

import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("""0.12.2"""):
				raise Exception("""requires fairseq >= 0.12.2""")
if version.parse(fairseq.__version__) > version.parse("""2"""):
				raise Exception("""requires fairseq < v2""")

logging.set_verbosity_info()
_a:     Union[str, Any]							=      logging.get_logger(__name__)

_a:     str							=      """Hello, World!"""
_a:     Any							=      """en_XX"""



def    __lowerCAmelCase   (    A							,  A							,  A     ):
	UpperCAmelCase_												=      Path("data_bin"     )
	UpperCAmelCase_												=      FairseqXmodModel.from_pretrained(
	    model_name_or_path=str(Path(_lowercase     ).parent     )							,  checkpoint_file=Path(_lowercase     ).name							,  _name="xmod_base"							,  arch="xmod_base"							,  task="multilingual_masked_lm"							,  data_name_or_path=str(_lowercase     )							,  bpe="sentencepiece"							,  sentencepiece_model=str(Path(_lowercase     ).parent / "sentencepiece.bpe.model"     )							,  src_dict=str(data_dir / "dict.txt"     )							,  )
	xmod.eval()  # disable dropout
	print(_lowercase     )

	UpperCAmelCase_												=      xmod.model.encoder.sentence_encoder
	UpperCAmelCase_												=      XmodConfig(
	    vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings							,  hidden_size=xmod.cfg.model.encoder_embed_dim							,  num_hidden_layers=xmod.cfg.model.encoder_layers							,  num_attention_heads=xmod.cfg.model.encoder_attention_heads							,  intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim							,  max_position_embeddings=514							,  type_vocab_size=1							,  layer_norm_eps=1E-5							,  pre_norm=xmod.cfg.model.encoder_normalize_before							,  adapter_reduction_factor=getattr(xmod.cfg.model							,  "bottleneck"							,  2     )							,  adapter_layer_norm=xmod.cfg.model.adapter_layer_norm							,  adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm							,  ln_before_adapter=xmod.cfg.model.ln_before_adapter							,  languages=xmod.cfg.model.languages							,  )
	if classification_head:
		UpperCAmelCase_												=      xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]

	print("Our X-MOD config:"							,  _lowercase     )

	UpperCAmelCase_												=      XmodForSequenceClassification(_lowercase     ) if classification_head else XmodForMaskedLM(_lowercase     )
	model.eval()

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

	UpperCAmelCase_												=      xmod_sent_encoder.layernorm_embedding.weight
	UpperCAmelCase_												=      xmod_sent_encoder.layernorm_embedding.bias

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

		# self attention
		UpperCAmelCase_												=      layer.attention.self
		if not (
		    xmod_layer.self_attn.k_proj.weight.data.shape
		    == xmod_layer.self_attn.q_proj.weight.data.shape
		    == xmod_layer.self_attn.v_proj.weight.data.shape
		    == torch.Size((config.hidden_size, config.hidden_size)     )
		):
			raise AssertionError("Dimensions of self-attention weights do not match."     )

		UpperCAmelCase_												=      xmod_layer.self_attn.q_proj.weight
		UpperCAmelCase_												=      xmod_layer.self_attn.q_proj.bias
		UpperCAmelCase_												=      xmod_layer.self_attn.k_proj.weight
		UpperCAmelCase_												=      xmod_layer.self_attn.k_proj.bias
		UpperCAmelCase_												=      xmod_layer.self_attn.v_proj.weight
		UpperCAmelCase_												=      xmod_layer.self_attn.v_proj.bias

		# self-attention output
		UpperCAmelCase_												=      layer.attention.output
		if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
			raise AssertionError("Dimensions of self-attention output weights do not match."     )
		UpperCAmelCase_												=      xmod_layer.self_attn.out_proj.weight
		UpperCAmelCase_												=      xmod_layer.self_attn.out_proj.bias
		UpperCAmelCase_												=      xmod_layer.self_attn_layer_norm.weight
		UpperCAmelCase_												=      xmod_layer.self_attn_layer_norm.bias

