
import argparse
import logging
import os
from datetime import datetime

import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, SequentialSampler, Subset
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    GlueDataset,
    default_data_collator,
    glue_compute_metrics,
    glue_output_modes,
    glue_processors,
    set_seed,
)
from transformers.trainer_utils import is_main_process


__a        = logging.getLogger(__name__)




def     a						(							snake_case__: Dict ):


				'''simple docstring'''
				lowercase_					  = p * torch.log(snake_case__ )
				lowercase_					  = 0
				return -plogp.sum(dim=-1 )




def     a						(							snake_case__: Union[str, Any] ):


				'''simple docstring'''
				logger.info('''lv, h >\t''' + '''\t'''.join(F'''{x + 1}''' for x in range(len(snake_case__ ) ) ) )
				for row in range(len(snake_case__ ) ):
								if tensor.dtype != torch.long:
												logger.info(F'''layer {row + 1}:\t''' + '''\t'''.join(F'''{x:.5f}''' for x in tensor[row].cpu().data ) )
								else:
												logger.info(F'''layer {row + 1}:\t''' + '''\t'''.join(F'''{x:d}''' for x in tensor[row].cpu().data ) )




def     a						(							snake_case__: Optional[Any]						,       snake_case__: str						,       snake_case__: Optional[Any]						,       snake_case__: List[str]=True						,       snake_case__: List[str]=True						,       snake_case__: int=None						,       snake_case__: Tuple=False ):


				'''simple docstring'''
				lowercase_  ,     lowercase_					  = model.config.num_hidden_layers, model.config.num_attention_heads
				lowercase_					  = torch.zeros(snake_case__						,       snake_case__ ).to(args.device )
				lowercase_					  = torch.zeros(snake_case__						,       snake_case__ ).to(args.device )

				if head_mask is None:
								lowercase_					  = torch.ones(snake_case__						,       snake_case__ ).to(args.device )

				head_mask.requires_grad_(requires_grad=snake_case__ )
				# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
				if actually_pruned:
								lowercase_					  = None

				lowercase_					  = None
				lowercase_					  = None
				lowercase_					  = 0.0

				for step, inputs in enumerate(tqdm(snake_case__						,       desc='''Iteration'''						,       disable=args.local_rank not in [-1, 0] ) ):
								for k, v in inputs.items():
												lowercase_					  = v.to(args.device )

								# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
								lowercase_					  = model(**snake_case__						,       head_mask=snake_case__ )
								lowercase_  ,     lowercase_  ,     lowercase_					  = (
								    outputs[0],
								    outputs[1],
								    outputs[-1],
								)  # Loss and logits are the first, attention the last
								loss.backward()  # Backpropagate to populate the gradients in the head mask

								if compute_entropy:
												for layer, attn in enumerate(snake_case__ ):
																lowercase_					  = entropy(attn.detach() ) * inputs['''attention_mask'''].float().unsqueeze(1 )
																attn_entropy[layer] += masked_entropy.sum(-1 ).sum(0 ).detach()

								if compute_importance:
												head_importance += head_mask.grad.abs().detach()

								# Also store our logits/labels if we want to compute metrics afterwards
								if preds is None:
												lowercase_					  = logits.detach().cpu().numpy()
												lowercase_					  = inputs['''labels'''].detach().cpu().numpy()
								else:
												lowercase_					  = np.append(snake_case__						,       logits.detach().cpu().numpy()						,       axis=0 )
												lowercase_					  = np.append(snake_case__						,       inputs['''labels'''].detach().cpu().numpy()						,       axis=0 )

								tot_tokens += inputs["attention_mask"].float().detach().sum().data

				# Normalize
				attn_entropy /= tot_tokens
				head_importance /= tot_tokens
				# Layerwise importance normalization
				if not args.dont_normalize_importance_by_layer:
								lowercase_					  = 2
								lowercase_					  = torch.pow(torch.pow(snake_case__						,       snake_case__ ).sum(-1 )						,       1 / exponent )
								head_importance /= norm_by_layer.unsqueeze(-1 ) + 1e-2_0

				if not args.dont_normalize_global_importance:
								lowercase_					  = (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

