
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


lowercase						:      int							=							logging.getLogger(__name__)







def 		_snake_case(						SCREAMING_SNAKE_CASE__       )				->					List[Any]:
       random.seed(SCREAMING_SNAKE_CASE__       )
       np.random.seed(SCREAMING_SNAKE_CASE__       )
       torch.manual_seed(SCREAMING_SNAKE_CASE__       )
       torch.cuda.manual_seed_all(SCREAMING_SNAKE_CASE__       )







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )				->					Optional[Any]:
       model.eval()
       lowercase							:  List[str] 			=   next(model.parameters()       ).device
       lowercase							:  Optional[int] 			=   1
       lowercase							:  Dict 			=   torch.zeros((eval_batch_size, context_len)      ,			dtype=torch.long      ,			device=SCREAMING_SNAKE_CASE__       )
       lowercase							:  str 			=   DataLoader(SCREAMING_SNAKE_CASE__      ,			shuffle=SCREAMING_SNAKE_CASE__      ,			batch_size=SCREAMING_SNAKE_CASE__       )
       lowercase							:  List[str] 			=   torch.zeros(1      ,			device=SCREAMING_SNAKE_CASE__       )
       lowercase							:  int 			=   0
       for batch in eval_dataloader:
              batch.to(SCREAMING_SNAKE_CASE__       )
              # pad
              context.zero_()
              for i in range(SCREAMING_SNAKE_CASE__       ):
                     lowercase							:  Optional[int] 			=   batch[i]
              lowercase							:  Any 			=   model(SCREAMING_SNAKE_CASE__      ,			labels=SCREAMING_SNAKE_CASE__       )
              eval_loss += outputs[0].sum().item()
              nb_eval_examples += batch.size(0       )
       lowercase							:  Optional[int] 			=   eval_loss / nb_eval_examples
       lowercase							:  Optional[Any] 			=   torch.exp(SCREAMING_SNAKE_CASE__       )
       model.train()
       return perplexity







def 		_snake_case(						SCREAMING_SNAKE_CASE__="gpt2"       )				->					str:
       lowercase							:  Any 			=   GPTaLMHeadModel.from_pretrained(SCREAMING_SNAKE_CASE__      ,			output_hidden_states=SCREAMING_SNAKE_CASE__       )
       torch.save(model.state_dict()      ,			model_name + """local.pt"""       )
       return model







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )				->					List[str]:
       lowercase							:  Union[str, Any] 			=   copy.deepcopy(SCREAMING_SNAKE_CASE__       )
       model.to(SCREAMING_SNAKE_CASE__       )

       lowercase							:  List[Any] 			=   ["""bias""", """LayerNorm.weight"""]
       lowercase							:  Union[str, Any] 			=   [
           {
               """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},
       ]
       lowercase							:  Dict 			=   AdamW(SCREAMING_SNAKE_CASE__      ,			lr=5e-5      ,			eps=1e-8       )
       lowercase							:  List[str] 			=   get_linear_schedule_with_warmup(SCREAMING_SNAKE_CASE__      ,			0      ,			SCREAMING_SNAKE_CASE__       )
       torch.cuda.empty_cache()
       return model, lm_optimizer, lm_scheduler







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )				->					Optional[int]:
       lowercase							:  Optional[Any] 			=   np.array(SCREAMING_SNAKE_CASE__       ).mean()
       lowercase							:  Optional[int] 			=   np.array(SCREAMING_SNAKE_CASE__       ).std()
       lowercase							:  Tuple 			=   (real_perps - avg) / std
       lowercase							:  Optional[int] 			=   list(zip(SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )       )
       joblib.dump(SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__="dev.jbl"      ,			SCREAMING_SNAKE_CASE__=recopy_gpta      ,			)				->					Union[str, Any]:
       lowercase							:  Optional[int] 			=   []
       lowercase							:  str 			=   []
       lowercase							:  Tuple 			=   []

