




from scipy.stats import spearmanr

import datasets


UpperCamelCase__ :   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.
'''

UpperCamelCase__ :   List[str]						=							'''
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
'''

UpperCamelCase__ :   Optional[Any]						=							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 				lowerCAmelCase_       (					datasets.Metric							):






				def 		snake_case	(			self ):
							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 		snake_case	(			self      ,snake_case__      ,snake_case__      ,snake_case__=False ):
							SCREAMING_SNAKE_CASE_   : str					  =      spearmanr(snake_case__      ,snake_case__ )
							if return_pvalue:
										return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
							else:
										return {"spearmanr": results[0]}









from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
						import tensorflow as tf

						from transformers.models.xglm.modeling_tf_xglm import (
						    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFXGLMForCausalLM,
						    TFXGLMModel,
						)







@require_tf
class 				lowerCAmelCase_       :
				__a     :       List[Any]						=     XGLMConfig
				__a     :       List[Any]						=     {}
				__a     :       List[str]						=     "gelu"






				def __init__(			self      ,snake_case__      ,snake_case__=14      ,snake_case__=7      ,snake_case__=True      ,snake_case__=True      ,snake_case__=True      ,snake_case__=99      ,snake_case__=32      ,snake_case__=2      ,snake_case__=4      ,snake_case__=37      ,snake_case__="gelu"      ,snake_case__=0.1      ,snake_case__=0.1      ,snake_case__=512      ,snake_case__=0.02      ,):
							SCREAMING_SNAKE_CASE_   : int					  =      parent
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      batch_size
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      seq_length
							SCREAMING_SNAKE_CASE_   : List[str]					  =      is_training
							SCREAMING_SNAKE_CASE_   : int					  =      use_input_mask
							SCREAMING_SNAKE_CASE_   : Tuple					  =      use_labels
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      vocab_size
							SCREAMING_SNAKE_CASE_   : int					  =      d_model
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      num_hidden_layers
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      num_attention_heads
							SCREAMING_SNAKE_CASE_   : List[str]					  =      ffn_dim
							SCREAMING_SNAKE_CASE_   : Any					  =      activation_function
							SCREAMING_SNAKE_CASE_   : Dict					  =      activation_dropout
							SCREAMING_SNAKE_CASE_   : int					  =      attention_dropout
							SCREAMING_SNAKE_CASE_   : str					  =      max_position_embeddings
							SCREAMING_SNAKE_CASE_   : Any					  =      initializer_range
							SCREAMING_SNAKE_CASE_   : int					  =      None
							SCREAMING_SNAKE_CASE_   : str					  =      0
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      2
							SCREAMING_SNAKE_CASE_   : Any					  =      1






				def 		snake_case	(			self ):
							return XGLMConfig.from_pretrained('facebook/xglm-564M' )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      tf.clip_by_value(
							    ids_tensor([self.batch_size, self.seq_length]      ,self.vocab_size )      ,clip_value_min=0      ,clip_value_max=3 )

							SCREAMING_SNAKE_CASE_   : List[str]					  =      None
							if self.use_input_mask:
										SCREAMING_SNAKE_CASE_   : List[str]					  =      random_attention_mask([self.batch_size, self.seq_length] )

							SCREAMING_SNAKE_CASE_   : List[Any]					  =      self.get_config()

							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      floats_tensor([self.num_hidden_layers, self.num_attention_heads]      ,2 )

							return (
							    config,
							    input_ids,
							    input_mask,
							    head_mask,
							)






				def 		snake_case	(			self ):
							return XGLMConfig(
							    vocab_size=self.vocab_size      ,d_model=self.hidden_size      ,num_layers=self.num_hidden_layers      ,attention_heads=self.num_attention_heads      ,ffn_dim=self.ffn_dim      ,activation_function=self.activation_function      ,activation_dropout=self.activation_dropout      ,attention_dropout=self.attention_dropout      ,max_position_embeddings=self.max_position_embeddings      ,initializer_range=self.initializer_range      ,use_cache=snake_case__      ,bos_token_id=self.bos_token_id      ,eos_token_id=self.eos_token_id      ,pad_token_id=self.pad_token_id      ,return_dict=snake_case__      ,)






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : str					  =      self.prepare_config_and_inputs()

							(
							    (
							    SCREAMING_SNAKE_CASE_
							)  ,    (
							    SCREAMING_SNAKE_CASE_
							)  ,    (
							    SCREAMING_SNAKE_CASE_
							)  ,    (
							    SCREAMING_SNAKE_CASE_
							)  ,    
							)   : List[Any]					  =      config_and_inputs

