






"""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      _lowercase :


      def __init__(    self				:	Any							,   a				:	Optional[Any]							,   a				:	List[Any]=9_9							,   a				:	List[Any]=1_3							,   a				:	List[Any]=7							,   a				:	Dict=9							,   a				:	List[Any]=True							,   a				:	int=True							,   a				:	List[Any]=False							,   a				:	int=3_2							,   a				:	List[Any]=5							,   a				:	int=4							,   a				:	Tuple=3_7							,   a				:	Optional[int]=8							,   a				:	Any=0.1							,   a				:	int=0.0_0_2							,   a				:	str=1							,   a				:	Optional[Any]=0							,   a				:	List[str]=0							,   a				:	Dict=None							,   a				:	Any=None							,   ):





            """simple docstring"""







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


      def _UpperCamelCase						(    self				:	List[str]						):





            """simple docstring"""







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


      def _UpperCamelCase						(    self				:	List[Any]							,   a				:	Union[str, Any]							,   a				:	Any							,   a				:	Optional[Any]							,   a				:	List[str]=None							,   a				:	Optional[int]=None							,   a				:	Optional[Any]=None							,   a				:	int=None							,   a				:	Tuple=None							,   ):





            """simple docstring"""







            if attention_mask is None:
                  __snake_case      :    List[str]  			=input_ids.ne(config.pad_token_id						)
            if decoder_attention_mask is None:
                  __snake_case      :    int  			=decoder_input_ids.ne(config.pad_token_id						)
            if head_mask is None:
                  __snake_case      :    str  			=torch.ones(config.num_hidden_layers							,   config.num_attention_heads							,   device=_a						)
            if decoder_head_mask is None:
                  __snake_case      :    List[str]  			=torch.ones(config.num_decoder_layers							,   config.num_attention_heads							,   device=_a						)
            if cross_attn_head_mask is None:
                  __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 _UpperCamelCase						(    self				:	Any						):





            """simple docstring"""







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

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


      def _UpperCamelCase						(    self				:	Any						):





            """simple docstring"""







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


      def _UpperCamelCase						(    self				:	Optional[int]						):





            """simple docstring"""







            return TaConfig(
                vocab_size=1_6_6							,   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 _UpperCamelCase						(    self				:	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 _UpperCamelCase						(    self				:	Tuple							,   a				:	str							,   a				:	Union[str, Any]							,   a				:	List[str]							,   a				:	int							,   a				:	List[str]							,   a				:	List[Any]							,   ):





            """simple docstring"""







            __snake_case      :    str  			=UMTaModel(config=_a						)
            model.to(_a						)
            model.eval()
            __snake_case      :    Dict  			=model(
                input_ids=_a							,   decoder_input_ids=_a							,   attention_mask=_a							,   decoder_attention_mask=_a							,   )
            __snake_case      :    Optional[Any]  			=model(input_ids=_a							,   decoder_input_ids=_a						)
            __snake_case      :    Optional[Any]  			=result.last_hidden_state
            __snake_case      :    Dict  			=result.past_key_values
            __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 _UpperCamelCase						(    self				:	int							,   a				:	Optional[int]							,   a				:	Any							,   a				:	int							,   a				:	Optional[Any]							,   a				:	Any							,   a				:	Any							,   ):





            """simple docstring"""







            __snake_case      :    int  			=UMTaModel(config=_a						).get_decoder().to(_a						).eval()
            # first forward pass
            __snake_case      :    str  			=model(_a							,   use_cache=_a						)
            __snake_case      :    str  			=model(_a						)
            __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						)

            __snake_case      :    Dict  			=outputs.to_tuple()

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

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

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

            # select random slice
            __snake_case      :    List[str]  			=ids_tensor((1,)							,   output_from_past.shape[-1]						).item()
            __snake_case      :    Optional[Any]  			=output_from_no_past[:, -1, random_slice_idx].detach()
            __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 _UpperCamelCase						(    self				:	List[str]							,   a				:	Tuple							,   a				:	Dict							,   ):





            """simple docstring"""







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



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


      def _UpperCamelCase						(    self				:	int						):





            """simple docstring"""







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


      @unittest.skip('''Test has a segmentation fault on torch 1.8.0'''						)
      def _UpperCamelCase						(    self				:	int						):





            """simple docstring"""







            __snake_case      :    Optional[Any]  			=self.model_tester.prepare_config_and_inputs()
            __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 _UpperCamelCase						(    self				:	Tuple						):





            """simple docstring"""







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


      def _UpperCamelCase						(    self				:	int						):





            """simple docstring"""







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

            __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()						):
                  __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":
                        __snake_case      :    str  			=torch.ones(
                            config.num_decoder_layers							,   config.num_heads							,   device=_a						)

                  __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
                  __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 _UpperCamelCase						(    self				:	int						):





            """simple docstring"""







            pass



@require_torch
@require_sentencepiece
@require_tokenizers
class      _lowercase (     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 _UpperCamelCase						(    self				:	int						):





            """simple docstring"""







            __snake_case      :    Optional[Any]  			=UMTaForConditionalGeneration.from_pretrained('''google/umt5-small'''							,   return_dict=_a						).to(_a						)
            __snake_case      :    str  			=AutoTokenizer.from_pretrained('''google/umt5-small'''							,   use_fast=_a							,   legacy=_a						)
            __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>.""",
            ]
            __snake_case      :    Tuple  			=tokenizer(_a							,   return_tensors='''pt'''							,   padding=_a						).input_ids
            # fmt: off
            __snake_case      :    Union[str, Any]  			=torch.tensor(
                [
                    [ 3_8_5_3_0, 2_1_0_7_0_3, 2_5_6_2_9_9, 1_4_1_0, 2_5_6_2_9_8, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                    [   8_2_6, 3_2_1, 6_7_1, 2_5_9_2_2, 2_5_6_2_9_9, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                    [  1_4_6_0, 3_3_9, 3_1_2, 1_9_0_1_4, 1_0_6_2_0, 7_5_8, 2_5_6_2_9_9, 2_3_5_5,2_7_4, 1, 0, 0, 0, 0, 0, 0,0, 0],
                    [   5_1_7, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 3_0_1, 2_5_6_2_9_8, 2_7_5, 1_1_9_9_8_3,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
                    [   3_2_0, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 2_2_3_4, 2_8_9, 2_2_7_5, 3_3_3,6_1_3_9_1, 2_8_9, 2_5_6_2_9_8, 5_4_3, 2_5_6_2_9_7, 1_6_8_7_1_4, 3_2_9, 2_5_6_2_9_6,2_7_4, 1],
                ]						)
            # fmt: on
            torch.testing.assert_allclose(_a							,   _a						)

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










"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase_	:				Any										= {"""configuration_reformer""": ["""REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ReformerConfig"""]}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_	:				List[str]										= ["""ReformerTokenizer"""]

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_	:				List[str]										= ["""ReformerTokenizerFast"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_	:				Optional[int]										= [
        """REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """ReformerAttention""",
        """ReformerForMaskedLM""",
        """ReformerForQuestionAnswering""",
        """ReformerForSequenceClassification""",
        """ReformerLayer""",
        """ReformerModel""",
        """ReformerModelWithLMHead""",
        """ReformerPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_reformer import ReformerTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_reformer_fast import ReformerTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_reformer import (
            REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            ReformerAttention,
            ReformerForMaskedLM,
            ReformerForQuestionAnswering,
            ReformerForSequenceClassification,
            ReformerLayer,
            ReformerModel,
            ReformerModelWithLMHead,
            ReformerPreTrainedModel,
        )

else:
    import sys

    UpperCamelCase_	:				Tuple										= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


