





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,
)


_UpperCAmelCase				:       Union[str, Any]	   =				getLogger(__name__)


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase = 8						,	_UpperCAmelCase = 1024						,	_UpperCAmelCase="val"						,	_UpperCAmelCase=None						,	_UpperCAmelCase=False						,	_UpperCAmelCase="summarization"						,	_UpperCAmelCase=None						,	_UpperCAmelCase=1						,	_UpperCAmelCase = None						,	_UpperCAmelCase=""						,	**_UpperCAmelCase						,	)			->     Dict:
    lowerCamelCase__    :      List[str]					 =		str(_UpperCAmelCase )
    assert local_rank is not None
    torch.distributed.init_process_group(backend='nccl'						,	rank=_UpperCAmelCase )

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

    lowerCamelCase__    :      List[Any]					 =		AutoTokenizer.from_pretrained(_UpperCAmelCase )
    logger.info(F"""Inferred tokenizer type: {tokenizer.__class__}""" )  # if this is wrong, check config.model_type.

    if max_source_length is None:
        lowerCamelCase__    :      Tuple					 =		tokenizer.model_max_length
    if prefix is None:
        lowerCamelCase__    :      List[str]					 =		prefix or getattr(model.config						,	'prefix'						,	'' ) or ''
    lowerCamelCase__    :      List[str]					 =		SeqaSeqDataset(
        _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	max_target_length=1024						,	type_path=_UpperCAmelCase						,	n_obs=_UpperCAmelCase						,	prefix=_UpperCAmelCase						,	**_UpperCAmelCase						,	)
    # 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.
    lowerCamelCase__    :      Optional[Any]					 =		ds.make_sortish_sampler(_UpperCAmelCase						,	distributed=_UpperCAmelCase						,	add_extra_examples=_UpperCAmelCase						,	shuffle=_UpperCAmelCase )
    lowerCamelCase__    :      str					 =		DataLoader(_UpperCAmelCase						,	sampler=_UpperCAmelCase						,	batch_size=_UpperCAmelCase						,	collate_fn=ds.collate_fn )
    lowerCamelCase__    :      Dict					 =		[]
    for batch in tqdm(_UpperCAmelCase ):
        lowerCamelCase__    :      Union[str, Any]					 =		model.generate(
            input_ids=batch['input_ids'].to(model.device )						,	attention_mask=batch['attention_mask'].to(model.device )						,	num_return_sequences=_UpperCAmelCase						,	num_beams=_UpperCAmelCase						,	**_UpperCAmelCase						,	)
        lowerCamelCase__    :      Dict					 =		tokenizer.batch_decode(_UpperCAmelCase						,	skip_special_tokens=_UpperCAmelCase						,	clean_up_tokenization_spaces=_UpperCAmelCase )
        lowerCamelCase__    :      Any					 =		batch['ids']
        if num_return_sequences > 1:
            lowerCamelCase__    :      int					 =		chunks(_UpperCAmelCase						,	_UpperCAmelCase )  # batch size chunks, each of size num_return_seq
        for i, pred in enumerate(_UpperCAmelCase ):
            results.append({'pred': pred, 'id': ids[i].item()} )
    save_json(_UpperCAmelCase						,	_UpperCAmelCase )
    return results, sampler.num_replicas


def        SCREAMING_SNAKE_CASE      (       )			->     Dict:
    lowerCamelCase__    :      Dict					 =		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=_UpperCAmelCase						,	help='like cnn_dm/test.source' )
    parser.add_argument(
        '--model_name'						,	type=_UpperCAmelCase						,	help='like facebook/bart-large-cnn,t5-base, etc.'						,	default='sshleifer/distilbart-xsum-12-3'						,	)
    parser.add_argument('--save_dir'						,	type=_UpperCAmelCase						,	help='where to save'						,	default='tmp_gen' )
    parser.add_argument('--max_source_length'						,	type=_UpperCAmelCase						,	default=_UpperCAmelCase )
    parser.add_argument(
        '--type_path'						,	type=_UpperCAmelCase						,	default='test'						,	help='which subset to evaluate typically train/val/test' )
    parser.add_argument('--task'						,	type=_UpperCAmelCase						,	default='summarization'						,	help='used for task_specific_params + metrics' )
    parser.add_argument('--bs'						,	type=_UpperCAmelCase						,	default=8						,	required=_UpperCAmelCase						,	help='batch size' )
    parser.add_argument(
        '--local_rank'						,	type=_UpperCAmelCase						,	default=-1						,	required=_UpperCAmelCase						,	help='should be passed by distributed.launch' )