				# Print/save matrices
				np.save(os.path.join(args.output_dir						,       '''attn_entropy.npy''' )						,       attn_entropy.detach().cpu().numpy() )
				np.save(os.path.join(args.output_dir						,       '''head_importance.npy''' )						,       head_importance.detach().cpu().numpy() )

				logger.info('''Attention entropies''' )
				print_ad_tensor(snake_case__ )
				logger.info('''Head importance scores''' )
				print_ad_tensor(snake_case__ )
				logger.info('''Head ranked by importance scores''' )
				lowercase_					  = torch.zeros(head_importance.numel()						,       dtype=torch.long						,       device=args.device )
				lowercase_					  = torch.arange(
				    head_importance.numel()						,       device=args.device )
				lowercase_					  = head_ranks.view_as(snake_case__ )
				print_ad_tensor(snake_case__ )

				return attn_entropy, head_importance, preds, labels




def     a						(							snake_case__: List[Any]						,       snake_case__: List[str]						,       snake_case__: Tuple ):


				'''simple docstring'''
				lowercase_  ,     lowercase_  ,     lowercase_  ,     lowercase_					  = compute_heads_importance(snake_case__						,       snake_case__						,       snake_case__						,       compute_entropy=snake_case__ )
				lowercase_					  = np.argmax(snake_case__						,       axis=1 ) if args.output_mode == '''classification''' else np.squeeze(snake_case__ )
				lowercase_					  = glue_compute_metrics(args.task_name						,       snake_case__						,       snake_case__ )[args.metric_name]
				logger.info('''Pruning: original score: %f, threshold: %f'''						,       snake_case__						,       original_score * args.masking_threshold )

				lowercase_					  = torch.ones_like(snake_case__ )
				lowercase_					  = max(1						,       int(new_head_mask.numel() * args.masking_amount ) )

				lowercase_					  = original_score
				while current_score >= original_score * args.masking_threshold:
								lowercase_					  = new_head_mask.clone()  # save current head mask
								# heads from least important to most - keep only not-masked heads
								lowercase_					  = float('''Inf''' )
								lowercase_					  = head_importance.view(-1 ).sort()[1]

								if len(snake_case__ ) <= num_to_mask:
												break

								# mask heads
								lowercase_					  = current_heads_to_mask[:num_to_mask]
								logger.info('''Heads to mask: %s'''						,       str(current_heads_to_mask.tolist() ) )
								lowercase_					  = new_head_mask.view(-1 )
								lowercase_					  = 0.0
								lowercase_					  = new_head_mask.view_as(snake_case__ )
								lowercase_					  = new_head_mask.clone().detach()
								print_ad_tensor(snake_case__ )

								# Compute metric and head importance again
								lowercase_  ,     lowercase_  ,     lowercase_  ,     lowercase_					  = compute_heads_importance(
								    snake_case__						,       snake_case__						,       snake_case__						,       compute_entropy=snake_case__						,       head_mask=snake_case__ )
								lowercase_					  = np.argmax(snake_case__						,       axis=1 ) if args.output_mode == '''classification''' else np.squeeze(snake_case__ )
								lowercase_					  = glue_compute_metrics(args.task_name						,       snake_case__						,       snake_case__ )[args.metric_name]
								logger.info(
								    '''Masking: current score: %f, remaining heads %d (%.1f percents)'''						,       snake_case__						,       new_head_mask.sum()						,       new_head_mask.sum() / new_head_mask.numel() * 100						,       )

				logger.info('''Final head mask''' )
				print_ad_tensor(snake_case__ )
				np.save(os.path.join(args.output_dir						,       '''head_mask.npy''' )						,       head_mask.detach().cpu().numpy() )

				return head_mask




def     a						(							snake_case__: List[str]						,       snake_case__: Optional[int]						,       snake_case__: str						,       snake_case__: List[str] ):


