
import gc
import unittest

from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
     CTRLForSequenceClassification,
     CTRLLMHeadModel,
     CTRLModel,
 )


class 						__lowerCAmelCase	:



      """simple docstring"""







      def __init__(      self		:       str				,       _snake_case		:       Union[str, Any]				,       _snake_case		:       Dict=14				,       _snake_case		:       Optional[int]=7				,       _snake_case		:       Tuple=True				,       _snake_case		:       Union[str, Any]=True				,       _snake_case		:       Any=True				,       _snake_case		:       str=True				,       _snake_case		:       Optional[int]=True				,       _snake_case		:       Any=99				,       _snake_case		:       Optional[Any]=32				,       _snake_case		:       Union[str, Any]=5				,       _snake_case		:       Any=4				,       _snake_case		:       Optional[Any]=37				,       _snake_case		:       str="gelu"				,       _snake_case		:       Union[str, Any]=0.1				,       _snake_case		:       Tuple=0.1				,       _snake_case		:       Dict=512				,       _snake_case		:       Union[str, Any]=16				,       _snake_case		:       Any=2				,       _snake_case		:       Dict=0.02				,       _snake_case		:       List[Any]=3				,       _snake_case		:       Any=4				,       _snake_case		:       List[Any]=None				,       ):
          __lowercase		:			str	    =       parent
          __lowercase		:			List[Any]	    =       batch_size
          __lowercase		:			Optional[Any]	    =       seq_length
          __lowercase		:			Any	    =       is_training
          __lowercase		:			int	    =       use_token_type_ids
          __lowercase		:			Optional[int]	    =       use_input_mask
          __lowercase		:			Optional[Any]	    =       use_labels
          __lowercase		:			Any	    =       use_mc_token_ids
          __lowercase		:			Optional[Any]	    =       vocab_size
          __lowercase		:			str	    =       hidden_size
          __lowercase		:			Tuple	    =       num_hidden_layers
          __lowercase		:			Optional[Any]	    =       num_attention_heads
          __lowercase		:			Tuple	    =       intermediate_size
          __lowercase		:			Union[str, Any]	    =       hidden_act
          __lowercase		:			Optional[Any]	    =       hidden_dropout_prob
          __lowercase		:			Dict	    =       attention_probs_dropout_prob
          __lowercase		:			Optional[Any]	    =       max_position_embeddings
          __lowercase		:			int	    =       type_vocab_size
          __lowercase		:			int	    =       type_sequence_label_size
          __lowercase		:			List[str]	    =       initializer_range
          __lowercase		:			Optional[int]	    =       num_labels
          __lowercase		:			Optional[Any]	    =       num_choices
          __lowercase		:			Optional[Any]	    =       scope
          __lowercase		:			Dict	    =       self.vocab_size - 1




      def 		snake_case_			(      self		:       Optional[Any]				):
          __lowercase		:			Optional[int]	    =       ids_tensor([self.batch_size, self.seq_length]				,       self.vocab_size				)

          __lowercase		:			Optional[int]	    =       None
          if self.use_input_mask:
              __lowercase		:			int	    =       random_attention_mask([self.batch_size, self.seq_length]				)

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

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

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

          __lowercase		:			Any	    =       self.get_config()

          __lowercase		:			List[str]	    =       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		:       str				):
          return 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				,       )




      def 		snake_case_			(      self		:       Tuple				,       _snake_case		:       Union[str, Any]				,       _snake_case		:       Optional[int]				,       _snake_case		:       List[str]				,       _snake_case		:       Tuple				,       _snake_case		:       Any				,       *_snake_case		:       Dict				):
          __lowercase		:			List[Any]	    =       CTRLModel(config=_snake_case				)
          model.to(_snake_case				)
          model.eval()

          model(_snake_case				,       token_type_ids=_snake_case				,       head_mask=_snake_case				)
          model(_snake_case				,       token_type_ids=_snake_case				)
          __lowercase		:			List[str]	    =       model(_snake_case				)
          self.parent.assertEqual(result.last_hidden_state.shape				,       (self.batch_size, self.seq_length, self.hidden_size)				)
          self.parent.assertEqual(len(result.past_key_values				)				,       config.n_layer				)




      def 		snake_case_			(      self		:       List[Any]				,       _snake_case		:       int				,       _snake_case		:       Union[str, Any]				,       _snake_case		:       Union[str, Any]				,       _snake_case		:       int				,       _snake_case		:       Dict				,       *_snake_case		:       Optional[Any]				):
          __lowercase		:			Dict	    =       CTRLLMHeadModel(_snake_case				)
          model.to(_snake_case				)
          model.eval()

          __lowercase		:			Optional[Any]	    =       model(_snake_case				,       token_type_ids=_snake_case				,       labels=_snake_case				)
          self.parent.assertEqual(result.loss.shape				,       ()				)
          self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.seq_length, self.vocab_size)				)




      def 		snake_case_			(      self		:       List[str]				):
          __lowercase		:			List[Any]	    =       self.prepare_config_and_inputs()

          (
              (
              __lowercase
          )    ,      (
              __lowercase
          )    ,      (
              __lowercase
          )    ,      (
              __lowercase
          )    ,      (
              __lowercase
          )    ,      (
              __lowercase
          )    ,      (
              __lowercase
          )    ,      (
              __lowercase
          )    ,      (
              __lowercase
          )    ,      
          )		:			Tuple	    =       config_and_inputs

