






"""simple docstring"""
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     UpperCamelCase_ :





	def __init__(   self      :				List[Any]			,			lowerCAmelCase_      :				List[str]			,			lowerCAmelCase_      :				List[str]=14			,			lowerCAmelCase_      :				Tuple=7			,			lowerCAmelCase_      :				List[Any]=True			,			lowerCAmelCase_      :				Tuple=True			,			lowerCAmelCase_      :				List[str]=True			,			lowerCAmelCase_      :				Optional[int]=True			,			lowerCAmelCase_      :				Optional[int]=True			,			lowerCAmelCase_      :				Optional[int]=99			,			lowerCAmelCase_      :				Union[str, Any]=32			,			lowerCAmelCase_      :				Union[str, Any]=5			,			lowerCAmelCase_      :				Union[str, Any]=4			,			lowerCAmelCase_      :				Any=37			,			lowerCAmelCase_      :				Any="gelu"			,			lowerCAmelCase_      :				Union[str, Any]=0.1			,			lowerCAmelCase_      :				Dict=0.1			,			lowerCAmelCase_      :				str=512			,			lowerCAmelCase_      :				List[str]=16			,			lowerCAmelCase_      :				List[Any]=2			,			lowerCAmelCase_      :				Tuple=0.0_2			,			lowerCAmelCase_      :				Any=3			,			lowerCAmelCase_      :				List[str]=4			,			lowerCAmelCase_      :				Dict=None			,			)		->    List[Any]:
				UpperCAmelCase_  :				str      =      parent
				UpperCAmelCase_  :				Dict      =      batch_size
				UpperCAmelCase_  :				Any      =      seq_length
				UpperCAmelCase_  :				Any      =      is_training
				UpperCAmelCase_  :				str      =      use_token_type_ids
				UpperCAmelCase_  :				str      =      use_input_mask
				UpperCAmelCase_  :				List[Any]      =      use_labels
				UpperCAmelCase_  :				Tuple      =      use_mc_token_ids
				UpperCAmelCase_  :				Dict      =      vocab_size
				UpperCAmelCase_  :				Union[str, Any]      =      hidden_size
				UpperCAmelCase_  :				int      =      num_hidden_layers
				UpperCAmelCase_  :				Union[str, Any]      =      num_attention_heads
				UpperCAmelCase_  :				Any      =      intermediate_size
				UpperCAmelCase_  :				Dict      =      hidden_act
				UpperCAmelCase_  :				int      =      hidden_dropout_prob
				UpperCAmelCase_  :				Any      =      attention_probs_dropout_prob
				UpperCAmelCase_  :				Union[str, Any]      =      max_position_embeddings
				UpperCAmelCase_  :				str      =      type_vocab_size
				UpperCAmelCase_  :				Any      =      type_sequence_label_size
				UpperCAmelCase_  :				List[str]      =      initializer_range
				UpperCAmelCase_  :				Dict      =      num_labels
				UpperCAmelCase_  :				Optional[Any]      =      num_choices
				UpperCAmelCase_  :				Optional[int]      =      scope
				UpperCAmelCase_  :				Any      =      self.vocab_size - 1





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    List[str]:
				UpperCAmelCase_  :				List[Any]      =      ids_tensor([self.batch_size, self.seq_length]			,			self.vocab_size		)

				UpperCAmelCase_  :				Any      =      None
				if self.use_input_mask:
							UpperCAmelCase_  :				str      =      random_attention_mask([self.batch_size, self.seq_length]		)

				UpperCAmelCase_  :				Tuple      =      None
				if self.use_token_type_ids:
							UpperCAmelCase_  :				Tuple      =      ids_tensor([self.batch_size, self.seq_length]			,			self.type_vocab_size		)

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

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

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

				UpperCAmelCase_  :				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   _SCREAMING_SNAKE_CASE   (   self      :				Dict		)		->    Tuple:
				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   _SCREAMING_SNAKE_CASE   (   self      :				Dict			,			lowerCAmelCase_      :				List[str]			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				Optional[Any]			,			lowerCAmelCase_      :				Dict			,			*lowerCAmelCase_      :				int		)		->    List[str]:
				UpperCAmelCase_  :				str      =      CTRLModel(config=lowerCAmelCase_		)
				model.to(lowerCAmelCase_		)
				model.eval()

				model(lowerCAmelCase_			,			token_type_ids=lowerCAmelCase_			,			head_mask=lowerCAmelCase_		)
				model(lowerCAmelCase_			,			token_type_ids=lowerCAmelCase_		)
				UpperCAmelCase_  :				Union[str, Any]      =      model(lowerCAmelCase_		)
				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   _SCREAMING_SNAKE_CASE   (   self      :				int			,			lowerCAmelCase_      :				Tuple			,			lowerCAmelCase_      :				Any			,			lowerCAmelCase_      :				List[Any]			,			lowerCAmelCase_      :				Union[str, Any]			,			lowerCAmelCase_      :				int			,			*lowerCAmelCase_      :				Any		)		->    List[Any]:
				UpperCAmelCase_  :				Any      =      CTRLLMHeadModel(lowerCAmelCase_		)
				model.to(lowerCAmelCase_		)
				model.eval()