				'''simple docstring'''
				lowercase_					  = datetime.now()
				lowercase_  ,     lowercase_  ,     lowercase_  ,     lowercase_					  = compute_heads_importance(
				    snake_case__						,       snake_case__						,       snake_case__						,       compute_entropy=snake_case__						,       compute_importance=snake_case__						,       head_mask=snake_case__ )
				lowercase_					  = np.argmax(snake_case__						,       axis=1 ) if args.output_mode == '''classification''' else np.squeeze(snake_case__ )
				lowercase_					  = glue_compute_metrics(args.task_name						,       snake_case__						,       snake_case__ )[args.metric_name]
				lowercase_					  = datetime.now() - before_time

				lowercase_					  = sum(p.numel() for p in model.parameters() )
				lowercase_					  = {
				    layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(snake_case__ ) )
				}

				assert sum(len(snake_case__ ) for h in heads_to_prune.values() ) == (1 - head_mask.long()).sum().item()
				model.prune_heads(snake_case__ )
				lowercase_					  = sum(p.numel() for p in model.parameters() )

				lowercase_					  = datetime.now()
				lowercase_  ,     lowercase_  ,     lowercase_  ,     lowercase_					  = compute_heads_importance(
				    snake_case__						,       snake_case__						,       snake_case__						,       compute_entropy=snake_case__						,       compute_importance=snake_case__						,       head_mask=snake_case__						,       actually_pruned=snake_case__						,       )
				lowercase_					  = np.argmax(snake_case__						,       axis=1 ) if args.output_mode == '''classification''' else np.squeeze(snake_case__ )
				lowercase_					  = glue_compute_metrics(args.task_name						,       snake_case__						,       snake_case__ )[args.metric_name]
				lowercase_					  = datetime.now() - before_time

				logger.info(
				    '''Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)'''						,       snake_case__						,       snake_case__						,       pruned_num_params / original_num_params * 100						,       )
				logger.info('''Pruning: score with masking: %f score with pruning: %f'''						,       snake_case__						,       snake_case__ )
				logger.info('''Pruning: speed ratio (new timing / original timing): %f percents'''						,       original_time / new_time * 100 )




def     a						(							):


				'''simple docstring'''
				lowercase_					  = argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '''--data_dir'''						,       default=snake_case__						,       type=snake_case__						,       required=snake_case__						,       help='''The input data dir. Should contain the .tsv files (or other data files) for the task.'''						,       )
				parser.add_argument(
				    '''--model_name_or_path'''						,       default=snake_case__						,       type=snake_case__						,       required=snake_case__						,       help='''Path to pretrained model or model identifier from huggingface.co/models'''						,       )
				parser.add_argument(
				    '''--task_name'''						,       default=snake_case__						,       type=snake_case__						,       required=snake_case__						,       help='''The name of the task to train selected in the list: ''' + ''', '''.join(glue_processors.keys() )						,       )
				parser.add_argument(
				    '''--output_dir'''						,       default=snake_case__						,       type=snake_case__						,       required=snake_case__						,       help='''The output directory where the model predictions and checkpoints will be written.'''						,       )

				# Other parameters
				parser.add_argument(
				    '''--config_name'''						,       default=''''''						,       type=snake_case__						,       help='''Pretrained config name or path if not the same as model_name_or_path'''						,       )
				parser.add_argument(
				    '''--tokenizer_name'''						,       default=''''''						,       type=snake_case__						,       help='''Pretrained tokenizer name or path if not the same as model_name_or_path'''						,       )
				parser.add_argument(
				    '''--cache_dir'''						,       default=snake_case__						,       type=snake_case__						,       help='''Where do you want to store the pre-trained models downloaded from huggingface.co'''						,       )
				parser.add_argument(
				    '''--data_subset'''						,       type=snake_case__						,       default=-1						,       help='''If > 0: limit the data to a subset of data_subset instances.''' )
				parser.add_argument(
				    '''--overwrite_output_dir'''						,       action='''store_true'''						,       help='''Whether to overwrite data in output directory''' )
				parser.add_argument(
				    '''--overwrite_cache'''						,       action='''store_true'''						,       help='''Overwrite the cached training and evaluation sets''' )

