
'''simple docstring'''

import math
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
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     a_    	=					42
     a_    	=					None







def 	_snake_case						(      A_       :    int     ,		A_       :    List[str]=0.999     ,		A_       :    List[Any]="cosine"     ,		):
  """simple docstring"""







  if alpha_transform_type == "cosine":

    def alpha_bar_fn(A_       :    List[Any]   ):
      return math.cos((t + 0.008) / 1.008 * math.pi / 2   ) ** 2

  elif alpha_transform_type == "exp":

    def alpha_bar_fn(A_       :    List[str]   ):
      return math.exp(t * -12.0   )

  else:
    raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''   )

  a_      :	int      =							[]
  for i in range(A_   ):
    a_      :	Optional[Any]      =							i / num_diffusion_timesteps
    a_      :	List[Any]      =							(i + 1) / num_diffusion_timesteps
    betas.append(min(1 - alpha_bar_fn(A_   ) / alpha_bar_fn(A_   )     ,		A_   )   )
  return torch.tensor(A_     ,		dtype=torch.floataa   )




class  _UpperCAmelCase ( lowerCAmelCase__	,lowerCAmelCase__    ):

     """simple docstring"""

     @register_to_config
     def __init__(      self							,		lowerCAmelCase_ = 10_00							,		lowerCAmelCase_ = "fixed_small_log"							,		lowerCAmelCase_ = True							,		lowerCAmelCase_ = 1.0							,		lowerCAmelCase_ = "epsilon"							,		lowerCAmelCase_ = "squaredcos_cap_v2"							,		):

       '''simple docstring'''

       if beta_schedule != "squaredcos_cap_v2":
         raise ValueError("""UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'""" )

       a_      :	Optional[Any]      =							betas_for_alpha_bar(lowerCAmelCase_ )

       a_      :	Optional[Any]      =							1.0 - self.betas
       a_      :	List[str]      =							torch.cumprod(self.alphas							,		dim=0 )
       a_      :	Any      =							torch.tensor(1.0 )

       # standard deviation of the initial noise distribution
       a_      :	Any      =							1.0

       # setable values
       a_      :	List[Any]      =							None
       a_      :	Any      =							torch.from_numpy(np.arange(0							,		lowerCAmelCase_ )[::-1].copy() )

       a_      :	Any      =							variance_type


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_ = None ):

       '''simple docstring'''

       return sample


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_ = None ):

       '''simple docstring'''

       a_      :	List[str]      =							num_inference_steps
       a_      :	Dict      =							(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
       a_      :	Optional[Any]      =							(np.arange(0							,		lowerCAmelCase_ ) * step_ratio).round()[::-1].copy().astype(np.intaa )
       a_      :	List[Any]      =							torch.from_numpy(lowerCAmelCase_ ).to(lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_=None							,		lowerCAmelCase_=None							,		lowerCAmelCase_=None ):

       '''simple docstring'''

       if prev_timestep is None:
         a_      :	Tuple      =							t - 1

       a_      :	Optional[Any]      =							self.alphas_cumprod[t]
       a_      :	Union[str, Any]      =							self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
       a_      :	Optional[Any]      =							1 - alpha_prod_t
       a_      :	str      =							1 - alpha_prod_t_prev

       if prev_timestep == t - 1:
         a_      :	int      =							self.betas[t]
       else:
         a_      :	Tuple      =							1 - alpha_prod_t / alpha_prod_t_prev

       # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
       # and sample from it to get previous sample
       # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
       a_      :	str      =							beta_prod_t_prev / beta_prod_t * beta

       if variance_type is None:
         a_      :	int      =							self.config.variance_type

       # hacks - were probably added for training stability
       if variance_type == "fixed_small_log":
         a_      :	List[Any]      =							torch.log(torch.clamp(lowerCAmelCase_							,		min=1E-2_0 ) )
         a_      :	List[Any]      =							torch.exp(0.5 * variance )
       elif variance_type == "learned_range":
         # NOTE difference with DDPM scheduler
         a_      :	Tuple      =							variance.log()
         a_      :	int      =							beta.log()

         a_      :	List[Any]      =							(predicted_variance + 1) / 2
         a_      :	Dict      =							frac * max_log + (1 - frac) * min_log

       return variance


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ = None							,		lowerCAmelCase_=None							,		lowerCAmelCase_ = True							,		):

       '''simple docstring'''

       a_      :	Tuple      =							timestep

       if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
         a_      , a_      :	Optional[Any]      =							torch.split(lowerCAmelCase_							,		sample.shape[1]							,		dim=1 )
       else:
         a_      :	Optional[int]      =							None

       # 1. compute alphas, betas
       if prev_timestep is None:
         a_      :	List[Any]      =							t - 1

       a_      :	int      =							self.alphas_cumprod[t]
       a_      :	List[str]      =							self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
       a_      :	Any      =							1 - alpha_prod_t
       a_      :	Any      =							1 - alpha_prod_t_prev

       if prev_timestep == t - 1:
         a_      :	Optional[int]      =							self.betas[t]
         a_      :	Any      =							self.alphas[t]
       else:
         a_      :	List[Any]      =							1 - alpha_prod_t / alpha_prod_t_prev
         a_      :	List[str]      =							1 - beta

       # 2. compute predicted original sample from predicted noise also called
       # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
       if self.config.prediction_type == "epsilon":
         a_      :	List[str]      =							(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
       elif self.config.prediction_type == "sample":
         a_      :	Optional[int]      =							model_output
       else:
         raise ValueError(
             f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`'''
             """ for the UnCLIPScheduler.""" )

