
"""simple docstring"""







from copy import deepcopy
from pathlib import Path
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import Dataset

from .deepspeed import is_deepspeed_zeroa_enabled
from .generation.configuration_utils import GenerationConfig
from .trainer import Trainer
from .utils import logging


if TYPE_CHECKING:
							from .data.data_collator import DataCollator
							from .modeling_utils import PreTrainedModel
							from .tokenization_utils_base import PreTrainedTokenizerBase
							from .trainer_callback import TrainerCallback
							from .trainer_utils import EvalPrediction, PredictionOutput
							from .training_args import TrainingArguments


lowerCamelCase__     =						logging.get_logger(__name__)




class 					A__			(      _lowerCamelCase):

		def __init__( self			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = (None, None)			,						_SCREAMING_SNAKE_CASE = None			,						):
			super().__init__(
			    model=_SCREAMING_SNAKE_CASE			,						args=_SCREAMING_SNAKE_CASE			,						data_collator=_SCREAMING_SNAKE_CASE			,						train_dataset=_SCREAMING_SNAKE_CASE			,						eval_dataset=_SCREAMING_SNAKE_CASE			,						tokenizer=_SCREAMING_SNAKE_CASE			,						model_init=_SCREAMING_SNAKE_CASE			,						compute_metrics=_SCREAMING_SNAKE_CASE			,						callbacks=_SCREAMING_SNAKE_CASE			,						optimizers=_SCREAMING_SNAKE_CASE			,						preprocess_logits_for_metrics=_SCREAMING_SNAKE_CASE			,						)

			# Override self.model.generation_config if a GenerationConfig is specified in args.
			# Priority: args.generation_config > model.generation_config > default GenerationConfig.
			if self.args.generation_config is not None:
				__lowerCAmelCase     :		Tuple		     = self.load_generation_config(self.args.generation_config					)
				__lowerCAmelCase     :		int		     = gen_config

		@staticmethod
		def 				__lowerCamelCase				( _SCREAMING_SNAKE_CASE					):

			# GenerationConfig provided, nothing to do
			if isinstance(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					):
				return deepcopy(_SCREAMING_SNAKE_CASE					)

			# str or Path
			__lowerCAmelCase     :		Dict		     = Path(_SCREAMING_SNAKE_CASE					) if isinstance(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					) else gen_config_arg
			__lowerCAmelCase     :		Any		     = None

			# Figuring if it is path pointing to a file, pointing to a directory or else a model id or URL
			# This step is required in order to determine config_file_name
			if pretrained_model_name.is_file():
				__lowerCAmelCase     :		List[Any]		     = pretrained_model_name.name
				__lowerCAmelCase     :		Tuple		     = pretrained_model_name.parent
			# dir path
			elif pretrained_model_name.is_dir():
				pass
			# model id or URL
			else:
				__lowerCAmelCase     :		List[Any]		     = gen_config_arg

			__lowerCAmelCase     :		Optional[int]		     = GenerationConfig.from_pretrained(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					)
			return gen_config

		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = "eval"			,						**_SCREAMING_SNAKE_CASE			,						):
			__lowerCAmelCase     :		Any		     = gen_kwargs.copy()
			if gen_kwargs.get('max_length'					) is None and gen_kwargs.get('max_new_tokens'					) is None:
				__lowerCAmelCase     :		Optional[Any]		     = self.args.generation_max_length
			__lowerCAmelCase     :		Tuple		     = (
			    gen_kwargs['num_beams'] if gen_kwargs.get('num_beams'					) is not None else self.args.generation_num_beams
			)
			__lowerCAmelCase     :		Optional[int]		     = gen_kwargs

			return super().evaluate(_SCREAMING_SNAKE_CASE			,						ignore_keys=_SCREAMING_SNAKE_CASE			,						metric_key_prefix=_SCREAMING_SNAKE_CASE					)

		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = "test"			,						**_SCREAMING_SNAKE_CASE			,						):
			__lowerCAmelCase     :		Dict		     = gen_kwargs.copy()
			if gen_kwargs.get('max_length'					) is None and gen_kwargs.get('max_new_tokens'					) is None:
				__lowerCAmelCase     :		Optional[Any]		     = self.args.generation_max_length
			__lowerCAmelCase     :		int		     = (
			    gen_kwargs['num_beams'] if gen_kwargs.get('num_beams'					) is not None else self.args.generation_num_beams
			)
			__lowerCAmelCase     :		Tuple		     = gen_kwargs

			return super().predict(_SCREAMING_SNAKE_CASE			,						ignore_keys=_SCREAMING_SNAKE_CASE			,						metric_key_prefix=_SCREAMING_SNAKE_CASE					)