    parser.add_argument(
        '--n_obs'						,	type=_UpperCAmelCase						,	default=_UpperCAmelCase						,	required=_UpperCAmelCase						,	help='How many observations. Defaults to all.' )
    parser.add_argument(
        '--num_return_sequences'						,	type=_UpperCAmelCase						,	default=1						,	required=_UpperCAmelCase						,	help='How many sequences to return' )
    parser.add_argument(
        '--sync_timeout'						,	type=_UpperCAmelCase						,	default=600						,	required=_UpperCAmelCase						,	help='How long should master process wait for other processes to finish.'						,	)
    parser.add_argument('--src_lang'						,	type=_UpperCAmelCase						,	default=_UpperCAmelCase						,	required=_UpperCAmelCase )
    parser.add_argument('--tgt_lang'						,	type=_UpperCAmelCase						,	default=_UpperCAmelCase						,	required=_UpperCAmelCase )
    parser.add_argument(
        '--prefix'						,	type=_UpperCAmelCase						,	required=_UpperCAmelCase						,	default=_UpperCAmelCase						,	help='will be added to the begininng of src examples' )
    parser.add_argument('--fp16'						,	action='store_true' )
    parser.add_argument('--debug'						,	action='store_true' )
    lowerCamelCase__    :      int					 =		time.time()
    lowerCamelCase__ ,							lowerCamelCase__    :      Optional[int]					 =		parser.parse_known_args()
    lowerCamelCase__    :      Union[str, Any]					 =		parse_numeric_n_bool_cl_kwargs(_UpperCAmelCase )
    if generate_kwargs and args.local_rank <= 0:
        print(F"""parsed the following generate kwargs: {generate_kwargs}""" )
    lowerCamelCase__    :      Union[str, Any]					 =		Path(args.save_dir + '_tmp' )
    Path(_UpperCAmelCase ).mkdir(exist_ok=_UpperCAmelCase )  # this handles locking.
    lowerCamelCase__    :      str					 =		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.
    lowerCamelCase__    :      Any					 =		{}
    if args.src_lang is not None:
        lowerCamelCase__    :      int					 =		args.src_lang
    if args.tgt_lang is not None:
        lowerCamelCase__    :      Union[str, Any]					 =		args.tgt_lang

    Path(args.save_dir ).mkdir(exist_ok=_UpperCAmelCase )
    lowerCamelCase__ ,							lowerCamelCase__    :      Tuple					 =		eval_data_dir(
        args.data_dir						,	_UpperCAmelCase						,	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=_UpperCAmelCase						,	**_UpperCAmelCase						,	)

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

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


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     List:
    lowerCamelCase__    :      Union[str, Any]					 =		[]
    for partial_result in partial_results:
        records.extend(_UpperCAmelCase )
    lowerCamelCase__    :      Tuple					 =		sorted(_UpperCAmelCase						,	key=lambda _UpperCAmelCase : x["id"] )
    lowerCamelCase__    :      Union[str, Any]					 =		[x['pred'] for x in records]
    return preds


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase )			->     List[Dict[str, List]]:
    # WAIT FOR lots of .json files
    lowerCamelCase__    :      Optional[Any]					 =		time.time()
    logger.info('waiting for all nodes to finish' )
    lowerCamelCase__    :      List[str]					 =		None
    while (time.time() - start_wait) < timeout:
        lowerCamelCase__    :      Any					 =		list(save_dir.glob('rank_*.json' ) )
        if len(_UpperCAmelCase ) < num_replicas:
            continue
        try:
            # make sure all json files are fully saved
            lowerCamelCase__    :      Any					 =		lmap(_UpperCAmelCase						,	_UpperCAmelCase )
            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()












