from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
      import PIL

if is_torch_available():
      import torch


lowercase_  : List[str]								=					logging.get_logger(__name__)






class 		_lowerCamelCase		( UpperCamelCase_     ):
    __a						=   ["pixel_values"]
    def __init__(	self			,			lowerCAmelCase = True			,			lowerCAmelCase = None			,			lowerCAmelCase = PILImageResampling.BILINEAR			,			lowerCAmelCase = True			,			lowerCAmelCase = 1 / 255			,			lowerCAmelCase = True			,			lowerCAmelCase = None			,			lowerCAmelCase = True			,			**lowerCAmelCase			,			)							->       None:
     super().__init__(**lowerCAmelCase )
     SCREAMING_SNAKE_CASE__: Tuple=   size if size is not None else {'''shortest_edge''': 224}
     SCREAMING_SNAKE_CASE__: Any=   get_size_dict(lowerCAmelCase			,			default_to_square=lowerCAmelCase )
     SCREAMING_SNAKE_CASE__: Optional[int]=   crop_size if crop_size is not None else {'''height''': 256, '''width''': 256}
     SCREAMING_SNAKE_CASE__: Tuple=   get_size_dict(lowerCAmelCase			,			param_name='''crop_size''' )

     SCREAMING_SNAKE_CASE__: List[str]=   do_resize
     SCREAMING_SNAKE_CASE__: List[Any]=   size
     SCREAMING_SNAKE_CASE__: Optional[int]=   resample
     SCREAMING_SNAKE_CASE__: Any=   do_rescale
     SCREAMING_SNAKE_CASE__: str=   rescale_factor
     SCREAMING_SNAKE_CASE__: str=   do_center_crop
     SCREAMING_SNAKE_CASE__: int=   crop_size
     SCREAMING_SNAKE_CASE__: List[str]=   do_flip_channel_order
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase = PIL.Image.BILINEAR			,			lowerCAmelCase = None			,			**lowerCAmelCase			,			)							->       np.ndarray:
     SCREAMING_SNAKE_CASE__: Any=   get_size_dict(lowerCAmelCase			,			default_to_square=lowerCAmelCase )
     if "shortest_edge" not in size:
      raise ValueError(f'The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}' )
     SCREAMING_SNAKE_CASE__: Dict=   get_resize_output_image_size(lowerCAmelCase			,			size=size['''shortest_edge''']			,			default_to_square=lowerCAmelCase )
     return resize(lowerCAmelCase			,			size=lowerCAmelCase			,			resample=lowerCAmelCase			,			data_format=lowerCAmelCase			,			**lowerCAmelCase )
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase = None			,			**lowerCAmelCase			,			)							->       np.ndarray:
     SCREAMING_SNAKE_CASE__: Optional[Any]=   get_size_dict(lowerCAmelCase )
     if "height" not in size or "width" not in size:
      raise ValueError(f'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}' )
     return center_crop(lowerCAmelCase			,			size=(size['''height'''], size['''width'''])			,			data_format=lowerCAmelCase			,			**lowerCAmelCase )
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase = None			,			**lowerCAmelCase			,			)							->       List[Any]:
     return rescale(lowerCAmelCase			,			scale=lowerCAmelCase			,			data_format=lowerCAmelCase			,			**lowerCAmelCase )
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase = None )							->       np.ndarray:
     return flip_channel_order(lowerCAmelCase			,			data_format=lowerCAmelCase )
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase = None			,			lowerCAmelCase = None			,			lowerCAmelCase = None			,			lowerCAmelCase = None			,			lowerCAmelCase = None			,			lowerCAmelCase = None			,			lowerCAmelCase = None			,			lowerCAmelCase = None			,			lowerCAmelCase = None			,			lowerCAmelCase = ChannelDimension.FIRST			,			**lowerCAmelCase			,			)							->       PIL.Image.Image:
     SCREAMING_SNAKE_CASE__: int=   do_resize if do_resize is not None else self.do_resize
     SCREAMING_SNAKE_CASE__: List[str]=   resample if resample is not None else self.resample
     SCREAMING_SNAKE_CASE__: List[str]=   do_rescale if do_rescale is not None else self.do_rescale
     SCREAMING_SNAKE_CASE__: Optional[int]=   rescale_factor if rescale_factor is not None else self.rescale_factor
     SCREAMING_SNAKE_CASE__: str=   do_center_crop if do_center_crop is not None else self.do_center_crop
     SCREAMING_SNAKE_CASE__: Optional[Any]=   (
         do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
     )

