






"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
			from ...onnx.config import PatchingSpec
			from ...tokenization_utils_base import PreTrainedTokenizerBase


lowerCamelCase_					=      logging.get_logger(__name__)

lowerCamelCase_					=      {
    '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json''',
    '''allenai/longformer-large-4096''': '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json''',
    '''allenai/longformer-large-4096-finetuned-triviaqa''': (
        '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json'''
    ),
    '''allenai/longformer-base-4096-extra.pos.embd.only''': (
        '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json'''
    ),
    '''allenai/longformer-large-4096-extra.pos.embd.only''': (
        '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json'''
    ),
}







class 			UpperCamelCase_       (__A     ):
	__magic_name__	      =				'''longformer'''





	def __init__(      self					: Optional[Any]	,	lowerCAmelCase_					: Union[List[int], int] = 512	,	lowerCAmelCase_					: int = 2	,	lowerCAmelCase_					: int = 1	,	lowerCAmelCase_					: int = 0	,	lowerCAmelCase_					: int = 2	,	lowerCAmelCase_					: int = 30_522	,	lowerCAmelCase_					: int = 768	,	lowerCAmelCase_					: int = 12	,	lowerCAmelCase_					: int = 12	,	lowerCAmelCase_					: int = 3_072	,	lowerCAmelCase_					: str = "gelu"	,	lowerCAmelCase_					: float = 0.1	,	lowerCAmelCase_					: float = 0.1	,	lowerCAmelCase_					: int = 512	,	lowerCAmelCase_					: int = 2	,	lowerCAmelCase_					: float = 0.0_2	,	lowerCAmelCase_					: float = 1e-12	,	lowerCAmelCase_					: bool = False	,	**lowerCAmelCase_					: Tuple	,	)						->			Dict:
				super().__init__(pad_token_id=lowerCAmelCase_	,	**lowerCAmelCase_						)

				UpperCAmelCase_     : Dict			     =					attention_window
				UpperCAmelCase_     : Union[str, Any]			     =					sep_token_id
				UpperCAmelCase_     : Dict			     =					bos_token_id
				UpperCAmelCase_     : Any			     =					eos_token_id
				UpperCAmelCase_     : Optional[Any]			     =					vocab_size
				UpperCAmelCase_     : int			     =					hidden_size
				UpperCAmelCase_     : str			     =					num_hidden_layers
				UpperCAmelCase_     : Dict			     =					num_attention_heads
				UpperCAmelCase_     : int			     =					hidden_act
				UpperCAmelCase_     : Optional[Any]			     =					intermediate_size
				UpperCAmelCase_     : Optional[Any]			     =					hidden_dropout_prob
				UpperCAmelCase_     : Union[str, Any]			     =					attention_probs_dropout_prob
				UpperCAmelCase_     : List[str]			     =					max_position_embeddings
				UpperCAmelCase_     : int			     =					type_vocab_size
				UpperCAmelCase_     : Dict			     =					initializer_range
				UpperCAmelCase_     : str			     =					layer_norm_eps
				UpperCAmelCase_     : List[str]			     =					onnx_export







class 			UpperCamelCase_       (__A     ):





	def __init__(      self					: Any	,	lowerCAmelCase_					: "PretrainedConfig"	,	lowerCAmelCase_					: str = "default"	,	lowerCAmelCase_					: "List[PatchingSpec]" = None						)						->			int:
				super().__init__(lowerCAmelCase_	,	lowerCAmelCase_	,	lowerCAmelCase_						)
				UpperCAmelCase_     : Any			     =					True





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: List[Any]						)						->			Mapping[str, Mapping[int, str]]:
				if self.task == "multiple-choice":
							UpperCAmelCase_     : List[Any]			     =					{0: "batch", 1: "choice", 2: "sequence"}
				else:
							UpperCAmelCase_     : Dict			     =					{0: "batch", 1: "sequence"}
				return OrderedDict(
				    [
				        ("input_ids", dynamic_axis),
				        ("attention_mask", dynamic_axis),
				        ("global_attention_mask", dynamic_axis),
				    ]						)





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: List[Any]						)						->			Mapping[str, Mapping[int, str]]:
				UpperCAmelCase_     : Any			     =					super().outputs
				if self.task == "default":
							UpperCAmelCase_     : str			     =					{0: "batch"}
				return outputs





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]						)						->			float:
				return 1e-4