		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE = None			,						**_SCREAMING_SNAKE_CASE			,						):

			if not self.args.predict_with_generate or prediction_loss_only:
				return super().prediction_step(
				    _SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						prediction_loss_only=_SCREAMING_SNAKE_CASE			,						ignore_keys=_SCREAMING_SNAKE_CASE					)

			__lowerCAmelCase     :		str		     = 'labels' in inputs
			__lowerCAmelCase     :		List[str]		     = self._prepare_inputs(_SCREAMING_SNAKE_CASE					)

			# XXX: adapt synced_gpus for fairscale as well
			# Priority (handled in generate):
			# gen_kwargs > model.generation_config > default GenerationConfig()

			if len(_SCREAMING_SNAKE_CASE					) == 0 and hasattr(self			,						'_gen_kwargs'					):
				__lowerCAmelCase     :		Dict		     = self._gen_kwargs.copy()

			if gen_kwargs.get('max_length'					) is None and gen_kwargs.get('max_new_tokens'					) is None:
				__lowerCAmelCase     :		Tuple		     = self.model.config.max_length
			__lowerCAmelCase     :		Optional[Any]		     = (
			    gen_kwargs['num_beams'] if gen_kwargs.get('num_beams'					) is not None else self.model.config.num_beams
			)
			__lowerCAmelCase     :		Union[str, Any]		     = True if is_deepspeed_zeroa_enabled() else False
			__lowerCAmelCase     :		int		     = (
			    gen_kwargs['synced_gpus'] if gen_kwargs.get('synced_gpus'					) is not None else default_synced_gpus
			)

			# If the `decoder_input_ids` was created from `labels`, evict the former, so that the model can freely generate
			# (otherwise, it would continue generating from the padded `decoder_input_ids`)
			if (
			    "labels" in inputs
			    and "decoder_input_ids" in inputs
			    and inputs["labels"].shape == inputs["decoder_input_ids"].shape
			):
				__lowerCAmelCase     :		Optional[int]		     = {k: v for k, v in inputs.items() if k != 'decoder_input_ids'}
			__lowerCAmelCase     :		Union[str, Any]		     = self.model.generate(**_SCREAMING_SNAKE_CASE			,						**_SCREAMING_SNAKE_CASE					)

			# Temporary hack to ensure the generation config is not initialized for each iteration of the evaluation loop
			# TODO: remove this hack when the legacy code that initializes generation_config from a model config is
			# removed in https://github.com/huggingface/transformers/blob/98d88b23f54e5a23e741833f1e973fdf600cc2c5/src/transformers/generation/utils.py#L1183
			if self.model.generation_config._from_model_config:
				__lowerCAmelCase     :		str		     = False

			# Retrieves GenerationConfig from model.generation_config
			__lowerCAmelCase     :		List[Any]		     = self.model.generation_config
			# in case the batch is shorter than max length, the output should be padded
			if generated_tokens.shape[-1] < gen_config.max_length:
				__lowerCAmelCase     :		Any		     = self._pad_tensors_to_max_len(_SCREAMING_SNAKE_CASE			,						gen_config.max_length					)
			elif gen_config.max_new_tokens is not None and generated_tokens.shape[-1] < gen_config.max_new_tokens + 1:
				__lowerCAmelCase     :		Optional[Any]		     = self._pad_tensors_to_max_len(_SCREAMING_SNAKE_CASE			,						gen_config.max_new_tokens + 1					)

			with torch.no_grad():
				if has_labels:
					with self.compute_loss_context_manager():
						__lowerCAmelCase     :		List[Any]		     = model(**_SCREAMING_SNAKE_CASE					)
					if self.label_smoother is not None:
						__lowerCAmelCase     :		Optional[int]		     = self.label_smoother(_SCREAMING_SNAKE_CASE			,						inputs['labels']					).mean().detach()
					else:
						__lowerCAmelCase     :		Optional[int]		     = (outputs['loss'] if isinstance(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					) else outputs[0]).mean().detach()
				else:
					__lowerCAmelCase     :		Optional[int]		     = None