				parser.add_argument(
				    '''--dont_normalize_importance_by_layer'''						,       action='''store_true'''						,       help='''Don\'t normalize importance score by layers''' )
				parser.add_argument(
				    '''--dont_normalize_global_importance'''						,       action='''store_true'''						,       help='''Don\'t normalize all importance scores between 0 and 1'''						,       )

				parser.add_argument(
				    '''--try_masking'''						,       action='''store_true'''						,       help='''Whether to try to mask head until a threshold of accuracy.''' )
				parser.add_argument(
				    '''--masking_threshold'''						,       default=0.9						,       type=snake_case__						,       help='''masking threshold in term of metrics (stop masking when metric < threshold * original metric value).'''						,       )
				parser.add_argument(
				    '''--masking_amount'''						,       default=0.1						,       type=snake_case__						,       help='''Amount to heads to masking at each masking step.''' )
				parser.add_argument('''--metric_name'''						,       default='''acc'''						,       type=snake_case__						,       help='''Metric to use for head masking.''' )

				parser.add_argument(
				    '''--max_seq_length'''						,       default=128						,       type=snake_case__						,       help=(
				        '''The maximum total input sequence length after WordPiece tokenization. \n'''
				        '''Sequences longer than this will be truncated, sequences shorter padded.'''
				    )						,       )
				parser.add_argument('''--batch_size'''						,       default=1						,       type=snake_case__						,       help='''Batch size.''' )

				parser.add_argument('''--seed'''						,       type=snake_case__						,       default=42 )
				parser.add_argument('''--local_rank'''						,       type=snake_case__						,       default=-1						,       help='''local_rank for distributed training on gpus''' )
				parser.add_argument('''--no_cuda'''						,       action='''store_true'''						,       help='''Whether not to use CUDA when available''' )
				parser.add_argument('''--server_ip'''						,       type=snake_case__						,       default=''''''						,       help='''Can be used for distant debugging.''' )
				parser.add_argument('''--server_port'''						,       type=snake_case__						,       default=''''''						,       help='''Can be used for distant debugging.''' )
				lowercase_					  = parser.parse_args()

				if args.server_ip and args.server_port:
								# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
								import ptvsd

								print('''Waiting for debugger attach''' )
								ptvsd.enable_attach(address=(args.server_ip, args.server_port)						,       redirect_output=snake_case__ )
								ptvsd.wait_for_attach()

				# Setup devices and distributed training
				if args.local_rank == -1 or args.no_cuda:
								lowercase_					  = torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu''' )
								lowercase_					  = 0 if args.no_cuda else torch.cuda.device_count()
				else:
								torch.cuda.set_device(args.local_rank )
								lowercase_					  = torch.device('''cuda'''						,       args.local_rank )
								lowercase_					  = 1
								torch.distributed.init_process_group(backend='''nccl''' )  # Initializes the distributed backend

				# Setup logging
				logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN )
				logger.info('''device: {} n_gpu: {}, distributed: {}'''.format(args.device						,       args.n_gpu						,       bool(args.local_rank != -1 ) ) )
				# Set the verbosity to info of the Transformers logger (on main process only):
				if is_main_process(args.local_rank ):
								transformers.utils.logging.set_verbosity_info()
								transformers.utils.logging.enable_default_handler()
								transformers.utils.logging.enable_explicit_format()

				# Set seeds
				set_seed(args.seed )

				# Prepare GLUE task
				lowercase_					  = args.task_name.lower()
				if args.task_name not in glue_processors:
								raise ValueError('''Task not found: %s''' % (args.task_name) )
				lowercase_					  = glue_processors[args.task_name]()
				lowercase_					  = glue_output_modes[args.task_name]
				lowercase_					  = processor.get_labels()
				lowercase_					  = len(snake_case__ )

				# Load pretrained model and tokenizer
				#
				# Distributed training:
				# The .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.

