



'''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 					lowerCamelCase_    :
  """simple docstring"""




  def __init__( self      :	Any			,	_a      :	Optional[int]			,	_a      :	List[str]=99			,	_a      :	Dict=13			,	_a      :	List[str]=7			,	_a      :	Any=9			,	_a      :	List[str]=True			,	_a      :	str=True			,	_a      :	str=False			,	_a      :	Tuple=32			,	_a      :	Any=5			,	_a      :	int=4			,	_a      :	Optional[Any]=37			,	_a      :	Tuple=8			,	_a      :	List[Any]=0.1			,	_a      :	Any=0.002			,	_a      :	Tuple=1			,	_a      :	Union[str, Any]=0			,	_a      :	List[str]=0			,	_a      :	int=None			,	_a      :	Optional[int]=None			,	)					->					Union[str, Any]:
    __lowerCamelCase       :       str           = parent
    __lowerCamelCase       :       Union[str, Any]           = batch_size
    __lowerCamelCase       :       Optional[int]           = encoder_seq_length
    __lowerCamelCase       :       Optional[int]           = decoder_seq_length
    # For common tests
    __lowerCamelCase       :       Any           = self.decoder_seq_length
    __lowerCamelCase       :       List[Any]           = is_training
    __lowerCamelCase       :       Dict           = use_attention_mask
    __lowerCamelCase       :       List[Any]           = use_labels
    __lowerCamelCase       :       List[str]           = vocab_size
    __lowerCamelCase       :       Optional[Any]           = hidden_size
    __lowerCamelCase       :       Union[str, Any]           = num_hidden_layers
    __lowerCamelCase       :       Dict           = num_attention_heads
    __lowerCamelCase       :       Union[str, Any]           = d_ff
    __lowerCamelCase       :       Optional[Any]           = relative_attention_num_buckets
    __lowerCamelCase       :       List[Any]           = dropout_rate
    __lowerCamelCase       :       List[str]           = initializer_factor
    __lowerCamelCase       :       Dict           = eos_token_id
    __lowerCamelCase       :       Any           = pad_token_id
    __lowerCamelCase       :       Tuple           = decoder_start_token_id
    __lowerCamelCase       :       List[str]           = None
    __lowerCamelCase       :       Dict           = decoder_layers




  def 						_lowercase     ( self      :	Optional[int]   )					->					List[str]:
    return TaConfig.from_pretrained('google/umt5-base'   )




  def 						_lowercase     ( self      :	Tuple			,	_a      :	Optional[Any]			,	_a      :	List[Any]			,	_a      :	Any			,	_a      :	Tuple=None			,	_a      :	Optional[Any]=None			,	_a      :	Union[str, Any]=None			,	_a      :	Optional[Any]=None			,	_a      :	List[str]=None			,	)					->					int:
    if attention_mask is None:
      __lowerCamelCase       :       Union[str, Any]           = input_ids.ne(config.pad_token_id   )
    if decoder_attention_mask is None:
      __lowerCamelCase       :       List[Any]           = decoder_input_ids.ne(config.pad_token_id   )
    if head_mask is None:
      __lowerCamelCase       :       Dict           = torch.ones(config.num_hidden_layers			,	config.num_attention_heads			,	device=_a   )
    if decoder_head_mask is None:
      __lowerCamelCase       :       List[Any]           = torch.ones(config.num_decoder_layers			,	config.num_attention_heads			,	device=_a   )
    if cross_attn_head_mask is None:
      __lowerCamelCase       :       Tuple           = 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 						_lowercase     ( self      :	Dict   )					->					str:
    __lowerCamelCase       :       List[Any]           = ids_tensor([self.batch_size, self.encoder_seq_length]			,	self.vocab_size   )
    __lowerCamelCase       :       Any           = ids_tensor([self.batch_size, self.decoder_seq_length]			,	self.vocab_size   )

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

    __lowerCamelCase       :       Dict           = self.get_config()
    __lowerCamelCase       :       List[Any]           = config.num_attention_heads
    __lowerCamelCase       :       Optional[Any]           = self.prepare_inputs_dict(_a			,	_a			,	_a   )
    return config, input_dict




  def 						_lowercase     ( self      :	Union[str, Any]   )					->					Optional[int]:
    __lowerCamelCase       ,__lowerCamelCase       :       int           = self.prepare_config_and_inputs()
    return config, inputs_dict




  def 						_lowercase     ( self      :	Optional[Any]   )					->					List[Any]:
    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 						_lowercase     ( self      :	List[str]   )					->					Optional[int]:
    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 						_lowercase     ( self      :	Union[str, Any]			,	_a      :	List[Any]			,	_a      :	Union[str, Any]			,	_a      :	Optional[Any]			,	_a      :	Any			,	_a      :	Optional[int]			,	_a      :	Dict			,	)					->					Optional[int]:
    __lowerCamelCase       :       List[Any]           = UMTaModel(config=_a   )
    model.to(_a   )
    model.eval()
    __lowerCamelCase       :       Dict           = model(
        input_ids=_a			,	decoder_input_ids=_a			,	attention_mask=_a			,	decoder_attention_mask=_a			,	)
    __lowerCamelCase       :       Optional[Any]           = model(input_ids=_a			,	decoder_input_ids=_a   )
    __lowerCamelCase       :       Optional[int]           = result.last_hidden_state
    __lowerCamelCase       :       int           = result.past_key_values
    __lowerCamelCase       :       str           = 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 						_lowercase     ( self      :	Optional[Any]			,	_a      :	Dict			,	_a      :	List[Any]			,	_a      :	str			,	_a      :	int			,	_a      :	Optional[int]			,	_a      :	Dict			,	)					->					Any:
    __lowerCamelCase       :       Union[str, Any]           = UMTaModel(config=_a   ).get_decoder().to(_a   ).eval()
    # first forward pass
    __lowerCamelCase       :       int           = model(_a			,	use_cache=_a   )
    __lowerCamelCase       :       str           = model(_a   )
    __lowerCamelCase       :       List[Any]           = model(_a			,	use_cache=_a   )

