





from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers import (
          TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFTransfoXLForSequenceClassification,
          TFTransfoXLLMHeadModel,
          TFTransfoXLModel,
      )
class        SCREAMING_SNAKE_CASE     :


   def __init__(					self    :       Tuple    ,  a    :       int    ,  )->     List[Any]:





     """simple docstring"""







     lowercase__			    =						parent
     lowercase__			    =						13
     lowercase__			    =						7
     lowercase__			    =						30
     lowercase__			    =						self.seq_length + self.mem_len
     lowercase__			    =						15
     lowercase__			    =						True
     lowercase__			    =						True
     lowercase__			    =						99
     lowercase__			    =						[10, 50, 80]
     lowercase__			    =						32
     lowercase__			    =						32
     lowercase__			    =						4
     lowercase__			    =						8
     lowercase__			    =						128
     lowercase__			    =						2
     lowercase__			    =						2
     lowercase__			    =						None
     lowercase__			    =						1
     lowercase__			    =						0
     lowercase__			    =						3
     lowercase__			    =						self.vocab_size - 1
     lowercase__			    =						0.01


   def  SCREAMING_SNAKE_CASE_							(					self    :       Union[str, Any] )->     List[Any]:





     """simple docstring"""







     lowercase__			    =						ids_tensor([self.batch_size, self.seq_length]    ,  self.vocab_size )
     lowercase__			    =						ids_tensor([self.batch_size, self.seq_length]    ,  self.vocab_size )

     lowercase__			    =						None
     if self.use_labels:
       lowercase__			    =						ids_tensor([self.batch_size, self.seq_length]    ,  self.vocab_size )

     lowercase__			    =						TransfoXLConfig(
         vocab_size=self.vocab_size    ,  mem_len=self.mem_len    ,  clamp_len=self.clamp_len    ,  cutoffs=self.cutoffs    ,  d_model=self.hidden_size    ,  d_embed=self.d_embed    ,  n_head=self.num_attention_heads    ,  d_head=self.d_head    ,  d_inner=self.d_inner    ,  div_val=self.div_val    ,  n_layer=self.num_hidden_layers    ,  eos_token_id=self.eos_token_id    ,  pad_token_id=self.vocab_size - 1    ,  init_range=self.init_range    ,  num_labels=self.num_labels    ,  )

     return (config, input_ids_a, input_ids_a, lm_labels)


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





     """simple docstring"""







     random.seed(self.seed )
     tf.random.set_seed(self.seed )


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[Any]    ,  a    :       Union[str, Any]    ,  a    :       List[str]    ,  a    :       Union[str, Any]    ,  a    :       Any )->     Optional[int]:





     """simple docstring"""







     lowercase__			    =						TFTransfoXLModel(a )

     lowercase__    ,					lowercase__			    =						model(a ).to_tuple()

     lowercase__			    =						{'input_ids': input_ids_a, 'mems': mems_a}

     lowercase__    ,					lowercase__			    =						model(a ).to_tuple()

     self.parent.assertEqual(hidden_states_a.shape    ,  (self.batch_size, self.seq_length, self.hidden_size) )
     self.parent.assertEqual(hidden_states_a.shape    ,  (self.batch_size, self.seq_length, self.hidden_size) )
     self.parent.assertListEqual(
         [mem.shape for mem in mems_a]    ,  [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers    ,  )
     self.parent.assertListEqual(
         [mem.shape for mem in mems_a]    ,  [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers    ,  )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple    ,  a    :       Union[str, Any]    ,  a    :       Tuple    ,  a    :       int    ,  a    :       Tuple )->     str:





     """simple docstring"""







     lowercase__			    =						TFTransfoXLLMHeadModel(a )

     lowercase__    ,					lowercase__			    =						model(a ).to_tuple()

     lowercase__			    =						{'input_ids': input_ids_a, 'labels': lm_labels}
     lowercase__    ,					lowercase__			    =						model(a ).to_tuple()

     lowercase__    ,					lowercase__			    =						model([input_ids_a, mems_a] ).to_tuple()

     lowercase__			    =						{'input_ids': input_ids_a, 'mems': mems_a, 'labels': lm_labels}

     lowercase__    ,					lowercase__			    =						model(a ).to_tuple()

     self.parent.assertEqual(lm_logits_a.shape    ,  (self.batch_size, self.seq_length, self.vocab_size) )
     self.parent.assertListEqual(
         [mem.shape for mem in mems_a]    ,  [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers    ,  )

     self.parent.assertEqual(lm_logits_a.shape    ,  (self.batch_size, self.seq_length, self.vocab_size) )
     self.parent.assertListEqual(
         [mem.shape for mem in mems_a]    ,  [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers    ,  )


   def  SCREAMING_SNAKE_CASE_							(					self    :       str    ,  a    :       Optional[Any]    ,  a    :       Tuple    ,  a    :       int    ,  a    :       Dict )->     Dict:





     """simple docstring"""







     lowercase__			    =						TFTransfoXLForSequenceClassification(a )
     lowercase__			    =						model(a )
     self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.num_labels) )


   def  SCREAMING_SNAKE_CASE_							(					self    :       str )->     str:





     """simple docstring"""







     lowercase__			    =						self.prepare_config_and_inputs()
     ((lowercase__)    ,					(lowercase__)    ,					(lowercase__)    ,					(lowercase__))			    =						config_and_inputs
     lowercase__			    =						{'input_ids': input_ids_a}
     return config, inputs_dict






@require_tf
class        SCREAMING_SNAKE_CASE     (UpperCAmelCase    ,			UpperCAmelCase    ,			unittest.TestCase   ):
   _UpperCamelCase					:	int				      = (
       (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
   )
   _UpperCamelCase					:	Tuple				      = () if is_tf_available() else ()
   _UpperCamelCase					:	List[str]				      = (
       {
           'feature-extraction': TFTransfoXLModel,
           'text-classification': TFTransfoXLForSequenceClassification,
           'text-generation': TFTransfoXLLMHeadModel,
           'zero-shot': TFTransfoXLForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )
   # TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
   _UpperCamelCase					:	Optional[int]				      = False
   _UpperCamelCase					:	Union[str, Any]				      = False
   _UpperCamelCase					:	Tuple				      = False
   _UpperCamelCase					:	Dict				      = False


   def  SCREAMING_SNAKE_CASE_							(					self    :       Union[str, Any]    ,  a    :       Any    ,  a    :       Union[str, Any]    ,  a    :       List[str]    ,  a    :       Tuple    ,  a    :       Dict )->     str:





     """simple docstring"""







     if pipeline_test_casse_name == "TextGenerationPipelineTests":
       # Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
       # `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
       # tokenizer.
       return True

     return False


   def  SCREAMING_SNAKE_CASE_							(					self    :       int )->     Any:





     """simple docstring"""







     lowercase__			    =						TFTransfoXLModelTester(self )
     lowercase__			    =						ConfigTester(self    ,  config_class=a    ,  d_embed=37 )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any] )->     str:





     """simple docstring"""







     self.config_tester.run_common_tests()


   def  SCREAMING_SNAKE_CASE_							(					self    :       Any )->     Dict:





     """simple docstring"""







     self.model_tester.set_seed()
     lowercase__			    =						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_transfo_xl_model(*a )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Any )->     List[str]:





     """simple docstring"""







     self.model_tester.set_seed()
     lowercase__			    =						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_transfo_xl_lm_head(*a )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[int] )->     str:





     """simple docstring"""







     lowercase__			    =						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*a )


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





     """simple docstring"""







     lowercase__    ,					lowercase__			    =						self.model_tester.prepare_config_and_inputs_for_common()
     lowercase__			    =						[TFTransfoXLForSequenceClassification]

     for model_class in self.all_model_classes:
       lowercase__			    =						model_class(a )
       assert isinstance(model.get_input_embeddings()    ,  tf.keras.layers.Layer )
       if model_class in list_other_models_with_output_ebd:
         lowercase__			    =						model.get_output_embeddings()
         assert isinstance(a    ,  tf.keras.layers.Layer )
         lowercase__			    =						model.get_bias()
         assert name is None
       else:
         lowercase__			    =						model.get_output_embeddings()
         assert x is None
         lowercase__			    =						model.get_bias()
         assert name is None


   def  SCREAMING_SNAKE_CASE_							(					self    :       Dict )->     Union[str, Any]:





     """simple docstring"""







     pass


   @slow
   def  SCREAMING_SNAKE_CASE_							(					self    :       List[Any] )->     Any:





     """simple docstring"""







     for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       lowercase__			    =						TFTransfoXLModel.from_pretrained(a )
       self.assertIsNotNone(a )