       # Initialize the transformer model
       lowercase							:  Any 			=   copy.deepcopy(SCREAMING_SNAKE_CASE__       )
       orig_model.to(device="""cpu"""       )
       torch.cuda.empty_cache()

       # Compute perplexity of initial transformer model for comparison
       model.train()
       lowercase			,    lowercase			,    lowercase							:  Optional[Any] 			=   recopy_model(SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )

       for step in tqdm(range(SCREAMING_SNAKE_CASE__       )       ):
              lowercase							:  Optional[int] 			=   torch.zeros((1, context_len)      ,			dtype=torch.long      ,			device=SCREAMING_SNAKE_CASE__       )
              lowercase							:  List[Any] 			=   random.choice(SCREAMING_SNAKE_CASE__       )
              lowercase							:  List[str] 			=   random.randint(0      ,			len(story[0]       ) - context_len - 1       )
              lowercase							:  Any 			=   story[0][start : start + context_len]
              lm_optimizer.zero_grad()
              lowercase							:  Dict 			=   model(SCREAMING_SNAKE_CASE__      ,			labels=SCREAMING_SNAKE_CASE__       )
              lowercase							:  List[str] 			=   outputs[0]
              lowercase							:  str 			=   compute_perplexity(SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )
              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
              lowercase							:  List[Any] 			=   compute_perplexity(SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )

              # Periodically save the stored (X, IG(X)) pairs
              if step % 1_000 == 0 and step > 1:
                     intermittent_save(SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )

              # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
              lowercase			,    lowercase			,    lowercase							:  int 			=   recopy_model(SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )

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

       intermittent_save(SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__="data/tokenized_stories_train_wikitext103.jbl"      ,			SCREAMING_SNAKE_CASE__=100      ,			SCREAMING_SNAKE_CASE__=1_026      ,			SCREAMING_SNAKE_CASE__=True       )				->					str:
       lowercase							:  Any 			=   joblib.load(SCREAMING_SNAKE_CASE__       )
       print("""data loaded"""       )
       lowercase							:  Dict 			=   []
       if trim:
              for i, example in enumerate(SCREAMING_SNAKE_CASE__       ):
                     if len(example[0]       ) > min_len:
                            lowercase							:  List[str] 			=   random.randint(0      ,			len(example[0]       ) - context_len - 1       )
                            objective_set.append(example[0, start : start + context_len]       )
                     if len(SCREAMING_SNAKE_CASE__       ) >= number:
                            break
              lowercase							:  Union[str, Any] 			=   []
              for j in range(i + 1      ,			len(SCREAMING_SNAKE_CASE__       )       ):
                     if len(data[j][0]       ) > min_len:
                            train_data.append(data[j]       )
       else:
              lowercase							:  Tuple 			=   data[0:number]
              lowercase							:  Optional[Any] 			=   data[number:]

       joblib.dump(SCREAMING_SNAKE_CASE__      ,			"""objective_set.jbl"""       )
       print("""objective set saved"""       )
       return train_data, objective_set







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__=50      ,			SCREAMING_SNAKE_CASE__="secondary_learner.pt"       )				->					Union[str, Any]:
       lowercase							:  int 			=   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
       lowercase							:  int 			=   train_dataset[:512]
       lowercase							:  Optional[int] 			=   train_dataset[512:]
       lowercase							:  Dict 			=   DataLoader(SCREAMING_SNAKE_CASE__      ,			shuffle=SCREAMING_SNAKE_CASE__      ,			batch_size=SCREAMING_SNAKE_CASE__       )
       lowercase							:  List[str] 			=   DataLoader(SCREAMING_SNAKE_CASE__      ,			shuffle=SCREAMING_SNAKE_CASE__      ,			batch_size=SCREAMING_SNAKE_CASE__       )

