



'''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 				SCREAMING_SNAKE_CASE:







	"""simple docstring"""






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







	def      A	(						self			:     int		)			->		Optional[int]:
								UpperCAmelCase :       int 							=		ids_tensor([self.batch_size, self.seq_length]		,			self.vocab_size		)

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

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

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

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

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

								UpperCAmelCase :       Any 							=		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			:     Union[str, Any]		)			->		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      A	(						self			:     Tuple		,			__snake_case			:     Optional[int]		,			__snake_case			:     List[str]		,			__snake_case			:     Optional[int]		,			__snake_case			:     Optional[int]		,			__snake_case			:     str		,			*__snake_case			:     Any		)			->		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 :       str 							=		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      A	(						self			:     str		,			__snake_case			:     List[Any]		,			__snake_case			:     Optional[Any]		,			__snake_case			:     Union[str, Any]		,			__snake_case			:     int		,			__snake_case			:     Any		,			*__snake_case			:     List[str]		)			->		Dict:
								UpperCAmelCase :       List[Any] 							=		CTRLLMHeadModel(lowerCamelCase__		)
								model.to(lowerCamelCase__		)
								model.eval()

								UpperCAmelCase :       Union[str, Any] 							=		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      A	(						self			:     Dict		)			->		List[str]:
								UpperCAmelCase :       Dict 							=		self.prepare_config_and_inputs()

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

								UpperCAmelCase :       Optional[Any] 							=		{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''head_mask''': head_mask}

								return config, inputs_dict







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

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







	"""simple docstring"""






	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      A	(						self			:     List[str]		,			__snake_case			:     Optional[Any]		,			__snake_case			:     Dict		,			__snake_case			:     Any		,			__snake_case			:     int		,			__snake_case			:     Union[str, Any]		)			->		List[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      A	(						self			:     Any		)			->		Any:
								UpperCAmelCase :       List[str] 							=		CTRLModelTester(self		)
								UpperCAmelCase :       List[Any] 							=		ConfigTester(self		,			config_class=lowerCamelCase__		,			n_embd=37		)







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







	def      A	(						self			:     List[Any]		)			->		Tuple:
								self.config_tester.run_common_tests()







	def      A	(						self			:     Optional[int]		)			->		int:
								UpperCAmelCase :       int 							=		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_ctrl_model(*lowerCamelCase__		)







	def      A	(						self			:     Union[str, Any]		)			->		List[str]:
								UpperCAmelCase :       Dict 							=		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      A	(						self			:     Dict		)			->		Any:
								pass







	@slow
	def      A	(						self			:     str		)			->		str:


								for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															UpperCAmelCase :       str 							=		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      A	(						self			:     int		)			->		Dict:
								pass

@require_torch
class 				SCREAMING_SNAKE_CASE(							unittest.TestCase			):







	"""simple docstring"""






	def      A	(						self			:     Union[str, Any]		)			->		Union[str, Any]:
								super().tearDown()
								# clean-up as much as possible GPU memory occupied by PyTorch
								gc.collect()
								torch.cuda.empty_cache()







	@slow
	def      A	(						self			:     List[str]		)			->		int:
								UpperCAmelCase :       Dict 							=		CTRLLMHeadModel.from_pretrained('''ctrl'''		)
								model.to(lowerCamelCase__		)
								UpperCAmelCase :       int 							=		torch.tensor(
								    [[11859, 0, 1611, 8]]		,			dtype=torch.long		,			device=lowerCamelCase__		)  # Legal the president is
								UpperCAmelCase :       Optional[int] 							=		[
								    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 :       Any 							=		model.generate(lowerCamelCase__		,			do_sample=lowerCamelCase__		)
								self.assertListEqual(output_ids[0].tolist()		,			lowerCamelCase__		)












from pathlib import Path

import fire



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ->   List[str]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						Path(_SCREAMING_SNAKE_CASE	)
      SCREAMING_SNAKE_CASE		=						Path(_SCREAMING_SNAKE_CASE	)
      dest_dir.mkdir(exist_ok=_SCREAMING_SNAKE_CASE	)
      for path in src_dir.iterdir():
            SCREAMING_SNAKE_CASE		=						[x.rstrip() for x in list(path.open().readlines()	)][:n]
            SCREAMING_SNAKE_CASE		=						dest_dir.joinpath(path.name	)
            print(_SCREAMING_SNAKE_CASE	)
            dest_path.open("""w"""	).write("""\n""".join(_SCREAMING_SNAKE_CASE	)	)


if __name__ == "__main__":
      fire.Fire(minify)


