


'''simple docstring'''
from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask


if is_tf_available():
					import tensorflow as tf

					from transformers import TFBlipTextModel
					from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST




class        _UpperCamelCase	:


				'''simple docstring'''


				def __init__(     self							,      a_							,      a_=1_2							,      a_=7							,      a_=True							,      a_=True							,      a_=True							,      a_=9_9							,      a_=3_2							,      a_=3_2							,      a_=2							,      a_=4							,      a_=3_7							,      a_=0.1							,      a_=0.1							,      a_=5_1_2							,      a_=0.02							,      a_=0							,      a_=None							,      )    ->  Union[str, Any]:
					lowercase						:		int   					=		parent
					lowercase						:		List[Any]   					=		batch_size
					lowercase						:		Any   					=		seq_length
					lowercase						:		List[Any]   					=		is_training
					lowercase						:		Tuple   					=		use_input_mask
					lowercase						:		int   					=		use_labels
					lowercase						:		List[str]   					=		vocab_size
					lowercase						:		List[Any]   					=		hidden_size
					lowercase						:		List[str]   					=		projection_dim
					lowercase						:		Dict   					=		num_hidden_layers
					lowercase						:		Optional[int]   					=		num_attention_heads
					lowercase						:		List[Any]   					=		intermediate_size
					lowercase						:		Any   					=		dropout
					lowercase						:		Optional[Any]   					=		attention_dropout
					lowercase						:		Tuple   					=		max_position_embeddings
					lowercase						:		List[str]   					=		initializer_range
					lowercase						:		Optional[int]   					=		scope
					lowercase						:		Optional[int]   					=		bos_token_id

				def 	a__		(     self							)    ->  int:
					lowercase						:		List[str]   					=		ids_tensor([self.batch_size, self.seq_length]							,      self.vocab_size							)

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

					if input_mask is not None:
						lowercase						:		Optional[int]   					=		input_mask.numpy()
						lowercase					,		lowercase						:		List[str]   					=		input_mask.shape
						lowercase						:		str   					=		np.random.randint(1							,      seq_length - 1							,      size=(batch_size,)							)
						for batch_idx, start_index in enumerate(lowercase_							):
							lowercase						:		Optional[int]   					=		1
							lowercase						:		List[str]   					=		0

					lowercase						:		List[str]   					=		self.get_config()

					return config, input_ids, tf.convert_to_tensor(lowercase_							)

				def 	a__		(     self							)    ->  List[Any]:
					return BlipTextConfig(
					    vocab_size=self.vocab_size							,      hidden_size=self.hidden_size							,      projection_dim=self.projection_dim							,      num_hidden_layers=self.num_hidden_layers							,      num_attention_heads=self.num_attention_heads							,      intermediate_size=self.intermediate_size							,      dropout=self.dropout							,      attention_dropout=self.attention_dropout							,      max_position_embeddings=self.max_position_embeddings							,      initializer_range=self.initializer_range							,      bos_token_id=self.bos_token_id							,      )

				def 	a__		(     self							,      a_							,      a_							,      a_							)    ->  List[Any]:
					lowercase						:		Any   					=		TFBlipTextModel(config=lowercase_							)
					lowercase						:		Optional[int]   					=		model(lowercase_							,      attention_mask=lowercase_							,      training=lowercase_							)
					lowercase						:		List[Any]   					=		model(lowercase_							,      training=lowercase_							)
					self.parent.assertEqual(result.last_hidden_state.shape							,      (self.batch_size, self.seq_length, self.hidden_size)							)
					self.parent.assertEqual(result.pooler_output.shape							,      (self.batch_size, self.hidden_size)							)


				def 	a__		(     self							)    ->  List[str]:
					lowercase						:		Any   					=		self.prepare_config_and_inputs()
					lowercase					,		lowercase					,		lowercase						:		List[str]   					=		config_and_inputs
					lowercase						:		Union[str, Any]   					=		{"input_ids": input_ids, "attention_mask": input_mask}
					return config, inputs_dict




@require_tf
class        _UpperCamelCase	(    lowercase__						,			unittest.TestCase):


				'''simple docstring'''


				_snake_case						       =				(TFBlipTextModel,) if is_tf_available() else ()
				_snake_case						       =				False
				_snake_case						       =				False
				_snake_case						       =				False

				def 	a__		(     self							)    ->  Optional[Any]:
					lowercase						:		int   					=		BlipTextModelTester(self							)
					lowercase						:		List[str]   					=		ConfigTester(self							,      config_class=lowercase_							,      hidden_size=3_7							)

				def 	a__		(     self							)    ->  Dict:
					self.config_tester.run_common_tests()

				def 	a__		(     self							)    ->  int:
					lowercase						:		Optional[int]   					=		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*lowercase_							)

				def 	a__		(     self							)    ->  Union[str, Any]:
					pass

				def 	a__		(     self							)    ->  Dict:
					pass

				@unittest.skip(reason="Blip does not use inputs_embeds"							)
				def 	a__		(     self							)    ->  Optional[Any]:
					pass

				@unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING"							)
				def 	a__		(     self							)    ->  List[Any]:
					pass

				@unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING"							)
				def 	a__		(     self							)    ->  Dict:
					pass

				@slow
				def 	a__		(     self							)    ->  Optional[int]:
					for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						lowercase						:		List[Any]   					=		TFBlipTextModel.from_pretrained(lowercase_							)
						self.assertIsNotNone(lowercase_							)


				def 	a__		(     self							,      a_=True							)    ->  str:
					super().test_pt_tf_model_equivalence(allow_missing_keys=lowercase_							)






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