			if self.args.prediction_loss_only:
				return loss, None, None

			if has_labels:
				__lowerCAmelCase     :		Tuple		     = inputs['labels']
				if labels.shape[-1] < gen_config.max_length:
					__lowerCAmelCase     :		List[str]		     = self._pad_tensors_to_max_len(_SCREAMING_SNAKE_CASE			,						gen_config.max_length					)
				elif gen_config.max_new_tokens is not None and labels.shape[-1] < gen_config.max_new_tokens + 1:
					__lowerCAmelCase     :		int		     = self._pad_tensors_to_max_len(_SCREAMING_SNAKE_CASE			,						gen_config.max_new_tokens + 1					)
			else:
				__lowerCAmelCase     :		Optional[int]		     = None

			return loss, generated_tokens, labels







		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					):
			if self.tokenizer is not None and hasattr(self.tokenizer			,						'pad_token_id'					):
				# If PAD token is not defined at least EOS token has to be defined
				__lowerCAmelCase     :		Any		     = (
				    self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id
				)
			else:
				if self.model.config.pad_token_id is not None:
					__lowerCAmelCase     :		List[str]		     = self.model.config.pad_token_id
				else:
					raise ValueError('Pad_token_id must be set in the configuration of the model, in order to pad tensors'					)

			__lowerCAmelCase     :		Dict		     = pad_token_id * torch.ones(
			    (tensor.shape[0], max_length)			,						dtype=tensor.dtype			,						device=tensor.device					)
			__lowerCAmelCase     :		Tuple		     = tensor
			return padded_tensor

"""simple docstring"""







import collections
import inspect
import unittest

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

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


if is_torch_available():
							import torch
							from torch import nn

							from transformers import NatBackbone, NatForImageClassification, NatModel
							from transformers.models.nat.modeling_nat import NAT_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor




class 					A__			:

		def __init__( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE=13			,						_SCREAMING_SNAKE_CASE=64			,						_SCREAMING_SNAKE_CASE=4			,						_SCREAMING_SNAKE_CASE=3			,						_SCREAMING_SNAKE_CASE=16			,						_SCREAMING_SNAKE_CASE=[1, 2, 1]			,						_SCREAMING_SNAKE_CASE=[2, 4, 8]			,						_SCREAMING_SNAKE_CASE=3			,						_SCREAMING_SNAKE_CASE=2.0			,						_SCREAMING_SNAKE_CASE=True			,						_SCREAMING_SNAKE_CASE=0.0			,						_SCREAMING_SNAKE_CASE=0.0			,						_SCREAMING_SNAKE_CASE=0.1			,						_SCREAMING_SNAKE_CASE="gelu"			,						_SCREAMING_SNAKE_CASE=True			,						_SCREAMING_SNAKE_CASE=0.02			,						_SCREAMING_SNAKE_CASE=1E-5			,						_SCREAMING_SNAKE_CASE=True			,						_SCREAMING_SNAKE_CASE=None			,						_SCREAMING_SNAKE_CASE=True			,						_SCREAMING_SNAKE_CASE=10			,						_SCREAMING_SNAKE_CASE=["stage1", "stage2"]			,						_SCREAMING_SNAKE_CASE=[1, 2]			,						):
			__lowerCAmelCase     :		List[str]		     = parent
			__lowerCAmelCase     :		str		     = batch_size
			__lowerCAmelCase     :		Dict		     = image_size
			__lowerCAmelCase     :		Optional[Any]		     = patch_size
			__lowerCAmelCase     :		List[str]		     = num_channels
			__lowerCAmelCase     :		Tuple		     = embed_dim
			__lowerCAmelCase     :		Tuple		     = depths
			__lowerCAmelCase     :		List[Any]		     = num_heads
			__lowerCAmelCase     :		Any		     = kernel_size
			__lowerCAmelCase     :		List[str]		     = mlp_ratio
			__lowerCAmelCase     :		int		     = qkv_bias
			__lowerCAmelCase     :		Dict		     = hidden_dropout_prob
			__lowerCAmelCase     :		List[Any]		     = attention_probs_dropout_prob
			__lowerCAmelCase     :		Any		     = drop_path_rate
			__lowerCAmelCase     :		Optional[int]		     = hidden_act
			__lowerCAmelCase     :		Optional[int]		     = patch_norm
			__lowerCAmelCase     :		str		     = layer_norm_eps
			__lowerCAmelCase     :		Any		     = initializer_range
			__lowerCAmelCase     :		str		     = is_training
			__lowerCAmelCase     :		Optional[int]		     = scope
			__lowerCAmelCase     :		Optional[int]		     = use_labels
			__lowerCAmelCase     :		Optional[Any]		     = num_labels
			__lowerCAmelCase     :		Any		     = out_features
			__lowerCAmelCase     :		Any		     = out_indices

