import argparse
import json
import os
import pickle
import shutil

import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset

from transformers import (
    BertConfig,
    BertForMaskedLM,
    BertTokenizer,
    DistilBertConfig,
    DistilBertForMaskedLM,
    DistilBertTokenizer,
    GPTaConfig,
    GPTaLMHeadModel,
    GPTaTokenizer,
    RobertaConfig,
    RobertaForMaskedLM,
    RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed


SCREAMING_SNAKE_CASE :	List[str]							     =		{
    """distilbert""": (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    """roberta""": (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    """bert""": (BertConfig, BertForMaskedLM, BertTokenizer),
    """gpt2""": (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}



def   __A		(			_A					):



 """simple docstring"""


 assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
 assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
 if args.mlm:
  assert os.path.isfile(args.token_counts					)
  assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
 else:
  assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])

 assert args.teacher_type == args.student_type or (
     args.student_type == "distilbert" and args.teacher_type == "bert"
 )
 assert os.path.isfile(args.student_config					)
 if args.student_pretrained_weights is not None:
  assert os.path.isfile(args.student_pretrained_weights					)

 if args.freeze_token_type_embds:
  assert args.student_type in ["roberta"]

 assert args.alpha_ce >= 0.0
 assert args.alpha_mlm >= 0.0
 assert args.alpha_clm >= 0.0
 assert args.alpha_mse >= 0.0
 assert args.alpha_cos >= 0.0
 assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0



def   __A		(			_A							,				_A					):



 """simple docstring"""


 if args.student_type == "roberta":
  __a       =  False
 elif args.student_type == "gpt2":
  __a       =  False



def   __A		(			_A							,				_A					):



 """simple docstring"""


 if args.student_type == "roberta":
  __a       =  False



def   __A		(			):



 """simple docstring"""


 __a       =  argparse.ArgumentParser(description="Training"					)
 parser.add_argument("--force"							,				action="store_true"							,				help="Overwrite dump_path if it already exists."					)

 parser.add_argument(
     "--dump_path"							,				type=_A							,				required=_A							,				help="The output directory (log, checkpoints, parameters, etc.)"					)
 parser.add_argument(
     "--data_file"							,				type=_A							,				required=_A							,				help="The binarized file (tokenized + tokens_to_ids) and grouped by sequence."							,				)

 parser.add_argument(
     "--student_type"							,				type=_A							,				choices=["distilbert", "roberta", "gpt2"]							,				required=_A							,				help="The student type (DistilBERT, RoBERTa)."							,				)
 parser.add_argument("--student_config"							,				type=_A							,				required=_A							,				help="Path to the student configuration."					)
 parser.add_argument(
     "--student_pretrained_weights"							,				default=_A							,				type=_A							,				help="Load student initialization checkpoint."					)

 parser.add_argument(
     "--teacher_type"							,				choices=["bert", "roberta", "gpt2"]							,				required=_A							,				help="Teacher type (BERT, RoBERTa)."					)
 parser.add_argument("--teacher_name"							,				type=_A							,				required=_A							,				help="The teacher model."					)

 parser.add_argument("--temperature"							,				default=2.0							,				type=_A							,				help="Temperature for the softmax temperature."					)
 parser.add_argument(
     "--alpha_ce"							,				default=0.5							,				type=_A							,				help="Linear weight for the distillation loss. Must be >=0."					)
 parser.add_argument(
     "--alpha_mlm"							,				default=0.0							,				type=_A							,				help="Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag."							,				)
 parser.add_argument("--alpha_clm"							,				default=0.5							,				type=_A							,				help="Linear weight for the CLM loss. Must be >=0."					)
 parser.add_argument("--alpha_mse"							,				default=0.0							,				type=_A							,				help="Linear weight of the MSE loss. Must be >=0."					)
 parser.add_argument(
     "--alpha_cos"							,				default=0.0							,				type=_A							,				help="Linear weight of the cosine embedding loss. Must be >=0."					)

