



"""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


lowercase  							=					logging.get_logger(__name__)


lowercase  							=					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 			lowercase__			(      A    ):
							'''simple docstring'''







							@add_start_docstrings(snake_case						)
							def __call__(  self	,       snake_case	,       snake_case	,       **snake_case						)			->							bool:
									raise NotImplementedError('StoppingCriteria needs to be subclassed'						)







class 			lowercase__			(      A    ):
							'''simple docstring'''







							def __init__(  self	,       snake_case	,       snake_case = None						)			->							Optional[int]:
									_UpperCAmelCase							 =		max_length
									_UpperCAmelCase							 =		max_position_embeddings






							@add_start_docstrings(snake_case						)
							def __call__(  self	,       snake_case	,       snake_case	,       **snake_case						)			->							bool:
									_UpperCAmelCase							 =		input_ids.shape[-1]
									_UpperCAmelCase							 =		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 			lowercase__			(      A    ):
							'''simple docstring'''







							def __init__(  self	,       snake_case	,       snake_case						)			->							Tuple:
									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	,       )
									_UpperCAmelCase							 =		start_length
									_UpperCAmelCase							 =		max_new_tokens
									_UpperCAmelCase							 =		start_length + max_new_tokens






							@add_start_docstrings(snake_case						)
							def __call__(  self	,       snake_case	,       snake_case	,       **snake_case						)			->							bool:
									return input_ids.shape[-1] >= self.max_length







class 			lowercase__			(      A    ):
							'''simple docstring'''







							def __init__(  self	,       snake_case	,       snake_case = None						)			->							int:
									_UpperCAmelCase							 =		max_time
									_UpperCAmelCase							 =		time.time() if initial_timestamp is None else initial_timestamp






							@add_start_docstrings(snake_case						)
							def __call__(  self	,       snake_case	,       snake_case	,       **snake_case						)			->							bool:
									return time.time() - self.initial_timestamp > self.max_time







class 			lowercase__			(      A    ):
							'''simple docstring'''







							@add_start_docstrings(snake_case						)
							def __call__(  self	,       snake_case	,       snake_case	,       **snake_case						)			->							bool:
									return any(criteria(snake_case	,       snake_case						) for criteria in self						)






							@property
							def        lowerCamelCase_   (  self						)			->							Optional[int]:
									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 	UpperCAmelCase     (		A	:						StoppingCriteriaList     ,				A	:						int       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		stopping_criteria.max_length
		_UpperCAmelCase							 =		deepcopy(A       )
		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'     ,				A       )
		elif stopping_max_length is None:
				new_stopping_criteria.append(MaxLengthCriteria(max_length=A       )       )
		return new_stopping_criteria







"""simple docstring"""





from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput




class 			lowercase__			(      nn.Module    ):
							'''simple docstring'''







							def __init__(  self	,       snake_case = 16	,       snake_case = 88	,       snake_case = None	,       snake_case = 1	,       snake_case = 0.0	,       snake_case = 32	,       snake_case = None	,       snake_case = False	,       snake_case = None	,       snake_case = None	,       snake_case = "geglu"	,       snake_case = None	,       )			->							str:
									super().__init__()
									_UpperCAmelCase							 =		nn.ModuleList(
									    [
									        TransformeraDModel(
									            num_attention_heads=snake_case	,       attention_head_dim=snake_case	,       in_channels=snake_case	,       num_layers=snake_case	,       dropout=snake_case	,       norm_num_groups=snake_case	,       cross_attention_dim=snake_case	,       attention_bias=snake_case	,       sample_size=snake_case	,       num_vector_embeds=snake_case	,       activation_fn=snake_case	,       num_embeds_ada_norm=snake_case	,       )
									        for _ in range(2						)
									    ]						)

									# Variables that can be set by a pipeline:

									# The ratio of transformer1 to transformer2's output states to be combined during inference
									_UpperCAmelCase							 =		0.5

									# The shape of `encoder_hidden_states` is expected to be
									# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
									_UpperCAmelCase							 =		[77, 257]

									# Which transformer to use to encode which condition.
									# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
									_UpperCAmelCase							 =		[1, 0]






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case=None	,       snake_case=None	,       snake_case=None	,       snake_case = True	,       )			->							Any:
									_UpperCAmelCase							 =		hidden_states

									_UpperCAmelCase							 =		[]
									_UpperCAmelCase							 =		0
									# attention_mask is not used yet
									for i in range(2						):
											# for each of the two transformers, pass the corresponding condition tokens
											_UpperCAmelCase							 =		encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
											_UpperCAmelCase							 =		self.transformer_index_for_condition[i]
											_UpperCAmelCase							 =		self.transformers[transformer_index](
											    snake_case	,       encoder_hidden_states=snake_case	,       timestep=snake_case	,       cross_attention_kwargs=snake_case	,       return_dict=snake_case	,       )[0]
											encoded_states.append(encoded_state - input_states						)
											tokens_start += self.condition_lengths[i]

									_UpperCAmelCase							 =		encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
									_UpperCAmelCase							 =		output_states + input_states

									if not return_dict:
											return (output_states,)

									return TransformeraDModelOutput(sample=snake_case						)


