

import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


a					:List[str]    = get_tests_dir("fixtures/test_sentencepiece.model")

@require_sentencepiece
@require_tokenizers
class      __a							(UpperCamelCase_   ,	unittest.TestCase):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:List[Any]       = ReformerTokenizer
     _SCREAMING_SNAKE_CASE						:Union[str, Any]       = ReformerTokenizerFast
     _SCREAMING_SNAKE_CASE						:Union[str, Any]       = True
     _SCREAMING_SNAKE_CASE						:Union[str, Any]       = False
     _SCREAMING_SNAKE_CASE						:List[Any]       = True

     def 					_a			(						self  )  ->      List[str]:


           """simple docstring"""


           super().setUp()

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	ReformerTokenizer(_a	,    keep_accents=_a  )
           tokenizer.save_pretrained(self.tmpdirname  )

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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      str      		=	"""<s>"""
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	1

           self.assertEqual(self.get_tokenizer()._convert_token_to_id(_a  )	,    _a  )
           self.assertEqual(self.get_tokenizer()._convert_id_to_token(_a  )	,    _a  )

     def 					_a			(						self  )  ->      List[str]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	list(self.get_tokenizer().get_vocab().keys()  )

           self.assertEqual(vocab_keys[0]	,    """<unk>"""  )
           self.assertEqual(vocab_keys[1]	,    """<s>"""  )
           self.assertEqual(vocab_keys[-1]	,    """j"""  )
           self.assertEqual(len(_a  )	,    1_000  )

     def 					_a			(						self  )  ->      Any:


           """simple docstring"""


           self.assertEqual(self.get_tokenizer().vocab_size	,    1_000  )

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


           """simple docstring"""


           if not self.test_rust_tokenizer:
                 return

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.get_tokenizer()
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	self.get_rust_tokenizer()

           SCREAMING_SNAKE_CASE__   :      int      		=	"""I was born in 92000, and this is falsé."""

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	tokenizer.tokenize(_a  )
           SCREAMING_SNAKE_CASE__   :      Any      		=	rust_tokenizer.tokenize(_a  )
           self.assertListEqual(_a	,    _a  )

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	tokenizer.encode(_a	,    add_special_tokens=_a  )
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	rust_tokenizer.encode(_a	,    add_special_tokens=_a  )
           self.assertListEqual(_a	,    _a  )

           SCREAMING_SNAKE_CASE__   :      str      		=	self.get_rust_tokenizer()
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	tokenizer.encode(_a  )
           SCREAMING_SNAKE_CASE__   :      Any      		=	rust_tokenizer.encode(_a  )
           self.assertListEqual(_a	,    _a  )

     def 					_a			(						self	,    _a=15  )  ->      Union[str, Any]:


           """simple docstring"""


           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''  ):
                       SCREAMING_SNAKE_CASE__   :      Dict      		=	self.rust_tokenizer_class.from_pretrained(_a	,    **_a  )

                       # Simple input
                       SCREAMING_SNAKE_CASE__   :      List[Any]      		=	"""This is a simple input"""
                       SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	["""This is a simple input 1""", """This is a simple input 2"""]
                       SCREAMING_SNAKE_CASE__   :      int      		=	("""This is a simple input""", """This is a pair""")
                       SCREAMING_SNAKE_CASE__   :      Dict      		=	[
                           ("""This is a simple input 1""", """This is a simple input 2"""),
                           ("""This is a simple pair 1""", """This is a simple pair 2"""),
                       ]

                       # Simple input tests
                       self.assertRaises(_a	,    tokenizer_r.encode	,    _a	,    max_length=_a	,    padding="""max_length"""  )

                       # Simple input
                       self.assertRaises(_a	,    tokenizer_r.encode_plus	,    _a	,    max_length=_a	,    padding="""max_length"""  )

                       # Simple input
                       self.assertRaises(
                           _a	,    tokenizer_r.batch_encode_plus	,    _a	,    max_length=_a	,    padding="""max_length"""	,    )

                       # Pair input
                       self.assertRaises(_a	,    tokenizer_r.encode	,    _a	,    max_length=_a	,    padding="""max_length"""  )

                       # Pair input
                       self.assertRaises(_a	,    tokenizer_r.encode_plus	,    _a	,    max_length=_a	,    padding="""max_length"""  )

                       # Pair input
                       self.assertRaises(
                           _a	,    tokenizer_r.batch_encode_plus	,    _a	,    max_length=_a	,    padding="""max_length"""	,    )

     def 					_a			(						self  )  ->      Optional[Any]:


           """simple docstring"""


           pass

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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	ReformerTokenizer(_a	,    keep_accents=_a  )

           SCREAMING_SNAKE_CASE__   :      Any      		=	tokenizer.tokenize("""This is a test"""  )
           self.assertListEqual(_a	,    ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]  )

           self.assertListEqual(
               tokenizer.convert_tokens_to_ids(_a  )	,    [285, 46, 10, 170, 382]	,    )

           SCREAMING_SNAKE_CASE__   :      Any      		=	tokenizer.tokenize("""I was born in 92000, and this is falsé."""  )
           self.assertListEqual(
               _a	,    [
                   SPIECE_UNDERLINE + """I""",
                   SPIECE_UNDERLINE + """was""",
                   SPIECE_UNDERLINE + """b""",
                   """or""",
                   """n""",
                   SPIECE_UNDERLINE + """in""",
                   SPIECE_UNDERLINE + """""",
                   """9""",
                   """2""",
                   """0""",
                   """0""",
                   """0""",
                   """,""",
                   SPIECE_UNDERLINE + """and""",
                   SPIECE_UNDERLINE + """this""",
                   SPIECE_UNDERLINE + """is""",
                   SPIECE_UNDERLINE + """f""",
                   """al""",
                   """s""",
                   """é""",
                   """.""",
               ]	,    )
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	tokenizer.convert_tokens_to_ids(_a  )
           self.assertListEqual(
               _a	,    [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]	,    )

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	tokenizer.convert_ids_to_tokens(_a  )
           self.assertListEqual(
               _a	,    [
                   SPIECE_UNDERLINE + """I""",
                   SPIECE_UNDERLINE + """was""",
                   SPIECE_UNDERLINE + """b""",
                   """or""",
                   """n""",
                   SPIECE_UNDERLINE + """in""",
                   SPIECE_UNDERLINE + """""",
                   """<unk>""",
                   """2""",
                   """0""",
                   """0""",
                   """0""",
                   """,""",
                   SPIECE_UNDERLINE + """and""",
                   SPIECE_UNDERLINE + """this""",
                   SPIECE_UNDERLINE + """is""",
                   SPIECE_UNDERLINE + """f""",
                   """al""",
                   """s""",
                   """<unk>""",
                   """.""",
               ]	,    )

     @cached_property
     def 					_a			(						self  )  ->      Optional[Any]:


           """simple docstring"""


           return ReformerTokenizer.from_pretrained("""google/reformer-crime-and-punishment"""  )

     @slow
     def 					_a			(						self  )  ->      Tuple:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Any      		=	"""Hello World!"""
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	[126, 32, 262, 152, 38, 72, 287]

           self.assertListEqual(_a	,    self.big_tokenizer.encode(_a  )  )

     @slow
     def 					_a			(						self  )  ->      Union[str, Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      int      		=	(
               """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
               """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
           )
           SCREAMING_SNAKE_CASE__   :      int      		=	[
               108,
               265,
               24,
               111,
               4,
               258,
               156,
               35,
               28,
               275,
               3,
               259,
               297,
               260,
               84,
               4,
               35,
               110,
               44,
               8,
               259,
               91,
               268,
               21,
               11,
               209,
               274,
               109,
               266,
               277,
               117,
               86,
               93,
               315,
               258,
               278,
               258,
               277,
               258,
               0,
               258,
               288,
               258,
               319,
               258,
               0,
               258,
               0,
               258,
               0,
               258,
               0,
               258,
               287,
               258,
               315,
               258,
               289,
               258,
               278,
               99,
               269,
               266,
               262,
               8,
               259,
               241,
               4,
               217,
               230,
               268,
               266,
               55,
               168,
               106,
               75,
               193,
               266,
               223,
               27,
               49,
               26,
               282,
               25,
               264,
               299,
               19,
               26,
               0,
               258,
               277,
               117,
               86,
               93,
               176,
               183,
               270,
               11,
               262,
               42,
               61,
               265,
           ]

           self.assertListEqual(_a	,    self.big_tokenizer.encode(_a  )  )

     @require_torch
     @slow
     def 					_a			(						self  )  ->      Tuple:


           """simple docstring"""


           import torch

           from transformers import ReformerConfig, ReformerModel

           # Build sequence
           SCREAMING_SNAKE_CASE__   :      Dict      		=	list(self.big_tokenizer.get_vocab().keys()  )[:10]
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	""" """.join(_a  )
           SCREAMING_SNAKE_CASE__   :      str      		=	self.big_tokenizer.encode_plus(_a	,    return_tensors="""pt"""  )
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	self.big_tokenizer.batch_encode_plus([sequence, sequence]	,    return_tensors="""pt"""  )

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	ReformerConfig()
           # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
           SCREAMING_SNAKE_CASE__   :      Any      		=	encoded_sequence["""input_ids"""].shape
           SCREAMING_SNAKE_CASE__   :      Any      		=	ReformerModel(_a  )

           # Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
           assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

           with torch.no_grad():
                 model(**_a  )
                 model(**_a  )





     @slow
     def 					_a			(						self  )  ->      List[str]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	{"""input_ids""": [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
           # fmt: on

           # This tokenizer does not know some characters like ")".
           # That is the reason why we use very simple texts here.
           # Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	[
               """This is a very simple sentence.""",
               """The quick brown fox jumps over the lazy dog.""",
           ]

           self.tokenizer_integration_test_util(
               expected_encoding=_a	,    model_name="""google/reformer-crime-and-punishment"""	,    revision="""0e6c3decb8211d49bf881013425dc8b0448b3f5a"""	,    padding=_a	,    sequences=_a	,    )







"""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							:






     '''simple docstring'''

     def __init__(						self	,    _a	,    _a=99	,    _a=13	,    _a=7	,    _a=9	,    _a=True	,    _a=True	,    _a=False	,    _a=32	,    _a=5	,    _a=4	,    _a=37	,    _a=8	,    _a=0.1	,    _a=0.002	,    _a=1	,    _a=0	,    _a=0	,    _a=None	,    _a=None	,    )  ->      Any:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Dict      		=	parent
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	batch_size
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	encoder_seq_length
           SCREAMING_SNAKE_CASE__   :      str      		=	decoder_seq_length
           # For common tests
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self.decoder_seq_length
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	is_training
           SCREAMING_SNAKE_CASE__   :      Dict      		=	use_attention_mask
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	use_labels
           SCREAMING_SNAKE_CASE__   :      str      		=	vocab_size
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	hidden_size
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	num_hidden_layers
           SCREAMING_SNAKE_CASE__   :      Any      		=	num_attention_heads
           SCREAMING_SNAKE_CASE__   :      Any      		=	d_ff
           SCREAMING_SNAKE_CASE__   :      Any      		=	relative_attention_num_buckets
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	dropout_rate
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	initializer_factor
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	eos_token_id
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	pad_token_id
           SCREAMING_SNAKE_CASE__   :      Any      		=	decoder_start_token_id
           SCREAMING_SNAKE_CASE__   :      Any      		=	None
           SCREAMING_SNAKE_CASE__   :      str      		=	decoder_layers

     def 					_a			(						self  )  ->      Tuple:


