



'''simple docstring'''







import itertools
import os
import subprocess
from os.path import dirname

from parameterized import parameterized

from tests.trainer.test_trainer import TrainerIntegrationCommon  # noqa
from transformers import is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_tests_dir,
    require_deepspeed,
    require_torch_gpu,
    slow,
)
from transformers.trainer_utils import set_seed


if is_torch_available():
							from tests.trainer.test_trainer import (  # noqa
							    RegressionModelConfig,
							    RegressionPreTrainedModel,
							    get_regression_trainer,
							)


set_seed(42)

SCREAMING_SNAKE_CASE__     : Optional[Any]				=   get_tests_dir("""fixtures""")
SCREAMING_SNAKE_CASE__     : Any				=   os.path.join(dirname(get_tests_dir()))
SCREAMING_SNAKE_CASE__     : List[str]				=   dirname(os.path.abspath(__file__))

# default torch.distributed port
SCREAMING_SNAKE_CASE__     : Any				=   """10999"""

SCREAMING_SNAKE_CASE__     : Optional[Any]				=   """t5-small"""

# *** Working Models ***
SCREAMING_SNAKE_CASE__     : Optional[Any]				=   """hf-internal-testing/tiny-albert"""
SCREAMING_SNAKE_CASE__     : Union[str, Any]				=   """sshleifer/bart-tiny-random"""
SCREAMING_SNAKE_CASE__     : Optional[Any]				=   """hf-internal-testing/tiny-bert"""
SCREAMING_SNAKE_CASE__     : Optional[Any]				=   """hf-internal-testing/tiny-random-bigbird_pegasus"""
SCREAMING_SNAKE_CASE__     : Tuple				=   """hf-internal-testing/tiny-random-big_bird"""
SCREAMING_SNAKE_CASE__     : int				=   """hf-internal-testing/tiny-random-blenderbot"""
SCREAMING_SNAKE_CASE__     : List[Any]				=   """bigscience/bigscience-small-testing"""
SCREAMING_SNAKE_CASE__     : Any				=   """hf-internal-testing/tiny-random-deberta"""
SCREAMING_SNAKE_CASE__     : str				=   """hf-internal-testing/tiny-random-deberta-v2"""
SCREAMING_SNAKE_CASE__     : Tuple				=   """sshleifer/tiny-distilbert-base-cased"""
SCREAMING_SNAKE_CASE__     : Dict				=   """hf-internal-testing/tiny-electra"""
SCREAMING_SNAKE_CASE__     : Dict				=   """hf-internal-testing/tiny-random-flaubert"""
SCREAMING_SNAKE_CASE__     : List[str]				=   """stas/tiny-wmt19-en-de"""
SCREAMING_SNAKE_CASE__     : Optional[Any]				=   """hf-internal-testing/tiny-random-funnel"""
SCREAMING_SNAKE_CASE__     : List[str]				=   """sshleifer/tiny-gpt2"""
SCREAMING_SNAKE_CASE__     : Tuple				=   """hf-internal-testing/tiny-random-gptj"""
SCREAMING_SNAKE_CASE__     : Union[str, Any]				=   """hf-internal-testing/tiny-random-gpt_neo"""
SCREAMING_SNAKE_CASE__     : List[str]				=   """hf-internal-testing/tiny-layoutlm"""
SCREAMING_SNAKE_CASE__     : Optional[Any]				=   """hf-internal-testing/tiny-random-led"""
SCREAMING_SNAKE_CASE__     : Dict				=   """hf-internal-testing/tiny-random-longformer"""
SCREAMING_SNAKE_CASE__     : Optional[int]				=   """stas/tiny-m2m_100"""  # hf tiny model is unsuitable
SCREAMING_SNAKE_CASE__     : List[Any]				=   """sshleifer/tiny-marian-en-de"""
SCREAMING_SNAKE_CASE__     : Tuple				=   """sshleifer/tiny-mbart"""
SCREAMING_SNAKE_CASE__     : List[Any]				=   """hf-internal-testing/tiny-random-mobilebert"""
SCREAMING_SNAKE_CASE__     : Tuple				=   """hf-internal-testing/tiny-random-mpnet"""
SCREAMING_SNAKE_CASE__     : int				=   """stas/pegasus-cnn_dailymail-tiny-random"""
SCREAMING_SNAKE_CASE__     : Optional[int]				=   """hf-internal-testing/tiny-random-prophetnet"""
SCREAMING_SNAKE_CASE__     : Tuple				=   """sshleifer/tiny-distilroberta-base"""
SCREAMING_SNAKE_CASE__     : Optional[int]				=   """hf-internal-testing/tiny-random-squeezebert"""
SCREAMING_SNAKE_CASE__     : Optional[Any]				=   """patrickvonplaten/t5-tiny-random"""
SCREAMING_SNAKE_CASE__     : int				=   """hf-internal-testing/tiny-random-t5-v1.1"""
SCREAMING_SNAKE_CASE__     : Optional[Any]				=   """hf-internal-testing/tiny-random-vit"""
SCREAMING_SNAKE_CASE__     : Union[str, Any]				=   """hf-internal-testing/tiny-xlm-roberta"""
SCREAMING_SNAKE_CASE__     : List[str]				=   """sshleifer/tiny-xlnet-base-cased"""


