






import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel



class  A__       :





      def __init__(			self	,							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_=0.0_02	,							A_=1	,							A_=0	,							A_=0	,							A_=None	,							A_=None	,							):
       '''simple docstring'''




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





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




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





      def 	__UpperCamelCase(			self	,							A_	,							A_	,							A_	,							A_=None	,							A_=None	,							A_=None	,							A_=None	,							A_=None	,							):
       '''simple docstring'''




       if attention_mask is None:
        UpperCamelCase     : int	     =       input_ids.ne(config.pad_token_id       )
       if decoder_attention_mask is None:
        UpperCamelCase     : List[Any]	     =       decoder_input_ids.ne(config.pad_token_id       )
       if head_mask is None:
        UpperCamelCase     : Any	     =       torch.ones(config.num_hidden_layers	,							config.num_attention_heads	,							device=A_       )
       if decoder_head_mask is None:
        UpperCamelCase     : Dict	     =       torch.ones(config.num_decoder_layers	,							config.num_attention_heads	,							device=A_       )
       if cross_attn_head_mask is None:
        UpperCamelCase     : 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       ):
       '''simple docstring'''




       UpperCamelCase     : Tuple	     =       ids_tensor([self.batch_size, self.encoder_seq_length]	,							self.vocab_size       )
       UpperCamelCase     : Dict	     =       ids_tensor([self.batch_size, self.decoder_seq_length]	,							self.vocab_size       )

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

       UpperCamelCase     : Dict	     =       self.get_config()
       UpperCamelCase     : Any	     =       config.num_attention_heads
       UpperCamelCase     : Optional[int]	     =       self.prepare_inputs_dict(A_	,							A_	,							A_       )
       return config, input_dict





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Optional[Any]	     =       self.prepare_config_and_inputs()
       return config, inputs_dict





      def 	__UpperCamelCase(			self       ):
       '''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 	__UpperCamelCase(			self       ):
       '''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	,							A_	,							A_	,							A_	,							A_	,							A_	,							A_	,							):
       '''simple docstring'''




       UpperCamelCase     : Dict	     =       UMTaModel(config=A_       )
       model.to(A_       )
       model.eval()
       UpperCamelCase     : int	     =       model(
           input_ids=A_	,							decoder_input_ids=A_	,							attention_mask=A_	,							decoder_attention_mask=A_	,							)
       UpperCamelCase     : str	     =       model(input_ids=A_	,							decoder_input_ids=A_       )
       UpperCamelCase     : Dict	     =       result.last_hidden_state
       UpperCamelCase     : Tuple	     =       result.past_key_values
       UpperCamelCase     : List[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 	__UpperCamelCase(			self	,							A_	,							A_	,							A_	,							A_	,							A_	,							A_	,							):
       '''simple docstring'''




       UpperCamelCase     : Dict	     =       UMTaModel(config=A_       ).get_decoder().to(A_       ).eval()
       # first forward pass
       UpperCamelCase     : Optional[Any]	     =       model(A_	,							use_cache=A_       )
       UpperCamelCase     : List[Any]	     =       model(A_       )
       UpperCamelCase     : List[Any]	     =       model(A_	,							use_cache=A_       )

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

       UpperCamelCase     : int	     =       outputs.to_tuple()

       # create hypothetical next token and extent to next_input_ids
       UpperCamelCase     : Optional[int]	     =       ids_tensor((self.batch_size, 1)	,							config.vocab_size       )

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

       UpperCamelCase     : Union[str, Any]	     =       model(A_       )["last_hidden_state"]
       UpperCamelCase     : List[str]	     =       model(A_	,							past_key_values=A_       )["last_hidden_state"]

       # select random slice
       UpperCamelCase     : Optional[int]	     =       ids_tensor((1,)	,							output_from_past.shape[-1]       ).item()
       UpperCamelCase     : List[str]	     =       output_from_no_past[:, -1, random_slice_idx].detach()
       UpperCamelCase     : 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 	__UpperCamelCase(			self	,							A_	,							A_	,							):
       '''simple docstring'''




       UpperCamelCase     : Any	     =       UMTaModel(config=A_       ).to(A_       ).half().eval()
       UpperCamelCase     : int	     =       model(**A_       )["last_hidden_state"]
       self.parent.assertFalse(torch.isnan(A_       ).any().item()       )



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





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Dict	     =       UMTaModelTester(self       )





      @unittest.skip("Test has a segmentation fault on torch 1.8.0"       )
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : str	     =       self.model_tester.prepare_config_and_inputs()
       UpperCamelCase     : Optional[Any]	     =       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       ):
       '''simple docstring'''




