
'''simple docstring'''







import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
							import jax
							import jax.numpy as jnp

							from transformers.modeling_flax_pytorch_utils import (
							    convert_pytorch_state_dict_to_flax,
							    load_flax_weights_in_pytorch_model,
							)
							from transformers.models.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
							import torch






class 					lowerCAmelCase__  :






					def __init__(  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE=14							,				__SCREAMING_SNAKE_CASE=7							,				__SCREAMING_SNAKE_CASE=True							,				__SCREAMING_SNAKE_CASE=True							,				__SCREAMING_SNAKE_CASE=False							,				__SCREAMING_SNAKE_CASE=True							,				__SCREAMING_SNAKE_CASE=99							,				__SCREAMING_SNAKE_CASE=32							,				__SCREAMING_SNAKE_CASE=4							,				__SCREAMING_SNAKE_CASE=4							,				__SCREAMING_SNAKE_CASE=[[["global", "local"], 2]]							,				__SCREAMING_SNAKE_CASE=37							,				__SCREAMING_SNAKE_CASE="gelu"							,				__SCREAMING_SNAKE_CASE=0.1							,				__SCREAMING_SNAKE_CASE=0.1							,				__SCREAMING_SNAKE_CASE=5_12							,				__SCREAMING_SNAKE_CASE=7							,				__SCREAMING_SNAKE_CASE=0.02							,				):


											"""simple docstring"""





											lowercase_      :	List[str]						=			parent
											lowercase_      :	Any						=			batch_size
											lowercase_      :	Optional[int]						=			seq_length
											lowercase_      :	List[Any]						=			is_training
											lowercase_      :	Optional[Any]						=			use_input_mask
											lowercase_      :	Union[str, Any]						=			use_token_type_ids
											lowercase_      :	Tuple						=			use_labels
											lowercase_      :	List[str]						=			vocab_size
											lowercase_      :	Any						=			hidden_size
											lowercase_      :	Union[str, Any]						=			num_hidden_layers
											lowercase_      :	Optional[Any]						=			num_attention_heads
											lowercase_      :	int						=			attention_types
											lowercase_      :	Union[str, Any]						=			intermediate_size
											lowercase_      :	Union[str, Any]						=			hidden_act
											lowercase_      :	str						=			hidden_dropout_prob
											lowercase_      :	str						=			attention_probs_dropout_prob
											lowercase_      :	Optional[int]						=			max_position_embeddings
											lowercase_      :	Any						=			window_size
											lowercase_      :	List[str]						=			initializer_range
											lowercase_      :	Tuple						=			None
											lowercase_      :	Dict						=			vocab_size - 1
											lowercase_      :	Union[str, Any]						=			vocab_size - 1
											lowercase_      :	List[Any]						=			vocab_size - 1






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	List[Any]						=			ids_tensor([self.batch_size, self.seq_length]							,				self.vocab_size						)

											lowercase_      :	Optional[int]						=			None
											if self.use_input_mask:
																	lowercase_      :	int						=			random_attention_mask([self.batch_size, self.seq_length]						)

											lowercase_      :	List[Any]						=			GPTNeoConfig(
											    vocab_size=self.vocab_size							,				hidden_size=self.hidden_size							,				num_layers=self.num_hidden_layers							,				num_heads=self.num_attention_heads							,				max_position_embeddings=self.max_position_embeddings							,				use_cache=__SCREAMING_SNAKE_CASE							,				bos_token_id=self.bos_token_id							,				eos_token_id=self.eos_token_id							,				pad_token_id=self.pad_token_id							,				window_size=self.window_size							,				attention_types=self.attention_types							,				)

											return (config, input_ids, input_mask)






