
"""simple docstring"""



import os
import unittest

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    BertTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english




@require_tokenizers
class 		a_						( _UpperCAmelCase     ,						unittest.TestCase	):
    a      :				List[str]     =				BertTokenizer
    a      :				List[Any]     =				BertTokenizerFast
    a      :				Tuple     =				True
    a      :				List[str]     =				True
    a      :				List[str]     =				filter_non_english







    def      _snake_case					(			self			:       Tuple     )		->Optional[Any]:






        '''simple docstring'''






        super().setUp()

        _UpperCAmelCase          =   [
            """[UNK]""",
            """[CLS]""",
            """[SEP]""",
            """[PAD]""",
            """[MASK]""",
            """want""",
            """##want""",
            """##ed""",
            """wa""",
            """un""",
            """runn""",
            """##ing""",
            """,""",
            """low""",
            """lowest""",
        ]
        _UpperCAmelCase          =   os.path.join(self.tmpdirname     ,					VOCAB_FILES_NAMES["""vocab_file"""]     )
        with open(self.vocab_file     ,					"""w"""     ,					encoding="""utf-8"""     ) as vocab_writer:
            vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]     )     )







    def      _snake_case					(			self			:       List[str]     ,					__UpperCamelCase			:       str     )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   """UNwant\u00E9d,running"""
        _UpperCAmelCase          =   """unwanted, running"""
        return input_text, output_text







    def      _snake_case					(			self			:       Tuple     )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   self.tokenizer_class(self.vocab_file     )

        _UpperCAmelCase          =   tokenizer.tokenize("""UNwant\u00E9d,running"""     )
        self.assertListEqual(__UpperCamelCase     ,					["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]     )
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCamelCase     )     ,					[9, 6, 7, 12, 10, 11]     )







    def      _snake_case					(			self			:       Dict     )		->List[str]:






        '''simple docstring'''






        if not self.test_rust_tokenizer:
            return

        _UpperCAmelCase          =   self.get_tokenizer()
        _UpperCAmelCase          =   self.get_rust_tokenizer()

        _UpperCAmelCase          =   """UNwant\u00E9d,running"""

        _UpperCAmelCase          =   tokenizer.tokenize(__UpperCamelCase     )
        _UpperCAmelCase          =   rust_tokenizer.tokenize(__UpperCamelCase     )
        self.assertListEqual(__UpperCamelCase     ,					__UpperCamelCase     )

        _UpperCAmelCase          =   tokenizer.encode(__UpperCamelCase     ,					add_special_tokens=__UpperCamelCase     )
        _UpperCAmelCase          =   rust_tokenizer.encode(__UpperCamelCase     ,					add_special_tokens=__UpperCamelCase     )
        self.assertListEqual(__UpperCamelCase     ,					__UpperCamelCase     )

        _UpperCAmelCase          =   self.get_rust_tokenizer()
        _UpperCAmelCase          =   tokenizer.encode(__UpperCamelCase     )
        _UpperCAmelCase          =   rust_tokenizer.encode(__UpperCamelCase     )
        self.assertListEqual(__UpperCamelCase     ,					__UpperCamelCase     )

        # With lower casing
        _UpperCAmelCase          =   self.get_tokenizer(do_lower_case=__UpperCamelCase     )
        _UpperCAmelCase          =   self.get_rust_tokenizer(do_lower_case=__UpperCamelCase     )

        _UpperCAmelCase          =   """UNwant\u00E9d,running"""

        _UpperCAmelCase          =   tokenizer.tokenize(__UpperCamelCase     )
        _UpperCAmelCase          =   rust_tokenizer.tokenize(__UpperCamelCase     )
        self.assertListEqual(__UpperCamelCase     ,					__UpperCamelCase     )

        _UpperCAmelCase          =   tokenizer.encode(__UpperCamelCase     ,					add_special_tokens=__UpperCamelCase     )
        _UpperCAmelCase          =   rust_tokenizer.encode(__UpperCamelCase     ,					add_special_tokens=__UpperCamelCase     )
        self.assertListEqual(__UpperCamelCase     ,					__UpperCamelCase     )

