



'''simple docstring'''



# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)
@flax.struct.dataclass
class 						lowerCamelCase_				:
						_lowerCAmelCase  : CommonSchedulerState

						# setable values
						_lowerCAmelCase  : jnp.ndarray
						_lowerCAmelCase  : jnp.ndarray
						_lowerCAmelCase  : Optional[int]						  =					None







						@classmethod
						def        __lowercase     (	cls :   Dict					,     lowerCAmelCase__ :   Dict					,     lowerCAmelCase__ :   str					,     lowerCAmelCase__ :   str					):

													"""simple docstring"""
													return cls(common=__UpperCamelCase					,     init_noise_sigma=__UpperCamelCase					,     timesteps=__UpperCamelCase					)


@dataclass
class 						lowerCamelCase_				(       __a      ):
						_lowerCAmelCase  : DDPMSchedulerState


class 						lowerCamelCase_				(       __a     ,					__a      ):
						_lowerCAmelCase  : int						  =					[e.name for e in FlaxKarrasDiffusionSchedulers]

						_lowerCAmelCase  : jnp.dtype







						@property
						def        __lowercase     (	self :   str					):

													"""simple docstring"""
													return True







						@register_to_config
						def __init__(	self :   Optional[Any]					,     lowerCAmelCase__ :   Union[str, Any] = 10_00					,     lowerCAmelCase__ :   str = 0.0001					,     lowerCAmelCase__ :   int = 0.02					,     lowerCAmelCase__ :   Any = "linear"					,     lowerCAmelCase__ :   str = None					,     lowerCAmelCase__ :   Optional[int] = "fixed_small"					,     lowerCAmelCase__ :   Tuple = True					,     lowerCAmelCase__ :   List[str] = "epsilon"					,     lowerCAmelCase__ :   List[Any] = jnp.floataa					,     ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      dtype







						def        __lowercase     (	self :   Dict					,     lowerCAmelCase__ :   int = None					):

													"""simple docstring"""
													if common is None:
																				SCREAMING_SNAKE_CASE       :   List[str]         =      CommonSchedulerState.create(self					)

													# standard deviation of the initial noise distribution
													SCREAMING_SNAKE_CASE       :   List[str]         =      jnp.array(1.0					,     dtype=self.dtype					)

													SCREAMING_SNAKE_CASE       :   Any         =      jnp.arange(0					,     self.config.num_train_timesteps					).round()[::-1]

													return DDPMSchedulerState.create(
													    common=__UpperCamelCase					,     init_noise_sigma=__UpperCamelCase					,     timesteps=__UpperCamelCase					,     )







						def        __lowercase     (	self :   Dict					,     lowerCAmelCase__ :   Optional[int]					,     lowerCAmelCase__ :   List[str]					,     lowerCAmelCase__ :   List[Any] = None					):

													"""simple docstring"""
													return sample







						def        __lowercase     (	self :   List[Any]					,     lowerCAmelCase__ :   List[Any]					,     lowerCAmelCase__ :   int					,     lowerCAmelCase__ :   List[Any] = ()					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Tuple         =      self.config.num_train_timesteps // num_inference_steps
													# creates integer timesteps by multiplying by ratio
													# rounding to avoid issues when num_inference_step is power of 3
													SCREAMING_SNAKE_CASE       :   Any         =      (jnp.arange(0					,     __UpperCamelCase					) * step_ratio).round()[::-1]

													return state.replace(
													    num_inference_steps=__UpperCamelCase					,     timesteps=__UpperCamelCase					,     )







						def        __lowercase     (	self :   Dict					,     lowerCAmelCase__ :   Optional[Any]					,     lowerCAmelCase__ :   List[Any]					,     lowerCAmelCase__ :   Dict=None					,     lowerCAmelCase__ :   str=None					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   List[Any]         =      state.common.alphas_cumprod[t]
													SCREAMING_SNAKE_CASE       :   Dict         =      jnp.where(t > 0					,     state.common.alphas_cumprod[t - 1]					,     jnp.array(1.0					,     dtype=self.dtype					)					)