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
SCREAMING_SNAKE_CASE__     : str				=   """hf-internal-testing/tiny-random-mt5"""
SCREAMING_SNAKE_CASE__     : Tuple				=   """hf-internal-testing/tiny-random-camembert"""
SCREAMING_SNAKE_CASE__     : str				=   """hf-internal-testing/tiny-random-openai-gpt"""

# missing tokenizer files
SCREAMING_SNAKE_CASE__     : Optional[int]				=   """hf-internal-testing/tiny-random-convbert"""
SCREAMING_SNAKE_CASE__     : Any				=   """hf-internal-testing/tiny-random-layoutlmv2"""
SCREAMING_SNAKE_CASE__     : str				=   """hf-internal-testing/tiny-random-hubert"""

# issues with tokenizer
SCREAMING_SNAKE_CASE__     : Optional[int]				=   """hf-internal-testing/tiny-random-ctrl"""
SCREAMING_SNAKE_CASE__     : Any				=   """hf-internal-testing/tiny-random-transfo-xl"""  # same as ctrl

# other issues with tiny models
SCREAMING_SNAKE_CASE__     : str				=   """hf-internal-testing/tiny-random-ibert"""  # multiple issues with either mlm/qa/clas
SCREAMING_SNAKE_CASE__     : Dict				=   """hf-internal-testing/tiny-random-reformer"""  # multiple issues with either mlm/qa/clas

# *** Lacking official examples to test with ***
# or not working with examples
SCREAMING_SNAKE_CASE__     : Any				=   """hf-internal-testing/tiny-random-dpr"""
# - "dpr"  examples/research_projects/rag-end2end-retriever/
SCREAMING_SNAKE_CASE__     : Tuple				=   """hf-internal-testing/tiny-random-rag"""
# - "rag" research_projects
SCREAMING_SNAKE_CASE__     : Tuple				=   """"""
# - "luke" Entities classes - no plan to make such example
SCREAMING_SNAKE_CASE__     : Optional[int]				=   """hf-internal-testing/tiny-random-lxmert"""
# - "lxmert" doesn't work with run_qa.py
SCREAMING_SNAKE_CASE__     : Optional[Any]				=   """hf-internal-testing/tiny-random-clip"""
# - "clip" nothing under pytorch examples - XXX: Suraj is working on adding some - check by end of Sep
SCREAMING_SNAKE_CASE__     : Optional[int]				=   """hf-internal-testing/tiny-random-speech_to_text"""
# - "speech_to_text", nothing under pytorch examples


# *** Reactive mode ***
# models with low usage, unstable API, things about to change - do nothing about the following until someone runs into a problem
SCREAMING_SNAKE_CASE__     : List[Any]				=   """hf-internal-testing/tiny-random-tapas"""







