import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
				from .tokenization_nllb import NllbTokenizer
else:
				_lowercase:	Dict       			=		None


_lowercase:	int       			=		logging.get_logger(__name__)


_lowercase:	Optional[int]       			=		{'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

_lowercase:	Optional[Any]       			=		{
    '''vocab_file''': {
        '''facebook/nllb-200-distilled-600M''': (
            '''https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/nllb-200-distilled-600M''': (
            '''https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json'''
        ),
    },
}

_lowercase:	str       			=		{
    '''facebook/nllb-large-en-ro''': 1_0_2_4,
    '''facebook/nllb-200-distilled-600M''': 1_0_2_4,
}

# fmt: off
_lowercase:	Dict       			=		['''ace_Arab''', '''ace_Latn''', '''acm_Arab''', '''acq_Arab''', '''aeb_Arab''', '''afr_Latn''', '''ajp_Arab''', '''aka_Latn''', '''amh_Ethi''', '''apc_Arab''', '''arb_Arab''', '''ars_Arab''', '''ary_Arab''', '''arz_Arab''', '''asm_Beng''', '''ast_Latn''', '''awa_Deva''', '''ayr_Latn''', '''azb_Arab''', '''azj_Latn''', '''bak_Cyrl''', '''bam_Latn''', '''ban_Latn''', '''bel_Cyrl''', '''bem_Latn''', '''ben_Beng''', '''bho_Deva''', '''bjn_Arab''', '''bjn_Latn''', '''bod_Tibt''', '''bos_Latn''', '''bug_Latn''', '''bul_Cyrl''', '''cat_Latn''', '''ceb_Latn''', '''ces_Latn''', '''cjk_Latn''', '''ckb_Arab''', '''crh_Latn''', '''cym_Latn''', '''dan_Latn''', '''deu_Latn''', '''dik_Latn''', '''dyu_Latn''', '''dzo_Tibt''', '''ell_Grek''', '''eng_Latn''', '''epo_Latn''', '''est_Latn''', '''eus_Latn''', '''ewe_Latn''', '''fao_Latn''', '''pes_Arab''', '''fij_Latn''', '''fin_Latn''', '''fon_Latn''', '''fra_Latn''', '''fur_Latn''', '''fuv_Latn''', '''gla_Latn''', '''gle_Latn''', '''glg_Latn''', '''grn_Latn''', '''guj_Gujr''', '''hat_Latn''', '''hau_Latn''', '''heb_Hebr''', '''hin_Deva''', '''hne_Deva''', '''hrv_Latn''', '''hun_Latn''', '''hye_Armn''', '''ibo_Latn''', '''ilo_Latn''', '''ind_Latn''', '''isl_Latn''', '''ita_Latn''', '''jav_Latn''', '''jpn_Jpan''', '''kab_Latn''', '''kac_Latn''', '''kam_Latn''', '''kan_Knda''', '''kas_Arab''', '''kas_Deva''', '''kat_Geor''', '''knc_Arab''', '''knc_Latn''', '''kaz_Cyrl''', '''kbp_Latn''', '''kea_Latn''', '''khm_Khmr''', '''kik_Latn''', '''kin_Latn''', '''kir_Cyrl''', '''kmb_Latn''', '''kon_Latn''', '''kor_Hang''', '''kmr_Latn''', '''lao_Laoo''', '''lvs_Latn''', '''lij_Latn''', '''lim_Latn''', '''lin_Latn''', '''lit_Latn''', '''lmo_Latn''', '''ltg_Latn''', '''ltz_Latn''', '''lua_Latn''', '''lug_Latn''', '''luo_Latn''', '''lus_Latn''', '''mag_Deva''', '''mai_Deva''', '''mal_Mlym''', '''mar_Deva''', '''min_Latn''', '''mkd_Cyrl''', '''plt_Latn''', '''mlt_Latn''', '''mni_Beng''', '''khk_Cyrl''', '''mos_Latn''', '''mri_Latn''', '''zsm_Latn''', '''mya_Mymr''', '''nld_Latn''', '''nno_Latn''', '''nob_Latn''', '''npi_Deva''', '''nso_Latn''', '''nus_Latn''', '''nya_Latn''', '''oci_Latn''', '''gaz_Latn''', '''ory_Orya''', '''pag_Latn''', '''pan_Guru''', '''pap_Latn''', '''pol_Latn''', '''por_Latn''', '''prs_Arab''', '''pbt_Arab''', '''quy_Latn''', '''ron_Latn''', '''run_Latn''', '''rus_Cyrl''', '''sag_Latn''', '''san_Deva''', '''sat_Beng''', '''scn_Latn''', '''shn_Mymr''', '''sin_Sinh''', '''slk_Latn''', '''slv_Latn''', '''smo_Latn''', '''sna_Latn''', '''snd_Arab''', '''som_Latn''', '''sot_Latn''', '''spa_Latn''', '''als_Latn''', '''srd_Latn''', '''srp_Cyrl''', '''ssw_Latn''', '''sun_Latn''', '''swe_Latn''', '''swh_Latn''', '''szl_Latn''', '''tam_Taml''', '''tat_Cyrl''', '''tel_Telu''', '''tgk_Cyrl''', '''tgl_Latn''', '''tha_Thai''', '''tir_Ethi''', '''taq_Latn''', '''taq_Tfng''', '''tpi_Latn''', '''tsn_Latn''', '''tso_Latn''', '''tuk_Latn''', '''tum_Latn''', '''tur_Latn''', '''twi_Latn''', '''tzm_Tfng''', '''uig_Arab''', '''ukr_Cyrl''', '''umb_Latn''', '''urd_Arab''', '''uzn_Latn''', '''vec_Latn''', '''vie_Latn''', '''war_Latn''', '''wol_Latn''', '''xho_Latn''', '''ydd_Hebr''', '''yor_Latn''', '''yue_Hant''', '''zho_Hans''', '''zho_Hant''', '''zul_Latn''']



