



import copy
import logging
import random

import joblib
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AdamW, GPTaLMHeadModel, get_linear_schedule_with_warmup


__snake_case		:str 						=	logging.getLogger(__name__)





def 	__snake_case      (     _UpperCAmelCase							):
    random.seed(UpperCamelCase_							)
    np.random.seed(UpperCamelCase_							)
    torch.manual_seed(UpperCamelCase_							)
    torch.cuda.manual_seed_all(UpperCamelCase_							)





def 	__snake_case      (     _UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase							):
    model.eval()
    __a					=							next(model.parameters()							).device
    __a					=							1
    __a					=							torch.zeros((eval_batch_size, context_len)  ,					dtype=torch.long  ,					device=UpperCamelCase_							)
    __a					=							DataLoader(UpperCamelCase_  ,					shuffle=UpperCamelCase_  ,					batch_size=UpperCamelCase_							)
    __a					=							torch.zeros(1  ,					device=UpperCamelCase_							)
    __a					=							0
    for batch in eval_dataloader:
        batch.to(UpperCamelCase_							)
        # pad
        context.zero_()
        for i in range(UpperCamelCase_							):
            __a					=							batch[i]
        __a					=							model(UpperCamelCase_  ,					labels=UpperCamelCase_							)
        eval_loss += outputs[0].sum().item()
        nb_eval_examples += batch.size(0							)
    __a					=							eval_loss / nb_eval_examples
    __a					=							torch.exp(UpperCamelCase_							)
    model.train()
    return perplexity





def 	__snake_case      (     _UpperCAmelCase="gpt2"							):
    __a					=							GPTaLMHeadModel.from_pretrained(UpperCamelCase_  ,					output_hidden_states=UpperCamelCase_							)
    torch.save(model.state_dict()  ,					model_name + '''local.pt'''							)
    return model





def 	__snake_case      (     _UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase							):
    __a					=							copy.deepcopy(UpperCamelCase_							)
    model.to(UpperCamelCase_							)

    __a					=							['bias', 'LayerNorm.weight']
    __a					=							[
        {
            'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay							)],
            'weight_decay': 0.0,
        },
        {'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay							)], 'weight_decay': 0.0},
    ]
    __a					=							AdamW(UpperCamelCase_  ,					lr=5E-5  ,					eps=1E-8							)
    __a					=							get_linear_schedule_with_warmup(UpperCamelCase_  ,					0  ,					UpperCamelCase_							)
    torch.cuda.empty_cache()
    return model, lm_optimizer, lm_scheduler





def 	__snake_case      (     _UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase							):
    __a					=							np.array(UpperCamelCase_							).mean()
    __a					=							np.array(UpperCamelCase_							).std()
    __a					=							(real_perps - avg) / std
    __a					=							list(zip(UpperCamelCase_  ,					UpperCamelCase_  ,					UpperCamelCase_							)							)
    joblib.dump(UpperCamelCase_  ,					UpperCamelCase_							)





def 	__snake_case      (     _UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase="dev.jbl"  ,					_UpperCAmelCase=recopy_gpta  ,					):
    __a					=							[]
    __a					=							[]
    __a					=							[]

    # Initialize the transformer model
    __a					=							copy.deepcopy(UpperCamelCase_							)
    orig_model.to(device='''cpu'''							)
    torch.cuda.empty_cache()

    # Compute perplexity of initial transformer model for comparison
    model.train()
    __a					=							recopy_model(UpperCamelCase_  ,					UpperCamelCase_  ,					UpperCamelCase_							)

    for step in tqdm(range(UpperCamelCase_							)							):
        __a					=							torch.zeros((1, context_len)  ,					dtype=torch.long  ,					device=UpperCamelCase_							)
        __a					=							random.choice(UpperCamelCase_							)
        __a					=							random.randint(0  ,					len(story[0]							) - context_len - 1							)
        __a					=							story[0][start : start + context_len]
        lm_optimizer.zero_grad()
        __a					=							model(UpperCamelCase_  ,					labels=UpperCamelCase_							)
        __a					=							outputs[0]
        __a					=							compute_perplexity(UpperCamelCase_  ,					UpperCamelCase_  ,					UpperCamelCase_							)
        model.train()
        lm_loss.backward()
        # Do LM backprop
        torch.nn.utils.clip_grad_norm_(model.parameters()  ,					3.0							)
        lm_optimizer.step()
        lm_scheduler.step()  # Update learning rate schedule