													# 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
													SCREAMING_SNAKE_CASE       :   Any         =      (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

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

													# hacks - were probably added for training stability
													if variance_type == "fixed_small":
																				SCREAMING_SNAKE_CASE       :   Any         =      jnp.clip(__UpperCamelCase					,     a_min=1e-20					)
													# for rl-diffuser https://arxiv.org/abs/2205.09991
													elif variance_type == "fixed_small_log":
																				SCREAMING_SNAKE_CASE       :   Dict         =      jnp.log(jnp.clip(__UpperCamelCase					,     a_min=1e-20					)					)
													elif variance_type == "fixed_large":
																				SCREAMING_SNAKE_CASE       :   Any         =      state.common.betas[t]
													elif variance_type == "fixed_large_log":
																				# Glide max_log
																				SCREAMING_SNAKE_CASE       :   Tuple         =      jnp.log(state.common.betas[t]					)
													elif variance_type == "learned":
																				return predicted_variance
													elif variance_type == "learned_range":
																				SCREAMING_SNAKE_CASE       :   Optional[Any]         =      variance
																				SCREAMING_SNAKE_CASE       :   str         =      state.common.betas[t]
																				SCREAMING_SNAKE_CASE       :   Optional[int]         =      (predicted_variance + 1) / 2
																				SCREAMING_SNAKE_CASE       :   int         =      frac * max_log + (1 - frac) * min_log

													return variance







						def        __lowercase     (	self :   List[Any]					,     lowerCAmelCase__ :   Optional[int]					,     lowerCAmelCase__ :   Optional[int]					,     lowerCAmelCase__ :   Tuple					,     lowerCAmelCase__ :   Tuple					,     lowerCAmelCase__ :   Any = None					,     lowerCAmelCase__ :   int = True					,     ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Dict         =      timestep

													if key is None:
																				SCREAMING_SNAKE_CASE       :   Tuple         =      jax.random.PRNGKey(0					)

													if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
																				SCREAMING_SNAKE_CASE       :   Tuple         =      jnp.split(__UpperCamelCase					,     sample.shape[1]					,     axis=1					)
													else:
																				SCREAMING_SNAKE_CASE       :   List[Any]         =      None

													# 1. compute alphas, betas
													SCREAMING_SNAKE_CASE       :   int         =      state.common.alphas_cumprod[t]
													SCREAMING_SNAKE_CASE       :   int         =      jnp.where(t > 0					,     state.common.alphas_cumprod[t - 1]					,     jnp.array(1.0					,     dtype=self.dtype					)					)
													SCREAMING_SNAKE_CASE       :   List[str]         =      1 - alpha_prod_t
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      1 - alpha_prod_t_prev

													# 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":
																				SCREAMING_SNAKE_CASE       :   List[Any]         =      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
													elif self.config.prediction_type == "sample":
																				SCREAMING_SNAKE_CASE       :   List[Any]         =      model_output
													elif self.config.prediction_type == "v_prediction":
																				SCREAMING_SNAKE_CASE       :   str         =      (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
													else:
																				raise ValueError(
																				    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` """
																				    ''' for the FlaxDDPMScheduler.'''					)

													# 3. Clip "predicted x_0"
													if self.config.clip_sample:
																				SCREAMING_SNAKE_CASE       :   Any         =      jnp.clip(__UpperCamelCase					,     -1					,     1					)

													# 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
													SCREAMING_SNAKE_CASE       :   Dict         =      (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
													SCREAMING_SNAKE_CASE       :   int         =      state.common.alphas[t] ** 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
													SCREAMING_SNAKE_CASE       :   List[str]         =      pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

													# 6. Add noise
													def random_variance():
																				SCREAMING_SNAKE_CASE       :   List[str]         =      jax.random.split(__UpperCamelCase					,     num=1					)
																				SCREAMING_SNAKE_CASE       :   Optional[int]         =      jax.random.normal(__UpperCamelCase					,     shape=model_output.shape					,     dtype=self.dtype					)
																				return (self._get_variance(__UpperCamelCase					,     __UpperCamelCase					,     predicted_variance=__UpperCamelCase					) ** 0.5) * noise

