






from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin



@dataclass
class   lowerCamelCase				(_snake_case      ):







							'''simple docstring'''



							_snake_case   :   torch.FloatTensor
							_snake_case   :   torch.FloatTensor
							_snake_case   :   Optional[torch.FloatTensor]							=  None





class   lowerCamelCase				(_snake_case			,  _snake_case      ):







							'''simple docstring'''



							_snake_case   :   str							=  2



							@register_to_config
							def __init__(							self    ,		_UpperCamelCase = 0.02    ,		_UpperCamelCase = 1_0_0    ,		_UpperCamelCase = 1.0_07    ,		_UpperCamelCase = 8_0    ,		_UpperCamelCase = 0.05    ,		_UpperCamelCase = 5_0    ,		)     ->							Dict:
												# standard deviation of the initial noise distribution
												UpperCAmelCase_   :				Optional[int]						=						sigma_max

												# setable values
												UpperCAmelCase_   :				int						=						None
												UpperCAmelCase_   :				np.IntTensor						=						None
												UpperCAmelCase_   :				torch.FloatTensor						=						None  # sigma(t_i)



							def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase = None       )     ->							torch.FloatTensor:
												return sample



							def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase = None       )     ->							str:
												UpperCAmelCase_   :				Tuple						=						num_inference_steps
												UpperCAmelCase_   :				Optional[int]						=						np.arange(0    ,		self.num_inference_steps       )[::-1].copy()
												UpperCAmelCase_   :				Union[str, Any]						=						torch.from_numpy(_UpperCamelCase       ).to(_UpperCamelCase       )
												UpperCAmelCase_   :				str						=						[
												    (
												        self.config.sigma_max**2
												        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
												    )
												    for i in self.timesteps
												]
												UpperCAmelCase_   :				Optional[Any]						=						torch.tensor(_UpperCamelCase    ,		dtype=torch.floataa    ,		device=_UpperCamelCase       )



							def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase = None       )     ->							Tuple[torch.FloatTensor, float]:
												if self.config.s_min <= sigma <= self.config.s_max:
																	UpperCAmelCase_   :				str						=						min(self.config.s_churn / self.num_inference_steps    ,		2**0.5 - 1       )
												else:
																	UpperCAmelCase_   :				Any						=						0

												# sample eps ~ N(0, S_noise^2 * I)
												UpperCAmelCase_   :				int						=						self.config.s_noise * randn_tensor(sample.shape    ,		generator=_UpperCamelCase       ).to(sample.device       )
												UpperCAmelCase_   :				Optional[Any]						=						sigma + gamma * sigma
												UpperCAmelCase_   :				List[Any]						=						sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

												return sample_hat, sigma_hat



							def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase = True    ,		)     ->							Union[KarrasVeOutput, Tuple]:
												UpperCAmelCase_   :				int						=						sample_hat + sigma_hat * model_output
												UpperCAmelCase_   :				Dict						=						(sample_hat - pred_original_sample) / sigma_hat
												UpperCAmelCase_   :				List[Any]						=						sample_hat + (sigma_prev - sigma_hat) * derivative

												if not return_dict:
																	return (sample_prev, derivative)

												return KarrasVeOutput(
												    prev_sample=_UpperCamelCase    ,		derivative=_UpperCamelCase    ,		pred_original_sample=_UpperCamelCase       )



							def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase = True    ,		)     ->							Union[KarrasVeOutput, Tuple]:
												UpperCAmelCase_   :				int						=						sample_prev + sigma_prev * model_output
												UpperCAmelCase_   :				Union[str, Any]						=						(sample_prev - pred_original_sample) / sigma_prev
												UpperCAmelCase_   :				List[Any]						=						sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

												if not return_dict:
																	return (sample_prev, derivative)

												return KarrasVeOutput(
												    prev_sample=_UpperCamelCase    ,		derivative=_UpperCamelCase    ,		pred_original_sample=_UpperCamelCase       )



							def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							Optional[int]:
												raise NotImplementedError()














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