class        lowerCamelCase__				(   UpperCAmelCase				):
			UpperCamelCase__    							=VOCAB_FILES_NAMES
			UpperCamelCase__    							=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase__    							=PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase__    							=["input_ids", "attention_mask"]
			UpperCamelCase__    							=NllbTokenizer

			UpperCamelCase__    							=[]
			UpperCamelCase__    							=[]





			def __init__(       self	:	str  ,  lowercase__	:	Optional[Any]=None  ,  lowercase__	:	List[str]=None  ,  lowercase__	:	Optional[Any]="<s>"  ,  lowercase__	:	Any="</s>"  ,  lowercase__	:	Optional[Any]="</s>"  ,  lowercase__	:	Optional[int]="<s>"  ,  lowercase__	:	Dict="<unk>"  ,  lowercase__	:	str="<pad>"  ,  lowercase__	:	List[str]="<mask>"  ,  lowercase__	:	Optional[Any]=None  ,  lowercase__	:	int=None  ,  lowercase__	:	Union[str, Any]=None  ,  lowercase__	:	Tuple=False  ,  **lowercase__	:	str  ,  ):
										# Mask token behave like a normal word, i.e. include the space before it
										_lowerCAmelCase							=							AddedToken(lowercase__  ,  lstrip=lowercase__  ,  rstrip=lowercase__						) if isinstance(lowercase__  ,  lowercase__						) else mask_token
										_lowerCAmelCase							=							legacy_behaviour
										super().__init__(
										    vocab_file=lowercase__  ,  tokenizer_file=lowercase__  ,  bos_token=lowercase__  ,  eos_token=lowercase__  ,  sep_token=lowercase__  ,  cls_token=lowercase__  ,  unk_token=lowercase__  ,  pad_token=lowercase__  ,  mask_token=lowercase__  ,  src_lang=lowercase__  ,  tgt_lang=lowercase__  ,  additional_special_tokens=lowercase__  ,  legacy_behaviour=lowercase__  ,  **lowercase__  ,  )

										_lowerCAmelCase							=							vocab_file
										_lowerCAmelCase							=							False if not self.vocab_file else True

										_lowerCAmelCase							=							FAIRSEQ_LANGUAGE_CODES.copy()

										if additional_special_tokens is not None:
																	# Only add those special tokens if they are not already there.
																	_additional_special_tokens.extend(
																	    [t for t in additional_special_tokens if t not in _additional_special_tokens]						)

										self.add_special_tokens({'additional_special_tokens': _additional_special_tokens}						)
										_lowerCAmelCase							=							{
										    lang_code: self.convert_tokens_to_ids(lowercase__						) for lang_code in FAIRSEQ_LANGUAGE_CODES
										}

