





import inspect
import unittest

from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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 torch import nn

    from transformers import ViTMSNForImageClassification, ViTMSNModel
    from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import ViTImageProcessor

class    lowerCAmelCase							:
 def __init__(      self  :      Optional[int]			,     UpperCAmelCase  :      Union[str, Any]			,     UpperCAmelCase  :      Union[str, Any]=13			,     UpperCAmelCase  :      Dict=30			,     UpperCAmelCase  :      str=2			,     UpperCAmelCase  :      Tuple=3			,     UpperCAmelCase  :      Dict=True			,     UpperCAmelCase  :      Dict=True			,     UpperCAmelCase  :      Optional[Any]=32			,     UpperCAmelCase  :      Optional[Any]=5			,     UpperCAmelCase  :      List[Any]=4			,     UpperCAmelCase  :      Optional[int]=37			,     UpperCAmelCase  :      Dict="gelu"			,     UpperCAmelCase  :      Dict=0.1			,     UpperCAmelCase  :      Dict=0.1			,     UpperCAmelCase  :      Union[str, Any]=10			,     UpperCAmelCase  :      Union[str, Any]=0.0_2			,     UpperCAmelCase  :      Optional[Any]=None			,     )			->	Union[str, Any]:
     lowerCamelCase__    :      Any					 =		parent
     lowerCamelCase__    :      Optional[int]					 =		batch_size
     lowerCamelCase__    :      int					 =		image_size
     lowerCamelCase__    :      Tuple					 =		patch_size
     lowerCamelCase__    :      str					 =		num_channels
     lowerCamelCase__    :      Optional[int]					 =		is_training
     lowerCamelCase__    :      List[str]					 =		use_labels
     lowerCamelCase__    :      Dict					 =		hidden_size
     lowerCamelCase__    :      Optional[Any]					 =		num_hidden_layers
     lowerCamelCase__    :      Optional[int]					 =		num_attention_heads
     lowerCamelCase__    :      str					 =		intermediate_size
     lowerCamelCase__    :      int					 =		hidden_act
     lowerCamelCase__    :      Tuple					 =		hidden_dropout_prob
     lowerCamelCase__    :      List[Any]					 =		attention_probs_dropout_prob
     lowerCamelCase__    :      Optional[Any]					 =		type_sequence_label_size
     lowerCamelCase__    :      int					 =		initializer_range
     lowerCamelCase__    :      Optional[int]					 =		scope

     # in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
     lowerCamelCase__    :      Tuple					 =		(image_size // patch_size) ** 2
     lowerCamelCase__    :      Dict					 =		num_patches + 1
 def      A_	(      self  :      Optional[Any]	)			->	int:
     lowerCamelCase__    :      Union[str, Any]					 =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

     lowerCamelCase__    :      Tuple					 =		None
     if self.use_labels:
         lowerCamelCase__    :      str					 =		ids_tensor([self.batch_size]			,     self.type_sequence_label_size	)

     lowerCamelCase__    :      Dict					 =		self.get_config()

     return config, pixel_values, labels
 def      A_	(      self  :      Union[str, Any]	)			->	Union[str, Any]:
     return ViTMSNConfig(
         image_size=self.image_size			,     patch_size=self.patch_size			,     num_channels=self.num_channels			,     hidden_size=self.hidden_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			,     initializer_range=self.initializer_range			,     )
 def      A_	(      self  :      Optional[Any]			,     UpperCAmelCase  :      List[str]			,     UpperCAmelCase  :      Dict			,     UpperCAmelCase  :      Dict	)			->	Dict:
     lowerCamelCase__    :      Optional[Any]					 =		ViTMSNModel(config=UpperCAmelCase	)
     model.to(UpperCAmelCase	)
     model.eval()
     lowerCamelCase__    :      Dict					 =		model(UpperCAmelCase	)
     self.parent.assertEqual(result.last_hidden_state.shape			,     (self.batch_size, self.seq_length, self.hidden_size)	)
 def      A_	(      self  :      Tuple			,     UpperCAmelCase  :      Tuple			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      Any	)			->	List[Any]:
     lowerCamelCase__    :      List[Any]					 =		self.type_sequence_label_size
     lowerCamelCase__    :      Dict					 =		ViTMSNForImageClassification(UpperCAmelCase	)
     model.to(UpperCAmelCase	)
     model.eval()
     lowerCamelCase__    :      Tuple					 =		model(UpperCAmelCase			,     labels=UpperCAmelCase	)
     print('Pixel and labels shape: {pixel_values.shape}, {labels.shape}'	)
     print('Labels: {labels}'	)
     self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.type_sequence_label_size)	)

