

"""simple docstring"""





from .constants import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SAFE_WEIGHTS_INDEX_NAME,
    SAFE_WEIGHTS_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    TORCH_LAUNCH_PARAMS,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
)
from .dataclasses import (
    BnbQuantizationConfig,
    ComputeEnvironment,
    CustomDtype,
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    DynamoBackend,
    FPaRecipeKwargs,
    FullyShardedDataParallelPlugin,
    GradientAccumulationPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    KwargsHandler,
    LoggerType,
    MegatronLMPlugin,
    PrecisionType,
    ProjectConfiguration,
    RNGType,
    SageMakerDistributedType,
    TensorInformation,
    TorchDynamoPlugin,
)
from .environment import get_int_from_env, parse_choice_from_env, parse_flag_from_env
from .imports import (
    get_ccl_version,
    is_abit_bnb_available,
    is_abit_bnb_available,
    is_aim_available,
    is_bfaa_available,
    is_bnb_available,
    is_botoa_available,
    is_ccl_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_fpa_available,
    is_ipex_available,
    is_megatron_lm_available,
    is_mlflow_available,
    is_mps_available,
    is_npu_available,
    is_rich_available,
    is_safetensors_available,
    is_sagemaker_available,
    is_tensorboard_available,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)
from .modeling import (
    check_device_map,
    check_tied_parameters_in_config,
    check_tied_parameters_on_same_device,
    compute_module_sizes,
    convert_file_size_to_int,
    dtype_byte_size,
    find_tied_parameters,
    get_balanced_memory,
    get_max_layer_size,
    get_max_memory,
    get_mixed_precision_context_manager,
    id_tensor_storage,
    infer_auto_device_map,
    load_checkpoint_in_model,
    load_offloaded_weights,
    load_state_dict,
    named_module_tensors,
    retie_parameters,
    set_module_tensor_to_device,
    shard_checkpoint,
)
from .offload import (
    OffloadedWeightsLoader,
    PrefixedDataset,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
    save_offload_index,
)
from .operations import (
    broadcast,
    broadcast_object_list,
    concatenate,
    convert_outputs_to_fpaa,
    convert_to_fpaa,
    find_batch_size,
    find_device,
    gather,
    gather_object,
    get_data_structure,
    honor_type,
    initialize_tensors,
    is_namedtuple,
    is_tensor_information,
    is_torch_tensor,
    listify,
    pad_across_processes,
    recursively_apply,
    reduce,
    send_to_device,
    slice_tensors,
)
from .versions import compare_versions, is_torch_version


if is_deepspeed_available():
							from .deepspeed import (
							    DeepSpeedEngineWrapper,
							    DeepSpeedOptimizerWrapper,
							    DeepSpeedSchedulerWrapper,
							    DummyOptim,
							    DummyScheduler,
							    HfDeepSpeedConfig,
							)

from .bnb import has_abit_bnb_layers, load_and_quantize_model
from .fsdp_utils import load_fsdp_model, load_fsdp_optimizer, save_fsdp_model, save_fsdp_optimizer
from .launch import (
    PrepareForLaunch,
    _filter_args,
    prepare_deepspeed_cmd_env,
    prepare_multi_gpu_env,
    prepare_sagemager_args_inputs,
    prepare_simple_launcher_cmd_env,
    prepare_tpu,
)
from .megatron_lm import (
    AbstractTrainStep,
    BertTrainStep,
    GPTTrainStep,
    MegatronEngine,
    MegatronLMDummyDataLoader,
    MegatronLMDummyScheduler,
    MegatronLMOptimizerWrapper,
    MegatronLMSchedulerWrapper,
    TaTrainStep,
    avg_losses_across_data_parallel_group,
    gather_across_data_parallel_groups,
)
from .megatron_lm import initialize as megatron_lm_initialize
from .megatron_lm import prepare_data_loader as megatron_lm_prepare_data_loader
from .megatron_lm import prepare_model as megatron_lm_prepare_model
from .megatron_lm import prepare_optimizer as megatron_lm_prepare_optimizer
from .megatron_lm import prepare_scheduler as megatron_lm_prepare_scheduler
from .memory import find_executable_batch_size, release_memory
from .other import (
    extract_model_from_parallel,
    get_pretty_name,
    is_port_in_use,
    merge_dicts,
    patch_environment,
    save,
    wait_for_everyone,
    write_basic_config,
)
from .random import set_seed, synchronize_rng_state, synchronize_rng_states
from .torch_xla import install_xla
from .tqdm import tqdm
from .transformer_engine import convert_model, has_transformer_engine_layers