        _UpperCAmelCase          =   self.get_rust_tokenizer()
        _UpperCAmelCase          =   tokenizer.encode(__UpperCamelCase     )
        _UpperCAmelCase          =   rust_tokenizer.encode(__UpperCamelCase     )
        self.assertListEqual(__UpperCamelCase     ,					__UpperCamelCase     )







    def      _snake_case					(			self			:       Any     )		->List[str]:






        '''simple docstring'''






        _UpperCAmelCase          =   BasicTokenizer()

        self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz"""     )     ,					["""ah""", """\u535A""", """\u63A8""", """zz"""]     )







    def      _snake_case					(			self			:       str     )		->str:






        '''simple docstring'''






        _UpperCAmelCase          =   BasicTokenizer(do_lower_case=__UpperCamelCase     )

        self.assertListEqual(
            tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """     )     ,					["""hello""", """!""", """how""", """are""", """you""", """?"""]     )
        self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""     )     ,					["""hello"""]     )







    def      _snake_case					(			self			:       int     )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase          =   BasicTokenizer(do_lower_case=__UpperCamelCase     ,					strip_accents=__UpperCamelCase     )

        self.assertListEqual(
            tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """     )     ,					["""hällo""", """!""", """how""", """are""", """you""", """?"""]     )
        self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""     )     ,					["""h\u00E9llo"""]     )







    def      _snake_case					(			self			:       Optional[Any]     )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   BasicTokenizer(do_lower_case=__UpperCamelCase     ,					strip_accents=__UpperCamelCase     )

        self.assertListEqual(
            tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """     )     ,					["""hallo""", """!""", """how""", """are""", """you""", """?"""]     )
        self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""     )     ,					["""hello"""]     )







    def      _snake_case					(			self			:       Union[str, Any]     )		->int:






        '''simple docstring'''






        _UpperCAmelCase          =   BasicTokenizer(do_lower_case=__UpperCamelCase     )

        self.assertListEqual(
            tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """     )     ,					["""hallo""", """!""", """how""", """are""", """you""", """?"""]     )
        self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""     )     ,					["""hello"""]     )







    def      _snake_case					(			self			:       Optional[int]     )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   BasicTokenizer(do_lower_case=__UpperCamelCase     )

        self.assertListEqual(
            tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """     )     ,					["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""]     )







    def      _snake_case					(			self			:       Dict     )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase          =   BasicTokenizer(do_lower_case=__UpperCamelCase     ,					strip_accents=__UpperCamelCase     )

        self.assertListEqual(
            tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """     )     ,					["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""]     )







    def      _snake_case					(			self			:       Union[str, Any]     )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase          =   BasicTokenizer(do_lower_case=__UpperCamelCase     ,					strip_accents=__UpperCamelCase     )

        self.assertListEqual(
            tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """     )     ,					["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""]     )







    def      _snake_case					(			self			:       List[Any]     )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase          =   BasicTokenizer(do_lower_case=__UpperCamelCase     ,					never_split=["""[UNK]"""]     )

        self.assertListEqual(
            tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU? [UNK]"""     )     ,					["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""]     )







    def      _snake_case					(			self			:       List[Any]     )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase          =   BasicTokenizer()
        _UpperCAmelCase          =   """a\n'll !!to?'d of, can't."""
        _UpperCAmelCase          =   ["""a""", """'""", """ll""", """!""", """!""", """to""", """?""", """'""", """d""", """of""", """,""", """can""", """'""", """t""", """."""]
        self.assertListEqual(tokenizer.tokenize(__UpperCamelCase     )     ,					__UpperCamelCase     )







    def      _snake_case					(			self			:       Tuple     )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   ["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]

        _UpperCAmelCase          =   {}
        for i, token in enumerate(__UpperCamelCase     ):
            _UpperCAmelCase          =   i
        _UpperCAmelCase          =   WordpieceTokenizer(vocab=__UpperCamelCase     ,					unk_token="""[UNK]"""     )

        self.assertListEqual(tokenizer.tokenize(""""""     )     ,					[]     )

        self.assertListEqual(tokenizer.tokenize("""unwanted running"""     )     ,					["""un""", """##want""", """##ed""", """runn""", """##ing"""]     )

        self.assertListEqual(tokenizer.tokenize("""unwantedX running"""     )     ,					["""[UNK]""", """runn""", """##ing"""]     )







    def      _snake_case					(			self			:       Optional[int]     )		->str:






        '''simple docstring'''






        self.assertTrue(_is_whitespace(""" """     )     )
        self.assertTrue(_is_whitespace("""\t"""     )     )
        self.assertTrue(_is_whitespace("""\r"""     )     )
        self.assertTrue(_is_whitespace("""\n"""     )     )
        self.assertTrue(_is_whitespace("""\u00A0"""     )     )

        self.assertFalse(_is_whitespace("""A"""     )     )
        self.assertFalse(_is_whitespace("""-"""     )     )







    def      _snake_case					(			self			:       Tuple     )		->Optional[int]:






        '''simple docstring'''






        self.assertTrue(_is_control("""\u0005"""     )     )

        self.assertFalse(_is_control("""A"""     )     )
        self.assertFalse(_is_control(""" """     )     )
        self.assertFalse(_is_control("""\t"""     )     )
        self.assertFalse(_is_control("""\r"""     )     )







    def      _snake_case					(			self			:       Tuple     )		->Union[str, Any]:






        '''simple docstring'''






        self.assertTrue(_is_punctuation("""-"""     )     )
        self.assertTrue(_is_punctuation("""$"""     )     )
        self.assertTrue(_is_punctuation("""`"""     )     )
        self.assertTrue(_is_punctuation("""."""     )     )

        self.assertFalse(_is_punctuation("""A"""     )     )
        self.assertFalse(_is_punctuation(""" """     )     )







    def      _snake_case					(			self			:       Dict     )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase          =   self.get_tokenizer()
        _UpperCAmelCase          =   self.get_rust_tokenizer()

        # Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
        self.assertListEqual([tokenizer.tokenize(__UpperCamelCase     ) for t in ["""Test""", """\xad""", """test"""]]     ,					[["""[UNK]"""], [], ["""[UNK]"""]]     )

        self.assertListEqual(
            [rust_tokenizer.tokenize(__UpperCamelCase     ) for t in ["""Test""", """\xad""", """test"""]]     ,					[["""[UNK]"""], [], ["""[UNK]"""]]     )







    @slow
    def      _snake_case					(			self			:       Optional[int]     )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase          =   self.tokenizer_class.from_pretrained("""bert-base-uncased"""     )

        _UpperCAmelCase          =   tokenizer.encode("""sequence builders"""     ,					add_special_tokens=__UpperCamelCase     )
        _UpperCAmelCase          =   tokenizer.encode("""multi-sequence build"""     ,					add_special_tokens=__UpperCamelCase     )

        _UpperCAmelCase          =   tokenizer.build_inputs_with_special_tokens(__UpperCamelCase     )
        _UpperCAmelCase          =   tokenizer.build_inputs_with_special_tokens(__UpperCamelCase     ,					__UpperCamelCase     )

        assert encoded_sentence == [1_01] + text + [1_02]
        assert encoded_pair == [1_01] + text + [1_02] + text_a + [1_02]







    def      _snake_case					(			self			:       Optional[Any]     )		->Dict:






        '''simple docstring'''






        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""     ):
                _UpperCAmelCase          =   self.rust_tokenizer_class.from_pretrained(__UpperCamelCase     ,					**__UpperCamelCase     )

                _UpperCAmelCase          =   f"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
                _UpperCAmelCase          =   tokenizer_r.encode_plus(
                    __UpperCamelCase     ,					return_attention_mask=__UpperCamelCase     ,					return_token_type_ids=__UpperCamelCase     ,					return_offsets_mapping=__UpperCamelCase     ,					add_special_tokens=__UpperCamelCase     ,					)