     # test greyscale images
     lowerCamelCase__    :      Optional[int]					 =		1
     lowerCamelCase__    :      Tuple					 =		ViTMSNForImageClassification(UpperCAmelCase	)
     model.to(UpperCAmelCase	)
     model.eval()

     lowerCamelCase__    :      str					 =		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
     lowerCamelCase__    :      Any					 =		model(UpperCAmelCase	)
     self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.type_sequence_label_size)	)







 def      A_	(      self  :      List[str]	)			->	List[Any]:
     lowerCamelCase__    :      List[Any]					 =		self.prepare_config_and_inputs()
     lowerCamelCase__ ,							lowerCamelCase__ ,							lowerCamelCase__    :      List[str]					 =		config_and_inputs
     lowerCamelCase__    :      Optional[int]					 =		{'pixel_values': pixel_values}
     return config, inputs_dict


@require_torch
class    lowerCAmelCase							(					__UpperCamelCase,  __UpperCamelCase,  unittest.TestCase			):
 UpperCAmelCase__													=		(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
 UpperCAmelCase__													=		(
     {"""feature-extraction""": ViTMSNModel, """image-classification""": ViTMSNForImageClassification}
     if is_torch_available()
     else {}
 )

 UpperCAmelCase__													=		False
 UpperCAmelCase__													=		False
 UpperCAmelCase__													=		False
 UpperCAmelCase__													=		False
 def      A_	(      self  :      Any	)			->	Union[str, Any]:
     lowerCamelCase__    :      Any					 =		ViTMSNModelTester(self	)
     lowerCamelCase__    :      List[str]					 =		ConfigTester(self			,     config_class=UpperCAmelCase			,     has_text_modality=UpperCAmelCase			,     hidden_size=37	)
 def      A_	(      self  :      int	)			->	str:
     self.config_tester.run_common_tests()
 @unittest.skip(reason='ViTMSN does not use inputs_embeds'	)
 def      A_	(      self  :      Optional[int]	)			->	Any:
     pass
 def      A_	(      self  :      Optional[int]	)			->	str:
     lowerCamelCase__ ,							lowerCamelCase__    :      Any					 =		self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         lowerCamelCase__    :      int					 =		model_class(UpperCAmelCase	)
         self.assertIsInstance(model.get_input_embeddings()			,     (nn.Module)	)
         lowerCamelCase__    :      Union[str, Any]					 =		model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(UpperCAmelCase			,     nn.Linear	)	)
 def      A_	(      self  :      List[str]	)			->	Optional[Any]:
     lowerCamelCase__ ,							lowerCamelCase__    :      int					 =		self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         lowerCamelCase__    :      Tuple					 =		model_class(UpperCAmelCase	)
         lowerCamelCase__    :      Union[str, Any]					 =		inspect.signature(model.forward	)
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         lowerCamelCase__    :      Dict					 =		[*signature.parameters.keys()]

         lowerCamelCase__    :      str					 =		['pixel_values']
         self.assertListEqual(arg_names[:1]			,     UpperCAmelCase	)
 def      A_	(      self  :      Tuple	)			->	Dict:
     lowerCamelCase__    :      int					 =		self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*UpperCAmelCase	)
 def      A_	(      self  :      Union[str, Any]	)			->	int:
     lowerCamelCase__    :      Tuple					 =		self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase	)







 @slow
 def      A_	(      self  :      int	)			->	Any:
     for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         lowerCamelCase__    :      List[Any]					 =		ViTMSNModel.from_pretrained(UpperCAmelCase	)
         self.assertIsNotNone(UpperCAmelCase	)


def        SCREAMING_SNAKE_CASE      (       )			->     Union[str, Any]:
    lowerCamelCase__    :      Optional[Any]					 =		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
    return image


@require_torch
@require_vision
class    lowerCAmelCase							(					unittest.TestCase			):
 @cached_property
 def      A_	(      self  :      Dict	)			->	List[str]:
     return ViTImageProcessor.from_pretrained('facebook/vit-msn-small'	) if is_vision_available() else None







 @slow
 def      A_	(      self  :      Optional[int]	)			->	List[str]:
     torch.manual_seed(2	)
     lowerCamelCase__    :      Dict					 =		ViTMSNForImageClassification.from_pretrained('facebook/vit-msn-small'	).to(UpperCAmelCase	)

     lowerCamelCase__    :      Union[str, Any]					 =		self.default_image_processor
     lowerCamelCase__    :      Any					 =		prepare_img()
     lowerCamelCase__    :      Tuple					 =		image_processor(images=UpperCAmelCase			,     return_tensors='pt'	).to(UpperCAmelCase	)

     # forward pass
     with torch.no_grad():
         lowerCamelCase__    :      str					 =		model(**UpperCAmelCase	)

     # verify the logits
     lowerCamelCase__    :      Any					 =		torch.Size((1, 1000)	)
     self.assertEqual(outputs.logits.shape			,     UpperCAmelCase	)

     lowerCamelCase__    :      int					 =		torch.tensor([-0.0_8_0_3, -0.4_4_5_4, -0.2_3_7_5]	).to(UpperCAmelCase	)