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

    __lowerCamelCase       ,__lowerCamelCase       :       int           = outputs.to_tuple()

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

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

    __lowerCamelCase       :       List[str]           = model(_a   )['last_hidden_state']
    __lowerCamelCase       :       Optional[Any]           = model(_a			,	past_key_values=_a   )['last_hidden_state']

    # select random slice
    __lowerCamelCase       :       Any           = ids_tensor((1,)			,	output_from_past.shape[-1]   ).item()
    __lowerCamelCase       :       List[str]           = output_from_no_past[:, -1, random_slice_idx].detach()
    __lowerCamelCase       :       Dict           = 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 						_lowercase     ( self      :	List[Any]			,	_a      :	int			,	_a      :	Any			,	)					->					Union[str, Any]:
    __lowerCamelCase       :       str           = UMTaModel(config=_a   ).to(_a   ).half().eval()
    __lowerCamelCase       :       Dict           = model(**_a   )['last_hidden_state']
    self.parent.assertFalse(torch.isnan(_a   ).any().item()   )






@require_torch
class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_   ,   unittest.TestCase     ):
  """simple docstring"""




  a_												=(
      (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
  )
  a_												=(UMTaForConditionalGeneration,) if is_torch_available() else ()
  a_												=(
      {
          """conversational""": UMTaForConditionalGeneration,
          """feature-extraction""": UMTaModel,
          """summarization""": UMTaForConditionalGeneration,
          """text2text-generation""": UMTaForConditionalGeneration,
          """translation""": UMTaForConditionalGeneration,
          """question-answering""": UMTaForQuestionAnswering,
      }
      if is_torch_available()
      else {}
  )
  a_												=True
  a_												=False
  a_												=False
  a_												=True
  a_												=True
  # The small UMT5 model needs higher percentages for CPU/MP tests
  a_												=[0.8, 0.9]




  def 						_lowercase     ( self      :	str   )					->					str:
    __lowerCamelCase       :       Dict           = UMTaModelTester(self   )




  @unittest.skip('Test has a segmentation fault on torch 1.8.0'   )
  def 						_lowercase     ( self      :	Dict   )					->					List[str]:
    __lowerCamelCase       :       Optional[Any]           = self.model_tester.prepare_config_and_inputs()
    __lowerCamelCase       :       str           = 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 						_lowercase     ( self      :	List[Any]   )					->					Any:
    __lowerCamelCase       :       Union[str, Any]           = self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model_fpaa_forward(*_a   )




  def 						_lowercase     ( self      :	int   )					->					Any:
    __lowerCamelCase       :       Any           = ['encoder_attentions', 'decoder_attentions', 'cross_attentions']
    __lowerCamelCase       :       Dict           = self.model_tester.prepare_config_and_inputs()
    __lowerCamelCase       :       Union[str, Any]           = config_and_inputs[0]
    __lowerCamelCase       :       Optional[Any]           = UMTaForConditionalGeneration(_a   ).eval()
    model.to(_a   )

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

      __lowerCamelCase       :       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
      __lowerCamelCase       :       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 						_lowercase     ( self      :	Tuple   )					->					List[Any]:
    pass






@require_torch
@require_sentencepiece
@require_tokenizers
class 					lowerCamelCase_    (				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 						_lowercase     ( self      :	str   )					->					Dict:
    __lowerCamelCase       :       Tuple           = UMTaForConditionalGeneration.from_pretrained('google/umt5-small'			,	return_dict=_a   ).to(_a   )
    __lowerCamelCase       :       List[str]           = AutoTokenizer.from_pretrained('google/umt5-small'			,	use_fast=_a			,	legacy=_a   )
    __lowerCamelCase       :       str           = [
        '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       :       Optional[Any]           = tokenizer(_a			,	return_tensors='pt'			,	padding=_a   ).input_ids
    # fmt: off
    __lowerCamelCase       :       Optional[int]           = torch.tensor(
        [
            [ 3_8530, 21_0703, 25_6299, 1410, 25_6298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
            [   826, 321, 671, 2_5922, 25_6299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
            [  1460, 339, 312, 1_9014, 1_0620, 758, 25_6299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
            [   517, 25_6299, 1_4869, 281, 301, 25_6298, 275, 11_9983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
            [   320, 25_6299, 1_4869, 281, 2234, 289, 2275, 333,6_1391, 289, 25_6298, 543, 25_6297, 16_8714, 329, 25_6296,274, 1],
        ]   )
    # fmt: on
    torch.testing.assert_allclose(_a			,	_a   )

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











'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_UpperCamelCase											=						{
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase											=						[
       'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'BioGptForCausalLM',
       'BioGptForTokenClassification',
       'BioGptForSequenceClassification',
       'BioGptModel',
       'BioGptPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
   from .tokenization_biogpt import BioGptTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_biogpt import (
          BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          BioGptForCausalLM,
          BioGptForSequenceClassification,
          BioGptForTokenClassification,
          BioGptModel,
          BioGptPreTrainedModel,
      )


else:
   import sys

   _UpperCamelCase											=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






