






import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
	import jax.numpy as jnp

	from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
	    FlaxRobertaPreLayerNormForCausalLM,
	    FlaxRobertaPreLayerNormForMaskedLM,
	    FlaxRobertaPreLayerNormForMultipleChoice,
	    FlaxRobertaPreLayerNormForQuestionAnswering,
	    FlaxRobertaPreLayerNormForSequenceClassification,
	    FlaxRobertaPreLayerNormForTokenClassification,
	    FlaxRobertaPreLayerNormModel,
	)

class     _SCREAMING_SNAKE_CASE       (			unittest.TestCase      ):





	def __init__(		self				:					Any ,  __lowerCamelCase				:					List[Any] ,  __lowerCamelCase				:					Any=13 ,  __lowerCamelCase				:					Tuple=7 ,  __lowerCamelCase				:					str=True ,  __lowerCamelCase				:					Optional[int]=True ,  __lowerCamelCase				:					str=True ,  __lowerCamelCase				:					List[str]=True ,  __lowerCamelCase				:					Tuple=99 ,  __lowerCamelCase				:					Union[str, Any]=32 ,  __lowerCamelCase				:					int=5 ,  __lowerCamelCase				:					Optional[Any]=4 ,  __lowerCamelCase				:					Union[str, Any]=37 ,  __lowerCamelCase				:					Any="gelu" ,  __lowerCamelCase				:					Optional[Any]=0.1 ,  __lowerCamelCase				:					List[Any]=0.1 ,  __lowerCamelCase				:					List[str]=512 ,  __lowerCamelCase				:					List[Any]=16 ,  __lowerCamelCase				:					List[Any]=2 ,  __lowerCamelCase				:					List[Any]=0.02 ,  __lowerCamelCase				:					Union[str, Any]=4 ,  ):
		UpperCamelCase     :int          =	parent
		UpperCamelCase     :Optional[Any]          =	batch_size
		UpperCamelCase     :Optional[int]          =	seq_length
		UpperCamelCase     :Dict          =	is_training
		UpperCamelCase     :Optional[int]          =	use_attention_mask
		UpperCamelCase     :List[str]          =	use_token_type_ids
		UpperCamelCase     :str          =	use_labels
		UpperCamelCase     :Optional[int]          =	vocab_size
		UpperCamelCase     :Union[str, Any]          =	hidden_size
		UpperCamelCase     :str          =	num_hidden_layers
		UpperCamelCase     :List[str]          =	num_attention_heads
		UpperCamelCase     :str          =	intermediate_size
		UpperCamelCase     :Dict          =	hidden_act
		UpperCamelCase     :Union[str, Any]          =	hidden_dropout_prob
		UpperCamelCase     :List[Any]          =	attention_probs_dropout_prob
		UpperCamelCase     :Optional[int]          =	max_position_embeddings
		UpperCamelCase     :List[str]          =	type_vocab_size
		UpperCamelCase     :Dict          =	type_sequence_label_size
		UpperCamelCase     :Optional[int]          =	initializer_range
		UpperCamelCase     :int          =	num_choices





	def 						_A (		self				:					Dict	):
		UpperCamelCase     :int          =	ids_tensor([self.batch_size, self.seq_length] ,  self.vocab_size	)

		UpperCamelCase     :str          =	None
		if self.use_attention_mask:
			UpperCamelCase     :List[Any]          =	random_attention_mask([self.batch_size, self.seq_length]	)

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

		UpperCamelCase     :Dict          =	RobertaPreLayerNormConfig(
		    vocab_size=self.vocab_size ,  hidden_size=self.hidden_size ,  num_hidden_layers=self.num_hidden_layers ,  num_attention_heads=self.num_attention_heads ,  intermediate_size=self.intermediate_size ,  hidden_act=self.hidden_act ,  hidden_dropout_prob=self.hidden_dropout_prob ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob ,  max_position_embeddings=self.max_position_embeddings ,  type_vocab_size=self.type_vocab_size ,  is_decoder=snake_case_ ,  initializer_range=self.initializer_range ,  )

		return config, input_ids, token_type_ids, attention_mask





	def 						_A (		self				:					int	):
		UpperCamelCase     :int          =	self.prepare_config_and_inputs()
		UpperCamelCase     :Tuple          =	config_and_inputs
		UpperCamelCase     :Tuple          =	{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
		return config, inputs_dict





	def 						_A (		self				:					Dict	):
		UpperCamelCase     :Union[str, Any]          =	self.prepare_config_and_inputs()
		UpperCamelCase     :List[Any]          =	config_and_inputs

