




import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


UpperCAmelCase_		:				Union[str, Any]									=     "scheduler_config.json"




class 		__A      (			UpperCamelCase__			):
						UpperCamelCase     						=					1
						UpperCamelCase     						=					2
						UpperCamelCase     						=					3
						UpperCamelCase     						=					4
						UpperCamelCase     						=					5




@dataclass
class 		__A      (			UpperCamelCase__			):
						UpperCamelCase     						=					42




class 		__A      :
						UpperCamelCase     						=					SCHEDULER_CONFIG_NAME
						UpperCamelCase     						=					["""dtype"""]
						UpperCamelCase     						=					[]
						UpperCamelCase     						=					True

						@classmethod
						def     A__						(		cls   :Dict    , __snake_case   :Dict[str, Any] = None    , __snake_case   :Optional[str] = None    , __snake_case   :List[str]=False    , **__snake_case   :Tuple    , ):
										'''simple docstring'''

										__magic_name__	:	List[str]								=cls.load_config(
										    pretrained_model_name_or_path=__snake_case    , subfolder=__snake_case    , return_unused_kwargs=__snake_case    , **__snake_case    , )
										__magic_name__	:	int								=cls.from_config(__snake_case    , return_unused_kwargs=__snake_case    , **__snake_case )

										if hasattr(__snake_case    , """create_state""" ) and getattr(__snake_case    , """has_state"""    , __snake_case ):
														__magic_name__	:	Optional[Any]								=scheduler.create_state()

										if return_unused_kwargs:
														return scheduler, state, unused_kwargs

										return scheduler, state

						def     A__						(		self   :Optional[Any]    , __snake_case   :Union[str, os.PathLike]    , __snake_case   :bool = False    , **__snake_case   :Optional[int] ):
										'''simple docstring'''

										self.save_config(save_directory=__snake_case    , push_to_hub=__snake_case    , **__snake_case )

						@property
						def     A__						(		self   :Union[str, Any] ):
										'''simple docstring'''

										return self._get_compatibles()


						@classmethod
						def     A__						(		cls   :Dict ):
										'''simple docstring'''

										__magic_name__	:	List[str]								=list(set([cls.__name__] + cls._compatibles ) )
										__magic_name__	:	Dict								=importlib.import_module(__name__.split(""".""" )[0] )
										__magic_name__	:	List[Any]								=[
										    getattr(__snake_case    , __snake_case ) for c in compatible_classes_str if hasattr(__snake_case    , __snake_case )
										]
										return compatible_classes




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase   ):
				assert len(lowerCamelCase   ) >= x.ndim
				return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowerCamelCase   ) - x.ndim)   )	,     lowerCamelCase   )




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase=0.9_9_9	,     lowerCamelCase=jnp.floataa   ):

				def alpha_bar(lowerCamelCase   ):
								return math.cos((time_step + 0.0_0_8) / 1.0_0_8 * math.pi / 2   ) ** 2

				__magic_name__	:	List[Any]								=[]
				for i in range(lowerCamelCase   ):
								__magic_name__	:	str								=i / num_diffusion_timesteps
								__magic_name__	:	Any								=(i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar(lowerCamelCase   ) / alpha_bar(lowerCamelCase   )	,     lowerCamelCase   )   )
				return jnp.array(lowerCamelCase	,     dtype=lowerCamelCase   )




@flax.struct.dataclass
class 		__A      :
						UpperCamelCase     						=					42
						UpperCamelCase     						=					42
						UpperCamelCase     						=					42

						@classmethod
						def     A__						(		cls   :int    , __snake_case   :List[str] ):
										'''simple docstring'''

