


'''simple docstring'''





import math
import warnings
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin




@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class 							UpperCAmelCase ( _snake_case	):
 UpperCAmelCase				=				42
 UpperCAmelCase				=				None


def 					a__    (	UpperCamelCase_	:			Any,      UpperCamelCase_	:			int=0.999,      UpperCamelCase_	:			Tuple="cosine",      ):
   if alpha_transform_type == "cosine":

      def alpha_bar_fn(UpperCamelCase_	:			Dict	):
         return math.cos((t + 0.008) / 1.008 * math.pi / 2	) ** 2

   elif alpha_transform_type == "exp":

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

   else:
      raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}'''	)

   UpperCAmelCase__  :Any            =			[]
   for i in range(UpperCamelCase_	):
      UpperCAmelCase__  :Union[str, Any]            =			i / num_diffusion_timesteps
      UpperCAmelCase__  :List[Any]            =			(i + 1) / num_diffusion_timesteps
      betas.append(min(1 - alpha_bar_fn(UpperCamelCase_	) / alpha_bar_fn(UpperCamelCase_	),      UpperCamelCase_	)	)
   return torch.tensor(UpperCamelCase_,      dtype=torch.floataa	)




class 							UpperCAmelCase ( _snake_case	,   _snake_case	):
 UpperCAmelCase				=				[e.name for e in KarrasDiffusionSchedulers]
 UpperCAmelCase				=				1


 @register_to_config
 def __init__(			self       :  Optional[int] ,					__lowerCamelCase       :  int = 1_0_0_0 ,					__lowerCamelCase       :  float = 0.00_01 ,					__lowerCamelCase       :  float = 0.02 ,					__lowerCamelCase       :  str = "linear" ,					__lowerCamelCase       :  Optional[Union[np.ndarray, List[float]]] = None ,					__lowerCamelCase       :  Optional[bool] = False ,					__lowerCamelCase       :  str = "epsilon" ,					__lowerCamelCase       :  str = "linspace" ,					__lowerCamelCase       :  int = 0 ,					):
    if trained_betas is not None:
       UpperCAmelCase__  :Dict            =			torch.tensor(__lowerCamelCase ,					dtype=torch.floataa   )
    elif beta_schedule == "linear":
       UpperCAmelCase__  :List[Any]            =			torch.linspace(__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase ,					dtype=torch.floataa   )
    elif beta_schedule == "scaled_linear":
       # this schedule is very specific to the latent diffusion model.
       UpperCAmelCase__  :Tuple            =			(
           torch.linspace(beta_start**0.5 ,					beta_end**0.5 ,					__lowerCamelCase ,					dtype=torch.floataa   ) ** 2
       )
    elif beta_schedule == "squaredcos_cap_v2":
       # Glide cosine schedule
       UpperCAmelCase__  :Union[str, Any]            =			betas_for_alpha_bar(__lowerCamelCase   )
    else:
       raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''   )

    UpperCAmelCase__  :Optional[Any]            =			1.0 - self.betas
    UpperCAmelCase__  :List[Any]            =			torch.cumprod(self.alphas ,					dim=0   )

    UpperCAmelCase__  :Union[str, Any]            =			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5   )
    UpperCAmelCase__  :Union[str, Any]            =			np.concatenate([sigmas[::-1], [0.0]]   ).astype(np.floataa   )
    UpperCAmelCase__  :List[Any]            =			torch.from_numpy(__lowerCamelCase   )

    # setable values
    UpperCAmelCase__  :Optional[Any]            =			None
    UpperCAmelCase__  :int            =			use_karras_sigmas
    self.set_timesteps(__lowerCamelCase ,					__lowerCamelCase   )
    UpperCAmelCase__  :Dict            =			[]
    UpperCAmelCase__  :Optional[int]            =			False


 @property
 def 					__SCREAMING_SNAKE_CASE				(			self       :  Dict   ):
    # standard deviation of the initial noise distribution
    if self.config.timestep_spacing in ["linspace", "trailing"]:
       return self.sigmas.max()

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


 def 					__SCREAMING_SNAKE_CASE				(			self       :  str ,					__lowerCamelCase       :  torch.FloatTensor ,					__lowerCamelCase       :  Union[float, torch.FloatTensor]   ):
    if isinstance(__lowerCamelCase ,					torch.Tensor   ):
       UpperCAmelCase__  :Any            =			timestep.to(self.timesteps.device   )
    UpperCAmelCase__  :Tuple            =			(self.timesteps == timestep).nonzero().item()
    UpperCAmelCase__  :str            =			self.sigmas[step_index]
    UpperCAmelCase__  :Union[str, Any]            =			sample / ((sigma**2 + 1) ** 0.5)
    UpperCAmelCase__  :str            =			True
    return sample