		UpperCamelCase     :List[str]          =	True
		UpperCamelCase     :Dict          =	floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
		UpperCamelCase     :Tuple          =	ids_tensor([self.batch_size, self.seq_length] ,  vocab_size=2	)

		return (
		    config,
		    input_ids,
		    token_type_ids,
		    encoder_hidden_states,
		    encoder_attention_mask,
		)

@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class     _SCREAMING_SNAKE_CASE       (			__lowercase		,   unittest.TestCase      ):
	snake_case__							:     Tuple     	=  True

	snake_case__							:     Optional[int]     	=  (
	    (
	        FlaxRobertaPreLayerNormModel,
	        FlaxRobertaPreLayerNormForCausalLM,
	        FlaxRobertaPreLayerNormForMaskedLM,
	        FlaxRobertaPreLayerNormForSequenceClassification,
	        FlaxRobertaPreLayerNormForTokenClassification,
	        FlaxRobertaPreLayerNormForMultipleChoice,
	        FlaxRobertaPreLayerNormForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)





	def 						_A (		self				:					Optional[int]	):
		UpperCamelCase     :Any          =	FlaxRobertaPreLayerNormModelTester(self	)





	@slow
	def 						_A (		self				:					List[str]	):
		for model_class_name in self.all_model_classes:
			UpperCamelCase     :str          =	model_class_name.from_pretrained("""andreasmadsen/efficient_mlm_m0.40""" ,  from_pt=snake_case_	)
			UpperCamelCase     :Union[str, Any]          =	model(np.ones((1, 1)	)	)
			self.assertIsNotNone(snake_case_	)

@require_flax
class     _SCREAMING_SNAKE_CASE       (			unittest.TestCase      ):





	@slow
	def 						_A (		self				:					Tuple	):
		UpperCamelCase     :Tuple          =	FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("""andreasmadsen/efficient_mlm_m0.40""" ,  from_pt=snake_case_	)

		UpperCamelCase     :Optional[int]          =	np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] ,  dtype=jnp.intaa	)
		UpperCamelCase     :str          =	model(snake_case_	)[0]
		UpperCamelCase     :Optional[int]          =	[1, 11, 50_265]
		self.assertEqual(list(output.shape	) ,  snake_case_	)
		# compare the actual values for a slice.
		UpperCamelCase     :int          =	np.array(
		    [[[40.4880, 18.0199, -5.2367], [-1.8877, -4.0885, 10.7085], [-2.2613, -5.6110, 7.2665]]] ,  dtype=np.floataa	)
		self.assertTrue(np.allclose(output[:, :3, :3] ,  snake_case_ ,  atol=1E-4	)	)





	@slow
	def 						_A (		self				:					Dict	):
		UpperCamelCase     :Optional[int]          =	FlaxRobertaPreLayerNormModel.from_pretrained("""andreasmadsen/efficient_mlm_m0.40""" ,  from_pt=snake_case_	)

		UpperCamelCase     :Optional[Any]          =	np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] ,  dtype=jnp.intaa	)
		UpperCamelCase     :str          =	model(snake_case_	)[0]
		# compare the actual values for a slice.
		UpperCamelCase     :int          =	np.array(
		    [[[0.0208, -0.0356, 0.0237], [-0.1569, -0.0411, -0.2626], [0.1879, 0.0125, -0.0089]]] ,  dtype=np.floataa	)
		self.assertTrue(np.allclose(output[:, :3, :3] ,  snake_case_ ,  atol=1E-4	)	)


import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
    SeqaSeqDataset,
    calculate_bleu,
    calculate_rouge,
    chunks,
    lmap,
    load_json,
    parse_numeric_n_bool_cl_kwargs,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


lowercase__       =getLogger(__name__)




