"""simple docstring"""


from sklearn.metrics import mean_absolute_error

import datasets


__UpperCamelCase       :    Dict         =		"\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n"

__UpperCamelCase       :    str         =		"\\nMean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual\nvalues.\n"


__UpperCamelCase       :    List[str]         =		"\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n                 \"raw_values\" : Returns a full set of errors in case of multioutput input.\n\n                 \"uniform_average\" : Errors of all outputs are averaged with uniform weight.\n\nReturns:\n    mae : mean absolute error.\n        If multioutput is \"raw_values\", then mean absolute error is returned for each output separately. If multioutput is \"uniform_average\" or an ndarray of weights, then the weighted average of all output errors is returned.\n        MAE output is non-negative floating point. The best value is 0.0.\nExamples:\n\n    >>> mae_metric = datasets.load_metric(\"mae\")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mae': 0.5}\n\n    If you're using multi-dimensional lists, then set the config as follows :\n\n    >>> mae_metric = datasets.load_metric(\"mae\", \"multilist\")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mae': 0.75}\n    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput='raw_values')\n    >>> print(results)\n    {'mae': array([0.5, 1. ])}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,    _KWARGS_DESCRIPTION   )
class   UpperCAmelCase_	(				datasets.Metric   ):
    def 		_lowerCamelCase      (	self			:       Any		)						->    str:
       return datasets.MetricInfo(
           description=_DESCRIPTION   ,						citation=_CITATION   ,						inputs_description=_KWARGS_DESCRIPTION   ,						features=datasets.Features(self._get_feature_types()		)   ,						reference_urls=[
               "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html"
           ]   ,						)
    def 		_lowerCamelCase      (	self			:       Union[str, Any]		)						->    int:
       if self.config_name == "multilist":
          return {
              "predictions": datasets.Sequence(datasets.Value("float"		)		),
              "references": datasets.Sequence(datasets.Value("float"		)		),
          }
       else:
          return {
              "predictions": datasets.Value("float"		),
              "references": datasets.Value("float"		),
          }




    def 		_lowerCamelCase      (	self			:       Dict   ,						_lowercase			:       Optional[int]   ,						_lowercase			:       Optional[int]   ,						_lowercase			:       Optional[int]=None   ,						_lowercase			:       List[Any]="uniform_average"		)						->    str:
       _lowercase							     =       mean_absolute_error(_lowercase   ,						_lowercase   ,						sample_weight=_lowercase   ,						multioutput=_lowercase		)

       return {"mae": mae_score}
"""simple docstring"""


from __future__ import annotations

import unittest

from transformers import OpenAIGPTConfig, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers.models.openai.modeling_tf_openai import (
      TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFOpenAIGPTDoubleHeadsModel,
      TFOpenAIGPTForSequenceClassification,
      TFOpenAIGPTLMHeadModel,
      TFOpenAIGPTModel,
  )
class   UpperCAmelCase_	:
    def __init__(	self			:       Tuple   ,						_lowercase			:       List[str]   ,						)						->    Tuple:
       _lowercase							     =       parent
       _lowercase							     =       1_3
       _lowercase							     =       7
       _lowercase							     =       True
       _lowercase							     =       True
       _lowercase							     =       True
       _lowercase							     =       True
       _lowercase							     =       True
       _lowercase							     =       9_9
       _lowercase							     =       3_2
       _lowercase							     =       2
       _lowercase							     =       4
       _lowercase							     =       3_7
       _lowercase							     =       "gelu"
       _lowercase							     =       0.1
       _lowercase							     =       0.1
       _lowercase							     =       5_1_2
       _lowercase							     =       1_6
       _lowercase							     =       2
       _lowercase							     =       0.02
       _lowercase							     =       3
       _lowercase							     =       4
       _lowercase							     =       None
       _lowercase							     =       self.vocab_size - 1
    def 		_lowerCamelCase      (	self			:       int		)						->    List[Any]:
       _lowercase							     =       ids_tensor([self.batch_size, self.seq_length]   ,						self.vocab_size		)

       _lowercase							     =       None
       if self.use_input_mask:
          _lowercase							     =       random_attention_mask([self.batch_size, self.seq_length]		)

       _lowercase							     =       None
       if self.use_token_type_ids:
          _lowercase							     =       ids_tensor([self.batch_size, self.seq_length]   ,						self.type_vocab_size		)

       _lowercase							     =       None
       if self.use_mc_token_ids:
          _lowercase							     =       ids_tensor([self.batch_size, self.num_choices]   ,						self.seq_length		)