										_lowerCAmelCase							=							src_lang if src_lang is not None else 'eng_Latn'
										_lowerCAmelCase							=							self.convert_tokens_to_ids(self._src_lang						)
										_lowerCAmelCase							=							tgt_lang
										self.set_src_lang_special_tokens(self._src_lang						)





			@property
			def   SCREAMING_SNAKE_CASE__     (       self	:	int						):
										return self._src_lang





			@src_lang.setter
			def   SCREAMING_SNAKE_CASE__     (       self	:	Dict  ,  lowercase__	:	str						):
										_lowerCAmelCase							=							new_src_lang
										self.set_src_lang_special_tokens(self._src_lang						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[int]  ,  lowercase__	:	List[int]  ,  lowercase__	:	Optional[List[int]] = None						):
										if token_ids_a is None:
																	return self.prefix_tokens + token_ids_a + self.suffix_tokens
										# We don't expect to process pairs, but leave the pair logic for API consistency
										return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens





			def   SCREAMING_SNAKE_CASE__     (       self	:	str  ,  lowercase__	:	List[int]  ,  lowercase__	:	Optional[List[int]] = None						):
										_lowerCAmelCase							=							[self.sep_token_id]
										_lowerCAmelCase							=							[self.cls_token_id]

										if token_ids_a is None:
																	return len(cls + token_ids_a + sep						) * [0]
										return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[int]  ,  lowercase__	:	str  ,  lowercase__	:	str  ,  lowercase__	:	Optional[str]  ,  lowercase__	:	Optional[str]  ,  **lowercase__	:	Optional[Any]						):
										if src_lang is None or tgt_lang is None:
																	raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'						)
										_lowerCAmelCase							=							src_lang
										_lowerCAmelCase							=							self(lowercase__  ,  add_special_tokens=lowercase__  ,  return_tensors=lowercase__  ,  **lowercase__						)
										_lowerCAmelCase							=							self.convert_tokens_to_ids(lowercase__						)
										_lowerCAmelCase							=							tgt_lang_id
										return inputs





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]  ,  lowercase__	:	List[str]  ,  lowercase__	:	str = "eng_Latn"  ,  lowercase__	:	Optional[List[str]] = None  ,  lowercase__	:	str = "fra_Latn"  ,  **lowercase__	:	Optional[Any]  ,  ):
										_lowerCAmelCase							=							src_lang
										_lowerCAmelCase							=							tgt_lang
										return super().prepare_seqaseq_batch(lowercase__  ,  lowercase__  ,  **lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[Any]						):
										return self.set_src_lang_special_tokens(self.src_lang						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Union[str, Any]						):
										return self.set_tgt_lang_special_tokens(self.tgt_lang						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Tuple  ,  lowercase__	:	Optional[Any]						):
										_lowerCAmelCase							=							self.convert_tokens_to_ids(lowercase__						)

										if self.legacy_behaviour:
																	_lowerCAmelCase							=							[]
																	_lowerCAmelCase							=							[self.eos_token_id, self.cur_lang_code]
										else:
																	_lowerCAmelCase							=							[self.cur_lang_code]
																	_lowerCAmelCase							=							[self.eos_token_id]

										_lowerCAmelCase							=							self.convert_ids_to_tokens(self.prefix_tokens						)
										_lowerCAmelCase							=							self.convert_ids_to_tokens(self.suffix_tokens						)

										_lowerCAmelCase							=							processors.TemplateProcessing(
										    single=prefix_tokens_str + ['$A'] + suffix_tokens_str  ,  pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str  ,  special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str  ,  self.prefix_tokens + self.suffix_tokens						)						)  ,  )





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[Any]  ,  lowercase__	:	str						):
										_lowerCAmelCase							=							self.convert_tokens_to_ids(lowercase__						)
										if self.legacy_behaviour:
																	_lowerCAmelCase							=							[]
																	_lowerCAmelCase							=							[self.eos_token_id, self.cur_lang_code]
										else:
																	_lowerCAmelCase							=							[self.cur_lang_code]
																	_lowerCAmelCase							=							[self.eos_token_id]

