


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       _lowercase			(							lowercase__):


       """simple docstring"""




       A__ 						=      ["loss", "ce_loss", "mlm_loss", "hid_loss_enc", "hid_loss_dec"]





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


             '''simple docstring'''



             assert Path(hparams.data_dir	).exists()
             lowerCamelCase__		:    int   =  Path(hparams.output_dir	)
             self.output_dir.mkdir(exist_ok=__lowerCamelCase	)

             lowerCamelCase__		:    List[str]   =  self.output_dir.joinpath("student"	)

             lowerCamelCase__		:    List[str]   =  str(__lowerCamelCase	)  # Tell lightning we are training the student
             lowerCamelCase__		:    Dict   =  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:
                   lowerCamelCase__		:    Optional[int]   =  AutoModelForSeqaSeqLM.from_pretrained(hparams.student	)
                   use_task_specific_params(__lowerCamelCase ,				hparams.task	)
                   lowerCamelCase__   ,       lowerCamelCase__		:    int   =  None, None
             else:
                   lowerCamelCase__   ,       lowerCamelCase__   ,       lowerCamelCase__		:    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:
                   lowerCamelCase__		:    List[Any]   =  hparams.length_penalty
             lowerCamelCase__		:    Dict   =  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":
                   lowerCamelCase__		:    Optional[Any]   =  len(student.get_encoder().block	)
                   lowerCamelCase__		:    Tuple   =  len(student.get_decoder().block	)
                   lowerCamelCase__		:    Optional[int]   =  len(teacher.get_encoder().block	)
                   lowerCamelCase__		:    Dict   =  len(teacher.get_decoder().block	)
             else:
                   lowerCamelCase__		:    int   =  student.config.encoder_layers
                   lowerCamelCase__		:    str   =  student.config.decoder_layers
                   lowerCamelCase__		:    List[str]   =  teacher.config.encoder_layers
                   lowerCamelCase__		:    List[Any]   =  teacher.config.decoder_layers

             lowerCamelCase__		:    Optional[Any]   =  not (hparams.student is None or hparams.teacher == hparams.student)
             lowerCamelCase__		:    List[str]   =  (not self.different_base_models) and hparams.alpha_hid > 0
             lowerCamelCase__		:    Union[str, Any]   =  self.different_base_models or (student_encoder_layers != teacher_encoder_layers)
             # self.different_encoder determines whether we need to run the teacher encoder
             lowerCamelCase__		:    Tuple   =  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:
                   lowerCamelCase__		:    List[str]   =  list(range(__lowerCamelCase	)	)
             if d_layer_ids is None:
                   lowerCamelCase__		:    str   =  list(range(__lowerCamelCase	)	)

             lowerCamelCase__   ,       lowerCamelCase__		:    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:
                         lowerCamelCase__		:    Union[str, Any]   =  get_layers_to_supervise(
                             n_student=len(self.e_layer_ids	) ,				n_teacher=__lowerCamelCase	)
                         lowerCamelCase__		:    Dict   =  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
                         lowerCamelCase__		:    int   =  self.e_layer_ids
                         lowerCamelCase__		:    List[str]   =  self.d_layer_ids
             else:
                   lowerCamelCase__		:    List[Any]   =  None
                   lowerCamelCase__		:    Tuple   =  None

             lowerCamelCase__		:    str   =  nn.KLDivLoss(reduction="batchmean"	)
             lowerCamelCase__		:    Any   =  2.0
             lowerCamelCase__		:    List[str]   =  hparams.alpha_mlm
             lowerCamelCase__		:    Any   =  hparams.alpha_ce
             lowerCamelCase__		:    Any   =  hparams.alpha_hid
             gc.collect()
             torch.cuda.empty_cache()





       def 						lowerCAmelCase					(       self							:      List[Any] ,				__lowerCamelCase							:      List[str] ,				__lowerCamelCase							:      Optional[int] ,				__lowerCamelCase							:      Dict	):


