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


SCREAMING_SNAKE_CASE__		:					Any						=					logging.get_logger(__name__)

class       snake_case					(      UpperCamelCase_						):
  lowercase_			=     ['pixel_values']




  def __init__(      self     :	List[Any]     , a_     :	bool = True     , a_     :	Dict[str, int] = None     , a_     :	PILImageResampling = PILImageResampling.BILINEAR     , a_     :	bool = True     , a_     :	Union[int, float] = 1 / 255     , a_     :	bool = True     , a_     :	Dict[str, int] = None     , a_     :	bool = True     , **a_     :	Any     , )->       None:




       """simple docstring"""
       super().__init__(**a_				)
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		size if size is not None else {'shortest_edge': 224}
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		get_size_dict(a_     , default_to_square=a_				)
       SCREAMING_SNAKE_CASE__				:				int			=		crop_size if crop_size is not None else {'height': 256, 'width': 256}
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		get_size_dict(a_     , param_name='crop_size'				)

       SCREAMING_SNAKE_CASE__				:				Any			=		do_resize
       SCREAMING_SNAKE_CASE__				:				Any			=		size
       SCREAMING_SNAKE_CASE__				:				int			=		resample
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		do_rescale
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		rescale_factor
       SCREAMING_SNAKE_CASE__				:				Tuple			=		do_center_crop
       SCREAMING_SNAKE_CASE__				:				Dict			=		crop_size
       SCREAMING_SNAKE_CASE__				:				str			=		do_flip_channel_order




  def 	__lowercase(      self     :	str     , a_     :	np.ndarray     , a_     :	Dict[str, int]     , a_     :	PILImageResampling = PIL.Image.BILINEAR     , a_     :	Optional[Union[str, ChannelDimension]] = None     , **a_     :	List[str]     , )->       np.ndarray:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				str			=		get_size_dict(a_     , default_to_square=a_				)
       if "shortest_edge" not in size:
            raise ValueError(F'''The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'''				)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		get_resize_output_image_size(a_     , size=size['shortest_edge']     , default_to_square=a_				)
       return resize(a_     , size=a_     , resample=a_     , data_format=a_     , **a_				)




  def 	__lowercase(      self     :	int     , a_     :	np.ndarray     , a_     :	Dict[str, int]     , a_     :	Optional[Union[str, ChannelDimension]] = None     , **a_     :	List[Any]     , )->       np.ndarray:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[str]			=		get_size_dict(a_				)
       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(a_     , size=(size['height'], size['width'])     , data_format=a_     , **a_				)




  def 	__lowercase(      self     :	Optional[int]     , a_     :	np.ndarray     , a_     :	Union[int, float]     , a_     :	Optional[Union[str, ChannelDimension]] = None     , **a_     :	List[Any]     , )->       Tuple:




       """simple docstring"""
       return rescale(a_     , scale=a_     , data_format=a_     , **a_				)




  def 	__lowercase(      self     :	str     , a_     :	np.ndarray     , a_     :	Optional[Union[str, ChannelDimension]] = None				)->       np.ndarray:




       """simple docstring"""
       return flip_channel_order(a_     , data_format=a_				)




  def 	__lowercase(      self     :	List[Any]     , a_     :	ImageInput     , a_     :	bool = None     , a_     :	Dict[str, int] = None     , a_     :	PILImageResampling = None     , a_     :	bool = None     , a_     :	float = None     , a_     :	bool = None     , a_     :	Dict[str, int] = None     , a_     :	bool = None     , a_     :	Optional[Union[str, TensorType]] = None     , a_     :	ChannelDimension = ChannelDimension.FIRST     , **a_     :	Union[str, Any]     , )->       PIL.Image.Image:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Any			=		do_resize if do_resize is not None else self.do_resize
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		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__				:				Dict			=		rescale_factor if rescale_factor is not None else self.rescale_factor
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		do_center_crop if do_center_crop is not None else self.do_center_crop
       SCREAMING_SNAKE_CASE__				:				Tuple			=		(
           do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
       )

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

       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		make_list_of_images(a_				)

       if not valid_images(a_				):
            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__				:				Tuple			=		[to_numpy_array(a_				) for image in images]

       if do_resize:
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		[self.resize(image=a_     , size=a_     , resample=a_				) for image in images]

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

       if do_rescale:
            SCREAMING_SNAKE_CASE__				:				List[Any]			=		[self.rescale(image=a_     , scale=a_				) for image in images]

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

       SCREAMING_SNAKE_CASE__				:				List[str]			=		[to_channel_dimension_format(a_     , a_				) for image in images]