   @unittest.skip(reason='This model doesn\'t play well with fit() due to not returning a single loss.' )
   def  SCREAMING_SNAKE_CASE_							(					self    :       Union[str, Any] )->     List[str]:





     """simple docstring"""







     pass






@require_tf
class        SCREAMING_SNAKE_CASE     (unittest.TestCase   ):


   @unittest.skip('Skip test until #12651 is resolved.' )
   @slow
   def  SCREAMING_SNAKE_CASE_							(					self    :       Dict )->     List[str]:





     """simple docstring"""







     lowercase__			    =						TFTransfoXLLMHeadModel.from_pretrained('transfo-xl-wt103' )
     # fmt: off
     lowercase__			    =						tf.convert_to_tensor([[33,1_297,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,22,1_706,17,20_098,5,3_215,21,37,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,6_224,831,16_002,2,8,603,78_967,29_546,23,803,20,25,416,5,8,232,4,277,6,1_855,4_601,3,29_546,54,8,3_609,5,57_211,49,4,1,277,18,8,1_755,15_691,3,341,25,416,693,42_573,71,17,401,94,31,17_919,2,29_546,7_873,18,1,435,23,11_011,755,5,5_167,3,7_983,98,84,2,29_546,3_267,8,3_609,4,1,4_865,1_075,2,6_087,71,6,346,8,5_854,3,29_546,824,1_400,1_868,2,19,160,2,311,8,5_496,2,20_920,17,25,15_097,3,24,24,0]]    ,  dtype=tf.intaa )  # noqa: E231
     # fmt: on
     #  In 1991 , the remains of Russian Tsar Nicholas II and his family
     #  ( except for Alexei and Maria ) are discovered .
     #  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
     #  remainder of the story . 1883 Western Siberia ,
     #  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
     #  Rasputin has a vision and denounces one of the men as a horse thief . Although his
     #  father initially slaps him for making such an accusation , Rasputin watches as the
     #  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
     #  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
     #  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

     # fmt: off
     lowercase__			    =						[33,1_297,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,22,1_706,17,20_098,5,3_215,21,37,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,6_224,831,16_002,2,8,603,78_967,29_546,23,803,20,25,416,5,8,232,4,277,6,1_855,4_601,3,29_546,54,8,3_609,5,57_211,49,4,1,277,18,8,1_755,15_691,3,341,25,416,693,42_573,71,17,401,94,31,17_919,2,29_546,7_873,18,1,435,23,11_011,755,5,5_167,3,7_983,98,84,2,29_546,3_267,8,3_609,4,1,4_865,1_075,2,6_087,71,6,346,8,5_854,3,29_546,824,1_400,1_868,2,19,160,2,311,8,5_496,2,20_920,17,25,15_097,3,24,24,0,33,1,1_857,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,28,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,0]  # noqa: E231
     # fmt: on
     #  In 1991, the remains of Russian Tsar Nicholas II and his family (
     #  except for Alexei and Maria ) are discovered. The voice of young son,
     #  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
     #  1883 Western Siberia, a young Grigori Rasputin is asked by his father
     #  and a group of men to perform magic. Rasputin has a vision and
     #  denounces one of the men as a horse thief. Although his father initially
     #  slaps him for making such an accusation, Rasputin watches as the man
     #  is chased outside and beaten. Twenty years later, Rasputin sees a vision
     #  of the Virgin Mary, prompting him to become a priest.
     #  Rasputin quickly becomes famous, with people, even a bishop, begging for
     #  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
     # Nicholas II and his family were discovered. The voice of <unk> young son,
     # Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

     lowercase__			    =						model.generate(a    ,  max_length=200    ,  do_sample=a )
     self.assertListEqual(output_ids[0].numpy().tolist()    ,  a )









from scipy.stats import spearmanr

import datasets


lowercase_					     =	"""
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.
"""

lowercase_					     =	"""
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
"""

lowercase_					     =	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        SCREAMING_SNAKE_CASE     (datasets.Metric   ):


   def  SCREAMING_SNAKE_CASE_							(					self    :       Union[str, Any] )->     Tuple:





     """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  SCREAMING_SNAKE_CASE_							(					self    :       int    ,  a    :       str    ,  a    :       Any    ,  a    :       str=False )->     Optional[int]:





     """simple docstring"""







     lowercase__			    =						spearmanr(a    ,  a )
     if return_pvalue:
       return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
     else:
       return {"spearmanr": results[0]}


