

# 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  UpperCAmelCase__	:






  """simple docstring"""


  a     =      42

  # setable values
  a     =      42
  a     =      42
  a     =      None






  @classmethod
  def    lowercase_	(							cls		:	Optional[Any]			,     __lowerCamelCase		:	CommonSchedulerState			,     __lowerCamelCase		:	jnp.ndarray			,     __lowerCamelCase		:	jnp.ndarray      )	->							Any:
       return cls(common=__lowerCamelCase			,     init_noise_sigma=__lowerCamelCase			,     timesteps=__lowerCamelCase      )






@dataclass
class  UpperCAmelCase__	( A__ ):






  """simple docstring"""


  a     =      42






class  UpperCAmelCase__	( A__		,  A__ ):






  """simple docstring"""


  a     =      [e.name for e in FlaxKarrasDiffusionSchedulers]

  a     =      42






  @property
  def    lowercase_	(							self		:	Any      )	->							Any:
       return True






  @register_to_config
  def __init__(							self		:	List[Any]			,     __lowerCamelCase		:	int = 1000			,     __lowerCamelCase		:	float = 0.0001			,     __lowerCamelCase		:	float = 0.02			,     __lowerCamelCase		:	str = "linear"			,     __lowerCamelCase		:	Optional[jnp.ndarray] = None			,     __lowerCamelCase		:	str = "fixed_small"			,     __lowerCamelCase		:	bool = True			,     __lowerCamelCase		:	str = "epsilon"			,     __lowerCamelCase		:	jnp.dtype = jnp.floataa			,     )	->							Tuple:
       SCREAMING_SNAKE_CASE__					=					dtype






  def    lowercase_	(							self		:	List[Any]			,     __lowerCamelCase		:	Optional[CommonSchedulerState] = None      )	->							DDPMSchedulerState:
       if common is None:
            SCREAMING_SNAKE_CASE__					=					CommonSchedulerState.create(self      )

       # standard deviation of the initial noise distribution
       SCREAMING_SNAKE_CASE__					=					jnp.array(1.0			,     dtype=self.dtype      )

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

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






  def    lowercase_	(							self		:	int			,     __lowerCamelCase		:	DDPMSchedulerState			,     __lowerCamelCase		:	jnp.ndarray			,     __lowerCamelCase		:	Optional[int] = None      )	->							jnp.ndarray:
       return sample






  def    lowercase_	(							self		:	Optional[int]			,     __lowerCamelCase		:	DDPMSchedulerState			,     __lowerCamelCase		:	int			,     __lowerCamelCase		:	Tuple = ()      )	->							DDPMSchedulerState:
       SCREAMING_SNAKE_CASE__					=					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__					=					(jnp.arange(0			,     __lowerCamelCase      ) * step_ratio).round()[::-1]

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






  def    lowercase_	(							self		:	str			,     __lowerCamelCase		:	DDPMSchedulerState			,     __lowerCamelCase		:	Union[str, Any]			,     __lowerCamelCase		:	int=None			,     __lowerCamelCase		:	str=None      )	->							int:
       SCREAMING_SNAKE_CASE__					=					state.common.alphas_cumprod[t]
       SCREAMING_SNAKE_CASE__					=					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__					=					(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

       if variance_type is None:
            SCREAMING_SNAKE_CASE__					=					self.config.variance_type

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

       return variance






  def    lowercase_	(							self		:	Any			,     __lowerCamelCase		:	DDPMSchedulerState			,     __lowerCamelCase		:	jnp.ndarray			,     __lowerCamelCase		:	int			,     __lowerCamelCase		:	jnp.ndarray			,     __lowerCamelCase		:	Optional[jax.random.KeyArray] = None			,     __lowerCamelCase		:	bool = True			,     )	->							Union[FlaxDDPMSchedulerOutput, Tuple]:
       SCREAMING_SNAKE_CASE__					=					timestep

       if key is None:
            SCREAMING_SNAKE_CASE__					=					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__,SCREAMING_SNAKE_CASE__					=					jnp.split(__lowerCamelCase			,     sample.shape[1]			,     axis=1      )
       else:
            SCREAMING_SNAKE_CASE__					=					None

       # 1. compute alphas, betas
       SCREAMING_SNAKE_CASE__					=					state.common.alphas_cumprod[t]
       SCREAMING_SNAKE_CASE__					=					jnp.where(t > 0			,     state.common.alphas_cumprod[t - 1]			,     jnp.array(1.0			,     dtype=self.dtype      )      )
       SCREAMING_SNAKE_CASE__					=					1 - alpha_prod_t
       SCREAMING_SNAKE_CASE__					=					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__					=					(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
       elif self.config.prediction_type == "sample":
            SCREAMING_SNAKE_CASE__					=					model_output
       elif self.config.prediction_type == "v_prediction":
            SCREAMING_SNAKE_CASE__					=					(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__					=					jnp.clip(__lowerCamelCase			,     -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__					=					(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
       SCREAMING_SNAKE_CASE__					=					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__					=					pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

       # 6. Add noise
       def random_variance():
            SCREAMING_SNAKE_CASE__					=					jax.random.split(__lowerCamelCase			,     num=1      )
            SCREAMING_SNAKE_CASE__					=					jax.random.normal(__lowerCamelCase			,     shape=model_output.shape			,     dtype=self.dtype      )
            return (self._get_variance(__lowerCamelCase			,     __lowerCamelCase			,     predicted_variance=__lowerCamelCase      ) ** 0.5) * noise