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE=False     ):
		# 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup
		# - it won't be able to handle that
		# 2. for now testing with just 2 gpus max (since some quality tests may give different
		# results with mode gpus because we use very little data)
		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      min(2 ,		get_gpu_count()     ) if distributed else 1
		SCREAMING_SNAKE_CASE_						:Any										=      os.environ.get('DS_TEST_PORT' ,		SCREAMING_SNAKE_CASE     )
		return F'deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'.split()







def  SCREAMING_SNAKE_CASE__ (				):
		SCREAMING_SNAKE_CASE_						:List[Any]										=      F'{FIXTURE_DIRECTORY}/tests_samples'
		SCREAMING_SNAKE_CASE_						:int										=      F'{data_dir_samples}/wmt_en_ro'
		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      F'{data_dir_samples}/xsum'
		SCREAMING_SNAKE_CASE_						:Optional[int]										=      '\n        --do_train\n        --max_train_samples 4\n        --per_device_train_batch_size 2\n        --num_train_epochs 1\n        --fp16\n        --report_to none\n        --overwrite_output_dir\n        '.split()

		# try to cover as many models as possible once (it's enough to run on one task per model)
		# but need a tiny model for each
		#
		# should have "{model_type.upper()}_TINY" corresponding vars defined, e.g., T5_TINY, etc.
		SCREAMING_SNAKE_CASE_						:Any										=      {
		    'trans': [
		        'bart',
		        'fsmt',
		        'm2m_100',
		        'marian',
		        'mbart',
		        't5',
		        't5_v1',
		        # "mt5", missing model files
		    ],
		    'sum': [
		        'pegasus',
		    ],
		    'clm': [
		        'big_bird',
		        'bigbird_pegasus',
		        'blenderbot',
		        'bloom',
		        'gpt2',
		        'gpt_neo',
		        'gptj',
		        'xlm-roberta',
		        'prophetnet',
		        # "camembert", missing model files
		    ],
		    'mlm': [
		        'albert',
		        'deberta',
		        'deberta-v2',
		        'distilbert',
		        'electra',
		        'flaubert',
		        'funnel',
		        'layoutlm',
		        # "reformer", # multiple issues with either mlm/qa/clas
		    ],
		    'qa': [
		        'led',
		        'longformer',
		        'mobilebert',
		        'mpnet',
		        'roberta',
		        'squeezebert',
		        # "convbert", # missing tokenizer files
		        # "layoutlmv2", missing model files
		    ],
		    'clas': [
		        'bert',
		        'xlnet',
		        # "hubert", # missing tokenizer files
		        # "ibert", # multiple issues with either mlm/qa/clas
		        # "transfo-xl", # tokenizer issues as ctrl
		        # "ctrl", # tokenizer issues
		        # "openai-gpt", missing model files
		        # "tapas", multiple issues
		    ],
		    'img_clas': [
		        'vit',
		    ],
		}

		SCREAMING_SNAKE_CASE_						:Optional[int]										=      F'{ROOT_DIRECTORY}/examples/pytorch'

		SCREAMING_SNAKE_CASE_						:Any										=      {
		    'trans': F'\n        {scripts_dir}/translation/run_translation.py\n        --train_file {data_dir_wmt}/train.json\n        --source_lang en\n        --target_lang ro\n        ',
		    'sum': F'\n        {scripts_dir}/summarization/run_summarization.py\n        --train_file {data_dir_xsum}/sample.json\n        --max_source_length 12\n        --max_target_length 12\n        --lang en\n        ',
		    'clm': F'\n        {scripts_dir}/language-modeling/run_clm.py\n        --train_file {FIXTURE_DIRECTORY}/sample_text.txt\n        --block_size 8\n        ',
		    'mlm': F'\n        {scripts_dir}/language-modeling/run_mlm.py\n        --train_file {FIXTURE_DIRECTORY}/sample_text.txt\n        ',
		    'qa': F'\n        {scripts_dir}/question-answering/run_qa.py\n        --train_file {data_dir_samples}/SQUAD/sample.json\n        ',
		    'clas': F'\n        {scripts_dir}/text-classification/run_glue.py\n        --train_file {data_dir_samples}/MRPC/train.csv\n        --max_seq_length 12\n        --task_name MRPC\n        ',
		    'img_clas': F'\n        {scripts_dir}/image-classification/run_image_classification.py\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --remove_unused_columns False\n            --max_steps 10\n            --image_processor_name {DS_TESTS_DIRECTORY}/vit_feature_extractor.json\n        ',
		}

		SCREAMING_SNAKE_CASE_						:Dict										=      get_launcher(distributed=SCREAMING_SNAKE_CASE     )

