






"""simple docstring"""



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__	:    List[str]			 = 'Hello, World!'
A__	:    Optional[int]			 = 'en_XX'

def 			_snake_case    (					lowerCamelCase__     :   str    ,						lowerCamelCase__     :   str    ,						lowerCamelCase__     :   bool  ):
							lowerCamelCase_	:							Tuple									=Path("data_bin"  )
							lowerCamelCase_	:							Optional[int]									=FairseqXmodModel.from_pretrained(
							    model_name_or_path=str(Path(lowerCAmelCase__  ).parent  )    ,						checkpoint_file=Path(lowerCAmelCase__  ).name    ,						_name="xmod_base"    ,						arch="xmod_base"    ,						task="multilingual_masked_lm"    ,						data_name_or_path=str(lowerCAmelCase__  )    ,						bpe="sentencepiece"    ,						sentencepiece_model=str(Path(lowerCAmelCase__  ).parent / "sentencepiece.bpe.model"  )    ,						src_dict=str(data_dir / "dict.txt"  )    ,						)
							xmod.eval()  # disable dropout
							print(lowerCAmelCase__  )

							lowerCamelCase_	:							Optional[Any]									=xmod.model.encoder.sentence_encoder
							lowerCamelCase_	:							str									=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:
														lowerCamelCase_	:							Dict									=xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

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

							lowerCamelCase_	:							Union[str, Any]									=XmodForSequenceClassification(lowerCAmelCase__  ) if classification_head else XmodForMaskedLM(lowerCAmelCase__  )
							model.eval()

							# Now let's copy all the weights.
							# Embeddings
							lowerCamelCase_	:							str									=xmod_sent_encoder.embed_tokens.weight
							lowerCamelCase_	:							Optional[Any]									=xmod_sent_encoder.embed_positions.weight
							lowerCamelCase_	:							Dict									=torch.zeros_like(
							    model.roberta.embeddings.token_type_embeddings.weight  )  # just zero them out b/c xmod doesn't use them.

							lowerCamelCase_	:							Optional[Any]									=xmod_sent_encoder.layernorm_embedding.weight
							lowerCamelCase_	:							Dict									=xmod_sent_encoder.layernorm_embedding.bias

							for i in range(config.num_hidden_layers  ):
														# Encoder: start of layer
														lowerCamelCase_	:							List[str]									=model.roberta.encoder.layer[i]
														lowerCamelCase_	:							Optional[Any]									=xmod_sent_encoder.layers[i]

														# self attention
														lowerCamelCase_	:							int									=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_	:							int									=xmod_layer.self_attn.q_proj.weight
														lowerCamelCase_	:							int									=xmod_layer.self_attn.q_proj.bias
														lowerCamelCase_	:							Dict									=xmod_layer.self_attn.k_proj.weight
														lowerCamelCase_	:							List[str]									=xmod_layer.self_attn.k_proj.bias
														lowerCamelCase_	:							Dict									=xmod_layer.self_attn.v_proj.weight
														lowerCamelCase_	:							Union[str, Any]									=xmod_layer.self_attn.v_proj.bias

														# self-attention output
														lowerCamelCase_	:							int									=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_	:							Dict									=xmod_layer.self_attn.out_proj.weight
														lowerCamelCase_	:							Optional[int]									=xmod_layer.self_attn.out_proj.bias
														lowerCamelCase_	:							List[Any]									=xmod_layer.self_attn_layer_norm.weight
														lowerCamelCase_	:							Optional[Any]									=xmod_layer.self_attn_layer_norm.bias

														# intermediate
														lowerCamelCase_	:							Optional[Any]									=layer.intermediate
														if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
																					raise AssertionError("Dimensions of intermediate weights do not match."  )
														lowerCamelCase_	:							Tuple									=xmod_layer.fca.weight
														lowerCamelCase_	:							Optional[Any]									=xmod_layer.fca.bias

														# output
														lowerCamelCase_	:							Any									=layer.output
														if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
																					raise AssertionError("Dimensions of feed-forward weights do not match."  )
														lowerCamelCase_	:							Union[str, Any]									=xmod_layer.fca.weight
														lowerCamelCase_	:							List[Any]									=xmod_layer.fca.bias
														lowerCamelCase_	:							Dict									=xmod_layer.final_layer_norm.weight
														lowerCamelCase_	:							Union[str, Any]									=xmod_layer.final_layer_norm.bias
														if bert_output.adapter_layer_norm is not None:
																					lowerCamelCase_	:							str									=xmod_layer.adapter_layer_norm.weight
																					lowerCamelCase_	:							Any									=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_	:							List[str]									=bert_output.adapter_modules[lang_code]
																					lowerCamelCase_	:							str									=xmod_layer.adapter_modules[lang_code]
																					lowerCamelCase_	:							Any									=from_adapter.fca.weight
																					lowerCamelCase_	:							Optional[int]									=from_adapter.fca.bias
																					lowerCamelCase_	:							List[str]									=from_adapter.fca.weight
																					lowerCamelCase_	:							Optional[Any]									=from_adapter.fca.bias

