
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 		lowercase_							(							snake_case_					):

 def __init__(   self  ,						__UpperCamelCase  ,						):




        """simple docstring"""


        UpperCamelCase_											=							parent
        UpperCamelCase_											=							1_3
        UpperCamelCase_											=							7
        UpperCamelCase_											=							True
        UpperCamelCase_											=							True
        UpperCamelCase_											=							True
        UpperCamelCase_											=							True
        UpperCamelCase_											=							True
        UpperCamelCase_											=							9_9
        UpperCamelCase_											=							3_2
        UpperCamelCase_											=							2
        UpperCamelCase_											=							4
        UpperCamelCase_											=							3_7
        UpperCamelCase_											=							"""gelu"""
        UpperCamelCase_											=							0.1
        UpperCamelCase_											=							0.1
        UpperCamelCase_											=							5_1_2
        UpperCamelCase_											=							1_6
        UpperCamelCase_											=							2
        UpperCamelCase_											=							0.02
        UpperCamelCase_											=							3
        UpperCamelCase_											=							4
        UpperCamelCase_											=							None
        UpperCamelCase_											=							self.vocab_size - 1

 def lowerCamelCase_					(   self  ):




        """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 lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						*__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							TFCTRLModel(config=lowercase_  )
        UpperCamelCase_											=							{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
        UpperCamelCase_											=							model(lowercase_  )

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

        UpperCamelCase_											=							model(lowercase_  )

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

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						*__UpperCamelCase  ):




        """simple docstring"""


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

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						*__UpperCamelCase  ):




        """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(lowercase_  )
        UpperCamelCase_											=							model(lowercase_  )
        self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.num_labels)  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							self.prepare_config_and_inputs()

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

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ):




        """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 lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							TFCTRLModelTester(self  )
        UpperCamelCase_											=							ConfigTester(self  ,						config_class=lowercase_  ,						n_embd=3_7  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        self.config_tester.run_common_tests()

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_ctrl_model(*lowercase_  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_ctrl_lm_head(*lowercase_  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_ctrl_for_sequence_classification(*lowercase_  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        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(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:
                      UpperCamelCase_											=							model.get_output_embeddings()
                      assert isinstance(lowercase_  ,						tf.keras.layers.Layer  )
                      UpperCamelCase_											=							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:
                      UpperCamelCase_											=							model.get_output_embeddings()
                      assert isinstance(lowercase_  ,						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 lowerCamelCase_					(   self  ):




        """simple docstring"""


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



@require_tf
class 		lowercase_							(							unittest.TestCase					):

 @slow
 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							TFCTRLLMHeadModel.from_pretrained("""ctrl"""  )
        UpperCamelCase_											=							tf.convert_to_tensor([[1_1_8_5_9, 0, 1_6_1_1, 8]]  ,						dtype=tf.intaa  )  # Legal the president is
        UpperCamelCase_											=							[
            1_1_8_5_9,
            0,
            1_6_1_1,
            8,
            5,
            1_5_0,
            2_6_4_4_9,
            2,
            1_9,
            3_4_8,
            4_6_9,
            3,
            2_5_9_5,
            4_8,
            2_0_7_4_0,
            2_4_6_5_3_3,
            2_4_6_5_3_3,
            1_9,
            3_0,
            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(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)