     SCREAMING_SNAKE_CASE__: List[Any]=   size if size is not None else self.size
     SCREAMING_SNAKE_CASE__: int=   get_size_dict(lowerCAmelCase			,			default_to_square=lowerCAmelCase )
     SCREAMING_SNAKE_CASE__: Optional[int]=   crop_size if crop_size is not None else self.crop_size
     SCREAMING_SNAKE_CASE__: Optional[Any]=   get_size_dict(lowerCAmelCase			,			param_name='''crop_size''' )

     SCREAMING_SNAKE_CASE__: Tuple=   make_list_of_images(lowerCAmelCase )

     if not valid_images(lowerCAmelCase ):
      raise ValueError(
          '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
          '''torch.Tensor, tf.Tensor or jax.ndarray.''' )

     if do_resize and size is None:
      raise ValueError('''Size must be specified if do_resize is True.''' )

     if do_rescale and rescale_factor is None:
      raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )

     if do_center_crop and crop_size is None:
      raise ValueError('''Crop size must be specified if do_center_crop is True.''' )

     # All transformations expect numpy arrays.
     SCREAMING_SNAKE_CASE__: Dict=   [to_numpy_array(lowerCAmelCase ) for image in images]

     if do_resize:
      SCREAMING_SNAKE_CASE__: Any=   [self.resize(image=lowerCAmelCase			,			size=lowerCAmelCase			,			resample=lowerCAmelCase ) for image in images]

     if do_center_crop:
      SCREAMING_SNAKE_CASE__: Dict=   [self.center_crop(image=lowerCAmelCase			,			size=lowerCAmelCase ) for image in images]

     if do_rescale:
      SCREAMING_SNAKE_CASE__: Dict=   [self.rescale(image=lowerCAmelCase			,			scale=lowerCAmelCase ) for image in images]

     # the pretrained checkpoints assume images are BGR, not RGB
     if do_flip_channel_order:
      SCREAMING_SNAKE_CASE__: Any=   [self.flip_channel_order(image=lowerCAmelCase ) for image in images]

     SCREAMING_SNAKE_CASE__: int=   [to_channel_dimension_format(lowerCAmelCase			,			lowerCAmelCase ) for image in images]

     SCREAMING_SNAKE_CASE__: Dict=   {'''pixel_values''': images}
     return BatchFeature(data=lowerCAmelCase			,			tensor_type=lowerCAmelCase )



    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase = None )							->       Union[str, Any]:
     SCREAMING_SNAKE_CASE__: Optional[Any]=   outputs.logits

     # Resize logits and compute semantic segmentation maps
     if target_sizes is not None:
      if len(lowerCAmelCase ) != len(lowerCAmelCase ):
       raise ValueError(
           '''Make sure that you pass in as many target sizes as the batch dimension of the logits''' )

      if is_torch_tensor(lowerCAmelCase ):
       SCREAMING_SNAKE_CASE__: Any=   target_sizes.numpy()

      SCREAMING_SNAKE_CASE__: Any=   []

      for idx in range(len(lowerCAmelCase ) ):
       SCREAMING_SNAKE_CASE__: str=   torch.nn.functional.interpolate(
           logits[idx].unsqueeze(dim=0 )			,			size=target_sizes[idx]			,			mode='''bilinear'''			,			align_corners=lowerCAmelCase )
       SCREAMING_SNAKE_CASE__: List[Any]=   resized_logits[0].argmax(dim=0 )
       semantic_segmentation.append(lowerCAmelCase )
     else:
      SCREAMING_SNAKE_CASE__: List[Any]=   logits.argmax(dim=1 )
      SCREAMING_SNAKE_CASE__: List[Any]=   [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]

     return semantic_segmentation





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