 parser.add_argument(
     "--mlm"							,				action="store_true"							,				help="The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM."					)
 parser.add_argument(
     "--mlm_mask_prop"							,				default=0.15							,				type=_A							,				help="Proportion of tokens for which we need to make a prediction."							,				)
 parser.add_argument("--word_mask"							,				default=0.8							,				type=_A							,				help="Proportion of tokens to mask out."					)
 parser.add_argument("--word_keep"							,				default=0.1							,				type=_A							,				help="Proportion of tokens to keep."					)
 parser.add_argument("--word_rand"							,				default=0.1							,				type=_A							,				help="Proportion of tokens to randomly replace."					)
 parser.add_argument(
     "--mlm_smoothing"							,				default=0.7							,				type=_A							,				help="Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec)."							,				)
 parser.add_argument("--token_counts"							,				type=_A							,				help="The token counts in the data_file for MLM."					)

 parser.add_argument(
     "--restrict_ce_to_mask"							,				action="store_true"							,				help="If true, compute the distillation loss only the [MLM] prediction distribution."							,				)
 parser.add_argument(
     "--freeze_pos_embs"							,				action="store_true"							,				help="Freeze positional embeddings during distillation. For student_type in ['roberta', 'gpt2'] only."							,				)
 parser.add_argument(
     "--freeze_token_type_embds"							,				action="store_true"							,				help="Freeze token type embeddings during distillation if existent. For student_type in ['roberta'] only."							,				)

 parser.add_argument("--n_epoch"							,				type=_A							,				default=3							,				help="Number of pass on the whole dataset."					)
 parser.add_argument("--batch_size"							,				type=_A							,				default=5							,				help="Batch size (for each process)."					)
 parser.add_argument(
     "--group_by_size"							,				action="store_false"							,				help="If true, group sequences that have similar length into the same batch. Default is true."							,				)

 parser.add_argument(
     "--gradient_accumulation_steps"							,				type=_A							,				default=50							,				help="Gradient accumulation for larger training batches."							,				)
 parser.add_argument("--warmup_prop"							,				default=0.05							,				type=_A							,				help="Linear warmup proportion."					)
 parser.add_argument("--weight_decay"							,				default=0.0							,				type=_A							,				help="Weight decay if we apply some."					)
 parser.add_argument("--learning_rate"							,				default=5E-4							,				type=_A							,				help="The initial learning rate for Adam."					)
 parser.add_argument("--adam_epsilon"							,				default=1E-6							,				type=_A							,				help="Epsilon for Adam optimizer."					)
 parser.add_argument("--max_grad_norm"							,				default=5.0							,				type=_A							,				help="Max gradient norm."					)
 parser.add_argument("--initializer_range"							,				default=0.02							,				type=_A							,				help="Random initialization range."					)

 parser.add_argument(
     "--fp16"							,				action="store_true"							,				help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"							,				)
 parser.add_argument(
     "--fp16_opt_level"							,				type=_A							,				default="O1"							,				help=(
         "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
         "See details at https://nvidia.github.io/apex/amp.html"
     )							,				)
 parser.add_argument("--n_gpu"							,				type=_A							,				default=1							,				help="Number of GPUs in the node."					)
 parser.add_argument("--local_rank"							,				type=_A							,				default=-1							,				help="Distributed training - Local rank"					)
 parser.add_argument("--seed"							,				type=_A							,				default=56							,				help="Random seed"					)

 parser.add_argument("--log_interval"							,				type=_A							,				default=500							,				help="Tensorboard logging interval."					)
 parser.add_argument("--checkpoint_interval"							,				type=_A							,				default=4000							,				help="Checkpoint interval."					)
 __a       =  parser.parse_args()
 sanity_checks(_A					)

 # ARGS #
 init_gpu_params(_A					)
 set_seed(_A					)
 if args.is_master:
  if os.path.exists(args.dump_path					):
   if not args.force:
    raise ValueError(
        f"""Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite"""
        " itUse `--force` if you want to overwrite it"					)
   else:
    shutil.rmtree(args.dump_path					)

  if not os.path.exists(args.dump_path					):
   os.makedirs(args.dump_path					)
  logger.info(f"""Experiment will be dumped and logged in {args.dump_path}"""					)