       _lowercase							     =       None
       _lowercase							     =       None
       _lowercase							     =       None
       if self.use_labels:
          _lowercase							     =       ids_tensor([self.batch_size]   ,						self.type_sequence_label_size		)
          _lowercase							     =       ids_tensor([self.batch_size, self.seq_length]   ,						self.num_labels		)
          _lowercase							     =       ids_tensor([self.batch_size]   ,						self.num_choices		)

       _lowercase							     =       OpenAIGPTConfig(
           vocab_size=self.vocab_size   ,						n_embd=self.hidden_size   ,						n_layer=self.num_hidden_layers   ,						n_head=self.num_attention_heads   ,						n_positions=self.max_position_embeddings   ,						pad_token_id=self.pad_token_id   ,						)

       _lowercase							     =       ids_tensor([self.num_hidden_layers, self.num_attention_heads]   ,						2		)

       return (
           config,
           input_ids,
           input_mask,
           head_mask,
           token_type_ids,
           mc_token_ids,
           sequence_labels,
           token_labels,
           choice_labels,
       )
    def 		_lowerCamelCase      (	self			:       Union[str, Any]   ,						_lowercase			:       Optional[int]   ,						_lowercase			:       str   ,						_lowercase			:       Dict   ,						_lowercase			:       List[Any]   ,						_lowercase			:       int   ,						*_lowercase			:       List[str]		)						->    Optional[int]:
       _lowercase							     =       TFOpenAIGPTModel(config=_lowercase		)
       _lowercase							     =       {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
       _lowercase							     =       model(_lowercase		)

       _lowercase							     =       [input_ids, input_mask]
       _lowercase							     =       model(_lowercase		)

       _lowercase							     =       model(_lowercase		)

       self.parent.assertEqual(result.last_hidden_state.shape   ,						(self.batch_size, self.seq_length, self.hidden_size)		)
    def 		_lowerCamelCase      (	self			:       Any   ,						_lowercase			:       Optional[Any]   ,						_lowercase			:       Optional[int]   ,						_lowercase			:       str   ,						_lowercase			:       Dict   ,						_lowercase			:       List[str]   ,						*_lowercase			:       Optional[Any]		)						->    Dict:
       _lowercase							     =       TFOpenAIGPTLMHeadModel(config=_lowercase		)
       _lowercase							     =       {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
       _lowercase							     =       model(_lowercase		)
       self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.seq_length, self.vocab_size)		)
    def 		_lowerCamelCase      (	self			:       Union[str, Any]   ,						_lowercase			:       Optional[int]   ,						_lowercase			:       int   ,						_lowercase			:       str   ,						_lowercase			:       int   ,						_lowercase			:       Optional[int]   ,						_lowercase			:       List[Any]   ,						*_lowercase			:       Tuple		)						->    Optional[int]:
       _lowercase							     =       TFOpenAIGPTDoubleHeadsModel(config=_lowercase		)

       _lowercase							     =       tf.tile(tf.expand_dims(_lowercase   ,						1		)   ,						(1, self.num_choices, 1)		)
       _lowercase							     =       tf.tile(tf.expand_dims(_lowercase   ,						1		)   ,						(1, self.num_choices, 1)		)
       _lowercase							     =       tf.tile(tf.expand_dims(_lowercase   ,						1		)   ,						(1, self.num_choices, 1)		)

       _lowercase							     =       {
           "input_ids": multiple_choice_inputs_ids,
           "mc_token_ids": mc_token_ids,
           "attention_mask": multiple_choice_input_mask,
           "token_type_ids": multiple_choice_token_type_ids,
       }
       _lowercase							     =       model(_lowercase		)
       self.parent.assertEqual(
           result.logits.shape   ,						(self.batch_size, self.num_choices, self.seq_length, self.vocab_size)		)
       self.parent.assertEqual(result.mc_logits.shape   ,						(self.batch_size, self.num_choices)		)
    def 		_lowerCamelCase      (	self			:       Union[str, Any]   ,						_lowercase			:       List[Any]   ,						_lowercase			:       Tuple   ,						_lowercase			:       Any   ,						_lowercase			:       Optional[Any]   ,						_lowercase			:       List[Any]   ,						*_lowercase			:       str		)						->    str:
       _lowercase							     =       self.num_labels
       _lowercase							     =       ids_tensor([self.batch_size]   ,						self.type_sequence_label_size		)
       _lowercase							     =       {
           "input_ids": input_ids,
           "attention_mask": input_mask,
           "token_type_ids": token_type_ids,
           "labels": sequence_labels,
       }
       _lowercase							     =       TFOpenAIGPTForSequenceClassification(_lowercase		)
       _lowercase							     =       model(_lowercase		)
       self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.num_labels)		)




    def 		_lowerCamelCase      (	self			:       str		)						->    Tuple:
       _lowercase							     =       self.prepare_config_and_inputs()