														# end of layer

							if xmod_sent_encoder.layer_norm is not None:
														lowerCamelCase_	:							Union[str, Any]									=xmod_sent_encoder.layer_norm.weight
														lowerCamelCase_	:							Dict									=xmod_sent_encoder.layer_norm.bias

							if classification_head:
														lowerCamelCase_	:							List[Any]									=xmod.model.classification_heads['''mnli'''].dense.weight
														lowerCamelCase_	:							Union[str, Any]									=xmod.model.classification_heads['''mnli'''].dense.bias
														lowerCamelCase_	:							Union[str, Any]									=xmod.model.classification_heads['''mnli'''].out_proj.weight
														lowerCamelCase_	:							str									=xmod.model.classification_heads['''mnli'''].out_proj.bias
							else:
														# LM Head
														lowerCamelCase_	:							Tuple									=xmod.model.encoder.lm_head.dense.weight
														lowerCamelCase_	:							int									=xmod.model.encoder.lm_head.dense.bias
														lowerCamelCase_	:							int									=xmod.model.encoder.lm_head.layer_norm.weight
														lowerCamelCase_	:							Optional[int]									=xmod.model.encoder.lm_head.layer_norm.bias
														lowerCamelCase_	:							List[str]									=xmod.model.encoder.lm_head.weight
														lowerCamelCase_	:							int									=xmod.model.encoder.lm_head.bias

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

							lowerCamelCase_	:							Optional[Any]									=model(lowerCAmelCase__  )[0]
							if classification_head:
														lowerCamelCase_	:							str									=xmod.model.classification_heads['''mnli'''](xmod.extract_features(lowerCAmelCase__  )  )
							else:
														lowerCamelCase_	:							Optional[Any]									=xmod.model(lowerCAmelCase__    ,						lang_id=[SAMPLE_LANGUAGE]  )[0]
							print(our_output.shape    ,						their_output.shape  )
							lowerCamelCase_	:							Optional[int]									=torch.max(torch.abs(our_output - their_output  )  ).item()
							print(F"""max_absolute_diff = {max_absolute_diff}"""  )  # ~ 1e-7
							lowerCamelCase_	:							List[str]									=torch.allclose(lowerCAmelCase__    ,						lowerCAmelCase__    ,						atol=1e-3  )
							print("Do both models output the same tensors?"    ,						"🔥" if success else "💩"  )
							if not success:
														raise Exception("Something went wRoNg"  )

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


if __name__ == "__main__":
						A__	:    Any			 = 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
						)











"""simple docstring"""



import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


A__	:    List[str]			 = datasets.utils.logging.get_logger(__name__)







@dataclass
class        lowercase__      (							datasets.BuilderConfig       ):
			_UpperCAmelCase		:int             =			10000
			_UpperCAmelCase		:Optional[List[str]]             =			None
			_UpperCAmelCase		:Optional[datasets.Features]             =			None







class        lowercase__      (							datasets.ArrowBasedBuilder       ):
			_UpperCAmelCase		:Optional[int]             =			ParquetConfig


			def      UpperCAmelCase__						(				self					:  Optional[int] ):
										return datasets.DatasetInfo(features=self.config.features )


			def      UpperCAmelCase__						(				self					:  List[Any]       ,	snake_case__					:  List[Any] ):
										if not self.config.data_files:
																	raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}""" )
										lowerCamelCase_	:							str									=dl_manager.download_and_extract(self.config.data_files )
										if isinstance(snake_case__       ,	(str, list, tuple) ):
																	lowerCamelCase_	:							Dict									=data_files
																	if isinstance(snake_case__       ,	snake_case__ ):
																								lowerCamelCase_	:							List[Any]									=[files]
																	# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
																	lowerCamelCase_	:							Any									=[dl_manager.iter_files(snake_case__ ) for file in files]
																	return [datasets.SplitGenerator(name=datasets.Split.TRAIN       ,	gen_kwargs={"files": files} )]
										lowerCamelCase_	:							Optional[int]									=[]
										for split_name, files in data_files.items():
																	if isinstance(snake_case__       ,	snake_case__ ):
																								lowerCamelCase_	:							Optional[int]									=[files]
																	# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
																	lowerCamelCase_	:							int									=[dl_manager.iter_files(snake_case__ ) for file in files]
																	# Infer features is they are stoed in the arrow schema
																	if self.info.features is None:
																								for file in itertools.chain.from_iterable(snake_case__ ):
																															with open(snake_case__       ,	"rb" ) as f:
																																						lowerCamelCase_	:							List[Any]									=datasets.Features.from_arrow_schema(pq.read_schema(snake_case__ ) )
																															break
																	splits.append(datasets.SplitGenerator(name=snake_case__       ,	gen_kwargs={"files": files} ) )
										return splits


			def      UpperCAmelCase__						(				self					:  int       ,	snake_case__					:  pa.Table ):
										if self.info.features is not None:
																	# more expensive cast to support nested features with keys in a different order
																	# allows str <-> int/float or str to Audio for example
																	lowerCamelCase_	:							List[str]									=table_cast(snake_case__       ,	self.info.features.arrow_schema )
										return pa_table


			def      UpperCAmelCase__						(				self					:  int       ,	snake_case__					:  Optional[Any] ):
										lowerCamelCase_	:							Tuple									=self.info.features.arrow_schema if self.info.features is not None else None
										if self.info.features is not None and self.config.columns is not None:
																	if sorted(field.name for field in schema ) != sorted(self.config.columns ):
																								raise ValueError(
																								    F"""Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'""" )
										for file_idx, file in enumerate(itertools.chain.from_iterable(snake_case__ ) ):
																	with open(snake_case__       ,	"rb" ) as f:
																								lowerCamelCase_	:							List[str]									=pq.ParquetFile(snake_case__ )
																								try:
																															for batch_idx, record_batch in enumerate(
																															    parquet_file.iter_batches(batch_size=self.config.batch_size       ,	columns=self.config.columns ) ):
																																						lowerCamelCase_	:							Union[str, Any]									=pa.Table.from_batches([record_batch] )
																																						# Uncomment for debugging (will print the Arrow table size and elements)
																																						# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																																						# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																																						yield F"""{file_idx}_{batch_idx}""", self._cast_table(snake_case__ )
																								except ValueError as e:
																															logger.error(F"""Failed to read file '{file}' with error {type(snake_case__ )}: {e}""" )
																															raise



