


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 XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('1.0.0a'):
   raise Exception('requires fairseq >= 1.0.0a')

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

_A			=						'Hello world! cécé herlolip'

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : str      ,					SCREAMING_SNAKE_CASE__      : str      ,					SCREAMING_SNAKE_CASE__      : bool    ):
     __UpperCamelCase   							=FairseqRobertaModel.from_pretrained(SCREAMING_SNAKE_CASE__    )
     roberta.eval()  # disable dropout
     __UpperCamelCase   							=roberta.model.encoder.sentence_encoder
     __UpperCamelCase   							=XLMRobertaConfig(
         vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings      ,					hidden_size=roberta.cfg.model.encoder_embed_dim      ,					num_hidden_layers=roberta.cfg.model.encoder_layers      ,					num_attention_heads=roberta.cfg.model.encoder_attention_heads      ,					intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim      ,					max_position_embeddings=5_14      ,					type_vocab_size=1      ,					layer_norm_eps=1E-5      ,					)
     if classification_head:
          __UpperCamelCase   							=roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]

     print('Our RoBERTa config:'      ,					SCREAMING_SNAKE_CASE__    )

     __UpperCamelCase   							=XLMRobertaXLForSequenceClassification(SCREAMING_SNAKE_CASE__    ) if classification_head else XLMRobertaXLForMaskedLM(SCREAMING_SNAKE_CASE__    )
     model.eval()

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

     __UpperCamelCase   							=roberta_sent_encoder.layer_norm.weight
     __UpperCamelCase   							=roberta_sent_encoder.layer_norm.bias

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

          __UpperCamelCase   							=layer.attention
          __UpperCamelCase   							=roberta_layer.self_attn_layer_norm.weight
          __UpperCamelCase   							=roberta_layer.self_attn_layer_norm.bias

          # self attention
          __UpperCamelCase   							=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)    )
          )

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

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

          # this one is final layer norm
          __UpperCamelCase   							=roberta_layer.final_layer_norm.weight
          __UpperCamelCase   							=roberta_layer.final_layer_norm.bias

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

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

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

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

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

     pathlib.Path(SCREAMING_SNAKE_CASE__    ).mkdir(parents=SCREAMING_SNAKE_CASE__      ,					exist_ok=SCREAMING_SNAKE_CASE__    )
     print(F'Saving model to {pytorch_dump_folder_path}'    )
     model.save_pretrained(SCREAMING_SNAKE_CASE__    )


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_xlm_roberta_xl_checkpoint_to_pytorch(
       args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
   )









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

import numpy as np
import torch

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






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    UpperCAmelCase__						:		torch.FloatTensor
    UpperCAmelCase__						:		Optional[torch.FloatTensor]              =  None

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Optional[Any]      ,					SCREAMING_SNAKE_CASE__      : int=0.999      ,					SCREAMING_SNAKE_CASE__      : str="cosine"      ,					):
     if alpha_transform_type == "cosine":

          def alpha_bar_fn(SCREAMING_SNAKE_CASE__      : Any    ):
               return math.cos((t + 0.008) / 1.008 * math.pi / 2    ) ** 2

     elif alpha_transform_type == "exp":

          def alpha_bar_fn(SCREAMING_SNAKE_CASE__      : Optional[Any]    ):
               return math.exp(t * -12.0    )

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

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






class    UpperCAmelCase__	( A_       ,  A_		):



    """simple docstring"""






    @register_to_config
    def __init__(      self ,       A_ = 1000 ,       A_ = "fixed_small_log" ,       A_ = True ,       A_ = 1.0 ,       A_ = "epsilon" ,       A_ = "squaredcos_cap_v2" ,       )  ->	Tuple:
         if beta_schedule != "squaredcos_cap_v2":
              raise ValueError('UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''   )

         __UpperCamelCase   							=betas_for_alpha_bar(A_   )

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

         # standard deviation of the initial noise distribution
         __UpperCamelCase   							=1.0

         # setable values
         __UpperCamelCase   							=None
         __UpperCamelCase   							=torch.from_numpy(np.arange(0 ,       A_   )[::-1].copy()   )

         __UpperCamelCase   							=variance_type






    def _a (      self ,       A_ ,       A_ = None   )  ->	torch.FloatTensor:
         return sample






    def _a (      self ,       A_ ,       A_ = None   )  ->	Tuple:
         __UpperCamelCase   							=num_inference_steps
         __UpperCamelCase   							=(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
         __UpperCamelCase   							=(np.arange(0 ,       A_   ) * step_ratio).round()[::-1].copy().astype(np.intaa   )
         __UpperCamelCase   							=torch.from_numpy(A_   ).to(A_   )






    def _a (      self ,       A_ ,       A_=None ,       A_=None ,       A_=None   )  ->	List[Any]:
         if prev_timestep is None:
              __UpperCamelCase   							=t - 1