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Any ,					__lowerCamelCase       :  Union[str, Any] ,					__lowerCamelCase       :  Tuple ,					__lowerCamelCase       :  Tuple   ):

    def lms_derivative(__lowerCamelCase       :  Optional[Any]   ):
       UpperCAmelCase__  :Tuple            =			1.0
       for k in range(__lowerCamelCase   ):
          if current_order == k:
             continue
          prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
       return prod

    UpperCAmelCase__  :int            =			integrate.quad(__lowerCamelCase ,					self.sigmas[t] ,					self.sigmas[t + 1] ,					epsrel=1e-4   )[0]

    return integrated_coeff


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Optional[Any] ,					__lowerCamelCase       :  int ,					__lowerCamelCase       :  Union[str, torch.device] = None   ):
    UpperCAmelCase__  :List[Any]            =			num_inference_steps

    # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
    if self.config.timestep_spacing == "linspace":
       UpperCAmelCase__  :List[Any]            =			np.linspace(0 ,					self.config.num_train_timesteps - 1 ,					__lowerCamelCase ,					dtype=__lowerCamelCase   )[
           ::-1
       ].copy()
    elif self.config.timestep_spacing == "leading":
       UpperCAmelCase__  :Any            =			self.config.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
       UpperCAmelCase__  :Optional[int]            =			(np.arange(0 ,					__lowerCamelCase   ) * step_ratio).round()[::-1].copy().astype(__lowerCamelCase   )
       timesteps += self.config.steps_offset
    elif self.config.timestep_spacing == "trailing":
       UpperCAmelCase__  :int            =			self.config.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
       UpperCAmelCase__  :Any            =			(np.arange(self.config.num_train_timesteps ,					0 ,					-step_ratio   )).round().copy().astype(__lowerCamelCase   )
       timesteps -= 1
    else:
       raise ValueError(
           f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''   )

    UpperCAmelCase__  :Optional[Any]            =			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5   )
    UpperCAmelCase__  :Tuple            =			np.log(__lowerCamelCase   )
    UpperCAmelCase__  :Tuple            =			np.interp(__lowerCamelCase ,					np.arange(0 ,					len(__lowerCamelCase   )   ) ,					__lowerCamelCase   )

    if self.use_karras_sigmas:
       UpperCAmelCase__  :Tuple            =			self._convert_to_karras(in_sigmas=__lowerCamelCase   )
       UpperCAmelCase__  :Union[str, Any]            =			np.array([self._sigma_to_t(__lowerCamelCase ,					__lowerCamelCase   ) for sigma in sigmas]   )

    UpperCAmelCase__  :Any            =			np.concatenate([sigmas, [0.0]]   ).astype(np.floataa   )

    UpperCAmelCase__  :List[str]            =			torch.from_numpy(__lowerCamelCase   ).to(device=__lowerCamelCase   )
    if str(__lowerCamelCase   ).startswith('''mps'''   ):
       # mps does not support float64
       UpperCAmelCase__  :Dict            =			torch.from_numpy(__lowerCamelCase   ).to(__lowerCamelCase ,					dtype=torch.floataa   )
    else:
       UpperCAmelCase__  :Optional[Any]            =			torch.from_numpy(__lowerCamelCase   ).to(device=__lowerCamelCase   )

    UpperCAmelCase__  :Union[str, Any]            =			[]


 def 					__SCREAMING_SNAKE_CASE				(			self       :  int ,					__lowerCamelCase       :  Union[str, Any] ,					__lowerCamelCase       :  Union[str, Any]   ):
    # get log sigma
    UpperCAmelCase__  :Optional[Any]            =			np.log(__lowerCamelCase   )

    # get distribution
    UpperCAmelCase__  :List[Any]            =			log_sigma - log_sigmas[:, np.newaxis]

    # get sigmas range
    UpperCAmelCase__  :List[str]            =			np.cumsum((dists >= 0) ,					axis=0   ).argmax(axis=0   ).clip(max=log_sigmas.shape[0] - 2   )
    UpperCAmelCase__  :List[str]            =			low_idx + 1

    UpperCAmelCase__  :str            =			log_sigmas[low_idx]
    UpperCAmelCase__  :Union[str, Any]            =			log_sigmas[high_idx]

    # interpolate sigmas
    UpperCAmelCase__  :List[str]            =			(low - log_sigma) / (low - high)
    UpperCAmelCase__  :Optional[Any]            =			np.clip(__lowerCamelCase ,					0 ,					1   )

    # transform interpolation to time range
    UpperCAmelCase__  :List[str]            =			(1 - w) * low_idx + w * high_idx
    UpperCAmelCase__  :Any            =			t.reshape(sigma.shape   )
    return t


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Union[str, Any] ,					__lowerCamelCase       :  torch.FloatTensor   ):
    UpperCAmelCase__  :float            =			in_sigmas[-1].item()
    UpperCAmelCase__  :float            =			in_sigmas[0].item()

    UpperCAmelCase__  :Union[str, Any]            =			7.0  # 7.0 is the value used in the paper
    UpperCAmelCase__  :Optional[Any]            =			np.linspace(0 ,					1 ,					self.num_inference_steps   )
    UpperCAmelCase__  :str            =			sigma_min ** (1 / rho)
    UpperCAmelCase__  :Optional[Any]            =			sigma_max ** (1 / rho)
    UpperCAmelCase__  :Union[str, Any]            =			(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
    return sigmas


 def 					__SCREAMING_SNAKE_CASE				(			self       :  List[Any] ,					__lowerCamelCase       :  torch.FloatTensor ,					__lowerCamelCase       :  Union[float, torch.FloatTensor] ,					__lowerCamelCase       :  torch.FloatTensor ,					__lowerCamelCase       :  int = 4 ,					__lowerCamelCase       :  bool = True ,					):
    if not self.is_scale_input_called:
       warnings.warn(
           '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
           '''See `StableDiffusionPipeline` for a usage example.'''   )

    if isinstance(__lowerCamelCase ,					torch.Tensor   ):
       UpperCAmelCase__  :Dict            =			timestep.to(self.timesteps.device   )
    UpperCAmelCase__  :Optional[int]            =			(self.timesteps == timestep).nonzero().item()
    UpperCAmelCase__  :Any            =			self.sigmas[step_index]

    # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
    if self.config.prediction_type == "epsilon":
       UpperCAmelCase__  :List[Any]            =			sample - sigma * model_output
    elif self.config.prediction_type == "v_prediction":
       # * c_out + input * c_skip
       UpperCAmelCase__  :Union[str, Any]            =			model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
    elif self.config.prediction_type == "sample":
       UpperCAmelCase__  :int            =			model_output
    else:
       raise ValueError(
           f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''   )

    # 2. Convert to an ODE derivative
    UpperCAmelCase__  :Dict            =			(sample - pred_original_sample) / sigma
    self.derivatives.append(__lowerCamelCase   )
    if len(self.derivatives   ) > order:
       self.derivatives.pop(0   )

    # 3. Compute linear multistep coefficients
    UpperCAmelCase__  :str            =			min(step_index + 1 ,					__lowerCamelCase   )
    UpperCAmelCase__  :List[str]            =			[self.get_lms_coefficient(__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase   ) for curr_order in range(__lowerCamelCase   )]

    # 4. Compute previous sample based on the derivatives path
    UpperCAmelCase__  :Tuple            =			sample + sum(
        coeff * derivative for coeff, derivative in zip(__lowerCamelCase ,					reversed(self.derivatives   )   )   )

    if not return_dict:
       return (prev_sample,)

    return LMSDiscreteSchedulerOutput(prev_sample=__lowerCamelCase ,					pred_original_sample=__lowerCamelCase   )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Optional[int] ,					__lowerCamelCase       :  torch.FloatTensor ,					__lowerCamelCase       :  torch.FloatTensor ,					__lowerCamelCase       :  torch.FloatTensor ,					):
    # Make sure sigmas and timesteps have the same device and dtype as original_samples
    UpperCAmelCase__  :Tuple            =			self.sigmas.to(device=original_samples.device ,					dtype=original_samples.dtype   )
    if original_samples.device.type == "mps" and torch.is_floating_point(__lowerCamelCase   ):
       # mps does not support float64
       UpperCAmelCase__  :List[str]            =			self.timesteps.to(original_samples.device ,					dtype=torch.floataa   )
       UpperCAmelCase__  :List[str]            =			timesteps.to(original_samples.device ,					dtype=torch.floataa   )
    else:
       UpperCAmelCase__  :Optional[int]            =			self.timesteps.to(original_samples.device   )
       UpperCAmelCase__  :List[str]            =			timesteps.to(original_samples.device   )

