




'''simple docstring'''






import warnings
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate






class 					lowercase_			(	lowerCAmelCase_       ):
     A_       							= CONFIG_NAME





     @register_to_config
     def __init__(    self    :				Optional[int]    ,       __lowerCamelCase    :				bool = True    ,       __lowerCamelCase    :				int = 8    ,       __lowerCamelCase    :				str = "lanczos"    ,       __lowerCamelCase    :				bool = True    ,       __lowerCamelCase    :				bool = False    ,       ):
           super().__init__()





     @staticmethod
     def  _lowerCAmelCase			(    __lowerCamelCase    :				np.ndarray ):
           if images.ndim == 3:
                 snake_case__					:      Optional[Any]  			=						images[None, ...]
           snake_case__					:      Any  			=						(images * 255).round().astype('uint8' )
           if images.shape[-1] == 1:
                 # special case for grayscale (single channel) images
                 snake_case__					:      Optional[Any]  			=						[Image.fromarray(image.squeeze()    ,       mode='L' ) for image in images]
           else:
                 snake_case__					:      Union[str, Any]  			=						[Image.fromarray(__lowerCamelCase ) for image in images]

           return pil_images





     @staticmethod
     def  _lowerCAmelCase			(    __lowerCamelCase    :				Union[List[PIL.Image.Image], PIL.Image.Image] ):
           if not isinstance(__lowerCamelCase    ,       __lowerCamelCase ):
                 snake_case__					:      int  			=						[images]
           snake_case__					:      Tuple  			=						[np.array(__lowerCamelCase ).astype(np.floataa ) / 2_5_5.0 for image in images]
           snake_case__					:      List[Any]  			=						np.stack(__lowerCamelCase    ,       axis=0 )

           return images





     @staticmethod
     def  _lowerCAmelCase			(    __lowerCamelCase    :				np.ndarray ):
           if images.ndim == 3:
                 snake_case__					:      Dict  			=						images[..., None]

           snake_case__					:      List[str]  			=						torch.from_numpy(images.transpose(0    ,       3    ,       1    ,       2 ) )
           return images





     @staticmethod
     def  _lowerCAmelCase			(    __lowerCamelCase    :				torch.FloatTensor ):
           snake_case__					:      Tuple  			=						images.cpu().permute(0    ,       2    ,       3    ,       1 ).float().numpy()
           return images





     @staticmethod
     def  _lowerCAmelCase			(    __lowerCamelCase    :				Optional[Any] ):
           return 2.0 * images - 1.0





     @staticmethod
     def  _lowerCAmelCase			(    __lowerCamelCase    :				int ):
           return (images / 2 + 0.5).clamp(0    ,       1 )





     @staticmethod
     def  _lowerCAmelCase			(    __lowerCamelCase    :				PIL.Image.Image ):
           snake_case__					:      Tuple  			=						image.convert('RGB' )
           return image





     def  _lowerCAmelCase			(    self    :				int    ,       __lowerCamelCase    :				PIL.Image.Image    ,       __lowerCamelCase    :				Optional[int] = None    ,       __lowerCamelCase    :				Optional[int] = None    ,       ):
           if height is None:
                 snake_case__					:      List[Any]  			=						image.height
           if width is None:
                 snake_case__					:      Dict  			=						image.width

           snake_case__			,       snake_case__					:      Union[str, Any]  			=						(
               x - x % self.config.vae_scale_factor for x in (width, height)
           )  # resize to integer multiple of vae_scale_factor
           snake_case__					:      Dict  			=						image.resize((width, height)    ,       resample=PIL_INTERPOLATION[self.config.resample] )
           return image





     def  _lowerCAmelCase			(    self    :				List[str]    ,       __lowerCamelCase    :				Union[torch.FloatTensor, PIL.Image.Image, np.ndarray]    ,       __lowerCamelCase    :				Optional[int] = None    ,       __lowerCamelCase    :				Optional[int] = None    ,       ):
           snake_case__					:      Dict  			=						(PIL.Image.Image, np.ndarray, torch.Tensor)
           if isinstance(__lowerCamelCase    ,       __lowerCamelCase ):
                 snake_case__					:      Optional[int]  			=						[image]
           elif not (isinstance(__lowerCamelCase    ,       __lowerCamelCase ) and all(isinstance(__lowerCamelCase    ,       __lowerCamelCase ) for i in image )):
                 raise ValueError(
                     F"Input is in incorrect format: {[type(__lowerCamelCase ) for i in image]}. Currently, we only support {', '.join(__lowerCamelCase )}" )

           if isinstance(image[0]    ,       PIL.Image.Image ):
                 if self.config.do_convert_rgb:
                       snake_case__					:      str  			=						[self.convert_to_rgb(__lowerCamelCase ) for i in image]
                 if self.config.do_resize:
                       snake_case__					:      Optional[int]  			=						[self.resize(__lowerCamelCase    ,       __lowerCamelCase    ,       __lowerCamelCase ) for i in image]
                 snake_case__					:      Union[str, Any]  			=						self.pil_to_numpy(__lowerCamelCase )  # to np
                 snake_case__					:      Union[str, Any]  			=						self.numpy_to_pt(__lowerCamelCase )  # to pt

           elif isinstance(image[0]    ,       np.ndarray ):
                 snake_case__					:      str  			=						np.concatenate(__lowerCamelCase    ,       axis=0 ) if image[0].ndim == 4 else np.stack(__lowerCamelCase    ,       axis=0 )
                 snake_case__					:      Tuple  			=						self.numpy_to_pt(__lowerCamelCase )
                 snake_case__			,       snake_case__			,       snake_case__			,       snake_case__					:      Any  			=						image.shape
                 if self.config.do_resize and (
                     height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
                 ):
                       raise ValueError(
                           F"Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}"
                           F"currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor" )

           elif isinstance(image[0]    ,       torch.Tensor ):
                 snake_case__					:      List[str]  			=						torch.cat(__lowerCamelCase    ,       axis=0 ) if image[0].ndim == 4 else torch.stack(__lowerCamelCase    ,       axis=0 )
                 snake_case__			,       snake_case__			,       snake_case__			,       snake_case__					:      Dict  			=						image.shape

                 # don't need any preprocess if the image is latents
                 if channel == 4:
                       return image

                 if self.config.do_resize and (
                     height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
                 ):
                       raise ValueError(
                           F"Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}"
                           F"currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor" )

        # expected range [0,1], normalize to [-1,1]
           snake_case__					:      str  			=						self.config.do_normalize
           if image.min() < 0:
                 warnings.warn(
                     'Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] '
                     F"when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]"    ,       __lowerCamelCase    ,       )
                 snake_case__					:      Dict  			=						False

           if do_normalize:
                 snake_case__					:      Optional[Any]  			=						self.normalize(__lowerCamelCase )

           return image





     def  _lowerCAmelCase			(    self    :				Optional[Any]    ,       __lowerCamelCase    :				torch.FloatTensor    ,       __lowerCamelCase    :				str = "pil"    ,       __lowerCamelCase    :				Optional[List[bool]] = None    ,       ):
           if not isinstance(__lowerCamelCase    ,       torch.Tensor ):
                 raise ValueError(
                     F"Input for postprocessing is in incorrect format: {type(__lowerCamelCase )}. We only support pytorch tensor" )
           if output_type not in ["latent", "pt", "np", "pil"]:
                 snake_case__					:      Optional[int]  			=						(
                     F"the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: "
                     '`pil`, `np`, `pt`, `latent`'
                 )
                 deprecate('Unsupported output_type'    ,       '1.0.0'    ,       __lowerCamelCase    ,       standard_warn=__lowerCamelCase )
                 snake_case__					:      Tuple  			=						'np'

           if output_type == "latent":
                 return image

           if do_denormalize is None:
                 snake_case__					:      str  			=						[self.config.do_normalize] * image.shape[0]

           snake_case__					:      List[Any]  			=						torch.stack(
               [self.denormalize(image[i] ) if do_denormalize[i] else image[i] for i in range(image.shape[0] )] )

           if output_type == "pt":
                 return image

           snake_case__					:      List[str]  			=						self.pt_to_numpy(__lowerCamelCase )

           if output_type == "np":
                 return image

           if output_type == "pil":
                 return self.numpy_to_pil(__lowerCamelCase )






class 					lowercase_			(	lowerCAmelCase_       ):
     A_       							= CONFIG_NAME





     @register_to_config
     def __init__(    self    :				Dict    ,       __lowerCamelCase    :				bool = True    ,       __lowerCamelCase    :				int = 8    ,       __lowerCamelCase    :				str = "lanczos"    ,       __lowerCamelCase    :				bool = True    ,       ):
           super().__init__()





     @staticmethod
     def  _lowerCAmelCase			(    __lowerCamelCase    :				str ):
           if images.ndim == 3:
                 snake_case__					:      Union[str, Any]  			=						images[None, ...]
           snake_case__					:      List[str]  			=						(images * 255).round().astype('uint8' )
           if images.shape[-1] == 1:
                 # special case for grayscale (single channel) images
                 snake_case__					:      List[str]  			=						[Image.fromarray(image.squeeze()    ,       mode='L' ) for image in images]
           else:
                 snake_case__					:      Optional[Any]  			=						[Image.fromarray(image[:, :, :3] ) for image in images]

           return pil_images





     @staticmethod
     def  _lowerCAmelCase			(    __lowerCamelCase    :				Any ):
           return image[:, :, 1] * 2**8 + image[:, :, 2]





     def  _lowerCAmelCase			(    self    :				Any    ,       __lowerCamelCase    :				List[str] ):
           if images.ndim == 3:
                 snake_case__					:      Tuple  			=						images[None, ...]
           snake_case__					:      Tuple  			=						images[:, :, :, 3:]
           if images.shape[-1] == 6:
                 snake_case__					:      List[str]  			=						(images_depth * 255).round().astype('uint8' )
                 snake_case__					:      str  			=						[
                     Image.fromarray(self.rgblike_to_depthmap(__lowerCamelCase )    ,       mode='I;16' ) for image_depth in images_depth
                 ]
           elif images.shape[-1] == 4:
                 snake_case__					:      Any  			=						(images_depth * 6_5_5_3_5.0).astype(np.uintaa )
                 snake_case__					:      Optional[int]  			=						[Image.fromarray(__lowerCamelCase    ,       mode='I;16' ) for image_depth in images_depth]
           else:
                 raise Exception('Not supported' )

           return pil_images





     def  _lowerCAmelCase			(    self    :				Any    ,       __lowerCamelCase    :				torch.FloatTensor    ,       __lowerCamelCase    :				str = "pil"    ,       __lowerCamelCase    :				Optional[List[bool]] = None    ,       ):
           if not isinstance(__lowerCamelCase    ,       torch.Tensor ):
                 raise ValueError(
                     F"Input for postprocessing is in incorrect format: {type(__lowerCamelCase )}. We only support pytorch tensor" )
           if output_type not in ["latent", "pt", "np", "pil"]:
                 snake_case__					:      List[str]  			=						(
                     F"the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: "
                     '`pil`, `np`, `pt`, `latent`'
                 )
                 deprecate('Unsupported output_type'    ,       '1.0.0'    ,       __lowerCamelCase    ,       standard_warn=__lowerCamelCase )
                 snake_case__					:      Any  			=						'np'

           if do_denormalize is None:
                 snake_case__					:      Optional[Any]  			=						[self.config.do_normalize] * image.shape[0]

           snake_case__					:      Optional[int]  			=						torch.stack(
               [self.denormalize(image[i] ) if do_denormalize[i] else image[i] for i in range(image.shape[0] )] )

           snake_case__					:      Union[str, Any]  			=						self.pt_to_numpy(__lowerCamelCase )

           if output_type == "np":
                 if image.shape[-1] == 6:
                       snake_case__					:      Optional[int]  			=						np.stack([self.rgblike_to_depthmap(im[:, :, 3:] ) for im in image]    ,       axis=0 )
                 else:
                       snake_case__					:      int  			=						image[:, :, :, 3:]
                 return image[:, :, :, :3], image_depth

           if output_type == "pil":
                 return self.numpy_to_pil(__lowerCamelCase ), self.numpy_to_depth(__lowerCamelCase )
           else:
                 raise Exception(F"This type {output_type} is not supported" )






'''simple docstring'''






from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


A_ 			=		{
    "configuration_ernie_m": ["ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP", "ErnieMConfig"],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A_ 			=		["ErnieMTokenizer"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A_ 			=		[
          "ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST",
          "ErnieMForMultipleChoice",
          "ErnieMForQuestionAnswering",
          "ErnieMForSequenceClassification",
          "ErnieMForTokenClassification",
          "ErnieMModel",
          "ErnieMPreTrainedModel",
          "ErnieMForInformationExtraction",
      ]


if TYPE_CHECKING:
      from .configuration_ernie_m import ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieMConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_ernie_m import ErnieMTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_ernie_m import (
                ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST,
                ErnieMForInformationExtraction,
                ErnieMForMultipleChoice,
                ErnieMForQuestionAnswering,
                ErnieMForSequenceClassification,
                ErnieMForTokenClassification,
                ErnieMModel,
                ErnieMPreTrainedModel,
            )


else:
      import sys

      A_ 			=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