                _UpperCAmelCase          =   tokenizer_r.do_lower_case if hasattr(__UpperCamelCase     ,					"""do_lower_case"""     ) else False
                _UpperCAmelCase          =   (
                    [
                        ((0, 0), tokenizer_r.cls_token),
                        ((0, 1), """A"""),
                        ((1, 2), ""","""),
                        ((3, 5), """na"""),
                        ((5, 6), """##ï"""),
                        ((6, 8), """##ve"""),
                        ((9, 15), tokenizer_r.mask_token),
                        ((16, 21), """Allen"""),
                        ((21, 23), """##NL"""),
                        ((23, 24), """##P"""),
                        ((25, 33), """sentence"""),
                        ((33, 34), """."""),
                        ((0, 0), tokenizer_r.sep_token),
                    ]
                    if not do_lower_case
                    else [
                        ((0, 0), tokenizer_r.cls_token),
                        ((0, 1), """a"""),
                        ((1, 2), ""","""),
                        ((3, 8), """naive"""),
                        ((9, 15), tokenizer_r.mask_token),
                        ((16, 21), """allen"""),
                        ((21, 23), """##nl"""),
                        ((23, 24), """##p"""),
                        ((25, 33), """sentence"""),
                        ((33, 34), """."""),
                        ((0, 0), tokenizer_r.sep_token),
                    ]
                )

                self.assertEqual(
                    [e[1] for e in expected_results]     ,					tokenizer_r.convert_ids_to_tokens(tokens["""input_ids"""]     )     )
                self.assertEqual([e[0] for e in expected_results]     ,					tokens["""offset_mapping"""]     )







    def      _snake_case					(			self			:       Optional[int]     )		->List[str]:






        '''simple docstring'''






        _UpperCAmelCase          =   ["""的""", """人""", """有"""]
        _UpperCAmelCase          =   """""".join(__UpperCamelCase     )
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""     ):
                _UpperCAmelCase          =   True
                _UpperCAmelCase          =   self.tokenizer_class.from_pretrained(__UpperCamelCase     ,					**__UpperCamelCase     )
                _UpperCAmelCase          =   self.rust_tokenizer_class.from_pretrained(__UpperCamelCase     ,					**__UpperCamelCase     )

                _UpperCAmelCase          =   tokenizer_p.encode(__UpperCamelCase     ,					add_special_tokens=__UpperCamelCase     )
                _UpperCAmelCase          =   tokenizer_r.encode(__UpperCamelCase     ,					add_special_tokens=__UpperCamelCase     )

                _UpperCAmelCase          =   tokenizer_r.convert_ids_to_tokens(__UpperCamelCase     )
                _UpperCAmelCase          =   tokenizer_p.convert_ids_to_tokens(__UpperCamelCase     )

                # it is expected that each Chinese character is not preceded by "##"
                self.assertListEqual(__UpperCamelCase     ,					__UpperCamelCase     )
                self.assertListEqual(__UpperCamelCase     ,					__UpperCamelCase     )

                _UpperCAmelCase          =   False
                _UpperCAmelCase          =   self.rust_tokenizer_class.from_pretrained(__UpperCamelCase     ,					**__UpperCamelCase     )
                _UpperCAmelCase          =   self.tokenizer_class.from_pretrained(__UpperCamelCase     ,					**__UpperCamelCase     )

                _UpperCAmelCase          =   tokenizer_r.encode(__UpperCamelCase     ,					add_special_tokens=__UpperCamelCase     )
                _UpperCAmelCase          =   tokenizer_p.encode(__UpperCamelCase     ,					add_special_tokens=__UpperCamelCase     )

                _UpperCAmelCase          =   tokenizer_r.convert_ids_to_tokens(__UpperCamelCase     )
                _UpperCAmelCase          =   tokenizer_p.convert_ids_to_tokens(__UpperCamelCase     )

