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, RandomSampler, TensorDataset
from tqdm import tqdm

from transformers import GPTaLMHeadModel


__a  :     str       =  logging.getLogger(__name__)





def 				UpperCAmelCase						(    lowercase							,			lowercase							):







				"""simple docstring"""



				if os.path.exists(lowercase							):
								if os.path.exists(os.path.join(lowercase							,			'''config.json'''							)							) and os.path.isfile(
								    os.path.join(lowercase							,			'''config.json'''							)							):
												os.remove(os.path.join(lowercase							,			'''config.json'''							)							)
								if os.path.exists(os.path.join(lowercase							,			'''pytorch_model.bin'''							)							) and os.path.isfile(
								    os.path.join(lowercase							,			'''pytorch_model.bin'''							)							):
												os.remove(os.path.join(lowercase							,			'''pytorch_model.bin'''							)							)
				else:
								os.makedirs(lowercase							)
				model.save_pretrained(lowercase							)





def 				UpperCAmelCase						(    lowercase							,			lowercase=False							):







				"""simple docstring"""



				__lowercase 			=							2
				if unlogit:
								__lowercase 			=							torch.pow(lowercase							,			lowercase							)
				__lowercase 			=							p * torch.log(lowercase							)
				__lowercase 			=							0
				return -plogp.sum(dim=-1							)





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



				logger.info('''lv, h >\t''' + '''\t'''.join(F"{x + 1}" for x in range(len(lowercase							)							)							)							)
				for row in range(len(lowercase							)							):
								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 				UpperCAmelCase						(    lowercase							,			lowercase							,			lowercase							,			lowercase=True							,			lowercase=True							,			lowercase=None							,			lowercase=False							):







				"""simple docstring"""



				__lowercase			,     __lowercase 			=							model.config.num_hidden_layers, model.config.num_attention_heads
				__lowercase 			=							torch.zeros(lowercase							,			lowercase							).to(args.device							)
				__lowercase 			=							torch.zeros(lowercase							,			lowercase							).to(args.device							)

				if head_mask is None:
								__lowercase 			=							torch.ones(lowercase							,			lowercase							).to(args.device							)

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

				__lowercase 			=							0.0
				__lowercase 			=							0.0
				for step, inputs in enumerate(tqdm(lowercase							,			desc='''Iteration'''							,			disable=args.local_rank not in [-1, 0]							)							):
								__lowercase 			=							tuple(t.to(args.device							) for t in inputs							)
								((__lowercase)			,     ) 			=							inputs

								# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
								__lowercase 			=							model(lowercase							,			labels=lowercase							,			head_mask=lowercase							)
								#  (loss), lm_logits, presents, (all hidden_states), (attentions)
								__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
								total_loss += loss.detach().cpu().numpy()
								if compute_entropy:
												for layer, attn in enumerate(lowercase							):
																__lowercase 			=							entropy(attn.detach()							,			lowercase							)
																attn_entropy[layer] += masked_entropy.sum(-1							).sum(0							).sum(0							).detach()

								if compute_importance:
												head_importance += head_mask.grad.abs().detach()
								tot_tokens += torch.ones_like(lowercase							).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(lowercase							,			lowercase							).sum(-1							)							,			1 / exponent							)
								head_importance /= norm_by_layer.unsqueeze(-1							) + 1E-20

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

				# Print matrices
				if compute_entropy:
								logger.info('''Attention entropies'''							)
								print_ad_tensor(lowercase							)
				if compute_importance:
								logger.info('''Head importance scores'''							)
								print_ad_tensor(lowercase							)
				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(lowercase							)
				print_ad_tensor(lowercase							)
				return attn_entropy, head_importance, total_loss





def 				UpperCAmelCase						(    lowercase							,			lowercase							,			lowercase							):







				"""simple docstring"""



				__lowercase			,     __lowercase			,     __lowercase 			=							compute_heads_importance(lowercase							,			lowercase							,			lowercase							,			compute_entropy=lowercase							)
				__lowercase 			=							1 / loss  # instead of downsteam score use the LM loss
				logger.info('''Pruning: original score: %f, threshold: %f'''							,			lowercase							,			original_score * args.masking_threshold							)

				__lowercase 			=							torch.ones_like(lowercase							)
				__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().detach()  # 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(lowercase							) <= num_to_mask:
												print('''BREAK BY 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(lowercase							)
								__lowercase 			=							new_head_mask.clone().detach()
								print_ad_tensor(lowercase							)

