

from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput





class 	snake_case_   (		nn.Module					):







			'''simple docstring'''

			def __init__(     self :				Dict							,   _UpperCamelCase :				int = 1_6							,   _UpperCamelCase :				int = 8_8							,   _UpperCamelCase :				Optional[int] = None							,   _UpperCamelCase :				int = 1							,   _UpperCamelCase :				float = 0.0							,   _UpperCamelCase :				int = 3_2							,   _UpperCamelCase :				Optional[int] = None							,   _UpperCamelCase :				bool = False							,   _UpperCamelCase :				Optional[int] = None							,   _UpperCamelCase :				Optional[int] = None							,   _UpperCamelCase :				str = "geglu"							,   _UpperCamelCase :				Optional[int] = None							,   )      ->Any:
									super().__init__()
									snake_case_   =	nn.ModuleList(
									    [
									        TransformeraDModel(
									            num_attention_heads=_UpperCamelCase							,   attention_head_dim=_UpperCamelCase							,   in_channels=_UpperCamelCase							,   num_layers=_UpperCamelCase							,   dropout=_UpperCamelCase							,   norm_num_groups=_UpperCamelCase							,   cross_attention_dim=_UpperCamelCase							,   attention_bias=_UpperCamelCase							,   sample_size=_UpperCamelCase							,   num_vector_embeds=_UpperCamelCase							,   activation_fn=_UpperCamelCase							,   num_embeds_ada_norm=_UpperCamelCase							,   )
									        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
									snake_case_   =	0.5

									# The shape of `encoder_hidden_states` is expected to be
									# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
									snake_case_   =	[7_7, 2_5_7]

									# 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])`
									snake_case_   =	[1, 0]







			def 				snake_case__(     self :				Dict							,   _UpperCamelCase :				str							,   _UpperCamelCase :				str							,   _UpperCamelCase :				Optional[Any]=None							,   _UpperCamelCase :				List[Any]=None							,   _UpperCamelCase :				List[str]=None							,   _UpperCamelCase :				bool = True							,   )      ->Optional[Any]:
									snake_case_   =	hidden_states

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

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

									if not return_dict:
															return (output_states,)

									return TransformeraDModelOutput(sample=_UpperCamelCase							)

'''simple docstring'''







import warnings

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


lowercase    :							int      =					logging.get_logger(__name__)

lowercase    :							Union[str, Any]      =					{
    "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/config.json",
    "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/config.json",
}


class 						__UpperCAmelCase      (					_lowerCamelCase						):
							__lowercase      		=       """xlnet"""
							__lowercase      		=       ["""mems"""]
							__lowercase      		=       {
							    """n_token""": """vocab_size""",  # Backward compatibility
							    """hidden_size""": """d_model""",
							    """num_attention_heads""": """n_head""",
							    """num_hidden_layers""": """n_layer""",
							}


							def __init__(       self    ,   lowerCAmelCase_=3_20_00    ,   lowerCAmelCase_=10_24    ,   lowerCAmelCase_=24    ,   lowerCAmelCase_=16    ,   lowerCAmelCase_=40_96    ,   lowerCAmelCase_="gelu"    ,   lowerCAmelCase_=True    ,   lowerCAmelCase_="bi"    ,   lowerCAmelCase_=0.02    ,   lowerCAmelCase_=1E-12    ,   lowerCAmelCase_=0.1    ,   lowerCAmelCase_=5_12    ,   lowerCAmelCase_=None    ,   lowerCAmelCase_=True    ,   lowerCAmelCase_=False    ,   lowerCAmelCase_=False    ,   lowerCAmelCase_=-1    ,   lowerCAmelCase_=False    ,   lowerCAmelCase_="last"    ,   lowerCAmelCase_=True    ,   lowerCAmelCase_="tanh"    ,   lowerCAmelCase_=0.1    ,   lowerCAmelCase_=5    ,   lowerCAmelCase_=5    ,   lowerCAmelCase_=5    ,   lowerCAmelCase_=1    ,   lowerCAmelCase_=2    ,   **lowerCAmelCase_    ,   ):





								"""simple docstring"""






								_snake_case   						=		vocab_size
								_snake_case   						=		d_model
								_snake_case   						=		n_layer
								_snake_case   						=		n_head
								if d_model % n_head != 0:
									raise ValueError(F'\'d_model % n_head\' ({d_model % n_head}) should be equal to 0'  )
								if "d_head" in kwargs:
									if kwargs["d_head"] != d_model // n_head:
										raise ValueError(
										    F'`d_head` ({kwargs["d_head"]}) should be equal to `d_model // n_head` ({d_model // n_head})'  )
								_snake_case   						=		d_model // n_head
								_snake_case   						=		ff_activation
								_snake_case   						=		d_inner
								_snake_case   						=		untie_r
								_snake_case   						=		attn_type

								_snake_case   						=		initializer_range
								_snake_case   						=		layer_norm_eps

								_snake_case   						=		dropout
								_snake_case   						=		mem_len
								_snake_case   						=		reuse_len
								_snake_case   						=		bi_data
								_snake_case   						=		clamp_len
								_snake_case   						=		same_length

								_snake_case   						=		summary_type
								_snake_case   						=		summary_use_proj
								_snake_case   						=		summary_activation
								_snake_case   						=		summary_last_dropout
								_snake_case   						=		start_n_top
								_snake_case   						=		end_n_top

								_snake_case   						=		bos_token_id
								_snake_case   						=		pad_token_id
								_snake_case   						=		eos_token_id

								if "use_cache" in kwargs:
									warnings.warn(
									    'The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`'
									    ' instead.'    ,   lowerCAmelCase_    ,   )
									_snake_case   						=		kwargs['use_cache']

								_snake_case   						=		use_mems_eval
								_snake_case   						=		use_mems_train
								super().__init__(pad_token_id=lowerCAmelCase_    ,   bos_token_id=lowerCAmelCase_    ,   eos_token_id=lowerCAmelCase_    ,   **lowerCAmelCase_  )


							@property
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								logger.info(F'The model {self.model_type} is one of the few models that has no sequence length limit.'  )
								return -1




							@max_position_embeddings.setter
							def  lowerCamelCase				(       self    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								raise NotImplementedError(
								    F'The model {self.model_type} is one of the few models that has no sequence length limit.'  )