             '''simple docstring'''



             lowerCamelCase__		:    List[Any]   =  mask[:, :, None].expand_as(__lowerCamelCase	)
             lowerCamelCase__		:    Tuple   =  s_logits.size(-1	)
             lowerCamelCase__		:    Optional[Any]   =  torch.masked_select(__lowerCamelCase ,				__lowerCamelCase	)  # (bs * seq_length * voc_size) modulo the 1s in mask
             lowerCamelCase__		:    Any   =  torch.masked_select(__lowerCamelCase ,				__lowerCamelCase	)  # (bs * seq_length * voc_size) modulo the 1s in mask
             lowerCamelCase__		:    List[Any]   =  s_logits_slct.view(-1 ,				__lowerCamelCase	)  # (bs * seq_length, voc_size) modulo the 1s in mask
             lowerCamelCase__		:    Any   =  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()
             lowerCamelCase__		:    int   =  (
                 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 						lowerCAmelCase					(       __lowerCamelCase							:      List[Any] ,				__lowerCamelCase							:      List[str]	):


             '''simple docstring'''



             SummarizationModule.add_model_specific_args(__lowerCamelCase ,				__lowerCamelCase	)
             add_distill_args(__lowerCamelCase	)
             return parser





       def 						lowerCAmelCase					(       self							:      List[Any] ,				__lowerCamelCase							:      dict	):


             '''simple docstring'''



             lowerCamelCase__		:    List[Any]   =  self.tokenizer.pad_token_id
             lowerCamelCase__   ,       lowerCamelCase__   ,       lowerCamelCase__		:    str   =  batch["input_ids"], batch["attention_mask"], batch["labels"]
             if isinstance(self.model ,				__lowerCamelCase	):
                   lowerCamelCase__		:    Optional[Any]   =  self.model._shift_right(__lowerCamelCase	)
             else:
                   lowerCamelCase__		:    Optional[Any]   =  shift_tokens_right(__lowerCamelCase ,				__lowerCamelCase	)

             # noinspection PyCallingNonCallable
             lowerCamelCase__		:    Union[str, Any]   =  self(
                 __lowerCamelCase ,				attention_mask=__lowerCamelCase ,				decoder_input_ids=__lowerCamelCase ,				output_hidden_states=self.do_calc_hidden_loss ,				output_attentions=__lowerCamelCase ,				use_cache=__lowerCamelCase ,				)
             lowerCamelCase__		:    List[str]   =  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
                   lowerCamelCase__		:    List[str]   =  nn.CrossEntropyLoss(ignore_index=__lowerCamelCase	)
                   lowerCamelCase__		:    Optional[int]   =  loss_fct(lm_logits.view(-1 ,				lm_logits.shape[-1]	) ,				labels.view(-1	)	)
             else:
                   lowerCamelCase__		:    str   =  nn.functional.log_softmax(__lowerCamelCase ,				dim=-1	)
                   lowerCamelCase__   ,       lowerCamelCase__		:    int   =  label_smoothed_nll_loss(
                       __lowerCamelCase ,				__lowerCamelCase ,				self.hparams.label_smoothing ,				ignore_index=__lowerCamelCase	)

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

             lowerCamelCase__		:    List[Any]   =  student_outputs[
                 "encoder_last_hidden_state"
             ]  # use this unless self.different_base_models
             lowerCamelCase__   ,       lowerCamelCase__		:    Optional[int]   =  zero_tensor(), zero_tensor()
             if self.different_encoder:  # compute encoder hidden state loss
                   lowerCamelCase__		:    Optional[int]   =  self.teacher.get_encoder()(
                       __lowerCamelCase ,				attention_mask=__lowerCamelCase ,				output_hidden_states=self.do_calc_hidden_loss ,				)
                   if self.different_base_models:
                         lowerCamelCase__		:    List[str]   =  all_teacher_encoder_outputs["last_hidden_state"]
                   elif self.do_calc_hidden_loss:
                         lowerCamelCase__		:    Union[str, Any]   =  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 ,				)

             lowerCamelCase__		:    Union[str, Any]   =  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 ,				)
             lowerCamelCase__		:    List[Any]   =  decoder_input_ids.ne(__lowerCamelCase	)
             lowerCamelCase__		:    Union[str, Any]   =  self.calc_ce_loss(__lowerCamelCase ,				__lowerCamelCase ,				teacher_outputs["logits"]	)
             if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
                   lowerCamelCase__		:    int   =  self.calc_hidden_loss(
                       __lowerCamelCase ,				student_outputs["decoder_hidden_states"] ,				teacher_outputs["decoder_hidden_states"] ,				self.d_matches ,				normalize_hidden=self.hparams.normalize_hidden ,				)

             lowerCamelCase__		:    Any   =  (
                 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 						lowerCAmelCase					(       __lowerCamelCase							:      Dict ,				__lowerCamelCase							:      str ,				__lowerCamelCase							:      Any ,				__lowerCamelCase							:      Optional[Any] ,				__lowerCamelCase							:      Union[str, Any]	):