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	Optional[Any]						=			self.prepare_config_and_inputs()
											lowercase_		,					lowercase_		,					lowercase_      :	Optional[Any]						=			config_and_inputs
											lowercase_      :	List[str]						=			{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
											return config, inputs_dict






					def       _snake_case       (  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											lowercase_      :	Union[str, Any]						=			20
											lowercase_      :	Any						=			model_class_name(__SCREAMING_SNAKE_CASE						)

											lowercase_      :	Dict						=			model.init_cache(input_ids.shape[0]							,				__SCREAMING_SNAKE_CASE						)
											lowercase_      :	Optional[Any]						=			jnp.ones((input_ids.shape[0], max_decoder_length)							,				dtype='''i4'''						)

											lowercase_      :	Tuple						=			jnp.broadcast_to(
											    jnp.arange(input_ids.shape[-1] - 1						)[None, :]							,				(input_ids.shape[0], input_ids.shape[-1] - 1)						)
											lowercase_      :	Tuple						=			model(
											    input_ids[:, :-1]							,				attention_mask=__SCREAMING_SNAKE_CASE							,				past_key_values=__SCREAMING_SNAKE_CASE							,				position_ids=__SCREAMING_SNAKE_CASE							,				)

											lowercase_      :	Dict						=			jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]							,				dtype='''i4'''						)
											lowercase_      :	Optional[int]						=			model(
											    input_ids[:, -1:]							,				attention_mask=__SCREAMING_SNAKE_CASE							,				past_key_values=outputs_cache.past_key_values							,				position_ids=__SCREAMING_SNAKE_CASE							,				)

											lowercase_      :	Optional[Any]						=			model(__SCREAMING_SNAKE_CASE						)

											lowercase_      :	List[Any]						=			np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
											self.parent.assertTrue(diff < 1E-3							,				msg=F'''Max diff is {diff}'''						)






					def       _snake_case       (  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											lowercase_      :	List[str]						=			20
											lowercase_      :	Dict						=			model_class_name(__SCREAMING_SNAKE_CASE						)

											lowercase_      :	Optional[int]						=			jnp.concatenate(
											    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])						)]							,				axis=-1							,				)

											lowercase_      :	Optional[int]						=			model.init_cache(input_ids.shape[0]							,				__SCREAMING_SNAKE_CASE						)
											lowercase_      :	int						=			jnp.broadcast_to(
											    jnp.arange(input_ids.shape[-1] - 1						)[None, :]							,				(input_ids.shape[0], input_ids.shape[-1] - 1)						)

											lowercase_      :	Optional[int]						=			model(
											    input_ids[:, :-1]							,				attention_mask=__SCREAMING_SNAKE_CASE							,				past_key_values=__SCREAMING_SNAKE_CASE							,				position_ids=__SCREAMING_SNAKE_CASE							,				)
											lowercase_      :	List[Any]						=			jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]							,				dtype='''i4'''						)
											lowercase_      :	Union[str, Any]						=			model(
											    input_ids[:, -1:]							,				past_key_values=outputs_cache.past_key_values							,				attention_mask=__SCREAMING_SNAKE_CASE							,				position_ids=__SCREAMING_SNAKE_CASE							,				)

											lowercase_      :	str						=			model(__SCREAMING_SNAKE_CASE							,				attention_mask=__SCREAMING_SNAKE_CASE						)

											lowercase_      :	Optional[Any]						=			np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
											self.parent.assertTrue(diff < 1E-3							,				msg=F'''Max diff is {diff}'''						)







@require_flax
class 					lowerCAmelCase__  (    lowerCamelCase_       ,		lowerCamelCase_       ,		unittest.TestCase	):
					lowerCAmelCase_	  =	(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
					lowerCAmelCase_	  =	(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	Tuple						=			FlaxGPTNeoModelTester(self						)






					def       _snake_case       (  self						):


											"""simple docstring"""





											for model_class_name in self.all_model_classes:
																	lowercase_		,					lowercase_		,					lowercase_      :	List[Any]						=			self.model_tester.prepare_config_and_inputs()
																	self.model_tester.check_use_cache_forward(__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						)






					def       _snake_case       (  self						):