"""simple docstring"""





import inspect
import unittest

import numpy as np

from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor


if is_flax_available():
							import jax

							from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel

if is_vision_available():
							from PIL import Image

							from transformers import BeitImageProcessor







class 				SCREAMING_SNAKE_CASE					(		unittest.TestCase       ):






					"""simple docstring"""



					def __init__(       self			:				Optional[Any]   ,lowercase_			:				List[str]   ,lowercase_			:				Any=1_0_0   ,lowercase_			:				str=1_3   ,lowercase_			:				Any=3_0   ,lowercase_			:				Optional[int]=2   ,lowercase_			:				Dict=3   ,lowercase_			:				Optional[int]=True   ,lowercase_			:				Union[str, Any]=True   ,lowercase_			:				Optional[Any]=3_2   ,lowercase_			:				List[Any]=5   ,lowercase_			:				Any=4   ,lowercase_			:				Optional[int]=3_7   ,lowercase_			:				List[Any]="gelu"   ,lowercase_			:				Dict=0.1   ,lowercase_			:				List[Any]=0.1   ,lowercase_			:				int=1_0   ,lowercase_			:				int=0.02   ,lowercase_			:				List[str]=3   ,):
						lowerCAmelCase__	:						int  				=	parent
						lowerCAmelCase__	:						Dict  				=	vocab_size
						lowerCAmelCase__	:						List[Any]  				=	batch_size
						lowerCAmelCase__	:						Union[str, Any]  				=	image_size
						lowerCAmelCase__	:						Optional[int]  				=	patch_size
						lowerCAmelCase__	:						str  				=	num_channels
						lowerCAmelCase__	:						str  				=	is_training
						lowerCAmelCase__	:						Optional[Any]  				=	use_labels
						lowerCAmelCase__	:						Union[str, Any]  				=	hidden_size
						lowerCAmelCase__	:						Optional[Any]  				=	num_hidden_layers
						lowerCAmelCase__	:						Any  				=	num_attention_heads
						lowerCAmelCase__	:						Union[str, Any]  				=	intermediate_size
						lowerCAmelCase__	:						List[Any]  				=	hidden_act
						lowerCAmelCase__	:						str  				=	hidden_dropout_prob
						lowerCAmelCase__	:						List[Any]  				=	attention_probs_dropout_prob
						lowerCAmelCase__	:						Union[str, Any]  				=	type_sequence_label_size
						lowerCAmelCase__	:						Dict  				=	initializer_range

						# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
						lowerCAmelCase__	:						Any  				=	(image_size // patch_size) ** 2
						lowerCAmelCase__	:						str  				=	num_patches + 1







					def        __lowerCAmelCase (       self			:				Tuple		):
						lowerCAmelCase__	:						int  				=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

						lowerCAmelCase__	:						Optional[int]  				=	None
						if self.use_labels:
							lowerCAmelCase__	:						str  				=	ids_tensor([self.batch_size]   ,self.type_sequence_label_size		)

						lowerCAmelCase__	:						List[str]  				=	BeitConfig(
						    vocab_size=self.vocab_size   ,image_size=self.image_size   ,patch_size=self.patch_size   ,num_channels=self.num_channels   ,hidden_size=self.hidden_size   ,num_hidden_layers=self.num_hidden_layers   ,num_attention_heads=self.num_attention_heads   ,intermediate_size=self.intermediate_size   ,hidden_act=self.hidden_act   ,hidden_dropout_prob=self.hidden_dropout_prob   ,attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,is_decoder=lowercase_   ,initializer_range=self.initializer_range   ,)

						return config, pixel_values, labels







					def        __lowerCAmelCase (       self			:				Union[str, Any]   ,lowercase_			:				str   ,lowercase_			:				Dict   ,lowercase_			:				Any		):
						lowerCAmelCase__	:						Any  				=	FlaxBeitModel(config=lowercase_		)
						lowerCAmelCase__	:						str  				=	model(lowercase_		)
						self.parent.assertEqual(result.last_hidden_state.shape   ,(self.batch_size, self.seq_length, self.hidden_size)		)