       SCREAMING_SNAKE_CASE__				:				Tuple			=		{'pixel_values': images}
       return BatchFeature(data=a_     , tensor_type=a_				)




  def 	__lowercase(      self     :	List[Any]     , a_     :	Optional[Any]     , a_     :	List[Tuple] = None				)->       Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Dict			=		outputs.logits

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

            if is_torch_tensor(a_				):
                 SCREAMING_SNAKE_CASE__				:				Optional[int]			=		target_sizes.numpy()

            SCREAMING_SNAKE_CASE__				:				Dict			=		[]

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

       return semantic_segmentation




import os
import socket
from contextlib import contextmanager

import torch

from ..commands.config.default import write_basic_config  # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version


if is_deepspeed_available():
      from deepspeed import DeepSpeedEngine

if is_tpu_available(check_device=False):
      import torch_xla.core.xla_model as xm







def     _a				(      lowercase__  :			int ):







     '''simple docstring'''


     if is_torch_version('<'     ,  '2.0.0' ) or not hasattr(lowercase__     ,  '_dynamo' ):
          return False
     return isinstance(lowercase__     ,  torch._dynamo.eval_frame.OptimizedModule )







def     _a				(      lowercase__  :			Optional[Any]     ,  lowercase__  :			bool = True ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		(torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

     SCREAMING_SNAKE_CASE__				:				Dict			=		is_compiled_module(lowercase__ )
     if is_compiled:
          SCREAMING_SNAKE_CASE__				:				Tuple			=		model
          SCREAMING_SNAKE_CASE__				:				int			=		model._orig_mod

     if is_deepspeed_available():
          options += (DeepSpeedEngine,)

     while isinstance(lowercase__     ,  lowercase__ ):
          SCREAMING_SNAKE_CASE__				:				Any			=		model.module

     if not keep_fpaa_wrapper:
          SCREAMING_SNAKE_CASE__				:				List[Any]			=		getattr(lowercase__     ,  'forward' )
          SCREAMING_SNAKE_CASE__				:				str			=		model.__dict__.pop('_original_forward'     ,  lowercase__ )
          if original_forward is not None:
               while hasattr(lowercase__     ,  '__wrapped__' ):
                    SCREAMING_SNAKE_CASE__				:				Dict			=		forward.__wrapped__
                    if forward == original_forward:
                         break
               SCREAMING_SNAKE_CASE__				:				Dict			=		forward
          if getattr(lowercase__     ,  '_converted_to_transformer_engine'     ,  lowercase__ ):
               convert_model(lowercase__     ,  to_transformer_engine=lowercase__ )

     if is_compiled:
          SCREAMING_SNAKE_CASE__				:				List[Any]			=		model
          SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		compiled_model

     return model







def     _a				(      ):







     '''simple docstring'''


     PartialState().wait_for_everyone()







def     _a				(      lowercase__  :			str     ,  lowercase__  :			Optional[Any] ):







     '''simple docstring'''


     if PartialState().distributed_type == DistributedType.TPU:
          xm.save(lowercase__     ,  lowercase__ )
     elif PartialState().local_process_index == 0:
          torch.save(lowercase__     ,  lowercase__ )







@contextmanager
def     _a				(      **lowercase__  :			str ):







     '''simple docstring'''


     for key, value in kwargs.items():
          SCREAMING_SNAKE_CASE__				:				int			=		str(lowercase__ )

     yield

     for key in kwargs:
          if key.upper() in os.environ:
               del os.environ[key.upper()]







def     _a				(      lowercase__  :			Optional[Any] ):







     '''simple docstring'''


     if not hasattr(lowercase__     ,  '__qualname__' ) and not hasattr(lowercase__     ,  '__name__' ):
          SCREAMING_SNAKE_CASE__				:				Any			=		getattr(lowercase__     ,  '__class__'     ,  lowercase__ )
     if hasattr(lowercase__     ,  '__qualname__' ):
          return obj.__qualname__
     if hasattr(lowercase__     ,  '__name__' ):
          return obj.__name__
     return str(lowercase__ )







def     _a				(      lowercase__  :			List[str]     ,  lowercase__  :			List[Any] ):







     '''simple docstring'''


     for key, value in source.items():
          if isinstance(lowercase__     ,  lowercase__ ):
               SCREAMING_SNAKE_CASE__				:				List[str]			=		destination.setdefault(lowercase__     ,  {} )
               merge_dicts(lowercase__     ,  lowercase__ )
          else:
               SCREAMING_SNAKE_CASE__				:				List[Any]			=		value

     return destination







def     _a				(      lowercase__  :			int = None ):







     '''simple docstring'''


     if port is None:
          SCREAMING_SNAKE_CASE__				:				int			=		2_95_00
     with socket.socket(socket.AF_INET     ,  socket.SOCK_STREAM ) as s:
          return s.connect_ex(('localhost', port) ) == 0



