

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  __snake_case					(      snake_case__    ):
		__lowerCAmelCase       :		Union[str, Any]  	=					CONFIG_NAME

		@register_to_config
		def __init__(   self  ,						_A = True  ,						_A = 8  ,						_A = "lanczos"  ,						_A = True  ,						_A = False  ,						):
						super().__init__()

		@staticmethod
		def   lowerCAmelCase__       (   _A):
						if images.ndim == 3:
										SCREAMING_SNAKE_CASE_													=  images[None, ...]
						SCREAMING_SNAKE_CASE_													=  (images * 255).round().astype('uint8')
						if images.shape[-1] == 1:
										# special case for grayscale (single channel) images
										SCREAMING_SNAKE_CASE_													=  [Image.fromarray(image.squeeze()  ,						mode='L') for image in images]
						else:
										SCREAMING_SNAKE_CASE_													=  [Image.fromarray(_A) for image in images]

						return pil_images

		@staticmethod
		def   lowerCAmelCase__       (   _A):
						if not isinstance(_A  ,						_A):
										SCREAMING_SNAKE_CASE_													=  [images]
						SCREAMING_SNAKE_CASE_													=  [np.array(_A).astype(np.floataa) / 2_5_5.0 for image in images]
						SCREAMING_SNAKE_CASE_													=  np.stack(_A  ,						axis=0)

						return images

		@staticmethod
		def   lowerCAmelCase__       (   _A):
						if images.ndim == 3:
										SCREAMING_SNAKE_CASE_													=  images[..., None]

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

		@staticmethod
		def   lowerCAmelCase__       (   _A):
						SCREAMING_SNAKE_CASE_													=  images.cpu().permute(0  ,						2  ,						3  ,						1).float().numpy()
						return images

		@staticmethod
		def   lowerCAmelCase__       (   _A):
						return 2.0 * images - 1.0

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

		@staticmethod
		def   lowerCAmelCase__       (   _A):
						SCREAMING_SNAKE_CASE_													=  image.convert('RGB')
						return image

		def   lowerCAmelCase__       (   self  ,						_A  ,						_A = None  ,						_A = None  ,						):
						if height is None:
										SCREAMING_SNAKE_CASE_													=  image.height
						if width is None:
										SCREAMING_SNAKE_CASE_													=  image.width

						SCREAMING_SNAKE_CASE_													=  (
						    x - x % self.config.vae_scale_factor for x in (width, height)
						)  # resize to integer multiple of vae_scale_factor
						SCREAMING_SNAKE_CASE_													=  image.resize((width, height)  ,						resample=PIL_INTERPOLATION[self.config.resample])
						return image

		def   lowerCAmelCase__       (   self  ,						_A  ,						_A = None  ,						_A = None  ,						):
						SCREAMING_SNAKE_CASE_													=  (PIL.Image.Image, np.ndarray, torch.Tensor)
						if isinstance(_A  ,						_A):
										SCREAMING_SNAKE_CASE_													=  [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:
														SCREAMING_SNAKE_CASE_													=  [self.convert_to_rgb(_A) for i in image]
										if self.config.do_resize:
														SCREAMING_SNAKE_CASE_													=  [self.resize(_A  ,						_A  ,						_A) for i in image]
										SCREAMING_SNAKE_CASE_													=  self.pil_to_numpy(_A)  # to np
										SCREAMING_SNAKE_CASE_													=  self.numpy_to_pt(_A)  # to pt

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

						if do_normalize:
										SCREAMING_SNAKE_CASE_													=  self.normalize(_A)

						return image


		def   lowerCAmelCase__       (   self  ,						_A  ,						_A = "pil"  ,						_A = None  ,						):
						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"]:
										SCREAMING_SNAKE_CASE_													=  (
										    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)
										SCREAMING_SNAKE_CASE_													=  "np"

						if output_type == "latent":
										return image

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

						SCREAMING_SNAKE_CASE_													=  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

						SCREAMING_SNAKE_CASE_													=  self.pt_to_numpy(_A)

						if output_type == "np":
										return image

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






class  __snake_case					(      snake_case__    ):
		__lowerCAmelCase       :		str  	=					CONFIG_NAME

		@register_to_config
		def __init__(   self  ,						_A = True  ,						_A = 8  ,						_A = "lanczos"  ,						_A = True  ,						):
						super().__init__()

		@staticmethod
		def   lowerCAmelCase__       (   _A):
						if images.ndim == 3:
										SCREAMING_SNAKE_CASE_													=  images[None, ...]
						SCREAMING_SNAKE_CASE_													=  (images * 255).round().astype('uint8')
						if images.shape[-1] == 1:
										# special case for grayscale (single channel) images
										SCREAMING_SNAKE_CASE_													=  [Image.fromarray(image.squeeze()  ,						mode='L') for image in images]
						else:
										SCREAMING_SNAKE_CASE_													=  [Image.fromarray(image[:, :, :3]) for image in images]

						return pil_images

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

		def   lowerCAmelCase__       (   self  ,						_A):
						if images.ndim == 3:
										SCREAMING_SNAKE_CASE_													=  images[None, ...]
						SCREAMING_SNAKE_CASE_													=  images[:, :, :, 3:]
						if images.shape[-1] == 6:
										SCREAMING_SNAKE_CASE_													=  (images_depth * 255).round().astype('uint8')
										SCREAMING_SNAKE_CASE_													=  [
										    Image.fromarray(self.rgblike_to_depthmap(_A)  ,						mode='I;16') for image_depth in images_depth
										]
						elif images.shape[-1] == 4:
										SCREAMING_SNAKE_CASE_													=  (images_depth * 65535.0).astype(np.uintaa)
										SCREAMING_SNAKE_CASE_													=  [Image.fromarray(_A  ,						mode='I;16') for image_depth in images_depth]
						else:
										raise Exception('Not supported')

						return pil_images


		def   lowerCAmelCase__       (   self  ,						_A  ,						_A = "pil"  ,						_A = None  ,						):
						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"]:
										SCREAMING_SNAKE_CASE_													=  (
										    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)
										SCREAMING_SNAKE_CASE_													=  "np"

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

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

						SCREAMING_SNAKE_CASE_													=  self.pt_to_numpy(_A)

						if output_type == "np":
										if image.shape[-1] == 6:
														SCREAMING_SNAKE_CASE_													=  np.stack([self.rgblike_to_depthmap(im[:, :, 3:]) for im in image]  ,						axis=0)
										else:
														SCREAMING_SNAKE_CASE_													=  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 ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__					: Optional[int]								=      logging.get_logger(__name__)

UpperCamelCase__					: int								=      {
    "google/pegasus-x-base": "https://huggingface.co/google/pegasus-x-base/resolve/main/config.json",
    "google/pegasus-x-large": "https://huggingface.co/google/pegasus-x-large/resolve/main/config.json",
    # See all PEGASUS-X models at https://huggingface.co/models?filter=pegasus-x
}



class  __snake_case					(      lowerCAmelCase__    ):
  __lowerCAmelCase       :		List[Any]  	=					'pegasus_x'
  __lowerCAmelCase       :		Optional[int]  	=					['past_key_values']
  __lowerCAmelCase       :		List[str]  	=					{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}

  def __init__(   self  ,						_A=96103  ,						_A=16384  ,						_A=16  ,						_A=4096  ,						_A=16  ,						_A=16  ,						_A=4096  ,						_A=16  ,						_A=0.0  ,						_A=0.0  ,						_A=True  ,						_A=True  ,						_A="gelu"  ,						_A=1024  ,						_A=0.1  ,						_A=0.0  ,						_A=0.0  ,						_A=0.0_2  ,						_A=0  ,						_A=True  ,						_A=0  ,						_A=1  ,						_A=1  ,						_A=32  ,						_A=512  ,						_A=True  ,						**_A  ,						):
      SCREAMING_SNAKE_CASE_													=  vocab_size
      SCREAMING_SNAKE_CASE_													=  max_position_embeddings
      SCREAMING_SNAKE_CASE_													=  d_model
      SCREAMING_SNAKE_CASE_													=  encoder_ffn_dim
      SCREAMING_SNAKE_CASE_													=  encoder_layers
      SCREAMING_SNAKE_CASE_													=  encoder_attention_heads
      SCREAMING_SNAKE_CASE_													=  decoder_ffn_dim
      SCREAMING_SNAKE_CASE_													=  decoder_layers
      SCREAMING_SNAKE_CASE_													=  decoder_attention_heads
      SCREAMING_SNAKE_CASE_													=  dropout
      SCREAMING_SNAKE_CASE_													=  attention_dropout
      SCREAMING_SNAKE_CASE_													=  activation_dropout
      SCREAMING_SNAKE_CASE_													=  activation_function
      SCREAMING_SNAKE_CASE_													=  init_std
      SCREAMING_SNAKE_CASE_													=  encoder_layerdrop
      SCREAMING_SNAKE_CASE_													=  decoder_layerdrop
      SCREAMING_SNAKE_CASE_													=  use_cache
      SCREAMING_SNAKE_CASE_													=  encoder_layers
      SCREAMING_SNAKE_CASE_													=  scale_embedding  # scale factor will be sqrt(d_model) if True

      SCREAMING_SNAKE_CASE_													=  num_global_tokens
      SCREAMING_SNAKE_CASE_													=  block_size
      SCREAMING_SNAKE_CASE_													=  stagger_local_blocks

      super().__init__(
          pad_token_id=_A  ,						eos_token_id=_A  ,						is_encoder_decoder=_A  ,						decoder_start_token_id=_A  ,						forced_eos_token_id=_A  ,						**_A  ,						)

  @property
  def   lowerCAmelCase__       (   self):
      return self.encoder_attention_heads


  @property
  def   lowerCAmelCase__       (   self):
      return self.d_model