       SCREAMING_SNAKE_CASE__					=					jnp.where(t > 0			,     random_variance()			,     jnp.zeros(model_output.shape			,     dtype=self.dtype      )      )

       SCREAMING_SNAKE_CASE__					=					pred_prev_sample + variance

       if not return_dict:
            return (pred_prev_sample, state)

       return FlaxDDPMSchedulerOutput(prev_sample=__lowerCamelCase			,     state=__lowerCamelCase      )






  def    lowercase_	(							self		:	Union[str, Any]			,     __lowerCamelCase		:	DDPMSchedulerState			,     __lowerCamelCase		:	jnp.ndarray			,     __lowerCamelCase		:	jnp.ndarray			,     __lowerCamelCase		:	jnp.ndarray			,     )	->							jnp.ndarray:
       return add_noise_common(state.common			,     __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase      )






  def    lowercase_	(							self		:	str			,     __lowerCamelCase		:	DDPMSchedulerState			,     __lowerCamelCase		:	jnp.ndarray			,     __lowerCamelCase		:	jnp.ndarray			,     __lowerCamelCase		:	jnp.ndarray			,     )	->							jnp.ndarray:
       return get_velocity_common(state.common			,     __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase      )







  def __len__(							self		:	Tuple      )	->							Any:
       return self.config.num_train_timesteps





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


_SCREAMING_SNAKE_CASE  :						Any         =    logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE  :						List[str]         =    {
    '''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__ ):






  """simple docstring"""


  a     =      "longformer"






  def __init__(							self		:	int			,     __lowerCamelCase		:	Union[List[int], int] = 512			,     __lowerCamelCase		:	int = 2			,     __lowerCamelCase		:	int = 1			,     __lowerCamelCase		:	int = 0			,     __lowerCamelCase		:	int = 2			,     __lowerCamelCase		:	int = 3_0522			,     __lowerCamelCase		:	int = 768			,     __lowerCamelCase		:	int = 12			,     __lowerCamelCase		:	int = 12			,     __lowerCamelCase		:	int = 3072			,     __lowerCamelCase		:	str = "gelu"			,     __lowerCamelCase		:	float = 0.1			,     __lowerCamelCase		:	float = 0.1			,     __lowerCamelCase		:	int = 512			,     __lowerCamelCase		:	int = 2			,     __lowerCamelCase		:	float = 0.02			,     __lowerCamelCase		:	float = 1e-12			,     __lowerCamelCase		:	bool = False			,     **__lowerCamelCase		:	Tuple			,     )	->							List[Any]:
       super().__init__(pad_token_id=__lowerCamelCase			,     **__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					attention_window
       SCREAMING_SNAKE_CASE__					=					sep_token_id
       SCREAMING_SNAKE_CASE__					=					bos_token_id
       SCREAMING_SNAKE_CASE__					=					eos_token_id
       SCREAMING_SNAKE_CASE__					=					vocab_size
       SCREAMING_SNAKE_CASE__					=					hidden_size
       SCREAMING_SNAKE_CASE__					=					num_hidden_layers
       SCREAMING_SNAKE_CASE__					=					num_attention_heads
       SCREAMING_SNAKE_CASE__					=					hidden_act
       SCREAMING_SNAKE_CASE__					=					intermediate_size
       SCREAMING_SNAKE_CASE__					=					hidden_dropout_prob
       SCREAMING_SNAKE_CASE__					=					attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__					=					max_position_embeddings
       SCREAMING_SNAKE_CASE__					=					type_vocab_size
       SCREAMING_SNAKE_CASE__					=					initializer_range
       SCREAMING_SNAKE_CASE__					=					layer_norm_eps
       SCREAMING_SNAKE_CASE__					=					onnx_export






class  UpperCAmelCase__	( A__ ):






  """simple docstring"""


  def __init__(							self		:	Dict			,     __lowerCamelCase		:	"PretrainedConfig"			,     __lowerCamelCase		:	str = "default"			,     __lowerCamelCase		:	"List[PatchingSpec]" = None      )	->							List[str]:
       super().__init__(__lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					True






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






  @property
  def    lowercase_	(							self		:	Optional[int]      )	->							Mapping[str, Mapping[int, str]]:
       SCREAMING_SNAKE_CASE__					=					super().outputs
       if self.task == "default":
            SCREAMING_SNAKE_CASE__					=					{0: '''batch'''}
       return outputs






  @property
  def    lowercase_	(							self		:	Optional[int]      )	->							float:
       return 1e-4






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







  def    lowercase_	(							self		:	Dict			,     __lowerCamelCase		:	"PreTrainedTokenizerBase"			,     __lowerCamelCase		:	int = -1			,     __lowerCamelCase		:	int = -1			,     __lowerCamelCase		:	bool = False			,     __lowerCamelCase		:	Optional[TensorType] = None			,     )	->							Mapping[str, Any]:
       SCREAMING_SNAKE_CASE__					=					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
       SCREAMING_SNAKE_CASE__					=					torch.zeros_like(inputs['''input_ids''']      )
       # make every second token global
       SCREAMING_SNAKE_CASE__					=					1

       return inputs


