


import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


SCREAMING_SNAKE_CASE							    =							'true'




def  a   (lowerCAmelCase__		,				lowerCAmelCase__=82		,				lowerCAmelCase__=16   ):
   set_seed(42   )
   __a					=  RegressionModel()
   __a					=  deepcopy(lowerCAmelCase__   )
   __a					=  RegressionDataset(length=lowerCAmelCase__   )
   __a					=  DataLoader(lowerCAmelCase__		,				batch_size=lowerCAmelCase__   )
   model.to(accelerator.device   )
   __a	,    __a					=  accelerator.prepare(lowerCAmelCase__		,				lowerCAmelCase__   )
   return model, ddp_model, dataloader




def  a   (lowerCAmelCase__		,				lowerCAmelCase__=False   ):
   __a					=  AutoTokenizer.from_pretrained("""hf-internal-testing/mrpc-bert-base-cased"""   )
   __a					=  load_dataset("""glue"""		,				"""mrpc"""		,				split="""validation"""   )

   def tokenize_function(lowerCAmelCase__   ):
      __a					=  tokenizer(examples["""sentence1"""]		,				examples["""sentence2"""]		,				truncation=lowerCAmelCase__		,				max_length=lowerCAmelCase__   )
      return outputs

   with accelerator.main_process_first():
      __a					=  dataset.map(
          lowerCAmelCase__		,				batched=lowerCAmelCase__		,				remove_columns=["""idx""", """sentence1""", """sentence2"""]		,				)

   __a					=  tokenized_datasets.rename_column("""label"""		,				"""labels"""   )

   def collate_fn(lowerCAmelCase__   ):
      if use_longest:
         return tokenizer.pad(lowerCAmelCase__		,				padding="""longest"""		,				return_tensors="""pt"""   )
      return tokenizer.pad(lowerCAmelCase__		,				padding="""max_length"""		,				max_length=128		,				return_tensors="""pt"""   )

   return DataLoader(lowerCAmelCase__		,				shuffle=lowerCAmelCase__		,				collate_fn=lowerCAmelCase__		,				batch_size=16   )




def  a   (lowerCAmelCase__		,				lowerCAmelCase__   ):
   __a					=  Accelerator(dispatch_batches=lowerCAmelCase__		,				split_batches=lowerCAmelCase__   )
   __a					=  get_dataloader(lowerCAmelCase__		,				not dispatch_batches   )
   __a					=  AutoModelForSequenceClassification.from_pretrained(
       """hf-internal-testing/mrpc-bert-base-cased"""		,				return_dict=lowerCAmelCase__   )
   __a	,    __a					=  accelerator.prepare(lowerCAmelCase__		,				lowerCAmelCase__   )
   return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator




def  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   ):
   __a					=  []
   for batch in dataloader:
      __a	,    __a					=  batch.values()
      with torch.no_grad():
         __a					=  model(lowerCAmelCase__   )
         __a	,    __a					=  accelerator.gather_for_metrics((logit, target)   )
         logits_and_targets.append((logit, target)   )
   __a	,    __a					=  [], []
   for logit, targ in logits_and_targets:
      logits.append(lowerCAmelCase__   )
      targs.append(lowerCAmelCase__   )
   __a	,    __a					=  torch.cat(lowerCAmelCase__   ), torch.cat(lowerCAmelCase__   )
   return logits, targs




def  a   (lowerCAmelCase__		,				lowerCAmelCase__=82		,				lowerCAmelCase__=False		,				lowerCAmelCase__=False		,				lowerCAmelCase__=16   ):
   __a	,    __a	,    __a					=  get_basic_setup(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   )
   __a	,    __a					=  generate_predictions(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   )
   assert (
       len(lowerCAmelCase__   ) == num_samples
   ), f'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(lowerCAmelCase__   )}'''




def  a   (lowerCAmelCase__ = False		,				lowerCAmelCase__ = False   ):
   __a					=  evaluate.load("""glue"""		,				"""mrpc"""   )
   __a	,    __a					=  get_mrpc_setup(lowerCAmelCase__		,				lowerCAmelCase__   )
   # First do baseline
   __a	,    __a	,    __a					=  setup["""no"""]
   model.to(lowerCAmelCase__   )
   model.eval()
   for batch in dataloader:
      batch.to(lowerCAmelCase__   )
      with torch.inference_mode():
         __a					=  model(**lowerCAmelCase__   )
      __a					=  outputs.logits.argmax(dim=-1   )
      metric.add_batch(predictions=lowerCAmelCase__		,				references=batch["""labels"""]   )
   __a					=  metric.compute()

