






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        a_		:

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE=14    ,	_SCREAMING_SNAKE_CASE=7    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=99    ,	_SCREAMING_SNAKE_CASE=32    ,	_SCREAMING_SNAKE_CASE=5    ,	_SCREAMING_SNAKE_CASE=4    ,	_SCREAMING_SNAKE_CASE=37    ,	_SCREAMING_SNAKE_CASE="gelu"    ,	_SCREAMING_SNAKE_CASE=0.1    ,	_SCREAMING_SNAKE_CASE=0.1    ,	_SCREAMING_SNAKE_CASE=512    ,	_SCREAMING_SNAKE_CASE=16    ,	_SCREAMING_SNAKE_CASE=2    ,	_SCREAMING_SNAKE_CASE=0.0_2    ,	_SCREAMING_SNAKE_CASE=3    ,	_SCREAMING_SNAKE_CASE=4    ,	_SCREAMING_SNAKE_CASE=None    ,	)						->   Any:



        """simple docstring"""






        UpperCamelCase       						=      parent
        UpperCamelCase       						=      batch_size
        UpperCamelCase       						=      seq_length
        UpperCamelCase       						=      is_training
        UpperCamelCase       						=      use_token_type_ids
        UpperCamelCase       						=      use_input_mask
        UpperCamelCase       						=      use_labels
        UpperCamelCase       						=      use_mc_token_ids
        UpperCamelCase       						=      vocab_size
        UpperCamelCase       						=      hidden_size
        UpperCamelCase       						=      num_hidden_layers
        UpperCamelCase       						=      num_attention_heads
        UpperCamelCase       						=      intermediate_size
        UpperCamelCase       						=      hidden_act
        UpperCamelCase       						=      hidden_dropout_prob
        UpperCamelCase       						=      attention_probs_dropout_prob
        UpperCamelCase       						=      max_position_embeddings
        UpperCamelCase       						=      type_vocab_size
        UpperCamelCase       						=      type_sequence_label_size
        UpperCamelCase       						=      initializer_range
        UpperCamelCase       						=      num_labels
        UpperCamelCase       						=      num_choices
        UpperCamelCase       						=      scope
        UpperCamelCase       						=      self.vocab_size - 1

  def 						A__		(   self   )						->   List[str]:



        """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       						=      self.get_config()

        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 						A__		(   self   )						->   List[Any]:



        """simple docstring"""






        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 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	*_SCREAMING_SNAKE_CASE   )						->   Optional[int]:



        """simple docstring"""






        UpperCamelCase       						=      CTRLModel(config=_SCREAMING_SNAKE_CASE   )
        model.to(_SCREAMING_SNAKE_CASE   )
        model.eval()

        model(_SCREAMING_SNAKE_CASE    ,	token_type_ids=_SCREAMING_SNAKE_CASE    ,	head_mask=_SCREAMING_SNAKE_CASE   )
        model(_SCREAMING_SNAKE_CASE    ,	token_type_ids=_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      model(_SCREAMING_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 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	*_SCREAMING_SNAKE_CASE   )						->   Tuple:



        """simple docstring"""






        UpperCamelCase       						=      CTRLLMHeadModel(_SCREAMING_SNAKE_CASE   )
        model.to(_SCREAMING_SNAKE_CASE   )
        model.eval()

        UpperCamelCase       						=      model(_SCREAMING_SNAKE_CASE    ,	token_type_ids=_SCREAMING_SNAKE_CASE    ,	labels=_SCREAMING_SNAKE_CASE   )
        self.parent.assertEqual(result.loss.shape    ,	()   )
        self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.seq_length, self.vocab_size)   )

  def 						A__		(   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, """head_mask""": head_mask}

        return config, inputs_dict


  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	*_SCREAMING_SNAKE_CASE   )						->   Optional[int]:



        """simple docstring"""






        UpperCamelCase       						=      self.num_labels
        UpperCamelCase       						=      CTRLForSequenceClassification(_SCREAMING_SNAKE_CASE   )
        model.to(_SCREAMING_SNAKE_CASE   )
        model.eval()
        UpperCamelCase       						=      ids_tensor([self.batch_size]    ,	self.type_sequence_label_size   )
        UpperCamelCase       						=      model(_SCREAMING_SNAKE_CASE    ,	token_type_ids=_SCREAMING_SNAKE_CASE    ,	labels=_SCREAMING_SNAKE_CASE   )
        self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.num_labels)   )






@require_torch
class        a_		(    lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, unittest.TestCase       ):
  lowercase  	=   (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
  lowercase  	=   (CTRLLMHeadModel,) if is_torch_available() else ()
  lowercase  	=   (
      {
          """feature-extraction""": CTRLModel,
          """text-classification""": CTRLForSequenceClassification,
          """text-generation""": CTRLLMHeadModel,
          """zero-shot""": CTRLForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  lowercase  	=   True
  lowercase  	=   False
  lowercase  	=   False

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   str:



        """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 						A__		(   self   )						->   Optional[int]:



        """simple docstring"""






        UpperCamelCase       						=      CTRLModelTester(self   )
        UpperCamelCase       						=      ConfigTester(self    ,	config_class=_SCREAMING_SNAKE_CASE    ,	n_embd=37   )

  def 						A__		(   self   )						->   Union[str, Any]:



        """simple docstring"""






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

  def 						A__		(   self   )						->   int:



        """simple docstring"""






        self.config_tester.run_common_tests()

  def 						A__		(   self   )						->   Dict:



        """simple docstring"""






        UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_ctrl_model(*_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self   )						->   List[str]:



        """simple docstring"""






        UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_lm_head_model(*_SCREAMING_SNAKE_CASE   )

  @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""   )
  def 						A__		(   self   )						->   Dict:



        """simple docstring"""






        pass

  @slow
  def 						A__		(   self   )						->   Optional[Any]:



        """simple docstring"""






        for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCamelCase       						=      CTRLModel.from_pretrained(_SCREAMING_SNAKE_CASE   )
              self.assertIsNotNone(_SCREAMING_SNAKE_CASE   )


  @unittest.skip("""The model doesn\'t support left padding"""   )  # and it's not used enough to be worth fixing :)
  def 						A__		(   self   )						->   Tuple:



        """simple docstring"""






        pass






@require_torch
class        a_		(    unittest.TestCase       ):

  def 						A__		(   self   )						->   str:



        """simple docstring"""






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


  @slow
  def 						A__		(   self   )						->   Any:



        """simple docstring"""






        UpperCamelCase       						=      CTRLLMHeadModel.from_pretrained("""ctrl"""   )
        model.to(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      torch.tensor(
            [[11859, 0, 1611, 8]]    ,	dtype=torch.long    ,	device=_SCREAMING_SNAKE_CASE   )  # Legal the president is
        UpperCamelCase       						=      [
            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

        UpperCamelCase       						=      model.generate(_SCREAMING_SNAKE_CASE    ,	do_sample=_SCREAMING_SNAKE_CASE   )
        self.assertListEqual(output_ids[0].tolist()    ,	_SCREAMING_SNAKE_CASE   )








'''simple docstring'''

from __future__ import annotations







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase					)->				dict[str, float]:
      if (voltage, current, resistance).count(0					) != 1:
            raise ValueError("""One and only one argument must be 0"""					)
      if resistance < 0:
            raise ValueError("""Resistance cannot be negative"""					)
      if voltage == 0:
            return {"voltage": float(current * resistance					)}
      elif current == 0:
            return {"current": voltage / resistance}
      elif resistance == 0:
            return {"resistance": voltage / current}
      else:
            raise ValueError("""Exactly one argument must be 0"""					)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