										_lowerCAmelCase							=							self.convert_ids_to_tokens(self.prefix_tokens						)
										_lowerCAmelCase							=							self.convert_ids_to_tokens(self.suffix_tokens						)

										_lowerCAmelCase							=							processors.TemplateProcessing(
										    single=prefix_tokens_str + ['$A'] + suffix_tokens_str  ,  pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str  ,  special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str  ,  self.prefix_tokens + self.suffix_tokens						)						)  ,  )





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[Any]  ,  lowercase__	:	str  ,  lowercase__	:	Optional[str] = None						):
										if not self.can_save_slow_tokenizer:
																	raise ValueError(
																	    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
																	    'tokenizer.'						)

										if not os.path.isdir(lowercase__						):
																	logger.error(f'Vocabulary path ({save_directory}) should be a directory.'						)
																	return
										_lowerCAmelCase							=							os.path.join(
										    lowercase__  ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']						)

										if os.path.abspath(self.vocab_file						) != os.path.abspath(lowercase__						):
																	copyfile(self.vocab_file  ,  lowercase__						)

										return (out_vocab_file,)







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	:	Union[str, Any]  ,  lowercase__	:	List[Any]  ,  lowercase__	:	Dict=99  ,  lowercase__	:	List[Any]=13  ,  lowercase__	:	Union[str, Any]=7  ,  lowercase__	:	int=9  ,  lowercase__	:	List[str]=True  ,  lowercase__	:	str=True  ,  lowercase__	:	Any=False  ,  lowercase__	:	int=32  ,  lowercase__	:	Any=5  ,  lowercase__	:	int=4  ,  lowercase__	:	int=37  ,  lowercase__	:	List[str]=8  ,  lowercase__	:	Optional[int]=0.1  ,  lowercase__	:	List[Any]=0.0_0_2  ,  lowercase__	:	str=1  ,  lowercase__	:	List[str]=0  ,  lowercase__	:	Optional[int]=0  ,  lowercase__	:	List[str]=None  ,  lowercase__	:	Any=None  ,  ):
										_lowerCAmelCase							=							parent
										_lowerCAmelCase							=							batch_size
										_lowerCAmelCase							=							encoder_seq_length
										_lowerCAmelCase							=							decoder_seq_length
										# For common tests
										_lowerCAmelCase							=							self.decoder_seq_length
										_lowerCAmelCase							=							is_training
										_lowerCAmelCase							=							use_attention_mask
										_lowerCAmelCase							=							use_labels
										_lowerCAmelCase							=							vocab_size
										_lowerCAmelCase							=							hidden_size
										_lowerCAmelCase							=							num_hidden_layers
										_lowerCAmelCase							=							num_attention_heads
										_lowerCAmelCase							=							d_ff
										_lowerCAmelCase							=							relative_attention_num_buckets
										_lowerCAmelCase							=							dropout_rate
										_lowerCAmelCase							=							initializer_factor
										_lowerCAmelCase							=							eos_token_id
										_lowerCAmelCase							=							pad_token_id
										_lowerCAmelCase							=							decoder_start_token_id
										_lowerCAmelCase							=							None
										_lowerCAmelCase							=							decoder_layers