def    __UpperCamelCase     (				lowerCAmelCase__							:				Tuple  ,   lowerCAmelCase__							:				str  ,   lowerCAmelCase__							:				str  ,   lowerCAmelCase__							:				int = 8  ,   lowerCAmelCase__							:				int = 1_0_2_4  ,   lowerCAmelCase__							:				Union[str, Any]="val"  ,   lowerCAmelCase__							:				int=None  ,   lowerCAmelCase__							:				Any=False  ,   lowerCAmelCase__							:				Union[str, Any]="summarization"  ,   lowerCAmelCase__							:				Tuple=None  ,   lowerCAmelCase__							:				Optional[int]=1  ,   lowerCAmelCase__							:				Dict = None  ,   lowerCAmelCase__							:				int=""  ,   **lowerCAmelCase__							:				int  ,   ):
 __a :  List[Any]						=  str(lowerCAmelCase__  )
 assert local_rank is not None
 torch.distributed.init_process_group(backend='''nccl'''  ,   rank=lowerCAmelCase__  )

 __a :  Tuple						=  Path(lowerCAmelCase__  )
 __a :  Dict						=  save_dir.joinpath(f"rank_{local_rank}_output.json"  )
 torch.cuda.set_device(lowerCAmelCase__  )
 __a :  Dict						=  AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase__  ).cuda()
 if fpaa:
  __a :  str						=  model.half()
 # determine if we need to increase num_beams
 use_task_specific_params(lowerCAmelCase__  ,   lowerCAmelCase__  )  # update config with task specific params
 __a :  List[str]						=  generate_kwargs.pop('''num_beams'''  ,   model.config.num_beams  )  # AttributeError risk?
 if num_return_sequences > num_beams:
  __a :  Dict						=  num_return_sequences

 __a :  Optional[Any]						=  AutoTokenizer.from_pretrained(lowerCAmelCase__  )
 logger.info(f"Inferred tokenizer type: {tokenizer.__class__}"  )  # if this is wrong, check config.model_type.

 if max_source_length is None:
  __a :  Dict						=  tokenizer.model_max_length
 if prefix is None:
  __a :  Dict						=  prefix or getattr(model.config  ,   '''prefix'''  ,   ''''''  ) or ''''''
 __a :  List[Any]						=  SeqaSeqDataset(
     lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   max_target_length=1_0_2_4  ,   type_path=lowerCAmelCase__  ,   n_obs=lowerCAmelCase__  ,   prefix=lowerCAmelCase__  ,   **lowerCAmelCase__  ,   )
 # I set shuffle=True for a more accurate progress bar.
 # If all the longest samples are first, the prog bar estimate is too high at the beginning.
 __a :  Tuple						=  ds.make_sortish_sampler(lowerCAmelCase__  ,   distributed=lowerCAmelCase__  ,   add_extra_examples=lowerCAmelCase__  ,   shuffle=lowerCAmelCase__  )
 __a :  List[Any]						=  DataLoader(lowerCAmelCase__  ,   sampler=lowerCAmelCase__  ,   batch_size=lowerCAmelCase__  ,   collate_fn=ds.collate_fn  )
 __a :  List[Any]						=  []
 for batch in tqdm(lowerCAmelCase__  ):
  __a :  Any						=  model.generate(
      input_ids=batch['''input_ids'''].to(model.device  )  ,   attention_mask=batch['''attention_mask'''].to(model.device  )  ,   num_return_sequences=lowerCAmelCase__  ,   num_beams=lowerCAmelCase__  ,   **lowerCAmelCase__  ,   )
  __a :  List[Any]						=  tokenizer.batch_decode(lowerCAmelCase__  ,   skip_special_tokens=lowerCAmelCase__  ,   clean_up_tokenization_spaces=lowerCAmelCase__  )
  __a :  int						=  batch['''ids''']
  if num_return_sequences > 1:
   __a :  List[str]						=  chunks(lowerCAmelCase__  ,   lowerCAmelCase__  )  # batch size chunks, each of size num_return_seq
  for i, pred in enumerate(lowerCAmelCase__  ):
   results.append({'''pred''': pred, '''id''': ids[i].item()}  )
 save_json(lowerCAmelCase__  ,   lowerCAmelCase__  )
 return results, sampler.num_replicas