import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel

class    lowerCAmelCase							:
 def __init__(      self  :      Tuple			,     UpperCAmelCase  :      Dict			,     UpperCAmelCase  :      Union[str, Any]=99			,     UpperCAmelCase  :      str=13			,     UpperCAmelCase  :      List[str]=7			,     UpperCAmelCase  :      str=9			,     UpperCAmelCase  :      Optional[int]=True			,     UpperCAmelCase  :      str=True			,     UpperCAmelCase  :      Any=False			,     UpperCAmelCase  :      Union[str, Any]=32			,     UpperCAmelCase  :      List[str]=5			,     UpperCAmelCase  :      Tuple=4			,     UpperCAmelCase  :      Union[str, Any]=37			,     UpperCAmelCase  :      int=8			,     UpperCAmelCase  :      List[str]=0.1			,     UpperCAmelCase  :      Any=0.0_0_2			,     UpperCAmelCase  :      Optional[Any]=1			,     UpperCAmelCase  :      List[Any]=0			,     UpperCAmelCase  :      Union[str, Any]=0			,     UpperCAmelCase  :      Tuple=None			,     UpperCAmelCase  :      Optional[Any]=None			,     )			->	Union[str, Any]:
     lowerCamelCase__    :      int					 =		parent
     lowerCamelCase__    :      Any					 =		batch_size
     lowerCamelCase__    :      Optional[int]					 =		encoder_seq_length
     lowerCamelCase__    :      int					 =		decoder_seq_length
     # For common tests
     lowerCamelCase__    :      List[str]					 =		self.decoder_seq_length
     lowerCamelCase__    :      Optional[int]					 =		is_training
     lowerCamelCase__    :      List[Any]					 =		use_attention_mask
     lowerCamelCase__    :      Optional[Any]					 =		use_labels
     lowerCamelCase__    :      Union[str, Any]					 =		vocab_size
     lowerCamelCase__    :      Union[str, Any]					 =		hidden_size
     lowerCamelCase__    :      Optional[Any]					 =		num_hidden_layers
     lowerCamelCase__    :      Any					 =		num_attention_heads
     lowerCamelCase__    :      str					 =		d_ff
     lowerCamelCase__    :      Optional[Any]					 =		relative_attention_num_buckets
     lowerCamelCase__    :      Any					 =		dropout_rate
     lowerCamelCase__    :      Any					 =		initializer_factor
     lowerCamelCase__    :      Union[str, Any]					 =		eos_token_id
     lowerCamelCase__    :      List[str]					 =		pad_token_id
     lowerCamelCase__    :      List[str]					 =		decoder_start_token_id
     lowerCamelCase__    :      List[Any]					 =		None
     lowerCamelCase__    :      Optional[Any]					 =		decoder_layers
 def      A_	(      self  :      List[Any]	)			->	int:
     return TaConfig.from_pretrained('google/umt5-base'	)
 def      A_	(      self  :      List[Any]			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      Optional[int]			,     UpperCAmelCase  :      Union[str, Any]			,     UpperCAmelCase  :      Tuple=None			,     UpperCAmelCase  :      List[str]=None			,     UpperCAmelCase  :      str=None			,     UpperCAmelCase  :      Optional[Any]=None			,     UpperCAmelCase  :      Optional[Any]=None			,     )			->	List[str]:
     if attention_mask is None:
         lowerCamelCase__    :      Optional[Any]					 =		input_ids.ne(config.pad_token_id	)
     if decoder_attention_mask is None:
         lowerCamelCase__    :      Optional[Any]					 =		decoder_input_ids.ne(config.pad_token_id	)
     if head_mask is None:
         lowerCamelCase__    :      int					 =		torch.ones(config.num_hidden_layers			,     config.num_attention_heads			,     device=UpperCAmelCase	)
     if decoder_head_mask is None:
         lowerCamelCase__    :      Dict					 =		torch.ones(config.num_decoder_layers			,     config.num_attention_heads			,     device=UpperCAmelCase	)
     if cross_attn_head_mask is None:
         lowerCamelCase__    :      Dict					 =		torch.ones(
             config.num_decoder_layers			,     config.num_attention_heads			,     device=UpperCAmelCase	)
     return {
         "input_ids": input_ids,
         "decoder_input_ids": decoder_input_ids,
         "attention_mask": attention_mask,
         "decoder_attention_mask": decoder_attention_mask,
         "head_mask": head_mask,
         "decoder_head_mask": decoder_head_mask,
         "cross_attn_head_mask": cross_attn_head_mask,
     }
 def      A_	(      self  :      str	)			->	List[str]:
     lowerCamelCase__    :      Any					 =		ids_tensor([self.batch_size, self.encoder_seq_length]			,     self.vocab_size	)
     lowerCamelCase__    :      Optional[Any]					 =		ids_tensor([self.batch_size, self.decoder_seq_length]			,     self.vocab_size	)