       # secondary learner model set up
       lowercase							:  List[str] 			=   nn.MSELoss()
       lowercase							:  Tuple 			=   nn.MSELoss(reduction="""sum"""       )
       secondary_learner.to(SCREAMING_SNAKE_CASE__       )
       lowercase							:  int 			=   torch.optim.Adam(secondary_learner.parameters()      ,			lr=0.00001       )
       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
       lowercase							:  List[str] 			=   float("""inf"""       )
       # Iterate through batches until we've used max_steps batches
       for epoch in range(int(SCREAMING_SNAKE_CASE__       )       ):
              lowercase							:  Union[str, Any] 			=   0.0
              secondary_learner.train()
              for step, batch in enumerate(SCREAMING_SNAKE_CASE__       ):
                     lowercase							:  Optional[int] 			=   batch[0].to(SCREAMING_SNAKE_CASE__       )
                     lowercase							:  int 			=   batch[1].to(SCREAMING_SNAKE_CASE__       )
                     lowercase							:  Union[str, Any] 			=   secondary_learner(SCREAMING_SNAKE_CASE__       )
                     q_optimizer.zero_grad()
                     lowercase							:  Union[str, Any] 			=   loss(SCREAMING_SNAKE_CASE__      ,			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(SCREAMING_SNAKE_CASE__       )):
                            lowercase							:  Any 			=   tr_q_loss / (step + 1)

                            secondary_learner.eval()
                            lowercase							:  Dict 			=   0.0
                            lowercase							:  Tuple 			=   0.0
                            lowercase							:  Dict 			=   []
                            lowercase							:  int 			=   []
                            # Compute performance of the secondary learner after this batch
                            for stepa, batcha in enumerate(SCREAMING_SNAKE_CASE__       ):
                                   lowercase							:  List[Any] 			=   batcha[0].to(SCREAMING_SNAKE_CASE__       )
                                   lowercase							:  Optional[int] 			=   batcha[1].to(SCREAMING_SNAKE_CASE__       )
                                   lowercase							:  str 			=   secondary_learner(SCREAMING_SNAKE_CASE__       )
                                   q_lossa += test_loss(SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       ).item()
                                   sum_qa += torch.sum(SCREAMING_SNAKE_CASE__       ).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(SCREAMING_SNAKE_CASE__       )
                            print(
                                """Epoch: """      ,			SCREAMING_SNAKE_CASE__      ,			"""step: """      ,			SCREAMING_SNAKE_CASE__      ,			"""Avg. q:"""      ,			sum_qa / len(SCREAMING_SNAKE_CASE__       )      ,			"""Train Loss: """      ,			SCREAMING_SNAKE_CASE__      ,			"""Test Loss: """      ,			SCREAMING_SNAKE_CASE__      ,			)
                            if q_lossa < best_test_loss:
                                   joblib.dump((predicted, actual)      ,			"""pred_vs_actual.jbl"""       )
                                   torch.save(secondary_learner.state_dict()      ,			SCREAMING_SNAKE_CASE__       )
                                   lowercase							:  List[str] 			=   q_lossa

                     secondary_learner.train()
       return secondary_learner




class __snake_case	(   nn.Module  ):
    def __init__(						self	,snake_case   ):



           '''simple docstring'''







           super(__A	,self   ).__init__()
           lowercase							:  Optional[int] 			=   model.transformer.wte
           lowercase							:  Tuple 			=   copy.deepcopy(model.transformer.wte.weight   )
           lowercase							:  Optional[int] 			=   nn.Convad(self.embeddings.weight.size(1   )	,256	,3	,padding=1   )
           lowercase							:  Union[str, Any] 			=   nn.Sequential(nn.Linear(256	,32   )	,nn.Dropout(p=0.1   )	,nn.Linear(32	,32   )	,nn.Linear(32	,1   )   )
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case   ):



           '''simple docstring'''







           lowercase							:  Optional[int] 			=   torch.max(self.conv(self.embeddings(__A   ).squeeze(1   ).transpose(1	,2   )   )	,2   )[0]
           lowercase							:  List[str] 			=   self.fc(__A   )
           return qs.squeeze(1   )
    @classmethod
    def    _SCREAMING_SNAKE_CASE (						cls	,snake_case	,snake_case   ):