					def        __lowerCAmelCase (       self			:				Dict   ,lowercase_			:				Any   ,lowercase_			:				Union[str, Any]   ,lowercase_			:				Any		):
						lowerCAmelCase__	:						Optional[int]  				=	FlaxBeitForMaskedImageModeling(config=lowercase_		)
						lowerCAmelCase__	:						str  				=	model(lowercase_		)
						self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.seq_length - 1, self.vocab_size)		)







					def        __lowerCAmelCase (       self			:				List[str]   ,lowercase_			:				str   ,lowercase_			:				List[str]   ,lowercase_			:				str		):
						lowerCAmelCase__	:						Dict  				=	self.type_sequence_label_size
						lowerCAmelCase__	:						Tuple  				=	FlaxBeitForImageClassification(config=lowercase_		)
						lowerCAmelCase__	:						List[str]  				=	model(lowercase_		)
						self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.type_sequence_label_size)		)

						# test greyscale images
						lowerCAmelCase__	:						int  				=	1
						lowerCAmelCase__	:						Tuple  				=	FlaxBeitForImageClassification(lowercase_		)

						lowerCAmelCase__	:						str  				=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
						lowerCAmelCase__	:						List[str]  				=	model(lowercase_		)







					def        __lowerCAmelCase (       self			:				List[Any]		):
						lowerCAmelCase__	:						List[str]  				=	self.prepare_config_and_inputs()
						(
						    (
						    lowerCAmelCase__
						)				,(
						    lowerCAmelCase__
						)				,(
						    lowerCAmelCase__
						)				,
						)	:						Optional[int]  				=	config_and_inputs
						lowerCAmelCase__	:						Dict  				=	{'''pixel_values''': pixel_values}
						return config, inputs_dict







@require_flax
class 				SCREAMING_SNAKE_CASE					(		a_    ,							unittest.TestCase       ):






					"""simple docstring"""



					lowercase__				 =       (
					    (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
					)







					def        __lowerCAmelCase (       self			:				int		):
						lowerCAmelCase__	:						Tuple  				=	FlaxBeitModelTester(self		)
						lowerCAmelCase__	:						str  				=	ConfigTester(self   ,config_class=lowercase_   ,has_text_modality=lowercase_   ,hidden_size=3_7		)







					def        __lowerCAmelCase (       self			:				Dict		):
						self.config_tester.run_common_tests()







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

						for model_class in self.all_model_classes:
							lowerCAmelCase__	:						List[str]  				=	model_class(lowercase_		)
							lowerCAmelCase__	:						Optional[int]  				=	inspect.signature(model.__call__		)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							lowerCAmelCase__	:						Tuple  				=	[*signature.parameters.keys()]

							lowerCAmelCase__	:						Optional[Any]  				=	['''pixel_values''']
							self.assertListEqual(arg_names[:1]   ,lowercase_		)







					def        __lowerCAmelCase (       self			:				Union[str, Any]		):
						lowerCAmelCase__				,lowerCAmelCase__	:						List[Any]  				=	self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
							with self.subTest(model_class.__name__		):
								lowerCAmelCase__	:						Union[str, Any]  				=	self._prepare_for_class(lowercase_   ,lowercase_		)
								lowerCAmelCase__	:						Dict  				=	model_class(lowercase_		)

								@jax.jit
								def model_jitted(lowercase_			:				Dict   ,**lowercase_			:				List[Any]		):
									return model(pixel_values=lowercase_   ,**lowercase_		)

								with self.subTest('''JIT Enabled'''		):
									lowerCAmelCase__	:						Any  				=	model_jitted(**lowercase_		).to_tuple()

								with self.subTest('''JIT Disabled'''		):
									with jax.disable_jit():
										lowerCAmelCase__	:						int  				=	model_jitted(**lowercase_		).to_tuple()

								self.assertEqual(len(lowercase_		)   ,len(lowercase_		)		)
								for jitted_output, output in zip(lowercase_   ,lowercase_		):
									self.assertEqual(jitted_output.shape   ,output.shape		)







					def        __lowerCAmelCase (       self			:				Any		):
						lowerCAmelCase__	:						Optional[int]  				=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*lowercase_		)







					def        __lowerCAmelCase (       self			:				str		):
						lowerCAmelCase__	:						Optional[Any]  				=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_lm(*lowercase_		)







					def        __lowerCAmelCase (       self			:				Any		):
						lowerCAmelCase__	:						Optional[Any]  				=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*lowercase_		)







