


import os
import sys
from pathlib import Path

import torch

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
from utils import load_json


lowerCamelCase		      = torch.cuda.is_available()
lowerCamelCase		      = [' Sam ate lunch today.', 'Sams lunch ingredients.']
lowerCamelCase		      = ['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
lowerCamelCase		      = {
    'max_tokens_per_batch': None,
    'supervise_forward': True,
    'normalize_hidden': True,
    'label_smoothing': 0.2,
    'eval_max_gen_length': None,
    'eval_beams': 1,
    'val_metric': 'loss',
    'save_top_k': 1,
    'adafactor': True,
    'early_stopping_patience': 2,
    'logger_name': 'default',
    'length_penalty': 0.5,
    'cache_dir': '',
    'task': 'summarization',
    'num_workers': 2,
    'alpha_hid': 0,
    'freeze_embeds': True,
    'enc_only': False,
    'tgt_suffix': '',
    'resume_from_checkpoint': None,
    'sortish_sampler': True,
    'student_decoder_layers': 1,
    'val_check_interval': 1.0,
    'output_dir': '',
    'fp16': False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    'no_teacher': False,
    'fp16_opt_level': 'O1',
    'gpus': 1 if CUDA_AVAILABLE else 0,
    'n_tpu_cores': 0,
    'max_grad_norm': 1.0,
    'do_train': True,
    'do_predict': True,
    'accumulate_grad_batches': 1,
    'server_ip': '',
    'server_port': '',
    'seed': 42,
    'model_name_or_path': 'sshleifer/bart-tiny-random',
    'config_name': '',
    'tokenizer_name': 'facebook/bart-large',
    'do_lower_case': False,
    'learning_rate': 0.3,
    'lr_scheduler': 'linear',
    'weight_decay': 0.0,
    'adam_epsilon': 1e-0_8,
    'warmup_steps': 0,
    'max_epochs': 1,
    'train_batch_size': 2,
    'eval_batch_size': 2,
    'max_source_length': 12,
    'max_target_length': 12,
    'val_max_target_length': 12,
    'test_max_target_length': 12,
    'fast_dev_run': False,
    'no_cache': False,
    'n_train': -1,
    'n_val': -1,
    'n_test': -1,
    'student_encoder_layers': 1,
    'freeze_encoder': False,
    'auto_scale_batch_size': False,
    'overwrite_output_dir': False,
    'student': None,
}




def 			a_			(					SCREAMING_SNAKE_CASE__      :   Optional[int]	,    SCREAMING_SNAKE_CASE__      :   Any  ):
       '''simple docstring'''





       _lowerCamelCase :	Any						='\n'.join(SCREAMING_SNAKE_CASE__  )
       Path(SCREAMING_SNAKE_CASE__  ).open('w'  ).writelines(SCREAMING_SNAKE_CASE__  )




def 			a_			(					SCREAMING_SNAKE_CASE__      :   str  ):
       '''simple docstring'''





       for split in ["train", "val", "test"]:
              _dump_articles(os.path.join(SCREAMING_SNAKE_CASE__	,    F'''{split}.source'''  )	,    SCREAMING_SNAKE_CASE__  )
              _dump_articles(os.path.join(SCREAMING_SNAKE_CASE__	,    F'''{split}.target'''  )	,    SCREAMING_SNAKE_CASE__  )
       return tmp_dir




class A						(     lowercase_  ):






      @classmethod
      def  lowerCamelCase (      cls		:  Union[str, Any]				)      ->		Union[str, Any]:
             """simple docstring"""




             return cls






      @require_torch_multi_gpu
      def  lowerCamelCase (      self		:  List[Any]				)      ->		List[Any]:
             """simple docstring"""




