




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 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE ):

      """simple docstring"""

      lowerCAmelCase__      :			Tuple     =       CONFIG_NAME







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







      @staticmethod
      def    snake_case						(  snake_case       :			np.ndarray					):
          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(snake_case					) for image in images]

          return pil_images







      @staticmethod
      def    snake_case						(  snake_case       :			Union[List[PIL.Image.Image], PIL.Image.Image]					):
          if not isinstance(snake_case			,							snake_case					):
              __UpperCamelCase			=    [images]
          __UpperCamelCase			=    [np.array(snake_case					).astype(np.floataa					) / 2_55.0 for image in images]
          __UpperCamelCase			=    np.stack(snake_case			,							axis=0					)

          return images







      @staticmethod
      def    snake_case						(  snake_case       :			np.ndarray					):
          if images.ndim == 3:
              __UpperCamelCase			=    images[..., None]

          __UpperCamelCase			=    torch.from_numpy(images.transpose(0			,							3			,							1			,							2					)					)
          return images







      @staticmethod
      def    snake_case						(  snake_case       :			torch.FloatTensor					):
          __UpperCamelCase			=    images.cpu().permute(0			,							2			,							3			,							1					).float().numpy()
          return images







      @staticmethod
      def    snake_case						(  snake_case       :			List[Any]					):
          return 2.0 * images - 1.0







      @staticmethod
      def    snake_case						(  snake_case       :			str					):
          return (images / 2 + 0.5).clamp(0			,							1					)







      @staticmethod
      def    snake_case						(  snake_case       :			PIL.Image.Image					):
          __UpperCamelCase			=    image.convert('''RGB'''					)
          return image







      def    snake_case						(  self       :			Optional[Any]			,							snake_case       :			PIL.Image.Image			,							snake_case       :			Optional[int] = None			,							snake_case       :			Optional[int] = None			,							):
          if height is None:
              __UpperCamelCase			=    image.height
          if width is None:
              __UpperCamelCase			=    image.width

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

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

          elif isinstance(image[0]			,							np.ndarray					):
              __UpperCamelCase			=    np.concatenate(snake_case			,							axis=0					) if image[0].ndim == 4 else np.stack(snake_case			,							axis=0					)
              __UpperCamelCase			=    self.numpy_to_pt(snake_case					)
              __UpperCamelCase		,   __UpperCamelCase		,   __UpperCamelCase		,   __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(snake_case			,							axis=0					) if image[0].ndim == 4 else torch.stack(snake_case			,							axis=0					)
              __UpperCamelCase		,   __UpperCamelCase		,   __UpperCamelCase		,   __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()}]"			,							snake_case			,							)
              __UpperCamelCase			=    False

          if do_normalize:
              __UpperCamelCase			=    self.normalize(snake_case					)

          return image







      def    snake_case						(  self       :			Union[str, Any]			,							snake_case       :			torch.FloatTensor			,							snake_case       :			str = "pil"			,							snake_case       :			Optional[List[bool]] = None			,							):
          if not isinstance(snake_case			,							torch.Tensor					):
              raise ValueError(
                  F"Input for postprocessing is in incorrect format: {type(snake_case					)}. 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'''			,							snake_case			,							standard_warn=snake_case					)
              __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(snake_case					)

          if output_type == "np":
              return image

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







class 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE ):

      """simple docstring"""

      lowerCAmelCase__      :			Optional[Any]     =       CONFIG_NAME







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







      @staticmethod
      def    snake_case						(  snake_case       :			str					):
          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    snake_case						(  snake_case       :			str					):
          return image[:, :, 1] * 2**8 + image[:, :, 2]







      def    snake_case						(  self       :			Dict			,							snake_case       :			List[str]					):
          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(snake_case					)			,							mode='''I;16'''					) for image_depth in images_depth
              ]
          elif images.shape[-1] == 4:
              __UpperCamelCase			=    (images_depth * 6_55_35.0).astype(np.uintaa					)
              __UpperCamelCase			=    [Image.fromarray(snake_case			,							mode='''I;16'''					) for image_depth in images_depth]
          else:
              raise Exception('''Not supported'''					)

          return pil_images







      def    snake_case						(  self       :			Dict			,							snake_case       :			torch.FloatTensor			,							snake_case       :			str = "pil"			,							snake_case       :			Optional[List[bool]] = None			,							):
          if not isinstance(snake_case			,							torch.Tensor					):
              raise ValueError(
                  F"Input for postprocessing is in incorrect format: {type(snake_case					)}. 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'''			,							snake_case			,							standard_warn=snake_case					)
              __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(snake_case					)

          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(snake_case					), self.numpy_to_depth(snake_case					)
          else:
              raise Exception(F"This type {output_type} is not supported"					)







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase      		=					logging.get_logger(__name__)

UpperCAmelCase      		=					{
    '''flaubert/flaubert_small_cased''': '''https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/config.json''',
    '''flaubert/flaubert_base_uncased''': '''https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/config.json''',
    '''flaubert/flaubert_base_cased''': '''https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/config.json''',
    '''flaubert/flaubert_large_cased''': '''https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/config.json''',
}




class  __lowerCAmelCase	(							a_    ):



