





import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput





def   lowerCamelCase__       (       UpperCamelCase__     :    Optional[Any]    ,      UpperCamelCase__     :    Tuple=0.999    ,      UpperCamelCase__     :    Optional[Any]="cosine"    ,      )     ->	int:



					'''simple docstring'''




					if alpha_transform_type == "cosine":

										def alpha_bar_fn(UpperCamelCase__     :    List[str]  ):
															return math.cos((t + 0.008) / 1.008 * math.pi / 2  ) ** 2

					elif alpha_transform_type == "exp":

										def alpha_bar_fn(UpperCamelCase__     :    Tuple  ):
															return math.exp(t * -12.0  )

					else:
										raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}'''  )

					_snake_case													=			[]
					for i in range(UpperCamelCase__  ):
										_snake_case													=			i / num_diffusion_timesteps
										_snake_case													=			(i + 1) / num_diffusion_timesteps
										betas.append(min(1 - alpha_bar_fn(UpperCamelCase__  ) / alpha_bar_fn(UpperCamelCase__  )    ,      UpperCamelCase__  )  )
					return torch.tensor(UpperCamelCase__    ,      dtype=torch.floataa  )




class      UpperCamelCase_						(					_lowerCamelCase							,					_lowerCamelCase   ):
					lowerCAmelCase_							=		[e.name for e in KarrasDiffusionSchedulers]
					lowerCAmelCase_							=		2




					@register_to_config
					def __init__(						self			,  lowerCAmelCase_ = 1000			,  lowerCAmelCase_ = 0.0_00_85			,  lowerCAmelCase_ = 0.0_12			,  lowerCAmelCase_ = "linear"			,  lowerCAmelCase_ = None			,  lowerCAmelCase_ = "epsilon"			,  lowerCAmelCase_ = "linspace"			,  lowerCAmelCase_ = 0			,  )						->							Optional[Any]:
										if trained_betas is not None:
															_snake_case													=			torch.tensor(lowerCAmelCase_			,  dtype=torch.floataa					)
										elif beta_schedule == "linear":
															_snake_case													=			torch.linspace(lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  dtype=torch.floataa					)
										elif beta_schedule == "scaled_linear":
															# this schedule is very specific to the latent diffusion model.
															_snake_case													=			(
															    torch.linspace(beta_start**0.5			,  beta_end**0.5			,  lowerCAmelCase_			,  dtype=torch.floataa					) ** 2
															)
										elif beta_schedule == "squaredcos_cap_v2":
															# Glide cosine schedule
															_snake_case													=			betas_for_alpha_bar(lowerCAmelCase_					)
										else:
															raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''					)

										_snake_case													=			1.0 - self.betas
										_snake_case													=			torch.cumprod(self.alphas			,  dim=0					)

										#  set all values
										self.set_timesteps(lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_					)




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_=None					)						->							str:
										if schedule_timesteps is None:
															_snake_case													=			self.timesteps

										_snake_case													=			(schedule_timesteps == timestep).nonzero()

										# The sigma index that is taken for the **very** first `step`
										# is always the second index (or the last index if there is only 1)
										# This way we can ensure we don't accidentally skip a sigma in
										# case we start in the middle of the denoising schedule (e.g. for image-to-image)
										if len(self._index_counter					) == 0:
															_snake_case													=			1 if len(lowerCAmelCase_					) > 1 else 0
										else:
															_snake_case													=			timestep.cpu().item() if torch.is_tensor(lowerCAmelCase_					) else timestep
															_snake_case													=			self._index_counter[timestep_int]

										return indices[pos].item()




					@property
					def 		lowerCAmelCase     (						self					)						->							Union[str, Any]:
										# standard deviation of the initial noise distribution
										if self.config.timestep_spacing in ["linspace", "trailing"]:
															return self.sigmas.max()

										return (self.sigmas.max() ** 2 + 1) ** 0.5




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  )						->							torch.FloatTensor:
										_snake_case													=			self.index_for_timestep(lowerCAmelCase_					)

										if self.state_in_first_order:
															_snake_case													=			self.sigmas[step_index]
										else:
															_snake_case													=			self.sigmas_interpol[step_index]

										_snake_case													=			sample / ((sigma**2 + 1) ** 0.5)
										return sample




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_ = None			,  lowerCAmelCase_ = None			,  )						->							List[Any]:
										_snake_case													=			num_inference_steps

										_snake_case													=			num_train_timesteps or self.config.num_train_timesteps

										# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
										if self.config.timestep_spacing == "linspace":
															_snake_case													=			np.linspace(0			,  num_train_timesteps - 1			,  lowerCAmelCase_			,  dtype=lowerCAmelCase_					)[::-1].copy()
										elif self.config.timestep_spacing == "leading":
															_snake_case													=			num_train_timesteps // self.num_inference_steps
															# creates integer timesteps by multiplying by ratio
															# casting to int to avoid issues when num_inference_step is power of 3
															_snake_case													=			(np.arange(0			,  lowerCAmelCase_					) * step_ratio).round()[::-1].copy().astype(lowerCAmelCase_					)
															timesteps += self.config.steps_offset
										elif self.config.timestep_spacing == "trailing":
															_snake_case													=			num_train_timesteps / self.num_inference_steps
															# creates integer timesteps by multiplying by ratio
															# casting to int to avoid issues when num_inference_step is power of 3
															_snake_case													=			(np.arange(lowerCAmelCase_			,  0			,  -step_ratio					)).round().copy().astype(lowerCAmelCase_					)
															timesteps -= 1
										else:
															raise ValueError(
															    F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''					)

										_snake_case													=			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
										_snake_case													=			torch.from_numpy(np.log(lowerCAmelCase_					)					).to(lowerCAmelCase_					)

										_snake_case													=			np.interp(lowerCAmelCase_			,  np.arange(0			,  len(lowerCAmelCase_					)					)			,  lowerCAmelCase_					)
										_snake_case													=			np.concatenate([sigmas, [0.0]]					).astype(np.floataa					)
										_snake_case													=			torch.from_numpy(lowerCAmelCase_					).to(device=lowerCAmelCase_					)

										# interpolate sigmas
										_snake_case													=			sigmas.log().lerp(sigmas.roll(1					).log()			,  0.5					).exp()

										_snake_case													=			torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2					), sigmas[-1:]]					)
										_snake_case													=			torch.cat(
										    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2					), sigmas_interpol[-1:]]					)

										if str(lowerCAmelCase_					).startswith('mps'					):
															# mps does not support float64
															_snake_case													=			torch.from_numpy(lowerCAmelCase_					).to(lowerCAmelCase_			,  dtype=torch.floataa					)
										else:
															_snake_case													=			torch.from_numpy(lowerCAmelCase_					).to(lowerCAmelCase_					)

										# interpolate timesteps
										_snake_case													=			self.sigma_to_t(lowerCAmelCase_					).to(lowerCAmelCase_			,  dtype=timesteps.dtype					)
										_snake_case													=			torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])			,  dim=-1					).flatten()

										_snake_case													=			torch.cat([timesteps[:1], interleaved_timesteps]					)

										_snake_case													=			None

										# for exp beta schedules, such as the one for `pipeline_shap_e.py`
										# we need an index counter
										_snake_case													=			defaultdict(lowerCAmelCase_					)




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							Tuple:
										# get log sigma
										_snake_case													=			sigma.log()

										# get distribution
										_snake_case													=			log_sigma - self.log_sigmas[:, None]

										# get sigmas range
										_snake_case													=			dists.ge(0					).cumsum(dim=0					).argmax(dim=0					).clamp(max=self.log_sigmas.shape[0] - 2					)
										_snake_case													=			low_idx + 1

										_snake_case													=			self.log_sigmas[low_idx]
										_snake_case													=			self.log_sigmas[high_idx]

										# interpolate sigmas
										_snake_case													=			(low - log_sigma) / (low - high)
										_snake_case													=			w.clamp(0			,  1					)

										# transform interpolation to time range
										_snake_case													=			(1 - w) * low_idx + w * high_idx
										_snake_case													=			t.view(sigma.shape					)
										return t




					@property
					def 		lowerCAmelCase     (						self					)						->							List[str]:
										return self.sample is None




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_ = True			,  )						->							Union[SchedulerOutput, Tuple]:
										_snake_case													=			self.index_for_timestep(lowerCAmelCase_					)

										# advance index counter by 1
										_snake_case													=			timestep.cpu().item() if torch.is_tensor(lowerCAmelCase_					) else timestep
										self._index_counter[timestep_int] += 1

										if self.state_in_first_order:
															_snake_case													=			self.sigmas[step_index]
															_snake_case													=			self.sigmas_interpol[step_index + 1]
															_snake_case													=			self.sigmas[step_index + 1]
										else:
															# 2nd order / KDPM2's method
															_snake_case													=			self.sigmas[step_index - 1]
															_snake_case													=			self.sigmas_interpol[step_index]
															_snake_case													=			self.sigmas[step_index]