								# Compute metric and head importance again
								__lowercase			,     __lowercase			,     __lowercase 			=							compute_heads_importance(
								    lowercase							,			lowercase							,			lowercase							,			compute_entropy=lowercase							,			head_mask=lowercase							)
								__lowercase 			=							1 / loss
								logger.info(
								    '''Masking: current score: %f, remaining heads %d (%.1f percents)'''							,			lowercase							,			new_head_mask.sum()							,			new_head_mask.sum() / new_head_mask.numel() * 100							,			)

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

				return head_mask





def 				UpperCAmelCase						(    lowercase							,			lowercase							,			lowercase							,			lowercase							):







				"""simple docstring"""



				__lowercase 			=							datetime.now()
				__lowercase			,     __lowercase			,     __lowercase 			=							compute_heads_importance(
				    lowercase							,			lowercase							,			lowercase							,			compute_entropy=lowercase							,			compute_importance=lowercase							,			head_mask=lowercase							)
				__lowercase 			=							1 / loss
				__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(lowercase							)							)
				}

				for k, v in heads_to_prune.items():
								if isinstance(lowercase							,			lowercase							):
												__lowercase 			=							[
												    v,
												]

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

				__lowercase 			=							datetime.now()
				__lowercase			,     __lowercase			,     __lowercase 			=							compute_heads_importance(
				    lowercase							,			lowercase							,			lowercase							,			compute_entropy=lowercase							,			compute_importance=lowercase							,			head_mask=lowercase							,			actually_pruned=lowercase							,			)

				__lowercase 			=							1 / loss
				__lowercase 			=							datetime.now() - before_time

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





def 				UpperCAmelCase						(    ):







				"""simple docstring"""



				__lowercase 			=							argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '''--data_dir'''							,			default=lowercase							,			type=lowercase							,			required=lowercase							,			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=lowercase							,			type=lowercase							,			required=lowercase							,			help='''Path to pretrained model or model identifier from huggingface.co/models'''							,			)
				parser.add_argument(
				    '''--output_dir'''							,			default=lowercase							,			type=lowercase							,			required=lowercase							,			help='''The output directory where the model predictions and checkpoints will be written.'''							,			)

				# Other parameters
				parser.add_argument(
				    '''--config_name'''							,			default=''''''							,			type=lowercase							,			help='''Pretrained config name or path if not the same as model_name_or_path'''							,			)
				parser.add_argument(
				    '''--tokenizer_name'''							,			default=''''''							,			type=lowercase							,			help='''Pretrained tokenizer name or path if not the same as model_name_or_path'''							,			)
				parser.add_argument(
				    '''--cache_dir'''							,			default=lowercase							,			type=lowercase							,			help='''Where do you want to store the pre-trained models downloaded from s3'''							,			)
				parser.add_argument(
				    '''--data_subset'''							,			type=lowercase							,			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=lowercase							,			help='''masking threshold in term of metrics (stop masking when metric < threshold * original metric value).'''							,			)
				parser.add_argument(
				    '''--masking_amount'''							,			default=0.1							,			type=lowercase							,			help='''Amount to heads to masking at each masking step.'''							)
				parser.add_argument('''--metric_name'''							,			default='''acc'''							,			type=lowercase							,			help='''Metric to use for head masking.'''							)

				parser.add_argument(
				    '''--max_seq_length'''							,			default=128							,			type=lowercase							,			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=lowercase							,			help='''Batch size.'''							)

				parser.add_argument('''--seed'''							,			type=lowercase							,			default=42							)
				parser.add_argument('''--local_rank'''							,			type=lowercase							,			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=lowercase							,			default=''''''							,			help='''Can be used for distant debugging.'''							)
				parser.add_argument('''--server_port'''							,			type=lowercase							,			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=lowercase							)
								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							)							)							)

				__lowercase 			=							GPTaLMHeadModel.from_pretrained(args.model_name_or_path							)

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

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

				# Prepare dataset
				__lowercase 			=							np.concatenate(
				    [
				        np.loadtxt(args.data_dir							,			dtype=np.intaa							),
				    ]							)
				__lowercase 			=							(torch.from_numpy(lowercase							),)
				__lowercase 			=							TensorDataset(*lowercase							)
				__lowercase 			=							RandomSampler(lowercase							)
				__lowercase 			=							DataLoader(lowercase							,			sampler=lowercase							,			batch_size=args.batch_size							)

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

				# 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(lowercase							,			lowercase							,			lowercase							)
								prune_heads(lowercase							,			lowercase							,			lowercase							,			lowercase							)


if __name__ == "__main__":
		main()
import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
		    MODEL_MAPPING,
		    LevitForImageClassification,
		    LevitForImageClassificationWithTeacher,
		    LevitModel,
		)
		from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import LevitImageProcessor




class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Optional[Any]:

							'''simple docstring'''


							__lowercase 			=							self.config_class(**self.inputs_dict						)
							self.parent.assertTrue(hasattr(lowerCAmelCase__      ,   '''hidden_sizes'''						)						)
							self.parent.assertTrue(hasattr(lowerCAmelCase__      ,   '''num_attention_heads'''						)						)




class    _UpperCamelCase      :


			"""simple docstring"""






			def __init__(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__=13      ,   lowerCAmelCase__=64      ,   lowerCAmelCase__=3      ,   lowerCAmelCase__=3      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__=1      ,   lowerCAmelCase__=16      ,   lowerCAmelCase__=[1_28, 2_56, 3_84]      ,   lowerCAmelCase__=[4, 6, 8]      ,   lowerCAmelCase__=[2, 3, 4]      ,   lowerCAmelCase__=[16, 16, 16]      ,   lowerCAmelCase__=0      ,   lowerCAmelCase__=[2, 2, 2]      ,   lowerCAmelCase__=[2, 2, 2]      ,   lowerCAmelCase__=0.02      ,   lowerCAmelCase__=True      ,   lowerCAmelCase__=True      ,   lowerCAmelCase__=2      ,   )     ->					Tuple:

							'''simple docstring'''


							__lowercase 			=							parent
							__lowercase 			=							batch_size
							__lowercase 			=							image_size
							__lowercase 			=							num_channels
							__lowercase 			=							kernel_size
							__lowercase 			=							stride
							__lowercase 			=							padding
							__lowercase 			=							hidden_sizes
							__lowercase 			=							num_attention_heads
							__lowercase 			=							depths
							__lowercase 			=							key_dim
							__lowercase 			=							drop_path_rate
							__lowercase 			=							patch_size
							__lowercase 			=							attention_ratio
							__lowercase 			=							mlp_ratio
							__lowercase 			=							initializer_range
							__lowercase 			=							[
							    ['''Subsample''', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
							    ['''Subsample''', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
							]
							__lowercase 			=							is_training
							__lowercase 			=							use_labels
							__lowercase 			=							num_labels
							__lowercase 			=							initializer_range





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Optional[int]:

							'''simple docstring'''


							__lowercase 			=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

							__lowercase 			=							None
							if self.use_labels:
											__lowercase 			=							ids_tensor([self.batch_size]      ,   self.num_labels						)

							__lowercase 			=							self.get_config()
							return config, pixel_values, labels





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


							return LevitConfig(
							    image_size=self.image_size      ,   num_channels=self.num_channels      ,   kernel_size=self.kernel_size      ,   stride=self.stride      ,   padding=self.padding      ,   patch_size=self.patch_size      ,   hidden_sizes=self.hidden_sizes      ,   num_attention_heads=self.num_attention_heads      ,   depths=self.depths      ,   key_dim=self.key_dim      ,   drop_path_rate=self.drop_path_rate      ,   mlp_ratio=self.mlp_ratio      ,   attention_ratio=self.attention_ratio      ,   initializer_range=self.initializer_range      ,   down_ops=self.down_ops      ,   )





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)     ->					Tuple:

							'''simple docstring'''


							__lowercase 			=							LevitModel(config=lowerCAmelCase__						)
							model.to(lowerCAmelCase__						)
							model.eval()
							__lowercase 			=							model(lowerCAmelCase__						)
							__lowercase 			=							(self.image_size, self.image_size)
							__lowercase			,     __lowercase 			=							image_size[0], image_size[1]
							for _ in range(4						):
											__lowercase 			=							floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1						)
											__lowercase 			=							floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1						)
							self.parent.assertEqual(
							    result.last_hidden_state.shape      ,   (self.batch_size, ceil(height / 4						) * ceil(width / 4						), self.hidden_sizes[-1])      ,   )





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)     ->					str:

							'''simple docstring'''


							__lowercase 			=							self.num_labels
							__lowercase 			=							LevitForImageClassification(lowerCAmelCase__						)
							model.to(lowerCAmelCase__						)
							model.eval()
							__lowercase 			=							model(lowerCAmelCase__      ,   labels=lowerCAmelCase__						)
							self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.num_labels)						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Union[str, Any]:

							'''simple docstring'''


							__lowercase 			=							self.prepare_config_and_inputs()
							__lowercase			,     __lowercase			,     __lowercase 			=							config_and_inputs
							__lowercase 			=							{'''pixel_values''': pixel_values}
							return config, inputs_dict




@require_torch
class    _UpperCamelCase      (						_UpperCAmelCase						,_UpperCAmelCase						,unittest.TestCase ):


			"""simple docstring"""