  # SAVE PARAMS #
  logger.info(f"""Param: {args}"""					)
  with open(os.path.join(args.dump_path							,				"parameters.json"					)							,				"w"					) as f:
   json.dump(vars(_A					)							,				_A							,				indent=4					)
  git_log(args.dump_path					)

 __a       ,     __a       ,     __a       =  MODEL_CLASSES[args.student_type]
 __a       ,     __a       ,     __a       =  MODEL_CLASSES[args.teacher_type]

 # TOKENIZER #
 __a       =  teacher_tokenizer_class.from_pretrained(args.teacher_name					)
 __a       =  {}
 for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
  __a       =  tokenizer.all_special_tokens.index(_A					)
  __a       =  tokenizer.all_special_ids[idx]
 logger.info(f"""Special tokens {special_tok_ids}"""					)
 __a       =  special_tok_ids
 __a       =  tokenizer.max_model_input_sizes[args.teacher_name]

 # DATA LOADER #
 logger.info(f"""Loading data from {args.data_file}"""					)
 with open(args.data_file							,				"rb"					) as fp:
  __a       =  pickle.load(_A					)

 if args.mlm:
  logger.info(f"""Loading token counts from {args.token_counts} (already pre-computed)"""					)
  with open(args.token_counts							,				"rb"					) as fp:
   __a       =  pickle.load(_A					)

  __a       =  np.maximum(_A							,				1					) ** -args.mlm_smoothing
  for idx in special_tok_ids.values():
   __a       =  0.0  # do not predict special tokens
  __a       =  torch.from_numpy(_A					)
 else:
  __a       =  None

 __a       =  LmSeqsDataset(params=_A							,				data=_A					)
 logger.info("Data loader created."					)

 # STUDENT #
 logger.info(f"""Loading student config from {args.student_config}"""					)
 __a       =  student_config_class.from_pretrained(args.student_config					)
 __a       =  True

 if args.student_pretrained_weights is not None:
  logger.info(f"""Loading pretrained weights from {args.student_pretrained_weights}"""					)
  __a       =  student_model_class.from_pretrained(args.student_pretrained_weights							,				config=_A					)
 else:
  __a       =  student_model_class(_A					)

 if args.n_gpu > 0:
  student.to(f"""cuda:{args.local_rank}"""					)
 logger.info("Student loaded."					)

 # TEACHER #
 __a       =  teacher_model_class.from_pretrained(args.teacher_name							,				output_hidden_states=_A					)
 if args.n_gpu > 0:
  teacher.to(f"""cuda:{args.local_rank}"""					)
 logger.info(f"""Teacher loaded from {args.teacher_name}."""					)

 # FREEZING #
 if args.freeze_pos_embs:
  freeze_pos_embeddings(_A							,				_A					)
 if args.freeze_token_type_embds:
  freeze_token_type_embeddings(_A							,				_A					)

 # SANITY CHECKS #
 assert student.config.vocab_size == teacher.config.vocab_size
 assert student.config.hidden_size == teacher.config.hidden_size
 assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
 if args.mlm:
  assert token_probs.size(0					) == stu_architecture_config.vocab_size

 # DISTILLER #
 torch.cuda.empty_cache()
 __a       =  Distiller(
     params=_A							,				dataset=_A							,				token_probs=_A							,				student=_A							,				teacher=_A					)
 distiller.train()
 logger.info("Let's go get some drinks."					)


if __name__ == "__main__":
  main()



import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
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
  SCREAMING_SNAKE_CASE :	List[Any]							     =		"""platform"""
  import jax
  import jax.numpy as jnp
  import numpy as np

  from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel
@require_flax
class    A_ :
  _SCREAMING_SNAKE_CASE    				=			PegasusConfig
  _SCREAMING_SNAKE_CASE    				=			{}
  _SCREAMING_SNAKE_CASE    				=			"""gelu"""

