


from __future__ import annotations

import unittest

from transformers import CTRLConfig, 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.ctrl.modeling_tf_ctrl import (
        TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFCTRLForSequenceClassification,
        TFCTRLLMHeadModel,
        TFCTRLModel,
    )



class 					__lowerCamelCase	(_a		):







       def __init__(	self:				List[str],A_:				Any,):






            '''simple docstring'''




            __UpperCamelCase				=	parent
            __UpperCamelCase				=	13
            __UpperCamelCase				=	7
            __UpperCamelCase				=	True
            __UpperCamelCase				=	True
            __UpperCamelCase				=	True
            __UpperCamelCase				=	True
            __UpperCamelCase				=	True
            __UpperCamelCase				=	99
            __UpperCamelCase				=	32
            __UpperCamelCase				=	2
            __UpperCamelCase				=	4
            __UpperCamelCase				=	37
            __UpperCamelCase				=	'gelu'
            __UpperCamelCase				=	0.1
            __UpperCamelCase				=	0.1
            __UpperCamelCase				=	512
            __UpperCamelCase				=	16
            __UpperCamelCase				=	2
            __UpperCamelCase				=	0.0_2
            __UpperCamelCase				=	3
            __UpperCamelCase				=	4
            __UpperCamelCase				=	None
            __UpperCamelCase				=	self.vocab_size - 1







       def 						snake_case_					(	self:				Dict			):






            '''simple docstring'''




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

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

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

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

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

            __UpperCamelCase				=	CTRLConfig(
                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,)

            __UpperCamelCase				=	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 						snake_case_					(	self:				Dict,A_:				List[Any],A_:				Optional[Any],A_:				Tuple,A_:				List[Any],A_:				Any,*A_:				Dict			):






            '''simple docstring'''




            __UpperCamelCase				=	TFCTRLModel(config=A_			)
            __UpperCamelCase				=	{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
            __UpperCamelCase				=	model(A_			)

            __UpperCamelCase				=	[input_ids, None, input_mask]  # None is the input for 'past'
            __UpperCamelCase				=	model(A_			)

            __UpperCamelCase				=	model(A_			)

            self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.hidden_size)			)







       def 						snake_case_					(	self:				Dict,A_:				List[str],A_:				Dict,A_:				Any,A_:				Optional[Any],A_:				Optional[int],*A_:				List[str]			):






            '''simple docstring'''




            __UpperCamelCase				=	TFCTRLLMHeadModel(config=A_			)
            __UpperCamelCase				=	{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
            __UpperCamelCase				=	model(A_			)
            self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length, self.vocab_size)			)







       def 						snake_case_					(	self:				int,A_:				Tuple,A_:				List[str],A_:				Union[str, Any],A_:				Dict,A_:				str,*A_:				List[Any]			):






            '''simple docstring'''




            __UpperCamelCase				=	self.num_labels
            __UpperCamelCase				=	ids_tensor([self.batch_size],self.type_sequence_label_size			)
            __UpperCamelCase				=	{
                'input_ids': input_ids,
                'token_type_ids': token_type_ids,
                'labels': sequence_labels,
            }
            __UpperCamelCase				=	TFCTRLForSequenceClassification(A_			)
            __UpperCamelCase				=	model(A_			)
            self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_labels)			)







       def 						snake_case_					(	self:				Dict			):






            '''simple docstring'''




            __UpperCamelCase				=	self.prepare_config_and_inputs()

            (
                (
                __UpperCamelCase
            ),  (
                __UpperCamelCase
            ),  (
                __UpperCamelCase
            ),  (
                __UpperCamelCase
            ),  (
                __UpperCamelCase
            ),  (
                __UpperCamelCase
            ),  (
                __UpperCamelCase
            ),  (
                __UpperCamelCase
            ),  (
                __UpperCamelCase
            ),  
            )				=	config_and_inputs

            __UpperCamelCase				=	{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
            return config, inputs_dict



@require_tf
class 					__lowerCamelCase	(_a							,     _a							,     unittest.TestCase		):
       _lowercase    							=      (TFCTRLModel, TFCTRLLMHeadModel, TFCTRLForSequenceClassification) if is_tf_available() else ()
       _lowercase    							=      (TFCTRLLMHeadModel,) if is_tf_available() else ()
       _lowercase    							=      (
           {
               """feature-extraction""": TFCTRLModel,
               """text-classification""": TFCTRLForSequenceClassification,
               """text-generation""": TFCTRLLMHeadModel,
               """zero-shot""": TFCTRLForSequenceClassification,
           }
           if is_tf_available()
           else {}
       )
       _lowercase    							=      False
       _lowercase    							=      False







       def 						snake_case_					(	self:				str,A_:				List[Any],A_:				List[str],A_:				int,A_:				int,A_:				Dict			):






            '''simple docstring'''




            if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
                 # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
                 # `CTRLConfig` 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 						snake_case_					(	self:				List[str]			):






            '''simple docstring'''




            __UpperCamelCase				=	TFCTRLModelTester(self			)
            __UpperCamelCase				=	ConfigTester(self,config_class=A_,n_embd=37			)







       def 						snake_case_					(	self:				Optional[int]			):






            '''simple docstring'''




            self.config_tester.run_common_tests()







       def 						snake_case_					(	self:				Any			):






            '''simple docstring'''




