





import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


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

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class SCREAMING_SNAKE_CASE_			(       lowercase_ ):





					"""simple docstring"""


					A__								=     42
					A__								=     None





def  __UpperCamelCase   (     a,  a=0.9_9_9,  a="cosine",  )			->Dict:
					if alpha_transform_type == "cosine":

										def alpha_bar_fn(a):
															return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2) ** 2

					elif alpha_transform_type == "exp":

										def alpha_bar_fn(a):
															return math.exp(t * -1_2.0)

					else:
										raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}")

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







class SCREAMING_SNAKE_CASE_			(       lowercase_				,							lowercase_ ):





					"""simple docstring"""


					A__								=     [e.name for e in KarrasDiffusionSchedulers]
					A__								=     1





					@register_to_config
					def __init__(			self						,	_lowerCAmelCase = 1000						,	_lowerCAmelCase = 0.0_001						,	_lowerCAmelCase = 0.02						,	_lowerCAmelCase = "linear"						,	_lowerCAmelCase = None						,	_lowerCAmelCase = "epsilon"						,	_lowerCAmelCase = "linear"						,	_lowerCAmelCase = False						,	_lowerCAmelCase = "linspace"						,	_lowerCAmelCase = 0						,	):
										if trained_betas is not None:
															lowerCamelCase__      =							torch.tensor(_lowerCAmelCase						,	dtype=torch.floataa						)
										elif beta_schedule == "linear":
															lowerCamelCase__      =							torch.linspace(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	dtype=torch.floataa						)
										elif beta_schedule == "scaled_linear":
															# this schedule is very specific to the latent diffusion model.
															lowerCamelCase__      =							(
															    torch.linspace(beta_start**0.5						,	beta_end**0.5						,	_lowerCAmelCase						,	dtype=torch.floataa						) ** 2
															)
										elif beta_schedule == "squaredcos_cap_v2":
															# Glide cosine schedule
															lowerCamelCase__      =							betas_for_alpha_bar(_lowerCAmelCase						)
										else:
															raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"						)

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

										lowerCamelCase__      =							np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
										lowerCamelCase__      =							np.concatenate([sigmas[::-1], [0.0]]						).astype(np.floataa						)
										lowerCamelCase__      =							torch.from_numpy(_lowerCAmelCase						)

										# setable values
										lowerCamelCase__      =							None
										lowerCamelCase__      =							np.linspace(0						,	num_train_timesteps - 1						,	_lowerCAmelCase						,	dtype=_lowerCAmelCase						)[::-1].copy()
										lowerCamelCase__      =							torch.from_numpy(_lowerCAmelCase						)
										lowerCamelCase__      =							False
										lowerCamelCase__      =							use_karras_sigmas





					@property
					def        __magic_name__			(			self						):
										# 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        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						):
										if isinstance(_lowerCAmelCase						,	torch.Tensor						):
															lowerCamelCase__      =							timestep.to(self.timesteps.device						)
										lowerCamelCase__      =							(self.timesteps == timestep).nonzero().item()
										lowerCamelCase__      =							self.sigmas[step_index]

										lowerCamelCase__      =							sample / ((sigma**2 + 1) ** 0.5)

										lowerCamelCase__      =							True
										return sample





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase = None						):
										lowerCamelCase__      =							num_inference_steps

										# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
										if self.config.timestep_spacing == "linspace":
															lowerCamelCase__      =							np.linspace(0						,	self.config.num_train_timesteps - 1						,	_lowerCAmelCase						,	dtype=_lowerCAmelCase						)[
															    ::-1
															].copy()
										elif self.config.timestep_spacing == "leading":
															lowerCamelCase__      =							self.config.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
															lowerCamelCase__      =							(np.arange(0						,	_lowerCAmelCase						) * step_ratio).round()[::-1].copy().astype(_lowerCAmelCase						)
															timesteps += self.config.steps_offset
										elif self.config.timestep_spacing == "trailing":
															lowerCamelCase__      =							self.config.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
															lowerCamelCase__      =							(np.arange(self.config.num_train_timesteps						,	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'."						)