             _lowerCamelCase :	Dict						={
                 'no_teacher': True,
                 'freeze_encoder': True,
                 'gpus': 2,
                 'overwrite_output_dir': True,
                 'sortish_sampler': True,
             }
             self._test_distiller_cli_fork(lowercase_    ,     check_contents=lowercase_				)






      def  lowerCamelCase (      self		:  str    ,     lowercase_		:  int    ,     lowercase_		:  int=True				)      ->		Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase :	str						={
                 'label_smoothing': 0.0,
                 'early_stopping_patience': -1,
                 'train_batch_size': 1,
                 'eval_batch_size': 2,
                 'max_epochs': 2,
                 'alpha_mlm': 0.2,
                 'alpha_ce': 0.8,
                 'do_predict': True,
                 'model_name_or_path': 'sshleifer/tinier_bart',
                 'teacher': CHEAP_ARGS['model_name_or_path'],
                 'val_check_interval': 0.5,
             }
             default_updates.update(lowercase_				)
             _lowerCamelCase :	Union[str, Any]						=CHEAP_ARGS.copy()
             _lowerCamelCase :	Any						=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)
             _lowerCamelCase :	Dict						=self.get_auto_remove_tmp_dir()
             args_d.update(data_dir=lowercase_    ,     output_dir=lowercase_    ,     **lowercase_				)

             def convert(lowercase_		:  Tuple    ,     lowercase_		:  Any				):
                    if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
                           return ""
                    if v is False or v is None:
                           return ""
                    if v is True:  # or len(str(v))==0:
                           return F'''--{k}'''
                    return F'''--{k}={v}'''

             _lowerCamelCase :	Union[str, Any]						=[x for x in (convert(lowercase_    ,     lowercase_				) for k, v in args_d.items()) if len(lowercase_				)]
             _lowerCamelCase :	Optional[int]						=[sys.executable, F'''{self.test_file_dir}/distillation.py'''] + cli_args
             execute_subprocess_async(lowercase_    ,     env=self.get_env()				)

             _lowerCamelCase :	List[str]						=os.listdir(lowercase_				)
             _lowerCamelCase :	str						={os.path.basename(lowercase_				) for p in contents}
             _lowerCamelCase :	Optional[Any]						=[p for p in contents if p.endswith('ckpt'				)]
             assert len(lowercase_				) > 0

             self.assertIn('test_generations.txt'    ,     lowercase_				)
             self.assertIn('test_results.txt'    ,     lowercase_				)

             # get the following from the module, (we don't have access to `model` here)
             _lowerCamelCase :	List[Any]						=os.path.join(lowercase_    ,     'metrics.json'				)
             _lowerCamelCase :	Any						='rouge2'

             _lowerCamelCase :	int						=load_json(lowercase_				)
             # {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
             print(lowercase_				)
             _lowerCamelCase :	List[str]						=metrics['val'][-1]
             self.assertGreaterEqual(last_step_stats['val_avg_gen_time']    ,     0.01				)
             self.assertIsInstance(last_step_stats[F'''val_avg_{val_metric}''']    ,     lowercase_				)
             self.assertEqual(len(metrics['test']				)    ,     1				)
             _lowerCamelCase :	Optional[Any]						=int(args_d['max_epochs'] * (1 / args_d['val_check_interval']) / 2 + 1				)
             self.assertEqual(len(metrics['val']				)    ,     lowercase_				)






import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('0.9.0'):
 raise Exception('requires fairseq >= 0.9.0')


logging.set_verbosity_info()
lowerCamelCase		      = logging.get_logger(__name__)

lowerCamelCase		      = 'Hello world! cécé herlolip'




def 			a_   (					SCREAMING_SNAKE_CASE__      :   str	,    SCREAMING_SNAKE_CASE__      :   str	,    SCREAMING_SNAKE_CASE__      :   bool  ):
       '''simple docstring'''





       _lowerCamelCase :	Dict						=FairseqRobertaModel.from_pretrained(SCREAMING_SNAKE_CASE__  )
       roberta.eval()  # disable dropout
       _lowerCamelCase :	int						=roberta.model.encoder.sentence_encoder
       _lowerCamelCase :	Union[str, Any]						=RobertaConfig(
           vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings	,    hidden_size=roberta.args.encoder_embed_dim	,    num_hidden_layers=roberta.args.encoder_layers	,    num_attention_heads=roberta.args.encoder_attention_heads	,    intermediate_size=roberta.args.encoder_ffn_embed_dim	,    max_position_embeddings=514	,    type_vocab_size=1	,    layer_norm_eps=1e-5	,    )
       if classification_head:
              _lowerCamelCase :	int						=roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]
       print('Our BERT config:'	,    SCREAMING_SNAKE_CASE__  )

       _lowerCamelCase :	Optional[int]						=RobertaForSequenceClassification(SCREAMING_SNAKE_CASE__  ) if classification_head else RobertaForMaskedLM(SCREAMING_SNAKE_CASE__  )
       model.eval()

