

'''simple docstring'''





import copy
import importlib.metadata
import json
import os
from dataclasses import dataclass
from typing import Any, Dict, Union

from packaging import version

from ..utils import is_torch_available, logging


if is_torch_available():
  import torch


__UpperCAmelCase    				=     logging.get_logger(__name__)





@dataclass
class A__    :



 """simple docstring"""







 def __init__(		self :	Tuple     ,						A_ :	str=False     ,						A_ :	Optional[Any]=False     ,						A_ :	Union[str, Any]=6.0     ,						A_ :	Dict=None     ,						A_ :	List[str]=False     ,						A_ :	Optional[int]=False     ,						A_ :	List[str]=None     ,						A_ :	Optional[int]="fp4"     ,						A_ :	List[Any]=False     ,						**A_ :	List[str]     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					Tuple        =    load_in_abit
      _lowerCAmelCase   :					Any        =    load_in_abit
      _lowerCAmelCase   :					str        =    llm_inta_threshold
      _lowerCAmelCase   :					Any        =    llm_inta_skip_modules
      _lowerCAmelCase   :					Optional[int]        =    llm_inta_enable_fpaa_cpu_offload
      _lowerCAmelCase   :					List[Any]        =    llm_inta_has_fpaa_weight
      _lowerCAmelCase   :					Tuple        =    bnb_abit_quant_type
      _lowerCAmelCase   :					int        =    bnb_abit_use_double_quant

      if bnb_abit_compute_dtype is None:
           _lowerCAmelCase   :					Any        =    torch.floataa
      elif isinstance(__UpperCamelCase     ,						__UpperCamelCase       ):
           _lowerCAmelCase   :					List[str]        =    getattr(__UpperCamelCase     ,						__UpperCamelCase       )
      elif isinstance(__UpperCamelCase     ,						torch.dtype       ):
           _lowerCAmelCase   :					List[Any]        =    bnb_abit_compute_dtype
      else:
           raise ValueError("bnb_4bit_compute_dtype must be a string or a torch.dtype"       )

      self.post_init()

 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      if not isinstance(self.llm_inta_threshold     ,						__UpperCamelCase       ):
           raise ValueError("llm_int8_threshold must be a float"       )

      if self.llm_inta_skip_modules is not None and not isinstance(self.llm_inta_skip_modules     ,						__UpperCamelCase       ):
           raise ValueError("llm_int8_skip_modules must be a list of strings"       )
      if not isinstance(self.llm_inta_enable_fpaa_cpu_offload     ,						__UpperCamelCase       ):
           raise ValueError("llm_int8_enable_fp32_cpu_offload must be a boolean"       )

      if not isinstance(self.llm_inta_has_fpaa_weight     ,						__UpperCamelCase       ):
           raise ValueError("llm_int8_has_fp16_weight must be a boolean"       )

      if self.bnb_abit_compute_dtype is not None and not isinstance(self.bnb_abit_compute_dtype     ,						torch.dtype       ):
           raise ValueError("bnb_4bit_compute_dtype must be torch.dtype"       )

      if not isinstance(self.bnb_abit_quant_type     ,						__UpperCamelCase       ):
           raise ValueError("bnb_4bit_quant_type must be a string"       )

      if not isinstance(self.bnb_abit_use_double_quant     ,						__UpperCamelCase       ):
           raise ValueError("bnb_4bit_use_double_quant must be a boolean"       )

      if self.load_in_abit and not version.parse(importlib.metadata.version("bitsandbytes"       )       ) >= version.parse(
          "0.39.0"       ):
           raise ValueError(
               "4 bit quantization requires bitsandbytes>=0.39.0 - please upgrade your bitsandbytes version"       )

 def    __magic_name__       (		self :	str       ):






      '''simple docstring'''




      return self.load_in_abit or self.load_in_abit

 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      if self.load_in_abit:
           return "llm_int8"
      elif self.load_in_abit and self.bnb_abit_quant_type == "fp4":
           return "fp4"
      elif self.load_in_abit and self.bnb_abit_quant_type == "nf4":
           return "nf4"
      else:
           return None

 @classmethod
 def    __magic_name__       (		cls :	Dict     ,						A_ :	Dict     ,						A_ :	Any     ,						**A_ :	Optional[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    cls(**__UpperCamelCase       )