           """simple docstring"""


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

     def 					_a			(						self	,    _a	,    _a	,    _a	,    _a=None	,    _a=None	,    _a=None	,    _a=None	,    _a=None	,    )  ->      Any:


           """simple docstring"""


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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Tuple      		=	ids_tensor([self.batch_size, self.encoder_seq_length]	,    self.vocab_size  )
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	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
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	input_ids.clamp(self.pad_token_id + 1  )
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	decoder_input_ids.clamp(self.pad_token_id + 1  )

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	self.get_config()
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	config.num_attention_heads
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self.prepare_inputs_dict(_a	,    _a	,    _a  )
           return config, input_dict

     def 					_a			(						self  )  ->      Any:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      int      		=	self.prepare_config_and_inputs()
           return config, inputs_dict

     def 					_a			(						self  )  ->      List[str]:


           """simple docstring"""


           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  )  ->      List[Any]:


           """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 					_a			(						self	,    _a	,    _a	,    _a	,    _a	,    _a	,    _a	,    )  ->      Optional[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      str      		=	UMTaModel(config=_a  )
           model.to(_a  )
           model.eval()
           SCREAMING_SNAKE_CASE__   :      Dict      		=	model(
               input_ids=_a	,    decoder_input_ids=_a	,    attention_mask=_a	,    decoder_attention_mask=_a	,    )
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	model(input_ids=_a	,    decoder_input_ids=_a  )
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	result.last_hidden_state
           SCREAMING_SNAKE_CASE__   :      Dict      		=	result.past_key_values
           SCREAMING_SNAKE_CASE__   :      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(_a  )	,    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	,    _a	,    _a	,    _a	,    _a	,    _a	,    _a	,    )  ->      Union[str, Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      int      		=	UMTaModel(config=_a  ).get_decoder().to(_a  ).eval()
           # first forward pass
           SCREAMING_SNAKE_CASE__   :      str      		=	model(_a	,    use_cache=_a  )
           SCREAMING_SNAKE_CASE__   :      str      		=	model(_a  )
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	model(_a	,    use_cache=_a  )

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

           SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      Dict      		=	outputs.to_tuple()

           # create hypothetical next token and extent to next_input_ids
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	ids_tensor((self.batch_size, 1)	,    config.vocab_size  )

           # append to next input_ids and
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	torch.cat([input_ids, next_tokens]	,    dim=-1  )

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	model(_a  )["""last_hidden_state"""]
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	model(_a	,    past_key_values=_a  )["""last_hidden_state"""]

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

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





     def 					_a			(						self	,    _a	,    _a	,    )  ->      List[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      int      		=	UMTaModel(config=_a  ).to(_a  ).half().eval()
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	model(**_a  )["""last_hidden_state"""]
           self.parent.assertFalse(torch.isnan(_a  ).any().item()  )





@require_torch
class      __a							(UpperCamelCase_   ,	UpperCamelCase_   ,	UpperCamelCase_   ,	unittest.TestCase):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Union[str, Any]       = (
         (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
     )
     _SCREAMING_SNAKE_CASE						:Optional[int]       = (UMTaForConditionalGeneration,) if is_torch_available() else ()
     _SCREAMING_SNAKE_CASE						:List[str]       = (
         {
             """conversational""": UMTaForConditionalGeneration,
             """feature-extraction""": UMTaModel,
             """summarization""": UMTaForConditionalGeneration,
             """text2text-generation""": UMTaForConditionalGeneration,
             """translation""": UMTaForConditionalGeneration,
             """question-answering""": UMTaForQuestionAnswering,
         }
         if is_torch_available()
         else {}
     )
     _SCREAMING_SNAKE_CASE						:Union[str, Any]       = True
     _SCREAMING_SNAKE_CASE						:Tuple       = False
     _SCREAMING_SNAKE_CASE						:Optional[Any]       = False
     _SCREAMING_SNAKE_CASE						:List[Any]       = True
     _SCREAMING_SNAKE_CASE						:List[str]       = True
     # The small UMT5 model needs higher percentages for CPU/MP tests
     _SCREAMING_SNAKE_CASE						:Union[str, Any]       = [0.8, 0.9]