					@slow
					def        __lowerCAmelCase (       self			:				List[str]		):
						for model_class_name in self.all_model_classes:
							lowerCAmelCase__	:						Tuple  				=	model_class_name.from_pretrained('''microsoft/beit-base-patch16-224'''		)
							lowerCAmelCase__	:						List[Any]  				=	model(np.ones((1, 3, 2_2_4, 2_2_4)		)		)
							self.assertIsNotNone(lowercase_		)

def   __SCREAMING_SNAKE_CASE  (      ):
	lowerCAmelCase__	:						List[str]  				=	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
	return image







@require_vision
@require_flax
class 				SCREAMING_SNAKE_CASE					(		unittest.TestCase       ):






					"""simple docstring"""



					@cached_property
					def        __lowerCAmelCase (       self			:				Dict		):
						return BeitImageProcessor.from_pretrained('''microsoft/beit-base-patch16-224'''		) if is_vision_available() else None







					@slow
					def        __lowerCAmelCase (       self			:				int		):
						lowerCAmelCase__	:						Tuple  				=	FlaxBeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k'''		)

						lowerCAmelCase__	:						List[str]  				=	self.default_image_processor
						lowerCAmelCase__	:						Optional[int]  				=	prepare_img()
						lowerCAmelCase__	:						Union[str, Any]  				=	image_processor(images=lowercase_   ,return_tensors='''np'''		).pixel_values

						# prepare bool_masked_pos
						lowerCAmelCase__	:						str  				=	np.ones((1, 1_9_6)   ,dtype=lowercase_		)

						# forward pass
						lowerCAmelCase__	:						Dict  				=	model(pixel_values=lowercase_   ,bool_masked_pos=lowercase_		)
						lowerCAmelCase__	:						str  				=	outputs.logits

						# verify the logits
						lowerCAmelCase__	:						Any  				=	(1, 1_9_6, 8_1_9_2)
						self.assertEqual(logits.shape   ,lowercase_		)

						lowerCAmelCase__	:						str  				=	np.array(
						    [[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]]		)

						self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3]   ,lowercase_   ,atol=1E-2		)		)







					@slow
					def        __lowerCAmelCase (       self			:				Optional[int]		):
						lowerCAmelCase__	:						List[str]  				=	FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224'''		)

						lowerCAmelCase__	:						Dict  				=	self.default_image_processor
						lowerCAmelCase__	:						Optional[Any]  				=	prepare_img()
						lowerCAmelCase__	:						Any  				=	image_processor(images=lowercase_   ,return_tensors='''np'''		)

						# forward pass
						lowerCAmelCase__	:						List[Any]  				=	model(**lowercase_		)
						lowerCAmelCase__	:						int  				=	outputs.logits

						# verify the logits
						lowerCAmelCase__	:						Dict  				=	(1, 1_0_0_0)
						self.assertEqual(logits.shape   ,lowercase_		)

						lowerCAmelCase__	:						Dict  				=	np.array([-1.2385, -1.0987, -1.0108]		)

						self.assertTrue(np.allclose(logits[0, :3]   ,lowercase_   ,atol=1E-4		)		)

						lowerCAmelCase__	:						Union[str, Any]  				=	2_8_1
						self.assertEqual(logits.argmax(-1		).item()   ,lowercase_		)







					@slow
					def        __lowerCAmelCase (       self			:				List[Any]		):
						lowerCAmelCase__	:						Tuple  				=	FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k'''		)

						lowerCAmelCase__	:						Union[str, Any]  				=	self.default_image_processor
						lowerCAmelCase__	:						str  				=	prepare_img()
						lowerCAmelCase__	:						Union[str, Any]  				=	image_processor(images=lowercase_   ,return_tensors='''np'''		)

						# forward pass
						lowerCAmelCase__	:						Tuple  				=	model(**lowercase_		)
						lowerCAmelCase__	:						int  				=	outputs.logits

						# verify the logits
						lowerCAmelCase__	:						Optional[int]  				=	(1, 2_1_8_4_1)
						self.assertEqual(logits.shape   ,lowercase_		)

						lowerCAmelCase__	:						Union[str, Any]  				=	np.array([1.6881, -0.2787, 0.5901]		)

						self.assertTrue(np.allclose(logits[0, :3]   ,lowercase_   ,atol=1E-4		)		)

						lowerCAmelCase__	:						Optional[int]  				=	2_3_9_6
						self.assertEqual(logits.argmax(-1		).item()   ,lowercase_		)




