




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__       (   snake_case__ ):
						_a				:       Any   =       CONFIG_NAME






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




											"""simple docstring"""



											super().__init__()






						@staticmethod
						def 							__SCREAMING_SNAKE_CASE(  _A     ):




											"""simple docstring"""



											if images.ndim == 3:
																__lowerCAmelCase							  =			images[None, ...]
											__lowerCAmelCase							  =			(images * 2_5_5).round().astype("uint8"     )
											if images.shape[-1] == 1:
																# special case for grayscale (single channel) images
																__lowerCAmelCase							  =			[Image.fromarray(image.squeeze()						,     mode="L"     ) for image in images]
											else:
																__lowerCAmelCase							  =			[Image.fromarray(__lowercase     ) for image in images]

											return pil_images






						@staticmethod
						def 							__SCREAMING_SNAKE_CASE(  _A     ):




											"""simple docstring"""



											if not isinstance(__lowercase						,     __lowercase     ):
																__lowerCAmelCase							  =			[images]
											__lowerCAmelCase							  =			[np.array(__lowercase     ).astype(np.floataa     ) / 2_5_5.0 for image in images]
											__lowerCAmelCase							  =			np.stack(__lowercase						,     axis=0     )

											return images






						@staticmethod
						def 							__SCREAMING_SNAKE_CASE(  _A     ):




											"""simple docstring"""



											if images.ndim == 3:
																__lowerCAmelCase							  =			images[..., None]

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






						@staticmethod
						def 							__SCREAMING_SNAKE_CASE(  _A     ):




											"""simple docstring"""



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






						@staticmethod
						def 							__SCREAMING_SNAKE_CASE(  _A     ):




											"""simple docstring"""



											return 2.0 * images - 1.0






						@staticmethod
						def 							__SCREAMING_SNAKE_CASE(  _A     ):




											"""simple docstring"""



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






						@staticmethod
						def 							__SCREAMING_SNAKE_CASE(  _A     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			image.convert("RGB"     )
											return image






						def 							__SCREAMING_SNAKE_CASE(  self						,     _A						,     _A = None						,     _A = None						,     ):




											"""simple docstring"""



											if height is None:
																__lowerCAmelCase							  =			image.height
											if width is None:
																__lowerCAmelCase							  =			image.width

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






						def 							__SCREAMING_SNAKE_CASE(  self						,     _A						,     _A = None						,     _A = None						,     ):




											"""simple docstring"""



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

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

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

											if do_normalize:
																__lowerCAmelCase							  =			self.normalize(__lowercase     )

											return image






						def 							__SCREAMING_SNAKE_CASE(  self						,     _A						,     _A = "pil"						,     _A = None						,     ):




											"""simple docstring"""



											if not isinstance(__lowercase						,     torch.Tensor     ):
																raise ValueError(
																    f"""Input for postprocessing is in incorrect format: {type(__lowercase     )}. We only support pytorch tensor"""     )
											if output_type not in ["latent", "pt", "np", "pil"]:
																__lowerCAmelCase							  =			(
																    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"						,     __lowercase						,     standard_warn=__lowercase     )
																__lowerCAmelCase							  =			'np'

											if output_type == "latent":
																return image

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

											__lowerCAmelCase							  =			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

											__lowerCAmelCase							  =			self.pt_to_numpy(__lowercase     )

											if output_type == "np":
																return image

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






class      a__       (   snake_case__ ):
						_a				:       Union[str, Any]   =       CONFIG_NAME






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




											"""simple docstring"""



											super().__init__()






						@staticmethod
						def 							__SCREAMING_SNAKE_CASE(  _A     ):




											"""simple docstring"""



											if images.ndim == 3:
																__lowerCAmelCase							  =			images[None, ...]
											__lowerCAmelCase							  =			(images * 2_5_5).round().astype("uint8"     )
											if images.shape[-1] == 1:
																# special case for grayscale (single channel) images
																__lowerCAmelCase							  =			[Image.fromarray(image.squeeze()						,     mode="L"     ) for image in images]
											else:
																__lowerCAmelCase							  =			[Image.fromarray(image[:, :, :3]     ) for image in images]

											return pil_images






						@staticmethod
						def 							__SCREAMING_SNAKE_CASE(  _A     ):




											"""simple docstring"""



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






						def 							__SCREAMING_SNAKE_CASE(  self						,     _A     ):




											"""simple docstring"""



											if images.ndim == 3:
																__lowerCAmelCase							  =			images[None, ...]
											__lowerCAmelCase							  =			images[:, :, :, 3:]
											if images.shape[-1] == 6:
																__lowerCAmelCase							  =			(images_depth * 2_5_5).round().astype("uint8"     )
																__lowerCAmelCase							  =			[
																    Image.fromarray(self.rgblike_to_depthmap(__lowercase     )						,     mode="I;16"     ) for image_depth in images_depth
																]
											elif images.shape[-1] == 4:
																__lowerCAmelCase							  =			(images_depth * 6_5_5_3_5.0).astype(np.uintaa     )
																__lowerCAmelCase							  =			[Image.fromarray(__lowercase						,     mode="I;16"     ) for image_depth in images_depth]
											else:
																raise Exception("Not supported"     )

											return pil_images






						def 							__SCREAMING_SNAKE_CASE(  self						,     _A						,     _A = "pil"						,     _A = None						,     ):