      _lowerCAmelCase   :					Optional[Any]        =    []
      for key, value in kwargs.items():
           if hasattr(__UpperCamelCase     ,						__UpperCamelCase       ):
                setattr(__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase       )
                to_remove.append(__UpperCamelCase       )
      for key in to_remove:
           kwargs.pop(__UpperCamelCase     ,						__UpperCamelCase       )

      if return_unused_kwargs:
           return config, kwargs
      else:
           return config

 def    __magic_name__       (		self :	int     ,						A_ :	Union[str, os.PathLike]       ):






      '''simple docstring'''




      with open(__UpperCamelCase     ,						"w"     ,						encoding="utf-8"       ) as writer:
           _lowerCAmelCase   :					int        =    self.to_dict()
           _lowerCAmelCase   :					Tuple        =    json.dumps(__UpperCamelCase     ,						indent=2     ,						sort_keys=__UpperCamelCase       ) + "\n"

           writer.write(__UpperCamelCase       )

 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    copy.deepcopy(self.__dict__       )
      _lowerCAmelCase   :					Optional[Any]        =    str(output["bnb_4bit_compute_dtype"]       ).split("."       )[1]

      return output

 def __repr__(		self :	Union[str, Any]       ):






      '''simple docstring'''




      return F'''{self.__class__.__name__} {self.to_json_string()}'''

 def    __magic_name__       (		self :	Union[str, Any]     ,						A_ :	bool = True       ):






      '''simple docstring'''




      if use_diff is True:
           _lowerCAmelCase   :					List[Any]        =    self.to_diff_dict()
      else:
           _lowerCAmelCase   :					Optional[Any]        =    self.to_dict()
      return json.dumps(__UpperCamelCase     ,						indent=2     ,						sort_keys=__UpperCamelCase       ) + "\n"


 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    self.to_dict()

      # get the default config dict
      _lowerCAmelCase   :					Optional[Any]        =    BitsAndBytesConfig().to_dict()

      _lowerCAmelCase   :					Any        =    {}

      # only serialize values that differ from the default config
      for key, value in config_dict.items():
           if value != default_config_dict[key]:
                _lowerCAmelCase   :					int        =    value

      return serializable_config_dict









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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
  import torch


__UpperCAmelCase    				=     logging.get_logger(__name__)





class A__    ( A ):



 """simple docstring"""







 _lowercase     :					int      =						['''pixel_values''']

 def __init__(		self :	Union[str, Any]     ,						A_ :	bool = True     ,						A_ :	Optional[Dict[str, int]] = None     ,						A_ :	PILImageResampling = PILImageResampling.BILINEAR     ,						A_ :	bool = True     ,						A_ :	Dict[str, int] = None     ,						A_ :	bool = True     ,						A_ :	Union[int, float] = 1 / 2_5_5     ,						A_ :	bool = True     ,						A_ :	Optional[Union[float, List[float]]] = None     ,						A_ :	Optional[Union[float, List[float]]] = None     ,						**A_ :	Optional[Any]     ,						):






      '''simple docstring'''




      super().__init__(**A_       )
      _lowerCAmelCase   :					Optional[Any]        =    size if size is not None else {"shortest_edge": 2_5_6}
      _lowerCAmelCase   :					Optional[int]        =    get_size_dict(A_     ,						default_to_square=A_       )
      _lowerCAmelCase   :					Dict        =    crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
      _lowerCAmelCase   :					Tuple        =    get_size_dict(A_     ,						param_name="crop_size"       )
      _lowerCAmelCase   :					Dict        =    do_resize
      _lowerCAmelCase   :					Optional[Any]        =    size
      _lowerCAmelCase   :					Union[str, Any]        =    resample
      _lowerCAmelCase   :					List[Any]        =    do_center_crop
      _lowerCAmelCase   :					str        =    crop_size
      _lowerCAmelCase   :					int        =    do_rescale
      _lowerCAmelCase   :					Tuple        =    rescale_factor
      _lowerCAmelCase   :					str        =    do_normalize
      _lowerCAmelCase   :					List[Any]        =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      _lowerCAmelCase   :					str        =    image_std if image_std is not None else IMAGENET_STANDARD_STD

 def    __magic_name__       (		self :	Dict     ,						A_ :	np.ndarray     ,						A_ :	Dict[str, int]     ,						A_ :	PILImageResampling = PILImageResampling.BICUBIC     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	Tuple     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    get_size_dict(A_     ,						default_to_square=A_       )
      if "shortest_edge" not in size:
           raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''       )
      _lowerCAmelCase   :					Optional[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    __magic_name__       (		self :	List[Any]     ,						A_ :	np.ndarray     ,						A_ :	Dict[str, int]     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	Dict     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    get_size_dict(A_       )
      if "height" not in size or "width" not in size:
           raise ValueError(F'''The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}'''       )
      return center_crop(A_     ,						size=(size["height"], size["width"])     ,						data_format=A_     ,						**A_       )

 def    __magic_name__       (		self :	int     ,						A_ :	np.ndarray     ,						A_ :	float     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	List[Any]       ):