     # we need to clamp the input ids here to avoid having pad token in between
     # this is because for NllbMoe the position_ids are prepared such that
     # all pad tokens have pos id = 2 and rest are between 2..seq_length
     # and the seq_length here is seq_length - num_pad_tokens
     # but when using past, there is no way of knowing if the past input ids had
     # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
     # position_ids being off by num_pad_tokens in past input
     lowerCamelCase__    :      List[str]					 =		input_ids.clamp(self.pad_token_id + 1	)
     lowerCamelCase__    :      Union[str, Any]					 =		decoder_input_ids.clamp(self.pad_token_id + 1	)

     lowerCamelCase__    :      Dict					 =		self.get_config()
     lowerCamelCase__    :      Tuple					 =		config.num_attention_heads
     lowerCamelCase__    :      Any					 =		self.prepare_inputs_dict(UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase	)
     return config, input_dict
 def      A_	(      self  :      Tuple	)			->	Union[str, Any]:
     lowerCamelCase__ ,							lowerCamelCase__    :      Dict					 =		self.prepare_config_and_inputs()
     return config, inputs_dict
 def      A_	(      self  :      Optional[int]	)			->	List[str]:
     return TaConfig(
         vocab_size=166			,     d_model=self.hidden_size			,     d_ff=self.d_ff			,     d_kv=self.hidden_size // self.num_attention_heads			,     num_layers=self.num_hidden_layers			,     num_decoder_layers=self.decoder_layers			,     num_heads=self.num_attention_heads			,     relative_attention_num_buckets=self.relative_attention_num_buckets			,     dropout_rate=self.dropout_rate			,     initializer_factor=self.initializer_factor			,     eos_token_id=self.eos_token_id			,     bos_token_id=self.pad_token_id			,     pad_token_id=self.pad_token_id			,     decoder_start_token_id=self.decoder_start_token_id			,     )
 def      A_	(      self  :      Union[str, Any]	)			->	Dict:
     return TaConfig(
         vocab_size=self.vocab_size			,     d_model=self.hidden_size			,     d_ff=self.d_ff			,     d_kv=self.hidden_size // self.num_attention_heads			,     num_layers=self.num_hidden_layers			,     num_decoder_layers=self.decoder_layers			,     num_heads=self.num_attention_heads			,     relative_attention_num_buckets=self.relative_attention_num_buckets			,     dropout_rate=self.dropout_rate			,     initializer_factor=self.initializer_factor			,     eos_token_id=self.eos_token_id			,     bos_token_id=self.pad_token_id			,     pad_token_id=self.pad_token_id			,     decoder_start_token_id=self.decoder_start_token_id			,     )
 def      A_	(      self  :      Any			,     UpperCAmelCase  :      Union[str, Any]			,     UpperCAmelCase  :      List[str]			,     UpperCAmelCase  :      int			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      Any			,     UpperCAmelCase  :      Dict			,     )			->	str:
     lowerCamelCase__    :      Dict					 =		UMTaModel(config=UpperCAmelCase	)
     model.to(UpperCAmelCase	)
     model.eval()
     lowerCamelCase__    :      Optional[int]					 =		model(
         input_ids=UpperCAmelCase			,     decoder_input_ids=UpperCAmelCase			,     attention_mask=UpperCAmelCase			,     decoder_attention_mask=UpperCAmelCase			,     )
     lowerCamelCase__    :      Any					 =		model(input_ids=UpperCAmelCase			,     decoder_input_ids=UpperCAmelCase	)
     lowerCamelCase__    :      Dict					 =		result.last_hidden_state
     lowerCamelCase__    :      Any					 =		result.past_key_values
     lowerCamelCase__    :      List[Any]					 =		result.encoder_last_hidden_state

     self.parent.assertEqual(encoder_output.size()			,     (self.batch_size, self.encoder_seq_length, self.hidden_size)	)
     self.parent.assertEqual(decoder_output.size()			,     (self.batch_size, self.decoder_seq_length, self.hidden_size)	)
     # There should be `num_layers` key value embeddings stored in decoder_past
     self.parent.assertEqual(len(UpperCAmelCase	)			,     config.num_layers	)
     # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
     self.parent.assertEqual(len(decoder_past[0]	)			,     4	)
 def      A_	(      self  :      Optional[int]			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      Union[str, Any]			,     UpperCAmelCase  :      List[Any]			,     UpperCAmelCase  :      int			,     UpperCAmelCase  :      List[str]			,     UpperCAmelCase  :      Optional[Any]			,     )			->	Optional[int]:
     lowerCamelCase__    :      List[Any]					 =		UMTaModel(config=UpperCAmelCase	).get_decoder().to(UpperCAmelCase	).eval()
     # first forward pass
     lowerCamelCase__    :      Tuple					 =		model(UpperCAmelCase			,     use_cache=UpperCAmelCase	)
     lowerCamelCase__    :      List[Any]					 =		model(UpperCAmelCase	)
     lowerCamelCase__    :      int					 =		model(UpperCAmelCase			,     use_cache=UpperCAmelCase	)

