






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__			= logging.get_logger(__name__)


lowerCamelCase__			= r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):
            Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax
            or scores for each vocabulary token after SoftMax.
        kwargs (`Dict[str, Any]`, *optional*):
            Additional stopping criteria specific kwargs.

    Return:
        `bool`. `False` indicates we should continue, `True` indicates we should stop.

"""






class    SCREAMING_SNAKE_CASE			(							SCREAMING_SNAKE_CASE__					):





							@add_start_docstrings(snake_case__	)
							def __call__(   self		:		Any      ,				__lowercase		:		List[Any]      ,				__lowercase		:		Optional[int]      ,				**__lowercase		:		Any	):

											'''simple docstring'''
											raise NotImplementedError("""StoppingCriteria needs to be subclassed"""	)






class    SCREAMING_SNAKE_CASE			(							SCREAMING_SNAKE_CASE__					):





							def __init__(   self		:		Optional[Any]      ,				__lowercase		:		Optional[Any]      ,				__lowercase		:		List[Any] = None	):

											'''simple docstring'''
											__a       =		max_length
											__a       =		max_position_embeddings







							@add_start_docstrings(snake_case__	)
							def __call__(   self		:		List[Any]      ,				__lowercase		:		Dict      ,				__lowercase		:		Optional[int]      ,				**__lowercase		:		int	):

											'''simple docstring'''
											__a       =		input_ids.shape[-1]
											__a       =		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    SCREAMING_SNAKE_CASE			(							SCREAMING_SNAKE_CASE__					):





							def __init__(   self		:		int      ,				__lowercase		:		Optional[Any]      ,				__lowercase		:		Tuple	):

											'''simple docstring'''
											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."""      ,				snake_case__      ,				)
											__a       =		start_length
											__a       =		max_new_tokens
											__a       =		start_length + max_new_tokens







							@add_start_docstrings(snake_case__	)
							def __call__(   self		:		int      ,				__lowercase		:		Optional[Any]      ,				__lowercase		:		int      ,				**__lowercase		:		List[str]	):

											'''simple docstring'''
											return input_ids.shape[-1] >= self.max_length






class    SCREAMING_SNAKE_CASE			(							SCREAMING_SNAKE_CASE__					):





							def __init__(   self		:		Any      ,				__lowercase		:		Optional[Any]      ,				__lowercase		:		Tuple = None	):

											'''simple docstring'''
											__a       =		max_time
											__a       =		time.time() if initial_timestamp is None else initial_timestamp







							@add_start_docstrings(snake_case__	)
							def __call__(   self		:		List[str]      ,				__lowercase		:		Optional[int]      ,				__lowercase		:		List[Any]      ,				**__lowercase		:		Tuple	):

											'''simple docstring'''
											return time.time() - self.initial_timestamp > self.max_time






class    SCREAMING_SNAKE_CASE			(							SCREAMING_SNAKE_CASE__					):





							@add_start_docstrings(snake_case__	)
							def __call__(   self		:		List[Any]      ,				__lowercase		:		Union[str, Any]      ,				__lowercase		:		Optional[Any]      ,				**__lowercase		:		Optional[int]	):

											'''simple docstring'''
											return any(criteria(snake_case__      ,				snake_case__	) for criteria in self	)







							@property
							def 						UpperCamelCase_					(   self		:		Any	):

											'''simple docstring'''
											for stopping_criterium in self:
															if isinstance(snake_case__      ,				snake_case__	):
																			return stopping_criterium.max_length
															elif isinstance(snake_case__      ,				snake_case__	):
																			return stopping_criterium.max_length
											return None



def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Any				,     _SCREAMING_SNAKE_CASE       :			Union[str, Any]    ):
				"""simple docstring"""







				__a       =		stopping_criteria.max_length
				__a       =		deepcopy(_SCREAMING_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"""				,     _SCREAMING_SNAKE_CASE    )
				elif stopping_max_length is None:
								new_stopping_criteria.append(MaxLengthCriteria(max_length=_SCREAMING_SNAKE_CASE    )    )
				return new_stopping_criteria





from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase__			= logging.get_logger(__name__)

lowerCamelCase__			= {
    """alibaba-damo/mgp-str-base""": """https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json""",
}






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):
							__lowerCamelCase	:	Dict    ='mgp-str'





							def __init__(   self		:		Optional[Any]      ,				__lowercase		:		Dict=[32, 128]      ,				__lowercase		:		Tuple=4      ,				__lowercase		:		Optional[Any]=3      ,				__lowercase		:		str=27      ,				__lowercase		:		Any=38      ,				__lowercase		:		str=50257      ,				__lowercase		:		List[str]=30522      ,				__lowercase		:		Tuple=768      ,				__lowercase		:		str=12      ,				__lowercase		:		Optional[Any]=12      ,				__lowercase		:		str=4.0      ,				__lowercase		:		Union[str, Any]=True      ,				__lowercase		:		List[Any]=False      ,				__lowercase		:		Union[str, Any]=1E-5      ,				__lowercase		:		Optional[int]=0.0      ,				__lowercase		:		Dict=0.0      ,				__lowercase		:		Union[str, Any]=0.0      ,				__lowercase		:		List[str]=False      ,				__lowercase		:		List[Any]=0.02      ,				**__lowercase		:		List[Any]      ,				):

											'''simple docstring'''
											super().__init__(**__lowercase	)

											__a       =		image_size
											__a       =		patch_size
											__a       =		num_channels
											__a       =		max_token_length
											__a       =		num_character_labels
											__a       =		num_bpe_labels
											__a       =		num_wordpiece_labels
											__a       =		hidden_size
											__a       =		num_hidden_layers
											__a       =		num_attention_heads
											__a       =		mlp_ratio
											__a       =		distilled
											__a       =		layer_norm_eps
											__a       =		drop_rate
											__a       =		qkv_bias
											__a       =		attn_drop_rate
											__a       =		drop_path_rate
											__a       =		output_aa_attentions
											__a       =		initializer_range