											"""simple docstring"""





											for model_class_name in self.all_model_classes:
																	lowercase_		,					lowercase_		,					lowercase_      :	str						=			self.model_tester.prepare_config_and_inputs()
																	self.model_tester.check_use_cache_forward_with_attn_mask(
																	    __SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						)






					@slow
					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	int						=			GPTaTokenizer.from_pretrained('''gpt2'''							,				pad_token='''<|endoftext|>'''							,				padding_side='''left'''						)
											lowercase_      :	List[Any]						=			tokenizer(['''Hello this is a long string''', '''Hey''']							,				return_tensors='''np'''							,				padding=__SCREAMING_SNAKE_CASE							,				truncation=__SCREAMING_SNAKE_CASE						)

											lowercase_      :	Dict						=			FlaxGPTNeoForCausalLM.from_pretrained('''EleutherAI/gpt-neo-125M'''						)
											lowercase_      :	int						=			False
											lowercase_      :	List[Any]						=			model.config.eos_token_id

											lowercase_      :	Optional[int]						=			jax.jit(model.generate						)

											lowercase_      :	Any						=			jit_generate(
											    inputs['''input_ids''']							,				attention_mask=inputs['''attention_mask''']							,				pad_token_id=tokenizer.pad_token_id						).sequences

											lowercase_      :	List[str]						=			tokenizer.batch_decode(__SCREAMING_SNAKE_CASE							,				skip_special_tokens=__SCREAMING_SNAKE_CASE						)

											lowercase_      :	Optional[int]						=			[
											    '''Hello this is a long string of text.\n\nI\'m trying to get the text of the''',
											    '''Hey, I\'m a little late to the party. I\'m going to''',
											]

											self.assertListEqual(__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						)






					@is_pt_flax_cross_test
					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_		,					lowercase_      :	Optional[int]						=			self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																	with self.subTest(model_class.__name__						):
																							# prepare inputs
																							lowercase_      :	List[Any]						=			self._prepare_for_class(__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						)
																							lowercase_      :	str						=			{k: torch.tensor(v.tolist()						) for k, v in prepared_inputs_dict.items()}

																							# load corresponding PyTorch class
																							lowercase_      :	Dict						=			model_class.__name__[4:]  # Skip the "Flax" at the beginning
																							lowercase_      :	Any						=			getattr(__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						)

																							lowercase_		,					lowercase_      :	int						=			pt_inputs['''input_ids'''].shape
																							lowercase_      :	Optional[int]						=			np.random.randint(0							,				seq_length - 1							,				size=(batch_size,)						)
																							for batch_idx, start_index in enumerate(__SCREAMING_SNAKE_CASE						):
																													lowercase_      :	Optional[Any]						=			0
																													lowercase_      :	Any						=			1
																													lowercase_      :	int						=			0
																													lowercase_      :	Optional[Any]						=			1
																							lowercase_      :	int						=			pt_model_class(__SCREAMING_SNAKE_CASE						).eval()
																							lowercase_      :	Any						=			model_class(__SCREAMING_SNAKE_CASE							,				dtype=jnp.floataa						)

																							lowercase_      :	Dict						=			convert_pytorch_state_dict_to_flax(pt_model.state_dict()							,				__SCREAMING_SNAKE_CASE						)
																							lowercase_      :	Optional[int]						=			fx_state

																							with torch.no_grad():
																													lowercase_      :	List[str]						=			pt_model(**__SCREAMING_SNAKE_CASE						).to_tuple()

																							lowercase_      :	str						=			fx_model(**__SCREAMING_SNAKE_CASE						).to_tuple()
																							self.assertEqual(len(__SCREAMING_SNAKE_CASE						)							,				len(__SCREAMING_SNAKE_CASE						)							,				'''Output lengths differ between Flax and PyTorch'''						)
																							for fx_output, pt_output in zip(__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						):
																													self.assert_almost_equals(fx_output[:, -1]							,				pt_output[:, -1].numpy()							,				4E-2						)