           '''simple docstring'''







           lowercase							:  Any 			=   cls(__A   )  # this calls __init__
           lowercase							:  int 			=   torch.load(__A   )
           secondary_learner.load_state_dict(__A   )
           lowercase							:  Optional[int] 			=   model.transformer.wte
           lowercase							:  Dict 			=   copy.deepcopy(model.transformer.wte.weight   )
           return secondary_learner







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:
  lowercase						:      Any							=							False

class __snake_case	:
    def __init__(						self	,snake_case	,snake_case=14	,snake_case=7	,snake_case=True	,snake_case=False	,snake_case=True	,snake_case=True	,snake_case=True	,snake_case=99	,snake_case=32	,snake_case=5	,snake_case=37	,snake_case="gelu"	,snake_case=0.1	,snake_case=0.1	,snake_case=512	,snake_case=16	,snake_case=2	,snake_case=3	,snake_case=4	,snake_case=None	,):



           '''simple docstring'''







           lowercase							:  Optional[Any] 			=   parent
           lowercase							:  str 			=   batch_size
           lowercase							:  str 			=   seq_length
           lowercase							:  int 			=   is_training
           lowercase							:  Optional[Any] 			=   use_token_type_ids
           lowercase							:  List[str] 			=   use_input_mask
           lowercase							:  List[Any] 			=   use_labels
           lowercase							:  List[Any] 			=   use_mc_token_ids
           lowercase							:  int 			=   vocab_size
           lowercase							:  Any 			=   hidden_size
           lowercase							:  Optional[int] 			=   num_hidden_layers
           lowercase							:  Optional[int] 			=   intermediate_size
           lowercase							:  Optional[int] 			=   hidden_act
           lowercase							:  Optional[int] 			=   hidden_dropout_prob
           lowercase							:  List[str] 			=   attention_probs_dropout_prob
           lowercase							:  Dict 			=   max_position_embeddings
           lowercase							:  Optional[Any] 			=   type_vocab_size
           lowercase							:  str 			=   type_sequence_label_size
           lowercase							:  int 			=   num_labels
           lowercase							:  List[Any] 			=   num_choices
           lowercase							:  int 			=   scope
           lowercase							:  Any 			=   vocab_size - 1
           lowercase							:  str 			=   vocab_size - 1
           lowercase							:  int 			=   vocab_size - 1
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           return RwkvConfig.from_pretrained("""sgugger/rwkv-4-pile-7b"""   )
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case=False	,snake_case=False	,snake_case=False   ):



           '''simple docstring'''







           lowercase							:  Optional[Any] 			=   ids_tensor([self.batch_size, self.seq_length]	,self.vocab_size   )

           lowercase							:  Tuple 			=   None
           if self.use_input_mask:
                  lowercase							:  List[str] 			=   random_attention_mask([self.batch_size, self.seq_length]   )

           lowercase							:  List[Any] 			=   None
           if self.use_token_type_ids:
                  lowercase							:  str 			=   ids_tensor([self.batch_size, self.seq_length]	,self.type_vocab_size   )

           lowercase							:  Any 			=   None
           if self.use_mc_token_ids:
                  lowercase							:  Tuple 			=   ids_tensor([self.batch_size, self.num_choices]	,self.seq_length   )

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

           lowercase							:  List[str] 			=   self.get_config(
               gradient_checkpointing=snake_case	,scale_attn_by_inverse_layer_idx=snake_case	,reorder_and_upcast_attn=snake_case	,)

           return (
               config,
               input_ids,
               input_mask,
               None,
               token_type_ids,
               mc_token_ids,
               sequence_labels,
               token_labels,
               choice_labels,
           )
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case=False	,snake_case=False	,snake_case=False   ):



           '''simple docstring'''