    UpperCAmelCase__  :Union[str, Any]            =			[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

    UpperCAmelCase__  :List[str]            =			original_samples + noise * sigma
    return noisy_samples





 def __len__(			self       :  Optional[Any]   ):
    return self.config.num_train_timesteps







'''simple docstring'''





import unittest
from unittest.util import safe_repr

from transformers import AutoTokenizer, RwkvConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
        RwkvForCausalLM,
        RwkvModel,
    )
    from transformers.pytorch_utils import is_torch_greater_or_equal_than_2_0
else:
    __lowerCamelCase       				=	False




class 							UpperCAmelCase :


 def __init__(			self       :  int ,					__lowerCamelCase       :  str ,					__lowerCamelCase       :  int=1_4 ,					__lowerCamelCase       :  Tuple=7 ,					__lowerCamelCase       :  int=True ,					__lowerCamelCase       :  Tuple=False ,					__lowerCamelCase       :  Any=True ,					__lowerCamelCase       :  List[Any]=True ,					__lowerCamelCase       :  Dict=True ,					__lowerCamelCase       :  List[Any]=9_9 ,					__lowerCamelCase       :  Dict=3_2 ,					__lowerCamelCase       :  Optional[int]=5 ,					__lowerCamelCase       :  Union[str, Any]=3_7 ,					__lowerCamelCase       :  Dict="gelu" ,					__lowerCamelCase       :  Any=0.1 ,					__lowerCamelCase       :  Any=0.1 ,					__lowerCamelCase       :  Optional[int]=5_1_2 ,					__lowerCamelCase       :  Tuple=1_6 ,					__lowerCamelCase       :  str=2 ,					__lowerCamelCase       :  str=3 ,					__lowerCamelCase       :  Any=4 ,					__lowerCamelCase       :  Tuple=None ,					):
    UpperCAmelCase__  :Any            =			parent
    UpperCAmelCase__  :Optional[Any]            =			batch_size
    UpperCAmelCase__  :str            =			seq_length
    UpperCAmelCase__  :Optional[int]            =			is_training
    UpperCAmelCase__  :Optional[Any]            =			use_token_type_ids
    UpperCAmelCase__  :Dict            =			use_input_mask
    UpperCAmelCase__  :List[Any]            =			use_labels
    UpperCAmelCase__  :List[str]            =			use_mc_token_ids
    UpperCAmelCase__  :List[Any]            =			vocab_size
    UpperCAmelCase__  :Dict            =			hidden_size
    UpperCAmelCase__  :Union[str, Any]            =			num_hidden_layers
    UpperCAmelCase__  :List[Any]            =			intermediate_size
    UpperCAmelCase__  :int            =			hidden_act
    UpperCAmelCase__  :Optional[Any]            =			hidden_dropout_prob
    UpperCAmelCase__  :Optional[int]            =			attention_probs_dropout_prob
    UpperCAmelCase__  :str            =			max_position_embeddings
    UpperCAmelCase__  :int            =			type_vocab_size
    UpperCAmelCase__  :List[Any]            =			type_sequence_label_size
    UpperCAmelCase__  :int            =			num_labels
    UpperCAmelCase__  :str            =			num_choices
    UpperCAmelCase__  :Optional[int]            =			scope
    UpperCAmelCase__  :Optional[Any]            =			vocab_size - 1
    UpperCAmelCase__  :str            =			vocab_size - 1
    UpperCAmelCase__  :Tuple            =			vocab_size - 1


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Optional[Any]   ):
    return RwkvConfig.from_pretrained('''sgugger/rwkv-4-pile-7b'''   )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Tuple ,					__lowerCamelCase       :  Dict=False ,					__lowerCamelCase       :  Dict=False ,					__lowerCamelCase       :  List[str]=False   ):
    UpperCAmelCase__  :Union[str, Any]            =			ids_tensor([self.batch_size, self.seq_length] ,					self.vocab_size   )

    UpperCAmelCase__  :Dict            =			None
    if self.use_input_mask:
       UpperCAmelCase__  :Optional[int]            =			random_attention_mask([self.batch_size, self.seq_length]   )

    UpperCAmelCase__  :Tuple            =			None
    if self.use_token_type_ids:
       UpperCAmelCase__  :Any            =			ids_tensor([self.batch_size, self.seq_length] ,					self.type_vocab_size   )

    UpperCAmelCase__  :str            =			None
    if self.use_mc_token_ids:
       UpperCAmelCase__  :Optional[Any]            =			ids_tensor([self.batch_size, self.num_choices] ,					self.seq_length   )