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







from typing import Any, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler

from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
    Adafactor,
    AdamW,
    get_constant_schedule,
    get_constant_schedule_with_warmup,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available


if is_fairscale_available():
				from fairscale.optim import OSS


__snake_case								=  logging.get_logger(__name__)

__snake_case								=  {
    """linear""": get_linear_schedule_with_warmup,
    """cosine""": get_cosine_schedule_with_warmup,
    """cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
    """polynomial""": get_polynomial_decay_schedule_with_warmup,
    """constant""": get_constant_schedule,
    """constant_w_warmup""": get_constant_schedule_with_warmup,
}




class 						lowercase__  (						_UpperCAmelCase  ):
	def __init__(			self							:   str       ,   UpperCAmelCase_							:   int=None       ,   UpperCAmelCase_							:   List[str]=None       ,   *UpperCAmelCase_							:   Union[str, Any]       ,   **UpperCAmelCase_							:   Optional[Any]							):
						super().__init__(*UpperCAmelCase_       ,   **UpperCAmelCase_							)

						if config is None:
											assert isinstance(self.model       ,   UpperCAmelCase_							), (
											    "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
											    F' {self.model.__class__}'
											)
											SCREAMING_SNAKE_CASE__													= self.model.config
						else:
											SCREAMING_SNAKE_CASE__													= config

						SCREAMING_SNAKE_CASE__													= data_args
						SCREAMING_SNAKE_CASE__													= self.config.tgt_vocab_size if isinstance(self.config       ,   UpperCAmelCase_							) else self.config.vocab_size

						if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
											assert self.config.pad_token_id is not None, (
											    "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
											    " calculation or doing label smoothing."
											)

						if self.config.pad_token_id is None and self.config.eos_token_id is not None:
											logger.warning(
											    F'The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for'
											    ' padding..'							)

						if self.args.label_smoothing == 0:
											SCREAMING_SNAKE_CASE__													= torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id							)
						else:
											# dynamically import label_smoothed_nll_loss
											from utils import label_smoothed_nll_loss

											SCREAMING_SNAKE_CASE__													= label_smoothed_nll_loss
	def      A_				(			self							:   Tuple       ,   UpperCAmelCase_							:   int							):
						if self.optimizer is None:
											SCREAMING_SNAKE_CASE__													= ['bias', 'LayerNorm.weight']
											SCREAMING_SNAKE_CASE__													= [
											    {
											        'params': [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay							)],
											        'weight_decay': self.args.weight_decay,
											    },
											    {
											        'params': [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay							)],
											        'weight_decay': 0.0,
											    },
											]
											SCREAMING_SNAKE_CASE__													= Adafactor if self.args.adafactor else AdamW
											if self.args.adafactor:
																SCREAMING_SNAKE_CASE__													= Adafactor
																SCREAMING_SNAKE_CASE__													= {'scale_parameter': False, 'relative_step': False}
											else:
																SCREAMING_SNAKE_CASE__													= AdamW
																SCREAMING_SNAKE_CASE__													= {
																    'betas': (self.args.adam_betaa, self.args.adam_betaa),
																    'eps': self.args.adam_epsilon,
																}
											SCREAMING_SNAKE_CASE__													= self.args.learning_rate
											if self.sharded_ddp:
																SCREAMING_SNAKE_CASE__													= OSS(
																    params=UpperCAmelCase_       ,   optim=UpperCAmelCase_       ,   **UpperCAmelCase_       ,   )
											else:
																SCREAMING_SNAKE_CASE__													= optimizer_cls(UpperCAmelCase_       ,   **UpperCAmelCase_							)

						if self.lr_scheduler is None:
											SCREAMING_SNAKE_CASE__													= self._get_lr_scheduler(UpperCAmelCase_							)
						else:  # ignoring --lr_scheduler
											logger.warning('scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.'							)
	def      A_				(			self							:   str       ,   UpperCAmelCase_							:   List[Any]							):
						SCREAMING_SNAKE_CASE__													= arg_to_scheduler[self.args.lr_scheduler]
						if self.args.lr_scheduler == "constant":
											SCREAMING_SNAKE_CASE__													= schedule_func(self.optimizer							)
						elif self.args.lr_scheduler == "constant_w_warmup":
											SCREAMING_SNAKE_CASE__													= schedule_func(self.optimizer       ,   num_warmup_steps=self.args.warmup_steps							)
						else:
											SCREAMING_SNAKE_CASE__													= schedule_func(
											    self.optimizer       ,   num_warmup_steps=self.args.warmup_steps       ,   num_training_steps=UpperCAmelCase_							)
						return scheduler
	def      A_				(			self							:   List[str]							):
						if isinstance(self.train_dataset       ,   torch.utils.data.IterableDataset							):
											return None
						elif is_torch_tpu_available():
											return get_tpu_sampler(self.train_dataset							)
						else:
											if self.args.sortish_sampler:
																self.train_dataset.make_sortish_sampler(
																    self.args.per_device_train_batch_size       ,   distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED)       ,   )