							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      {
							    'input_ids': input_ids,
							    'head_mask': head_mask,
							}

							return config, inputs_dict







@require_tf
class 				lowerCAmelCase_       (					lowerCamelCase_						,     lowerCamelCase_						,     unittest.TestCase							):
				__a     :       Dict						=     (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
				__a     :       Dict						=     (TFXGLMForCausalLM,) if is_tf_available() else ()
				__a     :       Union[str, Any]						=     (
				    {"feature-extraction": TFXGLMModel, "text-generation": TFXGLMForCausalLM} if is_tf_available() else {}
				)
				__a     :       Dict						=     False
				__a     :       Union[str, Any]						=     False
				__a     :       List[str]						=     False






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : int					  =      TFXGLMModelTester(self )
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      ConfigTester(self      ,config_class=snake_case__      ,n_embd=37 )






				def 		snake_case	(			self ):
							self.config_tester.run_common_tests()






				@slow
				def 		snake_case	(			self ):
							for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      TFXGLMModel.from_pretrained(snake_case__ )
										self.assertIsNotNone(snake_case__ )






				@unittest.skip(reason='Currently, model embeddings are going to undergo a major refactor.' )
				def 		snake_case	(			self ):
							super().test_resize_token_embeddings()







@require_tf
class 				lowerCAmelCase_       (					unittest.TestCase							):






				@slow
				def 		snake_case	(			self      ,snake_case__=True ):
							SCREAMING_SNAKE_CASE_   : Dict					  =      TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )
							SCREAMING_SNAKE_CASE_   : Any					  =      tf.convert_to_tensor([[2, 268, 9865]]      ,dtype=tf.intaa )  # The dog
							# </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
							# fmt: off
							SCREAMING_SNAKE_CASE_   : Tuple					  =      [2, 268, 9865, 67, 11, 1988, 57252, 9865, 5, 984, 67, 1988, 213838, 1658, 53, 70446, 33, 6657, 278, 1581]
							# fmt: on
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      model.generate(snake_case__      ,do_sample=snake_case__      ,num_beams=1 )
							if verify_outputs:
										self.assertListEqual(output_ids[0].numpy().tolist()      ,snake_case__ )






				@slow
				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Tuple					  =      XGLMTokenizer.from_pretrained('facebook/xglm-564M' )
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )

							tf.random.set_seed(0 )
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      tokenizer('Today is a nice day and'      ,return_tensors='tf' )
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      tokenized.input_ids
							# forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
							with tf.device(':/CPU:0' ):
										SCREAMING_SNAKE_CASE_   : List[Any]					  =      model.generate(snake_case__      ,do_sample=snake_case__      ,seed=[7, 0] )
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      tokenizer.decode(output_ids[0]      ,skip_special_tokens=snake_case__ )

							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      (
							    'Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'
							)
							self.assertEqual(snake_case__      ,snake_case__ )






				@slow
				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )
							SCREAMING_SNAKE_CASE_   : Tuple					  =      XGLMTokenizer.from_pretrained('facebook/xglm-564M' )

							SCREAMING_SNAKE_CASE_   : List[Any]					  =      'left'

							# use different length sentences to test batching
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      [
							    'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
							    'left-padding, such as in batched generation. The output for the sequence below should be the same '
							    'regardless of whether left padding is applied or not. When',
							    'Hello, my dog is a little',
							]

							SCREAMING_SNAKE_CASE_   : Any					  =      tokenizer(snake_case__      ,return_tensors='tf'      ,padding=snake_case__ )
							SCREAMING_SNAKE_CASE_   : int					  =      inputs['input_ids']

							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      model.generate(input_ids=snake_case__      ,attention_mask=inputs['attention_mask']      ,max_new_tokens=12 )

							SCREAMING_SNAKE_CASE_   : List[str]					  =      tokenizer(sentences[0]      ,return_tensors='tf' ).input_ids
							SCREAMING_SNAKE_CASE_   : Any					  =      model.generate(input_ids=snake_case__      ,max_new_tokens=12 )

							SCREAMING_SNAKE_CASE_   : List[str]					  =      tokenizer(sentences[1]      ,return_tensors='tf' ).input_ids
							SCREAMING_SNAKE_CASE_   : Any					  =      model.generate(input_ids=snake_case__      ,max_new_tokens=12 )

							SCREAMING_SNAKE_CASE_   : List[str]					  =      tokenizer.batch_decode(snake_case__      ,skip_special_tokens=snake_case__ )
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      tokenizer.decode(output_non_padded[0]      ,skip_special_tokens=snake_case__ )
							SCREAMING_SNAKE_CASE_   : Tuple					  =      tokenizer.decode(output_padded[0]      ,skip_special_tokens=snake_case__ )

							SCREAMING_SNAKE_CASE_   : str					  =      [
							    'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
							    'left-padding, such as in batched generation. The output for the sequence below should be the same '
							    'regardless of whether left padding is applied or not. When left padding is applied, the sequence will be '
							    'a single',
							    'Hello, my dog is a little bit of a shy one, but he is very friendly',
							]
							self.assertListEqual(snake_case__      ,snake_case__ )
							self.assertListEqual(snake_case__      ,[non_padded_sentence, padded_sentence] )