    UpperCAmelCase__  :Optional[Any]            =			None
    UpperCAmelCase__  :List[Any]            =			None
    UpperCAmelCase__  :Any            =			None
    if self.use_labels:
       UpperCAmelCase__  :Optional[Any]            =			ids_tensor([self.batch_size] ,					self.type_sequence_label_size   )
       UpperCAmelCase__  :int            =			ids_tensor([self.batch_size, self.seq_length] ,					self.num_labels   )
       UpperCAmelCase__  :Tuple            =			ids_tensor([self.batch_size] ,					self.num_choices   )

    UpperCAmelCase__  :Optional[int]            =			self.get_config(
        gradient_checkpointing=__lowerCamelCase ,					scale_attn_by_inverse_layer_idx=__lowerCamelCase ,					reorder_and_upcast_attn=__lowerCamelCase ,					)

    return (
        config,
        input_ids,
        input_mask,
        None,
        token_type_ids,
        mc_token_ids,
        sequence_labels,
        token_labels,
        choice_labels,
    )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  str ,					__lowerCamelCase       :  Union[str, Any]=False ,					__lowerCamelCase       :  int=False ,					__lowerCamelCase       :  int=False   ):
    return RwkvConfig(
        vocab_size=self.vocab_size ,					hidden_size=self.hidden_size ,					num_hidden_layers=self.num_hidden_layers ,					intermediate_size=self.intermediate_size ,					activation_function=self.hidden_act ,					resid_pdrop=self.hidden_dropout_prob ,					attn_pdrop=self.attention_probs_dropout_prob ,					n_positions=self.max_position_embeddings ,					type_vocab_size=self.type_vocab_size ,					use_cache=__lowerCamelCase ,					bos_token_id=self.bos_token_id ,					eos_token_id=self.eos_token_id ,					pad_token_id=self.pad_token_id ,					gradient_checkpointing=__lowerCamelCase ,					scale_attn_by_inverse_layer_idx=__lowerCamelCase ,					reorder_and_upcast_attn=__lowerCamelCase ,					)


 def 					__SCREAMING_SNAKE_CASE				(			self       :  str   ):
    UpperCAmelCase__  :int            =			self.get_config()
    UpperCAmelCase__  :Any            =			3_0_0
    return config


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Tuple   ):
    (
        (
        UpperCAmelCase__
    )      ,	(
        UpperCAmelCase__
    )      ,	(
        UpperCAmelCase__
    )      ,	(
        UpperCAmelCase__
    )      ,	(
        UpperCAmelCase__
    )      ,	(
        UpperCAmelCase__
    )      ,	(
        UpperCAmelCase__
    )      ,	(
        UpperCAmelCase__
    )      ,	(
        UpperCAmelCase__
    )      ,	
    )  :Dict            =			self.prepare_config_and_inputs()

    UpperCAmelCase__  :List[str]            =			floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
    UpperCAmelCase__  :Dict            =			ids_tensor([self.batch_size, self.seq_length] ,					vocab_size=2   )

    return (
        config,
        input_ids,
        input_mask,
        head_mask,
        token_type_ids,
        sequence_labels,
        token_labels,
        choice_labels,
        encoder_hidden_states,
        encoder_attention_mask,
    )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Optional[Any] ,					__lowerCamelCase       :  Optional[Any] ,					__lowerCamelCase       :  Tuple ,					__lowerCamelCase       :  Dict ,					__lowerCamelCase       :  Union[str, Any] ,					__lowerCamelCase       :  Optional[int] ,					*__lowerCamelCase       :  Optional[Any]   ):
    UpperCAmelCase__  :str            =			True
    UpperCAmelCase__  :Optional[Any]            =			RwkvModel(config=__lowerCamelCase   )
    model.to(__lowerCamelCase   )
    model.eval()

    UpperCAmelCase__  :Optional[int]            =			model(__lowerCamelCase   )

    self.parent.assertEqual(result.last_hidden_state.shape ,					(self.batch_size, self.seq_length, self.hidden_size)   )
    self.parent.assertEqual(len(result.hidden_states   ) ,					config.num_hidden_layers + 1   )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Dict ,					__lowerCamelCase       :  Tuple ,					__lowerCamelCase       :  Dict ,					__lowerCamelCase       :  Dict ,					__lowerCamelCase       :  Optional[int] ,					__lowerCamelCase       :  Union[str, Any] ,					*__lowerCamelCase       :  List[Any]   ):
    UpperCAmelCase__  :int            =			RwkvForCausalLM(__lowerCamelCase   )
    model.to(__lowerCamelCase   )
    model.eval()