             '''simple docstring'''



             lowerCamelCase__		:    Union[str, 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}"
             lowerCamelCase__		:    List[str]   =  attention_mask.to(hidden_states[0]	)
             lowerCamelCase__		:    Optional[int]   =  mask.sum() * hidden_states[0].size(-1	)
             lowerCamelCase__		:    List[str]   =  torch.stack([hidden_states[i] for i in range(len(__lowerCamelCase	)	)]	)
             lowerCamelCase__		:    Tuple   =  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:
                   lowerCamelCase__		:    Any   =  nn.functional.layer_norm(__lowerCamelCase ,				student_states.shape[1:]	)
                   lowerCamelCase__		:    Dict   =  nn.functional.layer_norm(__lowerCamelCase ,				teacher_states.shape[1:]	)
             lowerCamelCase__		:    Union[str, Any]   =  nn.functional.mse_loss(__lowerCamelCase ,				__lowerCamelCase ,				reduction="none"	)
             lowerCamelCase__		:    Dict   =  (mse * mask.unsqueeze(0	).unsqueeze(-1	)).sum() / valid_count
             return masked_mse



def 				lowercase_		(		_A      :   Tuple						):

      """simple docstring"""




      parser.add_argument("--teacher"   , type=_A						)
      parser.add_argument("--alpha_ce"   , default=0.8   , type=_A						)
      parser.add_argument("--alpha_mlm"   , default=0.2   , type=_A						)
      parser.add_argument("--alpha_hid"   , default=0.0   , type=_A   , required=_A						)
      parser.add_argument("--student"   , type=_A   , required=_A						)
      parser.add_argument("--student_decoder_layers"   , default=12   , type=_A   , required=_A						)
      parser.add_argument("--student_encoder_layers"   , default=12   , type=_A   , required=_A						)
      parser.add_argument("--no_teacher"   , action="store_true"   , default=_A						)
      parser.add_argument("--length_penalty"   , type=_A   , default=-1						)
      parser.add_argument("--supervise_forward"   , action="store_true"   , default=_A						)
      parser.add_argument("--normalize_hidden"   , action="store_true"   , default=_A						)







class       _lowercase			(							lowercase__):


       """simple docstring"""




       A__ 						=      "translation"
       A__ 						=      ["bleu"]
       A__ 						=      "bleu"





       def __init__(       self							:      Union[str, Any] ,				__lowerCamelCase							:      List[Any] ,				**__lowerCamelCase							:      Tuple	):


             '''simple docstring'''



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





       def 						lowerCAmelCase					(       self							:      Tuple ,				__lowerCamelCase							:      Dict ,				__lowerCamelCase							:      str	):


             '''simple docstring'''



             return calculate_bleu(__lowerCamelCase ,				__lowerCamelCase	)





       @staticmethod
       def 						lowerCAmelCase					(       __lowerCamelCase							:      Dict ,				__lowerCamelCase							:      Optional[Any]	):


             '''simple docstring'''



             TranslationModule.add_model_specific_args(__lowerCamelCase ,				__lowerCamelCase	)
             add_distill_args(__lowerCamelCase	)
             return parser



def 				lowercase_		(		_A      :   Any						):

      """simple docstring"""




      if args.no_teacher:
            lowerCamelCase__		:    int   =  TranslationModule if "translation" in args.task else SummarizationModule
      else:  # DISTILL WITH TEACHER
            lowerCamelCase__		:    Optional[Any]   =  TranslationDistiller if "translation" in args.task else SummarizationDistiller
      lowerCamelCase__		:    str   =  module_cls.__name__
      print(F"using module {args.setup_cls}"						)
      lowerCamelCase__		:    int   =  module_cls(_A						)
      return model

def 				lowercase_		(		_A      :   Dict						):

      """simple docstring"""