        # Compute perplexity after back-propagating on the selected context
        __a					=							compute_perplexity(UpperCamelCase_  ,					UpperCamelCase_  ,					UpperCamelCase_							)

        # Periodically save the stored (X, IG(X)) pairs
        if step % 1000 == 0 and step > 1:
            intermittent_save(UpperCamelCase_  ,					UpperCamelCase_  ,					UpperCamelCase_  ,					UpperCamelCase_							)

        # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
        __a					=							recopy_model(UpperCamelCase_  ,					UpperCamelCase_  ,					UpperCamelCase_							)

        past_perps.append(past_perp.item()							)
        real_perps.append(orig_perp - real_perp.item()							)
        contexts.append(np.array(context.cpu()							)							)

    intermittent_save(UpperCamelCase_  ,					UpperCamelCase_  ,					UpperCamelCase_  ,					UpperCamelCase_							)





def 	__snake_case      (     _UpperCAmelCase  ,					_UpperCAmelCase="data/tokenized_stories_train_wikitext103.jbl"  ,					_UpperCAmelCase=100  ,					_UpperCAmelCase=1026  ,					_UpperCAmelCase=True							):
    __a					=							joblib.load(UpperCamelCase_							)
    print('''data loaded'''							)
    __a					=							[]
    if trim:
        for i, example in enumerate(UpperCamelCase_							):
            if len(example[0]							) > min_len:
                __a					=							random.randint(0  ,					len(example[0]							) - context_len - 1							)
                objective_set.append(example[0, start : start + context_len]							)
            if len(UpperCamelCase_							) >= number:
                break
        __a					=							[]
        for j in range(i + 1  ,					len(UpperCamelCase_							)							):
            if len(data[j][0]							) > min_len:
                train_data.append(data[j]							)
    else:
        __a					=							data[0:number]
        __a					=							data[number:]

    joblib.dump(UpperCamelCase_  ,					'''objective_set.jbl'''							)
    print('''objective set saved'''							)
    return train_data, objective_set





def 	__snake_case      (     _UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase=50  ,					_UpperCAmelCase="secondary_learner.pt"							):
    __a					=							torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''							)
    # We will use the first 512 pairs from our dataset as a test set for
    # our secondary learner and the rest to train
    __a					=							train_dataset[:512]
    __a					=							train_dataset[512:]
    __a					=							DataLoader(UpperCamelCase_  ,					shuffle=UpperCamelCase_  ,					batch_size=UpperCamelCase_							)
    __a					=							DataLoader(UpperCamelCase_  ,					shuffle=UpperCamelCase_  ,					batch_size=UpperCamelCase_							)

    # secondary learner model set up
    __a					=							nn.MSELoss()
    __a					=							nn.MSELoss(reduction='''sum'''							)
    secondary_learner.to(UpperCamelCase_							)
    __a					=							torch.optim.Adam(secondary_learner.parameters()  ,					lr=0.0_00_01							)
    secondary_learner.train()

    # TODO in original code this is written as number of actual batches seen
    # not number of items seen but other places it is number of items instead.
    # improve consistency! changed this to epochs for clarity
    __a					=							float('''inf'''							)
    # Iterate through batches until we've used max_steps batches
    for epoch in range(int(UpperCamelCase_							)							):
        __a					=							0.0
        secondary_learner.train()
        for step, batch in enumerate(UpperCamelCase_							):
            __a					=							batch[0].to(UpperCamelCase_							)
            __a					=							batch[1].to(UpperCamelCase_							)
            __a					=							secondary_learner(UpperCamelCase_							)
            q_optimizer.zero_grad()
            __a					=							loss(UpperCamelCase_  ,					real_q.float()							)
            q_loss.backward()
            q_optimizer.step()
            tr_q_loss += q_loss.item()