			def   SCREAMING_SNAKE_CASE__     (       self	:	str						):
										return TaConfig.from_pretrained('google/umt5-base'						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	int  ,  lowercase__	:	List[str]  ,  lowercase__	:	Any  ,  lowercase__	:	int  ,  lowercase__	:	int=None  ,  lowercase__	:	str=None  ,  lowercase__	:	int=None  ,  lowercase__	:	Optional[int]=None  ,  lowercase__	:	List[Any]=None  ,  ):
										if attention_mask is None:
																	_lowerCAmelCase							=							input_ids.ne(config.pad_token_id						)
										if decoder_attention_mask is None:
																	_lowerCAmelCase							=							decoder_input_ids.ne(config.pad_token_id						)
										if head_mask is None:
																	_lowerCAmelCase							=							torch.ones(config.num_hidden_layers  ,  config.num_attention_heads  ,  device=lowercase__						)
										if decoder_head_mask is None:
																	_lowerCAmelCase							=							torch.ones(config.num_decoder_layers  ,  config.num_attention_heads  ,  device=lowercase__						)
										if cross_attn_head_mask is None:
																	_lowerCAmelCase							=							torch.ones(
																	    config.num_decoder_layers  ,  config.num_attention_heads  ,  device=lowercase__						)
										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   SCREAMING_SNAKE_CASE__     (       self	:	List[Any]						):
										_lowerCAmelCase							=							ids_tensor([self.batch_size, self.encoder_seq_length]  ,  self.vocab_size						)
										_lowerCAmelCase							=							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							=							input_ids.clamp(self.pad_token_id + 1						)
										_lowerCAmelCase							=							decoder_input_ids.clamp(self.pad_token_id + 1						)

										_lowerCAmelCase							=							self.get_config()
										_lowerCAmelCase							=							config.num_attention_heads
										_lowerCAmelCase							=							self.prepare_inputs_dict(lowercase__  ,  lowercase__  ,  lowercase__						)
										return config, input_dict





			def   SCREAMING_SNAKE_CASE__     (       self	:	Dict						):
										_lowerCAmelCase			,		_lowerCAmelCase							=							self.prepare_config_and_inputs()
										return config, inputs_dict





			def   SCREAMING_SNAKE_CASE__     (       self	:	Tuple						):
										return TaConfig(
										    vocab_size=1_66  ,  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   SCREAMING_SNAKE_CASE__     (       self	:	Optional[Any]						):
										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   SCREAMING_SNAKE_CASE__     (       self	:	Dict  ,  lowercase__	:	List[str]  ,  lowercase__	:	str  ,  lowercase__	:	Any  ,  lowercase__	:	Optional[int]  ,  lowercase__	:	Tuple  ,  lowercase__	:	Dict  ,  ):
										_lowerCAmelCase							=							UMTaModel(config=lowercase__						)
										model.to(lowercase__						)
										model.eval()
										_lowerCAmelCase							=							model(
										    input_ids=lowercase__  ,  decoder_input_ids=lowercase__  ,  attention_mask=lowercase__  ,  decoder_attention_mask=lowercase__  ,  )
										_lowerCAmelCase							=							model(input_ids=lowercase__  ,  decoder_input_ids=lowercase__						)
										_lowerCAmelCase							=							result.last_hidden_state
										_lowerCAmelCase							=							result.past_key_values
										_lowerCAmelCase							=							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(lowercase__						)  ,  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   SCREAMING_SNAKE_CASE__     (       self	:	Any  ,  lowercase__	:	Tuple  ,  lowercase__	:	Tuple  ,  lowercase__	:	List[Any]  ,  lowercase__	:	Dict  ,  lowercase__	:	str  ,  lowercase__	:	List[Any]  ,  ):
										_lowerCAmelCase							=							UMTaModel(config=lowercase__						).get_decoder().to(lowercase__						).eval()
										# first forward pass
										_lowerCAmelCase							=							model(lowercase__  ,  use_cache=lowercase__						)
										_lowerCAmelCase							=							model(lowercase__						)
										_lowerCAmelCase							=							model(lowercase__  ,  use_cache=lowercase__						)

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

										_lowerCAmelCase			,		_lowerCAmelCase							=							outputs.to_tuple()

										# create hypothetical next token and extent to next_input_ids
										_lowerCAmelCase							=							ids_tensor((self.batch_size, 1)  ,  config.vocab_size						)

										# append to next input_ids and
										_lowerCAmelCase							=							torch.cat([input_ids, next_tokens]  ,  dim=-1						)

										_lowerCAmelCase							=							model(lowercase__						)['last_hidden_state']
										_lowerCAmelCase							=							model(lowercase__  ,  past_key_values=lowercase__						)['last_hidden_state']

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

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





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[int]  ,  lowercase__	:	Tuple  ,  lowercase__	:	Dict  ,  ):
										_lowerCAmelCase							=							UMTaModel(config=lowercase__						).to(lowercase__						).half().eval()
										_lowerCAmelCase							=							model(**lowercase__						)['last_hidden_state']
										self.parent.assertFalse(torch.isnan(lowercase__						).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   SCREAMING_SNAKE_CASE__     (       self	:	List[str]						):
										_lowerCAmelCase							=							UMTaModelTester(self						)