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		Dict		     = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

			__lowerCAmelCase     :		Union[str, Any]		     = None
			if self.use_labels:
				__lowerCAmelCase     :		int		     = ids_tensor([self.batch_size]			,						self.num_labels					)

			__lowerCAmelCase     :		Tuple		     = self.get_config()

			return config, pixel_values, labels

		def 				__lowerCamelCase				( self					):
			return NatConfig(
			    num_labels=self.num_labels			,						image_size=self.image_size			,						patch_size=self.patch_size			,						num_channels=self.num_channels			,						embed_dim=self.embed_dim			,						depths=self.depths			,						num_heads=self.num_heads			,						kernel_size=self.kernel_size			,						mlp_ratio=self.mlp_ratio			,						qkv_bias=self.qkv_bias			,						hidden_dropout_prob=self.hidden_dropout_prob			,						attention_probs_dropout_prob=self.attention_probs_dropout_prob			,						drop_path_rate=self.drop_path_rate			,						hidden_act=self.hidden_act			,						patch_norm=self.patch_norm			,						layer_norm_eps=self.layer_norm_eps			,						initializer_range=self.initializer_range			,						out_features=self.out_features			,						out_indices=self.out_indices			,						)

		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					):
			__lowerCAmelCase     :		Any		     = NatModel(config=_SCREAMING_SNAKE_CASE					)
			model.to(_SCREAMING_SNAKE_CASE					)
			model.eval()
			__lowerCAmelCase     :		Union[str, Any]		     = model(_SCREAMING_SNAKE_CASE					)

			__lowerCAmelCase     :		str		     = (config.image_size // config.patch_size) // (2 ** (len(config.depths					) - 1))
			__lowerCAmelCase     :		Any		     = int(config.embed_dim * 2 ** (len(config.depths					) - 1)					)

			self.parent.assertEqual(
			    result.last_hidden_state.shape			,						(self.batch_size, expected_height, expected_width, expected_dim)					)

		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					):
			__lowerCAmelCase     :		Dict		     = NatForImageClassification(_SCREAMING_SNAKE_CASE					)
			model.to(_SCREAMING_SNAKE_CASE					)
			model.eval()
			__lowerCAmelCase     :		Dict		     = model(_SCREAMING_SNAKE_CASE			,						labels=_SCREAMING_SNAKE_CASE					)
			self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.num_labels)					)

			# test greyscale images
			__lowerCAmelCase     :		Union[str, Any]		     = 1
			__lowerCAmelCase     :		Any		     = NatForImageClassification(_SCREAMING_SNAKE_CASE					)
			model.to(_SCREAMING_SNAKE_CASE					)
			model.eval()

			__lowerCAmelCase     :		Optional[int]		     = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
			__lowerCAmelCase     :		List[str]		     = model(_SCREAMING_SNAKE_CASE					)
			self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.num_labels)					)

		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					):
			__lowerCAmelCase     :		Tuple		     = NatBackbone(config=_SCREAMING_SNAKE_CASE					)
			model.to(_SCREAMING_SNAKE_CASE					)
			model.eval()
			__lowerCAmelCase     :		Dict		     = model(_SCREAMING_SNAKE_CASE					)

			# verify hidden states
			self.parent.assertEqual(len(result.feature_maps					)			,						len(config.out_features					)					)
			self.parent.assertListEqual(list(result.feature_maps[0].shape					)			,						[self.batch_size, model.channels[0], 16, 16]					)

			# verify channels
			self.parent.assertEqual(len(model.channels					)			,						len(config.out_features					)					)

			# verify backbone works with out_features=None
			__lowerCAmelCase     :		List[Any]		     = None
			__lowerCAmelCase     :		List[Any]		     = NatBackbone(config=_SCREAMING_SNAKE_CASE					)
			model.to(_SCREAMING_SNAKE_CASE					)
			model.eval()
			__lowerCAmelCase     :		Dict		     = model(_SCREAMING_SNAKE_CASE					)