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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	UMTaModelTester(self  )

     @unittest.skip("""Test has a segmentation fault on torch 1.8.0"""  )
     def 					_a			(						self  )  ->      Any:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	self.model_tester.prepare_config_and_inputs()
           SCREAMING_SNAKE_CASE__   :      Dict      		=	UMTaModel(config_and_inputs[0]  ).to(_a  )
           with tempfile.TemporaryDirectory() as tmpdirname:
                 torch.onnx.export(
                     _a	,    (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])	,    f'''{tmpdirname}/t5_test.onnx'''	,    export_params=_a	,    opset_version=9	,    input_names=["""input_ids""", """decoder_input_ids"""]	,    )

     @unittest.skipIf(torch_device == """cpu"""	,    """Cant do half precision"""  )
     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Dict      		=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model_fpaa_forward(*_a  )

     def 					_a			(						self  )  ->      List[str]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      int      		=	["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	self.model_tester.prepare_config_and_inputs()
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	config_and_inputs[0]
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	UMTaForConditionalGeneration(_a  ).eval()
           model.to(_a  )

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	{
               """head_mask""": torch.zeros(config.num_layers	,    config.num_heads	,    device=_a  ),
               """decoder_head_mask""": torch.zeros(config.num_decoder_layers	,    config.num_heads	,    device=_a  ),
               """cross_attn_head_mask""": torch.zeros(config.num_decoder_layers	,    config.num_heads	,    device=_a  ),
           }

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

                 SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	model.generate(
                     config_and_inputs[1]["""input_ids"""]	,    num_beams=1	,    max_length=3	,    output_attentions=_a	,    return_dict_in_generate=_a	,    **_a	,    )
                 # We check the state of decoder_attentions and cross_attentions just from the last step
                 SCREAMING_SNAKE_CASE__   :      List[str]      		=	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  )  ->      Dict:


           """simple docstring"""


           pass





@require_torch
@require_sentencepiece
@require_tokenizers
class      __a							(unittest.TestCase):






     '''simple docstring'''

     @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  )  ->      Union[str, Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	UMTaForConditionalGeneration.from_pretrained("""google/umt5-small"""	,    return_dict=_a  ).to(_a  )
           SCREAMING_SNAKE_CASE__   :      str      		=	AutoTokenizer.from_pretrained("""google/umt5-small"""	,    use_fast=_a	,    legacy=_a  )
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	[
               """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>.""",
           ]
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	tokenizer(_a	,    return_tensors="""pt"""	,    padding=_a  ).input_ids
           # fmt: off
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	torch.tensor(
               [
                   [ 38_530, 210_703, 256_299, 1_410, 256_298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                   [   826, 321, 671, 25_922, 256_299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                   [  1_460, 339, 312, 19_014, 10_620, 758, 256_299, 2_355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
                   [   517, 256_299, 14_869, 281, 301, 256_298, 275, 119_983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
                   [   320, 256_299, 14_869, 281, 2_234, 289, 2_275, 333,61_391, 289, 256_298, 543, 256_297, 168_714, 329, 256_296,274, 1],
               ]  )
           # fmt: on
           torch.testing.assert_allclose(_a	,    _a  )

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	model.generate(input_ids.to(_a  )  )
           SCREAMING_SNAKE_CASE__   :      int      		=	[
               """<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>""",
           ]
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	tokenizer.batch_decode(_a  )
           self.assertEqual(_a	,    _a  )




