




import argparse
import os
from pathlib import Path

import fairseq
import torch
from packaging import version
from torch import nn

from transformers import (
    BartConfig,
    BartForConditionalGeneration,
    BartForSequenceClassification,
    BartModel,
    BartTokenizer,
)
from transformers.utils import logging


lowerCamelCase__			:       List[str]         =							['bart.large', 'bart.large.mnli', 'bart.large.cnn', 'bart_xsum/model.pt']
lowerCamelCase__			:       Any         =							{'bart.large': BartModel, 'bart.large.mnli': BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse('0.9.0'):
   raise Exception('requires fairseq >= 0.9.0')


logging.set_verbosity_info()
lowerCamelCase__			:       Union[str, Any]         =							logging.get_logger(__name__)

lowerCamelCase__			:       int         =							' Hello world! cécé herlolip'

lowerCamelCase__			:       str         =							[
    ('model.classification_heads.mnli.dense.weight', 'classification_head.dense.weight'),
    ('model.classification_heads.mnli.dense.bias', 'classification_head.dense.bias'),
    ('model.classification_heads.mnli.out_proj.weight', 'classification_head.out_proj.weight'),
    ('model.classification_heads.mnli.out_proj.bias', 'classification_head.out_proj.bias'),
]

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Tuple							) ->			Any:
      SCREAMING_SNAKE_CASE_     						=      [
          'encoder.version',
          'decoder.version',
          'model.encoder.version',
          'model.decoder.version',
          '_float_tensor',
      ]
      for k in ignore_keys:
            state_dict.pop(__UpperCAmelCase      ,      __UpperCAmelCase							)

def  UpperCAmelCase_							(				__UpperCAmelCase						:     int      ,      __UpperCAmelCase						:     List[Any]      ,      __UpperCAmelCase						:     Tuple							) ->			Dict:
      SCREAMING_SNAKE_CASE_     						=      dct.pop(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_     						=      val

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Any							) ->			Any:
      SCREAMING_SNAKE_CASE_     						=      torch.load(__UpperCAmelCase      ,      map_location='cpu'							)
      SCREAMING_SNAKE_CASE_     						=      torch.hub.load('pytorch/fairseq'      ,      'bart.large.cnn'							).eval()
      hub_interface.model.load_state_dict(sd['model']							)
      return hub_interface

def  UpperCAmelCase_							(				__UpperCAmelCase						:     str							) ->			Union[str, Any]:
      SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      emb.weight.shape
      SCREAMING_SNAKE_CASE_     						=      nn.Linear(__UpperCAmelCase      ,      __UpperCAmelCase      ,      bias=__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_     						=      emb.weight.data
      return lin_layer

@torch.no_grad()
def  UpperCAmelCase_							(				__UpperCAmelCase						:     Optional[Any]      ,      __UpperCAmelCase						:     Any      ,      __UpperCAmelCase						:     Optional[Any]=None							) ->			Optional[Any]:
      if not os.path.exists(__UpperCAmelCase							):
            SCREAMING_SNAKE_CASE_     						=      torch.hub.load('pytorch/fairseq'      ,      __UpperCAmelCase							).eval()
      else:
            SCREAMING_SNAKE_CASE_     						=      load_xsum_checkpoint(__UpperCAmelCase							)

      bart.model.upgrade_state_dict(bart.model.state_dict()							)
      if hf_checkpoint_name is None:
            SCREAMING_SNAKE_CASE_     						=      checkpoint_path.replace('.'      ,      '-'							)
      SCREAMING_SNAKE_CASE_     						=      BartConfig.from_pretrained(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_     						=      bart.encode(__UpperCAmelCase							).unsqueeze(0							)
      SCREAMING_SNAKE_CASE_     						=      BartTokenizer.from_pretrained(__UpperCAmelCase							).encode(__UpperCAmelCase      ,      return_tensors='pt'							).unsqueeze(0							)
      if not torch.eq(__UpperCAmelCase      ,      __UpperCAmelCase							).all():
            raise ValueError(
                f"converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}"							)

      if checkpoint_path == "bart.large.mnli":
            SCREAMING_SNAKE_CASE_     						=      bart.state_dict()
            remove_ignore_keys_(__UpperCAmelCase							)
            SCREAMING_SNAKE_CASE_     						=      state_dict['model.decoder.embed_tokens.weight']
            for src, dest in mnli_rename_keys:
                  rename_key(__UpperCAmelCase      ,      __UpperCAmelCase      ,      __UpperCAmelCase							)
            SCREAMING_SNAKE_CASE_     						=      BartForSequenceClassification(__UpperCAmelCase							).eval()
            model.load_state_dict(__UpperCAmelCase							)
            SCREAMING_SNAKE_CASE_     						=      bart.predict('mnli'      ,      __UpperCAmelCase      ,      return_logits=__UpperCAmelCase							)
            SCREAMING_SNAKE_CASE_     						=      model(__UpperCAmelCase							)[0]  # logits
      else:  # no classification heads to worry about
            SCREAMING_SNAKE_CASE_     						=      bart.model.state_dict()
            remove_ignore_keys_(__UpperCAmelCase							)
            SCREAMING_SNAKE_CASE_     						=      state_dict['decoder.embed_tokens.weight']
            SCREAMING_SNAKE_CASE_     						=      bart.extract_features(__UpperCAmelCase							)
            if hf_checkpoint_name == "facebook/bart-large":
                  SCREAMING_SNAKE_CASE_     						=      BartModel(__UpperCAmelCase							).eval()
                  model.load_state_dict(__UpperCAmelCase							)
                  SCREAMING_SNAKE_CASE_     						=      model(__UpperCAmelCase							).model[0]
            else:
                  SCREAMING_SNAKE_CASE_     						=      BartForConditionalGeneration(__UpperCAmelCase							).eval()  # an existing summarization ckpt
                  model.model.load_state_dict(__UpperCAmelCase							)
                  if hasattr(__UpperCAmelCase      ,      'lm_head'							):
                        SCREAMING_SNAKE_CASE_     						=      make_linear_from_emb(model.model.shared							)
                  SCREAMING_SNAKE_CASE_     						=      model.model(__UpperCAmelCase							)[0]

