



"""simple docstring"""







from typing import Mapping

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


A     =      logging.get_logger(__name__)

A     =      {
    'google/long-t5-local-base': 'https://huggingface.co/google/long-t5-local-base/blob/main/config.json',
    'google/long-t5-local-large': 'https://huggingface.co/google/long-t5-local-large/blob/main/config.json',
    'google/long-t5-tglobal-base': 'https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json',
    'google/long-t5-tglobal-large': 'https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json',
}






class   UpperCAmelCase__		(   UpperCamelCase  ):
		lowerCAmelCase_							:						Optional[int]							=  """longt5"""
		lowerCAmelCase_							:						Optional[Any]							=  ["""past_key_values"""]
		lowerCAmelCase_							:						int							=  {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}



		def __init__(			self	:     Dict   ,				snake_case	:     Optional[Any]=32_128   ,				snake_case	:     int=512   ,				snake_case	:     Optional[int]=64   ,				snake_case	:     List[Any]=2_048   ,				snake_case	:     Union[str, Any]=6   ,				snake_case	:     str=None   ,				snake_case	:     int=8   ,				snake_case	:     Dict=127   ,				snake_case	:     Union[str, Any]=16   ,				snake_case	:     Dict=32   ,				snake_case	:     str=128   ,				snake_case	:     Any=0.1   ,				snake_case	:     Optional[Any]=1E-6   ,				snake_case	:     List[str]=1.0   ,				snake_case	:     Tuple="relu"   ,				snake_case	:     Dict=True   ,				snake_case	:     Any="local"   ,				snake_case	:     List[Any]=True   ,				snake_case	:     int=0   ,				snake_case	:     int=1   ,				**snake_case	:     Dict   ,				)      ->    Any:






									'''simple docstring'''

									A    =       vocab_size
									A    =       d_model
									A    =       d_kv
									A    =       d_ff
									A    =       num_layers
									# default = symmetry
									A    =       num_decoder_layers if num_decoder_layers is not None else self.num_layers
									A    =       num_heads
									A    =       local_radius
									A    =       global_block_size
									A    =       relative_attention_num_buckets
									A    =       relative_attention_max_distance
									A    =       dropout_rate
									A    =       layer_norm_epsilon
									A    =       initializer_factor
									A    =       feed_forward_proj
									A    =       encoder_attention_type
									A    =       use_cache

									A    =       self.feed_forward_proj.split('-'					)
									A    =       act_info[-1]
									A    =       act_info[0] == 'gated'

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

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






class   UpperCAmelCase__		(   UpperCamelCase  ):



		@property
		def        A_		(			self	:     Any					)      ->    Mapping[str, Mapping[int, str]]:






									'''simple docstring'''

									A    =       {
									    'input_ids': {0: 'batch', 1: 'encoder_sequence'},
									    'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
									}
									if self.use_past:
																A    =       'past_encoder_sequence + sequence'
																A    =       {0: 'batch'}
																A    =       {0: 'batch', 1: 'past_decoder_sequence + sequence'}
									else:
																A    =       {0: 'batch', 1: 'decoder_sequence'}
																A    =       {0: 'batch', 1: 'decoder_sequence'}

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

									return common_inputs



		@property
		def        A_		(			self	:     List[Any]					)      ->    int:






									'''simple docstring'''

									return 13








"""simple docstring"""







import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput

def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__=0.9_99					,   lowerCamelCase__="cosine"					,   )			->				int:
							if alpha_transform_type == "cosine":

														def alpha_bar_fn(lowerCamelCase__      ):
																					return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2      ) ** 2

							elif alpha_transform_type == "exp":

														def alpha_bar_fn(lowerCamelCase__      ):
																					return math.exp(t * -12.0      )