																							with tempfile.TemporaryDirectory() as tmpdirname:
																													pt_model.save_pretrained(__SCREAMING_SNAKE_CASE						)
																													lowercase_      :	str						=			model_class.from_pretrained(__SCREAMING_SNAKE_CASE							,				from_pt=__SCREAMING_SNAKE_CASE						)

																							lowercase_      :	Optional[int]						=			fx_model_loaded(**__SCREAMING_SNAKE_CASE						).to_tuple()
																							self.assertEqual(
																							    len(__SCREAMING_SNAKE_CASE						)							,				len(__SCREAMING_SNAKE_CASE						)							,				'''Output lengths differ between Flax and PyTorch'''						)
																							for fx_output_loaded, pt_output in zip(__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						):
																													self.assert_almost_equals(fx_output_loaded[:, -1]							,				pt_output[:, -1].numpy()							,				4E-2						)






					@is_pt_flax_cross_test
					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_		,					lowercase_      :	Union[str, Any]						=			self.model_tester.prepare_config_and_inputs_for_common()
											for model_class in self.all_model_classes:
																	with self.subTest(model_class.__name__						):
																							# prepare inputs
																							lowercase_      :	Tuple						=			self._prepare_for_class(__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						)
																							lowercase_      :	Optional[int]						=			{k: torch.tensor(v.tolist()						) for k, v in prepared_inputs_dict.items()}

																							# load corresponding PyTorch class
																							lowercase_      :	Dict						=			model_class.__name__[4:]  # Skip the "Flax" at the beginning
																							lowercase_      :	int						=			getattr(__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						)

																							lowercase_      :	Optional[Any]						=			pt_model_class(__SCREAMING_SNAKE_CASE						).eval()
																							lowercase_      :	List[str]						=			model_class(__SCREAMING_SNAKE_CASE							,				dtype=jnp.floataa						)

																							lowercase_      :	Dict						=			load_flax_weights_in_pytorch_model(__SCREAMING_SNAKE_CASE							,				fx_model.params						)
																							lowercase_		,					lowercase_      :	List[str]						=			pt_inputs['''input_ids'''].shape
																							lowercase_      :	List[str]						=			np.random.randint(0							,				seq_length - 1							,				size=(batch_size,)						)
																							for batch_idx, start_index in enumerate(__SCREAMING_SNAKE_CASE						):
																													lowercase_      :	List[Any]						=			0
																													lowercase_      :	Optional[Any]						=			1
																													lowercase_      :	Union[str, Any]						=			0
																													lowercase_      :	List[Any]						=			1

																							# make sure weights are tied in PyTorch
																							pt_model.tie_weights()

																							with torch.no_grad():
																													lowercase_      :	Any						=			pt_model(**__SCREAMING_SNAKE_CASE						).to_tuple()

																							lowercase_      :	List[Any]						=			fx_model(**__SCREAMING_SNAKE_CASE						).to_tuple()
																							self.assertEqual(len(__SCREAMING_SNAKE_CASE						)							,				len(__SCREAMING_SNAKE_CASE						)							,				'''Output lengths differ between Flax and PyTorch'''						)
																							for fx_output, pt_output in zip(__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						):
																													self.assert_almost_equals(fx_output[:, -1]							,				pt_output[:, -1].numpy()							,				4E-2						)

																							with tempfile.TemporaryDirectory() as tmpdirname:
																													fx_model.save_pretrained(__SCREAMING_SNAKE_CASE						)
																													lowercase_      :	Optional[Any]						=			pt_model_class.from_pretrained(__SCREAMING_SNAKE_CASE							,				from_flax=__SCREAMING_SNAKE_CASE						)

																							with torch.no_grad():
																													lowercase_      :	List[Any]						=			pt_model_loaded(**__SCREAMING_SNAKE_CASE						).to_tuple()

																							self.assertEqual(
																							    len(__SCREAMING_SNAKE_CASE						)							,				len(__SCREAMING_SNAKE_CASE						)							,				'''Output lengths differ between Flax and PyTorch'''						)
																							for fx_output, pt_output in zip(__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						):
																													self.assert_almost_equals(fx_output[:, -1]							,				pt_output[:, -1].numpy()							,				4E-2						)