				UpperCAmelCase_  :				int      =      model(lowerCAmelCase_			,			token_type_ids=lowerCAmelCase_			,			labels=lowerCAmelCase_		)
				self.parent.assertEqual(result.loss.shape			,			()		)
				self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.seq_length, self.vocab_size)		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict		)		->    int:
				UpperCAmelCase_  :				Optional[Any]      =      self.prepare_config_and_inputs()

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

				UpperCAmelCase_  :				List[str]      =      {"input_ids": input_ids, "token_type_ids": token_type_ids, "head_mask": head_mask}

				return config, inputs_dict





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







@require_torch
class     UpperCamelCase_ (__A       ,		__A       ,		__A       ,		unittest.TestCase  ):
	__magic_name__			   =       (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
	__magic_name__			   =       (CTRLLMHeadModel,) if is_torch_available() else ()
	__magic_name__			   =       (
	    {
	        '''feature-extraction''': CTRLModel,
	        '''text-classification''': CTRLForSequenceClassification,
	        '''text-generation''': CTRLLMHeadModel,
	        '''zero-shot''': CTRLForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	__magic_name__			   =       True
	__magic_name__			   =       False
	__magic_name__			   =       False





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple			,			lowerCAmelCase_      :				Optional[int]			,			lowerCAmelCase_      :				Optional[Any]			,			lowerCAmelCase_      :				Optional[int]			,			lowerCAmelCase_      :				List[str]			,			lowerCAmelCase_      :				Any		)		->    Tuple:
				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   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]		)		->    Tuple:
				UpperCAmelCase_  :				Union[str, Any]      =      CTRLModelTester(self		)
				UpperCAmelCase_  :				List[str]      =      ConfigTester(self			,			config_class=lowerCAmelCase_			,			n_embd=37		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]		)		->    List[Any]:
				super().tearDown()
				# clean-up as much as possible GPU memory occupied by PyTorch
				gc.collect()
				torch.cuda.empty_cache()





	def   _SCREAMING_SNAKE_CASE   (   self      :				int		)		->    Any:
				self.config_tester.run_common_tests()





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple		)		->    int:
				UpperCAmelCase_  :				Optional[int]      =      self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_ctrl_model(*lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]		)		->    int:
				UpperCAmelCase_  :				int      =      self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_lm_head_model(*lowerCAmelCase_		)





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





	@slow
	def   _SCREAMING_SNAKE_CASE   (   self      :				Any		)		->    List[str]:
				for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							UpperCAmelCase_  :				Dict      =      CTRLModel.from_pretrained(lowerCAmelCase_		)
							self.assertIsNotNone(lowerCAmelCase_		)





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







@require_torch
class     UpperCamelCase_ (unittest.TestCase  ):





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





	@slow
	def   _SCREAMING_SNAKE_CASE   (   self      :				int		)		->    Optional[int]:
				UpperCAmelCase_  :				List[str]      =      CTRLLMHeadModel.from_pretrained("ctrl"		)
				model.to(lowerCAmelCase_		)
				UpperCAmelCase_  :				Any      =      torch.tensor(
				    [[11_859, 0, 1_611, 8]]			,			dtype=torch.long			,			device=lowerCAmelCase_		)  # Legal the president is
				UpperCAmelCase_  :				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

				UpperCAmelCase_  :				Union[str, Any]      =      model.generate(lowerCAmelCase_			,			do_sample=lowerCAmelCase_		)
				self.assertListEqual(output_ids[0].tolist()			,			lowerCAmelCase_		)











"""simple docstring"""
lowerCamelCase_       			=   {
    '''a''': '''AAAAA''',
    '''b''': '''AAAAB''',
    '''c''': '''AAABA''',
    '''d''': '''AAABB''',
    '''e''': '''AABAA''',
    '''f''': '''AABAB''',
    '''g''': '''AABBA''',
    '''h''': '''AABBB''',
    '''i''': '''ABAAA''',
    '''j''': '''BBBAA''',
    '''k''': '''ABAAB''',
    '''l''': '''ABABA''',
    '''m''': '''ABABB''',
    '''n''': '''ABBAA''',
    '''o''': '''ABBAB''',
    '''p''': '''ABBBA''',
    '''q''': '''ABBBB''',
    '''r''': '''BAAAA''',
    '''s''': '''BAAAB''',
    '''t''': '''BAABA''',
    '''u''': '''BAABB''',
    '''v''': '''BBBAB''',
    '''w''': '''BABAA''',
    '''x''': '''BABAB''',
    '''y''': '''BABBA''',
    '''z''': '''BABBB''',
    ''' ''': ''' ''',
}


lowerCamelCase_       			=   {value: key for key, value in encode_dict.items()}

def        snake_case   (     A__		):
			UpperCAmelCase_  :				Union[str, Any]      =      ""
			for letter in word.lower():
						if letter.isalpha() or letter == " ":
									encoded += encode_dict[letter]
						else:
									raise Exception("encode() accepts only letters of the alphabet and spaces"		)
			return encoded

def        snake_case   (     A__		):
			if set(A__		) - {"A", "B", " "} != set():
						raise Exception("decode() accepts only 'A', 'B' and spaces"		)
			UpperCAmelCase_  :				Dict      =      ""
			for word in coded.split():
						while len(A__		) != 0:
									decoded += decode_dict[word[:5]]
									UpperCAmelCase_  :				str      =      word[5:]
						decoded += " "
			return decoded.strip()


if __name__ == "__main__":
			from doctest import testmod

			testmod()



