						max_new_tokens
           lowerCamelCase       :       List[Any]   =						start_length + max_new_tokens







     @add_start_docstrings(_SCREAMING_SNAKE_CASE						)
     def __call__(						self:    List[Any]  ,		__a:    torch.LongTensor  ,		__a:    torch.FloatTensor  ,		**__a:    Tuple						)->     bool:
           return input_ids.shape[-1] >= self.max_length


class       A__						(    __SCREAMING_SNAKE_CASE):


     """simple docstring"""

     def __init__(						self:    Optional[int]  ,		__a:    float  ,		__a:    Optional[float] = None						)->     Any:
           lowerCamelCase       :       Optional[int]   =						max_time
           lowerCamelCase       :       str   =						time.time() if initial_timestamp is None else initial_timestamp







     @add_start_docstrings(_SCREAMING_SNAKE_CASE						)
     def __call__(						self:    Optional[Any]  ,		__a:    torch.LongTensor  ,		__a:    torch.FloatTensor  ,		**__a:    Optional[Any]						)->     bool:
           return time.time() - self.initial_timestamp > self.max_time


class       A__						(    __SCREAMING_SNAKE_CASE):


     """simple docstring"""

     @add_start_docstrings(_SCREAMING_SNAKE_CASE						)
     def __call__(						self:    Optional[Any]  ,		__a:    torch.LongTensor  ,		__a:    torch.FloatTensor  ,		**__a:    int						)->     bool:
           return any(criteria(_SCREAMING_SNAKE_CASE  ,		_SCREAMING_SNAKE_CASE						) for criteria in self						)







     @property
     def 		a__    (						self:    int						)->     Optional[int]:

           for stopping_criterium in self:
                 if isinstance(_SCREAMING_SNAKE_CASE  ,		_SCREAMING_SNAKE_CASE						):
                       return stopping_criterium.max_length
                 elif isinstance(_SCREAMING_SNAKE_CASE  ,		_SCREAMING_SNAKE_CASE						):
                       return stopping_criterium.max_length
           return None




def 	snake_case		(  UpperCamelCase__							:    str      ,					UpperCamelCase__							:    int     ) ->   StoppingCriteriaList:
      lowerCamelCase       :       Optional[int]   =						stopping_criteria.max_length
      lowerCamelCase       :       Any   =						deepcopy(__snake_case     )
      if stopping_max_length is not None and stopping_max_length != max_length:
            warnings.warn("""You set different `max_length` for stopping criteria and `max_length` parameter"""      ,					__snake_case     )
      elif stopping_max_length is None:
            new_stopping_criteria.append(MaxLengthCriteria(max_length=__snake_case     )     )
      return new_stopping_criteria









"""simple docstring"""






import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class      A__  (    __SCREAMING_SNAKE_CASE    ,						__SCREAMING_SNAKE_CASE    ,						__SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	@register_to_config
	def __init__(	self:						List[str]			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						float			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						str			,     _SCREAMING_SNAKE_CASE:						bool = False			,     )							->			Any:

				"""simple docstring"""

				super().__init__()

				__lowerCAmelCase :    str				=			nn.Embedding(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Dict				=			nn.Embedding(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    str				=			False

				__lowerCAmelCase :    Optional[Any]				=			nn.Dropout(p=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Dict				=			TaConfig(
				    vocab_size=_SCREAMING_SNAKE_CASE			,     d_model=_SCREAMING_SNAKE_CASE			,     num_heads=_SCREAMING_SNAKE_CASE			,     d_kv=_SCREAMING_SNAKE_CASE			,     d_ff=_SCREAMING_SNAKE_CASE			,     dropout_rate=_SCREAMING_SNAKE_CASE			,     feed_forward_proj=_SCREAMING_SNAKE_CASE			,     is_decoder=_SCREAMING_SNAKE_CASE			,     is_encoder_decoder=_SCREAMING_SNAKE_CASE			,     )

				__lowerCAmelCase :    str				=			nn.ModuleList()
				for lyr_num in range(_SCREAMING_SNAKE_CASE):
							__lowerCAmelCase :    int				=			TaBlock(_SCREAMING_SNAKE_CASE)
							self.encoders.append(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Dict				=			TaLayerNorm(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Any				=			nn.Dropout(p=_SCREAMING_SNAKE_CASE)







	def 				_SCREAMING_SNAKE_CASE  (	self:						Any			,     _SCREAMING_SNAKE_CASE:						Tuple			,     _SCREAMING_SNAKE_CASE:						str)							->			List[str]:

				"""simple docstring"""

				__lowerCAmelCase :    Union[str, Any]				=			self.token_embedder(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Optional[Any]				=			encoder_input_tokens.shape[1]
				__lowerCAmelCase :    List[Any]				=			torch.arange(_SCREAMING_SNAKE_CASE			,     device=encoder_input_tokens.device)
				x += self.position_encoding(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    List[Any]				=			self.dropout_pre(_SCREAMING_SNAKE_CASE)

				# inverted the attention mask
				__lowerCAmelCase :    List[Any]				=			encoder_input_tokens.size()
				__lowerCAmelCase :    Any				=			self.get_extended_attention_mask(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)

				for lyr in self.encoders:
							__lowerCAmelCase :    Union[str, Any]				=			lyr(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)[0]
				__lowerCAmelCase :    int				=			self.layer_norm(_SCREAMING_SNAKE_CASE)

				return self.dropout_post(_SCREAMING_SNAKE_CASE), encoder_inputs_mask