			# verify feature maps
			self.parent.assertEqual(len(result.feature_maps					)			,						1					)
			self.parent.assertListEqual(list(result.feature_maps[0].shape					)			,						[self.batch_size, model.channels[-1], 4, 4]					)

			# verify channels
			self.parent.assertEqual(len(model.channels					)			,						1					)







		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		Dict		     = self.prepare_config_and_inputs()
			__lowerCAmelCase     ,  __lowerCAmelCase     ,  __lowerCAmelCase     :		str		     = config_and_inputs
			__lowerCAmelCase     :		Tuple		     = {'pixel_values': pixel_values}
			return config, inputs_dict




@require_natten
@require_torch
class 					A__			(      _lowerCamelCase			,	_lowerCamelCase			,	unittest.TestCase):
		A_		:			str							  =      (
		    (
		        NatModel,
		        NatForImageClassification,
		        NatBackbone,
		    )
		    if is_torch_available()
		    else ()
		)
		A_		:			int							  =      (
		    {'feature-extraction': NatModel, 'image-classification': NatForImageClassification}
		    if is_torch_available()
		    else {}
		)
		A_		:			Union[str, Any]							  =      False

		A_		:			List[Any]							  =      False
		A_		:			Optional[Any]							  =      False
		A_		:			Union[str, Any]							  =      False
		A_		:			int							  =      False

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		int		     = NatModelTester(self					)
			__lowerCAmelCase     :		Dict		     = ConfigTester(self			,						config_class=_SCREAMING_SNAKE_CASE			,						embed_dim=37					)

		def 				__lowerCamelCase				( self					):
			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 				__lowerCamelCase				( self					):
			return

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		List[Any]		     = self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE					)

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		Optional[int]		     = self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE					)

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		str		     = self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_backbone(*_SCREAMING_SNAKE_CASE					)

		@unittest.skip(reason='Nat does not use inputs_embeds'					)
		def 				__lowerCamelCase				( self					):
			pass

		@unittest.skip(reason='Nat does not use feedforward chunking'					)
		def 				__lowerCamelCase				( self					):
			pass

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     ,  __lowerCAmelCase     :		Dict		     = self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
				__lowerCAmelCase     :		Optional[Any]		     = model_class(_SCREAMING_SNAKE_CASE					)
				self.assertIsInstance(model.get_input_embeddings()			,						(nn.Module)					)
				__lowerCAmelCase     :		List[str]		     = model.get_output_embeddings()
				self.assertTrue(x is None or isinstance(_SCREAMING_SNAKE_CASE			,						nn.Linear					)					)

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     ,  __lowerCAmelCase     :		Optional[int]		     = self.model_tester.prepare_config_and_inputs_for_common()

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

				__lowerCAmelCase     :		Dict		     = ['pixel_values']
				self.assertListEqual(arg_names[:1]			,						_SCREAMING_SNAKE_CASE					)

		def 				__lowerCamelCase				( self					):
			self.skipTest('Nat\'s attention operation is handled entirely by NATTEN.'					)

		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					):
			__lowerCAmelCase     :		List[str]		     = model_class(_SCREAMING_SNAKE_CASE					)
			model.to(_SCREAMING_SNAKE_CASE					)
			model.eval()

			with torch.no_grad():
				__lowerCAmelCase     :		Any		     = model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					)					)

			__lowerCAmelCase     :		str		     = outputs.hidden_states

			__lowerCAmelCase     :		Any		     = getattr(
			    self.model_tester			,						'expected_num_hidden_layers'			,						len(self.model_tester.depths					) + 1					)
			self.assertEqual(len(_SCREAMING_SNAKE_CASE					)			,						_SCREAMING_SNAKE_CASE					)

			# Nat has a different seq_length
			__lowerCAmelCase     :		Dict		     = (
			    config.patch_size
			    if isinstance(config.patch_size			,						collections.abc.Iterable					)
			    else (config.patch_size, config.patch_size)
			)