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

							A    =       []
							for i in range(lowerCamelCase__      ):
														A    =       i / num_diffusion_timesteps
														A    =       (i + 1) / num_diffusion_timesteps
														betas.append(min(1 - alpha_bar_fn(lowerCamelCase__      ) / alpha_bar_fn(lowerCamelCase__      )					,   lowerCamelCase__      )      )
							return torch.tensor(lowerCamelCase__					,   dtype=torch.floataa      )






class   UpperCAmelCase__		(   UpperCamelCase ,UpperCamelCase  ):
		lowerCAmelCase_							:						int							=  [e.name for e in KarrasDiffusionSchedulers]
		lowerCAmelCase_							:						Union[str, Any]							=  2



		@register_to_config
		def __init__(			self	:     Any   ,				snake_case	:     int = 1_000   ,				snake_case	:     float = 0.00085   ,				snake_case	:     float = 0.012   ,				snake_case	:     str = "linear"   ,				snake_case	:     Optional[Union[np.ndarray, List[float]]] = None   ,				snake_case	:     str = "epsilon"   ,				snake_case	:     str = "linspace"   ,				snake_case	:     int = 0   ,				)      ->    Dict:






									'''simple docstring'''

									if trained_betas is not None:
																A    =       torch.tensor(snake_case   ,				dtype=torch.floataa					)
									elif beta_schedule == "linear":
																A    =       torch.linspace(snake_case   ,				snake_case   ,				snake_case   ,				dtype=torch.floataa					)
									elif beta_schedule == "scaled_linear":
																# this schedule is very specific to the latent diffusion model.
																A    =       (
																    torch.linspace(beta_start**0.5   ,				beta_end**0.5   ,				snake_case   ,				dtype=torch.floataa					) ** 2
																)
									elif beta_schedule == "squaredcos_cap_v2":
																# Glide cosine schedule
																A    =       betas_for_alpha_bar(snake_case					)
									else:
																raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""					)

									A    =       1.0 - self.betas
									A    =       torch.cumprod(self.alphas   ,				dim=0					)

									#  set all values
									self.set_timesteps(snake_case   ,				snake_case   ,				snake_case					)



		def        A_		(			self	:     str   ,				snake_case	:     List[Any]   ,				snake_case	:     str=None					)      ->    Optional[Any]:






									'''simple docstring'''

									if schedule_timesteps is None:
																A    =       self.timesteps

									A    =       (schedule_timesteps == timestep).nonzero()

									# The sigma index that is taken for the **very** first `step`
									# is always the second index (or the last index if there is only 1)
									# This way we can ensure we don't accidentally skip a sigma in
									# case we start in the middle of the denoising schedule (e.g. for image-to-image)
									if len(self._index_counter					) == 0:
																A    =       1 if len(snake_case					) > 1 else 0
									else:
																A    =       timestep.cpu().item() if torch.is_tensor(snake_case					) else timestep
																A    =       self._index_counter[timestep_int]

									return indices[pos].item()



		@property
		def        A_		(			self	:     Optional[int]					)      ->    Union[str, Any]:






									'''simple docstring'''

									if self.config.timestep_spacing in ["linspace", "trailing"]:
																return self.sigmas.max()

									return (self.sigmas.max() ** 2 + 1) ** 0.5



		def        A_		(			self	:     Union[str, Any]   ,				snake_case	:     torch.FloatTensor   ,				snake_case	:     Union[float, torch.FloatTensor]   ,				)      ->    torch.FloatTensor:






									'''simple docstring'''

									A    =       self.index_for_timestep(snake_case					)

									if self.state_in_first_order:
																A    =       self.sigmas[step_index]
									else:
																A    =       self.sigmas_interpol[step_index - 1]

									A    =       sample / ((sigma**2 + 1) ** 0.5)
									return sample



		def        A_		(			self	:     Union[str, Any]   ,				snake_case	:     int   ,				snake_case	:     Union[str, torch.device] = None   ,				snake_case	:     Optional[int] = None   ,				)      ->    Any:






									'''simple docstring'''