					@slow
					def       _snake_case       (  self						):


											"""simple docstring"""





											for model_class_name in self.all_model_classes:
																	lowercase_      :	List[str]						=			model_class_name.from_pretrained('''EleutherAI/gpt-neo-125M'''						)
																	lowercase_      :	Any						=			model(np.ones((1, 1)						)						)
																	self.assertIsNotNone(__SCREAMING_SNAKE_CASE						)





'''simple docstring'''







import collections
import inspect
import unittest

from transformers import DinatConfig
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 DinatBackbone, DinatForImageClassification, DinatModel
							from transformers.models.dinat.modeling_dinat import DINAT_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor






class 					lowerCAmelCase__  :






					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=[[3], [1, 2], [1]]							,				__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]							,				):


											"""simple docstring"""





											lowercase_      :	Optional[int]						=			parent
											lowercase_      :	Optional[Any]						=			batch_size
											lowercase_      :	str						=			image_size
											lowercase_      :	Optional[Any]						=			patch_size
											lowercase_      :	Union[str, Any]						=			num_channels
											lowercase_      :	List[Any]						=			embed_dim
											lowercase_      :	Optional[int]						=			depths
											lowercase_      :	Any						=			num_heads
											lowercase_      :	Any						=			kernel_size
											lowercase_      :	str						=			dilations
											lowercase_      :	Optional[Any]						=			mlp_ratio
											lowercase_      :	Optional[Any]						=			qkv_bias
											lowercase_      :	Union[str, Any]						=			hidden_dropout_prob
											lowercase_      :	str						=			attention_probs_dropout_prob
											lowercase_      :	List[Any]						=			drop_path_rate
											lowercase_      :	str						=			hidden_act
											lowercase_      :	Optional[int]						=			patch_norm
											lowercase_      :	Optional[int]						=			layer_norm_eps
											lowercase_      :	str						=			initializer_range
											lowercase_      :	Optional[Any]						=			is_training
											lowercase_      :	Any						=			scope
											lowercase_      :	Dict						=			use_labels
											lowercase_      :	Union[str, Any]						=			num_labels
											lowercase_      :	List[str]						=			out_features
											lowercase_      :	str						=			out_indices






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	int						=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

											lowercase_      :	List[str]						=			None
											if self.use_labels:
																	lowercase_      :	List[str]						=			ids_tensor([self.batch_size]							,				self.num_labels						)

											lowercase_      :	Union[str, Any]						=			self.get_config()

											return config, pixel_values, labels






					def       _snake_case       (  self						):


											"""simple docstring"""





											return DinatConfig(
											    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							,				dilations=self.dilations							,				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       _snake_case       (  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											lowercase_      :	Optional[int]						=			DinatModel(config=__SCREAMING_SNAKE_CASE						)
											model.to(__SCREAMING_SNAKE_CASE						)
											model.eval()
											lowercase_      :	Union[str, Any]						=			model(__SCREAMING_SNAKE_CASE						)

											lowercase_      :	int						=			(config.image_size // config.patch_size) // (2 ** (len(config.depths						) - 1))
											lowercase_      :	Dict						=			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       _snake_case       (  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											lowercase_      :	int						=			DinatForImageClassification(__SCREAMING_SNAKE_CASE						)
											model.to(__SCREAMING_SNAKE_CASE						)
											model.eval()
											lowercase_      :	Union[str, Any]						=			model(__SCREAMING_SNAKE_CASE							,				labels=__SCREAMING_SNAKE_CASE						)
											self.parent.assertEqual(result.logits.shape							,				(self.batch_size, self.num_labels)						)