		SCREAMING_SNAKE_CASE_						:Tuple										=      {}
		for task, args in tasks.items():
				SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      args.split()
				for model in tasksamodels[task]:
						SCREAMING_SNAKE_CASE_						:Any										=      globals()[F'{model.upper().replace("-" ,		"_"     )}_TINY']
						SCREAMING_SNAKE_CASE_						:Any										=      F'--model_name_or_path {model_name}'.split()
						SCREAMING_SNAKE_CASE_						:Dict										=      launcher + args + args_model + args_main

						# # generation special case
						# if task == "gen":
						#     launcher = f"deepspeed --num_nodes 1 --num_gpus 1".split()
						#     args_model += f"--model_type {model}".split()
						#     cmds[f"{task}_{model}"] = launcher + args + args_model
						# else:

		return cmds


SCREAMING_SNAKE_CASE__     : Tuple				=   make_task_cmds()

SCREAMING_SNAKE_CASE__     : List[str]				=   """zero2"""
SCREAMING_SNAKE_CASE__     : Optional[int]				=   """zero3"""

SCREAMING_SNAKE_CASE__     : List[str]				=   [ZEROa, ZEROa]







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		# customize the test name generator function as we want both params to appear in the sub-test
		# name, as by default it shows only the first param
		SCREAMING_SNAKE_CASE_						:Optional[int]										=      parameterized.to_safe_name('_'.join(str(SCREAMING_SNAKE_CASE     ) for x in param.args     )     )
		return F'{func.__name__}_{param_based_name}'


# Cartesian-product of zero stages with models to test
SCREAMING_SNAKE_CASE__     : Optional[Any]				=   list(itertools.product(stages, task_cmds.keys()))





@slow
@require_deepspeed
@require_torch_gpu
class 				__lowerCAmelCase(							lowerCAmelCase__      ):






			def 				_lowercase	(   self  :							Union[str, Any]      ,       SCREAMING_SNAKE_CASE  :							List[Any]      ,       SCREAMING_SNAKE_CASE  :							Union[str, Any]	):




					"""simple docstring"""







					if task not in task_cmds:
							raise ValueError(f'don\'t know of task {task}, have {task_cmds.keys()}'	)

					SCREAMING_SNAKE_CASE_						:List[Any]										=      task_cmds[task]
					SCREAMING_SNAKE_CASE_						:Any										=      f'--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json'.split()

					SCREAMING_SNAKE_CASE_						:int										=      self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_						:List[str]										=      f'--output_dir {output_dir}'.split()

					cmd += args_ds + args_out

					return cmd, output_dir






			@parameterized.expand(SCREAMING_SNAKE_CASE      ,       name_func=SCREAMING_SNAKE_CASE	)
			def 				_lowercase	(   self  :							Any      ,       SCREAMING_SNAKE_CASE  :							Dict      ,       SCREAMING_SNAKE_CASE  :							Any	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:Optional[int]										=      self.get_task_cmd(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)

					# 1. generate the checkpoint
					cmd += "--save_steps 1".split()
					# keep for quick debug
					# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] + cmd)); die
					execute_subprocess_async(SCREAMING_SNAKE_CASE      ,       env=self.get_env()	)

					# 2. test that the fp32 weights get reconsolidated
					SCREAMING_SNAKE_CASE_						:Dict										=      f'{output_dir}/checkpoint-1'
					SCREAMING_SNAKE_CASE_						:str										=      f'{chkpt_dir}/out.bin'
					SCREAMING_SNAKE_CASE_						:str										=      f'{chkpt_dir}/zero_to_fp32.py {chkpt_dir} {recovered_model_path}'
					# keep for quick debug
					# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
					subprocess.check_call(SCREAMING_SNAKE_CASE      ,       shell=SCREAMING_SNAKE_CASE	)
					assert os.path.exists(SCREAMING_SNAKE_CASE	), f'{recovered_model_path} was not found'

					# possibly could also test that the resulting saved model is usable but given that we use
					# random models we won't know if it's any good