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Any						)						->			int:
				# needs to be >= 14 to support tril operator
				return max(super().default_onnx_opset	,	14						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[int]	,	lowerCAmelCase_					: "PreTrainedTokenizerBase"	,	lowerCAmelCase_					: int = -1	,	lowerCAmelCase_					: int = -1	,	lowerCAmelCase_					: bool = False	,	lowerCAmelCase_					: Optional[TensorType] = None	,	)						->			Mapping[str, Any]:
				UpperCAmelCase_     : int			     =					super().generate_dummy_inputs(
				    preprocessor=lowerCAmelCase_	,	batch_size=lowerCAmelCase_	,	seq_length=lowerCAmelCase_	,	is_pair=lowerCAmelCase_	,	framework=lowerCAmelCase_						)
				import torch

				# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
				# makes the export fail randomly
				UpperCAmelCase_     : Any			     =					torch.zeros_like(inputs["input_ids"]						)
				# make every second token global
				UpperCAmelCase_     : Optional[int]			     =					1

				return inputs











"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin







@flax.struct.dataclass
class 			UpperCamelCase_       :
	# setable values
	__magic_name__	      =				None
	__magic_name__	      =				None
	__magic_name__	      =				None  # sigma(t_i)





	@classmethod
	def      _SCREAMING_SNAKE_CASE      (      cls					: List[str]						)						->			Optional[Any]:
				return cls()







@dataclass
class 			UpperCamelCase_       (__A     ):
	__magic_name__	      =				42
	__magic_name__	      =				42
	__magic_name__	      =				42







class 			UpperCamelCase_       (__A				,						__A     ):





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Tuple						)						->			str:
				return True





	@register_to_config
	def __init__(      self					: List[str]	,	lowerCAmelCase_					: float = 0.0_2	,	lowerCAmelCase_					: float = 100	,	lowerCAmelCase_					: float = 1.0_0_7	,	lowerCAmelCase_					: float = 80	,	lowerCAmelCase_					: float = 0.0_5	,	lowerCAmelCase_					: float = 50	,	)						->			Union[str, Any]:
				pass





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]						)						->			Tuple:
				return KarrasVeSchedulerState.create()





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: int	,	lowerCAmelCase_					: Tuple = ()						)						->			KarrasVeSchedulerState:
				UpperCAmelCase_     : Dict			     =					jnp.arange(0	,	lowerCAmelCase_						)[::-1].copy()
				UpperCAmelCase_     : Dict			     =					[
				    (
				        self.config.sigma_max**2
				        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
				    )
				    for i in timesteps
				]

				return state.replace(
				    num_inference_steps=lowerCAmelCase_	,	schedule=jnp.array(lowerCAmelCase_	,	dtype=jnp.floataa						)	,	timesteps=lowerCAmelCase_	,	)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: random.KeyArray	,	)						->			Tuple[jnp.ndarray, float]:
				if self.config.s_min <= sigma <= self.config.s_max:
							UpperCAmelCase_     : Any			     =					min(self.config.s_churn / state.num_inference_steps	,	2**0.5 - 1						)
				else:
							UpperCAmelCase_     : Optional[int]			     =					0

				# sample eps ~ N(0, S_noise^2 * I)
				UpperCAmelCase_     : List[Any]			     =					random.split(lowerCAmelCase_	,	num=1						)
				UpperCAmelCase_     : List[str]			     =					self.config.s_noise * random.normal(key=lowerCAmelCase_	,	shape=sample.shape						)
				UpperCAmelCase_     : Optional[Any]			     =					sigma + gamma * sigma
				UpperCAmelCase_     : Any			     =					sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

				return sample_hat, sigma_hat





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: bool = True	,	)						->			Union[FlaxKarrasVeOutput, Tuple]:
				UpperCAmelCase_     : Union[str, Any]			     =					sample_hat + sigma_hat * model_output
				UpperCAmelCase_     : List[Any]			     =					(sample_hat - pred_original_sample) / sigma_hat
				UpperCAmelCase_     : Union[str, Any]			     =					sample_hat + (sigma_prev - sigma_hat) * derivative

				if not return_dict:
							return (sample_prev, derivative, state)

				return FlaxKarrasVeOutput(prev_sample=lowerCAmelCase_	,	derivative=lowerCAmelCase_	,	state=lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: bool = True	,	)						->			Union[FlaxKarrasVeOutput, Tuple]:
				UpperCAmelCase_     : str			     =					sample_prev + sigma_prev * model_output
				UpperCAmelCase_     : Any			     =					(sample_prev - pred_original_sample) / sigma_prev
				UpperCAmelCase_     : int			     =					sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

				if not return_dict:
							return (sample_prev, derivative, state)

				return FlaxKarrasVeOutput(prev_sample=lowerCAmelCase_	,	derivative=lowerCAmelCase_	,	state=lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: List[Any]	,	lowerCAmelCase_					: List[str]	,	lowerCAmelCase_					: Optional[Any]						)						->			Dict:
				raise NotImplementedError()