      '''simple docstring'''




      return rescale(A_     ,						scale=A_     ,						data_format=A_     ,						**A_       )

 def    __magic_name__       (		self :	Tuple     ,						A_ :	np.ndarray     ,						A_ :	Union[float, List[float]]     ,						A_ :	Union[float, List[float]]     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	List[Any]     ,						):






      '''simple docstring'''




      return normalize(A_     ,						mean=A_     ,						std=A_     ,						data_format=A_     ,						**A_       )

 def    __magic_name__       (		self :	List[str]     ,						A_ :	ImageInput     ,						A_ :	Optional[bool] = None     ,						A_ :	Dict[str, int] = None     ,						A_ :	PILImageResampling = None     ,						A_ :	bool = None     ,						A_ :	Dict[str, int] = None     ,						A_ :	Optional[bool] = None     ,						A_ :	Optional[float] = None     ,						A_ :	Optional[bool] = None     ,						A_ :	Optional[Union[float, List[float]]] = None     ,						A_ :	Optional[Union[float, List[float]]] = None     ,						A_ :	Optional[Union[str, TensorType]] = None     ,						A_ :	Union[str, ChannelDimension] = ChannelDimension.FIRST     ,						**A_ :	int     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    do_resize if do_resize is not None else self.do_resize
      _lowerCAmelCase   :					Optional[Any]        =    size if size is not None else self.size
      _lowerCAmelCase   :					Optional[Any]        =    get_size_dict(A_     ,						default_to_square=A_       )
      _lowerCAmelCase   :					Tuple        =    resample if resample is not None else self.resample
      _lowerCAmelCase   :					str        =    do_center_crop if do_center_crop is not None else self.do_center_crop
      _lowerCAmelCase   :					List[Any]        =    crop_size if crop_size is not None else self.crop_size
      _lowerCAmelCase   :					str        =    get_size_dict(A_     ,						param_name="crop_size"       )
      _lowerCAmelCase   :					Union[str, Any]        =    do_rescale if do_rescale is not None else self.do_rescale
      _lowerCAmelCase   :					List[str]        =    rescale_factor if rescale_factor is not None else self.rescale_factor
      _lowerCAmelCase   :					int        =    do_normalize if do_normalize is not None else self.do_normalize
      _lowerCAmelCase   :					Optional[Any]        =    image_mean if image_mean is not None else self.image_mean
      _lowerCAmelCase   :					Any        =    image_std if image_std is not None else self.image_std

      _lowerCAmelCase   :					str        =    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_center_crop and crop_size is None:
           raise ValueError("Crop size must be specified if do_center_crop is True."       )

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

      if do_normalize and (image_mean is None or image_std is None):
           raise ValueError("Image mean and std must be specified if do_normalize is True."       )

      # All transformations expect numpy arrays.
      _lowerCAmelCase   :					Any        =    [to_numpy_array(A_       ) for image in images]

      if do_resize:
           _lowerCAmelCase   :					str        =    [self.resize(image=A_     ,						size=A_     ,						resample=A_       ) for image in images]

      if do_center_crop:
           _lowerCAmelCase   :					List[Any]        =    [self.center_crop(image=A_     ,						size=A_       ) for image in images]

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

      if do_normalize:
           _lowerCAmelCase   :					Tuple        =    [self.normalize(image=A_     ,						mean=A_     ,						std=A_       ) for image in images]

      _lowerCAmelCase   :					List[Any]        =    [to_channel_dimension_format(A_     ,						A_       ) for image in images]

      _lowerCAmelCase   :					Tuple        =    {"pixel_values": images}
      return BatchFeature(data=A_     ,						tensor_type=A_       )


 def    __magic_name__       (		self :	Optional[int]     ,						A_ :	Tuple     ,						A_ :	List[Tuple] = None       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    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_       ):
                _lowerCAmelCase   :					List[str]        =    target_sizes.numpy()

           _lowerCAmelCase   :					Tuple        =    []

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

      return semantic_segmentation