										lowerCamelCase__      =							np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
										lowerCamelCase__      =							np.log(_lowerCAmelCase						)

										if self.config.interpolation_type == "linear":
															lowerCamelCase__      =							np.interp(_lowerCAmelCase						,	np.arange(0						,	len(_lowerCAmelCase						)						)						,	_lowerCAmelCase						)
										elif self.config.interpolation_type == "log_linear":
															lowerCamelCase__      =							torch.linspace(np.log(sigmas[-1]						)						,	np.log(sigmas[0]						)						,	num_inference_steps + 1						).exp()
										else:
															raise ValueError(
															    F"{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either"
															    " 'linear' or 'log_linear'"						)

										if self.use_karras_sigmas:
															lowerCamelCase__      =							self._convert_to_karras(in_sigmas=_lowerCAmelCase						,	num_inference_steps=self.num_inference_steps						)
															lowerCamelCase__      =							np.array([self._sigma_to_t(_lowerCAmelCase						,	_lowerCAmelCase						) for sigma in sigmas]						)

										lowerCamelCase__      =							np.concatenate([sigmas, [0.0]]						).astype(np.floataa						)
										lowerCamelCase__      =							torch.from_numpy(_lowerCAmelCase						).to(device=_lowerCAmelCase						)
										if str(_lowerCAmelCase						).startswith("mps"						):
															# mps does not support float64
															lowerCamelCase__      =							torch.from_numpy(_lowerCAmelCase						).to(_lowerCAmelCase						,	dtype=torch.floataa						)
										else:
															lowerCamelCase__      =							torch.from_numpy(_lowerCAmelCase						).to(device=_lowerCAmelCase						)





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						):
										# get log sigma
										lowerCamelCase__      =							np.log(_lowerCAmelCase						)

										# get distribution
										lowerCamelCase__      =							log_sigma - log_sigmas[:, np.newaxis]

										# get sigmas range
										lowerCamelCase__      =							np.cumsum((dists >= 0)						,	axis=0						).argmax(axis=0						).clip(max=log_sigmas.shape[0] - 2						)
										lowerCamelCase__      =							low_idx + 1

										lowerCamelCase__      =							log_sigmas[low_idx]
										lowerCamelCase__      =							log_sigmas[high_idx]

										# interpolate sigmas
										lowerCamelCase__      =							(low - log_sigma) / (low - high)
										lowerCamelCase__      =							np.clip(_lowerCAmelCase						,	0						,	1						)

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





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						):
										lowerCamelCase__      =							in_sigmas[-1].item()
										lowerCamelCase__      =							in_sigmas[0].item()

										lowerCamelCase__      =							7.0  # 7.0 is the value used in the paper
										lowerCamelCase__      =							np.linspace(0						,	1						,	_lowerCAmelCase						)
										lowerCamelCase__      =							sigma_min ** (1 / rho)
										lowerCamelCase__      =							sigma_max ** (1 / rho)
										lowerCamelCase__      =							(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
										return sigmas





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase = 0.0						,	_lowerCAmelCase = 0.0						,	_lowerCAmelCase = float("inf"						)						,	_lowerCAmelCase = 1.0						,	_lowerCAmelCase = None						,	_lowerCAmelCase = True						,	):

										if (
										    isinstance(_lowerCAmelCase						,	_lowerCAmelCase						)
										    or isinstance(_lowerCAmelCase						,	torch.IntTensor						)
										    or isinstance(_lowerCAmelCase						,	torch.LongTensor						)
										):
															raise ValueError(
															    (
															        "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
															        " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
															        " one of the `scheduler.timesteps` as a timestep."
															    )						,	)

										if not self.is_scale_input_called:
															logger.warning(
															    "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
															    "See `StableDiffusionPipeline` for a usage example."						)

										if isinstance(_lowerCAmelCase						,	torch.Tensor						):
															lowerCamelCase__      =							timestep.to(self.timesteps.device						)

										lowerCamelCase__      =							(self.timesteps == timestep).nonzero().item()
										lowerCamelCase__      =							self.sigmas[step_index]

										lowerCamelCase__      =							min(s_churn / (len(self.sigmas						) - 1)						,	2**0.5 - 1						) if s_tmin <= sigma <= s_tmax else 0.0

										lowerCamelCase__      =							randn_tensor(
										    model_output.shape						,	dtype=model_output.dtype						,	device=model_output.device						,	generator=_lowerCAmelCase						)

										lowerCamelCase__      =							noise * s_noise
										lowerCamelCase__      =							sigma * (gamma + 1)

										if gamma > 0:
															lowerCamelCase__      =							sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

										# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
										# NOTE: "original_sample" should not be an expected prediction_type but is left in for
										# backwards compatibility
										if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
															lowerCamelCase__      =							model_output
										elif self.config.prediction_type == "epsilon":
															lowerCamelCase__      =							sample - sigma_hat * model_output
										elif self.config.prediction_type == "v_prediction":
															# * c_out + input * c_skip
															lowerCamelCase__      =							model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
										else:
															raise ValueError(
															    F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"						)

										# 2. Convert to an ODE derivative
										lowerCamelCase__      =							(sample - pred_original_sample) / sigma_hat

										lowerCamelCase__      =							self.sigmas[step_index + 1] - sigma_hat

										lowerCamelCase__      =							sample + derivative * dt

										if not return_dict:
															return (prev_sample,)

										return EulerDiscreteSchedulerOutput(prev_sample=_lowerCAmelCase						,	pred_original_sample=_lowerCAmelCase						)





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	):
										# Make sure sigmas and timesteps have the same device and dtype as original_samples
										lowerCamelCase__      =							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
															lowerCamelCase__      =							self.timesteps.to(original_samples.device						,	dtype=torch.floataa						)
															lowerCamelCase__      =							timesteps.to(original_samples.device						,	dtype=torch.floataa						)
										else:
															lowerCamelCase__      =							self.timesteps.to(original_samples.device						)
															lowerCamelCase__      =							timesteps.to(original_samples.device						)

										lowerCamelCase__      =							[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

										lowerCamelCase__      =							original_samples + noise * sigma
										return noisy_samples






					def __len__(			self						):
										return self.config.num_train_timesteps








import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("0.9.0"):
						raise Exception("requires fairseq >= 0.9.0")


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

A_        =  "Hello world! cécé herlolip"





def  __UpperCamelCase   (     a,  a,  a)			->Any:
					lowerCamelCase__      =							FairseqRobertaModel.from_pretrained(a)
					roberta.eval()  # disable dropout
					lowerCamelCase__      =							roberta.model.encoder.sentence_encoder
					lowerCamelCase__      =							RobertaConfig(
					    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings,  hidden_size=roberta.args.encoder_embed_dim,  num_hidden_layers=roberta.args.encoder_layers,  num_attention_heads=roberta.args.encoder_attention_heads,  intermediate_size=roberta.args.encoder_ffn_embed_dim,  max_position_embeddings=514,  type_vocab_size=1,  layer_norm_eps=1E-5,  )
					if classification_head:
										lowerCamelCase__      =							roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
					print("Our BERT config:",  a)

					lowerCamelCase__      =							RobertaForSequenceClassification(a) if classification_head else RobertaForMaskedLM(a)
					model.eval()

					# Now let's copy all the weights.
					# Embeddings
					lowerCamelCase__      =							roberta_sent_encoder.embed_tokens.weight
					lowerCamelCase__      =							roberta_sent_encoder.embed_positions.weight
					lowerCamelCase__      =							torch.zeros_like(
					    model.roberta.embeddings.token_type_embeddings.weight)  # just zero them out b/c RoBERTa doesn't use them.
					lowerCamelCase__      =							roberta_sent_encoder.emb_layer_norm.weight
					lowerCamelCase__      =							roberta_sent_encoder.emb_layer_norm.bias

					for i in range(config.num_hidden_layers):
										# Encoder: start of layer
										lowerCamelCase__      =							model.roberta.encoder.layer[i]
										lowerCamelCase__      =							roberta_sent_encoder.layers[i]