		# intermediate
		UpperCAmelCase_												=      layer.intermediate
		if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
			raise AssertionError("Dimensions of intermediate weights do not match."     )
		UpperCAmelCase_												=      xmod_layer.fca.weight
		UpperCAmelCase_												=      xmod_layer.fca.bias

		# output
		UpperCAmelCase_												=      layer.output
		if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
			raise AssertionError("Dimensions of feed-forward weights do not match."     )
		UpperCAmelCase_												=      xmod_layer.fca.weight
		UpperCAmelCase_												=      xmod_layer.fca.bias
		UpperCAmelCase_												=      xmod_layer.final_layer_norm.weight
		UpperCAmelCase_												=      xmod_layer.final_layer_norm.bias
		if bert_output.adapter_layer_norm is not None:
			UpperCAmelCase_												=      xmod_layer.adapter_layer_norm.weight
			UpperCAmelCase_												=      xmod_layer.adapter_layer_norm.bias

		if sorted(bert_output.adapter_modules.keys()     ) != sorted(xmod_layer.adapter_modules.keys()     ):
			raise AssertionError("Lists of language adapters do not match."     )
		for lang_code, adapter in xmod_layer.adapter_modules.items():
			UpperCAmelCase_												=      bert_output.adapter_modules[lang_code]
			UpperCAmelCase_												=      xmod_layer.adapter_modules[lang_code]
			UpperCAmelCase_												=      from_adapter.fca.weight
			UpperCAmelCase_												=      from_adapter.fca.bias
			UpperCAmelCase_												=      from_adapter.fca.weight
			UpperCAmelCase_												=      from_adapter.fca.bias

		# end of layer

	if xmod_sent_encoder.layer_norm is not None:
		UpperCAmelCase_												=      xmod_sent_encoder.layer_norm.weight
		UpperCAmelCase_												=      xmod_sent_encoder.layer_norm.bias

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

	# Let's check that we get the same results.
	UpperCAmelCase_												=      xmod.encode(_lowercase     ).unsqueeze(0     )  # batch of size 1
	model.roberta.set_default_language(_lowercase     )

	UpperCAmelCase_												=      model(_lowercase     )[0]
	if classification_head:
		UpperCAmelCase_												=      xmod.model.classification_heads["mnli"](xmod.extract_features(_lowercase     )     )
	else:
		UpperCAmelCase_												=      xmod.model(_lowercase							,  lang_id=[SAMPLE_LANGUAGE]     )[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(_lowercase							,  _lowercase							,  atol=1E-3     )
	print("Do both models output the same tensors?"							,  "🔥" if success else "💩"     )
	if not success:
		raise Exception("Something went wRoNg"     )

	Path(_lowercase     ).mkdir(parents=_lowercase							,  exist_ok=_lowercase     )
	print(F"Saving model to {pytorch_dump_folder_path}"     )
	model.save_pretrained(_lowercase     )


if __name__ == "__main__":
				_a:     List[str]							=      argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    """--xmod_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:     Union[str, Any]							=      parser.parse_args()
				convert_xmod_checkpoint_to_pytorch(
				    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
				)

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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
@flax.struct.dataclass
class       _lowerCAmelCase  :

 """simple docstring"""
 # setable values
 SCREAMING_SNAKE_CASE_				:					Optional[int]       						=None
 SCREAMING_SNAKE_CASE_				:					Optional[jnp.ndarray]       						=None
 SCREAMING_SNAKE_CASE_				:					Optional[jnp.ndarray]       						=None  # sigma(t_i)

 @classmethod
 def      __lowerCAmelCase  (						cls			:			Optional[Any]   ):






      """simple docstring"""



      return cls()




@dataclass
class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					jnp.ndarray
 SCREAMING_SNAKE_CASE_				:					jnp.ndarray
 SCREAMING_SNAKE_CASE_				:					KarrasVeSchedulerState




class       _lowerCAmelCase  (		__magic_name__			,	__magic_name__							):

 """simple docstring"""

 @property
 def      __lowerCAmelCase  (						self			:			Any   ):