			@unittest.skip('Test has a segmentation fault on torch 1.8.0'						)
			def   SCREAMING_SNAKE_CASE__     (       self	:	str						):
										_lowerCAmelCase							=							self.model_tester.prepare_config_and_inputs()
										_lowerCAmelCase							=							UMTaModel(config_and_inputs[0]						).to(lowercase__						)
										with tempfile.TemporaryDirectory() as tmpdirname:
																	torch.onnx.export(
																	    lowercase__  ,  (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])  ,  f'{tmpdirname}/t5_test.onnx'  ,  export_params=lowercase__  ,  opset_version=9  ,  input_names=['input_ids', 'decoder_input_ids']  ,  )





			@unittest.skipIf(torch_device == 'cpu'  ,  'Cant do half precision'						)
			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[int]						):
										_lowerCAmelCase							=							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model_fpaa_forward(*lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	str						):
										_lowerCAmelCase							=							['encoder_attentions', 'decoder_attentions', 'cross_attentions']
										_lowerCAmelCase							=							self.model_tester.prepare_config_and_inputs()
										_lowerCAmelCase							=							config_and_inputs[0]
										_lowerCAmelCase							=							UMTaForConditionalGeneration(lowercase__						).eval()
										model.to(lowercase__						)

										_lowerCAmelCase							=							{
										    'head_mask': torch.zeros(config.num_layers  ,  config.num_heads  ,  device=lowercase__						),
										    'decoder_head_mask': torch.zeros(config.num_decoder_layers  ,  config.num_heads  ,  device=lowercase__						),
										    'cross_attn_head_mask': torch.zeros(config.num_decoder_layers  ,  config.num_heads  ,  device=lowercase__						),
										}

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

																	_lowerCAmelCase							=							model.generate(
																	    config_and_inputs[1]['input_ids']  ,  num_beams=1  ,  max_length=3  ,  output_attentions=lowercase__  ,  return_dict_in_generate=lowercase__  ,  **lowercase__  ,  )
																	# We check the state of decoder_attentions and cross_attentions just from the last step
																	_lowerCAmelCase							=							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   SCREAMING_SNAKE_CASE__     (       self	:	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   SCREAMING_SNAKE_CASE__     (       self	:	str						):
										_lowerCAmelCase							=							UMTaForConditionalGeneration.from_pretrained('google/umt5-small'  ,  return_dict=lowercase__						).to(lowercase__						)
										_lowerCAmelCase							=							AutoTokenizer.from_pretrained('google/umt5-small'  ,  use_fast=lowercase__  ,  legacy=lowercase__						)
										_lowerCAmelCase							=							[
										    '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							=							tokenizer(lowercase__  ,  return_tensors='pt'  ,  padding=lowercase__						).input_ids
										# fmt: off
										_lowerCAmelCase							=							torch.tensor(
										    [
										        [ 3_85_30, 21_07_03, 25_62_99, 14_10, 25_62_98, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
										        [   8_26, 3_21, 6_71, 2_59_22, 25_62_99, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
										        [  14_60, 3_39, 3_12, 1_90_14, 1_06_20, 7_58, 25_62_99, 23_55,2_74, 1, 0, 0, 0, 0, 0, 0,0, 0],
										        [   5_17, 25_62_99, 1_48_69, 2_81, 3_01, 25_62_98, 2_75, 11_99_83,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
										        [   3_20, 25_62_99, 1_48_69, 2_81, 22_34, 2_89, 22_75, 3_33,6_13_91, 2_89, 25_62_98, 5_43, 25_62_97, 16_87_14, 3_29, 25_62_96,2_74, 1],
										    ]						)
										# fmt: on
										torch.testing.assert_allclose(lowercase__  ,  lowercase__						)

										_lowerCAmelCase							=							model.generate(input_ids.to(lowercase__						)						)
										_lowerCAmelCase							=							[
										    '<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							=							tokenizer.batch_decode(lowercase__						)
										self.assertEqual(lowercase__  ,  lowercase__						)