			__lowerCAmelCase     :		str		     = image_size[0] // patch_size[0]
			__lowerCAmelCase     :		Union[str, Any]		     = image_size[1] // patch_size[1]

			self.assertListEqual(
			    list(hidden_states[0].shape[-3:]					)			,						[height, width, self.model_tester.embed_dim]			,						)

			if model_class.__name__ != "NatBackbone":
				__lowerCAmelCase     :		Optional[Any]		     = outputs.reshaped_hidden_states
				self.assertEqual(len(_SCREAMING_SNAKE_CASE					)			,						_SCREAMING_SNAKE_CASE					)

				__lowerCAmelCase     ,  __lowerCAmelCase     ,  __lowerCAmelCase     ,  __lowerCAmelCase     :		str		     = reshaped_hidden_states[0].shape
				__lowerCAmelCase     :		List[str]		     = (
				    reshaped_hidden_states[0].view(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					).permute(0			,						2			,						3			,						1					)
				)
				self.assertListEqual(
				    list(reshaped_hidden_states.shape[-3:]					)			,						[height, width, self.model_tester.embed_dim]			,						)

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     ,  __lowerCAmelCase     :		Optional[int]		     = self.model_tester.prepare_config_and_inputs_for_common()

			__lowerCAmelCase     :		List[Any]		     = (
			    self.model_tester.image_size
			    if isinstance(self.model_tester.image_size			,						collections.abc.Iterable					)
			    else (self.model_tester.image_size, self.model_tester.image_size)
			)

			for model_class in self.all_model_classes:
				__lowerCAmelCase     :		Union[str, Any]		     = True
				self.check_hidden_states_output(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					)

				# check that output_hidden_states also work using config
				del inputs_dict["output_hidden_states"]
				__lowerCAmelCase     :		Dict		     = True

				self.check_hidden_states_output(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					)

		@slow
		def 				__lowerCamelCase				( self					):
			for model_name in NAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
				__lowerCAmelCase     :		List[Any]		     = NatModel.from_pretrained(_SCREAMING_SNAKE_CASE					)
				self.assertIsNotNone(_SCREAMING_SNAKE_CASE					)







		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     ,  __lowerCAmelCase     :		Optional[Any]		     = self.model_tester.prepare_config_and_inputs_for_common()

			__lowerCAmelCase     :		List[Any]		     = _config_zero_init(_SCREAMING_SNAKE_CASE					)
			for model_class in self.all_model_classes:
				__lowerCAmelCase     :		int		     = model_class(config=_SCREAMING_SNAKE_CASE					)
				for name, param in model.named_parameters():
					if "embeddings" not in name and param.requires_grad:
						self.assertIn(
						    ((param.data.mean() * 1E9).round() / 1E9).item()			,						[0.0, 1.0]			,						msg=f"Parameter {name} of model {model_class} seems not properly initialized"			,						)




@require_natten
@require_vision
@require_torch
class 					A__			(      unittest.TestCase):

		@cached_property
		def 				__lowerCamelCase				( self					):
			return AutoImageProcessor.from_pretrained('shi-labs/nat-mini-in1k-224'					) if is_vision_available() else None







		@slow
		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		Optional[int]		     = NatForImageClassification.from_pretrained('shi-labs/nat-mini-in1k-224'					).to(_SCREAMING_SNAKE_CASE					)
			__lowerCAmelCase     :		str		     = self.default_image_processor

			__lowerCAmelCase     :		str		     = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
			__lowerCAmelCase     :		Dict		     = image_processor(images=_SCREAMING_SNAKE_CASE			,						return_tensors='pt'					).to(_SCREAMING_SNAKE_CASE					)

			# forward pass
			with torch.no_grad():
				__lowerCAmelCase     :		Dict		     = model(**_SCREAMING_SNAKE_CASE					)

			# verify the logits
			__lowerCAmelCase     :		Optional[int]		     = torch.Size((1, 10_00)					)
			self.assertEqual(outputs.logits.shape			,						_SCREAMING_SNAKE_CASE					)
			__lowerCAmelCase     :		Dict		     = torch.tensor([0.3805, -0.8676, -0.3912]					).to(_SCREAMING_SNAKE_CASE					)
			self.assertTrue(torch.allclose(outputs.logits[0, :3]			,						_SCREAMING_SNAKE_CASE			,						atol=1E-4					)					)




@require_torch
@require_natten
class 					A__			(      unittest.TestCase			,	_lowerCamelCase):
		A_		:			str							  =      (NatBackbone,) if is_torch_available() else ()
		A_		:			Any							  =      NatConfig

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		Union[str, Any]		     = NatModelTester(self					)