           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=snake_case	,bos_token_id=self.bos_token_id	,eos_token_id=self.eos_token_id	,pad_token_id=self.pad_token_id	,gradient_checkpointing=snake_case	,scale_attn_by_inverse_layer_idx=snake_case	,reorder_and_upcast_attn=snake_case	,)
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  List[Any] 			=   self.get_config()
           lowercase							:  str 			=   300
           return config
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           (
               (
               lowercase
           )			,    (
               lowercase
           )			,    (
               lowercase
           )			,    (
               lowercase
           )			,    (
               lowercase
           )			,    (
               lowercase
           )			,    (
               lowercase
           )			,    (
               lowercase
           )			,    (
               lowercase
           )			,    
           )							:  Dict 			=   self.prepare_config_and_inputs()

           lowercase							:  List[str] 			=   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
           lowercase							:  Tuple 			=   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	,snake_case	,snake_case	,snake_case	,snake_case	,snake_case	,*snake_case   ):



           '''simple docstring'''







           lowercase							:  Optional[Any] 			=   True
           lowercase							:  Dict 			=   RwkvModel(config=snake_case   )
           model.to(snake_case   )
           model.eval()

           lowercase							:  Optional[Any] 			=   model(snake_case   )

           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	,snake_case	,snake_case	,snake_case	,snake_case	,snake_case	,*snake_case   ):



           '''simple docstring'''







           lowercase							:  int 			=   RwkvForCausalLM(snake_case   )
           model.to(snake_case   )
           model.eval()

           lowercase							:  Optional[int] 			=   model(snake_case	,labels=snake_case   )
           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	,snake_case	,snake_case	,snake_case	,snake_case	,snake_case	,*snake_case   ):



           '''simple docstring'''







           lowercase							:  str 			=   RwkvModel(config=snake_case   )
           model.to(snake_case   )
           model.eval()

           lowercase							:  int 			=   model(snake_case   )
           lowercase							:  int 			=   outputs.last_hidden_state

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

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

           self.parent.assertTrue(torch.allclose(torch.cat([output_one, output_two]	,dim=1   )	,snake_case	,atol=1e-5   )   )
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case	,snake_case	,snake_case	,snake_case	,snake_case	,*snake_case	,snake_case=False   ):



           '''simple docstring'''







           lowercase							:  Dict 			=   RwkvForCausalLM(snake_case   )
           model.to(snake_case   )
           if gradient_checkpointing:
                  model.gradient_checkpointing_enable()

           lowercase							:  List[str] 			=   model(snake_case	,labels=snake_case   )
           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   ):



           '''simple docstring'''







           lowercase							:  List[Any] 			=   self.prepare_config_and_inputs()

           (
               (
               lowercase
           )			,    (
               lowercase
           )			,    (
               lowercase
           )			,    (
               lowercase
           )			,    (
               lowercase
           )			,    (
               lowercase
           )			,    (
               lowercase
           )			,    (
               lowercase
           )			,    (
               lowercase
           )			,    
           )							:  Union[str, Any] 			=   config_and_inputs

           lowercase							:  Optional[Any] 			=   {"""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 __snake_case	(   lowerCAmelCase      ,			lowerCAmelCase      ,			lowerCAmelCase      ,			unittest.TestCase  ):
    _a	:		str=       (RwkvModel, RwkvForCausalLM) if is_torch_available() else ()
    _a	:		Tuple=       (
        {"feature-extraction": RwkvModel, "text-generation": RwkvForCausalLM} if is_torch_available() else {}
    )
    # all_generative_model_classes = (RwkvForCausalLM,) if is_torch_available() else ()
    _a	:		List[str]=       False
    _a	:		int=       False
    _a	:		Optional[Any]=       False
    _a	:		List[Any]=       False
    _a	:		int=       False  # Rwkv does not support head masking
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Dict 			=   RwkvModelTester(self   )
           lowercase							:  Any 			=   ConfigTester(
               self	,config_class=snake_case	,n_embd=37	,common_properties=["""hidden_size""", """num_hidden_layers"""]   )
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case	,snake_case	,snake_case=None   ):



