


'''simple docstring'''





import argparse
import gc
import os
import sys
from pathlib import Path
from typing import List  # noqa: F401

import pytorch_lightning as pl
import torch
from finetune import SummarizationModule, TranslationModule
from finetune import main as ft_main
from make_student import create_student_by_copying_alternating_layers, get_layers_to_supervise
from torch import nn

from transformers import AutoModelForSeqaSeqLM, MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import calculate_bleu, check_output_dir, freeze_params, label_smoothed_nll_loss, use_task_specific_params


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import generic_train  # noqa




class 							UpperCAmelCase ( _snake_case	):
 UpperCAmelCase				=				["loss", "ce_loss", "mlm_loss", "hid_loss_enc", "hid_loss_dec"]


 def __init__(			self       :  Optional[int] ,					__lowerCamelCase       :  str   ):
    assert Path(hparams.data_dir   ).exists()
    UpperCAmelCase__  :List[str]            =			Path(hparams.output_dir   )
    self.output_dir.mkdir(exist_ok=__lowerCamelCase   )

    UpperCAmelCase__  :Optional[int]            =			self.output_dir.joinpath('''student'''   )

    UpperCAmelCase__  :Union[str, Any]            =			str(__lowerCamelCase   )  # Tell lightning we are training the student
    UpperCAmelCase__  :Optional[int]            =			AutoModelForSeqaSeqLM.from_pretrained(hparams.teacher   ).eval()
    use_task_specific_params(__lowerCamelCase ,					hparams.task   )  # We copy good generation parameters to student by default
    if hparams.student is not None:
       UpperCAmelCase__  :int            =			AutoModelForSeqaSeqLM.from_pretrained(hparams.student   )
       use_task_specific_params(__lowerCamelCase ,					hparams.task   )
       UpperCAmelCase__      ,	UpperCAmelCase__  :Optional[Any]            =			None, None
    else:
       UpperCAmelCase__      ,	UpperCAmelCase__      ,	UpperCAmelCase__  :List[str]            =			create_student_by_copying_alternating_layers(
           __lowerCamelCase ,					e=hparams.student_encoder_layers ,					d=hparams.student_decoder_layers ,					save_path=__lowerCamelCase   )

    if hparams.length_penalty != -1:
       UpperCAmelCase__  :int            =			hparams.length_penalty
    UpperCAmelCase__  :Optional[Any]            =			hparams.teacher  # Use teacher's tokenizer
    super().__init__(__lowerCamelCase ,					model=__lowerCamelCase ,					config=student.config   )
    assert student.config.model_type == teacher.config.model_type, (
        f'''teacher, student model types should be the same, got {student.config.model_type} !='''
        f''' {teacher.config.model_type}'''
    )

    if student.config.model_type == "t5":
       UpperCAmelCase__  :Dict            =			len(student.get_encoder().block   )
       UpperCAmelCase__  :int            =			len(student.get_decoder().block   )
       UpperCAmelCase__  :str            =			len(teacher.get_encoder().block   )
       UpperCAmelCase__  :int            =			len(teacher.get_decoder().block   )
    else:
       UpperCAmelCase__  :Optional[int]            =			student.config.encoder_layers
       UpperCAmelCase__  :List[str]            =			student.config.decoder_layers
       UpperCAmelCase__  :Optional[Any]            =			teacher.config.encoder_layers
       UpperCAmelCase__  :List[str]            =			teacher.config.decoder_layers

    UpperCAmelCase__  :Optional[Any]            =			not (hparams.student is None or hparams.teacher == hparams.student)
    UpperCAmelCase__  :Optional[Any]            =			(not self.different_base_models) and hparams.alpha_hid > 0
    UpperCAmelCase__  :Any            =			self.different_base_models or (student_encoder_layers != teacher_encoder_layers)
    # self.different_encoder determines whether we need to run the teacher encoder
    UpperCAmelCase__  :Dict            =			teacher
    freeze_params(self.teacher   )

    if not self.different_encoder:  # To save RAM, delete teacher encoder and freeze student encoder.
       try:
          del self.teacher.model.encoder
       except AttributeError:  # T5
          del self.teacher.encoder

    if e_layer_ids is None:
       UpperCAmelCase__  :Optional[Any]            =			list(range(__lowerCamelCase   )   )
    if d_layer_ids is None:
       UpperCAmelCase__  :int            =			list(range(__lowerCamelCase   )   )

