
'''simple docstring'''







import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
							import tensorflow as tf

if is_keras_nlp_available():
							from transformers.models.gpta import TFGPTaTokenizer


_lowercase :  Optional[int]    							=       ["gpt2"]
_lowercase :  Union[str, Any]    							=       "gpt2"

if is_tf_available():






							class  lowerCAmelCase__   (  tf.Module     ):






												def __init__(   self				,							__SCREAMING_SNAKE_CASE		):


																		"""simple docstring"""





																		super().__init__()
																		lowercase_	:     int													=       tokenizer
																		lowercase_	:     List[str]													=       AutoConfig.from_pretrained(__SCREAMING_SNAKE_CASE		)
																		lowercase_	:     Optional[int]													=       TFGPTaLMHeadModel.from_config(__SCREAMING_SNAKE_CASE		)






												@tf.function(input_signature=(tf.TensorSpec((None,)				,							tf.string				,							name='''text'''		),)		)
												def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


																		"""simple docstring"""





																		lowercase_	:     Dict													=       self.tokenizer(__SCREAMING_SNAKE_CASE		)
																		lowercase_	:     Dict													=       tokenized['''input_ids'''].to_tensor()

																		lowercase_	:     int													=       tf.cast(input_ids_dense > 0				,							tf.intaa		)
																		# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

																		lowercase_	:     Any													=       self.model(input_ids=__SCREAMING_SNAKE_CASE				,							attention_mask=__SCREAMING_SNAKE_CASE		)['''logits''']

																		return outputs






@require_tf
@require_keras_nlp
class  lowerCAmelCase__   (  unittest.TestCase     ):






					def 	_snake_case    (   self		):


											"""simple docstring"""





											super().setUp()

											lowercase_	:     List[str]													=       [GPTaTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE		) for checkpoint in (TOKENIZER_CHECKPOINTS)]
											lowercase_	:     Dict													=       [TFGPTaTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE		) for checkpoint in TOKENIZER_CHECKPOINTS]
											assert len(self.tokenizers		) == len(self.tf_tokenizers		)

											lowercase_	:     Optional[Any]													=       [
											    '''This is a straightforward English test sentence.''',
											    '''This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.''',
											    '''Now we\'re going to add some Chinese: 一 二 三 一二三''',
											    '''And some much more rare Chinese: 齉 堃 齉堃''',
											    '''Je vais aussi écrire en français pour tester les accents''',
											    '''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
											]
											lowercase_	:     Optional[int]													=       list(zip(self.test_sentences				,							self.test_sentences[::-1]		)		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											for tokenizer, tf_tokenizer in zip(self.tokenizers				,							self.tf_tokenizers		):
																	for test_inputs in self.test_sentences:
																							lowercase_	:     str													=       tokenizer([test_inputs]				,							return_tensors='''tf'''		)
																							lowercase_	:     Tuple													=       tf_tokenizer([test_inputs]		)

																							for key in python_outputs.keys():
																													# convert them to numpy to avoid messing with ragged tensors
																													lowercase_	:     int													=       python_outputs[key].numpy()
																													lowercase_	:     List[str]													=       tf_outputs[key].numpy()

																													self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape		)		)
																													self.assertTrue(tf.reduce_all(tf.cast(__SCREAMING_SNAKE_CASE				,							tf.intaa		) == tf_outputs_values		)		)






					@slow
					def 	_snake_case    (   self		):


											"""simple docstring"""





											for tf_tokenizer in self.tf_tokenizers:
																	lowercase_	:     str													=       tf.function(__SCREAMING_SNAKE_CASE		)
																	for test_inputs in self.test_sentences:
																							lowercase_	:     str													=       tf.constant(__SCREAMING_SNAKE_CASE		)
																							lowercase_	:     Tuple													=       compiled_tokenizer(__SCREAMING_SNAKE_CASE		)
																							lowercase_	:     List[str]													=       tf_tokenizer(__SCREAMING_SNAKE_CASE		)

																							for key in eager_outputs.keys():
																													self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]		)		)






					@slow
					def 	_snake_case    (   self		):


											"""simple docstring"""





											for tf_tokenizer in self.tf_tokenizers:
																	lowercase_	:     Dict													=       ModelToSave(tokenizer=__SCREAMING_SNAKE_CASE		)
																	lowercase_	:     Tuple													=       tf.convert_to_tensor([self.test_sentences[0]]		)
																	lowercase_	:     Union[str, Any]													=       model.serving(__SCREAMING_SNAKE_CASE		)  # Build model with some sample inputs
																	with TemporaryDirectory() as tempdir:
																							lowercase_	:     Union[str, Any]													=       Path(__SCREAMING_SNAKE_CASE		) / '''saved.model'''
																							tf.saved_model.save(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							signatures={'''serving_default''': model.serving}		)
																							lowercase_	:     List[str]													=       tf.saved_model.load(__SCREAMING_SNAKE_CASE		)
																	lowercase_	:     Any													=       loaded_model.signatures['''serving_default'''](__SCREAMING_SNAKE_CASE		)['''output_0''']
																	# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
																	self.assertTrue(tf.reduce_all(out == loaded_output		)		)






					@slow
					def 	_snake_case    (   self		):