       # Now let's copy all the weights.
       # Embeddings
       _lowerCamelCase :	List[str]						=roberta_sent_encoder.embed_tokens.weight
       _lowerCamelCase :	int						=roberta_sent_encoder.embed_positions.weight
       _lowerCamelCase :	Tuple						=torch.zeros_like(
           model.roberta.embeddings.token_type_embeddings.weight  )  # just zero them out b/c RoBERTa doesn't use them.
       _lowerCamelCase :	Optional[Any]						=roberta_sent_encoder.emb_layer_norm.weight
       _lowerCamelCase :	int						=roberta_sent_encoder.emb_layer_norm.bias

       for i in range(config.num_hidden_layers  ):
              # Encoder: start of layer
              _lowerCamelCase :	BertLayer						=model.roberta.encoder.layer[i]
              _lowerCamelCase :	TransformerSentenceEncoderLayer						=roberta_sent_encoder.layers[i]

              # self attention
              _lowerCamelCase :	BertSelfAttention						=layer.attention.self
              assert (
                  roberta_layer.self_attn.k_proj.weight.data.shape
                  == roberta_layer.self_attn.q_proj.weight.data.shape
                  == roberta_layer.self_attn.v_proj.weight.data.shape
                  == torch.Size((config.hidden_size, config.hidden_size)  )
              )

              _lowerCamelCase :	Dict						=roberta_layer.self_attn.q_proj.weight
              _lowerCamelCase :	List[Any]						=roberta_layer.self_attn.q_proj.bias
              _lowerCamelCase :	Optional[Any]						=roberta_layer.self_attn.k_proj.weight
              _lowerCamelCase :	int						=roberta_layer.self_attn.k_proj.bias
              _lowerCamelCase :	Optional[Any]						=roberta_layer.self_attn.v_proj.weight
              _lowerCamelCase :	int						=roberta_layer.self_attn.v_proj.bias

              # self-attention output
              _lowerCamelCase :	BertSelfOutput						=layer.attention.output
              assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
              _lowerCamelCase :	Union[str, Any]						=roberta_layer.self_attn.out_proj.weight
              _lowerCamelCase :	str						=roberta_layer.self_attn.out_proj.bias
              _lowerCamelCase :	List[Any]						=roberta_layer.self_attn_layer_norm.weight
              _lowerCamelCase :	List[Any]						=roberta_layer.self_attn_layer_norm.bias

              # intermediate
              _lowerCamelCase :	BertIntermediate						=layer.intermediate
              assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
              _lowerCamelCase :	str						=roberta_layer.fca.weight
              _lowerCamelCase :	Optional[int]						=roberta_layer.fca.bias

              # output
              _lowerCamelCase :	BertOutput						=layer.output
              assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
              _lowerCamelCase :	str						=roberta_layer.fca.weight
              _lowerCamelCase :	List[Any]						=roberta_layer.fca.bias
              _lowerCamelCase :	List[str]						=roberta_layer.final_layer_norm.weight
              _lowerCamelCase :	Optional[int]						=roberta_layer.final_layer_norm.bias
              # end of layer

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

       # Let's check that we get the same results.
       _lowerCamelCase :	torch.Tensor						=roberta.encode(SCREAMING_SNAKE_CASE__  ).unsqueeze(0  )  # batch of size 1

       _lowerCamelCase :	List[Any]						=model(SCREAMING_SNAKE_CASE__  )[0]
       if classification_head:
              _lowerCamelCase :	int						=roberta.model.classification_heads['mnli'](roberta.extract_features(SCREAMING_SNAKE_CASE__  )  )
       else:
              _lowerCamelCase :	int						=roberta.model(SCREAMING_SNAKE_CASE__  )[0]
       print(our_output.shape	,    their_output.shape  )
       _lowerCamelCase :	Any						=torch.max(torch.abs(our_output - their_output  )  ).item()
       print(F'''max_absolute_diff = {max_absolute_diff}'''  )  # ~ 1e-7
       _lowerCamelCase :	Optional[Any]						=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'  )

       pathlib.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__":
 lowerCamelCase		      = argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--roberta_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.'
 )
 lowerCamelCase		      = parser.parse_args()
 convert_roberta_checkpoint_to_pytorch(
     args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
 )