def    __UpperCamelCase     (				):
 __a :  str						=  argparse.ArgumentParser(
     epilog='''Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate'''  )
 parser.add_argument('''--data_dir'''  ,   type=lowerCAmelCase__  ,   help='''like cnn_dm/test.source'''  )
 parser.add_argument(
     '''--model_name'''  ,   type=lowerCAmelCase__  ,   help='''like facebook/bart-large-cnn,t5-base, etc.'''  ,   default='''sshleifer/distilbart-xsum-12-3'''  ,   )
 parser.add_argument('''--save_dir'''  ,   type=lowerCAmelCase__  ,   help='''where to save'''  ,   default='''tmp_gen'''  )
 parser.add_argument('''--max_source_length'''  ,   type=lowerCAmelCase__  ,   default=lowerCAmelCase__  )
 parser.add_argument(
     '''--type_path'''  ,   type=lowerCAmelCase__  ,   default='''test'''  ,   help='''which subset to evaluate typically train/val/test'''  )
 parser.add_argument('''--task'''  ,   type=lowerCAmelCase__  ,   default='''summarization'''  ,   help='''used for task_specific_params + metrics'''  )
 parser.add_argument('''--bs'''  ,   type=lowerCAmelCase__  ,   default=8  ,   required=lowerCAmelCase__  ,   help='''batch size'''  )
 parser.add_argument(
     '''--local_rank'''  ,   type=lowerCAmelCase__  ,   default=-1  ,   required=lowerCAmelCase__  ,   help='''should be passed by distributed.launch'''  )

 parser.add_argument(
     '''--n_obs'''  ,   type=lowerCAmelCase__  ,   default=lowerCAmelCase__  ,   required=lowerCAmelCase__  ,   help='''How many observations. Defaults to all.'''  )
 parser.add_argument(
     '''--num_return_sequences'''  ,   type=lowerCAmelCase__  ,   default=1  ,   required=lowerCAmelCase__  ,   help='''How many sequences to return'''  )
 parser.add_argument(
     '''--sync_timeout'''  ,   type=lowerCAmelCase__  ,   default=6_0_0  ,   required=lowerCAmelCase__  ,   help='''How long should master process wait for other processes to finish.'''  ,   )
 parser.add_argument('''--src_lang'''  ,   type=lowerCAmelCase__  ,   default=lowerCAmelCase__  ,   required=lowerCAmelCase__  )
 parser.add_argument('''--tgt_lang'''  ,   type=lowerCAmelCase__  ,   default=lowerCAmelCase__  ,   required=lowerCAmelCase__  )
 parser.add_argument(
     '''--prefix'''  ,   type=lowerCAmelCase__  ,   required=lowerCAmelCase__  ,   default=lowerCAmelCase__  ,   help='''will be added to the begininng of src examples'''  )
 parser.add_argument('''--fp16'''  ,   action='''store_true'''  )
 parser.add_argument('''--debug'''  ,   action='''store_true'''  )
 __a :  int						=  time.time()
 __a				,       __a :  Tuple						=  parser.parse_known_args()
 __a :  Optional[int]						=  parse_numeric_n_bool_cl_kwargs(lowerCAmelCase__  )
 if generate_kwargs and args.local_rank <= 0:
  print(f"parsed the following generate kwargs: {generate_kwargs}"  )
 __a :  Union[str, Any]						=  Path(args.save_dir + '''_tmp'''  )
 Path(lowerCAmelCase__  ).mkdir(exist_ok=lowerCAmelCase__  )  # this handles locking.
 __a :  Dict						=  list(json_save_dir.glob('''rank_*.json'''  )  )
 if intermediate_files:
  raise ValueError(f"Found files at {json_save_dir} please move or remove them."  )
  # In theory, a node could finish and save before another node hits this. If this happens, we can address later.
 __a :  Optional[Any]						=  {}
 if args.src_lang is not None:
  __a :  int						=  args.src_lang
 if args.tgt_lang is not None:
  __a :  Optional[Any]						=  args.tgt_lang