       # 3. Clip "predicted x_0"
       if self.config.clip_sample:
         a_      :	Any      =							torch.clamp(
             lowerCAmelCase_							,		-self.config.clip_sample_range							,		self.config.clip_sample_range )

       # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
       # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
       a_      :	str      =							(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
       a_      :	Dict      =							alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

       # 5. Compute predicted previous sample µ_t
       # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
       a_      :	Any      =							pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

       # 6. Add noise
       a_      :	List[Any]      =							0
       if t > 0:
         a_      :	List[Any]      =							randn_tensor(
             model_output.shape							,		dtype=model_output.dtype							,		generator=lowerCAmelCase_							,		device=model_output.device )

         a_      :	List[Any]      =							self._get_variance(
             lowerCAmelCase_							,		predicted_variance=lowerCAmelCase_							,		prev_timestep=lowerCAmelCase_							,		)

         if self.variance_type == "fixed_small_log":
           a_      :	Dict      =							variance
         elif self.variance_type == "learned_range":
           a_      :	Any      =							(0.5 * variance).exp()
         else:
           raise ValueError(
               f'''variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`'''
               """ for the UnCLIPScheduler.""" )

         a_      :	List[str]      =							variance * variance_noise

       a_      :	Optional[Any]      =							pred_prev_sample + variance

       if not return_dict:
         return (pred_prev_sample,)

       return UnCLIPSchedulerOutput(prev_sample=lowerCAmelCase_							,		pred_original_sample=lowerCAmelCase_ )






     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		):

       '''simple docstring'''

       a_      :	List[str]      =							self.alphas_cumprod.to(device=original_samples.device							,		dtype=original_samples.dtype )
       a_      :	Union[str, Any]      =							timesteps.to(original_samples.device )

       a_      :	List[Any]      =							alphas_cumprod[timesteps] ** 0.5
       a_      :	Optional[Any]      =							sqrt_alpha_prod.flatten()
       while len(sqrt_alpha_prod.shape ) < len(original_samples.shape ):
         a_      :	int      =							sqrt_alpha_prod.unsqueeze(-1 )

       a_      :	Optional[Any]      =							(1 - alphas_cumprod[timesteps]) ** 0.5
       a_      :	str      =							sqrt_one_minus_alpha_prod.flatten()
       while len(sqrt_one_minus_alpha_prod.shape ) < len(original_samples.shape ):
         a_      :	str      =							sqrt_one_minus_alpha_prod.unsqueeze(-1 )

       a_      :	int      =							sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
       return noisy_samples




'''simple docstring'''

from typing import Mapping

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


__snake_case:  Tuple								=			logging.get_logger(__name__)

__snake_case:  Tuple								=			{
    "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
    "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
    "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
    "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
    "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}


class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     a_    	=					"t5"
     a_    	=					["past_key_values"]
     a_    	=					{"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}


     def __init__(      self							,		lowerCAmelCase_=3_21_28							,		lowerCAmelCase_=5_12							,		lowerCAmelCase_=64							,		lowerCAmelCase_=20_48							,		lowerCAmelCase_=6							,		lowerCAmelCase_=None							,		lowerCAmelCase_=8							,		lowerCAmelCase_=32							,		lowerCAmelCase_=1_28							,		lowerCAmelCase_=0.1							,		lowerCAmelCase_=1E-6							,		lowerCAmelCase_=1.0							,		lowerCAmelCase_="relu"							,		lowerCAmelCase_=True							,		lowerCAmelCase_=True							,		lowerCAmelCase_=0							,		lowerCAmelCase_=1							,		**lowerCAmelCase_							,		):

       '''simple docstring'''

       a_      :	List[Any]      =							vocab_size
       a_      :	List[str]      =							d_model
       a_      :	Union[str, Any]      =							d_kv
       a_      :	List[Any]      =							d_ff
       a_      :	int      =							num_layers
       a_      :	Any      =							(
           num_decoder_layers if num_decoder_layers is not None else self.num_layers
       )  # default = symmetry
       a_      :	Union[str, Any]      =							num_heads
       a_      :	int      =							relative_attention_num_buckets
       a_      :	Dict      =							relative_attention_max_distance
       a_      :	Union[str, Any]      =							dropout_rate
       a_      :	Union[str, Any]      =							layer_norm_epsilon
       a_      :	List[Any]      =							initializer_factor
       a_      :	Any      =							feed_forward_proj
       a_      :	List[Any]      =							use_cache

       a_      :	Dict      =							self.feed_forward_proj.split("""-""" )
       a_      :	Union[str, Any]      =							act_info[-1]
       a_      :	str      =							act_info[0] == """gated"""

       if len(lowerCAmelCase_ ) > 1 and act_info[0] != "gated" or len(lowerCAmelCase_ ) > 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'""" )

       # for backwards compatibility
       if feed_forward_proj == "gated-gelu":
         a_      :	List[str]      =							"""gelu_new"""

       super().__init__(
           pad_token_id=lowerCAmelCase_							,		eos_token_id=lowerCAmelCase_							,		is_encoder_decoder=lowerCAmelCase_							,		**lowerCAmelCase_							,		)







class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[Any]      =							{
           """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
           """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
       }
       if self.use_past:
         a_      :	Tuple      =							"""past_encoder_sequence + sequence"""
         a_      :	str      =							{0: """batch"""}
         a_      :	Any      =							{0: """batch""", 1: """past_decoder_sequence + sequence"""}
       else:
         a_      :	List[Any]      =							{0: """batch""", 1: """decoder_sequence"""}
         a_      :	Optional[int]      =							{0: """batch""", 1: """decoder_sequence"""}

       if self.use_past:
         self.fill_with_past_key_values_(lowerCAmelCase_							,		direction="""inputs""" )

       return common_inputs






     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return 13