													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      jnp.where(t > 0					,     random_variance()					,     jnp.zeros(model_output.shape					,     dtype=self.dtype					)					)

													SCREAMING_SNAKE_CASE       :   Optional[int]         =      pred_prev_sample + variance

													if not return_dict:
																				return (pred_prev_sample, state)

													return FlaxDDPMSchedulerOutput(prev_sample=__UpperCamelCase					,     state=__UpperCamelCase					)







						def        __lowercase     (	self :   List[str]					,     lowerCAmelCase__ :   Optional[Any]					,     lowerCAmelCase__ :   List[str]					,     lowerCAmelCase__ :   int					,     lowerCAmelCase__ :   Dict					,     ):

													"""simple docstring"""
													return add_noise_common(state.common					,     __UpperCamelCase					,     __UpperCamelCase					,     __UpperCamelCase					)







						def        __lowercase     (	self :   Dict					,     lowerCAmelCase__ :   List[str]					,     lowerCAmelCase__ :   List[Any]					,     lowerCAmelCase__ :   List[Any]					,     lowerCAmelCase__ :   Any					,     ):

													"""simple docstring"""
													return get_velocity_common(state.common					,     __UpperCamelCase					,     __UpperCamelCase					,     __UpperCamelCase					)







						def __len__(	self :   Optional[int]					):

													"""simple docstring"""
													return self.config.num_train_timesteps





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional

from packaging import version


if TYPE_CHECKING:
	from ... import PreTrainedTokenizer, TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging


SCREAMING_SNAKE_CASE		:     List[str]         =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE		:     str         =	{
    "bigscience/bloom": "https://huggingface.co/bigscience/bloom/resolve/main/config.json",
    "bigscience/bloom-560m": "https://huggingface.co/bigscience/bloom-560m/blob/main/config.json",
    "bigscience/bloom-1b1": "https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json",
    "bigscience/bloom-1b7": "https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json",
    "bigscience/bloom-3b": "https://huggingface.co/bigscience/bloom-3b/blob/main/config.json",
    "bigscience/bloom-7b1": "https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json",
}

class        UpperCamelCase						(				__a				):
						a__		:Any      =			'''bloom'''
						a__		:Optional[int]      =			['''past_key_values''']
						a__		:Tuple      =			{
						    '''num_hidden_layers''': '''n_layer''',
						    '''num_attention_heads''': '''n_head''',
						}





						def __init__(self			,     __UpperCamelCase=250_880			,     __UpperCamelCase=64			,     __UpperCamelCase=2			,     __UpperCamelCase=8			,     __UpperCamelCase=1E-5			,     __UpperCamelCase=0.02			,     __UpperCamelCase=True			,     __UpperCamelCase=1			,     __UpperCamelCase=2			,     __UpperCamelCase=False			,     __UpperCamelCase=0.0			,     __UpperCamelCase=0.0			,     __UpperCamelCase=1			,     __UpperCamelCase=False			,     **__UpperCamelCase			,     )				->				List[Any]:
												UpperCamelCase_		:							Dict  =		vocab_size
												# Backward compatibility with n_embed kwarg
												UpperCamelCase_		:							Optional[Any]  =		kwargs.pop("""n_embed"""			,     __UpperCamelCase						)
												UpperCamelCase_		:							List[Any]  =		hidden_size if n_embed is None else n_embed
												UpperCamelCase_		:							List[Any]  =		n_layer
												UpperCamelCase_		:							Optional[Any]  =		n_head
												UpperCamelCase_		:							List[str]  =		layer_norm_epsilon
												UpperCamelCase_		:							int  =		initializer_range
												UpperCamelCase_		:							List[str]  =		use_cache
												UpperCamelCase_		:							List[Any]  =		pretraining_tp
												UpperCamelCase_		:							Optional[int]  =		apply_residual_connection_post_layernorm
												UpperCamelCase_		:							Any  =		hidden_dropout
												UpperCamelCase_		:							Optional[Any]  =		attention_dropout