										# self attention
										lowerCamelCase__      =							layer.attention.self
										assert (
										    roberta_layer.self_attn.k_proj.weight.data.shape
										    == roberta_layer.self_attn.q_proj.weight.data.shape
										    == roberta_layer.self_attn.v_proj.weight.data.shape
										    == torch.Size((config.hidden_size, config.hidden_size))
										)

										lowerCamelCase__      =							roberta_layer.self_attn.q_proj.weight
										lowerCamelCase__      =							roberta_layer.self_attn.q_proj.bias
										lowerCamelCase__      =							roberta_layer.self_attn.k_proj.weight
										lowerCamelCase__      =							roberta_layer.self_attn.k_proj.bias
										lowerCamelCase__      =							roberta_layer.self_attn.v_proj.weight
										lowerCamelCase__      =							roberta_layer.self_attn.v_proj.bias

										# self-attention output
										lowerCamelCase__      =							layer.attention.output
										assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
										lowerCamelCase__      =							roberta_layer.self_attn.out_proj.weight
										lowerCamelCase__      =							roberta_layer.self_attn.out_proj.bias
										lowerCamelCase__      =							roberta_layer.self_attn_layer_norm.weight
										lowerCamelCase__      =							roberta_layer.self_attn_layer_norm.bias

										# intermediate
										lowerCamelCase__      =							layer.intermediate
										assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
										lowerCamelCase__      =							roberta_layer.fca.weight
										lowerCamelCase__      =							roberta_layer.fca.bias

										# output
										lowerCamelCase__      =							layer.output
										assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
										lowerCamelCase__      =							roberta_layer.fca.weight
										lowerCamelCase__      =							roberta_layer.fca.bias
										lowerCamelCase__      =							roberta_layer.final_layer_norm.weight
										lowerCamelCase__      =							roberta_layer.final_layer_norm.bias
										# end of layer

					if classification_head:
										lowerCamelCase__      =							roberta.model.classification_heads["mnli"].dense.weight
										lowerCamelCase__      =							roberta.model.classification_heads["mnli"].dense.bias
										lowerCamelCase__      =							roberta.model.classification_heads["mnli"].out_proj.weight
										lowerCamelCase__      =							roberta.model.classification_heads["mnli"].out_proj.bias
					else:
										# LM Head
										lowerCamelCase__      =							roberta.model.encoder.lm_head.dense.weight
										lowerCamelCase__      =							roberta.model.encoder.lm_head.dense.bias
										lowerCamelCase__      =							roberta.model.encoder.lm_head.layer_norm.weight
										lowerCamelCase__      =							roberta.model.encoder.lm_head.layer_norm.bias
										lowerCamelCase__      =							roberta.model.encoder.lm_head.weight
										lowerCamelCase__      =							roberta.model.encoder.lm_head.bias

					# Let's check that we get the same results.
					lowerCamelCase__      =							roberta.encode(a).unsqueeze(0)  # batch of size 1

					lowerCamelCase__      =							model(a)[0]
					if classification_head:
										lowerCamelCase__      =							roberta.model.classification_heads["mnli"](roberta.extract_features(a))
					else:
										lowerCamelCase__      =							roberta.model(a)[0]
					print(our_output.shape,  their_output.shape)
					lowerCamelCase__      =							torch.max(torch.abs(our_output - their_output)).item()
					print(f"max_absolute_diff = {max_absolute_diff}")  # ~ 1e-7
					lowerCamelCase__      =							torch.allclose(a,  a,  atol=1E-3)
					print("Do both models output the same tensors?",  "🔥" if success else "💩")
					if not success:
										raise Exception("Something went wRoNg")

					pathlib.Path(a).mkdir(parents=a,  exist_ok=a)
					print(f"Saving model to {pytorch_dump_folder_path}")
					model.save_pretrained(a)


if __name__ == "__main__":
						A_        =  argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--roberta_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."
						)
						parser.add_argument(
						    "--classification_head", action="store_true", help="Whether to convert a final classification head."
						)
						A_        =  parser.parse_args()
						convert_roberta_checkpoint_to_pytorch(
						    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
						)

