
'''simple docstring'''





import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


UpperCAmelCase    					=   logging.get_logger(__name__)  # pylint: disable=invalid-name

def      A (       A_							:					Union[List, PIL.Image.Image, torch.Tensor]       ):
						warnings.warn(
						    '''The preprocess method is deprecated and will be removed in a future version. Please'''
						    ''' use VaeImageProcessor.preprocess instead'''    ,							A_    ,							)
						if isinstance(A_    ,							torch.Tensor       ):
												return image
						elif isinstance(A_    ,							PIL.Image.Image       ):
												snake_case       :     str =    [image]

						if isinstance(image[0]    ,							PIL.Image.Image       ):
												snake_case,					snake_case       :     int =    image[0].size
												snake_case,					snake_case       :     Any =    (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

												snake_case       :     str =    [np.array(i.resize((w, h)    ,							resample=PIL_INTERPOLATION['''lanczos''']       )       )[None, :] for i in image]
												snake_case       :     List[Any] =    np.concatenate(A_    ,							axis=0       )
												snake_case       :     Any =    np.array(A_       ).astype(np.floataa       ) / 255.0
												snake_case       :     Union[str, Any] =    image.transpose(0    ,							3    ,							1    ,							2       )
												snake_case       :     List[Any] =    2.0 * image - 1.0
												snake_case       :     List[Any] =    torch.from_numpy(A_       )
						elif isinstance(image[0]    ,							torch.Tensor       ):
												snake_case       :     int =    torch.cat(A_    ,							dim=0       )
						return image

def      A (       A_							:					Union[List, PIL.Image.Image, torch.Tensor]       ):
						if isinstance(A_    ,							torch.Tensor       ):
												return mask
						elif isinstance(A_    ,							PIL.Image.Image       ):
												snake_case       :     str =    [mask]

						if isinstance(mask[0]    ,							PIL.Image.Image       ):
												snake_case,					snake_case       :     Dict =    mask[0].size
												snake_case,					snake_case       :     int =    (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
												snake_case       :     List[Any] =    [np.array(m.convert('''L'''       ).resize((w, h)    ,							resample=PIL_INTERPOLATION['''nearest''']       )       )[None, :] for m in mask]
												snake_case       :     Dict =    np.concatenate(A_    ,							axis=0       )
												snake_case       :     Optional[int] =    mask.astype(np.floataa       ) / 255.0
												snake_case       :     str =    0
												snake_case       :     str =    1
												snake_case       :     Union[str, Any] =    torch.from_numpy(A_       )
						elif isinstance(mask[0]    ,							torch.Tensor       ):
												snake_case       :     Union[str, Any] =    torch.cat(A_    ,							dim=0       )
						return mask





class 						a   (				__magic_name__    ):
				_snake_case		    = 42
				_snake_case		    = 42




				def __init__(							self	:				List[Any],    SCREAMING_SNAKE_CASE_	:				Tuple,    SCREAMING_SNAKE_CASE_	:				int      ):
										super().__init__()
										self.register_modules(unet=SCREAMING_SNAKE_CASE_,    scheduler=SCREAMING_SNAKE_CASE_      )




				@torch.no_grad()
				def __call__(							self	:				int,    SCREAMING_SNAKE_CASE_	:				Union[torch.Tensor, PIL.Image.Image],    SCREAMING_SNAKE_CASE_	:				Union[torch.Tensor, PIL.Image.Image],    SCREAMING_SNAKE_CASE_	:				int = 2_50,    SCREAMING_SNAKE_CASE_	:				float = 0.0,    SCREAMING_SNAKE_CASE_	:				int = 10,    SCREAMING_SNAKE_CASE_	:				int = 10,    SCREAMING_SNAKE_CASE_	:				Optional[Union[torch.Generator, List[torch.Generator]]] = None,    SCREAMING_SNAKE_CASE_	:				Optional[str] = "pil",    SCREAMING_SNAKE_CASE_	:				bool = True,    ):
										snake_case       :     Union[str, Any] =    image

										snake_case       :     List[str] =    _preprocess_image(SCREAMING_SNAKE_CASE_      )
										snake_case       :     Union[str, Any] =    original_image.to(device=self.device,    dtype=self.unet.dtype      )
										snake_case       :     Dict =    _preprocess_mask(SCREAMING_SNAKE_CASE_      )
										snake_case       :     List[str] =    mask_image.to(device=self.device,    dtype=self.unet.dtype      )

										snake_case       :     Optional[int] =    original_image.shape[0]

										# sample gaussian noise to begin the loop
										if isinstance(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      ) and len(SCREAMING_SNAKE_CASE_      ) != batch_size:
																raise ValueError(
																    F"""You have passed a list of generators of length {len(SCREAMING_SNAKE_CASE_      )}, but requested an effective batch"""
																    F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""      )

										snake_case       :     Tuple =    original_image.shape
										snake_case       :     Optional[int] =    randn_tensor(SCREAMING_SNAKE_CASE_,    generator=SCREAMING_SNAKE_CASE_,    device=self.device,    dtype=self.unet.dtype      )

										# set step values
										self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    self.device      )
										snake_case       :     int =    eta

										snake_case       :     Optional[int] =    self.scheduler.timesteps[0] + 1
										snake_case       :     List[Any] =    generator[0] if isinstance(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      ) else generator
										for i, t in enumerate(self.progress_bar(self.scheduler.timesteps      )      ):
																if t < t_last:
																						# predict the noise residual
																						snake_case       :     List[str] =    self.unet(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      ).sample
																						# compute previous image: x_t -> x_t-1
																						snake_case       :     Any =    self.scheduler.step(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      ).prev_sample

																else:
																						# compute the reverse: x_t-1 -> x_t
																						snake_case       :     Tuple =    self.scheduler.undo_step(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      )
																snake_case       :     List[Any] =    t

										snake_case       :     int =    (image / 2 + 0.5).clamp(0,    1      )
										snake_case       :     Union[str, Any] =    image.cpu().permute(0,    2,    3,    1      ).numpy()
										if output_type == "pil":
																snake_case       :     List[Any] =    self.numpy_to_pil(SCREAMING_SNAKE_CASE_      )

										if not return_dict:
																return (image,)

										return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_      )