    UpperCAmelCase__  :int            =			model(__lowerCamelCase ,					labels=__lowerCamelCase   )
    self.parent.assertEqual(result.loss.shape ,					()   )
    self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.seq_length, self.vocab_size)   )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Optional[int] ,					__lowerCamelCase       :  Optional[int] ,					__lowerCamelCase       :  Tuple ,					__lowerCamelCase       :  Optional[int] ,					__lowerCamelCase       :  Union[str, Any] ,					__lowerCamelCase       :  Tuple ,					*__lowerCamelCase       :  Optional[Any]   ):
    UpperCAmelCase__  :Optional[Any]            =			RwkvModel(config=__lowerCamelCase   )
    model.to(__lowerCamelCase   )
    model.eval()

    UpperCAmelCase__  :Tuple            =			model(__lowerCamelCase   )
    UpperCAmelCase__  :int            =			outputs.last_hidden_state

    UpperCAmelCase__  :Tuple            =			model(input_ids[:, :2]   )
    UpperCAmelCase__  :List[Any]            =			outputs.last_hidden_state

    # Using the state computed on the first inputs, we will get the same output
    UpperCAmelCase__  :Any            =			model(input_ids[:, 2:] ,					state=outputs.state   )
    UpperCAmelCase__  :Any            =			outputs.last_hidden_state

    self.parent.assertTrue(torch.allclose(torch.cat([output_one, output_two] ,					dim=1   ) ,					__lowerCamelCase ,					atol=1e-5   )   )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  str ,					__lowerCamelCase       :  int ,					__lowerCamelCase       :  Optional[int] ,					__lowerCamelCase       :  List[Any] ,					__lowerCamelCase       :  List[str] ,					__lowerCamelCase       :  Any ,					*__lowerCamelCase       :  Any ,					__lowerCamelCase       :  Tuple=False   ):
    UpperCAmelCase__  :Any            =			RwkvForCausalLM(__lowerCamelCase   )
    model.to(__lowerCamelCase   )
    if gradient_checkpointing:
       model.gradient_checkpointing_enable()

    UpperCAmelCase__  :Union[str, Any]            =			model(__lowerCamelCase ,					labels=__lowerCamelCase   )
    self.parent.assertEqual(result.loss.shape ,					()   )
    self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.seq_length, self.vocab_size)   )
    result.loss.backward()





 def 					__SCREAMING_SNAKE_CASE				(			self       :  Union[str, Any]   ):
    UpperCAmelCase__  :Optional[Any]            =			self.prepare_config_and_inputs()

    (
        (
        UpperCAmelCase__
    )      ,	(
        UpperCAmelCase__
    )      ,	(
        UpperCAmelCase__
    )      ,	(
        UpperCAmelCase__
    )      ,	(
        UpperCAmelCase__
    )      ,	(
        UpperCAmelCase__
    )      ,	(
        UpperCAmelCase__
    )      ,	(
        UpperCAmelCase__
    )      ,	(
        UpperCAmelCase__
    )      ,	
    )  :Any            =			config_and_inputs

    UpperCAmelCase__  :int            =			{'''input_ids''': input_ids}

    return config, inputs_dict




@unittest.skipIf(
    not is_torch_greater_or_equal_than_2_0	,   reason="See https://github.com/huggingface/transformers/pull/24204"	)
@require_torch
class 							UpperCAmelCase ( _snake_case	,   _snake_case	,   _snake_case	,   unittest.TestCase	):
 UpperCAmelCase				=				(RwkvModel, RwkvForCausalLM) if is_torch_available() else ()
 UpperCAmelCase				=				(
     {"feature-extraction": RwkvModel, "text-generation": RwkvForCausalLM} if is_torch_available() else {}
 )
 # all_generative_model_classes = (RwkvForCausalLM,) if is_torch_available() else ()
 UpperCAmelCase				=				False
 UpperCAmelCase				=				False
 UpperCAmelCase				=				False
 UpperCAmelCase				=				False
 UpperCAmelCase				=				False  # Rwkv does not support head masking


