
import numpy as np
from PIL import Image


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	) ->						np.ndarray:
    __lowercase		:			Dict	    =       np.array(__lowerCAmelCase	)
    if arr.shape[0] != arr.shape[1]:
        raise ValueError('''The input array is not a square matrix'''	)
    __lowercase		:			Dict	    =       0
    __lowercase		:			Any	    =       0
    __lowercase		:			List[str]	    =       0
    __lowercase		:			Optional[int]	    =       0

    # compute the shape of the output matrix
    __lowercase		:			int	    =       (arr.shape[0] - size) // stride + 1
    # initialize the output matrix with zeros of shape maxpool_shape
    __lowercase		:			Tuple	    =       np.zeros((maxpool_shape, maxpool_shape)	)

    while i < arr.shape[0]:
        if i + size > arr.shape[0]:
            # if the end of the matrix is reached, break
            break
        while j < arr.shape[1]:
            # if the end of the matrix is reached, break
            if j + size > arr.shape[1]:
                break
            # compute the maximum of the pooling matrix
            __lowercase		:			str	    =       np.max(arr[i : i + size, j : j + size]	)
            # shift the pooling matrix by stride of column pixels
            j += stride
            mat_j += 1

        # shift the pooling matrix by stride of row pixels
        i += stride
        mat_i += 1

        # reset the column index to 0
        __lowercase		:			int	    =       0
        __lowercase		:			List[str]	    =       0

    return updated_arr


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	) ->						np.ndarray:
    __lowercase		:			Optional[int]	    =       np.array(__lowerCAmelCase	)
    if arr.shape[0] != arr.shape[1]:
        raise ValueError('''The input array is not a square matrix'''	)
    __lowercase		:			Optional[int]	    =       0
    __lowercase		:			Any	    =       0
    __lowercase		:			Optional[int]	    =       0
    __lowercase		:			Optional[Any]	    =       0

    # compute the shape of the output matrix
    __lowercase		:			List[str]	    =       (arr.shape[0] - size) // stride + 1
    # initialize the output matrix with zeros of shape avgpool_shape
    __lowercase		:			Union[str, Any]	    =       np.zeros((avgpool_shape, avgpool_shape)	)

    while i < arr.shape[0]:
        # if the end of the matrix is reached, break
        if i + size > arr.shape[0]:
            break
        while j < arr.shape[1]:
            # if the end of the matrix is reached, break
            if j + size > arr.shape[1]:
                break
            # compute the average of the pooling matrix
            __lowercase		:			int	    =       int(np.average(arr[i : i + size, j : j + size]	)	)
            # shift the pooling matrix by stride of column pixels
            j += stride
            mat_j += 1

        # shift the pooling matrix by stride of row pixels
        i += stride
        mat_i += 1
        # reset the column index to 0
        __lowercase		:			Dict	    =       0
        __lowercase		:			List[Any]	    =       0

    return updated_arr


# Main Function
if __name__ == "__main__":
 from doctest import testmod

 testmod(name="avgpooling", verbose=True)

 # Loading the image
 __lowerCAmelCase					:			int			      =		Image.open("path_to_image")

 # Converting the image to numpy array and maxpooling, displaying the result
 # Ensure that the image is a square matrix

 Image.fromarray(maxpooling(np.array(image), size=3, stride=2)).show()

 # Converting the image to numpy array and averagepooling, displaying the result
 # Ensure that the image is a square matrix

 Image.fromarray(avgpooling(np.array(image), size=3, stride=2)).show()




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