     self.parent.assertTrue(len(UpperCAmelCase	) == len(UpperCAmelCase	)	)
     self.parent.assertTrue(len(UpperCAmelCase	) == len(UpperCAmelCase	) + 1	)

     lowerCamelCase__ ,							lowerCamelCase__    :      Dict					 =		outputs.to_tuple()

     # create hypothetical next token and extent to next_input_ids
     lowerCamelCase__    :      Optional[int]					 =		ids_tensor((self.batch_size, 1)			,     config.vocab_size	)

     # append to next input_ids and
     lowerCamelCase__    :      List[Any]					 =		torch.cat([input_ids, next_tokens]			,     dim=-1	)

     lowerCamelCase__    :      List[str]					 =		model(UpperCAmelCase	)['last_hidden_state']
     lowerCamelCase__    :      str					 =		model(UpperCAmelCase			,     past_key_values=UpperCAmelCase	)['last_hidden_state']

     # select random slice
     lowerCamelCase__    :      List[str]					 =		ids_tensor((1,)			,     output_from_past.shape[-1]	).item()
     lowerCamelCase__    :      Tuple					 =		output_from_no_past[:, -1, random_slice_idx].detach()
     lowerCamelCase__    :      List[str]					 =		output_from_past[:, 0, random_slice_idx].detach()

     # test that outputs are equal for slice
     self.parent.assertTrue(torch.allclose(UpperCAmelCase			,     UpperCAmelCase			,     atol=1e-3	)	)







 def      A_	(      self  :      Optional[int]			,     UpperCAmelCase  :      Optional[Any]			,     UpperCAmelCase  :      List[str]			,     )			->	Tuple:
     lowerCamelCase__    :      Union[str, Any]					 =		UMTaModel(config=UpperCAmelCase	).to(UpperCAmelCase	).half().eval()
     lowerCamelCase__    :      Optional[int]					 =		model(**UpperCAmelCase	)['last_hidden_state']
     self.parent.assertFalse(torch.isnan(UpperCAmelCase	).any().item()	)


@require_torch
class    lowerCAmelCase							(					__UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  unittest.TestCase			):
 UpperCAmelCase__													=		(
     (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
 )
 UpperCAmelCase__													=		(UMTaForConditionalGeneration,) if is_torch_available() else ()
 UpperCAmelCase__													=		(
     {
         """conversational""": UMTaForConditionalGeneration,
         """feature-extraction""": UMTaModel,
         """summarization""": UMTaForConditionalGeneration,
         """text2text-generation""": UMTaForConditionalGeneration,
         """translation""": UMTaForConditionalGeneration,
         """question-answering""": UMTaForQuestionAnswering,
     }
     if is_torch_available()
     else {}
 )
 UpperCAmelCase__													=		True
 UpperCAmelCase__													=		False
 UpperCAmelCase__													=		False
 UpperCAmelCase__													=		True
 UpperCAmelCase__													=		True
 # The small UMT5 model needs higher percentages for CPU/MP tests
 UpperCAmelCase__													=		[0.8, 0.9]
 def      A_	(      self  :      Union[str, Any]	)			->	List[Any]:
     lowerCamelCase__    :      Union[str, Any]					 =		UMTaModelTester(self	)
 @unittest.skip('Test has a segmentation fault on torch 1.8.0'	)
 def      A_	(      self  :      Tuple	)			->	int:
     lowerCamelCase__    :      str					 =		self.model_tester.prepare_config_and_inputs()
     lowerCamelCase__    :      Tuple					 =		UMTaModel(config_and_inputs[0]	).to(UpperCAmelCase	)
     with tempfile.TemporaryDirectory() as tmpdirname:
         torch.onnx.export(
             UpperCAmelCase			,     (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])			,     F"""{tmpdirname}/t5_test.onnx"""			,     export_params=UpperCAmelCase			,     opset_version=9			,     input_names=['input_ids', 'decoder_input_ids']			,     )
 @unittest.skipIf(torch_device == 'cpu'			,     'Cant do half precision'	)
 def      A_	(      self  :      Tuple	)			->	Optional[Any]:
     lowerCamelCase__    :      Any					 =		self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model_fpaa_forward(*UpperCAmelCase	)
 def      A_	(      self  :      List[Any]	)			->	str:
     lowerCamelCase__    :      int					 =		['encoder_attentions', 'decoder_attentions', 'cross_attentions']
     lowerCamelCase__    :      str					 =		self.model_tester.prepare_config_and_inputs()
     lowerCamelCase__    :      Any					 =		config_and_inputs[0]
     lowerCamelCase__    :      Any					 =		UMTaForConditionalGeneration(UpperCAmelCase	).eval()
     model.to(UpperCAmelCase	)

