






from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput



class   lowerCamelCase				(nn.Module      ):







		'''simple docstring'''



		def __init__(							self    ,		_UpperCamelCase = 1_6    ,		_UpperCamelCase = 8_8    ,		_UpperCamelCase = None    ,		_UpperCamelCase = 1    ,		_UpperCamelCase = 0.0    ,		_UpperCamelCase = 3_2    ,		_UpperCamelCase = None    ,		_UpperCamelCase = False    ,		_UpperCamelCase = None    ,		_UpperCamelCase = None    ,		_UpperCamelCase = "geglu"    ,		_UpperCamelCase = None    ,		)     ->							Union[str, Any]:
							super().__init__()
							UpperCAmelCase_   :				List[Any]						=						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
							UpperCAmelCase_   :				Tuple						=						0.5

							# The shape of `encoder_hidden_states` is expected to be
							# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
							UpperCAmelCase_   :				Union[str, Any]						=						[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])`
							UpperCAmelCase_   :				Optional[Any]						=						[1, 0]



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase=None    ,		_UpperCamelCase=None    ,		_UpperCamelCase=None    ,		_UpperCamelCase = True    ,		)     ->							Dict:
							UpperCAmelCase_   :				str						=						hidden_states

							UpperCAmelCase_   :				Dict						=						[]
							UpperCAmelCase_   :				int						=						0
							# attention_mask is not used yet
							for i in range(2       ):
												# for each of the two transformers, pass the corresponding condition tokens
												UpperCAmelCase_   :				Optional[Any]						=						encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
												UpperCAmelCase_   :				List[Any]						=						self.transformer_index_for_condition[i]
												UpperCAmelCase_   :				Optional[int]						=						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]

							UpperCAmelCase_   :				Dict						=						encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
							UpperCAmelCase_   :				List[str]						=						output_states + input_states

							if not return_dict:
												return (output_states,)

							return TransformeraDModelOutput(sample=_UpperCamelCase       )














from typing import Mapping

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


__UpperCAmelCase     							=						logging.get_logger(__name__)

__UpperCAmelCase     							=						{
    '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   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		_snake_case   :   Union[str, Any]							=  '''umt5'''
		_snake_case   :   Union[str, Any]							=  ['''past_key_values''']



		def __init__(							self    ,		_UpperCamelCase=2_5_0_1_1_2    ,		_UpperCamelCase=5_1_2    ,		_UpperCamelCase=6_4    ,		_UpperCamelCase=1_0_2_4    ,		_UpperCamelCase=8    ,		_UpperCamelCase=None    ,		_UpperCamelCase=6    ,		_UpperCamelCase=3_2    ,		_UpperCamelCase=1_2_8    ,		_UpperCamelCase=0.1    ,		_UpperCamelCase=1E-6    ,		_UpperCamelCase=1.0    ,		_UpperCamelCase="gated-gelu"    ,		_UpperCamelCase=True    ,		_UpperCamelCase=True    ,		_UpperCamelCase="T5Tokenizer"    ,		_UpperCamelCase=True    ,		_UpperCamelCase=0    ,		_UpperCamelCase=1    ,		_UpperCamelCase=0    ,		**_UpperCamelCase    ,		)     ->							List[Any]:
							super().__init__(
							    is_encoder_decoder=_UpperCamelCase    ,		tokenizer_class=_UpperCamelCase    ,		tie_word_embeddings=_UpperCamelCase    ,		pad_token_id=_UpperCamelCase    ,		eos_token_id=_UpperCamelCase    ,		decoder_start_token_id=_UpperCamelCase    ,		**_UpperCamelCase    ,		)
							UpperCAmelCase_   :				Union[str, Any]						=						vocab_size
							UpperCAmelCase_   :				List[str]						=						d_model
							UpperCAmelCase_   :				Any						=						d_kv
							UpperCAmelCase_   :				Optional[int]						=						d_ff
							UpperCAmelCase_   :				List[Any]						=						num_layers
							UpperCAmelCase_   :				Optional[Any]						=						(
							    num_decoder_layers if num_decoder_layers is not None else self.num_layers
							)  # default = symmetry
							UpperCAmelCase_   :				Optional[int]						=						num_heads
							UpperCAmelCase_   :				Optional[int]						=						relative_attention_num_buckets
							UpperCAmelCase_   :				Dict						=						relative_attention_max_distance
							UpperCAmelCase_   :				Tuple						=						dropout_rate
							UpperCAmelCase_   :				Union[str, Any]						=						layer_norm_epsilon
							UpperCAmelCase_   :				Optional[int]						=						initializer_factor
							UpperCAmelCase_   :				List[str]						=						feed_forward_proj
							UpperCAmelCase_   :				Any						=						use_cache

							UpperCAmelCase_   :				List[Any]						=						self.feed_forward_proj.split('-'       )
							UpperCAmelCase_   :				List[Any]						=						act_info[-1]
							UpperCAmelCase_   :				Union[str, Any]						=						act_info[0] == 'gated'

							if len(_UpperCamelCase       ) > 1 and act_info[0] != "gated" or len(_UpperCamelCase       ) > 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":
												UpperCAmelCase_   :				Optional[int]						=						'gelu_new'



		@property
		def  __UpperCAmelCase      (							self       )     ->							int:
							return self.d_model



		@property
		def  __UpperCAmelCase      (							self       )     ->							Any:
							return self.num_heads



		@property
		def  __UpperCAmelCase      (							self       )     ->							List[Any]:
							return self.num_layers





class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		@property
		# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
		def  __UpperCAmelCase      (							self       )     ->							Mapping[str, Mapping[int, str]]:
							UpperCAmelCase_   :				str						=						{
							    'input_ids': {0: 'batch', 1: 'encoder_sequence'},
							    'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
							}
							if self.use_past:
												UpperCAmelCase_   :				Optional[int]						=						'past_encoder_sequence + sequence'
												UpperCAmelCase_   :				str						=						{0: 'batch'}
												UpperCAmelCase_   :				Optional[int]						=						{0: 'batch', 1: 'past_decoder_sequence + sequence'}
							else:
												UpperCAmelCase_   :				Optional[int]						=						{0: 'batch', 1: 'decoder_sequence'}
												UpperCAmelCase_   :				Union[str, Any]						=						{0: 'batch', 1: 'decoder_sequence'}

							if self.use_past:
												self.fill_with_past_key_values_(_UpperCamelCase    ,		direction='inputs'       )

							return common_inputs



		@property
		# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
		def  __UpperCAmelCase      (							self       )     ->							int:
							return 1_3



		@property
		def  __UpperCAmelCase      (							self       )     ->							float:
							return 5E-4






