
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 			lowercase_		:


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


       def 	_snake_case				(    self				)						->    str:
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=ids_tensor([self.batch_size, self.seq_length]						,       self.vocab_size				)

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

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=None
              if self.use_token_type_ids:
                     SCREAMING_SNAKE_CASE_							:				Dict		=ids_tensor([self.batch_size, self.seq_length]						,       self.type_vocab_size				)

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

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

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.get_config()

              SCREAMING_SNAKE_CASE_							:				Dict		=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				)						->    Any:
              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						,       __A						,       __A						,       __A						,       __A						,       __A						,       *__A				)						->    Union[str, Any]:
              SCREAMING_SNAKE_CASE_							:				List[str]		=CTRLModel(config=__A				)
              model.to(__A				)
              model.eval()

              model(__A						,       token_type_ids=__A						,       head_mask=__A				)
              model(__A						,       token_type_ids=__A				)
              SCREAMING_SNAKE_CASE_							:				List[str]		=model(__A				)
              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						,       __A						,       __A						,       __A						,       __A						,       __A						,       *__A				)						->    Any:
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=CTRLLMHeadModel(__A				)
              model.to(__A				)
              model.eval()

              SCREAMING_SNAKE_CASE_							:				Any		=model(__A						,       token_type_ids=__A						,       labels=__A				)
              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				)						->    Any:
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.prepare_config_and_inputs()

              (
                  (
                  SCREAMING_SNAKE_CASE_
              )	,     (
                  SCREAMING_SNAKE_CASE_
              )	,     (
                  SCREAMING_SNAKE_CASE_
              )	,     (
                  SCREAMING_SNAKE_CASE_
              )	,     (
                  SCREAMING_SNAKE_CASE_
              )	,     (
                  SCREAMING_SNAKE_CASE_
              )	,     (
                  SCREAMING_SNAKE_CASE_
              )	,     (
                  SCREAMING_SNAKE_CASE_
              )	,     (
                  SCREAMING_SNAKE_CASE_
              )	,     
              )							:				List[str]		=config_and_inputs

              SCREAMING_SNAKE_CASE_							:				str		={'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''head_mask''': head_mask}

              return config, inputs_dict





       def 	_snake_case				(    self						,       __A						,       __A						,       __A						,       __A						,       *__A				)						->    Any:
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=self.num_labels
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=CTRLForSequenceClassification(__A				)
              model.to(__A				)
              model.eval()
              SCREAMING_SNAKE_CASE_							:				int		=ids_tensor([self.batch_size]						,       self.type_sequence_label_size				)
              SCREAMING_SNAKE_CASE_							:				int		=model(__A						,       token_type_ids=__A						,       labels=__A				)
              self.parent.assertEqual(result.logits.shape						,       (self.batch_size, self.num_labels)				)






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


       def 	_snake_case				(    self						,       __A						,       __A						,       __A						,       __A						,       __A				)						->    List[str]:
              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				)						->    Dict:
              SCREAMING_SNAKE_CASE_							:				Tuple		=CTRLModelTester(self				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=ConfigTester(self						,       config_class=__A						,       n_embd=37				)


       def 	_snake_case				(    self				)						->    int:
              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				)						->    Tuple:
              SCREAMING_SNAKE_CASE_							:				List[Any]		=self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_ctrl_model(*__A				)


       def 	_snake_case				(    self				)						->    Optional[int]:
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_lm_head_model(*__A				)


       @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''				)
       def 	_snake_case				(    self				)						->    Union[str, Any]:
              pass


       @slow
       def 	_snake_case				(    self				)						->    List[Any]:
              for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                     SCREAMING_SNAKE_CASE_							:				List[str]		=CTRLModel.from_pretrained(__A				)
                     self.assertIsNotNone(__A				)





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






@require_torch
class 			lowercase_		(      unittest.TestCase       ):


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





       @slow
       def 	_snake_case				(    self				)						->    Optional[int]:
              SCREAMING_SNAKE_CASE_							:				Dict		=CTRLLMHeadModel.from_pretrained('''ctrl'''				)
              model.to(__A				)
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=torch.tensor(
                  [[11_859, 0, 1_611, 8]]						,       dtype=torch.long						,       device=__A				)  # Legal the president is
              SCREAMING_SNAKE_CASE_							:				Dict		=[
                  11_859,
                  0,
                  1_611,
                  8,
                  5,
                  150,
                  26_449,
                  2,
                  19,
                  348,
                  469,
                  3,
                  2_595,
                  48,
                  20_740,
                  246_533,
                  246_533,
                  19,
                  30,
                  5,
              ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

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


def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     int | float | str				)			->						tuple[int, int]:
       try:
              SCREAMING_SNAKE_CASE_							:				int		=float(UpperCAmelCase_				)
       except ValueError:
              raise ValueError('''Please enter a valid number'''				)
       SCREAMING_SNAKE_CASE_							:				Any		=decimal - int(UpperCAmelCase_				)
       if fractional_part == 0:
              return int(UpperCAmelCase_				), 1
       else:
              SCREAMING_SNAKE_CASE_							:				Any		=len(str(UpperCAmelCase_				).split('''.'''				)[1]				)
              SCREAMING_SNAKE_CASE_							:				str		=int(decimal * (1_0**number_of_frac_digits)				)
              SCREAMING_SNAKE_CASE_							:				Any		=1_0**number_of_frac_digits
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Tuple		=denominator, numerator
              while True:
                     SCREAMING_SNAKE_CASE_							:				Any		=dividend % divisor
                     if remainder == 0:
                            break
                     SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Optional[int]		=divisor, remainder
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Optional[int]		=numerator / divisor, denominator / divisor
              return int(UpperCAmelCase_				), int(UpperCAmelCase_				)


if __name__ == "__main__":
 print(F"{decimal_to_fraction(2) = }")
 print(F"{decimal_to_fraction(89.0) = }")
 print(F"{decimal_to_fraction('67') = }")
 print(F"{decimal_to_fraction('45.0') = }")
 print(F"{decimal_to_fraction(1.5) = }")
 print(F"{decimal_to_fraction('6.25') = }")
 print(F"{decimal_to_fraction('78td') = }")