            # model trains fairly quickly so we won't wait for a full epoch
            # eval is triggered at eval_freq and end of epochs
            if (step % eval_freq == 0 and step > 0) or ((step + 1) == len(UpperCamelCase_							)):
                __a					=							tr_q_loss / (step + 1)

                secondary_learner.eval()
                __a					=							0.0
                __a					=							0.0
                __a					=							[]
                __a					=							[]
                # Compute performance of the secondary learner after this batch
                for stepa, batcha in enumerate(UpperCamelCase_							):
                    __a					=							batcha[0].to(UpperCamelCase_							)
                    __a					=							batcha[1].to(UpperCamelCase_							)
                    __a					=							secondary_learner(UpperCamelCase_							)
                    q_lossa += test_loss(UpperCamelCase_  ,					UpperCamelCase_							).item()
                    sum_qa += torch.sum(UpperCamelCase_							).item()
                    for ei, i in enumerate(predicted_qa.cpu().detach().numpy()							):
                        predicted.append(i.item()							)
                    for ei, i in enumerate(real_qa.cpu().detach().numpy()							):
                        actual.append(i.item()							)

                q_lossa /= len(UpperCamelCase_							)
                print(
                    '''Epoch: '''  ,					UpperCamelCase_  ,					'''step: '''  ,					UpperCamelCase_  ,					'''Avg. q:'''  ,					sum_qa / len(UpperCamelCase_							)  ,					'''Train Loss: '''  ,					UpperCamelCase_  ,					'''Test Loss: '''  ,					UpperCamelCase_  ,					)
                if q_lossa < best_test_loss:
                    joblib.dump((predicted, actual)  ,					'''pred_vs_actual.jbl'''							)
                    torch.save(secondary_learner.state_dict()  ,					UpperCamelCase_							)
                    __a					=							q_lossa

            secondary_learner.train()
    return secondary_learner

class        _A			(      nn.Module       ):



       def __init__(						self				:	str	,						__SCREAMING_SNAKE_CASE				:	List[str]):


           '''simple docstring'''

           super(lowercase_	,						self).__init__()
           __a					=							model.transformer.wte
           __a					=							copy.deepcopy(model.transformer.wte.weight)
           __a					=							nn.Convad(self.embeddings.weight.size(1)	,						256	,						3	,						padding=1)
           __a					=							nn.Sequential(nn.Linear(256	,						32)	,						nn.Dropout(p=0.1)	,						nn.Linear(32	,						32)	,						nn.Linear(32	,						1))



       def 						_lowerCamelCase     (						self				:	Tuple	,						__SCREAMING_SNAKE_CASE				:	Tuple):


           '''simple docstring'''

           __a					=							torch.max(self.conv(self.embeddings(lowercase_).squeeze(1).transpose(1	,						2))	,						2)[0]
           __a					=							self.fc(lowercase_)
           return qs.squeeze(1)





       @classmethod
       def 						_lowerCamelCase     (						cls				:	List[str]	,						__SCREAMING_SNAKE_CASE				:	Optional[int]	,						__SCREAMING_SNAKE_CASE				:	Any):


           '''simple docstring'''

           __a					=							cls(lowercase_)  # this calls __init__
           __a					=							torch.load(lowercase_)
           secondary_learner.load_state_dict(lowercase_)
           __a					=							model.transformer.wte
           __a					=							copy.deepcopy(model.transformer.wte.weight)
           return secondary_learner













"""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:
   _UpperCamelCase :    Union[str, Any]      =			False






class __a :