  def __init__(		self :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      Any=13     ,							__SCREAMING_SNAKE_CASE :      Any=7     ,							__SCREAMING_SNAKE_CASE :      Optional[int]=True     ,							__SCREAMING_SNAKE_CASE :      Any=False     ,							__SCREAMING_SNAKE_CASE :      Optional[int]=99     ,							__SCREAMING_SNAKE_CASE :      Optional[int]=32     ,							__SCREAMING_SNAKE_CASE :      List[str]=5     ,							__SCREAMING_SNAKE_CASE :      Tuple=4     ,							__SCREAMING_SNAKE_CASE :      int=37     ,							__SCREAMING_SNAKE_CASE :      str=0.1     ,							__SCREAMING_SNAKE_CASE :      Dict=0.1     ,							__SCREAMING_SNAKE_CASE :      Tuple=20     ,							__SCREAMING_SNAKE_CASE :      List[str]=2     ,							__SCREAMING_SNAKE_CASE :      Dict=1     ,							__SCREAMING_SNAKE_CASE :      List[Any]=0     ,							):
   __a       =  parent
   __a       =  batch_size
   __a       =  seq_length
   __a       =  is_training
   __a       =  use_labels
   __a       =  vocab_size
   __a       =  hidden_size
   __a       =  num_hidden_layers
   __a       =  num_attention_heads
   __a       =  intermediate_size

   __a       =  hidden_dropout_prob
   __a       =  attention_probs_dropout_prob
   __a       =  max_position_embeddings
   __a       =  eos_token_id
   __a       =  pad_token_id
   __a       =  bos_token_id

  def 		_UpperCAmelCase   (		self :      Optional[int]       ):
   __a       =  ids_tensor([self.batch_size, self.seq_length - 1]     ,							self.vocab_size       ).clip(3     ,							self.vocab_size       )
   __a       =  np.expand_dims(np.array([self.eos_token_id] * self.batch_size       )     ,							1       )
   __a       =  np.concatenate([input_ids, eos_tensor]     ,							axis=1       )

   __a       =  ids_tensor([self.batch_size, self.seq_length]     ,							self.vocab_size       )

   __a       =  self.config_cls(
       vocab_size=self.vocab_size     ,							d_model=self.hidden_size     ,							encoder_layers=self.num_hidden_layers     ,							decoder_layers=self.num_hidden_layers     ,							encoder_attention_heads=self.num_attention_heads     ,							decoder_attention_heads=self.num_attention_heads     ,							encoder_ffn_dim=self.intermediate_size     ,							decoder_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_ids=[2]     ,							bos_token_id=self.bos_token_id     ,							pad_token_id=self.pad_token_id     ,							decoder_start_token_id=self.pad_token_id     ,							**self.config_updates     ,							)
   __a       =  prepare_pegasus_inputs_dict(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )
   return config, inputs_dict

  def 		_UpperCAmelCase   (		self :      Tuple     ,							__SCREAMING_SNAKE_CASE :      Optional[int]     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      str       ):
   __a       =  20
   __a       =  model_class_name(__SCREAMING_SNAKE_CASE       )

   __a       =  model.encode(inputs_dict["input_ids"]       )

   __a       ,     __a       =  (
       inputs_dict["decoder_input_ids"],
       inputs_dict["decoder_attention_mask"],
   )

   __a       =  model.init_cache(decoder_input_ids.shape[0]     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )
   __a       =  jnp.ones((decoder_input_ids.shape[0], max_decoder_length)     ,							dtype="i4"       )

   __a       =  jnp.broadcast_to(
       jnp.arange(decoder_input_ids.shape[-1] - 1       )[None, :]     ,							(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)     ,							)
   __a       =  model.decode(
       decoder_input_ids[:, :-1]     ,							__SCREAMING_SNAKE_CASE     ,							decoder_attention_mask=__SCREAMING_SNAKE_CASE     ,							past_key_values=__SCREAMING_SNAKE_CASE     ,							decoder_position_ids=__SCREAMING_SNAKE_CASE     ,							)

   __a       =  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]     ,							dtype="i4"       )
   __a       =  model.decode(
       decoder_input_ids[:, -1:]     ,							__SCREAMING_SNAKE_CASE     ,							decoder_attention_mask=__SCREAMING_SNAKE_CASE     ,							past_key_values=outputs_cache.past_key_values     ,							decoder_position_ids=__SCREAMING_SNAKE_CASE     ,							)