										__magic_name__	:	Optional[int]								=scheduler.config

										if config.trained_betas is not None:
														__magic_name__	:	Any								=jnp.asarray(config.trained_betas    , dtype=scheduler.dtype )
										elif config.beta_schedule == "linear":
														__magic_name__	:	str								=jnp.linspace(config.beta_start    , config.beta_end    , config.num_train_timesteps    , dtype=scheduler.dtype )
										elif config.beta_schedule == "scaled_linear":
														# this schedule is very specific to the latent diffusion model.
														__magic_name__	:	List[Any]								=(
														    jnp.linspace(
														        config.beta_start**0.5    , config.beta_end**0.5    , config.num_train_timesteps    , dtype=scheduler.dtype )
														    ** 2
														)
										elif config.beta_schedule == "squaredcos_cap_v2":
														# Glide cosine schedule
														__magic_name__	:	Optional[int]								=betas_for_alpha_bar(config.num_train_timesteps    , dtype=scheduler.dtype )
										else:
														raise NotImplementedError(
														    f"beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}" )

										__magic_name__	:	Optional[int]								=1.0 - betas

										__magic_name__	:	Any								=jnp.cumprod(__snake_case    , axis=0 )

										return cls(
										    alphas=__snake_case    , betas=__snake_case    , alphas_cumprod=__snake_case    , )




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   ):
				__magic_name__	:	List[str]								=state.alphas_cumprod

				__magic_name__	:	List[Any]								=alphas_cumprod[timesteps] ** 0.5
				__magic_name__	:	int								=sqrt_alpha_prod.flatten()
				__magic_name__	:	List[Any]								=broadcast_to_shape_from_left(lowerCamelCase	,     original_samples.shape   )

				__magic_name__	:	int								=(1 - alphas_cumprod[timesteps]) ** 0.5
				__magic_name__	:	Optional[int]								=sqrt_one_minus_alpha_prod.flatten()
				__magic_name__	:	List[Any]								=broadcast_to_shape_from_left(lowerCamelCase	,     original_samples.shape   )

				return sqrt_alpha_prod, sqrt_one_minus_alpha_prod




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   ):
				__magic_name__	:	int								=get_sqrt_alpha_prod(lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   )
				__magic_name__	:	Optional[Any]								=sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
				return noisy_samples




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   ):
				__magic_name__	:	Dict								=get_sqrt_alpha_prod(lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   )
				__magic_name__	:	Optional[Any]								=sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
				return velocity










from typing import Mapping

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


UpperCAmelCase_		:				str									=     logging.get_logger(__name__)

UpperCAmelCase_		:				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 		__A      (			UpperCamelCase__			):
						UpperCamelCase     						=					"""t5"""
						UpperCamelCase     						=					["""past_key_values"""]
						UpperCamelCase     						=					{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}

						def __init__(		self   :Optional[Any]    , __snake_case   :Optional[Any]=3_21_28    , __snake_case   :Optional[int]=5_12    , __snake_case   :str=64    , __snake_case   :int=20_48    , __snake_case   :Union[str, Any]=6    , __snake_case   :Union[str, Any]=None    , __snake_case   :Union[str, Any]=8    , __snake_case   :List[str]=32    , __snake_case   :Dict=1_28    , __snake_case   :List[Any]=0.1    , __snake_case   :int=1E-6    , __snake_case   :List[str]=1.0    , __snake_case   :Optional[int]="relu"    , __snake_case   :List[str]=True    , __snake_case   :Union[str, Any]=True    , __snake_case   :Union[str, Any]=0    , __snake_case   :str=1    , **__snake_case   :int    , ):
										'''simple docstring'''

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

										__magic_name__	:	Tuple								=self.feed_forward_proj.split("""-""" )
										__magic_name__	:	int								=act_info[-1]
										__magic_name__	:	int								=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":
														__magic_name__	:	str								="""gelu_new"""

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




class 		__A      (			UpperCamelCase__			):

						@property
						def     A__						(		self   :List[Any] ):
										'''simple docstring'''

										__magic_name__	:	int								={
										    """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
										    """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
										}
										if self.use_past:
														__magic_name__	:	List[str]								="""past_encoder_sequence + sequence"""
														__magic_name__	:	List[Any]								={0: """batch"""}
														__magic_name__	:	Union[str, Any]								={0: """batch""", 1: """past_decoder_sequence + sequence"""}
										else:
														__magic_name__	:	int								={0: """batch""", 1: """decoder_sequence"""}
														__magic_name__	:	Tuple								={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   :Union[str, Any] ):
										'''simple docstring'''

										return 13