    UpperCAmelCase__      ,	UpperCAmelCase__  :Any            =			e_layer_ids, d_layer_ids  # type: List[int], List[int]

    if self.do_calc_hidden_loss:  # Intermediate supervision: Decide which layers to supervise
       if hparams.supervise_forward:
          UpperCAmelCase__  :List[Any]            =			get_layers_to_supervise(
              n_student=len(self.e_layer_ids   ) ,					n_teacher=__lowerCamelCase   )
          UpperCAmelCase__  :Optional[int]            =			get_layers_to_supervise(
              n_student=len(self.d_layer_ids   ) ,					n_teacher=__lowerCamelCase   )
       else:  # student layer should emulate hidden states of the teacher layer it was copied from
          UpperCAmelCase__  :Union[str, Any]            =			self.e_layer_ids
          UpperCAmelCase__  :Tuple            =			self.d_layer_ids
    else:
       UpperCAmelCase__  :str            =			None
       UpperCAmelCase__  :str            =			None

    UpperCAmelCase__  :Dict            =			nn.KLDivLoss(reduction='''batchmean'''   )
    UpperCAmelCase__  :List[str]            =			2.0
    UpperCAmelCase__  :Optional[Any]            =			hparams.alpha_mlm
    UpperCAmelCase__  :Optional[int]            =			hparams.alpha_ce
    UpperCAmelCase__  :Optional[Any]            =			hparams.alpha_hid
    gc.collect()
    torch.cuda.empty_cache()


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Optional[Any] ,					__lowerCamelCase       :  Optional[Any] ,					__lowerCamelCase       :  List[str] ,					__lowerCamelCase       :  Tuple   ):
    UpperCAmelCase__  :Optional[Any]            =			mask[:, :, None].expand_as(__lowerCamelCase   )
    UpperCAmelCase__  :Optional[Any]            =			s_logits.size(-1   )
    UpperCAmelCase__  :List[str]            =			torch.masked_select(__lowerCamelCase ,					__lowerCamelCase   )  # (bs * seq_length * voc_size) modulo the 1s in mask
    UpperCAmelCase__  :str            =			torch.masked_select(__lowerCamelCase ,					__lowerCamelCase   )  # (bs * seq_length * voc_size) modulo the 1s in mask
    UpperCAmelCase__  :Optional[Any]            =			s_logits_slct.view(-1 ,					__lowerCamelCase   )  # (bs * seq_length, voc_size) modulo the 1s in mask
    UpperCAmelCase__  :Tuple            =			t_logits_slct.view(-1 ,					__lowerCamelCase   )  # (bs * seq_length, voc_size) modulo the 1s in mask
    assert t_logits_slct.size() == s_logits_slct.size()
    UpperCAmelCase__  :Any            =			(
        self.ce_loss_fct(
            nn.functional.log_softmax(s_logits_slct / self.temperature ,					dim=-1   ) ,					nn.functional.softmax(t_logits_slct / self.temperature ,					dim=-1   ) ,					)
        * (self.temperature) ** 2
    )
    return loss_ce


 @staticmethod
 def 					__SCREAMING_SNAKE_CASE				(			__lowerCamelCase       :  List[str] ,					__lowerCamelCase       :  Optional[int]   ):
    SummarizationModule.add_model_specific_args(__lowerCamelCase ,					__lowerCamelCase   )
    add_distill_args(__lowerCamelCase   )
    return parser


 def 					__SCREAMING_SNAKE_CASE				(			self       :  str ,					__lowerCamelCase       :  dict   ):
    UpperCAmelCase__  :Union[str, Any]            =			self.tokenizer.pad_token_id
    UpperCAmelCase__      ,	UpperCAmelCase__      ,	UpperCAmelCase__  :List[Any]            =			batch['''input_ids'''], batch['''attention_mask'''], batch['''labels''']
    if isinstance(self.model ,					__lowerCamelCase   ):
       UpperCAmelCase__  :Dict            =			self.model._shift_right(__lowerCamelCase   )
    else:
       UpperCAmelCase__  :int            =			shift_tokens_right(__lowerCamelCase ,					__lowerCamelCase   )

    # noinspection PyCallingNonCallable
    UpperCAmelCase__  :Dict            =			self(
        __lowerCamelCase ,					attention_mask=__lowerCamelCase ,					decoder_input_ids=__lowerCamelCase ,					output_hidden_states=self.do_calc_hidden_loss ,					output_attentions=__lowerCamelCase ,					use_cache=__lowerCamelCase ,					)
    UpperCAmelCase__  :Dict            =			student_outputs['''logits''']