   __a       =  model.decode(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

   __a       =  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 		_UpperCAmelCase   (		self :      str     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      List[str]     ,							__SCREAMING_SNAKE_CASE :      List[str]       ):
   __a       =  20
   __a       =  model_class_name(__SCREAMING_SNAKE_CASE       )

   __a       =  model.encode(inputs_dict["input_ids"]       )

   __a       ,     __a       =  (
       inputs_dict["decoder_input_ids"],
       inputs_dict["decoder_attention_mask"],
   )

   __a       =  jnp.concatenate(
       [
           decoder_attention_mask,
           jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])       ),
       ]     ,							axis=-1     ,							)

   __a       =  model.init_cache(decoder_input_ids.shape[0]     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )
   __a       =  jnp.broadcast_to(
       jnp.arange(decoder_input_ids.shape[-1] - 1       )[None, :]     ,							(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)     ,							)

   __a       =  model.decode(
       decoder_input_ids[:, :-1]     ,							__SCREAMING_SNAKE_CASE     ,							decoder_attention_mask=__SCREAMING_SNAKE_CASE     ,							past_key_values=__SCREAMING_SNAKE_CASE     ,							decoder_position_ids=__SCREAMING_SNAKE_CASE     ,							)
   __a       =  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]     ,							dtype="i4"       )
   __a       =  model.decode(
       decoder_input_ids[:, -1:]     ,							__SCREAMING_SNAKE_CASE     ,							past_key_values=outputs_cache.past_key_values     ,							decoder_attention_mask=__SCREAMING_SNAKE_CASE     ,							decoder_position_ids=__SCREAMING_SNAKE_CASE     ,							)

   __a       =  model.decode(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							decoder_attention_mask=__SCREAMING_SNAKE_CASE       )

   __a       =  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   __A		(			_A							,				_A							,				_A							,				_A=None							,				_A=None							,				):



 """simple docstring"""


 if attention_mask is None:
  __a       =  np.not_equal(_A							,				config.pad_token_id					).astype(np.inta					)
 if decoder_attention_mask is None:
  __a       =  np.concatenate(
      [
          np.ones(decoder_input_ids[:, :1].shape							,				dtype=np.inta					),
          np.not_equal(decoder_input_ids[:, 1:]							,				config.pad_token_id					).astype(np.inta					),
      ]							,				axis=-1							,				)
 return {
     "input_ids": input_ids,
     "decoder_input_ids": decoder_input_ids,
     "attention_mask": attention_mask,
     "decoder_attention_mask": decoder_attention_mask,
 }






@require_flax
class    A_ (       a_			,       unittest.TestCase ):
  _SCREAMING_SNAKE_CASE    				=			(
      (
          FlaxPegasusForConditionalGeneration,
          FlaxPegasusModel,
      )
      if is_flax_available()
      else ()
  )
  _SCREAMING_SNAKE_CASE    				=			(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
  _SCREAMING_SNAKE_CASE    				=			True
  _SCREAMING_SNAKE_CASE    				=			False
  _SCREAMING_SNAKE_CASE    				=			False
  _SCREAMING_SNAKE_CASE    				=			False

  def 		_UpperCAmelCase   (		self :      int       ):
   __a       =  FlaxPegasusModelTester(self       )
   __a       =  ConfigTester(self     ,							config_class=__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Tuple       ):
   self.config_tester.run_common_tests()

  def 		_UpperCAmelCase   (		self :      Optional[int]       ):
   __a       ,     __a       =  self.model_tester.prepare_config_and_inputs_for_common()
   for model_class in self.all_model_classes:
    self.model_tester.check_use_cache_forward(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      str       ):
   __a       ,     __a       =  self.model_tester.prepare_config_and_inputs_for_common()
   for model_class in self.all_model_classes:
    self.model_tester.check_use_cache_forward_with_attn_mask(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Dict       ):
   __a       ,     __a       =  self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    with self.subTest(model_class.__name__       ):
     __a       =  self._prepare_for_class(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )
     __a       =  model_class(__SCREAMING_SNAKE_CASE       )