'''simple docstring'''





import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


UpperCAmelCase    					=   logging.get_logger(__name__)
logging.set_verbosity_info()

def      A (       A_							:					str    ,							A_							:					str       ):
						if "xprophetnet" in prophetnet_checkpoint_path:
												snake_case       :     Any =    XLMProphetNetForConditionalGenerationOld.from_pretrained(A_       )
												snake_case,					snake_case       :     Tuple =    XLMProphetNetForConditionalGeneration.from_pretrained(
												    A_    ,							output_loading_info=A_       )
						else:
												snake_case       :     Union[str, Any] =    ProphetNetForConditionalGenerationOld.from_pretrained(A_       )
												snake_case,					snake_case       :     List[Any] =    ProphetNetForConditionalGeneration.from_pretrained(
												    A_    ,							output_loading_info=A_       )

						snake_case       :     Union[str, Any] =    ['''key_proj''', '''value_proj''', '''query_proj''']

						snake_case       :     str =    {
						    '''self_attn''': '''ngram_self_attn''',
						    '''cross_attn''': '''encoder_attn''',
						    '''cross_attn_layer_norm''': '''encoder_attn_layer_norm''',
						    '''feed_forward_layer_norm''': '''final_layer_norm''',
						    '''feed_forward''': '''''',
						    '''intermediate''': '''fc1''',
						    '''output''': '''fc2''',
						    '''key_proj''': '''k_proj''',
						    '''query_proj''': '''q_proj''',
						    '''value_proj''': '''v_proj''',
						    '''word_embeddings''': '''embed_tokens''',
						    '''embeddings_layer_norm''': '''emb_layer_norm''',
						    '''relative_pos_embeddings''': '''relative_linear''',
						    '''ngram_embeddings''': '''ngram_input_embed''',
						    '''position_embeddings''': '''embed_positions''',
						}

						for key in loading_info["missing_keys"]:
												snake_case       :     Optional[Any] =    key.split('''.'''       )

												if attributes[0] == "lm_head":
																		snake_case       :     Optional[int] =    prophet
																		snake_case       :     Union[str, Any] =    prophet_old
												else:
																		snake_case       :     Optional[int] =    prophet.prophetnet
																		snake_case       :     Any =    prophet_old.model

												snake_case       :     Optional[Any] =    False
												for attribute in attributes:
																		if attribute in mapping:
																								snake_case       :     List[str] =    mapping[attribute]
																								if not hasattr(A_    ,							A_       ) and len(A_       ) > 0:
																														snake_case       :     str =    attribute
																		elif hasattr(A_    ,							A_       ):
																								snake_case       :     Any =    attribute

																		if attribute == "weight":
																								assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
																								snake_case       :     Optional[Any] =    old_model.weight
																								logger.info(F"""{attribute} is initialized."""       )
																								snake_case       :     Tuple =    True
																								break
																		elif attribute == "bias":
																								assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
																								snake_case       :     List[str] =    old_model.bias
																								logger.info(F"""{attribute} is initialized"""       )
																								snake_case       :     Tuple =    True
																								break
																		elif attribute in special_keys and hasattr(A_    ,							'''in_proj_weight'''       ):
																								snake_case       :     Union[str, Any] =    old_model.in_proj_weight.shape[0] // 3
																								snake_case       :     Any =    getattr(A_    ,							A_       )
																								param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
																								param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
																								if attribute == "query_proj":
																														snake_case       :     Tuple =    nn.Parameter(old_model.in_proj_weight[:embed_dim, :]       )
																														snake_case       :     List[Any] =    nn.Parameter(old_model.in_proj_bias[:embed_dim]       )

																								elif attribute == "key_proj":
																														snake_case       :     str =    nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]       )
																														snake_case       :     Any =    nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]       )
																								elif attribute == "value_proj":
																														snake_case       :     Tuple =    nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]       )
																														snake_case       :     List[str] =    nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]       )
																								snake_case       :     Optional[Any] =    True
																								break
																		elif attribute == "position_embeddings":
																								assert (
																								    model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
																								), "Hidden size has to match"
																								assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
																								snake_case       :     List[Any] =    nn.Parameter(old_model.embed_positions.weight[:512, :]       )
																								snake_case       :     Any =    True
																								break

																		if attribute.isdigit():
																								snake_case       :     Optional[Any] =    model[int(A_       )]
																								snake_case       :     List[str] =    old_model[int(A_       )]
																		else:
																								snake_case       :     Optional[Any] =    getattr(A_    ,							A_       )

																								if old_attribute == "":
																														snake_case       :     Union[str, Any] =    old_model
																								else:
																														if not hasattr(A_    ,							A_       ):
																																				raise ValueError(F"""{old_model} does not have {old_attribute}"""       )
																														snake_case       :     Tuple =    getattr(A_    ,							A_       )

												if not is_key_init:
																		raise ValueError(F"""{key} was not correctly initialized!"""       )

						print(F"""Saving model to {pytorch_dump_folder_path}"""       )
						prophet.save_pretrained(A_       )


if __name__ == "__main__":
						UpperCAmelCase    					=   argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--prophetnet_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
						)
						UpperCAmelCase    					=   parser.parse_args()
						convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)

