


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   A__							(						_a     ):
						UpperCAmelCase									=  CONFIG_NAME




						@register_to_config
						def __init__(				self    :					List[str] ,			_a    :					bool = True ,			_a    :					int = 8 ,			_a    :					str = "lanczos" ,			_a    :					bool = True ,			_a    :					bool = False ,			)							->   Optional[int]:


										"""simple docstring"""





										super().__init__()




						@staticmethod
						def 							__UpperCamelCase (				_a    :					np.ndarray     )							->   PIL.Image.Image:


										"""simple docstring"""





										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(_UpperCAmelCase     ) for image in images]

										return pil_images




						@staticmethod
						def 							__UpperCamelCase (				_a    :					Union[List[PIL.Image.Image], PIL.Image.Image]     )							->   np.ndarray:


										"""simple docstring"""





										if not isinstance(_UpperCAmelCase ,			_UpperCAmelCase     ):
														_SCREAMING_SNAKE_CASE	     =[images]
										_SCREAMING_SNAKE_CASE	     =[np.array(_UpperCAmelCase     ).astype(np.floataa     ) / 2_55.0 for image in images]
										_SCREAMING_SNAKE_CASE	     =np.stack(_UpperCAmelCase ,			axis=0     )

										return images




						@staticmethod
						def 							__UpperCamelCase (				_a    :					np.ndarray     )							->   torch.FloatTensor:


										"""simple docstring"""





										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 							__UpperCamelCase (				_a    :					torch.FloatTensor     )							->   np.ndarray:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =images.cpu().permute(0 ,			2 ,			3 ,			1     ).float().numpy()
										return images




						@staticmethod
						def 							__UpperCamelCase (				_a    :					List[Any]     )							->   List[Any]:


										"""simple docstring"""





										return 2.0 * images - 1.0




						@staticmethod
						def 							__UpperCamelCase (				_a    :					Any     )							->   List[str]:


										"""simple docstring"""





										return (images / 2 + 0.5).clamp(0 ,			1     )




						@staticmethod
						def 							__UpperCamelCase (				_a    :					PIL.Image.Image     )							->   PIL.Image.Image:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =image.convert('''RGB'''     )
										return image




						def 							__UpperCamelCase (				self    :					Tuple ,			_a    :					PIL.Image.Image ,			_a    :					Optional[int] = None ,			_a    :					Optional[int] = None ,			)							->   PIL.Image.Image:


										"""simple docstring"""





										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 							__UpperCamelCase (				self    :					Dict ,			_a    :					Union[torch.FloatTensor, PIL.Image.Image, np.ndarray] ,			_a    :					Optional[int] = None ,			_a    :					Optional[int] = None ,			)							->   torch.Tensor:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =(PIL.Image.Image, np.ndarray, torch.Tensor)
										if isinstance(_UpperCAmelCase ,			_UpperCAmelCase     ):
														_SCREAMING_SNAKE_CASE	     =[image]
										elif not (isinstance(_UpperCAmelCase ,			_UpperCAmelCase     ) and all(isinstance(_UpperCAmelCase ,			_UpperCAmelCase     ) for i in image     )):
														raise ValueError(
														    f"Input is in incorrect format: {[type(_UpperCAmelCase     ) for i in image]}. Currently, we only support {', '.join(_UpperCAmelCase     )}"     )

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

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

										if do_normalize:
														_SCREAMING_SNAKE_CASE	     =self.normalize(_UpperCAmelCase     )

										return image




						def 							__UpperCamelCase (				self    :					Union[str, Any] ,			_a    :					torch.FloatTensor ,			_a    :					str = "pil" ,			_a    :					Optional[List[bool]] = None ,			)							->   str:


										"""simple docstring"""





										if not isinstance(_UpperCAmelCase ,			torch.Tensor     ):
														raise ValueError(
														    f"Input for postprocessing is in incorrect format: {type(_UpperCAmelCase     )}. 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''' ,			_UpperCAmelCase ,			standard_warn=_UpperCAmelCase     )
														_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(_UpperCAmelCase     )

										if output_type == "np":
														return image

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

class   A__							(						_a     ):
						UpperCAmelCase									=  CONFIG_NAME




						@register_to_config
						def __init__(				self    :					int ,			_a    :					bool = True ,			_a    :					int = 8 ,			_a    :					str = "lanczos" ,			_a    :					bool = True ,			)							->   str:


										"""simple docstring"""





										super().__init__()




						@staticmethod
						def 							__UpperCamelCase (				_a    :					Any     )							->   Any:


										"""simple docstring"""





										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 							__UpperCamelCase (				_a    :					Dict     )							->   Any:


										"""simple docstring"""





										return image[:, :, 1] * 2**8 + image[:, :, 2]




						def 							__UpperCamelCase (				self    :					int ,			_a    :					Optional[int]     )							->   Any:


										"""simple docstring"""





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

										return pil_images




						def 							__UpperCamelCase (				self    :					Optional[int] ,			_a    :					torch.FloatTensor ,			_a    :					str = "pil" ,			_a    :					Optional[List[bool]] = None ,			)							->   str:


										"""simple docstring"""





										if not isinstance(_UpperCAmelCase ,			torch.Tensor     ):
														raise ValueError(
														    f"Input for postprocessing is in incorrect format: {type(_UpperCAmelCase     )}. 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''' ,			_UpperCAmelCase ,			standard_warn=_UpperCAmelCase     )
														_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(_UpperCAmelCase     )

