



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 	_a			(      snake_case_  ):






			def __init__(    self    ,  lowercase_    ,  )	->					Any:
									lowerCAmelCase	:   Optional[int]					     =					parent
									lowerCAmelCase	:   List[Any]					     =					13
									lowerCAmelCase	:   List[Any]					     =					7
									lowerCAmelCase	:   Dict					     =					True
									lowerCAmelCase	:   int					     =					True
									lowerCAmelCase	:   List[str]					     =					True
									lowerCAmelCase	:   Dict					     =					True
									lowerCAmelCase	:   Optional[Any]					     =					True
									lowerCAmelCase	:   Any					     =					99
									lowerCAmelCase	:   str					     =					32
									lowerCAmelCase	:   List[Any]					     =					2
									lowerCAmelCase	:   Dict					     =					4
									lowerCAmelCase	:   Tuple					     =					37
									lowerCAmelCase	:   int					     =					"""gelu"""
									lowerCAmelCase	:   List[str]					     =					0.1
									lowerCAmelCase	:   Union[str, Any]					     =					0.1
									lowerCAmelCase	:   Tuple					     =					512
									lowerCAmelCase	:   List[Any]					     =					16
									lowerCAmelCase	:   Optional[Any]					     =					2
									lowerCAmelCase	:   Tuple					     =					0.0_2
									lowerCAmelCase	:   Tuple					     =					3
									lowerCAmelCase	:   List[str]					     =					4
									lowerCAmelCase	:   List[str]					     =					None
									lowerCAmelCase	:   Optional[Any]					     =					self.vocab_size - 1






			def 							_snake_case   (    self )	->					Tuple:
									lowerCAmelCase	:   Optional[Any]					     =					ids_tensor([self.batch_size, self.seq_length]    ,  self.vocab_size )

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

									lowerCAmelCase	:   Optional[Any]					     =					None
									if self.use_token_type_ids:
															lowerCAmelCase	:   str					     =					ids_tensor([self.batch_size, self.seq_length]    ,  self.type_vocab_size )

									lowerCAmelCase	:   Optional[int]					     =					None
									if self.use_mc_token_ids:
															lowerCAmelCase	:   str					     =					ids_tensor([self.batch_size, self.num_choices]    ,  self.seq_length )

									lowerCAmelCase	:   Any					     =					None
									lowerCAmelCase	:   List[Any]					     =					None
									lowerCAmelCase	:   Optional[int]					     =					None
									if self.use_labels:
															lowerCAmelCase	:   List[Any]					     =					ids_tensor([self.batch_size]    ,  self.type_sequence_label_size )
															lowerCAmelCase	:   str					     =					ids_tensor([self.batch_size, self.seq_length]    ,  self.num_labels )
															lowerCAmelCase	:   str					     =					ids_tensor([self.batch_size]    ,  self.num_choices )

									lowerCAmelCase	:   Tuple					     =					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    ,  )

									lowerCAmelCase	:   Any					     =					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    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  *lowercase_ )	->					List[str]:
									lowerCAmelCase	:   Optional[int]					     =					TFCTRLModel(config=lowercase_ )
									lowerCAmelCase	:   Tuple					     =					{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
									lowerCAmelCase	:   int					     =					model(lowercase_ )

									lowerCAmelCase	:   Tuple					     =					[input_ids, None, input_mask]  # None is the input for 'past'
									lowerCAmelCase	:   List[str]					     =					model(lowercase_ )

									lowerCAmelCase	:   Union[str, Any]					     =					model(lowercase_ )

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






			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  *lowercase_ )	->					List[Any]:
									lowerCAmelCase	:   Optional[Any]					     =					TFCTRLLMHeadModel(config=lowercase_ )
									lowerCAmelCase	:   Optional[Any]					     =					{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
									lowerCAmelCase	:   Tuple					     =					model(lowercase_ )
									self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.seq_length, self.vocab_size) )






			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  *lowercase_ )	->					Optional[Any]:
									lowerCAmelCase	:   Dict					     =					self.num_labels
									lowerCAmelCase	:   int					     =					ids_tensor([self.batch_size]    ,  self.type_sequence_label_size )
									lowerCAmelCase	:   Any					     =					{
									    """input_ids""": input_ids,
									    """token_type_ids""": token_type_ids,
									    """labels""": sequence_labels,
									}
									lowerCAmelCase	:   Optional[Any]					     =					TFCTRLForSequenceClassification(lowercase_ )
									lowerCAmelCase	:   Tuple					     =					model(lowercase_ )
									self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.num_labels) )






			def 							_snake_case   (    self )	->					Union[str, Any]:
									lowerCAmelCase	:   Dict					     =					self.prepare_config_and_inputs()

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

									lowerCAmelCase	:   str					     =					{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
									return config, inputs_dict






