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 __snake_case    :




   def __init__(   self      :    Optional[int]	,     _lowercase      :    Optional[Any]	,     _lowercase      :    Dict=99	,     _lowercase      :    Optional[int]=13	,     _lowercase      :    int=7	,     _lowercase      :    Optional[int]=9	,     _lowercase      :    Tuple=True	,     _lowercase      :    List[Any]=True	,     _lowercase      :    Tuple=False	,     _lowercase      :    List[Any]=32	,     _lowercase      :    Optional[Any]=5	,     _lowercase      :    str=4	,     _lowercase      :    Any=37	,     _lowercase      :    List[Any]=8	,     _lowercase      :    Union[str, Any]=0.1	,     _lowercase      :    Optional[Any]=0.0_02	,     _lowercase      :    Dict=1	,     _lowercase      :    Dict=0	,     _lowercase      :    Dict=0	,     _lowercase      :    Tuple=None	,     _lowercase      :    Tuple=None	,     ):



     """simple docstring"""






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




   def      __a						(   self      :    Optional[Any]				):



     """simple docstring"""






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




   def      __a						(   self      :    int	,     _lowercase      :    List[str]	,     _lowercase      :    str	,     _lowercase      :    str	,     _lowercase      :    Any=None	,     _lowercase      :    Optional[Any]=None	,     _lowercase      :    str=None	,     _lowercase      :    Optional[Any]=None	,     _lowercase      :    Dict=None	,     ):



     """simple docstring"""






     if attention_mask is None:
       SCREAMING_SNAKE_CASE__      							=				input_ids.ne(config.pad_token_id				)
     if decoder_attention_mask is None:
       SCREAMING_SNAKE_CASE__      							=				decoder_input_ids.ne(config.pad_token_id				)
     if head_mask is None:
       SCREAMING_SNAKE_CASE__      							=				torch.ones(config.num_hidden_layers	,     config.num_attention_heads	,     device=_lowercase				)
     if decoder_head_mask is None:
       SCREAMING_SNAKE_CASE__      							=				torch.ones(config.num_decoder_layers	,     config.num_attention_heads	,     device=_lowercase				)
     if cross_attn_head_mask is None:
       SCREAMING_SNAKE_CASE__      							=				torch.ones(
           config.num_decoder_layers	,     config.num_attention_heads	,     device=_lowercase				)
     return {
         "input_ids": input_ids,
         "decoder_input_ids": decoder_input_ids,
         "attention_mask": attention_mask,
         "decoder_attention_mask": decoder_attention_mask,
         "head_mask": head_mask,
         "decoder_head_mask": decoder_head_mask,
         "cross_attn_head_mask": cross_attn_head_mask,
     }




   def      __a						(   self      :    Tuple				):



     """simple docstring"""






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

     SCREAMING_SNAKE_CASE__      							=				self.get_config()
     SCREAMING_SNAKE_CASE__      							=				config.num_attention_heads
     SCREAMING_SNAKE_CASE__      							=				self.prepare_inputs_dict(_lowercase	,     _lowercase	,     _lowercase				)
     return config, input_dict




   def      __a						(   self      :    str				):



     """simple docstring"""






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




   def      __a						(   self      :    int				):



     """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      __a						(   self      :    str				):



     """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      :    str	,     _lowercase      :    Tuple	,     _lowercase      :    List[Any]	,     _lowercase      :    Optional[int]	,     _lowercase      :    Tuple	,     _lowercase      :    int	,     _lowercase      :    Union[str, Any]	,     ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				UMTaModel(config=_lowercase				)
     model.to(_lowercase				)
     model.eval()
     SCREAMING_SNAKE_CASE__      							=				model(
         input_ids=_lowercase	,     decoder_input_ids=_lowercase	,     attention_mask=_lowercase	,     decoder_attention_mask=_lowercase	,     )
     SCREAMING_SNAKE_CASE__      							=				model(input_ids=_lowercase	,     decoder_input_ids=_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				result.last_hidden_state
     SCREAMING_SNAKE_CASE__      							=				result.past_key_values
     SCREAMING_SNAKE_CASE__      							=				result.encoder_last_hidden_state