   # Then do distributed
   __a	,    __a	,    __a					=  setup["""ddp"""]
   model.eval()
   for batch in dataloader:
      with torch.inference_mode():
         __a					=  model(**lowerCAmelCase__   )
      __a					=  outputs.logits.argmax(dim=-1   )
      __a					=  batch["""labels"""]
      __a	,    __a					=  accelerator.gather_for_metrics((preds, references)   )
      metric.add_batch(predictions=lowerCAmelCase__		,				references=lowerCAmelCase__   )
   __a					=  metric.compute()

   for key in "accuracy f1".split():
      assert math.isclose(
          baseline[key]		,				distributed[key]   ), f'''Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'''




def  a   ():
   __a					=  Accelerator(split_batches=lowerCAmelCase__		,				dispatch_batches=lowerCAmelCase__   )
   if accelerator.is_local_main_process:
      datasets.utils.logging.set_verbosity_warning()
      transformers.utils.logging.set_verbosity_warning()
   else:
      datasets.utils.logging.set_verbosity_error()
      transformers.utils.logging.set_verbosity_error()
   # These are a bit slower so they should only be ran on the GPU or TPU
   if torch.cuda.is_available() or is_tpu_available():
      if accelerator.is_local_main_process:
         print("""**Testing gather_for_metrics**"""   )
      for split_batches in [True, False]:
         for dispatch_batches in [True, False]:
            if accelerator.is_local_main_process:
               print(f'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`'''   )
            test_mrpc(lowerCAmelCase__		,				lowerCAmelCase__   )
            accelerator.state._reset_state()
   if accelerator.is_local_main_process:
      print("""**Test torch metrics**"""   )
   for split_batches in [True, False]:
      for dispatch_batches in [True, False]:
         __a					=  Accelerator(split_batches=lowerCAmelCase__		,				dispatch_batches=lowerCAmelCase__   )
         if accelerator.is_local_main_process:
            print(f'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'''   )
         test_torch_metrics(lowerCAmelCase__		,				99   )
         accelerator.state._reset_state()
   if accelerator.is_local_main_process:
      print("""**Test last batch is not dropped when perfectly divisible**"""   )
   __a					=  Accelerator()
   test_torch_metrics(lowerCAmelCase__		,				512   )
   accelerator.state._reset_state()




def  a   (lowerCAmelCase__   ):
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
  main()







'''simple docstring'''





import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def        a__     (					UpperCamelCase_						:      Tuple						):
   UpperCAmelCase__	:Dict      =      [
       '''encoder.version''',
       '''decoder.version''',
       '''model.encoder.version''',
       '''model.decoder.version''',
       '''decoder.output_projection.weight''',
       '''_float_tensor''',
       '''encoder.embed_positions._float_tensor''',
       '''decoder.embed_positions._float_tensor''',
   ]
   for k in ignore_keys:
      state_dict.pop(UpperCamelCase_,   UpperCamelCase_						)


def        a__     (					UpperCamelCase_						:      Union[str, Any]						):
   UpperCAmelCase__   ,						UpperCAmelCase__	:Optional[Any]      =      emb.weight.shape
   UpperCAmelCase__	:int      =      nn.Linear(UpperCamelCase_,   UpperCamelCase_,   bias=UpperCamelCase_						)
   UpperCAmelCase__	:Tuple      =      emb.weight.data
   return lin_layer


def        a__     (					UpperCamelCase_						:      str						):
   UpperCAmelCase__	:List[str]      =      torch.load(UpperCamelCase_,   map_location='''cpu'''						)
   UpperCAmelCase__	:List[Any]      =      mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
   UpperCAmelCase__	:List[Any]      =      mam_aaa['''model''']
   remove_ignore_keys_(UpperCamelCase_						)
   UpperCAmelCase__	:Tuple      =      state_dict['''encoder.embed_tokens.weight'''].shape[0]

   UpperCAmelCase__	:Union[str, Any]      =      MaMaaaConfig(
       vocab_size=UpperCamelCase_,   max_position_embeddings=1_024,   encoder_layers=args.encoder_layers,   decoder_layers=args.decoder_layers,   encoder_attention_heads=args.encoder_attention_heads,   decoder_attention_heads=args.decoder_attention_heads,   encoder_ffn_dim=args.encoder_ffn_embed_dim,   decoder_ffn_dim=args.decoder_ffn_embed_dim,   d_model=args.encoder_embed_dim,   encoder_layerdrop=args.encoder_layerdrop,   decoder_layerdrop=args.decoder_layerdrop,   dropout=args.dropout,   attention_dropout=args.attention_dropout,   activation_dropout=args.activation_dropout,   activation_function='''relu''',   )

   UpperCAmelCase__	:str      =      state_dict['''decoder.embed_tokens.weight''']
   UpperCAmelCase__	:Tuple      =      MaMaaaForConditionalGeneration(UpperCamelCase_						)
   model.model.load_state_dict(UpperCamelCase_,   strict=UpperCamelCase_						)
   UpperCAmelCase__	:Optional[int]      =      make_linear_from_emb(model.model.shared						)

   return model


if __name__ == "__main__":
    __lowerCamelCase												=						argparse.ArgumentParser()
    # Required parameters
    parser.add_argument('''fairseq_path''', type=str, help='''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.''')
    __lowerCamelCase												=						parser.parse_args()
    __lowerCamelCase												=						convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
    model.save_pretrained(args.pytorch_dump_folder_path)



