




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()
UpperCamelCase__    			=			logging.get_logger(__name__)

UpperCamelCase__    			=			"""Hello, World!"""
UpperCamelCase__    			=			"""en_XX"""







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

					__lowerCAmelCase				   =       xmod.model.encoder.sentence_encoder
					__lowerCAmelCase				   =       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=5_14      ,      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:
										__lowerCAmelCase				   =       xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]

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

					__lowerCAmelCase				   =       XmodForSequenceClassification(SCREAMING_SNAKE_CASE_     ) if classification_head else XmodForMaskedLM(SCREAMING_SNAKE_CASE_     )
					model.eval()

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

					__lowerCAmelCase				   =       xmod_sent_encoder.layernorm_embedding.weight
					__lowerCAmelCase				   =       xmod_sent_encoder.layernorm_embedding.bias

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

										# self attention
										__lowerCAmelCase				   =       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."     )

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

										# self-attention output
										__lowerCAmelCase				   =       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."     )
										__lowerCAmelCase				   =       xmod_layer.self_attn.out_proj.weight
										__lowerCAmelCase				   =       xmod_layer.self_attn.out_proj.bias
										__lowerCAmelCase				   =       xmod_layer.self_attn_layer_norm.weight
										__lowerCAmelCase				   =       xmod_layer.self_attn_layer_norm.bias

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

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

										# end of layer

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

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

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

					__lowerCAmelCase				   =       model(SCREAMING_SNAKE_CASE_     )[0]
					if classification_head:
										__lowerCAmelCase				   =       xmod.model.classification_heads["mnli"](xmod.extract_features(SCREAMING_SNAKE_CASE_     )     )
					else:
										__lowerCAmelCase				   =       xmod.model(SCREAMING_SNAKE_CASE_      ,      lang_id=[SAMPLE_LANGUAGE]     )[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(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"     )

					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(
					    """--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."""
					)
					UpperCamelCase__    			=			parser.parse_args()
					convert_xmod_checkpoint_to_pytorch(
					    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
					)











from __future__ import annotations
def      _A       (		SCREAMING_SNAKE_CASE     :   list						):





  """simple docstring"""



  if len(SCREAMING_SNAKE_CASE						) == 0:
    return []
  a__					,							a__	:					int										=min(SCREAMING_SNAKE_CASE						), max(SCREAMING_SNAKE_CASE						)
  a__	:					Optional[int]										=int(max_value - min_value						) + 1
  a__	:					list[list]										=[[] for _ in range(SCREAMING_SNAKE_CASE						)]

  for i in my_list:
    buckets[int(i - min_value						)].append(SCREAMING_SNAKE_CASE						)

  return [v for bucket in buckets for v in sorted(SCREAMING_SNAKE_CASE						)]


if __name__ == "__main__":
     from doctest import testmod

     testmod()
     assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
     assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]






