





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__    :




   """simple docstring"""



   def __init__(					self					,  lowercase					,  lowercase=14					,  lowercase=7					,  lowercase=True					,  lowercase=True					,  lowercase=True					,  lowercase=True					,  lowercase=True					,  lowercase=99					,  lowercase=32					,  lowercase=5					,  lowercase=4					,  lowercase=37					,  lowercase="gelu"					,  lowercase=0.1					,  lowercase=0.1					,  lowercase=512					,  lowercase=16					,  lowercase=2					,  lowercase=0.02					,  lowercase=3					,  lowercase=4					,  lowercase=None					,  )			->						List[Any]:







          '''simple docstring'''

          a__						:	Dict =				parent
          a__						:	List[Any] =				batch_size
          a__						:	Union[str, Any] =				seq_length
          a__						:	Optional[Any] =				is_training
          a__						:	Dict =				use_token_type_ids
          a__						:	Any =				use_input_mask
          a__						:	Optional[int] =				use_labels
          a__						:	str =				use_mc_token_ids
          a__						:	int =				vocab_size
          a__						:	Dict =				hidden_size
          a__						:	Optional[int] =				num_hidden_layers
          a__						:	Optional[Any] =				num_attention_heads
          a__						:	Any =				intermediate_size
          a__						:	List[Any] =				hidden_act
          a__						:	Dict =				hidden_dropout_prob
          a__						:	List[str] =				attention_probs_dropout_prob
          a__						:	str =				max_position_embeddings
          a__						:	str =				type_vocab_size
          a__						:	Union[str, Any] =				type_sequence_label_size
          a__						:	List[Any] =				initializer_range
          a__						:	Dict =				num_labels
          a__						:	int =				num_choices
          a__						:	Any =				scope
          a__						:	Optional[int] =				self.vocab_size - 1

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







          '''simple docstring'''

          a__						:	Optional[int] =				ids_tensor([self.batch_size, self.seq_length]					,  self.vocab_size)

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

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

          a__						:	Any =				None
          if self.use_mc_token_ids:
                 a__						:	Union[str, Any] =				ids_tensor([self.batch_size, self.num_choices]					,  self.seq_length)

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

          a__						:	str =				self.get_config()

          a__						:	int =				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 							__lowercase		(					self)			->						Dict:







          '''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 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  *lowercase)			->						Optional[int]:







          '''simple docstring'''

          a__						:	str =				CTRLModel(config=lowercase)
          model.to(lowercase)
          model.eval()

          model(lowercase					,  token_type_ids=lowercase					,  head_mask=lowercase)
          model(lowercase					,  token_type_ids=lowercase)
          a__						:	str =				model(lowercase)
          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 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  *lowercase)			->						str:







          '''simple docstring'''

          a__						:	Dict =				CTRLLMHeadModel(lowercase)
          model.to(lowercase)
          model.eval()

          a__						:	Tuple =				model(lowercase					,  token_type_ids=lowercase					,  labels=lowercase)
          self.parent.assertEqual(result.loss.shape					,  ())
          self.parent.assertEqual(result.logits.shape					,  (self.batch_size, self.seq_length, self.vocab_size))

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







          '''simple docstring'''

          a__						:	Dict =				self.prepare_config_and_inputs()

          (
              (
              a__
          ) ,							(
              a__
          ) ,							(
              a__
          ) ,							(
              a__
          ) ,							(
              a__
          ) ,							(
              a__
          ) ,							(
              a__
          ) ,							(
              a__
          ) ,							(
              a__
          ) ,							
          )						:	Optional[int] =				config_and_inputs

          a__						:	Union[str, Any] =				{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'head_mask': head_mask}

          return config, inputs_dict

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  *lowercase)			->						List[str]:







          '''simple docstring'''

          a__						:	Any =				self.num_labels
          a__						:	List[Any] =				CTRLForSequenceClassification(lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	str =				ids_tensor([self.batch_size]					,  self.type_sequence_label_size)
          a__						:	Dict =				model(lowercase					,  token_type_ids=lowercase					,  labels=lowercase)
          self.parent.assertEqual(result.logits.shape					,  (self.batch_size, self.num_labels))





@require_torch
class  A__    (							__UpperCAmelCase ,  __UpperCAmelCase ,  __UpperCAmelCase ,  unittest.TestCase	):




   """simple docstring"""



   __A   :				List[str]         =  (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   :				Union[str, Any]         =  True
   __A   :				Any         =  False
   __A   :				Optional[Any]         =  False

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase)			->						Any:







          '''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 							__lowercase		(					self)			->						Optional[int]:







          '''simple docstring'''

          a__						:	Any =				CTRLModelTester(self)
          a__						:	Optional[Any] =				ConfigTester(self					,  config_class=lowercase					,  n_embd=37)

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







          '''simple docstring'''

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

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







          '''simple docstring'''

          self.config_tester.run_common_tests()

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







          '''simple docstring'''

          a__						:	Optional[Any] =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_ctrl_model(*lowercase)

   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          a__						:	Union[str, Any] =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_lm_head_model(*lowercase)

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







          '''simple docstring'''

          pass

   @slow
   def 							__lowercase		(					self)			->						Optional[int]:







          '''simple docstring'''

          for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 a__						:	Optional[int] =				CTRLModel.from_pretrained(lowercase)
                 self.assertIsNotNone(lowercase)

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







          '''simple docstring'''

          pass





@require_torch
class  A__    (							unittest.TestCase	):




   """simple docstring"""



   def 							__lowercase		(					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()

   @slow
   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          a__						:	List[Any] =				CTRLLMHeadModel.from_pretrained('ctrl')
          model.to(lowercase)
          a__						:	int =				torch.tensor(
              [[1_1859, 0, 1611, 8]]					,  dtype=torch.long					,  device=lowercase)  # Legal the president is
          a__						:	List[str] =				[
              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

          a__						:	str =				model.generate(lowercase					,  do_sample=lowercase)
          self.assertListEqual(output_ids[0].tolist()					,  lowercase)









import math
import random



def A_	(		A__		,      A__ = False				)  ->		float:
       if deriv:
              return value * (1 - value)
       return 1 / (1 + math.exp(-value				))


# Initial Value
lowercase      :       Optional[Any]									=  0.02



def A_	(		A__		,      A__				)  ->		float:
       a__						:	int =				float(2 * (random.randint(1		,      100				)) - 1				)

       for _ in range(A__				):
              # Forward propagation
              a__						:	Union[str, Any] =				sigmoid_function(INITIAL_VALUE * weight				)
              # How much did we miss?
              a__						:	Optional[Any] =				(expected / 100) - layer_a
              # Error delta
              a__						:	Optional[int] =				layer_1_error * sigmoid_function(A__		,      A__				)
              # Update weight
              weight += INITIAL_VALUE * layer_1_delta

       return layer_a * 100


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    lowercase      :       Dict									=  int(input("""Expected value: """))
    lowercase      :       Dict									=  int(input("""Number of propagations: """))
    print(forward_propagation(expected, number_propagations))