 Path(args.save_dir  ).mkdir(exist_ok=lowerCAmelCase__  )
 __a				,       __a :  Tuple						=  eval_data_dir(
     args.data_dir  ,   lowerCAmelCase__  ,   args.model_name  ,   type_path=args.type_path  ,   bs=args.bs  ,   fpaa=args.fpaa  ,   task=args.task  ,   local_rank=args.local_rank  ,   n_obs=args.n_obs  ,   max_source_length=args.max_source_length  ,   num_return_sequences=args.num_return_sequences  ,   prefix=args.prefix  ,   dataset_kwargs=lowerCAmelCase__  ,   **lowerCAmelCase__  ,   )

 if args.local_rank <= 0:
  __a :  int						=  Path(args.save_dir  )
  save_dir.mkdir(exist_ok=lowerCAmelCase__  )
  __a :  List[str]						=  gather_results_from_each_node(lowerCAmelCase__  ,   lowerCAmelCase__  ,   args.sync_timeout  )
  __a :  int						=  combine_partial_results(lowerCAmelCase__  )
  if args.num_return_sequences > 1:
   __a :  List[Any]						=  save_dir.joinpath('''pseudolabel_results.json'''  )
   print(f"Saving aggregated results at {save_path}, intermediate in {json_save_dir}/"  )
   save_json(lowerCAmelCase__  ,   lowerCAmelCase__  )
   return
  __a :  Any						=  Path(args.data_dir  ).joinpath(args.type_path + '''.target'''  )
  with open(lowerCAmelCase__  ) as f:
   __a :  Optional[int]						=  [x.rstrip() for x in f.readlines()][: len(lowerCAmelCase__  )]

  # Calculate metrics, save metrics,  and save _generations.txt
  __a :  str						=  '''translation''' in args.task
  __a :  List[str]						=  calculate_bleu if calc_bleu else calculate_rouge
  __a :  Any						=  '''bleu''' if calc_bleu else '''rouge'''
  __a :  Dict						=  score_fn(lowerCAmelCase__  ,   lowerCAmelCase__  )
  __a :  Dict						=  len(lowerCAmelCase__  )
  __a :  str						=  time.time() - start_time
  __a :  List[str]						=  round(runtime / metrics['''n_obs''']  ,   4  )
  __a :  Tuple						=  num_replicas
  # TODO(@stas00): add whatever metadata to metrics
  __a :  Optional[int]						=  save_dir.joinpath(f"{args.type_path}_{metric_name}.json"  )
  save_json(lowerCAmelCase__  ,   lowerCAmelCase__  ,   indent=lowerCAmelCase__  )
  print(lowerCAmelCase__  )
  write_txt_file(lowerCAmelCase__  ,   save_dir.joinpath(f"{args.type_path}_generations.txt"  )  )
  if args.debug:
   write_txt_file(lowerCAmelCase__  ,   save_dir.joinpath(f"{args.type_path}.target"  )  )
  else:
   shutil.rmtree(lowerCAmelCase__  )




def    __UpperCamelCase     (				lowerCAmelCase__							:				Optional[int]  ):
 __a :  Optional[int]						=  []
 for partial_result in partial_results:
  records.extend(lowerCAmelCase__  )
 __a :  Tuple						=  sorted(lowerCAmelCase__  ,   key=lambda lowerCAmelCase__  : x["id"]  )
 __a :  Tuple						=  [x['''pred'''] for x in records]
 return preds




def    __UpperCamelCase     (				lowerCAmelCase__							:				Any  ,   lowerCAmelCase__							:				Optional[Any]  ,   lowerCAmelCase__							:				Dict  ):
 # WAIT FOR lots of .json files
 __a :  Tuple						=  time.time()
 logger.info('''waiting for all nodes to finish'''  )
 __a :  Optional[int]						=  None
 while (time.time() - start_wait) < timeout:
  __a :  Optional[int]						=  list(save_dir.glob('''rank_*.json'''  )  )
  if len(lowerCAmelCase__  ) < num_replicas:
   continue
  try:
   # make sure all json files are fully saved
   __a :  Tuple						=  lmap(lowerCAmelCase__  ,   lowerCAmelCase__  )
   return json_data
  except JSONDecodeError:
   continue
 else:
  raise TimeoutError('''Rank 0 gave up on waiting for other processes'''  )
 # Unreachable


if __name__ == "__main__":
      # Usage for MT:
      run_generate()
