




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       _UpperCamelCase   (					_a		):
      UpperCAmelCase_          =		CONFIG_NAME
      @register_to_config
      def __init__(      self :Any			,   lowerCamelCase :bool = True			,   lowerCamelCase :int = 8			,   lowerCamelCase :str = "lanczos"			,   lowerCamelCase :bool = True			,   lowerCamelCase :bool = False			,   )  ->      Dict:
       super().__init__()
      @staticmethod
      def 						UpperCAmelCase_    (      lowerCamelCase :np.ndarray   )  ->      Optional[int]:
       if images.ndim == 3:
        UpperCAmelCase__							=    images[None, ...]
       UpperCAmelCase__							=    (images * 255).round().astype("uint8"   )
       if images.shape[-1] == 1:
        # special case for grayscale (single channel) images
        UpperCAmelCase__							=    [Image.fromarray(image.squeeze()			,   mode="L"   ) for image in images]
       else:
        UpperCAmelCase__							=    [Image.fromarray(_a   ) for image in images]

       return pil_images
      @staticmethod
      def 						UpperCAmelCase_    (      lowerCamelCase :Union[List[PIL.Image.Image], PIL.Image.Image]   )  ->      Tuple:
       if not isinstance(_a			,   _a   ):
        UpperCAmelCase__							=    [images]
       UpperCAmelCase__							=    [np.array(_a   ).astype(np.floataa   ) / 255.0 for image in images]
       UpperCAmelCase__							=    np.stack(_a			,   axis=0   )

       return images
      @staticmethod
      def 						UpperCAmelCase_    (      lowerCamelCase :np.ndarray   )  ->      Optional[int]:
       if images.ndim == 3:
        UpperCAmelCase__							=    images[..., None]

       UpperCAmelCase__							=    torch.from_numpy(images.transpose(0			,   3			,   1			,   2   )   )
       return images
      @staticmethod
      def 						UpperCAmelCase_    (      lowerCamelCase :torch.FloatTensor   )  ->      str:
       UpperCAmelCase__							=    images.cpu().permute(0			,   2			,   3			,   1   ).float().numpy()
       return images
      @staticmethod
      def 						UpperCAmelCase_    (      lowerCamelCase :List[Any]   )  ->      Optional[Any]:
       return 2.0 * images - 1.0
      @staticmethod
      def 						UpperCAmelCase_    (      lowerCamelCase :List[Any]   )  ->      Union[str, Any]:
       return (images / 2 + 0.5).clamp(0			,   1   )
      @staticmethod
      def 						UpperCAmelCase_    (      lowerCamelCase :PIL.Image.Image   )  ->      Dict:
       UpperCAmelCase__							=    image.convert("RGB"   )
       return image
      def 						UpperCAmelCase_    (      self :str			,   lowerCamelCase :PIL.Image.Image			,   lowerCamelCase :Optional[int] = None			,   lowerCamelCase :Optional[int] = None			,   )  ->      List[str]:
       if height is None:
        UpperCAmelCase__							=    image.height
       if width is None:
        UpperCAmelCase__							=    image.width

       UpperCAmelCase__							=    (
           x - x % self.config.vae_scale_factor for x in (width, height)
       )  # resize to integer multiple of vae_scale_factor
       UpperCAmelCase__							=    image.resize((width, height)			,   resample=PIL_INTERPOLATION[self.config.resample]   )
       return image
      def 						UpperCAmelCase_    (      self :Any			,   lowerCamelCase :Union[torch.FloatTensor, PIL.Image.Image, np.ndarray]			,   lowerCamelCase :Optional[int] = None			,   lowerCamelCase :Optional[int] = None			,   )  ->      Union[str, Any]:
       UpperCAmelCase__							=    (PIL.Image.Image, np.ndarray, torch.Tensor)
       if isinstance(_a			,   _a   ):
        UpperCAmelCase__							=    [image]
       elif not (isinstance(_a			,   _a   ) and all(isinstance(_a			,   _a   ) for i in image   )):
        raise ValueError(
            f'''Input is in incorrect format: {[type(_a   ) for i in image]}. Currently, we only support {", ".join(_a   )}'''   )

       if isinstance(image[0]			,   PIL.Image.Image   ):
        if self.config.do_convert_rgb:
         UpperCAmelCase__							=    [self.convert_to_rgb(_a   ) for i in image]
        if self.config.do_resize:
         UpperCAmelCase__							=    [self.resize(_a			,   _a			,   _a   ) for i in image]
        UpperCAmelCase__							=    self.pil_to_numpy(_a   )  # to np
        UpperCAmelCase__							=    self.numpy_to_pt(_a   )  # to pt

       elif isinstance(image[0]			,   np.ndarray   ):
        UpperCAmelCase__							=    np.concatenate(_a			,   axis=0   ) if image[0].ndim == 4 else np.stack(_a			,   axis=0   )
        UpperCAmelCase__							=    self.numpy_to_pt(_a   )
        UpperCAmelCase__							=    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   ):
        UpperCAmelCase__							=    torch.cat(_a			,   axis=0   ) if image[0].ndim == 4 else torch.stack(_a			,   axis=0   )
        UpperCAmelCase__							=    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]
       UpperCAmelCase__							=    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()}]'''			,   _a			,   )
        UpperCAmelCase__							=    False

       if do_normalize:
        UpperCAmelCase__							=    self.normalize(_a   )

       return image







      def 						UpperCAmelCase_    (      self :int			,   lowerCamelCase :torch.FloatTensor			,   lowerCamelCase :str = "pil"			,   lowerCamelCase :Optional[List[bool]] = None			,   )  ->      Optional[Any]:
       if not isinstance(_a			,   torch.Tensor   ):
        raise ValueError(
            f'''Input for postprocessing is in incorrect format: {type(_a   )}. We only support pytorch tensor'''   )
       if output_type not in ["latent", "pt", "np", "pil"]:
        UpperCAmelCase__							=    (
            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"			,   _a			,   standard_warn=_a   )
        UpperCAmelCase__							=    "np"

       if output_type == "latent":
        return image

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

       UpperCAmelCase__							=    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

       UpperCAmelCase__							=    self.pt_to_numpy(_a   )

       if output_type == "np":
        return image

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



class       _UpperCamelCase   (					_a		):
      UpperCAmelCase_          =		CONFIG_NAME
      @register_to_config
      def __init__(      self :Optional[int]			,   lowerCamelCase :bool = True			,   lowerCamelCase :int = 8			,   lowerCamelCase :str = "lanczos"			,   lowerCamelCase :bool = True			,   )  ->      Dict:
       super().__init__()
      @staticmethod
      def 						UpperCAmelCase_    (      lowerCamelCase :str   )  ->      Union[str, Any]:
       if images.ndim == 3:
        UpperCAmelCase__							=    images[None, ...]
       UpperCAmelCase__							=    (images * 255).round().astype("uint8"   )
       if images.shape[-1] == 1:
        # special case for grayscale (single channel) images
        UpperCAmelCase__							=    [Image.fromarray(image.squeeze()			,   mode="L"   ) for image in images]
       else:
        UpperCAmelCase__							=    [Image.fromarray(image[:, :, :3]   ) for image in images]

       return pil_images
      @staticmethod
      def 						UpperCAmelCase_    (      lowerCamelCase :List[Any]   )  ->      str:
       return image[:, :, 1] * 2**8 + image[:, :, 2]
      def 						UpperCAmelCase_    (      self :Any			,   lowerCamelCase :Optional[Any]   )  ->      int:
       if images.ndim == 3:
        UpperCAmelCase__							=    images[None, ...]
       UpperCAmelCase__							=    images[:, :, :, 3:]
       if images.shape[-1] == 6:
        UpperCAmelCase__							=    (images_depth * 255).round().astype("uint8"   )
        UpperCAmelCase__							=    [
            Image.fromarray(self.rgblike_to_depthmap(_a   )			,   mode="I;16"   ) for image_depth in images_depth
        ]
       elif images.shape[-1] == 4:
        UpperCAmelCase__							=    (images_depth * 6_5535.0).astype(np.uintaa   )
        UpperCAmelCase__							=    [Image.fromarray(_a			,   mode="I;16"   ) for image_depth in images_depth]
       else:
        raise Exception("Not supported"   )

       return pil_images







      def 						UpperCAmelCase_    (      self :Optional[int]			,   lowerCamelCase :torch.FloatTensor			,   lowerCamelCase :str = "pil"			,   lowerCamelCase :Optional[List[bool]] = None			,   )  ->      List[Any]:
       if not isinstance(_a			,   torch.Tensor   ):
        raise ValueError(
            f'''Input for postprocessing is in incorrect format: {type(_a   )}. We only support pytorch tensor'''   )
       if output_type not in ["latent", "pt", "np", "pil"]:
        UpperCAmelCase__							=    (
            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"			,   _a			,   standard_warn=_a   )
        UpperCAmelCase__							=    "np"

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

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

       UpperCAmelCase__							=    self.pt_to_numpy(_a   )

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

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










from typing import TYPE_CHECKING

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


__UpperCAmelCase    	=  {
    '''configuration_ernie_m''': ['''ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ErnieMConfig'''],
}

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

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase    	=  [
		    '''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

		__UpperCAmelCase    	=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