  def __init__(self	:		str					,      lowercase_	:		Dict					,      lowercase_	:		str=14					,      lowercase_	:		List[str]=7					,      lowercase_	:		Any=True					,      lowercase_	:		List[str]=False					,      lowercase_	:		int=True					,      lowercase_	:		List[str]=True					,      lowercase_	:		str=True					,      lowercase_	:		Union[str, Any]=99					,      lowercase_	:		str=32					,      lowercase_	:		List[Any]=5					,      lowercase_	:		List[str]=37					,      lowercase_	:		str="gelu"					,      lowercase_	:		Any=0.1					,      lowercase_	:		Dict=0.1					,      lowercase_	:		List[Any]=512					,      lowercase_	:		Any=16					,      lowercase_	:		Union[str, Any]=2					,      lowercase_	:		Tuple=3					,      lowercase_	:		Optional[int]=4					,      lowercase_	:		int=None					,      )		->				Tuple:
   __A						:							Any      =						parent
   __A						:							int      =						batch_size
   __A						:							int      =						seq_length
   __A						:							int      =						is_training
   __A						:							int      =						use_token_type_ids
   __A						:							Union[str, Any]      =						use_input_mask
   __A						:							Any      =						use_labels
   __A						:							Union[str, Any]      =						use_mc_token_ids
   __A						:							List[Any]      =						vocab_size
   __A						:							List[str]      =						hidden_size
   __A						:							List[Any]      =						num_hidden_layers
   __A						:							Union[str, Any]      =						intermediate_size
   __A						:							Union[str, Any]      =						hidden_act
   __A						:							int      =						hidden_dropout_prob
   __A						:							Optional[Any]      =						attention_probs_dropout_prob
   __A						:							List[str]      =						max_position_embeddings
   __A						:							List[Any]      =						type_vocab_size
   __A						:							Tuple      =						type_sequence_label_size
   __A						:							Union[str, Any]      =						num_labels
   __A						:							Tuple      =						num_choices
   __A						:							int      =						scope
   __A						:							Union[str, Any]      =						vocab_size - 1
   __A						:							int      =						vocab_size - 1
   __A						:							Optional[Any]      =						vocab_size - 1



  def     A_					(self	:		List[str] )		->				Optional[int]:
   return RwkvConfig.from_pretrained('sgugger/rwkv-4-pile-7b' )



  def     A_					(self	:		Dict					,      lowercase_	:		str=False					,      lowercase_	:		Optional[int]=False					,      lowercase_	:		int=False )		->				List[str]:
   __A						:							List[Any]      =						ids_tensor([self.batch_size, self.seq_length]					,      self.vocab_size )

   __A						:							Optional[Any]      =						None
   if self.use_input_mask:
    __A						:							Tuple      =						random_attention_mask([self.batch_size, self.seq_length] )

   __A						:							Tuple      =						None
   if self.use_token_type_ids:
    __A						:							Optional[int]      =						ids_tensor([self.batch_size, self.seq_length]					,      self.type_vocab_size )

   __A						:							str      =						None
   if self.use_mc_token_ids:
    __A						:							int      =						ids_tensor([self.batch_size, self.num_choices]					,      self.seq_length )

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

   __A						:							Any      =						self.get_config(
       gradient_checkpointing=lowercase_					,      scale_attn_by_inverse_layer_idx=lowercase_					,      reorder_and_upcast_attn=lowercase_					,      )

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



  def     A_					(self	:		Optional[int]					,      lowercase_	:		Optional[int]=False					,      lowercase_	:		Dict=False					,      lowercase_	:		List[str]=False )		->				Any:
   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=lowercase_					,      bos_token_id=self.bos_token_id					,      eos_token_id=self.eos_token_id					,      pad_token_id=self.pad_token_id					,      gradient_checkpointing=lowercase_					,      scale_attn_by_inverse_layer_idx=lowercase_					,      reorder_and_upcast_attn=lowercase_					,      )



  def     A_					(self	:		Optional[int] )		->				Dict:
   __A						:							Any      =						self.get_config()
   __A						:							Any      =						300
   return config



  def     A_					(self	:		Tuple )		->				Dict:
   (
       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       
   )						:							Tuple      =						self.prepare_config_and_inputs()

   __A						:							Tuple      =						floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
   __A						:							List[str]      =						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     A_					(self	:		Any					,      lowercase_	:		Any					,      lowercase_	:		Union[str, Any]					,      lowercase_	:		Optional[Any]					,      lowercase_	:		List[Any]					,      lowercase_	:		str					,      *lowercase_	:		Any )		->				Tuple:
   __A						:							List[str]      =						True
   __A						:							str      =						RwkvModel(config=lowercase_ )
   model.to(lowercase_ )
   model.eval()