				lowercase_					  = AutoConfig.from_pretrained(
				    args.config_name if args.config_name else args.model_name_or_path						,       num_labels=snake_case__						,       finetuning_task=args.task_name						,       output_attentions=snake_case__						,       cache_dir=args.cache_dir						,       )
				lowercase_					  = AutoTokenizer.from_pretrained(
				    args.tokenizer_name if args.tokenizer_name else args.model_name_or_path						,       cache_dir=args.cache_dir						,       )
				lowercase_					  = AutoModelForSequenceClassification.from_pretrained(
				    args.model_name_or_path						,       from_tf=bool('''.ckpt''' in args.model_name_or_path )						,       config=snake_case__						,       cache_dir=args.cache_dir						,       )

				# Distributed and parallel training
				model.to(args.device )
				if args.local_rank != -1:
								lowercase_					  = nn.parallel.DistributedDataParallel(
								    snake_case__						,       device_ids=[args.local_rank]						,       output_device=args.local_rank						,       find_unused_parameters=snake_case__ )
				elif args.n_gpu > 1:
								lowercase_					  = nn.DataParallel(snake_case__ )

				# Print/save training arguments
				os.makedirs(args.output_dir						,       exist_ok=snake_case__ )
				torch.save(snake_case__						,       os.path.join(args.output_dir						,       '''run_args.bin''' ) )
				logger.info('''Training/evaluation parameters %s'''						,       snake_case__ )

				# Prepare dataset for the GLUE task
				lowercase_					  = GlueDataset(snake_case__						,       tokenizer=snake_case__						,       mode='''dev''' )
				if args.data_subset > 0:
								lowercase_					  = Subset(snake_case__						,       list(range(min(args.data_subset						,       len(snake_case__ ) ) ) ) )
				lowercase_					  = SequentialSampler(snake_case__ ) if args.local_rank == -1 else DistributedSampler(snake_case__ )
				lowercase_					  = DataLoader(
				    snake_case__						,       sampler=snake_case__						,       batch_size=args.batch_size						,       collate_fn=snake_case__ )

				# Compute head entropy and importance score
				compute_heads_importance(snake_case__						,       snake_case__						,       snake_case__ )

				# Try head masking (set heads to zero until the score goes under a threshole)
				# and head pruning (remove masked heads and see the effect on the network)
				if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
								lowercase_					  = mask_heads(snake_case__						,       snake_case__						,       snake_case__ )
								prune_heads(snake_case__						,       snake_case__						,       snake_case__						,       snake_case__ )


if __name__ == "__main__":
						main()






import unittest

import numpy as np

from transformers import ElectraConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
						from transformers.models.electra.modeling_flax_electra import (
						    FlaxElectraForCausalLM,
						    FlaxElectraForMaskedLM,
						    FlaxElectraForMultipleChoice,
						    FlaxElectraForPreTraining,
						    FlaxElectraForQuestionAnswering,
						    FlaxElectraForSequenceClassification,
						    FlaxElectraForTokenClassification,
						    FlaxElectraModel,
						)
class 			lowercase__(    unittest.TestCase							):



	"""simple docstring"""