			__a  : int			 =						(
			    (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
			    if is_torch_available()
			    else ()
			)
			__a  : List[str]			 =						(
			    {
			        '''feature-extraction''': LevitModel,
			        '''image-classification''': (LevitForImageClassification, LevitForImageClassificationWithTeacher),
			    }
			    if is_torch_available()
			    else {}
			)

			__a  : int			 =						False
			__a  : Dict			 =						False
			__a  : Optional[Any]			 =						False
			__a  : Optional[int]			 =						False
			__a  : Dict			 =						False





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[Any]:

							'''simple docstring'''


							__lowercase 			=							LevitModelTester(self						)
							__lowercase 			=							ConfigTester(self      ,   config_class=lowerCAmelCase__      ,   has_text_modality=lowerCAmelCase__      ,   hidden_size=37						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[Any]:

							'''simple docstring'''


							self.create_and_test_config_common_properties()
							self.config_tester.create_and_test_config_to_json_string()
							self.config_tester.create_and_test_config_to_json_file()
							self.config_tester.create_and_test_config_from_and_save_pretrained()
							self.config_tester.create_and_test_config_with_num_labels()
							self.config_tester.check_config_can_be_init_without_params()
							self.config_tester.check_config_arguments_init()





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[str]:

							'''simple docstring'''


							return





			@unittest.skip(reason='''Levit does not use inputs_embeds'''						)
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Union[str, Any]:

							'''simple docstring'''


							pass





			@unittest.skip(reason='''Levit does not support input and output embeddings'''						)
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[str]:

							'''simple docstring'''


							pass





			@unittest.skip(reason='''Levit does not output attentions'''						)
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Tuple:

							'''simple docstring'''


							pass





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


							__lowercase			,     __lowercase 			=							self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											__lowercase 			=							model_class(lowerCAmelCase__						)
											__lowercase 			=							inspect.signature(model.forward						)
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											__lowercase 			=							[*signature.parameters.keys()]

											__lowercase 			=							['''pixel_values''']
											self.assertListEqual(arg_names[:1]      ,   lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[str]:

							'''simple docstring'''


							def check_hidden_states_output(lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						):
											__lowercase 			=							model_class(lowerCAmelCase__						)
											model.to(lowerCAmelCase__						)
											model.eval()

											with torch.no_grad():
															__lowercase 			=							model(**self._prepare_for_class(lowerCAmelCase__      ,   lowerCAmelCase__						)						)

											__lowercase 			=							outputs.hidden_states

											__lowercase 			=							len(self.model_tester.depths						) + 1
											self.assertEqual(len(lowerCAmelCase__						)      ,   lowerCAmelCase__						)

											__lowercase 			=							(self.model_tester.image_size, self.model_tester.image_size)
											__lowercase			,     __lowercase 			=							image_size[0], image_size[1]
											for _ in range(4						):
															__lowercase 			=							floor(
															    (
															        (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
															        / self.model_tester.stride
															    )
															    + 1						)
															__lowercase 			=							floor(
															    (
															        (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
															        / self.model_tester.stride
															    )
															    + 1						)
											# verify the first hidden states (first block)
											self.assertListEqual(
											    list(hidden_states[0].shape[-2:]						)      ,   [
											        height * width,
											        self.model_tester.hidden_sizes[0],
											    ]      ,   )

							__lowercase			,     __lowercase 			=							self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											__lowercase 			=							True
											check_hidden_states_output(lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)

											# check that output_hidden_states also work using config
											del inputs_dict["output_hidden_states"]
											__lowercase 			=							True

											check_hidden_states_output(lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)





			@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''						)
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


							pass





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__=False						)     ->					str:

							'''simple docstring'''


							__lowercase 			=							super()._prepare_for_class(lowerCAmelCase__      ,   lowerCAmelCase__      ,   return_labels=lowerCAmelCase__						)

							if return_labels:
											if model_class.__name__ == "LevitForImageClassificationWithTeacher":
															del inputs_dict["labels"]

							return inputs_dict





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Union[str, Any]:

							'''simple docstring'''


							__lowercase 			=							self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Dict:

							'''simple docstring'''


							__lowercase 			=							self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[str]:

							'''simple docstring'''


							if not self.model_tester.is_training:
											return

							__lowercase			,     __lowercase 			=							self.model_tester.prepare_config_and_inputs_for_common()
							__lowercase 			=							True

							for model_class in self.all_model_classes:
											# LevitForImageClassificationWithTeacher supports inference-only
											if (
											    model_class in get_values(lowerCAmelCase__						)
											    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
											):
															continue
											__lowercase 			=							model_class(lowerCAmelCase__						)
											model.to(lowerCAmelCase__						)
											model.train()
											__lowercase 			=							self._prepare_for_class(lowerCAmelCase__      ,   lowerCAmelCase__      ,   return_labels=lowerCAmelCase__						)
											__lowercase 			=							model(**lowerCAmelCase__						).loss
											loss.backward()