       (
           (
           _lowercase
       )     ,  (
           _lowercase
       )     ,  (
           _lowercase
       )     ,  (
           _lowercase
       )     ,  (
           _lowercase
       )     ,  (
           _lowercase
       )     ,  (
           _lowercase
       )     ,  (
           _lowercase
       )     ,  (
           _lowercase
       )     ,  
       )							     =       config_and_inputs

       _lowercase							     =       {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
       return config, inputs_dict
@require_tf
class   UpperCAmelCase_	(				lowercase__,    lowercase__,    unittest.TestCase   ):
    snake_case_											=      (
        (TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel, TFOpenAIGPTDoubleHeadsModel, TFOpenAIGPTForSequenceClassification)
        if is_tf_available()
        else ()
    )
    snake_case_											=      (
        (TFOpenAIGPTLMHeadModel,) if is_tf_available() else ()
    )  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
    snake_case_											=      (
        {
            """feature-extraction""": TFOpenAIGPTModel,
            """text-classification""": TFOpenAIGPTForSequenceClassification,
            """text-generation""": TFOpenAIGPTLMHeadModel,
            """zero-shot""": TFOpenAIGPTForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )
    snake_case_											=      False
    snake_case_											=      False
    def 		_lowerCamelCase      (	self			:       Optional[int]   ,						_lowercase			:       Optional[int]   ,						_lowercase			:       List[str]   ,						_lowercase			:       Tuple   ,						_lowercase			:       Union[str, Any]   ,						_lowercase			:       Any		)						->    List[str]:
       if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
          # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
          # `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
          # tiny config could not be created.
          return True

       return False
    def 		_lowerCamelCase      (	self			:       Union[str, Any]		)						->    str:
       _lowercase							     =       TFOpenAIGPTModelTester(self		)
       _lowercase							     =       ConfigTester(self   ,						config_class=_lowercase   ,						n_embd=3_7		)
    def 		_lowerCamelCase      (	self			:       List[Any]		)						->    Optional[int]:
       self.config_tester.run_common_tests()
    def 		_lowerCamelCase      (	self			:       Any		)						->    List[str]:
       _lowercase							     =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_openai_gpt_model(*_lowercase		)
    def 		_lowerCamelCase      (	self			:       Optional[int]		)						->    List[Any]:
       _lowercase							     =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_openai_gpt_lm_head(*_lowercase		)
    def 		_lowerCamelCase      (	self			:       Optional[Any]		)						->    List[Any]:
       _lowercase							     =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_openai_gpt_double_head(*_lowercase		)
    def 		_lowerCamelCase      (	self			:       int		)						->    Union[str, Any]:
       _lowercase							     =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*_lowercase		)




    @slow
    def 		_lowerCamelCase      (	self			:       Tuple		)						->    Dict:
       for model_name in TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          _lowercase							     =       TFOpenAIGPTModel.from_pretrained(_lowercase		)
          self.assertIsNotNone(_lowercase		)
@require_tf
class   UpperCAmelCase_	(				unittest.TestCase   ):
    @slow
    def 		_lowerCamelCase      (	self			:       Optional[Any]		)						->    Union[str, Any]:
       _lowercase							     =       TFOpenAIGPTLMHeadModel.from_pretrained("openai-gpt"		)
       _lowercase							     =       tf.convert_to_tensor([[4_8_1, 4_7_3_5, 5_4_4]]   ,						dtype=tf.intaa		)  # the president is
       _lowercase							     =       [
           4_8_1,
           4_7_3_5,
           5_4_4,
           2_4_6,
           9_6_3,
           8_7_0,
           7_6_2,
           2_3_9,
           2_4_4,
           4_0_4_7_7,
           2_4_4,
           2_4_9,
           7_1_9,
           8_8_1,
           4_8_7,
           5_4_4,
           2_4_0,
           2_4_4,
           6_0_3,
           4_8_1,
       ]  # the president is a very good man. " \n " i\'m sure he is, " said the

       _lowercase							     =       model.generate(_lowercase   ,						do_sample=_lowercase		)
       self.assertListEqual(output_ids[0].numpy().tolist()   ,						_lowercase		)