											# test greyscale images
											lowercase_      :	Union[str, Any]						=			1
											lowercase_      :	List[str]						=			DinatForImageClassification(__SCREAMING_SNAKE_CASE						)
											model.to(__SCREAMING_SNAKE_CASE						)
											model.eval()

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






					def       _snake_case       (  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											lowercase_      :	Dict						=			DinatBackbone(config=__SCREAMING_SNAKE_CASE						)
											model.to(__SCREAMING_SNAKE_CASE						)
											model.eval()
											lowercase_      :	Union[str, Any]						=			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
											lowercase_      :	str						=			None
											lowercase_      :	Optional[int]						=			DinatBackbone(config=__SCREAMING_SNAKE_CASE						)
											model.to(__SCREAMING_SNAKE_CASE						)
											model.eval()
											lowercase_      :	str						=			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       _snake_case       (  self						):


											"""simple docstring"""





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







@require_natten
@require_torch
class 					lowerCAmelCase__  (    lowerCamelCase_       ,		lowerCamelCase_       ,		unittest.TestCase	):
					lowerCAmelCase_	  =	(
					    (
					        DinatModel,
					        DinatForImageClassification,
					        DinatBackbone,
					    )
					    if is_torch_available()
					    else ()
					)
					lowerCAmelCase_	  =	(
					    {'''feature-extraction''': DinatModel, '''image-classification''': DinatForImageClassification}
					    if is_torch_available()
					    else {}
					)
					lowerCAmelCase_	  =	False

					lowerCAmelCase_	  =	False
					lowerCAmelCase_	  =	False
					lowerCAmelCase_	  =	False
					lowerCAmelCase_	  =	False






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	Optional[int]						=			DinatModelTester(self						)
											lowercase_      :	Optional[Any]						=			ConfigTester(self							,				config_class=__SCREAMING_SNAKE_CASE							,				embed_dim=37						)






					def       _snake_case       (  self						):


											"""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       _snake_case       (  self						):


											"""simple docstring"""





											return






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	Tuple						=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE						)






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	List[str]						=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE						)






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	Any						=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_backbone(*__SCREAMING_SNAKE_CASE						)






					@unittest.skip(reason='''Dinat does not use inputs_embeds'''						)
					def       _snake_case       (  self						):


											"""simple docstring"""





											pass






					@unittest.skip(reason='''Dinat does not use feedforward chunking'''						)
					def       _snake_case       (  self						):


											"""simple docstring"""





											pass






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_		,					lowercase_      :	List[str]						=			self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																	lowercase_      :	str						=			model_class(__SCREAMING_SNAKE_CASE						)
																	self.assertIsInstance(model.get_input_embeddings()							,				(nn.Module)						)
																	lowercase_      :	int						=			model.get_output_embeddings()
																	self.assertTrue(x is None or isinstance(__SCREAMING_SNAKE_CASE							,				nn.Linear						)						)






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_		,					lowercase_      :	int						=			self.model_tester.prepare_config_and_inputs_for_common()

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

																	lowercase_      :	Dict						=			['''pixel_values''']
																	self.assertListEqual(arg_names[:1]							,				__SCREAMING_SNAKE_CASE						)






					def       _snake_case       (  self						):


											"""simple docstring"""





											self.skipTest('''Dinat\'s attention operation is handled entirely by NATTEN.'''						)






					def       _snake_case       (  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											lowercase_      :	Union[str, Any]						=			model_class(__SCREAMING_SNAKE_CASE						)
											model.to(__SCREAMING_SNAKE_CASE						)
											model.eval()

											with torch.no_grad():
																	lowercase_      :	Optional[Any]						=			model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						)						)

											lowercase_      :	int						=			outputs.hidden_states

											lowercase_      :	List[str]						=			getattr(
											    self.model_tester							,				'''expected_num_hidden_layers'''							,				len(self.model_tester.depths						) + 1						)
											self.assertEqual(len(__SCREAMING_SNAKE_CASE						)							,				__SCREAMING_SNAKE_CASE						)