          __lowercase		:			List[str]	    =       {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''head_mask''': head_mask}

          return config, inputs_dict




      def 		snake_case_			(      self		:       Dict				,       _snake_case		:       int				,       _snake_case		:       List[str]				,       _snake_case		:       Any				,       _snake_case		:       str				,       *_snake_case		:       List[Any]				):
          __lowercase		:			List[Any]	    =       self.num_labels
          __lowercase		:			str	    =       CTRLForSequenceClassification(_snake_case				)
          model.to(_snake_case				)
          model.eval()
          __lowercase		:			List[Any]	    =       ids_tensor([self.batch_size]				,       self.type_sequence_label_size				)
          __lowercase		:			int	    =       model(_snake_case				,       token_type_ids=_snake_case				,       labels=_snake_case				)
          self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.num_labels)				)






@require_torch
class 						__lowerCAmelCase	(	lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase      ):



      """simple docstring"""







      A__	:			Optional[Any]        =    (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
      A__	:			int        =    (CTRLLMHeadModel,) if is_torch_available() else ()
      A__	:			Dict        =    (
          {
              '''feature-extraction''': CTRLModel,
              '''text-classification''': CTRLForSequenceClassification,
              '''text-generation''': CTRLLMHeadModel,
              '''zero-shot''': CTRLForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      A__	:			str        =    True
      A__	:			Optional[Any]        =    False
      A__	:			Dict        =    False




      def 		snake_case_			(      self		:       str				,       _snake_case		:       Tuple				,       _snake_case		:       Any				,       _snake_case		:       Optional[Any]				,       _snake_case		:       Optional[Any]				,       _snake_case		:       Union[str, 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		:       List[Any]				):
          __lowercase		:			Optional[Any]	    =       CTRLModelTester(self				)
          __lowercase		:			Union[str, Any]	    =       ConfigTester(self				,       config_class=_snake_case				,       n_embd=37				)




      def 		snake_case_			(      self		:       Dict				):
          super().tearDown()
          # clean-up as much as possible GPU memory occupied by PyTorch
          gc.collect()
          torch.cuda.empty_cache()




      def 		snake_case_			(      self		:       Dict				):
          self.config_tester.run_common_tests()




      def 		snake_case_			(      self		:       Any				):
          __lowercase		:			Dict	    =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_ctrl_model(*_snake_case				)




      def 		snake_case_			(      self		:       Any				):
          __lowercase		:			Optional[Any]	    =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_lm_head_model(*_snake_case				)




      @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''				)
      def 		snake_case_			(      self		:       Dict				):
          pass




      @slow
      def 		snake_case_			(      self		:       int				):
          for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              __lowercase		:			Optional[int]	    =       CTRLModel.from_pretrained(_snake_case				)
              self.assertIsNotNone(_snake_case				)




      @unittest.skip('''The model doesn\'t support left padding'''				)  # and it's not used enough to be worth fixing :)
      def 		snake_case_			(      self		:       Union[str, Any]				):
          pass






@require_torch
class 						__lowerCAmelCase	(	unittest.TestCase      ):



      """simple docstring"""







      def 		snake_case_			(      self		:       Tuple				):
          super().tearDown()
          # clean-up as much as possible GPU memory occupied by PyTorch
          gc.collect()
          torch.cuda.empty_cache()




      @slow
      def 		snake_case_			(      self		:       List[str]				):
          __lowercase		:			List[str]	    =       CTRLLMHeadModel.from_pretrained('''ctrl'''				)
          model.to(_snake_case				)
          __lowercase		:			Optional[int]	    =       torch.tensor(
              [[1_1859, 0, 1611, 8]]				,       dtype=torch.long				,       device=_snake_case				)  # Legal the president is
          __lowercase		:			Optional[int]	    =       [
              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

          __lowercase		:			List[str]	    =       model.generate(_snake_case				,       do_sample=_snake_case				)
          self.assertListEqual(output_ids[0].tolist()				,       _snake_case				)




def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						int:

    if not isinstance(__lowerCAmelCase	,					__lowerCAmelCase	):
        raise ValueError('''multiplicative_persistence() only accepts integral values'''	)
    if num < 0:
        raise ValueError('''multiplicative_persistence() does not accept negative values'''	)

    __lowercase		:			Dict	    =       0
    __lowercase		:			Optional[int]	    =       str(__lowerCAmelCase	)

    while len(__lowerCAmelCase	) != 1:
        __lowercase		:			str	    =       [int(__lowerCAmelCase	) for i in num_string]

        __lowercase		:			Union[str, Any]	    =       1
        for i in range(0	,					len(__lowerCAmelCase	)	):
            total *= numbers[i]

        __lowercase		:			Any	    =       str(__lowerCAmelCase	)

        steps += 1
    return steps


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						int:

    if not isinstance(__lowerCAmelCase	,					__lowerCAmelCase	):
        raise ValueError('''additive_persistence() only accepts integral values'''	)
    if num < 0:
        raise ValueError('''additive_persistence() does not accept negative values'''	)

    __lowercase		:			Any	    =       0
    __lowercase		:			Union[str, Any]	    =       str(__lowerCAmelCase	)

    while len(__lowerCAmelCase	) != 1:
        __lowercase		:			List[str]	    =       [int(__lowerCAmelCase	) for i in num_string]

        __lowercase		:			Tuple	    =       0
        for i in range(0	,					len(__lowerCAmelCase	)	):
            total += numbers[i]

        __lowercase		:			List[str]	    =       str(__lowerCAmelCase	)

        steps += 1
    return steps


if __name__ == "__main__":
 import doctest

 doctest.testmod()