      Path(args.output_dir						).mkdir(exist_ok=_A						)
      check_output_dir(_A   , expected_items=3						)

      lowerCamelCase__		:    int   =  create_module(_A						)
      return ft_main(_A   , model=_A						)


if __name__ == "__main__":
    A				:   Optional[int]		= argparse.ArgumentParser()
    A				:   Optional[int]		= pl.Trainer.add_argparse_args(parser)
    A				:   Any		= SummarizationDistiller.add_model_specific_args(parser, os.getcwd())
    A				:   str		= parser.parse_args()

    distill_main(args)






import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
    import os

    # The slow tests are often failing with OOM error on GPU
    # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
    # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
    A				:   Optional[Any]		= "platform"

    import jax
    import jax.numpy as jnp

    from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer

def 				lowercase_		(		_A      :   Optional[Any]   , _A      :   str   , _A      :   Union[str, Any]=None   , _A      :   Optional[int]=None						):

      """simple docstring"""




      if attention_mask is None:
            lowerCamelCase__		:    Any   =  np.where(input_ids != config.pad_token_id   , 1   , 0						)
      return {
          "input_ids": input_ids,
          "attention_mask": attention_mask,
      }







@require_flax
class       _lowercase			:


       """simple docstring"""




       def __init__(       self							:      Dict ,				__lowerCamelCase							:      Dict ,				__lowerCamelCase							:      List[str]=13 ,				__lowerCamelCase							:      List[str]=7 ,				__lowerCamelCase							:      Dict=True ,				__lowerCamelCase							:      Optional[int]=False ,				__lowerCamelCase							:      str=99 ,				__lowerCamelCase							:      str=16 ,				__lowerCamelCase							:      List[Any]=2 ,				__lowerCamelCase							:      Optional[int]=4 ,				__lowerCamelCase							:      Tuple=4 ,				__lowerCamelCase							:      str="gelu" ,				__lowerCamelCase							:      List[str]=0.1 ,				__lowerCamelCase							:      str=0.1 ,				__lowerCamelCase							:      List[str]=20 ,				__lowerCamelCase							:      Optional[Any]=2 ,				__lowerCamelCase							:      Optional[Any]=1 ,				__lowerCamelCase							:      int=0 ,				__lowerCamelCase							:      str=16 ,				__lowerCamelCase							:      str=16 ,				__lowerCamelCase							:      int=0.0_2 ,				):


             '''simple docstring'''



             lowerCamelCase__		:    Any   =  parent
             lowerCamelCase__		:    Any   =  batch_size
             lowerCamelCase__		:    Dict   =  seq_length
             lowerCamelCase__		:    Optional[int]   =  is_training
             lowerCamelCase__		:    Any   =  use_labels
             lowerCamelCase__		:    List[Any]   =  vocab_size
             lowerCamelCase__		:    List[Any]   =  hidden_size
             lowerCamelCase__		:    Any   =  num_hidden_layers
             lowerCamelCase__		:    Any   =  num_attention_heads
             lowerCamelCase__		:    str   =  intermediate_size
             lowerCamelCase__		:    Optional[Any]   =  hidden_act
             lowerCamelCase__		:    int   =  hidden_dropout_prob
             lowerCamelCase__		:    Any   =  attention_probs_dropout_prob
             lowerCamelCase__		:    Any   =  max_position_embeddings
             lowerCamelCase__		:    List[Any]   =  eos_token_id
             lowerCamelCase__		:    Union[str, Any]   =  pad_token_id
             lowerCamelCase__		:    Optional[int]   =  bos_token_id
             lowerCamelCase__		:    Optional[Any]   =  embed_dim
             lowerCamelCase__		:    Dict   =  word_embed_proj_dim
             lowerCamelCase__		:    Tuple   =  initializer_range
             lowerCamelCase__		:    List[Any]   =  False





       def 						lowerCAmelCase					(       self							:      int	):


             '''simple docstring'''



             lowerCamelCase__		:    Optional[int]   =  np.clip(ids_tensor([self.batch_size, self.seq_length - 1] ,				self.vocab_size	) ,				3 ,				self.vocab_size	)
             lowerCamelCase__		:    Union[str, Any]   =  np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) ,				dtype=np.intaa	)) ,				-1	)

