


import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


UpperCamelCase								=	"""scheduler_config.json"""






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""
       snake_case      =    1
       snake_case      =    2
       snake_case      =    3
       snake_case      =    4
       snake_case      =    5






@dataclass
class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""
       snake_case      =    42






class   _lowerCamelCase :




       """simple docstring"""
       snake_case      =    SCHEDULER_CONFIG_NAME
       snake_case      =    ["dtype"]
       snake_case      =    []
       snake_case      =    True



       @classmethod
       def        _snake_case       (      cls				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE=False				,       **_SCREAMING_SNAKE_CASE				,       )->Any:




         '''simple docstring'''





         A_     ,       A_      :				List[Any]		     =					cls.load_config(
             pretrained_model_name_or_path=_SCREAMING_SNAKE_CASE				,       subfolder=_SCREAMING_SNAKE_CASE				,       return_unused_kwargs=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE				,       )
         A_     ,       A_      :				str		     =					cls.from_config(_SCREAMING_SNAKE_CASE				,       return_unused_kwargs=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)

         if hasattr(_SCREAMING_SNAKE_CASE				,       '''create_state'''		) and getattr(_SCREAMING_SNAKE_CASE				,       '''has_state'''				,       _SCREAMING_SNAKE_CASE		):
           A_      :				str		     =					scheduler.create_state()

         if return_unused_kwargs:
           return scheduler, state, unused_kwargs

         return scheduler, state



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE = False				,       **_SCREAMING_SNAKE_CASE		)->List[str]:




         '''simple docstring'''





         self.save_config(save_directory=_SCREAMING_SNAKE_CASE				,       push_to_hub=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)



       @property
       def        _snake_case       (      self		)->Any:




         '''simple docstring'''





         return self._get_compatibles()







       @classmethod
       def        _snake_case       (      cls		)->Tuple:




         '''simple docstring'''





         A_      :				Tuple		     =					list(set([cls.__name__] + cls._compatibles		)		)
         A_      :				Union[str, Any]		     =					importlib.import_module(__name__.split('''.'''		)[0]		)
         A_      :				Any		     =					[
             getattr(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		) for c in compatible_classes_str if hasattr(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)
         ]
         return compatible_classes




def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  assert len(SCREAMING_SNAKE_CASE       ) >= x.ndim
  return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(SCREAMING_SNAKE_CASE       ) - x.ndim)       ) ,			SCREAMING_SNAKE_CASE       )


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE=0.9_9_9 ,			SCREAMING_SNAKE_CASE=jnp.floataa       ):

  def alpha_bar(SCREAMING_SNAKE_CASE       ):
    return math.cos((time_step + 0.0_0_8) / 1.0_0_8 * math.pi / 2       ) ** 2

  A_      :				Optional[Any]		     =					[]
  for i in range(SCREAMING_SNAKE_CASE       ):
    A_      :				Optional[Any]		     =					i / num_diffusion_timesteps
    A_      :				Union[str, Any]		     =					(i + 1) / num_diffusion_timesteps
    betas.append(min(1 - alpha_bar(SCREAMING_SNAKE_CASE       ) / alpha_bar(SCREAMING_SNAKE_CASE       ) ,			SCREAMING_SNAKE_CASE       )       )
  return jnp.array(SCREAMING_SNAKE_CASE ,			dtype=SCREAMING_SNAKE_CASE       )






@flax.struct.dataclass
class   _lowerCamelCase :




       """simple docstring"""
       snake_case      =    42
       snake_case      =    42
       snake_case      =    42



       @classmethod
       def        _snake_case       (      cls				,       _SCREAMING_SNAKE_CASE		)->Tuple:




         '''simple docstring'''





         A_      :				Tuple		     =					scheduler.config

         if config.trained_betas is not None:
           A_      :				List[Any]		     =					jnp.asarray(config.trained_betas				,       dtype=scheduler.dtype		)
         elif config.beta_schedule == "linear":
           A_      :				List[Any]		     =					jnp.linspace(config.beta_start				,       config.beta_end				,       config.num_train_timesteps				,       dtype=scheduler.dtype		)
         elif config.beta_schedule == "scaled_linear":
           # this schedule is very specific to the latent diffusion model.
           A_      :				Tuple		     =					(
               jnp.linspace(
                   config.beta_start**0.5				,       config.beta_end**0.5				,       config.num_train_timesteps				,       dtype=scheduler.dtype		)
               ** 2
           )
         elif config.beta_schedule == "squaredcos_cap_v2":
           # Glide cosine schedule
           A_      :				Tuple		     =					betas_for_alpha_bar(config.num_train_timesteps				,       dtype=scheduler.dtype		)
         else:
           raise NotImplementedError(
               F'''beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}'''		)

         A_      :				int		     =					1.0 - betas

         A_      :				List[Any]		     =					jnp.cumprod(_SCREAMING_SNAKE_CASE				,       axis=0		)

         return cls(
             alphas=_SCREAMING_SNAKE_CASE				,       betas=_SCREAMING_SNAKE_CASE				,       alphas_cumprod=_SCREAMING_SNAKE_CASE				,       )




def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  A_      :				List[Any]		     =					state.alphas_cumprod

  A_      :				Tuple		     =					alphas_cumprod[timesteps] ** 0.5
  A_      :				Optional[int]		     =					sqrt_alpha_prod.flatten()
  A_      :				Union[str, Any]		     =					broadcast_to_shape_from_left(SCREAMING_SNAKE_CASE ,			original_samples.shape       )

  A_      :				Any		     =					(1 - alphas_cumprod[timesteps]) ** 0.5
  A_      :				Dict		     =					sqrt_one_minus_alpha_prod.flatten()
  A_      :				List[str]		     =					broadcast_to_shape_from_left(SCREAMING_SNAKE_CASE ,			original_samples.shape       )

  return sqrt_alpha_prod, sqrt_one_minus_alpha_prod


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  A_     ,       A_      :				Union[str, Any]		     =					get_sqrt_alpha_prod(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )
  A_      :				Union[str, Any]		     =					sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
  return noisy_samples







def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  A_     ,       A_      :				Dict		     =					get_sqrt_alpha_prod(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )
  A_      :				Union[str, Any]		     =					sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
  return velocity








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()
UpperCamelCase								=	logging.get_logger(__name__)

UpperCamelCase								=	"""Hello world! cécé herlolip"""


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  A_      :				List[Any]		     =					FairseqRobertaModel.from_pretrained(SCREAMING_SNAKE_CASE       )
  roberta.eval()  # disable dropout
  A_      :				Dict		     =					roberta.model.encoder.sentence_encoder
  A_      :				Optional[Any]		     =					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=514 ,			type_vocab_size=1 ,			layer_norm_eps=1e-5 ,			)
  if classification_head:
    A_      :				Optional[int]		     =					roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

  print('''Our RoBERTa config:''' ,			SCREAMING_SNAKE_CASE       )

  A_      :				List[str]		     =					XLMRobertaXLForSequenceClassification(SCREAMING_SNAKE_CASE       ) if classification_head else XLMRobertaXLForMaskedLM(SCREAMING_SNAKE_CASE       )
  model.eval()

  # Now let's copy all the weights.
  # Embeddings
  A_      :				str		     =					roberta_sent_encoder.embed_tokens.weight
  A_      :				int		     =					roberta_sent_encoder.embed_positions.weight
  A_      :				Union[str, Any]		     =					torch.zeros_like(
      model.roberta.embeddings.token_type_embeddings.weight       )  # just zero them out b/c RoBERTa doesn't use them.

  A_      :				Union[str, Any]		     =					roberta_sent_encoder.layer_norm.weight
  A_      :				int		     =					roberta_sent_encoder.layer_norm.bias

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

    A_      :				RobertaAttention		     =					layer.attention
    A_      :				Dict		     =					roberta_layer.self_attn_layer_norm.weight
    A_      :				str		     =					roberta_layer.self_attn_layer_norm.bias

    # self attention
    A_      :				BertSelfAttention		     =					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_      :				str		     =					roberta_layer.self_attn.q_proj.weight
    A_      :				List[str]		     =					roberta_layer.self_attn.q_proj.bias
    A_      :				int		     =					roberta_layer.self_attn.k_proj.weight
    A_      :				List[Any]		     =					roberta_layer.self_attn.k_proj.bias
    A_      :				Dict		     =					roberta_layer.self_attn.v_proj.weight
    A_      :				int		     =					roberta_layer.self_attn.v_proj.bias

    # self-attention output
    A_      :				BertSelfOutput		     =					layer.attention.output
    assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
    A_      :				Any		     =					roberta_layer.self_attn.out_proj.weight
    A_      :				Optional[Any]		     =					roberta_layer.self_attn.out_proj.bias

    # this one is final layer norm
    A_      :				Any		     =					roberta_layer.final_layer_norm.weight
    A_      :				int		     =					roberta_layer.final_layer_norm.bias

    # intermediate
    A_      :				BertIntermediate		     =					layer.intermediate
    assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
    A_      :				int		     =					roberta_layer.fca.weight
    A_      :				List[str]		     =					roberta_layer.fca.bias

    # output
    A_      :				BertOutput		     =					layer.output
    assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
    A_      :				Optional[int]		     =					roberta_layer.fca.weight
    A_      :				List[Any]		     =					roberta_layer.fca.bias
    # end of layer

  if classification_head:
    A_      :				str		     =					roberta.model.classification_heads['''mnli'''].dense.weight
    A_      :				int		     =					roberta.model.classification_heads['''mnli'''].dense.bias
    A_      :				str		     =					roberta.model.classification_heads['''mnli'''].out_proj.weight
    A_      :				Dict		     =					roberta.model.classification_heads['''mnli'''].out_proj.bias
  else:
    # LM Head
    A_      :				int		     =					roberta.model.encoder.lm_head.dense.weight
    A_      :				List[str]		     =					roberta.model.encoder.lm_head.dense.bias
    A_      :				Optional[Any]		     =					roberta.model.encoder.lm_head.layer_norm.weight
    A_      :				int		     =					roberta.model.encoder.lm_head.layer_norm.bias
    A_      :				Optional[int]		     =					roberta.model.encoder.lm_head.weight
    A_      :				Dict		     =					roberta.model.encoder.lm_head.bias

  # Let's check that we get the same results.
  A_      :				torch.Tensor		     =					roberta.encode(SCREAMING_SNAKE_CASE       ).unsqueeze(0       )  # batch of size 1

  A_      :				Union[str, Any]		     =					model(SCREAMING_SNAKE_CASE       )[0]
  if classification_head:
    A_      :				str		     =					roberta.model.classification_heads['''mnli'''](roberta.extract_features(SCREAMING_SNAKE_CASE       )       )
  else:
    A_      :				int		     =					roberta.model(SCREAMING_SNAKE_CASE       )[0]
  print(our_output.shape ,			their_output.shape       )
  A_      :				Any		     =					torch.max(torch.abs(our_output - their_output       )       ).item()
  print(f'''max_absolute_diff = {max_absolute_diff}'''       )  # ~ 1e-7
  A_      :				Tuple		     =					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__":
     UpperCamelCase								=	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."""
     )
     UpperCamelCase								=	parser.parse_args()
     convert_xlm_roberta_xl_checkpoint_to_pytorch(
         args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
     )




