
'''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


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


def A				(__lowerCamelCase				:str			,     __lowerCamelCase				:str		):
						if "xprophetnet" in prophetnet_checkpoint_path:
												_lowerCAmelCase  						=			XLMProphetNetForConditionalGenerationOld.from_pretrained(__lowerCamelCase		)
												_lowerCAmelCase						,   _lowerCAmelCase  						=			XLMProphetNetForConditionalGeneration.from_pretrained(
												    __lowerCamelCase			,     output_loading_info=__lowerCamelCase		)
						else:
												_lowerCAmelCase  						=			ProphetNetForConditionalGenerationOld.from_pretrained(__lowerCamelCase		)
												_lowerCAmelCase						,   _lowerCAmelCase  						=			ProphetNetForConditionalGeneration.from_pretrained(
												    __lowerCamelCase			,     output_loading_info=__lowerCamelCase		)

						_lowerCAmelCase  						=			["""key_proj""", """value_proj""", """query_proj"""]

						_lowerCAmelCase  						=			{
						    """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"]:
												_lowerCAmelCase  						=			key.split("""."""		)

												if attributes[0] == "lm_head":
																		_lowerCAmelCase  						=			prophet
																		_lowerCAmelCase  						=			prophet_old
												else:
																		_lowerCAmelCase  						=			prophet.prophetnet
																		_lowerCAmelCase  						=			prophet_old.model

												_lowerCAmelCase  						=			False
												for attribute in attributes:
																		if attribute in mapping:
																								_lowerCAmelCase  						=			mapping[attribute]
																								if not hasattr(__lowerCamelCase			,     __lowerCamelCase		) and len(__lowerCamelCase		) > 0:
																														_lowerCAmelCase  						=			attribute
																		elif hasattr(__lowerCamelCase			,     __lowerCamelCase		):
																								_lowerCAmelCase  						=			attribute

																		if attribute == "weight":
																								assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
																								_lowerCAmelCase  						=			old_model.weight
																								logger.info(f'{attribute} is initialized.'		)
																								_lowerCAmelCase  						=			True
																								break
																		elif attribute == "bias":
																								assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
																								_lowerCAmelCase  						=			old_model.bias
																								logger.info(f'{attribute} is initialized'		)
																								_lowerCAmelCase  						=			True
																								break
																		elif attribute in special_keys and hasattr(__lowerCamelCase			,     """in_proj_weight"""		):
																								_lowerCAmelCase  						=			old_model.in_proj_weight.shape[0] // 3
																								_lowerCAmelCase  						=			getattr(__lowerCamelCase			,     __lowerCamelCase		)
																								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":
																														_lowerCAmelCase  						=			nn.Parameter(old_model.in_proj_weight[:embed_dim, :]		)
																														_lowerCAmelCase  						=			nn.Parameter(old_model.in_proj_bias[:embed_dim]		)

																								elif attribute == "key_proj":
																														_lowerCAmelCase  						=			nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]		)
																														_lowerCAmelCase  						=			nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]		)
																								elif attribute == "value_proj":
																														_lowerCAmelCase  						=			nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]		)
																														_lowerCAmelCase  						=			nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]		)
																								_lowerCAmelCase  						=			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."
																								_lowerCAmelCase  						=			nn.Parameter(old_model.embed_positions.weight[:512, :]		)
																								_lowerCAmelCase  						=			True
																								break

																		if attribute.isdigit():
																								_lowerCAmelCase  						=			model[int(__lowerCamelCase		)]
																								_lowerCAmelCase  						=			old_model[int(__lowerCamelCase		)]
																		else:
																								_lowerCAmelCase  						=			getattr(__lowerCamelCase			,     __lowerCamelCase		)

																								if old_attribute == "":
																														_lowerCAmelCase  						=			old_model
																								else:
																														if not hasattr(__lowerCamelCase			,     __lowerCamelCase		):
																																				raise ValueError(f'{old_model} does not have {old_attribute}'		)
																														_lowerCAmelCase  						=			getattr(__lowerCamelCase			,     __lowerCamelCase		)

												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(__lowerCamelCase		)


if __name__ == "__main__":
						_lowercase    =		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."""
						)
						_lowercase    =		parser.parse_args()
						convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)


'''simple docstring'''



from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						def __init__( self   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



												super().__init__()

												# make sure scheduler can always be converted to DDIM
												_lowerCAmelCase  						=			DDIMScheduler.from_config(scheduler.config				)

												self.register_modules(unet=_lowercase   ,       scheduler=_lowercase				)




						@torch.no_grad()
						def __call__( self   ,       _lowercase = 1   ,       _lowercase = None   ,       _lowercase = 0.0   ,       _lowercase = 50   ,       _lowercase = None   ,       _lowercase = "pil"   ,       _lowercase = True   ,       ):

												"""simple docstring"""



												if isinstance(self.unet.config.sample_size   ,       _lowercase				):
																		_lowerCAmelCase  						=			(
																		    batch_size,
																		    self.unet.config.in_channels,
																		    self.unet.config.sample_size,
																		    self.unet.config.sample_size,
																		)
												else:
																		_lowerCAmelCase  						=			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

												if isinstance(_lowercase   ,       _lowercase				) and len(_lowercase				) != batch_size:
																		raise ValueError(
																		    F'You have passed a list of generators of length {len(_lowercase				)}, but requested an effective batch'
																		    F' size of {batch_size}. Make sure the batch size matches the length of the generators.'				)

												_lowerCAmelCase  						=			randn_tensor(_lowercase   ,       generator=_lowercase   ,       device=self.device   ,       dtype=self.unet.dtype				)

												# set step values
												self.scheduler.set_timesteps(_lowercase				)

												for t in self.progress_bar(self.scheduler.timesteps				):
																		# 1. predict noise model_output
																		_lowerCAmelCase  						=			self.unet(_lowercase   ,       _lowercase				).sample

																		# 2. predict previous mean of image x_t-1 and add variance depending on eta
																		# eta corresponds to η in paper and should be between [0, 1]
																		# do x_t -> x_t-1
																		_lowerCAmelCase  						=			self.scheduler.step(
																		    _lowercase   ,       _lowercase   ,       _lowercase   ,       eta=_lowercase   ,       use_clipped_model_output=_lowercase   ,       generator=_lowercase				).prev_sample

												_lowerCAmelCase  						=			(image / 2 + 0.5).clamp(0   ,       1				)
												_lowerCAmelCase  						=			image.cpu().permute(0   ,       2   ,       3   ,       1				).numpy()
												if output_type == "pil":
																		_lowerCAmelCase  						=			self.numpy_to_pil(_lowercase				)

												if not return_dict:
																		return (image,)

												return ImagePipelineOutput(images=_lowercase				)