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Optional[int]   ):
    UpperCAmelCase__  :Dict            =			RwkvModelTester(self   )
    UpperCAmelCase__  :Union[str, Any]            =			ConfigTester(
        self ,					config_class=__lowerCamelCase ,					n_embd=3_7 ,					common_properties=['''hidden_size''', '''num_hidden_layers''']   )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Dict ,					__lowerCamelCase       :  Optional[Any] ,					__lowerCamelCase       :  int ,					__lowerCamelCase       :  Optional[int]=None   ):
    if isinstance(__lowerCamelCase ,					torch.Tensor   ):
       UpperCAmelCase__      ,	UpperCAmelCase__  :Dict            =			member.max().item(), member.min().item()
    elif isinstance(__lowerCamelCase ,					__lowerCamelCase   ) or isinstance(__lowerCamelCase ,					__lowerCamelCase   ):
       UpperCAmelCase__      ,	UpperCAmelCase__  :List[Any]            =			max(__lowerCamelCase   ), min(__lowerCamelCase   )

    if not isinstance(__lowerCamelCase ,					__lowerCamelCase   ):
       raise TypeError('''container should be a list or tuple'''   )
    elif len(__lowerCamelCase   ) != 2:
       raise ValueError('''container should have 2 elements'''   )

    UpperCAmelCase__      ,	UpperCAmelCase__  :Any            =			container

    UpperCAmelCase__  :List[str]            =			(min_value >= expected_min) and (max_value <= expected_max)

    if not is_inside_interval:
       UpperCAmelCase__  :int            =			'''%s not found in %s''' % (safe_repr(__lowerCamelCase   ), safe_repr(__lowerCamelCase   ))
       self.fail(self._formatMessage(__lowerCamelCase ,					__lowerCamelCase   )   )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  str   ):
    self.config_tester.run_common_tests()


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Optional[Any]   ):
    UpperCAmelCase__  :Tuple            =			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_rwkv_model(*__lowerCamelCase   )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  int   ):
    UpperCAmelCase__  :str            =			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_causl_lm(*__lowerCamelCase   )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  str   ):
    UpperCAmelCase__  :Union[str, Any]            =			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_state_equivalency(*__lowerCamelCase   )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Optional[int]   ):
    UpperCAmelCase__      ,	UpperCAmelCase__  :Dict            =			self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       UpperCAmelCase__  :Optional[int]            =			model_class(config=__lowerCamelCase   )
       for name, param in model.named_parameters():
          if "time_decay" in name:
             if param.requires_grad:
                self.assertTrue(param.data.max().item() == 3.0   )
                self.assertTrue(param.data.min().item() == -5.0   )
          elif "time_first" in name:
             if param.requires_grad:
                # check if it's a ones like
                self.assertTrue(torch.allclose(param.data ,					torch.ones_like(param.data   ) ,					atol=1e-5 ,					rtol=1e-5   )   )
          elif any(x in name for x in ['''time_mix_key''', '''time_mix_receptance''']   ):
             if param.requires_grad:
                self.assertInterval(
                    param.data ,					[0.0, 1.0] ,					msg=f'''Parameter {name} of model {model_class} seems not properly initialized''' ,					)
          elif "time_mix_value" in name:
             if param.requires_grad:
                self.assertInterval(
                    param.data ,					[0.0, 1.3] ,					msg=f'''Parameter {name} of model {model_class} seems not properly initialized''' ,					)


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Union[str, Any]   ):
    UpperCAmelCase__      ,	UpperCAmelCase__  :int            =			self.model_tester.prepare_config_and_inputs_for_common()
    UpperCAmelCase__  :Dict            =			True

    UpperCAmelCase__  :Dict            =			getattr(self.model_tester ,					'''seq_length''' ,					__lowerCamelCase   )

    for model_class in self.all_model_classes:
       UpperCAmelCase__  :Tuple            =			True
       UpperCAmelCase__  :Optional[int]            =			False
       UpperCAmelCase__  :List[Any]            =			True
       UpperCAmelCase__  :Optional[int]            =			model_class(__lowerCamelCase   )
       model.to(__lowerCamelCase   )
       model.eval()

       UpperCAmelCase__  :List[Any]            =			self._prepare_for_class(__lowerCamelCase ,					__lowerCamelCase   )
       UpperCAmelCase__  :Tuple            =			inputs['''input_ids'''].shape[0]
       with torch.no_grad():
          UpperCAmelCase__  :List[str]            =			model(**__lowerCamelCase   )
       UpperCAmelCase__  :int            =			outputs.attentions
       self.assertEqual(len(__lowerCamelCase   ) ,					self.model_tester.num_hidden_layers   )

       # check that output_attentions also work using config
       del inputs_dict["output_attentions"]
       UpperCAmelCase__  :Optional[Any]            =			True
       UpperCAmelCase__  :Union[str, Any]            =			model_class(__lowerCamelCase   )
       model.to(__lowerCamelCase   )
       model.eval()