         __UpperCamelCase   							=self.alphas_cumprod[t]
         __UpperCamelCase   							=self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
         __UpperCamelCase   							=1 - alpha_prod_t
         __UpperCamelCase   							=1 - alpha_prod_t_prev

         if prev_timestep == t - 1:
              __UpperCamelCase   							=self.betas[t]
         else:
              __UpperCamelCase   							=1 - alpha_prod_t / alpha_prod_t_prev

         # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
         # and sample from it to get previous sample
         # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
         __UpperCamelCase   							=beta_prod_t_prev / beta_prod_t * beta

         if variance_type is None:
              __UpperCamelCase   							=self.config.variance_type

         # hacks - were probably added for training stability
         if variance_type == "fixed_small_log":
              __UpperCamelCase   							=torch.log(torch.clamp(A_ ,       min=1E-20   )   )
              __UpperCamelCase   							=torch.exp(0.5 * variance   )
         elif variance_type == "learned_range":
              # NOTE difference with DDPM scheduler
              __UpperCamelCase   							=variance.log()
              __UpperCamelCase   							=beta.log()

              __UpperCamelCase   							=(predicted_variance + 1) / 2
              __UpperCamelCase   							=frac * max_log + (1 - frac) * min_log

         return variance






    def _a (      self ,       A_ ,       A_ ,       A_ ,       A_ = None ,       A_=None ,       A_ = True ,       )  ->	Union[UnCLIPSchedulerOutput, Tuple]:
         __UpperCamelCase   							=timestep

         if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
              __UpperCamelCase							,				__UpperCamelCase   							=torch.split(A_ ,       sample.shape[1] ,       dim=1   )
         else:
              __UpperCamelCase   							=None

         # 1. compute alphas, betas
         if prev_timestep is None:
              __UpperCamelCase   							=t - 1

         __UpperCamelCase   							=self.alphas_cumprod[t]
         __UpperCamelCase   							=self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
         __UpperCamelCase   							=1 - alpha_prod_t
         __UpperCamelCase   							=1 - alpha_prod_t_prev

         if prev_timestep == t - 1:
              __UpperCamelCase   							=self.betas[t]
              __UpperCamelCase   							=self.alphas[t]
         else:
              __UpperCamelCase   							=1 - alpha_prod_t / alpha_prod_t_prev
              __UpperCamelCase   							=1 - beta

         # 2. compute predicted original sample from predicted noise also called
         # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
         if self.config.prediction_type == "epsilon":
              __UpperCamelCase   							=(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
         elif self.config.prediction_type == "sample":
              __UpperCamelCase   							=model_output
         else:
              raise ValueError(
                  f'prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`'
                  ' for the UnCLIPScheduler.'   )

         # 3. Clip "predicted x_0"
         if self.config.clip_sample:
              __UpperCamelCase   							=torch.clamp(
                  A_ ,       -self.config.clip_sample_range ,       self.config.clip_sample_range   )

         # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
         # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
         __UpperCamelCase   							=(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
         __UpperCamelCase   							=alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

         # 5. Compute predicted previous sample µ_t
         # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
         __UpperCamelCase   							=pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

         # 6. Add noise
         __UpperCamelCase   							=0
         if t > 0:
              __UpperCamelCase   							=randn_tensor(
                  model_output.shape ,       dtype=model_output.dtype ,       generator=A_ ,       device=model_output.device   )

              __UpperCamelCase   							=self._get_variance(
                  A_ ,       predicted_variance=A_ ,       prev_timestep=A_ ,       )

              if self.variance_type == "fixed_small_log":
                   __UpperCamelCase   							=variance
              elif self.variance_type == "learned_range":
                   __UpperCamelCase   							=(0.5 * variance).exp()
              else:
                   raise ValueError(
                       f'variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`'
                       ' for the UnCLIPScheduler.'   )

              __UpperCamelCase   							=variance * variance_noise

         __UpperCamelCase   							=pred_prev_sample + variance

         if not return_dict:
              return (pred_prev_sample,)

         return UnCLIPSchedulerOutput(prev_sample=A_ ,       pred_original_sample=A_   )






    def _a (      self ,       A_ ,       A_ ,       A_ ,       )  ->	torch.FloatTensor:
         # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
         __UpperCamelCase   							=self.alphas_cumprod.to(device=original_samples.device ,       dtype=original_samples.dtype   )
         __UpperCamelCase   							=timesteps.to(original_samples.device   )

         __UpperCamelCase   							=alphas_cumprod[timesteps] ** 0.5
         __UpperCamelCase   							=sqrt_alpha_prod.flatten()
         while len(sqrt_alpha_prod.shape   ) < len(original_samples.shape   ):
              __UpperCamelCase   							=sqrt_alpha_prod.unsqueeze(-1   )

         __UpperCamelCase   							=(1 - alphas_cumprod[timesteps]) ** 0.5
         __UpperCamelCase   							=sqrt_one_minus_alpha_prod.flatten()
         while len(sqrt_one_minus_alpha_prod.shape   ) < len(original_samples.shape   ):
              __UpperCamelCase   							=sqrt_one_minus_alpha_prod.unsqueeze(-1   )

         __UpperCamelCase   							=sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
         return noisy_samples