    # Same cross entropy vs. label smoothing logic as finetune.py
    assert lm_logits.shape[-1] == self.model.config.vocab_size
    if self.hparams.label_smoothing == 0:
       # Same behavior as modeling_bart.py, besides ignoring pad_token_id
       UpperCAmelCase__  :int            =			nn.CrossEntropyLoss(ignore_index=__lowerCamelCase   )
       UpperCAmelCase__  :Optional[Any]            =			loss_fct(lm_logits.view(-1 ,					lm_logits.shape[-1]   ) ,					labels.view(-1   )   )
    else:
       UpperCAmelCase__  :str            =			nn.functional.log_softmax(__lowerCamelCase ,					dim=-1   )
       UpperCAmelCase__      ,	UpperCAmelCase__  :Tuple            =			label_smoothed_nll_loss(
           __lowerCamelCase ,					__lowerCamelCase ,					self.hparams.label_smoothing ,					ignore_index=__lowerCamelCase   )

    def zero_tensor():
       return torch.tensor(0.0   ).type_as(__lowerCamelCase   )

    UpperCAmelCase__  :Optional[Any]            =			student_outputs[
        '''encoder_last_hidden_state'''
    ]  # use this unless self.different_base_models
    UpperCAmelCase__      ,	UpperCAmelCase__  :Tuple            =			zero_tensor(), zero_tensor()
    if self.different_encoder:  # compute encoder hidden state loss
       UpperCAmelCase__  :List[str]            =			self.teacher.get_encoder()(
           __lowerCamelCase ,					attention_mask=__lowerCamelCase ,					output_hidden_states=self.do_calc_hidden_loss ,					)
       if self.different_base_models:
          UpperCAmelCase__  :Optional[int]            =			all_teacher_encoder_outputs['''last_hidden_state''']
       elif self.do_calc_hidden_loss:
          UpperCAmelCase__  :Tuple            =			self.calc_hidden_loss(
              __lowerCamelCase ,					student_outputs['''encoder_hidden_states'''] ,					all_teacher_encoder_outputs['''hidden_states'''] ,					self.e_matches ,					normalize_hidden=self.hparams.normalize_hidden ,					)

    UpperCAmelCase__  :int            =			self.teacher(
        __lowerCamelCase ,					attention_mask=__lowerCamelCase ,					encoder_outputs=(teacher_enc_outputs,) ,					decoder_input_ids=__lowerCamelCase ,					output_hidden_states=self.do_calc_hidden_loss ,					use_cache=__lowerCamelCase ,					)
    UpperCAmelCase__  :Optional[int]            =			decoder_input_ids.ne(__lowerCamelCase   )
    UpperCAmelCase__  :int            =			self.calc_ce_loss(__lowerCamelCase ,					__lowerCamelCase ,					teacher_outputs['''logits''']   )
    if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
       UpperCAmelCase__  :str            =			self.calc_hidden_loss(
           __lowerCamelCase ,					student_outputs['''decoder_hidden_states'''] ,					teacher_outputs['''decoder_hidden_states'''] ,					self.d_matches ,					normalize_hidden=self.hparams.normalize_hidden ,					)

    UpperCAmelCase__  :str            =			(
        self.alpha_ce * loss_ce
        + self.alpha_mlm * student_lm_loss
        + self.hparams.alpha_hid * (hid_loss_enc + hid_loss_dec)
    )
    return blended_loss, loss_ce, student_lm_loss, hid_loss_enc, hid_loss_dec





 @staticmethod
 def 					__SCREAMING_SNAKE_CASE				(			__lowerCamelCase       :  Any ,					__lowerCamelCase       :  Any ,					__lowerCamelCase       :  Union[str, Any] ,					__lowerCamelCase       :  Tuple ,					__lowerCamelCase       :  Tuple   ):
    UpperCAmelCase__  :Any            =			'''expected list or tuple for hidden_states, got tensor of shape: '''
    assert not isinstance(__lowerCamelCase ,					torch.Tensor   ), f'''{msg}{hidden_states.shape}'''
    assert not isinstance(__lowerCamelCase ,					torch.Tensor   ), f'''{msg}{hidden_states_T.shape}'''
    UpperCAmelCase__  :Any            =			attention_mask.to(hidden_states[0]   )
    UpperCAmelCase__  :int            =			mask.sum() * hidden_states[0].size(-1   )
    UpperCAmelCase__  :Union[str, Any]            =			torch.stack([hidden_states[i] for i in range(len(__lowerCamelCase   )   )]   )
    UpperCAmelCase__  :Optional[Any]            =			torch.stack([hidden_states_T[j] for j in matches]   )
    assert student_states.shape == teacher_states.shape, f'''{student_states.shape} != {teacher_states.shape}'''
    if normalize_hidden:
       UpperCAmelCase__  :Union[str, Any]            =			nn.functional.layer_norm(__lowerCamelCase ,					student_states.shape[1:]   )
       UpperCAmelCase__  :Dict            =			nn.functional.layer_norm(__lowerCamelCase ,					teacher_states.shape[1:]   )
    UpperCAmelCase__  :Optional[Any]            =			nn.functional.mse_loss(__lowerCamelCase ,					__lowerCamelCase ,					reduction='''none'''   )
    UpperCAmelCase__  :Optional[int]            =			(mse * mask.unsqueeze(0   ).unsqueeze(-1   )).sum() / valid_count
    return masked_mse