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     A					:







							def __init__(    self   :	List[Any]   ,     __UpperCAmelCase   :	Optional[int]   ,     __UpperCAmelCase   :	Union[str, Any]=13   ,     __UpperCAmelCase   :	Optional[int]=64   ,     __UpperCAmelCase   :	List[Any]=4   ,     __UpperCAmelCase   :	Dict=3   ,     __UpperCAmelCase   :	Tuple=16   ,     __UpperCAmelCase   :	Union[str, Any]=[1, 2, 1]   ,     __UpperCAmelCase   :	Tuple=[2, 4, 8]   ,     __UpperCAmelCase   :	int=3   ,     __UpperCAmelCase   :	Optional[Any]=[[3], [1, 2], [1]]   ,     __UpperCAmelCase   :	Union[str, Any]=2.0   ,     __UpperCAmelCase   :	Optional[int]=True   ,     __UpperCAmelCase   :	List[str]=0.0   ,     __UpperCAmelCase   :	str=0.0   ,     __UpperCAmelCase   :	Tuple=0.1   ,     __UpperCAmelCase   :	Optional[int]="gelu"   ,     __UpperCAmelCase   :	int=True   ,     __UpperCAmelCase   :	Tuple=0.02   ,     __UpperCAmelCase   :	List[Any]=1E-5   ,     __UpperCAmelCase   :	Union[str, Any]=True   ,     __UpperCAmelCase   :	Tuple=None   ,     __UpperCAmelCase   :	Optional[Any]=True   ,     __UpperCAmelCase   :	Optional[int]=10   ,     __UpperCAmelCase   :	Optional[Any]=["stage1", "stage2"]   ,     __UpperCAmelCase   :	Tuple=[1, 2]   ,     )		->					int:

										"""simple docstring"""



										UpperCamelCase_     				= parent
										UpperCamelCase_     				= batch_size
										UpperCamelCase_     				= image_size
										UpperCamelCase_     				= patch_size
										UpperCamelCase_     				= num_channels
										UpperCamelCase_     				= embed_dim
										UpperCamelCase_     				= depths
										UpperCamelCase_     				= num_heads
										UpperCamelCase_     				= kernel_size
										UpperCamelCase_     				= dilations
										UpperCamelCase_     				= mlp_ratio
										UpperCamelCase_     				= qkv_bias
										UpperCamelCase_     				= hidden_dropout_prob
										UpperCamelCase_     				= attention_probs_dropout_prob
										UpperCamelCase_     				= drop_path_rate
										UpperCamelCase_     				= hidden_act
										UpperCamelCase_     				= patch_norm
										UpperCamelCase_     				= layer_norm_eps
										UpperCamelCase_     				= initializer_range
										UpperCamelCase_     				= is_training
										UpperCamelCase_     				= scope
										UpperCamelCase_     				= use_labels
										UpperCamelCase_     				= num_labels
										UpperCamelCase_     				= out_features
										UpperCamelCase_     				= out_indices







							def        lowercase__							(    self   :	Dict				)		->					Tuple:

										"""simple docstring"""



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

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

										UpperCamelCase_     				= self.get_config()

										return config, pixel_values, labels







							def        lowercase__							(    self   :	Any				)		->					List[str]:

										"""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        lowercase__							(    self   :	str   ,     __UpperCAmelCase   :	Tuple   ,     __UpperCAmelCase   :	List[Any]   ,     __UpperCAmelCase   :	Optional[int]				)		->					Union[str, Any]:

										"""simple docstring"""



										UpperCamelCase_     				= DinatModel(config=__UpperCAmelCase				)
										model.to(__UpperCAmelCase				)
										model.eval()
										UpperCamelCase_     				= model(__UpperCAmelCase				)

										UpperCamelCase_     				= UpperCamelCase_     				= (config.image_size // config.patch_size) // (2 ** (len(config.depths				) - 1))
										UpperCamelCase_     				= 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        lowercase__							(    self   :	List[str]   ,     __UpperCAmelCase   :	Any   ,     __UpperCAmelCase   :	Optional[Any]   ,     __UpperCAmelCase   :	Optional[Any]				)		->					str:

										"""simple docstring"""



										UpperCamelCase_     				= DinatForImageClassification(__UpperCAmelCase				)
										model.to(__UpperCAmelCase				)
										model.eval()
										UpperCamelCase_     				= model(__UpperCAmelCase   ,     labels=__UpperCAmelCase				)
										self.parent.assertEqual(result.logits.shape   ,     (self.batch_size, self.num_labels)				)

										# test greyscale images
										UpperCamelCase_     				= 1
										UpperCamelCase_     				= DinatForImageClassification(__UpperCAmelCase				)
										model.to(__UpperCAmelCase				)
										model.eval()