                # it is expected that only the first Chinese character is not preceded by "##".
                _UpperCAmelCase          =   [
                    f"""##{token}""" if idx != 0 else token for idx, token in enumerate(__UpperCamelCase     )
                ]
                self.assertListEqual(__UpperCamelCase     ,					__UpperCamelCase     )
                self.assertListEqual(__UpperCamelCase     ,					__UpperCamelCase     )

"""simple docstring"""



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 		a_						:







    def __init__(			self			:       Tuple     ,					__UpperCamelCase			:       Optional[int]     ,					__UpperCamelCase			:       List[Any]=99     ,					__UpperCamelCase			:       Optional[int]=13     ,					__UpperCamelCase			:       List[str]=7     ,					__UpperCamelCase			:       List[Any]=9     ,					__UpperCamelCase			:       List[Any]=True     ,					__UpperCamelCase			:       str=True     ,					__UpperCamelCase			:       int=False     ,					__UpperCamelCase			:       int=32     ,					__UpperCamelCase			:       Dict=5     ,					__UpperCamelCase			:       Optional[int]=4     ,					__UpperCamelCase			:       Any=37     ,					__UpperCamelCase			:       List[Any]=8     ,					__UpperCamelCase			:       Optional[int]=0.1     ,					__UpperCamelCase			:       str=0.0_0_2     ,					__UpperCamelCase			:       Union[str, Any]=1     ,					__UpperCamelCase			:       List[Any]=0     ,					__UpperCamelCase			:       Tuple=0     ,					__UpperCamelCase			:       Optional[int]=None     ,					__UpperCamelCase			:       Any=None     ,					)		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   parent
        _UpperCAmelCase          =   batch_size
        _UpperCAmelCase          =   encoder_seq_length
        _UpperCAmelCase          =   decoder_seq_length
        # For common tests
        _UpperCAmelCase          =   self.decoder_seq_length
        _UpperCAmelCase          =   is_training
        _UpperCAmelCase          =   use_attention_mask
        _UpperCAmelCase          =   use_labels
        _UpperCAmelCase          =   vocab_size
        _UpperCAmelCase          =   hidden_size
        _UpperCAmelCase          =   num_hidden_layers
        _UpperCAmelCase          =   num_attention_heads
        _UpperCAmelCase          =   d_ff
        _UpperCAmelCase          =   relative_attention_num_buckets
        _UpperCAmelCase          =   dropout_rate
        _UpperCAmelCase          =   initializer_factor
        _UpperCAmelCase          =   eos_token_id
        _UpperCAmelCase          =   pad_token_id
        _UpperCAmelCase          =   decoder_start_token_id
        _UpperCAmelCase          =   None
        _UpperCAmelCase          =   decoder_layers







    def      _snake_case					(			self			:       Union[str, Any]     )		->Union[str, Any]:






        '''simple docstring'''






        return TaConfig.from_pretrained("""google/umt5-base"""     )







    def      _snake_case					(			self			:       List[Any]     ,					__UpperCamelCase			:       Dict     ,					__UpperCamelCase			:       List[Any]     ,					__UpperCamelCase			:       Optional[int]     ,					__UpperCamelCase			:       Tuple=None     ,					__UpperCamelCase			:       Optional[Any]=None     ,					__UpperCamelCase			:       Optional[Any]=None     ,					__UpperCamelCase			:       Any=None     ,					__UpperCamelCase			:       str=None     ,					)		->int:






        '''simple docstring'''






        if attention_mask is None:
            _UpperCAmelCase          =   input_ids.ne(config.pad_token_id     )
        if decoder_attention_mask is None:
            _UpperCAmelCase          =   decoder_input_ids.ne(config.pad_token_id     )
        if head_mask is None:
            _UpperCAmelCase          =   torch.ones(config.num_hidden_layers     ,					config.num_attention_heads     ,					device=__UpperCamelCase     )
        if decoder_head_mask is None:
            _UpperCAmelCase          =   torch.ones(config.num_decoder_layers     ,					config.num_attention_heads     ,					device=__UpperCamelCase     )
        if cross_attn_head_mask is None:
            _UpperCAmelCase          =   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      _snake_case					(			self			:       List[Any]     )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   ids_tensor([self.batch_size, self.encoder_seq_length]     ,					self.vocab_size     )
        _UpperCAmelCase          =   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
        _UpperCAmelCase          =   input_ids.clamp(self.pad_token_id + 1     )
        _UpperCAmelCase          =   decoder_input_ids.clamp(self.pad_token_id + 1     )

        _UpperCAmelCase          =   self.get_config()
        _UpperCAmelCase          =   config.num_attention_heads
        _UpperCAmelCase          =   self.prepare_inputs_dict(__UpperCamelCase     ,					__UpperCamelCase     ,					__UpperCamelCase     )
        return config, input_dict







    def      _snake_case					(			self			:       Union[str, Any]     )		->Any:






        '''simple docstring'''






        _UpperCAmelCase    ,_UpperCAmelCase          =   self.prepare_config_and_inputs()
        return config, inputs_dict







    def      _snake_case					(			self			:       Dict     )		->List[str]:






        '''simple docstring'''






        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      _snake_case					(			self			:       Tuple     )		->Dict:






        '''simple docstring'''






        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      _snake_case					(			self			:       List[Any]     ,					__UpperCamelCase			:       Union[str, Any]     ,					__UpperCamelCase			:       Any     ,					__UpperCamelCase			:       Dict     ,					__UpperCamelCase			:       Optional[Any]     ,					__UpperCamelCase			:       Dict     ,					__UpperCamelCase			:       List[str]     ,					)		->List[str]:






        '''simple docstring'''






        _UpperCAmelCase          =   UMTaModel(config=__UpperCamelCase     )
        model.to(__UpperCamelCase     )
        model.eval()
        _UpperCAmelCase          =   model(
            input_ids=__UpperCamelCase     ,					decoder_input_ids=__UpperCamelCase     ,					attention_mask=__UpperCamelCase     ,					decoder_attention_mask=__UpperCamelCase     ,					)
        _UpperCAmelCase          =   model(input_ids=__UpperCamelCase     ,					decoder_input_ids=__UpperCamelCase     )
        _UpperCAmelCase          =   result.last_hidden_state
        _UpperCAmelCase          =   result.past_key_values
        _UpperCAmelCase          =   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      _snake_case					(			self			:       Union[str, Any]     ,					__UpperCamelCase			:       List[Any]     ,					__UpperCamelCase			:       Optional[int]     ,					__UpperCamelCase			:       Dict     ,					__UpperCamelCase			:       Optional[int]     ,					__UpperCamelCase			:       Union[str, Any]     ,					__UpperCamelCase			:       List[str]     ,					)		->str:






        '''simple docstring'''






        _UpperCAmelCase          =   UMTaModel(config=__UpperCamelCase     ).get_decoder().to(__UpperCamelCase     ).eval()
        # first forward pass
        _UpperCAmelCase          =   model(__UpperCamelCase     ,					use_cache=__UpperCamelCase     )
        _UpperCAmelCase          =   model(__UpperCamelCase     )
        _UpperCAmelCase          =   model(__UpperCamelCase     ,					use_cache=__UpperCamelCase     )

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

        _UpperCAmelCase    ,_UpperCAmelCase          =   outputs.to_tuple()

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

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

        _UpperCAmelCase          =   model(__UpperCamelCase     )["""last_hidden_state"""]
        _UpperCAmelCase          =   model(__UpperCamelCase     ,					past_key_values=__UpperCamelCase     )["""last_hidden_state"""]

        # select random slice
        _UpperCAmelCase          =   ids_tensor((1,)     ,					output_from_past.shape[-1]     ).item()
        _UpperCAmelCase          =   output_from_no_past[:, -1, random_slice_idx].detach()
        _UpperCAmelCase          =   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      _snake_case					(			self			:       Optional[int]     ,					__UpperCamelCase			:       int     ,					__UpperCamelCase			:       Dict     ,					)		->List[str]:






        '''simple docstring'''






        _UpperCAmelCase          =   UMTaModel(config=__UpperCamelCase     ).to(__UpperCamelCase     ).half().eval()
        _UpperCAmelCase          =   model(**__UpperCamelCase     )["""last_hidden_state"""]
        self.parent.assertFalse(torch.isnan(__UpperCamelCase     ).any().item()     )







@require_torch
class 		a_						( _UpperCAmelCase     ,						_UpperCAmelCase     ,						_UpperCAmelCase     ,						unittest.TestCase	):
    a      :				List[str]     =				(
        (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
    )
    a      :				Tuple     =				(UMTaForConditionalGeneration,) if is_torch_available() else ()
    a      :				Optional[Any]     =				(
        {
            'conversational': UMTaForConditionalGeneration,
            'feature-extraction': UMTaModel,
            'summarization': UMTaForConditionalGeneration,
            'text2text-generation': UMTaForConditionalGeneration,
            'translation': UMTaForConditionalGeneration,
            'question-answering': UMTaForQuestionAnswering,
        }
        if is_torch_available()
        else {}
    )
    a      :				Any     =				True
    a      :				Optional[int]     =				False
    a      :				Any     =				False
    a      :				Optional[int]     =				True
    a      :				Optional[Any]     =				True
    # The small UMT5 model needs higher percentages for CPU/MP tests
    a      :				int     =				[0.8, 0.9]







    def      _snake_case					(			self			:       Optional[Any]     )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   UMTaModelTester(self     )







    @unittest.skip("""Test has a segmentation fault on torch 1.8.0"""     )
    def      _snake_case					(			self			:       Union[str, Any]     )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        _UpperCAmelCase          =   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      _snake_case					(			self			:       Tuple     )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model_fpaa_forward(*__UpperCamelCase     )







    def      _snake_case					(			self			:       Any     )		->Any:






        '''simple docstring'''






        _UpperCAmelCase          =   ["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        _UpperCAmelCase          =   config_and_inputs[0]
        _UpperCAmelCase          =   UMTaForConditionalGeneration(__UpperCamelCase     ).eval()
        model.to(__UpperCamelCase     )

        _UpperCAmelCase          =   {
            """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()     ):
            _UpperCAmelCase          =   {name: mask}
            # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
            if name == "head_mask":
                _UpperCAmelCase          =   torch.ones(
                    config.num_decoder_layers     ,					config.num_heads     ,					device=__UpperCamelCase     )

            _UpperCAmelCase          =   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
            _UpperCAmelCase          =   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      _snake_case					(			self			:       Tuple     )		->List[Any]:






        '''simple docstring'''






        pass







@require_torch
@require_sentencepiece
@require_tokenizers
class 		a_						( 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      _snake_case					(			self			:       Optional[Any]     )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   UMTaForConditionalGeneration.from_pretrained("""google/umt5-small"""     ,					return_dict=__UpperCamelCase     ).to(__UpperCamelCase     )
        _UpperCAmelCase          =   AutoTokenizer.from_pretrained("""google/umt5-small"""     ,					use_fast=__UpperCamelCase     ,					legacy=__UpperCamelCase     )
        _UpperCAmelCase          =   [
            """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>.""",
        ]
        _UpperCAmelCase          =   tokenizer(__UpperCamelCase     ,					return_tensors="""pt"""     ,					padding=__UpperCamelCase     ).input_ids
        # fmt: off
        _UpperCAmelCase          =   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(__UpperCamelCase     ,					__UpperCamelCase     )

        _UpperCAmelCase          =   model.generate(input_ids.to(__UpperCamelCase     )     )
        _UpperCAmelCase          =   [
            """<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>""",
        ]
        _UpperCAmelCase          =   tokenizer.batch_decode(__UpperCamelCase     )
        self.assertEqual(__UpperCamelCase     ,					__UpperCamelCase     )