											# Dinat has a different seq_length
											lowercase_      :	Optional[Any]						=			(
											    config.patch_size
											    if isinstance(config.patch_size							,				collections.abc.Iterable						)
											    else (config.patch_size, config.patch_size)
											)

											lowercase_      :	Union[str, Any]						=			image_size[0] // patch_size[0]
											lowercase_      :	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__ != "DinatBackbone":
																	lowercase_      :	Any						=			outputs.reshaped_hidden_states
																	self.assertEqual(len(__SCREAMING_SNAKE_CASE						)							,				__SCREAMING_SNAKE_CASE						)

																	lowercase_		,					lowercase_		,					lowercase_		,					lowercase_      :	int						=			reshaped_hidden_states[0].shape
																	lowercase_      :	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       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_		,					lowercase_      :	List[Any]						=			self.model_tester.prepare_config_and_inputs_for_common()

											lowercase_      :	List[str]						=			(
											    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:
																	lowercase_      :	Dict						=			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"]
																	lowercase_      :	str						=			True

																	self.check_hidden_states_output(__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						)






					@slow
					def       _snake_case       (  self						):


											"""simple docstring"""





											for model_name in DINAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	lowercase_      :	Optional[Any]						=			DinatModel.from_pretrained(__SCREAMING_SNAKE_CASE						)
																	self.assertIsNotNone(__SCREAMING_SNAKE_CASE						)






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_		,					lowercase_      :	Tuple						=			self.model_tester.prepare_config_and_inputs_for_common()

											lowercase_      :	Tuple						=			_config_zero_init(__SCREAMING_SNAKE_CASE						)
											for model_class in self.all_model_classes:
																	lowercase_      :	List[str]						=			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 					lowerCAmelCase__  (    unittest.TestCase	):






					@cached_property
					def       _snake_case       (  self						):


											"""simple docstring"""





											return AutoImageProcessor.from_pretrained('''shi-labs/dinat-mini-in1k-224'''						) if is_vision_available() else None






					@slow
					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	Optional[int]						=			DinatForImageClassification.from_pretrained('''shi-labs/dinat-mini-in1k-224'''						).to(__SCREAMING_SNAKE_CASE						)
											lowercase_      :	str						=			self.default_image_processor

											lowercase_      :	Tuple						=			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
											lowercase_      :	Tuple						=			image_processor(images=__SCREAMING_SNAKE_CASE							,				return_tensors='''pt'''						).to(__SCREAMING_SNAKE_CASE						)

											# forward pass
											with torch.no_grad():
																	lowercase_      :	str						=			model(**__SCREAMING_SNAKE_CASE						)

											# verify the logits
											lowercase_      :	List[Any]						=			torch.Size((1, 10_00)						)
											self.assertEqual(outputs.logits.shape							,				__SCREAMING_SNAKE_CASE						)
											lowercase_      :	Union[str, Any]						=			torch.tensor([-0.1_545, -0.7_667, 0.4_642]						).to(__SCREAMING_SNAKE_CASE						)
											self.assertTrue(torch.allclose(outputs.logits[0, :3]							,				__SCREAMING_SNAKE_CASE							,				atol=1E-4						)						)







@require_torch
@require_natten
class 					lowerCAmelCase__  (    unittest.TestCase       ,		lowerCamelCase_	):
					lowerCAmelCase_	  =	(DinatBackbone,) if is_torch_available() else ()
					lowerCAmelCase_	  =	DinatConfig






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	Tuple						=			DinatModelTester(self						)