     lowerCamelCase__    :      Tuple					 =		{
         'head_mask': torch.zeros(config.num_layers			,     config.num_heads			,     device=UpperCAmelCase	),
         'decoder_head_mask': torch.zeros(config.num_decoder_layers			,     config.num_heads			,     device=UpperCAmelCase	),
         'cross_attn_head_mask': torch.zeros(config.num_decoder_layers			,     config.num_heads			,     device=UpperCAmelCase	),
     }

     for attn_name, (name, mask) in zip(UpperCAmelCase			,     head_masking.items()	):
         lowerCamelCase__    :      Union[str, Any]					 =		{name: mask}
         # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
         if name == "head_mask":
             lowerCamelCase__    :      Union[str, Any]					 =		torch.ones(
                 config.num_decoder_layers			,     config.num_heads			,     device=UpperCAmelCase	)

         lowerCamelCase__    :      Tuple					 =		model.generate(
             config_and_inputs[1]['input_ids']			,     num_beams=1			,     max_length=3			,     output_attentions=UpperCAmelCase			,     return_dict_in_generate=UpperCAmelCase			,     **UpperCAmelCase			,     )
         # We check the state of decoder_attentions and cross_attentions just from the last step
         lowerCamelCase__    :      Union[str, Any]					 =		out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
         self.assertEqual(sum([w.sum().item() for w in attn_weights]	)			,     0.0	)







 @unittest.skip('Does not work on the tiny model as we keep hitting edge cases.'	)
 def      A_	(      self  :      Optional[Any]	)			->	Optional[Any]:
     pass


@require_torch
@require_sentencepiece
@require_tokenizers
class    lowerCAmelCase							(					unittest.TestCase			):
 @slow
 @unittest.skip(
     'Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged'	)
 def      A_	(      self  :      Any	)			->	int:
     lowerCamelCase__    :      Optional[Any]					 =		UMTaForConditionalGeneration.from_pretrained('google/umt5-small'			,     return_dict=UpperCAmelCase	).to(UpperCAmelCase	)
     lowerCamelCase__    :      List[str]					 =		AutoTokenizer.from_pretrained('google/umt5-small'			,     use_fast=UpperCAmelCase			,     legacy=UpperCAmelCase	)
     lowerCamelCase__    :      Dict					 =		[
         'Bonjour monsieur <extra_id_0> bien <extra_id_1>.',
         'No se como puedo <extra_id_0>.',
         'This is the reason why we <extra_id_0> them.',
         'The <extra_id_0> walks in <extra_id_1>, seats',
         'A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.',
     ]
     lowerCamelCase__    :      Tuple					 =		tokenizer(UpperCAmelCase			,     return_tensors='pt'			,     padding=UpperCAmelCase	).input_ids
     # fmt: off
     lowerCamelCase__    :      Any					 =		torch.tensor(
         [
             [ 38530, 210703, 256299, 1410, 256298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
             [   826, 321, 671, 25922, 256299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
             [  1460, 339, 312, 19014, 10620, 758, 256299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
             [   517, 256299, 14869, 281, 301, 256298, 275, 119983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
             [   320, 256299, 14869, 281, 2234, 289, 2275, 333,61391, 289, 256298, 543, 256297, 168714, 329, 256296,274, 1],
         ]	)
     # fmt: on
     torch.testing.assert_allclose(UpperCAmelCase			,     UpperCAmelCase	)

     lowerCamelCase__    :      Optional[int]					 =		model.generate(input_ids.to(UpperCAmelCase	)	)
     lowerCamelCase__    :      List[Any]					 =		[
         '<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>',
         '<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
         '<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
         '<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
         '<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
     ]
     lowerCamelCase__    :      Union[str, Any]					 =		tokenizer.batch_decode(UpperCAmelCase	)
     self.assertEqual(UpperCAmelCase			,     UpperCAmelCase	)