@require_tf
class 	_a			(      snake_case_     , snake_case_     , unittest.TestCase  ):
			_UpperCamelCase:   Union[str, Any]	     =		(TFCTRLModel, TFCTRLLMHeadModel, TFCTRLForSequenceClassification) if is_tf_available() else ()
			_UpperCamelCase:   Union[str, Any]	     =		(TFCTRLLMHeadModel,) if is_tf_available() else ()
			_UpperCamelCase:   Union[str, Any]	     =		(
			    {
			        "feature-extraction": TFCTRLModel,
			        "text-classification": TFCTRLForSequenceClassification,
			        "text-generation": TFCTRLLMHeadModel,
			        "zero-shot": TFCTRLForSequenceClassification,
			    }
			    if is_tf_available()
			    else {}
			)
			_UpperCamelCase:   Any	     =		False
			_UpperCamelCase:   Any	     =		False






			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_ )	->					Optional[Any]:
									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 )	->					str:
									lowerCAmelCase	:   str					     =					TFCTRLModelTester(self )
									lowerCAmelCase	:   Tuple					     =					ConfigTester(self    ,  config_class=lowercase_    ,  n_embd=37 )






			def 							_snake_case   (    self )	->					Tuple:
									self.config_tester.run_common_tests()






			def 							_snake_case   (    self )	->					int:
									lowerCAmelCase	:   Union[str, Any]					     =					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_ctrl_model(*lowercase_ )






			def 							_snake_case   (    self )	->					Tuple:
									lowerCAmelCase	:   int					     =					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_ctrl_lm_head(*lowercase_ )






			def 							_snake_case   (    self )	->					Optional[int]:
									lowerCAmelCase	:   Tuple					     =					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_ctrl_for_sequence_classification(*lowercase_ )






			def 							_snake_case   (    self )	->					Any:
									lowerCAmelCase     ,	lowerCAmelCase	:   Optional[int]					     =					self.model_tester.prepare_config_and_inputs_for_common()
									lowerCAmelCase	:   int					     =					[TFCTRLLMHeadModel]
									lowerCAmelCase	:   Dict					     =					[TFCTRLForSequenceClassification]

									for model_class in self.all_model_classes:
															lowerCAmelCase	:   Any					     =					model_class(lowercase_ )
															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:
																					lowerCAmelCase	:   Tuple					     =					model.get_output_embeddings()
																					assert isinstance(lowercase_    ,  tf.keras.layers.Layer )
																					lowerCAmelCase	:   Dict					     =					model.get_bias()
																					assert isinstance(lowercase_    ,  lowercase_ )
																					for k, v in name.items():
																											assert isinstance(lowercase_    ,  tf.Variable )
															elif model_class in list_other_models_with_output_ebd:
																					lowerCAmelCase	:   Union[str, Any]					     =					model.get_output_embeddings()
																					assert isinstance(lowercase_    ,  tf.keras.layers.Layer )
																					lowerCAmelCase	:   str					     =					model.get_bias()
																					assert name is None
															else:
																					lowerCAmelCase	:   Optional[int]					     =					model.get_output_embeddings()
																					assert x is None
																					lowerCAmelCase	:   Optional[int]					     =					model.get_bias()
																					assert name is None






			@slow
			def 							_snake_case   (    self )	->					Optional[Any]:
									for model_name in TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															lowerCAmelCase	:   Dict					     =					TFCTRLModel.from_pretrained(lowercase_ )
															self.assertIsNotNone(lowercase_ )






@require_tf
class 	_a			(      unittest.TestCase  ):






			@slow
			def 							_snake_case   (    self )	->					Optional[Any]:
									lowerCAmelCase	:   Optional[int]					     =					TFCTRLLMHeadModel.from_pretrained("""ctrl""" )
									lowerCAmelCase	:   Optional[Any]					     =					tf.convert_to_tensor([[11859, 0, 1611, 8]]    ,  dtype=tf.intaa )  # Legal the president is
									lowerCAmelCase	:   Tuple					     =					[
									    11859,
									    0,
									    1611,
									    8,
									    5,
									    150,
									    26449,
									    2,
									    19,
									    348,
									    469,
									    3,
									    2595,
									    48,
									    20740,
									    246533,
									    246533,
									    19,
									    30,
									    5,
									]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

									lowerCAmelCase	:   str					     =					model.generate(lowercase_    ,  do_sample=lowercase_ )
									self.assertListEqual(output_ids[0].numpy().tolist()    ,  lowercase_ )






import numpy as np






class 	_a			:






			def __init__(    self    ,  lowercase_=5    ,  lowercase_=5 )	->					Union[str, Any]:
									lowerCAmelCase	:   Union[str, Any]					     =					depth
									lowerCAmelCase	:   Tuple					     =					0
									lowerCAmelCase	:   Tuple					     =					None
									lowerCAmelCase	:   Union[str, Any]					     =					None
									lowerCAmelCase	:   Any					     =					min_leaf_size
									lowerCAmelCase	:   Optional[int]					     =					None