     self.parent.assertEqual(encoder_output.size()	,     (self.batch_size, self.encoder_seq_length, self.hidden_size)				)
     self.parent.assertEqual(decoder_output.size()	,     (self.batch_size, self.decoder_seq_length, self.hidden_size)				)
     # There should be `num_layers` key value embeddings stored in decoder_past
     self.parent.assertEqual(len(_lowercase				)	,     config.num_layers				)
     # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
     self.parent.assertEqual(len(decoder_past[0]				)	,     4				)




   def      __a						(   self      :    Union[str, Any]	,     _lowercase      :    str	,     _lowercase      :    Dict	,     _lowercase      :    Dict	,     _lowercase      :    Any	,     _lowercase      :    Optional[Any]	,     _lowercase      :    Dict	,     ):



     """simple docstring"""






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

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

     SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				outputs.to_tuple()

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

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

     SCREAMING_SNAKE_CASE__      							=				model(_lowercase				)["""last_hidden_state"""]
     SCREAMING_SNAKE_CASE__      							=				model(_lowercase	,     past_key_values=_lowercase				)["""last_hidden_state"""]

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

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




   def      __a						(   self      :    List[str]	,     _lowercase      :    Any	,     _lowercase      :    Dict	,     ):



     """simple docstring"""






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







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




   def      __a						(   self      :    str				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				UMTaModelTester(self				)




   @unittest.skip("""Test has a segmentation fault on torch 1.8.0"""				)
   def      __a						(   self      :    Dict				):



     """simple docstring"""






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




   @unittest.skipIf(torch_device == """cpu"""	,     """Cant do half precision"""				)
   def      __a						(   self      :    Optional[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model_fpaa_forward(*_lowercase				)




   def      __a						(   self      :    str				):



     """simple docstring"""






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

     SCREAMING_SNAKE_CASE__      							=				{
         """head_mask""": torch.zeros(config.num_layers	,     config.num_heads	,     device=_lowercase				),
         """decoder_head_mask""": torch.zeros(config.num_decoder_layers	,     config.num_heads	,     device=_lowercase				),
         """cross_attn_head_mask""": torch.zeros(config.num_decoder_layers	,     config.num_heads	,     device=_lowercase				),
     }

     for attn_name, (name, mask) in zip(_lowercase	,     head_masking.items()				):
       SCREAMING_SNAKE_CASE__      							=				{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__      							=				torch.ones(
             config.num_decoder_layers	,     config.num_heads	,     device=_lowercase				)

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



     """simple docstring"""






     pass







@require_torch
@require_sentencepiece
@require_tokenizers
class __snake_case    (						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      __a						(   self      :    Dict				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				UMTaForConditionalGeneration.from_pretrained("""google/umt5-small"""	,     return_dict=_lowercase				).to(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				AutoTokenizer.from_pretrained("""google/umt5-small"""	,     use_fast=_lowercase	,     legacy=_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				[
         """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__      							=				tokenizer(_lowercase	,     return_tensors="""pt"""	,     padding=_lowercase				).input_ids
     # fmt: off
     SCREAMING_SNAKE_CASE__      							=				torch.tensor(
         [
             [ 3_85_30, 21_07_03, 25_62_99, 14_10, 25_62_98, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
             [   8_26, 3_21, 6_71, 2_59_22, 25_62_99, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
             [  14_60, 3_39, 3_12, 1_90_14, 1_06_20, 7_58, 25_62_99, 23_55,2_74, 1, 0, 0, 0, 0, 0, 0,0, 0],
             [   5_17, 25_62_99, 1_48_69, 2_81, 3_01, 25_62_98, 2_75, 11_99_83,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
             [   3_20, 25_62_99, 1_48_69, 2_81, 22_34, 2_89, 22_75, 3_33,6_13_91, 2_89, 25_62_98, 5_43, 25_62_97, 16_87_14, 3_29, 25_62_96,2_74, 1],
         ]				)
     # fmt: on
     torch.testing.assert_allclose(_lowercase	,     _lowercase				)

     SCREAMING_SNAKE_CASE__      							=				model.generate(input_ids.to(_lowercase				)				)
     SCREAMING_SNAKE_CASE__      							=				[
         """<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__      							=				tokenizer.batch_decode(_lowercase				)
     self.assertEqual(_lowercase	,     _lowercase				)

from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels
