




"""simple docstring"""

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 						_snake_case     (					_snake_case							:					Dict				,  _snake_case							:					List[str]				,  _snake_case							:					Tuple							)    ->					str:






      '''simple docstring'''


      _A					   =   FairseqRobertaModel.from_pretrained(A__							)
      roberta.eval()  # disable dropout
      _A					   =   roberta.model.encoder.sentence_encoder
      _A					   =   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:
            _A					   =   roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]

      print('Our RoBERTa config:'				,  A__							)

      _A					   =   XLMRobertaXLForSequenceClassification(A__							) if classification_head else XLMRobertaXLForMaskedLM(A__							)
      model.eval()

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

      _A					   =   roberta_sent_encoder.layer_norm.weight
      _A					   =   roberta_sent_encoder.layer_norm.bias

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

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

            # self attention
            _A					   =   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)							)
            )

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

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

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

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

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

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

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

      _A					   =   model(A__							)[0]
      if classification_head:
            _A					   =   roberta.model.classification_heads['mnli'](roberta.extract_features(A__							)							)
      else:
            _A					   =   roberta.model(A__							)[0]
      print(our_output.shape				,  their_output.shape							)
      _A					   =   torch.max(torch.abs(our_output - their_output							)							).item()
      print(F'''max_absolute_diff = {max_absolute_diff}'''							)  # ~ 1e-7
      _A					   =   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_xlm_roberta_xl_checkpoint_to_pytorch(
           args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
       )






'''simple docstring'''


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  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  42
    _SCREAMING_SNAKE_CASE	  =  None







def 				__lowerCamelCase				(   A__    ,						A__=0.999    ,						A__="cosine"    ,						)       ->	Tuple:






   """simple docstring"""
   if alpha_transform_type == "cosine":

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

   elif alpha_transform_type == "exp":

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

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

   UpperCamelCase					=  []
   for i in range(A__    ):
      UpperCamelCase					=  i / num_diffusion_timesteps
      UpperCamelCase					=  (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 (  _a   ,						_a		):






    """simple docstring"""





    @register_to_config
    def __init__(  self      :					List[str]  ,						UpperCamelCase__      :					int = 1_0_0_0  ,						UpperCamelCase__      :					str = "fixed_small_log"  ,						UpperCamelCase__      :					bool = True  ,						UpperCamelCase__      :					Optional[float] = 1.0  ,						UpperCamelCase__      :					str = "epsilon"  ,						UpperCamelCase__      :					str = "squaredcos_cap_v2"  ,						):


       """simple docstring"""







       if beta_schedule != "squaredcos_cap_v2":
          raise ValueError('UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\'' )

       UpperCamelCase					=  betas_for_alpha_bar(UpperCamelCase__ )

       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  ,						UpperCamelCase__ )[::-1].copy() )

       UpperCamelCase					=  variance_type






    def  A       (  self      :					Dict  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					Optional[int] = None ):


       """simple docstring"""







       return sample






    def  A       (  self      :					List[str]  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					Union[str, torch.device] = None ):


       """simple docstring"""







       UpperCamelCase					=  num_inference_steps
       UpperCamelCase					=  (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
       UpperCamelCase					=  (np.arange(0  ,						UpperCamelCase__ ) * step_ratio).round()[::-1].copy().astype(np.intaa )
       UpperCamelCase					=  torch.from_numpy(UpperCamelCase__ ).to(UpperCamelCase__ )






    def  A       (  self      :					Dict  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					Union[str, Any]=None  ,						UpperCamelCase__      :					Optional[int]=None  ,						UpperCamelCase__      :					Tuple=None ):


       """simple docstring"""







       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(UpperCamelCase__  ,						min=1E-2_0 ) )
          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      :					int  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					Optional[int] = None  ,						UpperCamelCase__      :					str=None  ,						UpperCamelCase__      :					bool = True  ,						):


       """simple docstring"""







       UpperCamelCase					=  timestep

       if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
          UpperCamelCase				,      UpperCamelCase					=  torch.split(UpperCamelCase__  ,						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(
              UpperCamelCase__  ,						-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=UpperCamelCase__  ,						device=model_output.device )

          UpperCamelCase					=  self._get_variance(
              UpperCamelCase__  ,						predicted_variance=UpperCamelCase__  ,						prev_timestep=UpperCamelCase__  ,						)

          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=UpperCamelCase__  ,						pred_original_sample=UpperCamelCase__ )






    def  A       (  self      :					int  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					torch.IntTensor  ,						):


       """simple docstring"""







       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