def 					a__    (				UpperCamelCase_	:			Any	):
   # NOTE: if --student argument was specified and the teacher and student base models
   # are different, the models still have to have the same tokenizer, specified by
   # --tokenizer_name. So, for example, you can distill from t5_large to t5_small but not
   # from bart to t5. This s because if the tokenizers are different, the output space
   # for the two models is also different and their logits are not comparable.
   parser.add_argument('''--teacher''',      type=UpperCamelCase_	)
   parser.add_argument('''--alpha_ce''',      default=0.8,      type=UpperCamelCase_	)
   parser.add_argument('''--alpha_mlm''',      default=0.2,      type=UpperCamelCase_	)
   parser.add_argument('''--alpha_hid''',      default=0.0,      type=UpperCamelCase_,      required=UpperCamelCase_	)
   parser.add_argument('''--student''',      type=UpperCamelCase_,      required=UpperCamelCase_	)
   parser.add_argument('''--student_decoder_layers''',      default=12,      type=UpperCamelCase_,      required=UpperCamelCase_	)
   parser.add_argument('''--student_encoder_layers''',      default=12,      type=UpperCamelCase_,      required=UpperCamelCase_	)
   parser.add_argument('''--no_teacher''',      action='''store_true''',      default=UpperCamelCase_	)
   parser.add_argument('''--length_penalty''',      type=UpperCamelCase_,      default=-1	)
   parser.add_argument('''--supervise_forward''',      action='''store_true''',      default=UpperCamelCase_	)
   parser.add_argument('''--normalize_hidden''',      action='''store_true''',      default=UpperCamelCase_	)




class 							UpperCAmelCase ( _snake_case	):
 UpperCAmelCase				=				"translation"
 UpperCAmelCase				=				["bleu"]
 UpperCAmelCase				=				"bleu"


 def __init__(			self       :  Dict ,					__lowerCamelCase       :  List[Any] ,					**__lowerCamelCase       :  List[Any]   ):
    super().__init__(__lowerCamelCase ,					**__lowerCamelCase   )
    assert hparams.src_lang is not None
    assert hparams.tgt_lang is not None
    UpperCAmelCase__  :List[str]            =			hparams.src_lang
    UpperCAmelCase__  :Optional[Any]            =			hparams.tgt_lang
    if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer ,					__lowerCamelCase   ):
       UpperCAmelCase__  :List[str]            =			self.tokenizer.lang_code_to_id[hparams.tgt_lang]


 def 					__SCREAMING_SNAKE_CASE				(			self       :  str ,					__lowerCamelCase       :  Optional[Any] ,					__lowerCamelCase       :  Optional[Any]   ):
    return calculate_bleu(__lowerCamelCase ,					__lowerCamelCase   )





 @staticmethod
 def 					__SCREAMING_SNAKE_CASE				(			__lowerCamelCase       :  Any ,					__lowerCamelCase       :  Optional[Any]   ):
    TranslationModule.add_model_specific_args(__lowerCamelCase ,					__lowerCamelCase   )
    add_distill_args(__lowerCamelCase   )
    return parser


def 					a__    (				UpperCamelCase_	:			List[str]	):
   if args.no_teacher:
      UpperCAmelCase__  :Dict            =			TranslationModule if '''translation''' in args.task else SummarizationModule
   else:  # DISTILL WITH TEACHER
      UpperCAmelCase__  :Tuple            =			TranslationDistiller if '''translation''' in args.task else SummarizationDistiller
   UpperCAmelCase__  :str            =			module_cls.__name__
   print(F'''using module {args.setup_cls}'''	)
   UpperCAmelCase__  :List[str]            =			module_cls(UpperCamelCase_	)
   return model


def 					a__    (				UpperCamelCase_	:			Optional[int]	):
   Path(args.output_dir	).mkdir(exist_ok=UpperCamelCase_	)
   check_output_dir(UpperCamelCase_,      expected_items=3	)

   UpperCAmelCase__  :Dict            =			create_module(UpperCamelCase_	)
   return ft_main(UpperCamelCase_,      model=UpperCamelCase_	)


if __name__ == "__main__":
    __lowerCamelCase       				=	argparse.ArgumentParser()
    __lowerCamelCase       				=	pl.Trainer.add_argparse_args(parser)
    __lowerCamelCase       				=	SummarizationDistiller.add_model_specific_args(parser, os.getcwd())
    __lowerCamelCase       				=	parser.parse_args()

    distill_main(args)







'''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   )