            __UpperCamelCase				=	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_ctrl_model(*A_			)







       def 						snake_case_					(	self:				int			):






            '''simple docstring'''




            __UpperCamelCase				=	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_ctrl_lm_head(*A_			)







       def 						snake_case_					(	self:				str			):






            '''simple docstring'''




            __UpperCamelCase				=	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_ctrl_for_sequence_classification(*A_			)







       def 						snake_case_					(	self:				List[str]			):






            '''simple docstring'''




            __UpperCamelCase,  __UpperCamelCase				=	self.model_tester.prepare_config_and_inputs_for_common()
            __UpperCamelCase				=	[TFCTRLLMHeadModel]
            __UpperCamelCase				=	[TFCTRLForSequenceClassification]

            for model_class in self.all_model_classes:
                 __UpperCamelCase				=	model_class(A_			)
                 model.build()  # may be needed for the get_bias() call below
                 assert isinstance(model.get_input_embeddings(),tf.keras.layers.Layer			)

                 if model_class in list_lm_models:
                      __UpperCamelCase				=	model.get_output_embeddings()
                      assert isinstance(A_,tf.keras.layers.Layer			)
                      __UpperCamelCase				=	model.get_bias()
                      assert isinstance(A_,A_			)
                      for k, v in name.items():
                           assert isinstance(A_,tf.Variable			)
                 elif model_class in list_other_models_with_output_ebd:
                      __UpperCamelCase				=	model.get_output_embeddings()
                      assert isinstance(A_,tf.keras.layers.Layer			)
                      __UpperCamelCase				=	model.get_bias()
                      assert name is None
                 else:
                      __UpperCamelCase				=	model.get_output_embeddings()
                      assert x is None
                      __UpperCamelCase				=	model.get_bias()
                      assert name is None







       @slow
       def 						snake_case_					(	self:				List[str]			):






            '''simple docstring'''




            for model_name in TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 __UpperCamelCase				=	TFCTRLModel.from_pretrained(A_			)
                 self.assertIsNotNone(A_			)



@require_tf
class 					__lowerCamelCase	(unittest.TestCase		):







       @slow
       def 						snake_case_					(	self:				Dict			):






            '''simple docstring'''




            __UpperCamelCase				=	TFCTRLLMHeadModel.from_pretrained('ctrl'			)
            __UpperCamelCase				=	tf.convert_to_tensor([[1_1859, 0, 1611, 8]],dtype=tf.intaa			)  # Legal the president is
            __UpperCamelCase				=	[
                1_1859,
                0,
                1611,
                8,
                5,
                150,
                2_6449,
                2,
                19,
                348,
                469,
                3,
                2595,
                48,
                2_0740,
                24_6533,
                24_6533,
                19,
                30,
                5,
            ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

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






import matplotlib.pyplot as plt
import numpy as np






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase				)			->				np.ndarray:


     """simple docstring"""


     __UpperCamelCase				=	len(_lowercase				)  # Number of training samples
     __UpperCamelCase				=	np.eye(_lowercase				)  # Initialize weights as identity matrix
     for j in range(_lowercase				):
          __UpperCamelCase				=	point - x_train[j]
          __UpperCamelCase				=	np.exp(diff @ diff.T / (-2.0 * tau**2)				)

     return weights






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)			->				np.ndarray:


     """simple docstring"""


     __UpperCamelCase				=	weight_matrix(_lowercase			,       _lowercase			,       _lowercase				)
     __UpperCamelCase				=	np.linalg.inv(x_train.T @ weight_mat @ x_train				) @ (
         x_train.T @ weight_mat @ y_train.T
     )

     return weight






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase				)			->				np.ndarray:


     """simple docstring"""


     __UpperCamelCase				=	np.zeros(len(_lowercase				)				)  # Initialize array of predictions
     for i, item in enumerate(_lowercase				):
          __UpperCamelCase				=	item @ local_weight(_lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)

     return y_pred






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase				)			->				tuple[np.ndarray, np.ndarray, np.ndarray]:


     """simple docstring"""


     import seaborn as sns

     __UpperCamelCase				=	sns.load_dataset(_lowercase				)
     __UpperCamelCase				=	np.array(data[x_name]				)
     __UpperCamelCase				=	np.array(data[y_name]				)

     __UpperCamelCase				=	np.ones(len(_lowercase				)				)

     # pairing elements of one and x_data
     __UpperCamelCase				=	np.column_stack((one, x_data)				)

     return x_train, x_data, y_data






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       )			->				None:


     """simple docstring"""


     __UpperCamelCase				=	np.sort(_lowercase			,       axis=0				)
     plt.scatter(_lowercase			,       _lowercase			,       color='blue'				)
     plt.plot(
         x_train_sorted[:, 1]			,       preds[x_train[:, 1].argsort(0				)]			,       color='yellow'			,       linewidth=5			,       )
     plt.title('Local Weighted Regression'				)
     plt.xlabel(_lowercase				)
     plt.ylabel(_lowercase				)
     plt.show()


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    # Demo with a dataset from the seaborn module
    __snake_case    ,					__snake_case    ,					__snake_case     =		load_data('''tips''', '''total_bill''', '''tip''')
    __snake_case     =		local_weight_regression(training_data_x, tip, 5)
    plot_preds(training_data_x, predictions, total_bill, tip, '''total_bill''', '''tip''')