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Any:

							'''simple docstring'''


							__lowercase			,     __lowercase 			=							self.model_tester.prepare_config_and_inputs_for_common()
							if not self.model_tester.is_training:
											return

							__lowercase 			=							False
							__lowercase 			=							True

							for model_class in self.all_model_classes:
											if model_class in get_values(lowerCAmelCase__						) or not model_class.supports_gradient_checkpointing:
															continue
											# LevitForImageClassificationWithTeacher supports inference-only
											if model_class.__name__ == "LevitForImageClassificationWithTeacher":
															continue
											__lowercase 			=							model_class(lowerCAmelCase__						)
											model.gradient_checkpointing_enable()
											model.to(lowerCAmelCase__						)
											model.train()
											__lowercase 			=							self._prepare_for_class(lowerCAmelCase__      ,   lowerCAmelCase__      ,   return_labels=lowerCAmelCase__						)
											__lowercase 			=							model(**lowerCAmelCase__						).loss
											loss.backward()





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


							__lowercase			,     __lowercase 			=							self.model_tester.prepare_config_and_inputs_for_common()

							__lowercase 			=							[
							    {'''title''': '''multi_label_classification''', '''num_labels''': 2, '''dtype''': torch.float},
							    {'''title''': '''single_label_classification''', '''num_labels''': 1, '''dtype''': torch.long},
							    {'''title''': '''regression''', '''num_labels''': 1, '''dtype''': torch.float},
							]

							for model_class in self.all_model_classes:
											if (
											    model_class
											    not in [
											        *get_values(lowerCAmelCase__						),
											    ]
											    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
											):
															continue

											for problem_type in problem_types:
															with self.subTest(msg=F"Testing {model_class} with {problem_type['title']}"						):
																			__lowercase 			=							problem_type['''title''']
																			__lowercase 			=							problem_type['''num_labels''']

																			__lowercase 			=							model_class(lowerCAmelCase__						)
																			model.to(lowerCAmelCase__						)
																			model.train()

																			__lowercase 			=							self._prepare_for_class(lowerCAmelCase__      ,   lowerCAmelCase__      ,   return_labels=lowerCAmelCase__						)

																			if problem_type["num_labels"] > 1:
																							__lowercase 			=							inputs['''labels'''].unsqueeze(1						).repeat(1      ,   problem_type['''num_labels''']						)

																			__lowercase 			=							inputs['''labels'''].to(problem_type['''dtype''']						)

																			# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
																			# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
																			# they have the same size." which is a symptom something in wrong for the regression problem.
																			# See https://github.com/huggingface/transformers/issues/11780
																			with warnings.catch_warnings(record=lowerCAmelCase__						) as warning_list:
																							__lowercase 			=							model(**lowerCAmelCase__						).loss
																			for w in warning_list:
																							if "Using a target size that is different to the input size" in str(w.message						):
																											raise ValueError(
																											    F"Something is going wrong in the regression problem: intercepted {w.message}"						)

																			loss.backward()





			@slow
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Dict:

							'''simple docstring'''


							for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__lowercase 			=							LevitModel.from_pretrained(lowerCAmelCase__						)
											self.assertIsNotNone(lowerCAmelCase__						)





def 				UpperCAmelCase						(    ):







				"""simple docstring"""



				__lowercase 			=							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
				return image




@require_torch
@require_vision
class    _UpperCamelCase      (						unittest.TestCase ):


			"""simple docstring"""






			@cached_property
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


							return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)





			@slow
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Dict:

							'''simple docstring'''


							__lowercase 			=							LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(
							    lowerCAmelCase__						)

							__lowercase 			=							self.default_image_processor
							__lowercase 			=							prepare_img()
							__lowercase 			=							image_processor(images=lowerCAmelCase__      ,   return_tensors='''pt'''						).to(lowerCAmelCase__						)

							# forward pass
							with torch.no_grad():
											__lowercase 			=							model(**lowerCAmelCase__						)

							# verify the logits
							__lowercase 			=							torch.Size((1, 10_00)						)
							self.assertEqual(outputs.logits.shape      ,   lowerCAmelCase__						)

							__lowercase 			=							torch.tensor([1.0448, -0.3745, -1.8317]						).to(lowerCAmelCase__						)

							self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,   lowerCAmelCase__      ,   atol=1E-4						)						)