										UpperCamelCase_     				= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
										UpperCamelCase_     				= model(__UpperCAmelCase				)
										self.parent.assertEqual(result.logits.shape   ,     (self.batch_size, self.num_labels)				)







							def        lowercase__							(    self   :	List[Any]   ,     __UpperCAmelCase   :	Dict   ,     __UpperCAmelCase   :	Union[str, Any]   ,     __UpperCAmelCase   :	Dict				)		->					Any:

										"""simple docstring"""



										UpperCamelCase_     				= DinatBackbone(config=__UpperCAmelCase				)
										model.to(__UpperCAmelCase				)
										model.eval()
										UpperCamelCase_     				= model(__UpperCAmelCase				)

										# 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
										UpperCamelCase_     				= None
										UpperCamelCase_     				= DinatBackbone(config=__UpperCAmelCase				)
										model.to(__UpperCAmelCase				)
										model.eval()
										UpperCamelCase_     				= model(__UpperCAmelCase				)

										# 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        lowercase__							(    self   :	int				)		->					Any:

										"""simple docstring"""



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







@require_natten
@require_torch
class     A					(				lowerCamelCase_  ,						lowerCamelCase_  ,						unittest.TestCase       ):
							_SCREAMING_SNAKE_CASE  :		int			  =       (
							    (
							        DinatModel,
							        DinatForImageClassification,
							        DinatBackbone,
							    )
							    if is_torch_available()
							    else ()
							)
							_SCREAMING_SNAKE_CASE  :		int			  =       (
							    {'''feature-extraction''': DinatModel, '''image-classification''': DinatForImageClassification}
							    if is_torch_available()
							    else {}
							)
							_SCREAMING_SNAKE_CASE  :		List[str]			  =       False

							_SCREAMING_SNAKE_CASE  :		Any			  =       False
							_SCREAMING_SNAKE_CASE  :		Dict			  =       False
							_SCREAMING_SNAKE_CASE  :		Union[str, Any]			  =       False
							_SCREAMING_SNAKE_CASE  :		Optional[Any]			  =       False







							def        lowercase__							(    self   :	Any				)		->					Any:

										"""simple docstring"""



										UpperCamelCase_     				= DinatModelTester(self				)
										UpperCamelCase_     				= ConfigTester(self   ,     config_class=__UpperCAmelCase   ,     embed_dim=37				)







							def        lowercase__							(    self   :	Optional[Any]				)		->					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        lowercase__							(    self   :	Dict				)		->					List[str]:

										"""simple docstring"""



										return







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

										"""simple docstring"""



										UpperCamelCase_     				= self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__UpperCAmelCase				)







							def        lowercase__							(    self   :	List[str]				)		->					List[Any]:

										"""simple docstring"""



										UpperCamelCase_     				= self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase				)







							def        lowercase__							(    self   :	Dict				)		->					Optional[int]:

										"""simple docstring"""



										UpperCamelCase_     				= self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_backbone(*__UpperCAmelCase				)







							@unittest.skip(reason='Dinat does not use inputs_embeds'				)
							def        lowercase__							(    self   :	Tuple				)		->					Optional[Any]:

										"""simple docstring"""



										pass







							@unittest.skip(reason='Dinat does not use feedforward chunking'				)
							def        lowercase__							(    self   :	List[Any]				)		->					List[Any]:

										"""simple docstring"""



										pass







							def        lowercase__							(    self   :	List[Any]				)		->					List[str]:

										"""simple docstring"""



										UpperCamelCase_				,   UpperCamelCase_     				= self.model_tester.prepare_config_and_inputs_for_common()

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







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

										"""simple docstring"""



										UpperCamelCase_				,   UpperCamelCase_     				= self.model_tester.prepare_config_and_inputs_for_common()

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

													UpperCamelCase_     				= ['pixel_values']
													self.assertListEqual(arg_names[:1]   ,     __UpperCAmelCase				)







							def        lowercase__							(    self   :	Dict				)		->					Optional[int]:

										"""simple docstring"""



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







							def        lowercase__							(    self   :	Optional[Any]   ,     __UpperCAmelCase   :	List[Any]   ,     __UpperCAmelCase   :	int   ,     __UpperCAmelCase   :	str   ,     __UpperCAmelCase   :	str				)		->					Tuple:

										"""simple docstring"""



										UpperCamelCase_     				= model_class(__UpperCAmelCase				)
										model.to(__UpperCAmelCase				)
										model.eval()

										with torch.no_grad():
													UpperCamelCase_     				= model(**self._prepare_for_class(__UpperCAmelCase   ,     __UpperCAmelCase				)				)

										UpperCamelCase_     				= outputs.hidden_states

										UpperCamelCase_     				= getattr(
										    self.model_tester   ,     'expected_num_hidden_layers'   ,     len(self.model_tester.depths				) + 1				)
										self.assertEqual(len(__UpperCAmelCase				)   ,     __UpperCAmelCase				)

										# Dinat has a different seq_length
										UpperCamelCase_     				= (
										    config.patch_size
										    if isinstance(config.patch_size   ,     collections.abc.Iterable				)
										    else (config.patch_size, config.patch_size)
										)

										UpperCamelCase_     				= image_size[0] // patch_size[0]
										UpperCamelCase_     				= 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":
													UpperCamelCase_     				= outputs.reshaped_hidden_states
													self.assertEqual(len(__UpperCAmelCase				)   ,     __UpperCAmelCase				)

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







							def        lowercase__							(    self   :	Tuple				)		->					List[str]:

										"""simple docstring"""



										UpperCamelCase_				,   UpperCamelCase_     				= self.model_tester.prepare_config_and_inputs_for_common()

										UpperCamelCase_     				= (
										    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:
													UpperCamelCase_     				= True
													self.check_hidden_states_output(__UpperCAmelCase   ,     __UpperCAmelCase   ,     __UpperCAmelCase   ,     __UpperCAmelCase				)

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

													self.check_hidden_states_output(__UpperCAmelCase   ,     __UpperCAmelCase   ,     __UpperCAmelCase   ,     __UpperCAmelCase				)







							@slow
							def        lowercase__							(    self   :	List[Any]				)		->					Dict:

										"""simple docstring"""



										for model_name in DINAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													UpperCamelCase_     				= DinatModel.from_pretrained(__UpperCAmelCase				)
													self.assertIsNotNone(__UpperCAmelCase				)







							def        lowercase__							(    self   :	List[Any]				)		->					str:

										"""simple docstring"""



										UpperCamelCase_				,   UpperCamelCase_     				= self.model_tester.prepare_config_and_inputs_for_common()

										UpperCamelCase_     				= _config_zero_init(__UpperCAmelCase				)
										for model_class in self.all_model_classes:
													UpperCamelCase_     				= model_class(config=__UpperCAmelCase				)
													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        lowercase__							(    self   :	Dict				)		->					Dict:

										"""simple docstring"""



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







							@slow
							def        lowercase__							(    self   :	Tuple				)		->					int:

										"""simple docstring"""



										UpperCamelCase_     				= DinatForImageClassification.from_pretrained('shi-labs/dinat-mini-in1k-224'				).to(__UpperCAmelCase				)
										UpperCamelCase_     				= self.default_image_processor

										UpperCamelCase_     				= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
										UpperCamelCase_     				= image_processor(images=__UpperCAmelCase   ,     return_tensors='pt'				).to(__UpperCAmelCase				)

										# forward pass
										with torch.no_grad():
													UpperCamelCase_     				= model(**__UpperCAmelCase				)

										# verify the logits
										UpperCamelCase_     				= torch.Size((1, 1000)				)
										self.assertEqual(outputs.logits.shape   ,     __UpperCAmelCase				)
										UpperCamelCase_     				= torch.tensor([-0.1_545, -0.7_667, 0.4_642]				).to(__UpperCAmelCase				)
										self.assertTrue(torch.allclose(outputs.logits[0, :3]   ,     __UpperCAmelCase   ,     atol=1E-4				)				)







@require_torch
@require_natten
class     A					(				unittest.TestCase  ,						lowerCamelCase_       ):
							_SCREAMING_SNAKE_CASE  :		str			  =       (DinatBackbone,) if is_torch_available() else ()
							_SCREAMING_SNAKE_CASE  :		int			  =       DinatConfig







							def        lowercase__							(    self   :	Optional[Any]				)		->					List[Any]:

										"""simple docstring"""



										UpperCamelCase_     				= DinatModelTester(self				)