											return (
											    RandomSampler(self.train_dataset							)
											    if self.args.local_rank == -1
											    else DistributedSampler(self.train_dataset							)
											)
	def      A_				(			self							:   Any       ,   UpperCAmelCase_							:   Tuple       ,   UpperCAmelCase_							:   Optional[Any]       ,   UpperCAmelCase_							:   Dict							):
						if self.args.label_smoothing == 0:
											if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
																# force training to ignore pad token
																SCREAMING_SNAKE_CASE__													= model(**UpperCAmelCase_       ,   use_cache=UpperCAmelCase_							)[0]
																SCREAMING_SNAKE_CASE__													= self.loss_fn(logits.view(-1       ,   logits.shape[-1]							)       ,   labels.view(-1							)							)
											else:
																# compute usual loss via models
																SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= model(**UpperCAmelCase_       ,   labels=UpperCAmelCase_       ,   use_cache=UpperCAmelCase_							)[:2]
						else:
											# compute label smoothed loss
											SCREAMING_SNAKE_CASE__													= model(**UpperCAmelCase_       ,   use_cache=UpperCAmelCase_							)[0]
											SCREAMING_SNAKE_CASE__													= torch.nn.functional.log_softmax(UpperCAmelCase_       ,   dim=-1							)
											SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= self.loss_fn(UpperCAmelCase_       ,   UpperCAmelCase_       ,   self.args.label_smoothing       ,   ignore_index=self.config.pad_token_id							)
						return loss, logits
	def      A_				(			self							:   Union[str, Any]       ,   UpperCAmelCase_							:   str       ,   UpperCAmelCase_							:   List[Any]							):
						SCREAMING_SNAKE_CASE__													= inputs.pop('labels'							)
						SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= self._compute_loss(UpperCAmelCase_       ,   UpperCAmelCase_       ,   UpperCAmelCase_							)
						return loss
	def      A_				(			self							:   List[str]       ,   UpperCAmelCase_							:   nn.Module       ,   UpperCAmelCase_							:   Dict[str, Union[torch.Tensor, Any]]       ,   UpperCAmelCase_							:   bool       ,   UpperCAmelCase_							:   Optional[List[str]] = None       ,   ):
						SCREAMING_SNAKE_CASE__													= self._prepare_inputs(UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= {
						    'max_length': self.data_args.val_max_target_length
						    if self.data_args is not None
						    else self.config.max_length,
						    'num_beams': self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
						}

						if self.args.predict_with_generate and not self.args.prediction_loss_only:
											SCREAMING_SNAKE_CASE__													= self.model.generate(
											    inputs['input_ids']       ,   attention_mask=inputs['attention_mask']       ,   **UpperCAmelCase_       ,   )
											# in case the batch is shorter than max length, the output should be padded
											if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
																SCREAMING_SNAKE_CASE__													= self._pad_tensors_to_max_len(UpperCAmelCase_       ,   gen_kwargs['max_length']							)

						SCREAMING_SNAKE_CASE__													= inputs.pop('labels'							)
						with torch.no_grad():
											# compute loss on predict data
											SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= self._compute_loss(UpperCAmelCase_       ,   UpperCAmelCase_       ,   UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= loss.mean().detach()
						if self.args.prediction_loss_only:
											return (loss, None, None)

						SCREAMING_SNAKE_CASE__													= generated_tokens if self.args.predict_with_generate else logits

						if labels.shape[-1] < gen_kwargs["max_length"]:
											SCREAMING_SNAKE_CASE__													= self._pad_tensors_to_max_len(UpperCAmelCase_       ,   gen_kwargs['max_length']							)

						return (loss, logits, labels)





	def      A_				(			self							:   Union[str, Any]       ,   UpperCAmelCase_							:   Tuple       ,   UpperCAmelCase_							:   List[str]							):
						# If PAD token is not defined at least EOS token has to be defined
						SCREAMING_SNAKE_CASE__													= self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id

						if pad_token_id is None:
											raise ValueError(
											    'Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be'
											    F' padded to `max_length`={max_length}'							)

						SCREAMING_SNAKE_CASE__													= pad_token_id * torch.ones(
						    (tensor.shape[0], max_length)       ,   dtype=tensor.dtype       ,   device=tensor.device							)
						SCREAMING_SNAKE_CASE__													= tensor
						return padded_tensor