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





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase					:     Optional[Any]					  =  logging.get_logger(__name__)

_lowerCamelCase					:     str					  =  {
    "google/reformer-crime-and-punishment": (
        "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/config.json"
    ),
    "google/reformer-enwik8": "https://huggingface.co/google/reformer-enwik8/resolve/main/config.json",
}
class 				__snake_case     (_a						):
				lowerCAmelCase__			=   "reformer"
				lowerCAmelCase__			=   ["past_buckets_states"]
				lowerCAmelCase__			=   {}



				def __init__(					self					:						Optional[int]						,		_UpperCAmelCase					:						str=64						,		_UpperCAmelCase					:						Tuple=["local", "lsh", "local", "lsh", "local", "lsh"]						,		_UpperCAmelCase					:						Optional[int]=1.0						,		_UpperCAmelCase					:						str=True						,		_UpperCAmelCase					:						List[Any]=[64, 64]						,		_UpperCAmelCase					:						str=[64, 192]						,		_UpperCAmelCase					:						List[Any]=0						,		_UpperCAmelCase					:						Optional[Any]=2						,		_UpperCAmelCase					:						Dict=512						,		_UpperCAmelCase					:						Optional[int]=None						,		_UpperCAmelCase					:						Tuple="relu"						,		_UpperCAmelCase					:						Tuple=0.05						,		_UpperCAmelCase					:						int=256						,		_UpperCAmelCase					:						List[str]=0.02						,		_UpperCAmelCase					:						Any=False						,		_UpperCAmelCase					:						Any=1E-12						,		_UpperCAmelCase					:						List[Any]=1						,		_UpperCAmelCase					:						Optional[Any]=0						,		_UpperCAmelCase					:						Union[str, Any]=0.05						,		_UpperCAmelCase					:						int=64						,		_UpperCAmelCase					:						List[str]=64						,		_UpperCAmelCase					:						Any=0.0						,		_UpperCAmelCase					:						Any=1						,		_UpperCAmelCase					:						Optional[Any]=0						,		_UpperCAmelCase					:						Union[str, Any]=4096						,		_UpperCAmelCase					:						Dict=12						,		_UpperCAmelCase					:						int=None						,		_UpperCAmelCase					:						List[Any]=1						,		_UpperCAmelCase					:						Union[str, Any]=0						,		_UpperCAmelCase					:						Any=320						,		_UpperCAmelCase					:						Any=False						,		_UpperCAmelCase					:						List[str]=True						,		_UpperCAmelCase					:						Optional[int]=None						,		**_UpperCAmelCase					:						Tuple						,		)     ->			List[str]:





								'''simple docstring'''




								_lowerCAmelCase			:			Union[str, Any]        =					hash_seed
								_lowerCAmelCase			:			int        =					vocab_size
								_lowerCAmelCase			:			Optional[Any]        =					attention_head_size
								_lowerCAmelCase			:			Dict        =					hidden_size
								_lowerCAmelCase			:			Union[str, Any]        =					num_attention_heads
								_lowerCAmelCase			:			int        =					num_hashes
								_lowerCAmelCase			:			Union[str, Any]        =					len(_UpperCAmelCase					)
								_lowerCAmelCase			:			int        =					tuple(_UpperCAmelCase					) if isinstance(_UpperCAmelCase						,		_UpperCAmelCase					) else num_buckets
								_lowerCAmelCase			:			Union[str, Any]        =					lsh_attn_chunk_length
								_lowerCAmelCase			:			List[str]        =					local_attn_chunk_length
								_lowerCAmelCase			:			int        =					lsh_num_chunks_after
								_lowerCAmelCase			:			Union[str, Any]        =					lsh_num_chunks_before
								_lowerCAmelCase			:			str        =					local_num_chunks_after
								_lowerCAmelCase			:			Dict        =					local_num_chunks_before
								_lowerCAmelCase			:			Any        =					hidden_act
								_lowerCAmelCase			:			List[str]        =					feed_forward_size
								_lowerCAmelCase			:			str        =					hidden_dropout_prob
								_lowerCAmelCase			:			Any        =					lsh_attention_probs_dropout_prob
								_lowerCAmelCase			:			Dict        =					local_attention_probs_dropout_prob
								_lowerCAmelCase			:			Tuple        =					max_position_embeddings
								_lowerCAmelCase			:			Any        =					initializer_range
								_lowerCAmelCase			:			List[str]        =					layer_norm_eps
								_lowerCAmelCase			:			Dict        =					axial_pos_embds
								_lowerCAmelCase			:			Union[str, Any]        =					tuple(_UpperCAmelCase					)
								_lowerCAmelCase			:			int        =					tuple(_UpperCAmelCase					)
								_lowerCAmelCase			:			List[str]        =					axial_norm_std
								_lowerCAmelCase			:			List[str]        =					chunk_size_lm_head
								_lowerCAmelCase			:			int        =					attn_layers
								_lowerCAmelCase			:			List[Any]        =					use_cache
								_lowerCAmelCase			:			Optional[int]        =					classifier_dropout
								super().__init__(
								    pad_token_id=_UpperCAmelCase						,		eos_token_id=_UpperCAmelCase						,		is_decoder=_UpperCAmelCase						,		tie_word_embeddings=_UpperCAmelCase						,		**_UpperCAmelCase						,		)