   __A						:							List[Any]      =						model(lowercase_ )

   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     A_					(self	:		List[str]					,      lowercase_	:		Dict					,      lowercase_	:		List[str]					,      lowercase_	:		Union[str, Any]					,      lowercase_	:		Optional[int]					,      lowercase_	:		List[Any]					,      *lowercase_	:		int )		->				Union[str, Any]:
   __A						:							Any      =						RwkvForCausalLM(lowercase_ )
   model.to(lowercase_ )
   model.eval()

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



  def     A_					(self	:		str					,      lowercase_	:		List[Any]					,      lowercase_	:		Dict					,      lowercase_	:		Tuple					,      lowercase_	:		str					,      lowercase_	:		Dict					,      *lowercase_	:		Optional[Any] )		->				Union[str, Any]:
   __A						:							Any      =						RwkvModel(config=lowercase_ )
   model.to(lowercase_ )
   model.eval()

   __A						:							Tuple      =						model(lowercase_ )
   __A						:							str      =						outputs.last_hidden_state

   __A						:							Optional[int]      =						model(input_ids[:, :2] )
   __A						:							Dict      =						outputs.last_hidden_state

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

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



  def     A_					(self	:		List[str]					,      lowercase_	:		Optional[int]					,      lowercase_	:		List[str]					,      lowercase_	:		int					,      lowercase_	:		int					,      lowercase_	:		Union[str, Any]					,      *lowercase_	:		Dict					,      lowercase_	:		List[Any]=False )		->				str:
   __A						:							Tuple      =						RwkvForCausalLM(lowercase_ )
   model.to(lowercase_ )
   if gradient_checkpointing:
    model.gradient_checkpointing_enable()

   __A						:							Optional[int]      =						model(lowercase_					,      labels=lowercase_ )
   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     A_					(self	:		int )		->				Tuple:
   __A						:							Union[str, Any]      =						self.prepare_config_and_inputs()

   (
       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       (
       __A
   )     ,       
   )						:							int      =						config_and_inputs

   __A						:							Tuple      =						{'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 __a ( snake_case_			,snake_case_			,snake_case_			,unittest.TestCase					):
  __lowercase     :     Any       						=       (RwkvModel, RwkvForCausalLM) if is_torch_available() else ()
  __lowercase     :     Dict       						=       (
      {'feature-extraction': RwkvModel, 'text-generation': RwkvForCausalLM} if is_torch_available() else {}
  )
  # all_generative_model_classes = (RwkvForCausalLM,) if is_torch_available() else ()
  __lowercase     :     List[str]       						=       False
  __lowercase     :     int       						=       False
  __lowercase     :     Optional[Any]       						=       False
  __lowercase     :     Union[str, Any]       						=       False
  __lowercase     :     Union[str, Any]       						=       False  # Rwkv does not support head masking



  def     A_					(self	:		List[str] )		->				Any:
   __A						:							List[str]      =						RwkvModelTester(self )
   __A						:							List[Any]      =						ConfigTester(
       self					,      config_class=lowercase_					,      n_embd=37					,      common_properties=['hidden_size', 'num_hidden_layers'] )



  def     A_					(self	:		int					,      lowercase_	:		Any					,      lowercase_	:		Optional[int]					,      lowercase_	:		Optional[Any]=None )		->				Tuple:
   if isinstance(lowercase_					,      torch.Tensor ):
    __A     ,       __A						:							str      =						member.max().item(), member.min().item()
   elif isinstance(lowercase_					,      lowercase_ ) or isinstance(lowercase_					,      lowercase_ ):
    __A     ,       __A						:							List[str]      =						max(lowercase_ ), min(lowercase_ )