			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_ )	->					Any:
									if labels.ndim != 1:
															print("""Error: Input labels must be one dimensional""" )

									return np.mean((labels - prediction) ** 2 )






			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_ )	->					Dict:
									if x.ndim != 1:
															print("""Error: Input data set must be one dimensional""" )
															return
									if len(lowercase_ ) != len(lowercase_ ):
															print("""Error: X and y have different lengths""" )
															return
									if y.ndim != 1:
															print("""Error: Data set labels must be one dimensional""" )
															return

									if len(lowercase_ ) < 2 * self.min_leaf_size:
															lowerCAmelCase	:   Tuple					     =					np.mean(lowercase_ )
															return

									if self.depth == 1:
															lowerCAmelCase	:   Tuple					     =					np.mean(lowercase_ )
															return

									lowerCAmelCase	:   List[str]					     =					0
									lowerCAmelCase	:   List[Any]					     =					self.mean_squared_error(lowercase_    ,  np.mean(lowercase_ ) ) * 2
									for i in range(len(lowercase_ ) ):
															if len(x[:i] ) < self.min_leaf_size:
																					continue
															elif len(x[i:] ) < self.min_leaf_size:
																					continue
															else:
																					lowerCAmelCase	:   int					     =					self.mean_squared_error(x[:i]    ,  np.mean(y[:i] ) )
																					lowerCAmelCase	:   Union[str, Any]					     =					self.mean_squared_error(x[i:]    ,  np.mean(y[i:] ) )
																					lowerCAmelCase	:   Tuple					     =					error_left + error_right
																					if error < min_error:
																											lowerCAmelCase	:   Tuple					     =					i
																											lowerCAmelCase	:   Tuple					     =					error

									if best_split != 0:
															lowerCAmelCase	:   int					     =					x[:best_split]
															lowerCAmelCase	:   List[str]					     =					y[:best_split]
															lowerCAmelCase	:   Dict					     =					x[best_split:]
															lowerCAmelCase	:   Tuple					     =					y[best_split:]

															lowerCAmelCase	:   Any					     =					x[best_split]
															lowerCAmelCase	:   str					     =					DecisionTree(
															    depth=self.depth - 1    ,  min_leaf_size=self.min_leaf_size )
															lowerCAmelCase	:   List[str]					     =					DecisionTree(
															    depth=self.depth - 1    ,  min_leaf_size=self.min_leaf_size )
															self.left.train(lowercase_    ,  lowercase_ )
															self.right.train(lowercase_    ,  lowercase_ )
									else:
															lowerCAmelCase	:   List[str]					     =					np.mean(lowercase_ )

									return






			def 							_snake_case   (    self    ,  lowercase_ )	->					int:
									if self.prediction is not None:
															return self.prediction
									elif self.left or self.right is not None:
															if x >= self.decision_boundary:
																					return self.right.predict(lowercase_ )
															else:
																					return self.left.predict(lowercase_ )
									else:
															print("""Error: Decision tree not yet trained""" )
															return None






class 	_a			:






			@staticmethod
			def 							_snake_case   (    lowercase_    ,  lowercase_ )	->					Optional[Any]:
									lowerCAmelCase	:   Optional[int]					     =					float(0 )
									for label in labels:
															squared_error_sum += (label - prediction) ** 2

									return float(squared_error_sum / labels.size )







def  _UpperCAmelCase  (				):


						'''simple docstring'''




						lowerCAmelCase	:   List[str]					     =					np.arange(-1.0			,1.0			,0.005  )
						lowerCAmelCase	:   List[str]					     =					np.sin(SCREAMING_SNAKE_CASE__  )

						lowerCAmelCase	:   Tuple					     =					DecisionTree(depth=1_0			,min_leaf_size=1_0  )
						tree.train(SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  )

						lowerCAmelCase	:   Dict					     =					(np.random.rand(1_0  ) * 2) - 1
						lowerCAmelCase	:   str					     =					np.array([tree.predict(SCREAMING_SNAKE_CASE__  ) for x in test_cases]  )
						lowerCAmelCase	:   Tuple					     =					np.mean((predictions - test_cases) ** 2  )

						print("""Test values: """ + str(SCREAMING_SNAKE_CASE__  )  )
						print("""Predictions: """ + str(SCREAMING_SNAKE_CASE__  )  )
						print("""Average error: """ + str(SCREAMING_SNAKE_CASE__  )  )


if __name__ == "__main__":
		main()
		import doctest

		doctest.testmod(name='mean_squarred_error', verbose=True)