             lowerCamelCase__		:    Tuple   =  OPTConfig(
                 vocab_size=self.vocab_size ,				hidden_size=self.hidden_size ,				num_hidden_layers=self.num_hidden_layers ,				num_attention_heads=self.num_attention_heads ,				ffn_dim=self.intermediate_size ,				dropout=self.hidden_dropout_prob ,				attention_dropout=self.attention_probs_dropout_prob ,				max_position_embeddings=self.max_position_embeddings ,				eos_token_id=self.eos_token_id ,				bos_token_id=self.bos_token_id ,				pad_token_id=self.pad_token_id ,				embed_dim=self.embed_dim ,				is_encoder_decoder=__lowerCamelCase ,				word_embed_proj_dim=self.word_embed_proj_dim ,				initializer_range=self.initializer_range ,				use_cache=__lowerCamelCase ,				)
             lowerCamelCase__		:    str   =  prepare_opt_inputs_dict(__lowerCamelCase ,				__lowerCamelCase	)
             return config, inputs_dict





       def 						lowerCAmelCase					(       self							:      Optional[int]	):


             '''simple docstring'''



             lowerCamelCase__   ,       lowerCamelCase__		:    Union[str, Any]   =  self.prepare_config_and_inputs()
             return config, inputs_dict





       def 						lowerCAmelCase					(       self							:      Optional[int] ,				__lowerCamelCase							:      int ,				__lowerCamelCase							:      List[Any] ,				__lowerCamelCase							:      Dict	):


             '''simple docstring'''



             lowerCamelCase__		:    List[Any]   =  20
             lowerCamelCase__		:    str   =  model_class_name(__lowerCamelCase	)

             lowerCamelCase__		:    int   =  inputs_dict["input_ids"]
             lowerCamelCase__		:    Dict   =  inputs_dict["attention_mask"]

             lowerCamelCase__		:    int   =  model.init_cache(input_ids.shape[0] ,				__lowerCamelCase	)
             lowerCamelCase__		:    List[Any]   =  jnp.ones((input_ids.shape[0], max_length) ,				dtype="i4"	)

             lowerCamelCase__		:    Optional[Any]   =  jnp.broadcast_to(
                 jnp.arange(input_ids.shape[-1] - 1	)[None, :] ,				(input_ids.shape[0], input_ids.shape[-1] - 1) ,				)
             lowerCamelCase__		:    Optional[int]   =  model(
                 input_ids[:, :-1] ,				attention_mask=__lowerCamelCase ,				past_key_values=__lowerCamelCase ,				position_ids=__lowerCamelCase ,				)

             lowerCamelCase__		:    Any   =  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,				dtype="i4"	)
             lowerCamelCase__		:    Optional[Any]   =  model(
                 input_ids[:, -1:] ,				attention_mask=__lowerCamelCase ,				past_key_values=outputs_cache.past_key_values ,				position_ids=__lowerCamelCase ,				)

             lowerCamelCase__		:    Union[str, Any]   =  model(__lowerCamelCase	)

             lowerCamelCase__		:    List[str]   =  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
             self.parent.assertTrue(diff < 1E-3 ,				msg=f"Max diff is {diff}"	)





       def 						lowerCAmelCase					(       self							:      List[str] ,				__lowerCamelCase							:      List[Any] ,				__lowerCamelCase							:      Optional[Any] ,				__lowerCamelCase							:      Optional[Any]	):


             '''simple docstring'''



             lowerCamelCase__		:    Union[str, Any]   =  20
             lowerCamelCase__		:    Dict   =  model_class_name(__lowerCamelCase	)

             lowerCamelCase__   ,       lowerCamelCase__		:    List[str]   =  (
                 inputs_dict["input_ids"],
                 inputs_dict["attention_mask"],
             )

             lowerCamelCase__		:    Tuple   =  jnp.concatenate(
                 [
                     attention_mask,
                     jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])	),
                 ] ,				axis=-1 ,				)

             lowerCamelCase__		:    List[Any]   =  model.init_cache(input_ids.shape[0] ,				__lowerCamelCase	)
             lowerCamelCase__		:    Dict   =  jnp.broadcast_to(
                 jnp.arange(input_ids.shape[-1] - 1	)[None, :] ,				(input_ids.shape[0], input_ids.shape[-1] - 1) ,				)

             lowerCamelCase__		:    List[Any]   =  model(
                 input_ids[:, :-1] ,				attention_mask=__lowerCamelCase ,				past_key_values=__lowerCamelCase ,				position_ids=__lowerCamelCase ,				)
             lowerCamelCase__		:    Dict   =  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,				dtype="i4"	)
             lowerCamelCase__		:    Optional[Any]   =  model(
                 input_ids[:, -1:] ,				past_key_values=outputs_cache.past_key_values ,				attention_mask=__lowerCamelCase ,				position_ids=__lowerCamelCase ,				)

             lowerCamelCase__		:    int   =  model(__lowerCamelCase ,				attention_mask=__lowerCamelCase	)

             lowerCamelCase__		:    Dict   =  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
             self.parent.assertTrue(diff < 1E-3 ,				msg=f"Max diff is {diff}"	)