       UpperCAmelCase__  :List[str]            =			self._prepare_for_class(__lowerCamelCase ,					__lowerCamelCase   )
       UpperCAmelCase__  :Optional[int]            =			inputs['''input_ids'''].shape[0]
       with torch.no_grad():
          UpperCAmelCase__  :Tuple            =			model(**__lowerCamelCase   )
       UpperCAmelCase__  :Union[str, Any]            =			outputs.attentions
       self.assertEqual(len(__lowerCamelCase   ) ,					self.model_tester.num_hidden_layers   )

       self.assertListEqual(
           list(attentions[0].shape[-3:]   ) ,					[batch_size, seq_len, config.hidden_size] ,					)
       UpperCAmelCase__  :Dict            =			len(__lowerCamelCase   )

       # Check attention is always last and order is fine
       UpperCAmelCase__  :int            =			True
       UpperCAmelCase__  :Tuple            =			True
       UpperCAmelCase__  :List[Any]            =			model_class(__lowerCamelCase   )
       model.to(__lowerCamelCase   )
       model.eval()

       UpperCAmelCase__  :Optional[int]            =			self._prepare_for_class(__lowerCamelCase ,					__lowerCamelCase   )
       UpperCAmelCase__  :int            =			inputs['''input_ids'''].shape[0]
       with torch.no_grad():
          UpperCAmelCase__  :Optional[int]            =			model(**__lowerCamelCase   )

       UpperCAmelCase__  :List[Any]            =			1
       self.assertEqual(out_len + added_hidden_states ,					len(__lowerCamelCase   )   )

       UpperCAmelCase__  :str            =			outputs.attentions

       self.assertEqual(len(__lowerCamelCase   ) ,					self.model_tester.num_hidden_layers   )
       self.assertListEqual(
           list(self_attentions[0].shape[-3:]   ) ,					[batch_size, seq_len, config.hidden_size] ,					)





 @slow
 def 					__SCREAMING_SNAKE_CASE				(			self       :  Dict   ):
    for model_name in RWKV_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       UpperCAmelCase__  :List[Any]            =			RwkvModel.from_pretrained(__lowerCamelCase   )
       self.assertIsNotNone(__lowerCamelCase   )




@unittest.skipIf(
    not is_torch_greater_or_equal_than_2_0	,   reason="See https://github.com/huggingface/transformers/pull/24204"	)
@slow
class 							UpperCAmelCase ( unittest.TestCase	):


 def 					__SCREAMING_SNAKE_CASE				(			self       :  str   ):
    UpperCAmelCase__  :List[Any]            =			'''RWKV/rwkv-4-169m-pile'''
    UpperCAmelCase__  :str            =			AutoTokenizer.from_pretrained(self.model_id   )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Any   ):
    UpperCAmelCase__  :Optional[int]            =			'''Hello my name is Jasmine and I am a newbie to the'''
    UpperCAmelCase__  :Union[str, Any]            =			RwkvForCausalLM.from_pretrained(self.model_id   ).to(__lowerCamelCase   )

    UpperCAmelCase__  :List[str]            =			self.tokenizer('''Hello my name is''' ,					return_tensors='''pt'''   ).input_ids.to(__lowerCamelCase   )
    UpperCAmelCase__  :str            =			model.generate(__lowerCamelCase ,					max_new_tokens=1_0   )
    UpperCAmelCase__  :str            =			self.tokenizer.decode(output[0].tolist()   )

    self.assertEqual(__lowerCamelCase ,					__lowerCamelCase   )





 def 					__SCREAMING_SNAKE_CASE				(			self       :  Union[str, Any]   ):
    UpperCAmelCase__  :str            =			'''Hello my name is Jasmine and I am a newbie to the'''

    UpperCAmelCase__  :int            =			self.tokenizer('''Hello my name is''' ,					return_tensors='''pt'''   ).input_ids.to(__lowerCamelCase   )
    UpperCAmelCase__  :Dict            =			RwkvForCausalLM.from_pretrained(self.model_id ,					torch_dtype=torch.bfloataa   ).to(__lowerCamelCase   )

    UpperCAmelCase__  :str            =			model.generate(__lowerCamelCase ,					max_new_tokens=1_0   )
    UpperCAmelCase__  :int            =			self.tokenizer.decode(output[0].tolist()   )

    self.assertEqual(__lowerCamelCase ,					__lowerCamelCase   )



