

"""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[str]			,    _lowercase     :       Union[str, Any]			,    _lowercase     :       Tuple=14			,    _lowercase     :       List[Any]=7			,    _lowercase     :       Optional[Any]=True			,    _lowercase     :       str=True			,    _lowercase     :       Union[str, Any]=True			,    _lowercase     :       Optional[Any]=True			,    _lowercase     :       List[str]=True			,    _lowercase     :       Union[str, Any]=99			,    _lowercase     :       Any=32			,    _lowercase     :       Union[str, Any]=5			,    _lowercase     :       List[Any]=4			,    _lowercase     :       Optional[Any]=37			,    _lowercase     :       Dict="gelu"			,    _lowercase     :       Tuple=0.1			,    _lowercase     :       List[str]=0.1			,    _lowercase     :       Optional[Any]=5_12			,    _lowercase     :       str=16			,    _lowercase     :       List[Any]=2			,    _lowercase     :       Union[str, Any]=0.02			,    _lowercase     :       int=3			,    _lowercase     :       List[Any]=4			,    _lowercase     :       Union[str, Any]=None			,    ):
		__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     :       Optional[Any]						):
		__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     :       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        a  (  self     :       str			,    _lowercase     :       str			,    _lowercase     :       Optional[Any]			,    _lowercase     :       str			,    _lowercase     :       Tuple			,    _lowercase     :       int			,    *_lowercase     :       Tuple						):
		__UpperCAmelCase      		= CTRLModel(config=_lowercase						)
		model.to(_lowercase						)
		model.eval()

		model(_lowercase			,    token_type_ids=_lowercase			,    head_mask=_lowercase						)
		model(_lowercase			,    token_type_ids=_lowercase						)
		__UpperCAmelCase      		= 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        a  (  self     :       Any			,    _lowercase     :       Any			,    _lowercase     :       List[str]			,    _lowercase     :       str			,    _lowercase     :       Optional[Any]			,    _lowercase     :       Any			,    *_lowercase     :       str						):
		__UpperCAmelCase      		= CTRLLMHeadModel(_lowercase						)
		model.to(_lowercase						)
		model.eval()

		__UpperCAmelCase      		= 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        a  (  self     :       Optional[Any]						):
		__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     :       Tuple			,    _lowercase     :       List[Any]			,    _lowercase     :       List[str]			,    _lowercase     :       Tuple			,    _lowercase     :       List[str]			,    *_lowercase     :       Optional[int]						):
		__UpperCAmelCase      		= self.num_labels
		__UpperCAmelCase      		= CTRLForSequenceClassification(_lowercase						)
		model.to(_lowercase						)
		model.eval()
		__UpperCAmelCase      		= ids_tensor([self.batch_size]			,    self.type_sequence_label_size						)
		__UpperCAmelCase      		= model(_lowercase			,    token_type_ids=_lowercase			,    labels=_lowercase						)
		self.parent.assertEqual(result.logits.shape			,    (self.batch_size, self.num_labels)						)






@require_torch
class      _UpperCAmelCase   (							_lowerCAmelCase		,    _lowerCAmelCase		,    _lowerCAmelCase		,    unittest.TestCase       ):
	a__		: List[str]					=					(CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
	a__		: int					=					(CTRLLMHeadModel,) if is_torch_available() else ()
	a__		: str					=					(
	    {
	        "feature-extraction": CTRLModel,
	        "text-classification": CTRLForSequenceClassification,
	        "text-generation": CTRLLMHeadModel,
	        "zero-shot": CTRLForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	a__		: List[str]					=					True
	a__		: Dict					=					False
	a__		: int					=					False



	def        a  (  self     :       Optional[int]			,    _lowercase     :       Union[str, Any]			,    _lowercase     :       List[Any]			,    _lowercase     :       Tuple			,    _lowercase     :       Union[str, Any]			,    _lowercase     :       Optional[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     :       List[Any]						):
		__UpperCAmelCase      		= CTRLModelTester(self						)
		__UpperCAmelCase      		= ConfigTester(self			,    config_class=_lowercase			,    n_embd=37						)



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



	def        a  (  self     :       Tuple						):
		self.config_tester.run_common_tests()



	def        a  (  self     :       Dict						):
		__UpperCAmelCase      		= self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_ctrl_model(*_lowercase						)



	def        a  (  self     :       Union[str, Any]						):
		__UpperCAmelCase      		= 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        a  (  self     :       Tuple						):
		pass



	@slow
	def        a  (  self     :       List[Any]						):
		for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			__UpperCAmelCase      		= 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        a  (  self     :       Union[str, Any]						):
		pass






@require_torch
class      _UpperCAmelCase   (							unittest.TestCase       ):



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



	@slow
	def        a  (  self     :       Union[str, Any]						):
		__UpperCAmelCase      		= CTRLLMHeadModel.from_pretrained('''ctrl'''						)
		model.to(_lowercase						)
		__UpperCAmelCase      		= torch.tensor(
		    [[1_18_59, 0, 16_11, 8]]			,    dtype=torch.long			,    device=_lowercase						)  # Legal the president is
		__UpperCAmelCase      		= [
		    1_18_59,
		    0,
		    16_11,
		    8,
		    5,
		    1_50,
		    2_64_49,
		    2,
		    19,
		    3_48,
		    4_69,
		    3,
		    25_95,
		    48,
		    2_07_40,
		    24_65_33,
		    24_65_33,
		    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(_lowercase			,    do_sample=_lowercase						)
		self.assertListEqual(output_ids[0].tolist()			,    _lowercase						)

import random




def    lowerCAmelCase_  (		lowercase:				int	,       lowercase:				float	,       lowercase:				bool = False		)     ->					dict:







 '''simple docstring'''
 _UpperCamelCase:  dict    						=			{i: [] for i in range(lowercase		)}

 # if probability is greater or equal than 1, then generate a complete graph
 if probability >= 1:
  return complete_graph(lowercase		)
 # if probability is lower or equal than 0, then return a graph without edges
 if probability <= 0:
  return graph

 # for each couple of nodes, add an edge from u to v
 # if the number randomly generated is greater than probability probability
 for i in range(lowercase		):
  for j in range(i + 1	,       lowercase		):
   if random.random() < probability:
    graph[i].append(lowercase		)
    if not directed:
     # if the graph is undirected, add an edge in from j to i, either
     graph[j].append(lowercase		)
 return graph




def    lowerCAmelCase_  (		lowercase:				int		)     ->					dict:







 '''simple docstring'''
 return {
     i: [j for j in range(lowercase		) if i != j] for i in range(lowercase		)
 }


if __name__ == "__main__":
  import doctest

  doctest.testmod()