@require_flax
class       _lowercase			(							lowercase__							,  unittest.TestCase							,  lowercase__):


       """simple docstring"""




       A__ 						=      (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
       A__ 						=      () if is_flax_available() else ()





       def 						lowerCAmelCase					(       self							:      int	):


             '''simple docstring'''



             lowerCamelCase__		:    int   =  FlaxOPTModelTester(self	)





       def 						lowerCAmelCase					(       self							:      Any	):


             '''simple docstring'''



             lowerCamelCase__   ,       lowerCamelCase__		:    Union[str, Any]   =  self.model_tester.prepare_config_and_inputs()
             for model_class in self.all_model_classes:
                   self.model_tester.check_use_cache_forward(__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase	)





       def 						lowerCAmelCase					(       self							:      str	):


             '''simple docstring'''



             lowerCamelCase__   ,       lowerCamelCase__		:    Tuple   =  self.model_tester.prepare_config_and_inputs()
             for model_class in self.all_model_classes:
                   self.model_tester.check_use_cache_forward_with_attn_mask(__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase	)





       @slow
       def 						lowerCAmelCase					(       self							:      str	):


             '''simple docstring'''



             for model_class_name in self.all_model_classes:
                   lowerCamelCase__		:    Tuple   =  model_class_name.from_pretrained("facebook/opt-125m"	)
                   lowerCamelCase__		:    Dict   =  np.ones((1, 1)	) * model.config.eos_token_id
                   lowerCamelCase__		:    int   =  model(__lowerCamelCase	)
                   self.assertIsNotNone(__lowerCamelCase	)







@require_sentencepiece
@require_flax
class       _lowercase			(							unittest.TestCase):


       """simple docstring"""




       @slow
       def 						lowerCAmelCase					(       self							:      Tuple	):


             '''simple docstring'''



             lowerCamelCase__		:    Optional[int]   =  FlaxOPTModel.from_pretrained("facebook/opt-350m"	)
             lowerCamelCase__		:    Optional[int]   =  jnp.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]	)
             lowerCamelCase__		:    int   =  model(input_ids=__lowerCamelCase	).last_hidden_state
             lowerCamelCase__		:    List[Any]   =  (1, 11, 512)
             self.assertEqual(output.shape ,				__lowerCamelCase	)
             lowerCamelCase__		:    Optional[Any]   =  jnp.array(
                 [[-0.2_8_6_7, -1.9_2_5_6, -0.3_0_6_2], [-1.2_7_1_1, -0.1_3_3_7, -0.1_8_9_7], [0.4_1_0_9, 0.1_1_8_7, -1.3_1_4_2]]	)
             self.assertTrue(jnp.allclose(output[:, :3, :3] ,				__lowerCamelCase ,				atol=4E-2	)	)







@require_flax
@slow
class       _lowercase			(							unittest.TestCase):


       """simple docstring"""




       def 						lowerCAmelCase					(       self							:      int	):


             '''simple docstring'''



             super().setUp()
             lowerCamelCase__		:    List[Any]   =  "facebook/opt-350m"





       def 						lowerCAmelCase					(       self							:      Optional[Any]	):


             '''simple docstring'''



             lowerCamelCase__		:    str   =  FlaxOPTForCausalLM.from_pretrained(self.path_model	)
             lowerCamelCase__		:    Optional[Any]   =  GPTaTokenizer.from_pretrained(self.path_model	)

             lowerCamelCase__		:    Tuple   =  [
                 "Today is a beautiful day and I want to",
                 "In the city of",
                 "Paris is the capital of France and",
                 "Computers and mobile phones have taken",
             ]
             # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
             lowerCamelCase__		:    int   =  tokenizer(__lowerCamelCase ,				return_tensors="jax" ,				padding=__lowerCamelCase ,				add_special_tokens=__lowerCamelCase	)
             lowerCamelCase__		:    str   =  model(inputs.input_ids ,				attention_mask=inputs.attention_mask	)[0].mean(axis=-1	)
             lowerCamelCase__		:    Any   =  jnp.array(
                 [
                     [1.3_8_5_1, -1_3.8_9_2_3, -1_0.5_2_2_9, -1_0.7_5_3_3, -0.2_3_0_9, -1_0.2_3_8_4, -0.5_3_6_5, -9.0_9_4_7, -5.1_6_7_0],
                     [-4.7_0_7_3, -1_0.6_2_7_6, -3.9_4_1_5, -2_1.5_2_4_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2],
                     [0.6_2_4_7, -3.4_2_2_9, -8.9_1_7_9, -1.4_2_9_7, -1_4.1_6_5_0, 1.4_1_4_6, -9.0_2_1_8, -0.2_7_0_3, -0.2_7_0_3],
                     [6.4_7_8_3, -1.9_9_1_3, -1_0.7_9_2_6, -2.3_3_3_6, 1.5_0_9_2, -0.9_9_7_4, -6.8_2_1_3, 1.3_4_7_7, 1.3_4_7_7],
                 ]	)
             self.assertTrue(jnp.allclose(__lowerCamelCase ,				__lowerCamelCase ,				atol=4E-2	)	)

             lowerCamelCase__		:    List[str]   =  jax.jit(__lowerCamelCase	)
             lowerCamelCase__		:    Optional[int]   =  model(inputs.input_ids ,				attention_mask=inputs.attention_mask	)[0].mean(axis=-1	)
             self.assertTrue(jnp.allclose(__lowerCamelCase ,				__lowerCamelCase ,				atol=4E-2	)	)







@require_flax
@slow
class       _lowercase			(							unittest.TestCase):


       """simple docstring"""




       @property
       def 						lowerCAmelCase					(       self							:      Optional[int]	):


             '''simple docstring'''



             return [
                 "Today is a beautiful day and I want",
                 "In the city of",
                 "Paris is the capital of France and",
                 "Computers and mobile phones have taken",
             ]





       def 						lowerCAmelCase					(       self							:      Union[str, Any]	):


             '''simple docstring'''



             lowerCamelCase__		:    List[Any]   =  "facebook/opt-125m"

             lowerCamelCase__		:    List[str]   =  [
                 "Today is a beautiful day and I want to",
                 "In the city of New York, the city",
                 "Paris is the capital of France and the capital",
                 "Computers and mobile phones have taken over the",
             ]

             lowerCamelCase__		:    str   =  []

             lowerCamelCase__		:    Tuple   =  FlaxOPTForCausalLM.from_pretrained(__lowerCamelCase	)
             lowerCamelCase__		:    List[Any]   =  GPTaTokenizer.from_pretrained(__lowerCamelCase	)

             for prompt in self.prompts:
                   lowerCamelCase__		:    Any   =  tokenizer(__lowerCamelCase ,				return_tensors="jax"	).input_ids

                   lowerCamelCase__		:    int   =  model.generate(__lowerCamelCase ,				max_length=10	)
                   lowerCamelCase__		:    Any   =  generated_ids[0]

                   lowerCamelCase__		:    List[Any]   =  tokenizer.batch_decode(__lowerCamelCase ,				skip_special_tokens=__lowerCamelCase	)
                   predicted_outputs += generated_string

             self.assertListEqual(__lowerCamelCase ,				__lowerCamelCase	)





       def 						lowerCAmelCase					(       self							:      Optional[Any]	):


             '''simple docstring'''



             lowerCamelCase__		:    Tuple   =  "facebook/opt-350m"

             lowerCamelCase__		:    Tuple   =  [
                 "Today is a beautiful day and I want to",
                 "In the city of San Francisco, the city",
                 "Paris is the capital of France and the capital",
                 "Computers and mobile phones have taken over the",
             ]

             lowerCamelCase__		:    Optional[int]   =  []
             lowerCamelCase__		:    List[Any]   =  FlaxOPTForCausalLM.from_pretrained(__lowerCamelCase	)
             lowerCamelCase__		:    Any   =  GPTaTokenizer.from_pretrained(__lowerCamelCase	)

             for prompt in self.prompts:
                   lowerCamelCase__		:    Optional[Any]   =  tokenizer(__lowerCamelCase ,				return_tensors="jax"	).input_ids

                   lowerCamelCase__		:    Optional[int]   =  model.generate(__lowerCamelCase ,				max_length=10	)
                   lowerCamelCase__		:    List[str]   =  generated_ids[0]

                   lowerCamelCase__		:    int   =  tokenizer.batch_decode(__lowerCamelCase ,				skip_special_tokens=__lowerCamelCase	)
                   predicted_outputs += generated_string

             self.assertListEqual(__lowerCamelCase ,				__lowerCamelCase	)





       def 						lowerCAmelCase					(       self							:      Any	):


             '''simple docstring'''



             lowerCamelCase__		:    List[str]   =  "facebook/opt-125m"
             lowerCamelCase__		:    Tuple   =  [
                 "Today is a beautiful day and I want to thank",
                 "In the city of Rome Canaver Canaver Canaver Canaver",
             ]
             lowerCamelCase__		:    List[str]   =  FlaxOPTForCausalLM.from_pretrained(__lowerCamelCase	)
             lowerCamelCase__		:    Optional[Any]   =  GPTaTokenizer.from_pretrained(__lowerCamelCase	)
             lowerCamelCase__		:    str   =  tokenizer(
                 [
                     "Today is a beautiful day and I want to",
                     "In the city of",
                 ] ,				return_tensors="jax" ,				padding=__lowerCamelCase ,				)

             lowerCamelCase__		:    str   =  jax.jit(model.generate	)

             lowerCamelCase__		:    Dict   =  jit_generate(inputs["input_ids"] ,				attention_mask=inputs["attention_mask"]	).sequences

             lowerCamelCase__		:    Optional[Any]   =  tokenizer.batch_decode(__lowerCamelCase ,				skip_special_tokens=__lowerCamelCase	)

             self.assertIsNotNone(__lowerCamelCase ,				__lowerCamelCase	)





       def 						lowerCAmelCase					(       self							:      Dict	):


             '''simple docstring'''



             lowerCamelCase__		:    Dict   =  "facebook/opt-350m"

             lowerCamelCase__		:    Union[str, Any]   =  GPTaTokenizer.from_pretrained(__lowerCamelCase	)
             lowerCamelCase__		:    Optional[Any]   =  FlaxOPTForCausalLM.from_pretrained(__lowerCamelCase	)

             lowerCamelCase__		:    str   =  "left"

             # use different length sentences to test batching
             lowerCamelCase__		:    Optional[Any]   =  [
                 "Hello, my dog is a little",
                 "Today, I",
             ]

             lowerCamelCase__		:    Tuple   =  tokenizer(__lowerCamelCase ,				return_tensors="jax" ,				padding=__lowerCamelCase	)
             lowerCamelCase__		:    List[Any]   =  inputs["input_ids"]

             lowerCamelCase__		:    Dict   =  model.generate(input_ids=__lowerCamelCase ,				attention_mask=inputs["attention_mask"] ,				trace=__lowerCamelCase	)

             lowerCamelCase__		:    List[Any]   =  tokenizer(sentences[0] ,				return_tensors="jax"	).input_ids
             lowerCamelCase__		:    str   =  model.generate(input_ids=__lowerCamelCase	)

             lowerCamelCase__		:    Dict   =  inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].sum()
             lowerCamelCase__		:    Optional[Any]   =  tokenizer(sentences[1] ,				return_tensors="jax"	).input_ids
             lowerCamelCase__		:    Union[str, Any]   =  model.generate(input_ids=__lowerCamelCase ,				max_length=model.config.max_length - num_paddings	)

             lowerCamelCase__		:    str   =  tokenizer.batch_decode(outputs[0] ,				skip_special_tokens=__lowerCamelCase	)
             lowerCamelCase__		:    List[str]   =  tokenizer.decode(output_non_padded[0][0] ,				skip_special_tokens=__lowerCamelCase	)
             lowerCamelCase__		:    int   =  tokenizer.decode(output_padded[0][0] ,				skip_special_tokens=__lowerCamelCase	)

             lowerCamelCase__		:    Dict   =  [
                 "Hello, my dog is a little bit of a dork.\nI'm a little bit",
                 "Today, I was in the middle of a conversation with a friend about the",
             ]
             self.assertListEqual(__lowerCamelCase ,				__lowerCamelCase	)
             self.assertListEqual(__lowerCamelCase ,				[non_padded_sentence, padded_sentence]	)