									A    =       num_inference_steps

									A    =       num_train_timesteps or self.config.num_train_timesteps

									# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
									if self.config.timestep_spacing == "linspace":
																A    =       np.linspace(0   ,				num_train_timesteps - 1   ,				snake_case   ,				dtype=snake_case					)[::-1].copy()
									elif self.config.timestep_spacing == "leading":
																A    =       num_train_timesteps // self.num_inference_steps
																# creates integer timesteps by multiplying by ratio
																# casting to int to avoid issues when num_inference_step is power of 3
																A    =       (np.arange(0   ,				snake_case					) * step_ratio).round()[::-1].copy().astype(snake_case					)
																timesteps += self.config.steps_offset
									elif self.config.timestep_spacing == "trailing":
																A    =       num_train_timesteps / self.num_inference_steps
																# creates integer timesteps by multiplying by ratio
																# casting to int to avoid issues when num_inference_step is power of 3
																A    =       (np.arange(snake_case   ,				0   ,				-step_ratio					)).round().copy().astype(snake_case					)
																timesteps -= 1
									else:
																raise ValueError(
																    f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""					)

									A    =       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
									A    =       torch.from_numpy(np.log(snake_case					)					).to(snake_case					)

									A    =       np.interp(snake_case   ,				np.arange(0   ,				len(snake_case					)					)   ,				snake_case					)
									A    =       np.concatenate([sigmas, [0.0]]					).astype(np.floataa					)
									A    =       torch.from_numpy(snake_case					).to(device=snake_case					)

									# compute up and down sigmas
									A    =       sigmas.roll(-1					)
									A    =       0.0
									A    =       (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
									A    =       (sigmas_next**2 - sigmas_up**2) ** 0.5
									A    =       0.0

									# compute interpolated sigmas
									A    =       sigmas.log().lerp(sigmas_down.log()   ,				0.5					).exp()
									A    =       0.0

									# set sigmas
									A    =       torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2					), sigmas[-1:]]					)
									A    =       torch.cat(
									    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2					), sigmas_interpol[-1:]]					)
									A    =       torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2					), sigmas_up[-1:]]					)
									A    =       torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2					), sigmas_down[-1:]]					)

									if str(snake_case					).startswith('mps'					):
																# mps does not support float64
																A    =       torch.from_numpy(snake_case					).to(snake_case   ,				dtype=torch.floataa					)
									else:
																A    =       torch.from_numpy(snake_case					).to(snake_case					)

									A    =       self.sigma_to_t(snake_case					).to(snake_case   ,				dtype=timesteps.dtype					)
									A    =       torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])   ,				dim=-1					).flatten()

									A    =       torch.cat([timesteps[:1], interleaved_timesteps]					)

									A    =       None

									# for exp beta schedules, such as the one for `pipeline_shap_e.py`
									# we need an index counter
									A    =       defaultdict(snake_case					)



		def        A_		(			self	:     Dict   ,				snake_case	:     Dict					)      ->    Tuple:






									'''simple docstring'''

									A    =       sigma.log()

									# get distribution
									A    =       log_sigma - self.log_sigmas[:, None]

									# get sigmas range
									A    =       dists.ge(0					).cumsum(dim=0					).argmax(dim=0					).clamp(max=self.log_sigmas.shape[0] - 2					)
									A    =       low_idx + 1

									A    =       self.log_sigmas[low_idx]
									A    =       self.log_sigmas[high_idx]

									# interpolate sigmas
									A    =       (low - log_sigma) / (low - high)
									A    =       w.clamp(0   ,				1					)

									# transform interpolation to time range
									A    =       (1 - w) * low_idx + w * high_idx
									A    =       t.view(sigma.shape					)
									return t



		@property
		def        A_		(			self	:     Any					)      ->    int:






									'''simple docstring'''

									return self.sample is None



		def        A_		(			self	:     Tuple   ,				snake_case	:     Union[torch.FloatTensor, np.ndarray]   ,				snake_case	:     Union[float, torch.FloatTensor]   ,				snake_case	:     Union[torch.FloatTensor, np.ndarray]   ,				snake_case	:     Optional[torch.Generator] = None   ,				snake_case	:     bool = True   ,				)      ->    Union[SchedulerOutput, Tuple]:






									'''simple docstring'''

									A    =       self.index_for_timestep(snake_case					)

									# advance index counter by 1
									A    =       timestep.cpu().item() if torch.is_tensor(snake_case					) else timestep
									self._index_counter[timestep_int] += 1

									if self.state_in_first_order:
																A    =       self.sigmas[step_index]
																A    =       self.sigmas_interpol[step_index]
																A    =       self.sigmas_up[step_index]
																A    =       self.sigmas_down[step_index - 1]
									else:
																# 2nd order / KPDM2's method
																A    =       self.sigmas[step_index - 1]
																A    =       self.sigmas_interpol[step_index - 1]
																A    =       self.sigmas_up[step_index - 1]
																A    =       self.sigmas_down[step_index - 1]

									# currently only gamma=0 is supported. This usually works best anyways.
									# We can support gamma in the future but then need to scale the timestep before
									# passing it to the model which requires a change in API
									A    =       0
									A    =       sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

									A    =       model_output.device
									A    =       randn_tensor(model_output.shape   ,				dtype=model_output.dtype   ,				device=snake_case   ,				generator=snake_case					)

									# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
									if self.config.prediction_type == "epsilon":
																A    =       sigma_hat if self.state_in_first_order else sigma_interpol
																A    =       sample - sigma_input * model_output
									elif self.config.prediction_type == "v_prediction":
																A    =       sigma_hat if self.state_in_first_order else sigma_interpol
																A    =       model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
																    sample / (sigma_input**2 + 1)
																)
									elif self.config.prediction_type == "sample":
																raise NotImplementedError('prediction_type not implemented yet: sample'					)
									else:
																raise ValueError(
																    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""					)

									if self.state_in_first_order:
																# 2. Convert to an ODE derivative for 1st order
																A    =       (sample - pred_original_sample) / sigma_hat
																# 3. delta timestep
																A    =       sigma_interpol - sigma_hat

																# store for 2nd order step
																A    =       sample
																A    =       dt
																A    =       sample + derivative * dt
									else:
																# DPM-Solver-2
																# 2. Convert to an ODE derivative for 2nd order
																A    =       (sample - pred_original_sample) / sigma_interpol
																# 3. delta timestep
																A    =       sigma_down - sigma_hat

																A    =       self.sample
																A    =       None

																A    =       sample + derivative * dt
																A    =       prev_sample + noise * sigma_up

									if not return_dict:
																return (prev_sample,)

									return SchedulerOutput(prev_sample=snake_case					)



		def        A_		(			self	:     Dict   ,				snake_case	:     torch.FloatTensor   ,				snake_case	:     torch.FloatTensor   ,				snake_case	:     torch.FloatTensor   ,				)      ->    torch.FloatTensor:






									'''simple docstring'''

									A    =       self.sigmas.to(device=original_samples.device   ,				dtype=original_samples.dtype					)
									if original_samples.device.type == "mps" and torch.is_floating_point(snake_case					):
																# mps does not support float64
																A    =       self.timesteps.to(original_samples.device   ,				dtype=torch.floataa					)
																A    =       timesteps.to(original_samples.device   ,				dtype=torch.floataa					)
									else:
																A    =       self.timesteps.to(original_samples.device					)
																A    =       timesteps.to(original_samples.device					)

									A    =       [self.index_for_timestep(snake_case   ,				snake_case					) for t in timesteps]

									A    =       sigmas[step_indices].flatten()
									while len(sigma.shape					) < len(original_samples.shape					):
																A    =       sigma.unsqueeze(-1					)

									A    =       original_samples + noise * sigma
									return noisy_samples



		def __len__(			self	:     List[Any]					)      ->    int:






									'''simple docstring'''

									return self.config.num_train_timesteps



