


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()
lowercase_	   =     logging.get_logger(__name__)

lowercase_	   =     'Hello, World!'
lowercase_	   =     'en_XX'
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							int		 =						Path('data_bin'						)
 __lowerCamelCase		:							Union[str, Any]		 =						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		:							Dict		 =						xmod.model.encoder.sentence_encoder
 __lowerCamelCase		:							List[Any]		 =						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		:							List[Any]		 =						xmod.model.classification_heads['mnli'].out_proj.weight.shape[0]

 print('Our X-MOD config:'      ,	SCREAMING_SNAKE_CASE__						)

 __lowerCamelCase		:							Tuple		 =						XmodForSequenceClassification(SCREAMING_SNAKE_CASE__						) if classification_head else XmodForMaskedLM(SCREAMING_SNAKE_CASE__						)
 model.eval()

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

 __lowerCamelCase		:							Union[str, Any]		 =						xmod_sent_encoder.layernorm_embedding.weight
 __lowerCamelCase		:							Union[str, Any]		 =						xmod_sent_encoder.layernorm_embedding.bias

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

  # self attention
  __lowerCamelCase		:							Optional[Any]		 =						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		:							Any		 =						xmod_layer.self_attn.q_proj.bias
  __lowerCamelCase		:							Tuple		 =						xmod_layer.self_attn.k_proj.weight
  __lowerCamelCase		:							Optional[int]		 =						xmod_layer.self_attn.k_proj.bias
  __lowerCamelCase		:							List[Any]		 =						xmod_layer.self_attn.v_proj.weight
  __lowerCamelCase		:							Dict		 =						xmod_layer.self_attn.v_proj.bias

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

  # intermediate
  __lowerCamelCase		:							List[Any]		 =						layer.intermediate
  if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
   raise AssertionError('Dimensions of intermediate weights do not match.'						)
  __lowerCamelCase		:							Union[str, Any]		 =						xmod_layer.fca.weight
  __lowerCamelCase		:							Optional[Any]		 =						xmod_layer.fca.bias

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

  # end of layer

 if xmod_sent_encoder.layer_norm is not None:
  __lowerCamelCase		:							Optional[int]		 =						xmod_sent_encoder.layer_norm.weight
  __lowerCamelCase		:							Optional[int]		 =						xmod_sent_encoder.layer_norm.bias

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

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

 __lowerCamelCase		:							Union[str, Any]		 =						model(SCREAMING_SNAKE_CASE__						)[0]
 if classification_head:
  __lowerCamelCase		:							Tuple		 =						xmod.model.classification_heads['mnli'](xmod.extract_features(SCREAMING_SNAKE_CASE__						)						)
 else:
  __lowerCamelCase		:							Any		 =						xmod.model(SCREAMING_SNAKE_CASE__      ,	lang_id=[SAMPLE_LANGUAGE]						)[0]
 print(our_output.shape      ,	their_output.shape						)
 __lowerCamelCase		:							str		 =						torch.max(torch.abs(our_output - their_output						)						).item()
 print(f'max_absolute_diff = {max_absolute_diff}'						)  # ~ 1e-7
 __lowerCamelCase		:							Optional[int]		 =						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__":
       lowercase_	   =     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.'
       )
       lowercase_	   =     parser.parse_args()
       convert_xmod_checkpoint_to_pytorch(
           args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
       )





import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


lowercase_	   =     5_0_0_0_0
lowercase_	   =     5_0_0_0

lowercase_   ,lowercase_	   =     os.path.split(__file__)
lowercase_	   =     os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))
@get_duration
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 for i in range(SCREAMING_SNAKE_CASE__						):
  __lowerCamelCase		:							Tuple		 =						dataset[i]
@get_duration
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 for i in range(0      ,	len(SCREAMING_SNAKE_CASE__						)      ,	SCREAMING_SNAKE_CASE__						):
  __lowerCamelCase		:							Optional[Any]		 =						dataset[i : i + batch_size]
@get_duration
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 with dataset.formatted_as(type=SCREAMING_SNAKE_CASE__						):
  for i in range(SCREAMING_SNAKE_CASE__						):
   __lowerCamelCase		:							str		 =						dataset[i]
@get_duration
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 with dataset.formatted_as(type=SCREAMING_SNAKE_CASE__						):
  for i in range(0      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
   __lowerCamelCase		:							int		 =						dataset[i : i + batch_size]
def 				UpperCamelCase__					(  ):
 __lowerCamelCase		:							Union[str, Any]		 =						{'num examples': SPEED_TEST_N_EXAMPLES}
 __lowerCamelCase		:							Optional[Any]		 =						[
     (read, {'length': SMALL_TEST}),
     (read, {'length': SPEED_TEST_N_EXAMPLES}),
     (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
     (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 100}),
     (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_000}),
     (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
     (read_formatted, {'type': 'pandas', 'length': SMALL_TEST}),
     (read_formatted, {'type': 'torch', 'length': SMALL_TEST}),
     (read_formatted, {'type': 'tensorflow', 'length': SMALL_TEST}),
     (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
     (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1_000}),
 ]

 __lowerCamelCase		:							Any		 =						[
     (read, {'length': SMALL_TEST}),
     (read, {'length': SPEED_TEST_N_EXAMPLES}),
     (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
     (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 100}),
     (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_000}),
     (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
     (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
     (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1_000}),
 ]
 with tempfile.TemporaryDirectory() as tmp_dir:
  print('generating dataset'						)
  __lowerCamelCase		:							Optional[int]		 =						datasets.Features(
      {'list': datasets.Sequence(datasets.Value('float32'						)						), 'numbers': datasets.Value('float32'						)}						)
  __lowerCamelCase		:							str		 =						generate_example_dataset(
      os.path.join(SCREAMING_SNAKE_CASE__      ,	'dataset.arrow'						)      ,	SCREAMING_SNAKE_CASE__      ,	num_examples=SCREAMING_SNAKE_CASE__      ,	seq_shapes={'list': (100,)}      ,	)
  print('first set of iterations'						)
  for func, kwargs in functions:
   print(func.__name__      ,	str(SCREAMING_SNAKE_CASE__						)						)
   __lowerCamelCase		:							Optional[int]		 =						func(SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__						)

  print('shuffling dataset'						)
  __lowerCamelCase		:							str		 =						dataset.shuffle()
  print('Second set of iterations (after shuffling'						)
  for func, kwargs in functions_shuffled:
   print('shuffled '      ,	func.__name__      ,	str(SCREAMING_SNAKE_CASE__						)						)
   __lowerCamelCase		:							int		 =						func(
       SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__						)

 with open(SCREAMING_SNAKE_CASE__      ,	'wb'						) as f:
  f.write(json.dumps(SCREAMING_SNAKE_CASE__						).encode('utf-8'						)						)


if __name__ == "__main__":  # useful to run the profiler
       benchmark_iterating()