	def __init__(	self  :      str	,							SCREAMING_SNAKE_CASE_  :      str	,							SCREAMING_SNAKE_CASE_  :      Optional[int]=1_3	,							SCREAMING_SNAKE_CASE_  :      int=7	,							SCREAMING_SNAKE_CASE_  :      Tuple=True	,							SCREAMING_SNAKE_CASE_  :      List[Any]=True	,							SCREAMING_SNAKE_CASE_  :      List[Any]=True	,							SCREAMING_SNAKE_CASE_  :      Dict=True	,							SCREAMING_SNAKE_CASE_  :      Optional[Any]=9_9	,							SCREAMING_SNAKE_CASE_  :      Any=2_4	,							SCREAMING_SNAKE_CASE_  :      int=3_2	,							SCREAMING_SNAKE_CASE_  :      Any=5	,							SCREAMING_SNAKE_CASE_  :      List[str]=4	,							SCREAMING_SNAKE_CASE_  :      Optional[int]=3_7	,							SCREAMING_SNAKE_CASE_  :      Optional[int]="gelu"	,							SCREAMING_SNAKE_CASE_  :      Any=0.1	,							SCREAMING_SNAKE_CASE_  :      List[Any]=0.1	,							SCREAMING_SNAKE_CASE_  :      Dict=5_1_2	,							SCREAMING_SNAKE_CASE_  :      Optional[int]=1_6	,							SCREAMING_SNAKE_CASE_  :      List[Any]=2	,							SCREAMING_SNAKE_CASE_  :      List[str]=0.02	,							SCREAMING_SNAKE_CASE_  :      List[str]=4	,							)	->					Optional[int]:
					lowercase_					  = parent
					lowercase_					  = batch_size
					lowercase_					  = seq_length
					lowercase_					  = is_training
					lowercase_					  = use_attention_mask
					lowercase_					  = use_token_type_ids
					lowercase_					  = use_labels
					lowercase_					  = vocab_size
					lowercase_					  = hidden_size
					lowercase_					  = embedding_size
					lowercase_					  = num_hidden_layers
					lowercase_					  = num_attention_heads
					lowercase_					  = intermediate_size
					lowercase_					  = hidden_act
					lowercase_					  = hidden_dropout_prob
					lowercase_					  = attention_probs_dropout_prob
					lowercase_					  = max_position_embeddings
					lowercase_					  = type_vocab_size
					lowercase_					  = type_sequence_label_size
					lowercase_					  = initializer_range
					lowercase_					  = num_choices
	def 						_lowercase  (	self  :      Optional[int]	)	->					Tuple:
					lowercase_					  = ids_tensor([self.batch_size, self.seq_length]	,							self.vocab_size	)

					lowercase_					  = None
					if self.use_attention_mask:
									lowercase_					  = random_attention_mask([self.batch_size, self.seq_length]	)

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

					lowercase_					  = ElectraConfig(
					    vocab_size=self.vocab_size	,							hidden_size=self.hidden_size	,							embedding_size=self.embedding_size	,							num_hidden_layers=self.num_hidden_layers	,							num_attention_heads=self.num_attention_heads	,							intermediate_size=self.intermediate_size	,							hidden_act=self.hidden_act	,							hidden_dropout_prob=self.hidden_dropout_prob	,							attention_probs_dropout_prob=self.attention_probs_dropout_prob	,							max_position_embeddings=self.max_position_embeddings	,							type_vocab_size=self.type_vocab_size	,							initializer_range=self.initializer_range	,							)

					return config, input_ids, token_type_ids, attention_mask




	def 						_lowercase  (	self  :      Any	)	->					str:
					lowercase_					  = self.prepare_config_and_inputs()
					lowercase_  ,     lowercase_  ,     lowercase_  ,     lowercase_					  = config_and_inputs
					lowercase_					  = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
					return config, inputs_dict

@require_flax
class 			lowercase__(    UpperCAmelCase			,						unittest.TestCase							):



	"""simple docstring"""




	a      :List[str]   					=     True

	a      :List[Any]   					=     (
	    (
	        FlaxElectraModel,
	        FlaxElectraForCausalLM,
	        FlaxElectraForMaskedLM,
	        FlaxElectraForPreTraining,
	        FlaxElectraForTokenClassification,
	        FlaxElectraForQuestionAnswering,
	        FlaxElectraForMultipleChoice,
	        FlaxElectraForSequenceClassification,
	    )
	    if is_flax_available()
	    else ()
	)
	def 						_lowercase  (	self  :      List[str]	)	->					Optional[Any]:
					lowercase_					  = FlaxElectraModelTester(self	)




	@slow
	def 						_lowercase  (	self  :      List[str]	)	->					int:
					for model_class_name in self.all_model_classes:
									if model_class_name == FlaxElectraForMaskedLM:
													lowercase_					  = model_class_name.from_pretrained('''google/electra-small-generator'''	)
									else:
													lowercase_					  = model_class_name.from_pretrained('''google/electra-small-discriminator'''	)
									lowercase_					  = model(np.ones((1, 1)	)	)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)