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

   __A     ,       __A						:							Optional[Any]      =						container

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

   if not is_inside_interval:
    __A						:							Optional[int]      =						'%s not found in %s' % (safe_repr(lowercase_ ), safe_repr(lowercase_ ))
    self.fail(self._formatMessage(lowercase_					,      lowercase_ ) )



  def     A_					(self	:		int )		->				Optional[Any]:
   self.config_tester.run_common_tests()



  def     A_					(self	:		Tuple )		->				str:
   __A						:							Dict      =						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_rwkv_model(*lowercase_ )



  def     A_					(self	:		List[Any] )		->				Union[str, Any]:
   __A						:							Any      =						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_causl_lm(*lowercase_ )



  def     A_					(self	:		Any )		->				Optional[int]:
   __A						:							Optional[int]      =						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_state_equivalency(*lowercase_ )



  def     A_					(self	:		List[str] )		->				int:
   __A     ,       __A						:							Optional[Any]      =						self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    __A						:							Optional[int]      =						model_class(config=lowercase_ )
    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     A_					(self	:		str )		->				Any:
   __A     ,       __A						:							List[str]      =						self.model_tester.prepare_config_and_inputs_for_common()
   __A						:							Optional[Any]      =						True

   __A						:							List[str]      =						getattr(self.model_tester					,      'seq_length'					,      lowercase_ )

   for model_class in self.all_model_classes:
    __A						:							str      =						True
    __A						:							str      =						False
    __A						:							List[str]      =						True
    __A						:							Optional[Any]      =						model_class(lowercase_ )
    model.to(lowercase_ )
    model.eval()

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

    # check that output_attentions also work using config
    del inputs_dict["output_attentions"]
    __A						:							Optional[int]      =						True
    __A						:							Dict      =						model_class(lowercase_ )
    model.to(lowercase_ )
    model.eval()

    __A						:							Any      =						self._prepare_for_class(lowercase_					,      lowercase_ )
    __A						:							Any      =						inputs['input_ids'].shape[0]
    with torch.no_grad():
     __A						:							List[Any]      =						model(**lowercase_ )
    __A						:							Optional[Any]      =						outputs.attentions
    self.assertEqual(len(lowercase_ )					,      self.model_tester.num_hidden_layers )

    self.assertListEqual(
        list(attentions[0].shape[-3:] )					,      [batch_size, seq_len, config.hidden_size]					,      )
    __A						:							Tuple      =						len(lowercase_ )

    # Check attention is always last and order is fine
    __A						:							Any      =						True
    __A						:							str      =						True
    __A						:							List[str]      =						model_class(lowercase_ )
    model.to(lowercase_ )
    model.eval()

    __A						:							List[Any]      =						self._prepare_for_class(lowercase_					,      lowercase_ )
    __A						:							Union[str, Any]      =						inputs['input_ids'].shape[0]
    with torch.no_grad():
     __A						:							Dict      =						model(**lowercase_ )

    __A						:							Dict      =						1
    self.assertEqual(out_len + added_hidden_states					,      len(lowercase_ ) )

    __A						:							Optional[Any]      =						outputs.attentions

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



  @slow
  def     A_					(self	:		Optional[Any] )		->				Any:
   for model_name in RWKV_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    __A						:							Optional[int]      =						RwkvModel.from_pretrained(lowercase_ )
    self.assertIsNotNone(lowercase_ )






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



  def     A_					(self	:		str )		->				Optional[Any]:
   __A						:							Optional[int]      =						'RWKV/rwkv-4-169m-pile'
   __A						:							Tuple      =						AutoTokenizer.from_pretrained(self.model_id )



  def     A_					(self	:		Optional[Any] )		->				str:
   __A						:							int      =						'Hello my name is Jasmine and I am a newbie to the'
   __A						:							str      =						RwkvForCausalLM.from_pretrained(self.model_id ).to(lowercase_ )

   __A						:							Union[str, Any]      =						self.tokenizer('Hello my name is'					,      return_tensors='pt' ).input_ids.to(lowercase_ )
   __A						:							List[str]      =						model.generate(lowercase_					,      max_new_tokens=10 )
   __A						:							List[str]      =						self.tokenizer.decode(output[0].tolist() )

   self.assertEqual(lowercase_					,      lowercase_ )



  def     A_					(self	:		Any )		->				Optional[Any]:
   __A						:							Optional[int]      =						'Hello my name is Jasmine and I am a newbie to the'

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

   __A						:							str      =						model.generate(lowercase_					,      max_new_tokens=10 )
   __A						:							Union[str, Any]      =						self.tokenizer.decode(output[0].tolist() )

   self.assertEqual(lowercase_					,      lowercase_ )