											"""simple docstring"""



											if not isinstance(__lowercase						,     torch.Tensor     ):
																raise ValueError(
																    f"""Input for postprocessing is in incorrect format: {type(__lowercase     )}. We only support pytorch tensor"""     )
											if output_type not in ["latent", "pt", "np", "pil"]:
																__lowerCAmelCase							  =			(
																    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"						,     __lowercase						,     standard_warn=__lowercase     )
																__lowerCAmelCase							  =			'np'

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

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

											__lowerCAmelCase							  =			self.pt_to_numpy(__lowercase     )

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

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










import copy

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


UpperCamelCase__						=		logging.get_logger(__name__)

UpperCamelCase__						=		{}


class 							__SCREAMING_SNAKE_CASE				(						lowerCamelCase    ):
					_lowerCamelCase						    =					"""fsmt_decoder"""

					def __init__(							self					,			__lowercase=0					,			__lowercase=0   ):







						'''simple docstring'''




						super().__init__()
						lowerCamelCase_				:						Tuple				  =				vocab_size
						lowerCamelCase_				:						Union[str, Any]				  =				bos_token_id


class 							__SCREAMING_SNAKE_CASE				(						lowerCamelCase    ):
					_lowerCamelCase						    =					"""fsmt"""
					_lowerCamelCase						    =					{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}

					def __init__(							self					,			__lowercase=["en", "de"]					,			__lowercase=42_024					,			__lowercase=42_024					,			__lowercase="relu"					,			__lowercase=1_024					,			__lowercase=200					,			__lowercase=1_024					,			__lowercase=4_096					,			__lowercase=12					,			__lowercase=16					,			__lowercase=0.0					,			__lowercase=4_096					,			__lowercase=12					,			__lowercase=16					,			__lowercase=0.0					,			__lowercase=0.0					,			__lowercase=0.1					,			__lowercase=0.0					,			__lowercase=0.02					,			__lowercase=2					,			__lowercase=True					,			__lowercase=True					,			__lowercase=False					,			__lowercase=5					,			__lowercase=1.0					,			__lowercase=False					,			__lowercase=True					,			__lowercase=1					,			__lowercase=0					,			__lowercase=2					,			__lowercase=2					,			**__lowercase					,			):







						'''simple docstring'''




						lowerCamelCase_				:						Any				  =				langs
						lowerCamelCase_				:						Union[str, Any]				  =				src_vocab_size
						lowerCamelCase_				:						Tuple				  =				tgt_vocab_size
						lowerCamelCase_				:						List[Any]				  =				d_model  # encoder_embed_dim and decoder_embed_dim

						lowerCamelCase_				:						str				  =				encoder_ffn_dim
						lowerCamelCase_				:						str				  =				encoder_layers
						lowerCamelCase_				:						Dict				  =				encoder_attention_heads
						lowerCamelCase_				:						Any				  =				encoder_layerdrop
						lowerCamelCase_				:						Union[str, Any]				  =				decoder_layerdrop
						lowerCamelCase_				:						Optional[int]				  =				decoder_ffn_dim
						lowerCamelCase_				:						List[str]				  =				decoder_layers
						lowerCamelCase_				:						int				  =				decoder_attention_heads
						lowerCamelCase_				:						List[str]				  =				max_position_embeddings
						lowerCamelCase_				:						int				  =				init_std  # Normal(0, this parameter)
						lowerCamelCase_				:						Optional[Any]				  =				activation_function

						lowerCamelCase_				:						Union[str, Any]				  =				DecoderConfig(vocab_size=__lowercase					,			bos_token_id=__lowercase   )
						if "decoder" in common_kwargs:
							del common_kwargs["decoder"]

						lowerCamelCase_				:						int				  =				scale_embedding  # scale factor will be sqrt(d_model) if True

						# 3 Types of Dropout
						lowerCamelCase_				:						Optional[int]				  =				attention_dropout
						lowerCamelCase_				:						Union[str, Any]				  =				activation_dropout
						lowerCamelCase_				:						Dict				  =				dropout

						lowerCamelCase_				:						Optional[int]				  =				use_cache
						super().__init__(
						    pad_token_id=__lowercase					,			bos_token_id=__lowercase					,			eos_token_id=__lowercase					,			decoder_start_token_id=__lowercase					,			is_encoder_decoder=__lowercase					,			tie_word_embeddings=__lowercase					,			forced_eos_token_id=__lowercase					,			max_length=__lowercase					,			num_beams=__lowercase					,			length_penalty=__lowercase					,			early_stopping=__lowercase					,			**__lowercase					,			)





					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						List[Any]				  =				copy.deepcopy(self.__dict__   )
						lowerCamelCase_				:						Optional[int]				  =				self.decoder.to_dict()
						lowerCamelCase_				:						Dict				  =				self.__class__.model_type
						return output