      """simple docstring"""



      return True

 @register_to_config
 def __init__(						self			:			Dict					, SCREAMING_SNAKE_CASE__			:			float = 0.02					, SCREAMING_SNAKE_CASE__			:			float = 1_00					, SCREAMING_SNAKE_CASE__			:			float = 1.007					, SCREAMING_SNAKE_CASE__			:			float = 80					, SCREAMING_SNAKE_CASE__			:			float = 0.05					, SCREAMING_SNAKE_CASE__			:			float = 50					, ):






      """simple docstring"""



      pass

 def      __lowerCAmelCase  (						self			:			int   ):






      """simple docstring"""



      return KarrasVeSchedulerState.create()

 def      __lowerCAmelCase  (						self			:			Union[str, Any]					, SCREAMING_SNAKE_CASE__			:			KarrasVeSchedulerState					, SCREAMING_SNAKE_CASE__			:			int					, SCREAMING_SNAKE_CASE__			:			Tuple = ()   ):






      """simple docstring"""



      UpperCamelCase						    =					jnp.arange(0					, SCREAMING_SNAKE_CASE__   )[::-1].copy()
      UpperCamelCase						    =					[
          (
              self.config.sigma_max**2
              * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
          )
          for i in timesteps
      ]

      return state.replace(
          num_inference_steps=SCREAMING_SNAKE_CASE__					, schedule=jnp.array(SCREAMING_SNAKE_CASE__					, dtype=jnp.floataa   )					, timesteps=SCREAMING_SNAKE_CASE__					, )

 def      __lowerCAmelCase  (						self			:			List[Any]					, SCREAMING_SNAKE_CASE__			:			KarrasVeSchedulerState					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			float					, SCREAMING_SNAKE_CASE__			:			random.KeyArray					, ):






      """simple docstring"""



      if self.config.s_min <= sigma <= self.config.s_max:
           UpperCamelCase						    =					min(self.config.s_churn / state.num_inference_steps					, 2**0.5 - 1   )
      else:
           UpperCamelCase						    =					0

      # sample eps ~ N(0, S_noise^2 * I)
      UpperCamelCase						    =					random.split(SCREAMING_SNAKE_CASE__					, num=1   )
      UpperCamelCase						    =					self.config.s_noise * random.normal(key=SCREAMING_SNAKE_CASE__					, shape=sample.shape   )
      UpperCamelCase						    =					sigma + gamma * sigma
      UpperCamelCase						    =					sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

      return sample_hat, sigma_hat

 def      __lowerCAmelCase  (						self			:			Optional[int]					, SCREAMING_SNAKE_CASE__			:			KarrasVeSchedulerState					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			float					, SCREAMING_SNAKE_CASE__			:			float					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			bool = True					, ):






      """simple docstring"""



      UpperCamelCase						    =					sample_hat + sigma_hat * model_output
      UpperCamelCase						    =					(sample_hat - pred_original_sample) / sigma_hat
      UpperCamelCase						    =					sample_hat + (sigma_prev - sigma_hat) * derivative

      if not return_dict:
           return (sample_prev, derivative, state)

      return FlaxKarrasVeOutput(prev_sample=SCREAMING_SNAKE_CASE__					, derivative=SCREAMING_SNAKE_CASE__					, state=SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			Tuple					, SCREAMING_SNAKE_CASE__			:			KarrasVeSchedulerState					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			float					, SCREAMING_SNAKE_CASE__			:			float					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			bool = True					, ):






      """simple docstring"""



      UpperCamelCase						    =					sample_prev + sigma_prev * model_output
      UpperCamelCase						    =					(sample_prev - pred_original_sample) / sigma_prev
      UpperCamelCase						    =					sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

      if not return_dict:
           return (sample_prev, derivative, state)

      return FlaxKarrasVeOutput(prev_sample=SCREAMING_SNAKE_CASE__					, derivative=SCREAMING_SNAKE_CASE__					, state=SCREAMING_SNAKE_CASE__   )






 def      __lowerCAmelCase  (						self			:			Any					, SCREAMING_SNAKE_CASE__			:			KarrasVeSchedulerState					, SCREAMING_SNAKE_CASE__			:			Any					, SCREAMING_SNAKE_CASE__			:			str					, SCREAMING_SNAKE_CASE__			:			int   ):






      """simple docstring"""



      raise NotImplementedError()