       UpperCamelCase     : List[str]	     =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model_fpaa_forward(*A_       )





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Optional[int]	     =       ["encoder_attentions", "decoder_attentions", "cross_attentions"]
       UpperCamelCase     : Dict	     =       self.model_tester.prepare_config_and_inputs()
       UpperCamelCase     : Tuple	     =       config_and_inputs[0]
       UpperCamelCase     : Tuple	     =       UMTaForConditionalGeneration(A_       ).eval()
       model.to(A_       )

       UpperCamelCase     : Any	     =       {
           "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()       ):
        UpperCamelCase     : Optional[int]	     =       {name: mask}
        # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
        if name == "head_mask":
         UpperCamelCase     : List[str]	     =       torch.ones(
             config.num_decoder_layers	,							config.num_heads	,							device=A_       )

        UpperCamelCase     : List[str]	     =       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
        UpperCamelCase     : Any	     =       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       ):
       '''simple docstring'''




       pass



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





      @slow
      @unittest.skip(
          "Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged"       )
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Tuple	     =       UMTaForConditionalGeneration.from_pretrained("google/umt5-small"	,							return_dict=A_       ).to(A_       )
       UpperCamelCase     : List[str]	     =       AutoTokenizer.from_pretrained("google/umt5-small"	,							use_fast=A_	,							legacy=A_       )
       UpperCamelCase     : Dict	     =       [
           "Bonjour monsieur <extra_id_0> bien <extra_id_1>.",
           "No se como puedo <extra_id_0>.",
           "This is the reason why we <extra_id_0> them.",
           "The <extra_id_0> walks in <extra_id_1>, seats",
           "A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.",
       ]
       UpperCamelCase     : Optional[Any]	     =       tokenizer(A_	,							return_tensors="pt"	,							padding=A_       ).input_ids
       # fmt: off
       UpperCamelCase     : 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_       )

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







from scipy.stats import spearmanr

import datasets


__lowerCamelCase	:					List[str]			  =					"""
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.

Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.

The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
"""

__lowerCamelCase	:					List[Any]			  =					"""
Args:
    predictions (`List[float]`): Predicted labels, as returned by a model.
    references (`List[float]`): Ground truth labels.
    return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns
            only the spearmanr score. Defaults to `False`.
Returns:
    spearmanr (`float`): Spearman correlation coefficient.
    p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.
Examples:
    Example 1:
        >>> spearmanr_metric = datasets.load_metric(\"spearmanr\")
        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
        >>> print(results)
        {'spearmanr': -0.7}

    Example 2:
        >>> spearmanr_metric = datasets.load_metric(\"spearmanr\")
        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
        ...                                     predictions=[10, 9, 2.5, 6, 4],
        ...                                     return_pvalue=True)
        >>> print(results['spearmanr'])
        -0.7
        >>> print(round(results['spearmanr_pvalue'], 2))
        0.19
"""

__lowerCamelCase	:					Optional[int]			  =					r"""\
@book{kokoska2000crc,
  title={CRC standard probability and statistics tables and formulae},
  author={Kokoska, Stephen and Zwillinger, Daniel},
  year={2000},
  publisher={Crc Press}
}
@article{2020SciPy-NMeth,
  author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
            Haberland, Matt and Reddy, Tyler and Cournapeau, David and
            Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
            Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
            Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
            Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
            Kern, Robert and Larson, Eric and Carey, C J and
            Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
            {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
            Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
            Harris, Charles R. and Archibald, Anne M. and
            Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
            {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
  title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
            Computing in Python}},
  journal = {Nature Methods},
  year    = {2020},
  volume  = {17},
  pages   = {261--272},
  adsurl  = {https://rdcu.be/b08Wh},
  doi     = {10.1038/s41592-019-0686-2},
}
"""



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,       _KWARGS_DESCRIPTION      )
class  A__       (	datasets.Metric      ):





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return datasets.MetricInfo(
           description=_DESCRIPTION	,							citation=_CITATION	,							inputs_description=_KWARGS_DESCRIPTION	,							features=datasets.Features(
               {
                   "predictions": datasets.Value("float"       ),
                   "references": datasets.Value("float"       ),
               }       )	,							reference_urls=["https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html"]	,							)





      def 	__UpperCamelCase(			self	,							A_	,							A_	,							A_=False       ):
       '''simple docstring'''




       UpperCamelCase     : Tuple	     =       spearmanr(A_	,							A_       )
       if return_pvalue:
        return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
       else:
        return {"spearmanr": results[0]}