     @jax.jit
     def encode_jitted(__SCREAMING_SNAKE_CASE :      Dict     ,							__SCREAMING_SNAKE_CASE :      Tuple=None     ,							**__SCREAMING_SNAKE_CASE :      Optional[Any]       ):
      return model.encode(input_ids=__SCREAMING_SNAKE_CASE     ,							attention_mask=__SCREAMING_SNAKE_CASE       )

     with self.subTest("JIT Enabled"       ):
      __a       =  encode_jitted(**__SCREAMING_SNAKE_CASE       ).to_tuple()

     with self.subTest("JIT Disabled"       ):
      with jax.disable_jit():
       __a       =  encode_jitted(**__SCREAMING_SNAKE_CASE       ).to_tuple()

     self.assertEqual(len(__SCREAMING_SNAKE_CASE       )     ,							len(__SCREAMING_SNAKE_CASE       )       )
     for jitted_output, output in zip(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       ):
      self.assertEqual(jitted_output.shape     ,							output.shape       )

  def 		_UpperCAmelCase   (		self :      List[str]       ):
   __a       ,     __a       =  self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    with self.subTest(model_class.__name__       ):
     __a       =  model_class(__SCREAMING_SNAKE_CASE       )
     __a       =  model.encode(inputs_dict["input_ids"]     ,							inputs_dict["attention_mask"]       )

     __a       =  {
         "decoder_input_ids": inputs_dict["decoder_input_ids"],
         "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
         "encoder_outputs": encoder_outputs,
     }

     @jax.jit
     def decode_jitted(__SCREAMING_SNAKE_CASE :      Any     ,							__SCREAMING_SNAKE_CASE :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      Tuple       ):
      return model.decode(
          decoder_input_ids=__SCREAMING_SNAKE_CASE     ,							decoder_attention_mask=__SCREAMING_SNAKE_CASE     ,							encoder_outputs=__SCREAMING_SNAKE_CASE     ,							)

     with self.subTest("JIT Enabled"       ):
      __a       =  decode_jitted(**__SCREAMING_SNAKE_CASE       ).to_tuple()

     with self.subTest("JIT Disabled"       ):
      with jax.disable_jit():
       __a       =  decode_jitted(**__SCREAMING_SNAKE_CASE       ).to_tuple()

     self.assertEqual(len(__SCREAMING_SNAKE_CASE       )     ,							len(__SCREAMING_SNAKE_CASE       )       )
     for jitted_output, output in zip(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       ):
      self.assertEqual(jitted_output.shape     ,							output.shape       )

  @slow
  def 		_UpperCAmelCase   (		self :      Tuple       ):
   for model_class_name in self.all_model_classes:
    __a       =  model_class_name.from_pretrained("google/pegasus-large"     ,							from_pt=__SCREAMING_SNAKE_CASE       )
    __a       =  np.ones((1, 1)       )
    __a       =  model(__SCREAMING_SNAKE_CASE       )
    self.assertIsNotNone(__SCREAMING_SNAKE_CASE       )

  @slow
  def 		_UpperCAmelCase   (		self :      str       ):
   __a       =  FlaxPegasusForConditionalGeneration.from_pretrained("google/pegasus-xsum"       )
   __a       =  PegasusTokenizer.from_pretrained("google/pegasus-xsum"       )

   __a       =  [
       " PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.",
       " The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" ",
   ]

   __a       =  [
       "California's largest electricity provider has turned off power to hundreds of thousands of customers.",
       "Pop group N-Dubz have revealed they were surprised to get four nominations for this year's Mobo Awards.",
   ]

   __a       =  tokenizer(__SCREAMING_SNAKE_CASE     ,							return_tensors="np"     ,							truncation=__SCREAMING_SNAKE_CASE     ,							max_length=5_12     ,							padding=__SCREAMING_SNAKE_CASE       )
   __a       =  model.generate(**__SCREAMING_SNAKE_CASE     ,							num_beams=2       ).sequences
   __a       =  tokenizer.batch_decode(__SCREAMING_SNAKE_CASE     ,							skip_special_tokens=__SCREAMING_SNAKE_CASE       )
   assert tgt_text == decoded