    # Check results
      if fairseq_output.shape != new_model_outputs.shape:
            raise ValueError(
                f"`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}"							)
      if (fairseq_output != new_model_outputs).any().item():
            raise ValueError('Some values in `fairseq_output` are different from `new_model_outputs`'							)
      Path(__UpperCAmelCase							).mkdir(exist_ok=__UpperCAmelCase							)
      model.save_pretrained(__UpperCAmelCase							)


if __name__ == "__main__":
   lowerCamelCase__			:       List[Any]         =							argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       'fairseq_path', type=str, help='bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'
   )
   parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
   parser.add_argument(
       '--hf_config', default=None, type=str, help='Which huggingface architecture to use: bart-large-xsum'
   )
   lowerCamelCase__			:       Any         =							parser.parse_args()
   convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)





import gc
import threading
import time

import psutil
import torch





class 	lowerCamelCase_							:


 '''simple docstring'''







 def __init__(							self  :		Optional[Any]						):
       SCREAMING_SNAKE_CASE_     						=      psutil.Process()
       SCREAMING_SNAKE_CASE_     						=      False



 def 				lowerCAmelCase_  (							self  :		Dict						):
       SCREAMING_SNAKE_CASE_     						=      -1

       while True:
             SCREAMING_SNAKE_CASE_     						=      max(self.process.memory_info().rss    ,    self.cpu_memory_peak						)

             # can't sleep or will not catch the peak right (this comment is here on purpose)
             if not self.peak_monitoring:
                   break



 def 				lowerCAmelCase_  (							self  :		List[str]						):
       SCREAMING_SNAKE_CASE_     						=      True
       SCREAMING_SNAKE_CASE_     						=      threading.Thread(target=self.peak_monitor						)
       SCREAMING_SNAKE_CASE_     						=      True
       self.thread.start()




 def 				lowerCAmelCase_  (							self  :		List[str]						):
       SCREAMING_SNAKE_CASE_     						=      False
       self.thread.join()
       return self.cpu_memory_peak


lowerCamelCase__			:       List[str]         =							PeakCPUMemory()

def  UpperCAmelCase_							(				) ->			Tuple:
      # Time
      SCREAMING_SNAKE_CASE_     						=      {'time': time.time()}

      gc.collect()
      torch.cuda.empty_cache()

      # CPU mem
      SCREAMING_SNAKE_CASE_     						=      psutil.Process().memory_info().rss
      cpu_peak_tracker.start()

      # GPU mem
      for i in range(torch.cuda.device_count()							):
            SCREAMING_SNAKE_CASE_     						=      torch.cuda.memory_allocated(__UpperCAmelCase							)
      torch.cuda.reset_peak_memory_stats()

      return measures

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Optional[int]							) ->			Optional[Any]:
      # Time
      SCREAMING_SNAKE_CASE_     						=      {'time': time.time() - start_measures['time']}

      gc.collect()
      torch.cuda.empty_cache()

      # CPU mem
      SCREAMING_SNAKE_CASE_     						=      (psutil.Process().memory_info().rss - start_measures['cpu']) / 2**20
      SCREAMING_SNAKE_CASE_     						=      (cpu_peak_tracker.stop() - start_measures['cpu']) / 2**20

      # GPU mem
      for i in range(torch.cuda.device_count()							):
            SCREAMING_SNAKE_CASE_     						=      (torch.cuda.memory_allocated(__UpperCAmelCase							) - start_measures[str(__UpperCAmelCase							)]) / 2**20
            SCREAMING_SNAKE_CASE_     						=      (torch.cuda.max_memory_allocated(__UpperCAmelCase							) - start_measures[str(__UpperCAmelCase							)]) / 2**20

      return measures





def  UpperCAmelCase_							(				__UpperCAmelCase						:     Optional[int]      ,      __UpperCAmelCase						:     Any							) ->			Optional[Any]:
      print(f"{description}:"							)
      print(f"- Time: {measures['time']:.2f}s"							)
      for i in range(torch.cuda.device_count()							):
            print(f"- GPU {i} allocated: {measures[str(__UpperCAmelCase							)]:.2f}MiB"							)
            SCREAMING_SNAKE_CASE_     						=      measures[f"{i}-peak"]
            print(f"- GPU {i} peak: {peak:.2f}MiB"							)
      print(f"- CPU RAM allocated: {measures['cpu']:.2f}MiB"							)
      print(f"- CPU RAM peak: {measures['cpu-peak']:.2f}MiB"							)