											"""simple docstring"""





											for tf_tokenizer in self.tf_tokenizers:
																	lowercase_	:     Dict													=       tf.convert_to_tensor([self.test_sentences[0]]		)
																	lowercase_	:     Optional[int]													=       tf_tokenizer(__SCREAMING_SNAKE_CASE		)  # Build model with some sample inputs

																	lowercase_	:     Tuple													=       tf_tokenizer.get_config()
																	lowercase_	:     int													=       TFGPTaTokenizer.from_config(__SCREAMING_SNAKE_CASE		)
																	lowercase_	:     List[Any]													=       model_from_config(__SCREAMING_SNAKE_CASE		)

																	for key in from_config_output.keys():
																							self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]		)		)






					@slow
					def 	_snake_case    (   self		):


											"""simple docstring"""





											for tf_tokenizer in self.tf_tokenizers:
																	# for the test to run
																	lowercase_	:     Union[str, Any]													=       12_31_23

																	for max_length in [3, 5, 10_24]:
																							lowercase_	:     Dict													=       tf.convert_to_tensor([self.test_sentences[0]]		)
																							lowercase_	:     Dict													=       tf_tokenizer(__SCREAMING_SNAKE_CASE				,							max_length=__SCREAMING_SNAKE_CASE		)

																							lowercase_	:     Optional[Any]													=       out['''input_ids'''].numpy().shape[1]

																							assert out_length == max_length










"""simple docstring"""





from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets


SCREAMING_SNAKE_CASE__					=   "\\n@inproceedings{wang2019glue,\n  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n  note={In the Proceedings of ICLR.},\n  year={2019}\n}\n"

SCREAMING_SNAKE_CASE__					=   "\\nGLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n"

SCREAMING_SNAKE_CASE__					=   "\nCompute GLUE evaluation metric associated to each GLUE dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\nReturns: depending on the GLUE subset, one or several of:\n    \"accuracy\": Accuracy\n    \"f1\": F1 score\n    \"pearson\": Pearson Correlation\n    \"spearmanr\": Spearman Correlation\n    \"matthews_correlation\": Matthew Correlation\nExamples:\n\n    >>> glue_metric = datasets.load_metric('glue', 'sst2')  # 'sst2' or any of [\"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'mrpc')  # 'mrpc' or 'qqp'\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0, 'f1': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'stsb')\n    >>> references = [0., 1., 2., 3., 4., 5.]\n    >>> predictions = [0., 1., 2., 3., 4., 5.]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print({\"pearson\": round(results[\"pearson\"], 2), \"spearmanr\": round(results[\"spearmanr\"], 2)})\n    {'pearson': 1.0, 'spearmanr': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'cola')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'matthews_correlation': 1.0}\n"


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Tuple							,						SCREAMING_SNAKE_CASE     :						List[Any]			):







      '''simple docstring'''




      return float((preds == labels).mean()			)


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Optional[Any]							,						SCREAMING_SNAKE_CASE     :						Any			):







      '''simple docstring'''




      lowerCAmelCase  						=       simple_accuracy(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)
      lowerCAmelCase  						=       float(fa_score(y_true=SCREAMING_SNAKE_CASE							,						y_pred=SCREAMING_SNAKE_CASE			)			)
      return {
          "accuracy": acc,
          "f1": fa,
      }


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						List[str]							,						SCREAMING_SNAKE_CASE     :						Tuple			):







      '''simple docstring'''




      lowerCAmelCase  						=       float(pearsonr(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)[0]			)
      lowerCAmelCase  						=       float(spearmanr(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)[0]			)
      return {
          "pearson": pearson_corr,
          "spearmanr": spearman_corr,
      }






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





      def     _snake_case			(       self )		->     List[str]:
            if self.config_name not in [
                "sst2",
                "mnli",
                "mnli_mismatched",
                "mnli_matched",
                "cola",
                "stsb",
                "mrpc",
                "qqp",
                "qnli",
                "rte",
                "wnli",
                "hans",
            ]:
                  raise KeyError(
                      """You should supply a configuration name selected in """
                      """[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", """
                      """\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]""" )
            return datasets.MetricInfo(
                description=_DESCRIPTION      ,						citation=_CITATION      ,						inputs_description=_KWARGS_DESCRIPTION      ,						features=datasets.Features(
                    {
                        """predictions""": datasets.Value("""int64""" if self.config_name != """stsb""" else """float32""" ),
                        """references""": datasets.Value("""int64""" if self.config_name != """stsb""" else """float32""" ),
                    } )      ,						codebase_urls=[]      ,						reference_urls=[]      ,						format="""numpy"""      ,						)





      def     _snake_case			(       self      ,						lowercase      ,						lowercase )		->     Any:
            if self.config_name == "cola":
                  return {"matthews_correlation": matthews_corrcoef(lowercase      ,						lowercase )}
            elif self.config_name == "stsb":
                  return pearson_and_spearman(lowercase      ,						lowercase )
            elif self.config_name in ["mrpc", "qqp"]:
                  return acc_and_fa(lowercase      ,						lowercase )
            elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
                  return {"accuracy": simple_accuracy(lowercase      ,						lowercase )}
            else:
                  raise KeyError(
                      """You should supply a configuration name selected in """
                      """[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", """
                      """\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]""" )