										# currently only gamma=0 is supported. This usually works best anyways.
										# We can support gamma in the future but then need to scale the timestep before
										# passing it to the model which requires a change in API
										_snake_case													=			0
										_snake_case													=			sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

										# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
										if self.config.prediction_type == "epsilon":
															_snake_case													=			sigma_hat if self.state_in_first_order else sigma_interpol
															_snake_case													=			sample - sigma_input * model_output
										elif self.config.prediction_type == "v_prediction":
															_snake_case													=			sigma_hat if self.state_in_first_order else sigma_interpol
															_snake_case													=			model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
															    sample / (sigma_input**2 + 1)
															)
										elif self.config.prediction_type == "sample":
															raise NotImplementedError('prediction_type not implemented yet: sample'					)
										else:
															raise ValueError(
															    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''					)

										if self.state_in_first_order:
															# 2. Convert to an ODE derivative for 1st order
															_snake_case													=			(sample - pred_original_sample) / sigma_hat
															# 3. delta timestep
															_snake_case													=			sigma_interpol - sigma_hat

															# store for 2nd order step
															_snake_case													=			sample
										else:
															# DPM-Solver-2
															# 2. Convert to an ODE derivative for 2nd order
															_snake_case													=			(sample - pred_original_sample) / sigma_interpol

															# 3. delta timestep
															_snake_case													=			sigma_next - sigma_hat

															_snake_case													=			self.sample
															_snake_case													=			None

										_snake_case													=			sample + derivative * dt

										if not return_dict:
															return (prev_sample,)

										return SchedulerOutput(prev_sample=lowerCAmelCase_					)




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  )						->							torch.FloatTensor:
										# Make sure sigmas and timesteps have the same device and dtype as original_samples
										_snake_case													=			self.sigmas.to(device=original_samples.device			,  dtype=original_samples.dtype					)
										if original_samples.device.type == "mps" and torch.is_floating_point(lowerCAmelCase_					):
															# mps does not support float64
															_snake_case													=			self.timesteps.to(original_samples.device			,  dtype=torch.floataa					)
															_snake_case													=			timesteps.to(original_samples.device			,  dtype=torch.floataa					)
										else:
															_snake_case													=			self.timesteps.to(original_samples.device					)
															_snake_case													=			timesteps.to(original_samples.device					)

										_snake_case													=			[self.index_for_timestep(lowerCAmelCase_			,  lowerCAmelCase_					) for t in timesteps]

										_snake_case													=			sigmas[step_indices].flatten()
										while len(sigma.shape					) < len(original_samples.shape					):
															_snake_case													=			sigma.unsqueeze(-1					)

										_snake_case													=			original_samples + noise * sigma
										return noisy_samples






					def __len__(						self					)						->							List[Any]:
										return self.config.num_train_timesteps










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   lowerCamelCase__       (       UpperCamelCase__     :    str    ,      UpperCamelCase__     :    str  )     ->	Union[str, Any]:



					'''simple docstring'''




					if "xprophetnet" in prophetnet_checkpoint_path:
										_snake_case													=			XLMProphetNetForConditionalGenerationOld.from_pretrained(UpperCamelCase__  )
										_snake_case							,       _snake_case													=			XLMProphetNetForConditionalGeneration.from_pretrained(
										    UpperCamelCase__    ,      output_loading_info=UpperCamelCase__  )
					else:
										_snake_case													=			ProphetNetForConditionalGenerationOld.from_pretrained(UpperCamelCase__  )
										_snake_case							,       _snake_case													=			ProphetNetForConditionalGeneration.from_pretrained(
										    UpperCamelCase__    ,      output_loading_info=UpperCamelCase__  )

					_snake_case													=			['key_proj', 'value_proj', 'query_proj']

					_snake_case													=			{
					    '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													=			key.split('.'  )

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

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

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

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

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

																				if old_attribute == "":
																									_snake_case													=			old_model
																				else:
																									if not hasattr(UpperCamelCase__    ,      UpperCamelCase__  ):
																														raise ValueError(F'''{old_model} does not have {old_attribute}'''  )
																									_snake_case													=			getattr(UpperCamelCase__    ,      UpperCamelCase__  )

										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(UpperCamelCase__  )


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)