												UpperCamelCase_		:							List[str]  =		bos_token_id
												UpperCamelCase_		:							Optional[Any]  =		eos_token_id
												UpperCamelCase_		:							str  =		slow_but_exact

												super().__init__(bos_token_id=__UpperCamelCase			,     eos_token_id=__UpperCamelCase			,     **__UpperCamelCase						)


class        UpperCamelCase						(				__a				):
						a__		:Dict      =			version.parse('''1.12'''				)





						def __init__(self			,     __UpperCamelCase			,     __UpperCamelCase = "default"			,     __UpperCamelCase = None			,     __UpperCamelCase = False			,     )				->				List[str]:
												super().__init__(__UpperCamelCase			,     task=__UpperCamelCase			,     patching_specs=__UpperCamelCase			,     use_past=__UpperCamelCase						)
												if not getattr(self._config			,     """pad_token_id"""			,     __UpperCamelCase						):
																		# TODO: how to do that better?
																		UpperCamelCase_		:							Optional[Any]  =		0





						@property
						def        A_							(self						)				->				Mapping[str, Mapping[int, str]]:
												UpperCamelCase_		:							str  =		OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}						)
												if self.use_past:
																		# BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
																		self.fill_with_past_key_values_(__UpperCamelCase			,     direction="""inputs"""			,     inverted_values_shape=__UpperCamelCase						)
																		UpperCamelCase_		:							int  =		{0: """batch""", 1: """past_sequence + sequence"""}
												else:
																		UpperCamelCase_		:							Dict  =		{0: """batch""", 1: """sequence"""}

												return common_inputs





						@property
						def        A_							(self						)				->				int:
												return self._config.n_layer





						@property
						def        A_							(self						)				->				int:
												return self._config.n_head





						@property
						def        A_							(self						)				->				float:
												return 1E-3





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase = -1			,     __UpperCamelCase = -1			,     __UpperCamelCase = False			,     __UpperCamelCase = None			,     )				->				Mapping[str, Any]:
												UpperCamelCase_		:							Union[str, Any]  =		super(__UpperCamelCase			,     self						).generate_dummy_inputs(
												    __UpperCamelCase			,     batch_size=__UpperCamelCase			,     seq_length=__UpperCamelCase			,     is_pair=__UpperCamelCase			,     framework=__UpperCamelCase						)

												# We need to order the input in the way they appears in the forward()
												UpperCamelCase_		:							List[Any]  =		OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}						)

												# Need to add the past_keys
												if self.use_past:
																		if not is_torch_available():
																								raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""						)
																		else:
																								import torch

																								UpperCamelCase_,UpperCamelCase_		:							List[str]  =		common_inputs["""input_ids"""].shape
																								# Not using the same length for past_key_values
																								UpperCamelCase_		:							List[str]  =		seqlen + 2
																								UpperCamelCase_		:							Tuple  =		self._config.hidden_size // self.num_attention_heads
																								UpperCamelCase_		:							List[Any]  =		(
																								    batch * self.num_attention_heads,
																								    head_dim,
																								    past_key_values_length,
																								)
																								UpperCamelCase_		:							str  =		(
																								    batch * self.num_attention_heads,
																								    past_key_values_length,
																								    head_dim,
																								)
																								UpperCamelCase_		:							Any  =		[
																								    (torch.zeros(__UpperCamelCase						), torch.zeros(__UpperCamelCase						)) for _ in range(self.num_layers						)
																								]

												UpperCamelCase_		:							int  =		common_inputs["""attention_mask"""]
												if self.use_past:
																		UpperCamelCase_		:							List[str]  =		ordered_inputs["""attention_mask"""].dtype
																		UpperCamelCase_		:							Any  =		torch.cat(
																		    [ordered_inputs["""attention_mask"""], torch.ones(__UpperCamelCase			,     __UpperCamelCase			,     dtype=__UpperCamelCase						)]			,     dim=1						)

												return ordered_inputs





						@property
						def        A_							(self						)				->				int:
												return 13






