


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_					:    List[Any]          =    logging.get_logger(__name__)

a_					:    Dict          =    "Hello, World!"
a_					:    Optional[int]          =    "en_XX"






def 	_A				(lowerCAmelCase__  :str				,						lowerCAmelCase__  :str				,						lowerCAmelCase__  :bool		)   ->  Tuple:




     '''simple docstring'''



     _a			      =							Path('data_bin'		)
     _a			      =							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__		)

     _a			      =							xmod.model.encoder.sentence_encoder
     _a			      =							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:
          _a			      =							xmod.model.classification_heads['mnli'].out_proj.weight.shape[0]

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

     _a			      =							XmodForSequenceClassification(lowerCAmelCase__		) if classification_head else XmodForMaskedLM(lowerCAmelCase__		)
     model.eval()

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

     _a			      =							xmod_sent_encoder.layernorm_embedding.weight
     _a			      =							xmod_sent_encoder.layernorm_embedding.bias

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

          # self attention
          _a			      =							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.'		)

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

          # self-attention output
          _a			      =							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.'		)
          _a			      =							xmod_layer.self_attn.out_proj.weight
          _a			      =							xmod_layer.self_attn.out_proj.bias
          _a			      =							xmod_layer.self_attn_layer_norm.weight
          _a			      =							xmod_layer.self_attn_layer_norm.bias

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

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

          # end of layer

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

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

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

     _a			      =							model(lowerCAmelCase__		)[0]
     if classification_head:
          _a			      =							xmod.model.classification_heads['mnli'](xmod.extract_features(lowerCAmelCase__		)		)
     else:
          _a			      =							xmod.model(lowerCAmelCase__				,						lang_id=[SAMPLE_LANGUAGE]		)[0]
     print(our_output.shape				,						their_output.shape		)
     _a			      =							torch.max(torch.abs(our_output - their_output		)		).item()
     print(f'max_absolute_diff = {max_absolute_diff}'		)  # ~ 1e-7
     _a			      =							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_					:    Union[str, 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_					:    List[str]          =    parser.parse_args()
 convert_xmod_checkpoint_to_pytorch(
     args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
 )





'''simple docstring'''






import logging

import numpy as np
import pytest
from scipy.linalg import eigh

logging.basicConfig(level=logging.INFO, format="%(message)s")






def 	_A				(lowerCAmelCase__  :np.ndarray		)   ->  np.ndarray:




     '''simple docstring'''



     return input_array.reshape((input_array.size, 1)		)






def 	_A				(lowerCAmelCase__  :np.ndarray				,						lowerCAmelCase__  :np.ndarray				,						lowerCAmelCase__  :int		)   ->  np.ndarray:




     '''simple docstring'''



     _a			      =							np.nan
     for i in range(lowerCAmelCase__		):
          _a			      =							features[:, labels == i]
          _a			      =							data.mean(1		)
          # Centralize the data of class i
          _a			      =							data - column_reshape(lowerCAmelCase__		)
          if i > 0:
               # If covariance_sum is not None
               covariance_sum += np.dot(lowerCAmelCase__				,						centered_data.T		)
          else:
               # If covariance_sum is np.nan (i.e. first loop)
               _a			      =							np.dot(lowerCAmelCase__				,						centered_data.T		)

     return covariance_sum / features.shape[1]






def 	_A				(lowerCAmelCase__  :np.ndarray				,						lowerCAmelCase__  :np.ndarray				,						lowerCAmelCase__  :int		)   ->  np.ndarray:




     '''simple docstring'''



     _a			      =							features.mean(1		)
     _a			      =							np.nan
     for i in range(lowerCAmelCase__		):
          _a			      =							features[:, labels == i]
          _a			      =							data.shape[1]
          _a			      =							data.mean(1		)
          if i > 0:
               # If covariance_sum is not None
               covariance_sum += device_data * np.dot(
                   column_reshape(lowerCAmelCase__		) - column_reshape(lowerCAmelCase__		)				,						(column_reshape(lowerCAmelCase__		) - column_reshape(lowerCAmelCase__		)).T				,						)
          else:
               # If covariance_sum is np.nan (i.e. first loop)
               _a			      =							device_data * np.dot(
                   column_reshape(lowerCAmelCase__		) - column_reshape(lowerCAmelCase__		)				,						(column_reshape(lowerCAmelCase__		) - column_reshape(lowerCAmelCase__		)).T				,						)

     return covariance_sum / features.shape[1]






def 	_A				(lowerCAmelCase__  :np.ndarray				,						lowerCAmelCase__  :int		)   ->  np.ndarray:




     '''simple docstring'''



     if features.any():
          _a			      =							features.mean(1		)
          # Center the dataset
          _a			      =							features - np.reshape(lowerCAmelCase__				,						(data_mean.size, 1)		)
          _a			      =							np.dot(lowerCAmelCase__				,						centered_data.T		) / features.shape[1]
          _a      ,   _a			      =							np.linalg.eigh(lowerCAmelCase__		)
          # Take all the columns in the reverse order (-1), and then takes only the first
          _a			      =							eigenvectors[:, ::-1][:, 0:dimensions]
          # Project the database on the new space
          _a			      =							np.dot(filtered_eigenvectors.T				,						lowerCAmelCase__		)
          logging.info('Principal Component Analysis computed'		)

          return projected_data
     else:
          logging.basicConfig(level=logging.ERROR				,						format='%(message)s'				,						force=lowerCAmelCase__		)
          logging.error('Dataset empty'		)
          raise AssertionError






def 	_A				(lowerCAmelCase__  :np.ndarray				,						lowerCAmelCase__  :np.ndarray				,						lowerCAmelCase__  :int				,						lowerCAmelCase__  :int		)   ->  np.ndarray:




     '''simple docstring'''



     assert classes > dimensions

     # Check if features have been already loaded
     if features.any:
          _a      ,   _a			      =							eigh(
              covariance_between_classes(lowerCAmelCase__				,						lowerCAmelCase__				,						lowerCAmelCase__		)				,						covariance_within_classes(lowerCAmelCase__				,						lowerCAmelCase__				,						lowerCAmelCase__		)				,						)
          _a			      =							eigenvectors[:, ::-1][:, :dimensions]
          _a      ,   _a      ,   _a			      =							np.linalg.svd(lowerCAmelCase__		)
          _a			      =							svd_matrix[:, 0:dimensions]
          _a			      =							np.dot(filtered_svd_matrix.T				,						lowerCAmelCase__		)
          logging.info('Linear Discriminant Analysis computed'		)

          return projected_data
     else:
          logging.basicConfig(level=logging.ERROR				,						format='%(message)s'				,						force=lowerCAmelCase__		)
          logging.error('Dataset empty'		)
          raise AssertionError






def 	_A				()   ->  None:




     '''simple docstring'''



     _a			      =							np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]		)
     _a			      =							np.array([0, 0, 0, 1, 1]		)
     _a			      =							2
     _a			      =							2

     # Assert that the function raises an AssertionError if dimensions > classes
     with pytest.raises(lowerCAmelCase__		) as error_info:
          _a			      =							linear_discriminant_analysis(
              lowerCAmelCase__				,						lowerCAmelCase__				,						lowerCAmelCase__				,						lowerCAmelCase__		)
          if isinstance(lowerCAmelCase__				,						np.ndarray		):
               raise AssertionError(
                   'Did not raise AssertionError for dimensions > classes'		)
          assert error_info.type is AssertionError






def 	_A				()   ->  None:




     '''simple docstring'''



     _a			      =							np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]		)
     _a			      =							2
     _a			      =							np.array([[6.9_2_8_2_0_3_2_3, 8.6_6_0_2_5_4_0_4, 1_0.3_9_2_3_0_4_8_5], [3.0, 3.0, 3.0]]		)

     with pytest.raises(lowerCAmelCase__		) as error_info:
          _a			      =							principal_component_analysis(lowerCAmelCase__				,						lowerCAmelCase__		)
          if not np.allclose(lowerCAmelCase__				,						lowerCAmelCase__		):
               raise AssertionError
          assert error_info.type is AssertionError


if __name__ == "__main__":
 import doctest

 doctest.testmod()