 '''simple docstring'''
 _lowerCamelCase    :						List[Any]					     =			'flaubert'
 _lowerCamelCase    :						List[str]					     =			{
     'hidden_size': 'emb_dim',
     'num_attention_heads': 'n_heads',
     'num_hidden_layers': 'n_layers',
     'n_words': 'vocab_size',  # For backward compatibility
 }

 def __init__(      self							:		List[Any]							,      lowerCamelCase							:		Tuple=False							,      lowerCamelCase							:		str=0.0							,      lowerCamelCase							:		int=3_0_1_4_5							,      lowerCamelCase							:		List[str]=2_0_4_8							,      lowerCamelCase							:		List[str]=1_2							,      lowerCamelCase							:		Dict=1_6							,      lowerCamelCase							:		Any=0.1							,      lowerCamelCase							:		List[str]=0.1							,      lowerCamelCase							:		List[str]=True							,      lowerCamelCase							:		Any=False							,      lowerCamelCase							:		Optional[int]=False							,      lowerCamelCase							:		int=False							,      lowerCamelCase							:		Optional[Any]=1							,      lowerCamelCase							:		int=True							,      lowerCamelCase							:		str=5_1_2							,      lowerCamelCase							:		Tuple=2_0_4_8**-0.5							,      lowerCamelCase							:		str=1e-12							,      lowerCamelCase							:		Any=0.0_2							,      lowerCamelCase							:		Dict=0							,      lowerCamelCase							:		Union[str, Any]=1							,      lowerCamelCase							:		List[Any]=2							,      lowerCamelCase							:		Union[str, Any]=3							,      lowerCamelCase							:		List[Any]=5							,      lowerCamelCase							:		int=True							,      lowerCamelCase							:		Dict="first"							,      lowerCamelCase							:		Optional[int]=True							,      lowerCamelCase							:		Any=None							,      lowerCamelCase							:		Optional[int]=True							,      lowerCamelCase							:		List[str]=0.1							,      lowerCamelCase							:		List[Any]=5							,      lowerCamelCase							:		Optional[Any]=5							,      lowerCamelCase							:		str=0							,      lowerCamelCase							:		Optional[int]=0							,      lowerCamelCase							:		List[str]=2							,      lowerCamelCase							:		Any=0							,      **lowerCamelCase							:		Tuple							,      )			->				Any:
        _UpperCamelCase  :							Optional[Any]	    =	pre_norm
        _UpperCamelCase  :							Optional[int]	    =	layerdrop
        _UpperCamelCase  :							Dict	    =	vocab_size
        _UpperCamelCase  :							Dict	    =	emb_dim
        _UpperCamelCase  :							int	    =	n_layers
        _UpperCamelCase  :							Tuple	    =	n_heads
        _UpperCamelCase  :							Tuple	    =	dropout
        _UpperCamelCase  :							Any	    =	attention_dropout
        _UpperCamelCase  :							Union[str, Any]	    =	gelu_activation
        _UpperCamelCase  :							Dict	    =	sinusoidal_embeddings
        _UpperCamelCase  :							str	    =	causal
        _UpperCamelCase  :							str	    =	asm
        _UpperCamelCase  :							Tuple	    =	n_langs
        _UpperCamelCase  :							Optional[int]	    =	use_lang_emb
        _UpperCamelCase  :							Optional[Any]	    =	layer_norm_eps
        _UpperCamelCase  :							Optional[Any]	    =	bos_index
        _UpperCamelCase  :							Dict	    =	eos_index
        _UpperCamelCase  :							Union[str, Any]	    =	pad_index
        _UpperCamelCase  :							Union[str, Any]	    =	unk_index
        _UpperCamelCase  :							Optional[Any]	    =	mask_index
        _UpperCamelCase  :							Any	    =	is_encoder
        _UpperCamelCase  :							List[str]	    =	max_position_embeddings
        _UpperCamelCase  :							Union[str, Any]	    =	embed_init_std
        _UpperCamelCase  :							Optional[int]	    =	init_std
        _UpperCamelCase  :							Any	    =	summary_type
        _UpperCamelCase  :							Any	    =	summary_use_proj
        _UpperCamelCase  :							Optional[int]	    =	summary_activation
        _UpperCamelCase  :							Union[str, Any]	    =	summary_proj_to_labels
        _UpperCamelCase  :							Dict	    =	summary_first_dropout
        _UpperCamelCase  :							Union[str, Any]	    =	start_n_top
        _UpperCamelCase  :							Any	    =	end_n_top
        _UpperCamelCase  :							List[Any]	    =	mask_token_id
        _UpperCamelCase  :							str	    =	lang_id

        if "n_words" in kwargs:
               _UpperCamelCase  :							Tuple	    =	kwargs['n_words']

        super().__init__(pad_token_id=lowerCamelCase							,      bos_token_id=lowerCamelCase							,      **lowerCamelCase	)







class  __lowerCAmelCase	(							a_    ):



 '''simple docstring'''

 @property
 def        __snake_case       (      self							:		str	)			->				Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
               _UpperCamelCase  :							int	    =	{0: 'batch', 1: 'choice', 2: 'sequence'}
        else:
               _UpperCamelCase  :							Dict	    =	{0: 'batch', 1: 'sequence'}
        return OrderedDict(
            [
                ('input_ids', dynamic_axis),
                ('attention_mask', dynamic_axis),
            ]	)