           '''simple docstring'''







           if isinstance(snake_case	,torch.Tensor   ):
                  lowercase			,    lowercase							:  Tuple 			=   member.max().item(), member.min().item()
           elif isinstance(snake_case	,snake_case   ) or isinstance(snake_case	,snake_case   ):
                  lowercase			,    lowercase							:  Dict 			=   max(snake_case   ), min(snake_case   )

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

           lowercase			,    lowercase							:  int 			=   container

           lowercase							:  Union[str, Any] 			=   (min_value >= expected_min) and (max_value <= expected_max)

           if not is_inside_interval:
                  lowercase							:  Optional[int] 			=   """%s not found in %s""" % (safe_repr(snake_case   ), safe_repr(snake_case   ))
                  self.fail(self._formatMessage(snake_case	,snake_case   )   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           self.config_tester.run_common_tests()
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Any 			=   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_rwkv_model(*snake_case   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Any 			=   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_causl_lm(*snake_case   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Any 			=   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_state_equivalency(*snake_case   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase			,    lowercase							:  Union[str, Any] 			=   self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  lowercase							:  List[Any] 			=   model_class(config=snake_case   )
                  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   ):



           '''simple docstring'''







           lowercase			,    lowercase							:  str 			=   self.model_tester.prepare_config_and_inputs_for_common()
           lowercase							:  List[Any] 			=   True

           lowercase							:  int 			=   getattr(self.model_tester	,"""seq_length"""	,snake_case   )

           for model_class in self.all_model_classes:
                  lowercase							:  Union[str, Any] 			=   True
                  lowercase							:  str 			=   False
                  lowercase							:  int 			=   True
                  lowercase							:  List[str] 			=   model_class(snake_case   )
                  model.to(snake_case   )
                  model.eval()

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

                  # check that output_attentions also work using config
                  del inputs_dict["output_attentions"]
                  lowercase							:  str 			=   True
                  lowercase							:  Any 			=   model_class(snake_case   )
                  model.to(snake_case   )
                  model.eval()

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

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

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

                  lowercase							:  Optional[int] 			=   self._prepare_for_class(snake_case	,snake_case   )
                  lowercase							:  Optional[Any] 			=   inputs["""input_ids"""].shape[0]
                  with torch.no_grad():
                         lowercase							:  Union[str, Any] 			=   model(**snake_case   )

                  lowercase							:  Tuple 			=   1
                  self.assertEqual(out_len + added_hidden_states	,len(snake_case   )   )

                  lowercase							:  Optional[Any] 			=   outputs.attentions

                  self.assertEqual(len(snake_case   )	,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   ):



           '''simple docstring'''







           for model_name in RWKV_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  lowercase							:  Dict 			=   RwkvModel.from_pretrained(snake_case   )
                  self.assertIsNotNone(snake_case   )






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



           '''simple docstring'''







           lowercase							:  List[Any] 			=   """RWKV/rwkv-4-169m-pile"""
           lowercase							:  str 			=   AutoTokenizer.from_pretrained(self.model_id   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  List[Any] 			=   """Hello my name is Jasmine and I am a newbie to the"""
           lowercase							:  List[Any] 			=   RwkvForCausalLM.from_pretrained(self.model_id   ).to(snake_case   )

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

           self.assertEqual(snake_case	,snake_case   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Any 			=   """Hello my name is Jasmine and I am a newbie to the"""

           lowercase							:  Optional[Any] 			=   self.tokenizer("""Hello my name is"""	,return_tensors="""pt"""   ).input_ids.to(snake_case   )
           lowercase							:  Union[str, Any] 			=   RwkvForCausalLM.from_pretrained(self.model_id	,torch_dtype=torch.bfloataa   ).to(snake_case   )

           lowercase							:  Tuple 			=   model.generate(snake_case	,max_new_tokens=10   )
           lowercase							:  Optional[int] 			=   self.tokenizer.decode(output[0].tolist()   )

           self.assertEqual(snake_case	,snake_case   )
