



# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


a_			=					logging.get_logger(__name__)

a_			=					{}
a_			=					{}
a_			=					{}





def 		snake_case_			(		_UpperCamelCase			: Tuple ,_UpperCamelCase			: Union[str, Any] ,_UpperCamelCase			: Optional[Any] = None ,):
   __lowerCamelCase													=    aliases if aliases is not None else []
   if format_type in _FORMAT_TYPES:
      logger.warning(
          F"""Overwriting format type '{format_type}' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})"""							)
   __lowerCamelCase													=    formatter_cls
   for alias in set(aliases + [format_type]							):
      if alias in _FORMAT_TYPES_ALIASES:
         logger.warning(
             F"""Overwriting format type alias '{alias}' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})"""							)
      __lowerCamelCase													=    format_type





def 		snake_case_			(		_UpperCamelCase			: List[str] ,_UpperCamelCase			: List[Any] ,_UpperCamelCase			: Any = None							):
   __lowerCamelCase													=    aliases if aliases is not None else []
   for alias in set(aliases + [format_type]							):
      __lowerCamelCase													=    unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=["""python"""])
_register_formatter(ArrowFormatter, """arrow""", aliases=["""pa""", """pyarrow"""])
_register_formatter(NumpyFormatter, """numpy""", aliases=["""np"""])
_register_formatter(PandasFormatter, """pandas""", aliases=["""pd"""])
_register_formatter(CustomFormatter, """custom""")

if config.TORCH_AVAILABLE:
  from .torch_formatter import TorchFormatter

  _register_formatter(TorchFormatter, """torch""", aliases=["""pt""", """pytorch"""])
else:
  a_			=					ValueError("""PyTorch needs to be installed to be able to return PyTorch tensors.""")
  _register_unavailable_formatter(_torch_error, """torch""", aliases=["""pt""", """pytorch"""])

if config.TF_AVAILABLE:
  from .tf_formatter import TFFormatter

  _register_formatter(TFFormatter, """tensorflow""", aliases=["""tf"""])
else:
  a_			=					ValueError("""Tensorflow needs to be installed to be able to return Tensorflow tensors.""")
  _register_unavailable_formatter(_tf_error, """tensorflow""", aliases=["""tf"""])

if config.JAX_AVAILABLE:
  from .jax_formatter import JaxFormatter

  _register_formatter(JaxFormatter, """jax""", aliases=[])
else:
  a_			=					ValueError("""JAX needs to be installed to be able to return JAX arrays.""")
  _register_unavailable_formatter(_jax_error, """jax""", aliases=[])





def 		snake_case_			(		_UpperCamelCase			: Any							):
   if format_type in _FORMAT_TYPES_ALIASES:
      return _FORMAT_TYPES_ALIASES[format_type]
   else:
      return format_type





def 		snake_case_			(		_UpperCamelCase			: Optional[Any] ,**_UpperCamelCase			: List[str]							):
   __lowerCamelCase													=    get_format_type_from_alias(_SCREAMING_SNAKE_CASE							)
   if format_type in _FORMAT_TYPES:
      return _FORMAT_TYPES[format_type](**_SCREAMING_SNAKE_CASE							)
   if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
      raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
   else:
      raise ValueError(
          F"""Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None							)}, but got '{format_type}'"""							)











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


a_			=					"""true"""





def 		a__			(		_UpperCamelCase			: Union[str, Any] ,_UpperCamelCase			: List[str]=82 ,_UpperCamelCase			: Optional[Any]=16							):
   set_seed(42							)
   __lowerCamelCase													=    RegressionModel()
   __lowerCamelCase													=    deepcopy(_UpperCamelCase							)
   __lowerCamelCase													=    RegressionDataset(length=_UpperCamelCase							)
   __lowerCamelCase													=    DataLoader(_UpperCamelCase ,batch_size=_UpperCamelCase							)
   model.to(accelerator.device							)
   __lowerCamelCase							,__lowerCamelCase													=    accelerator.prepare(_UpperCamelCase ,_UpperCamelCase							)
   return model, ddp_model, dataloader





def 		a__			(		_UpperCamelCase			: Accelerator ,_UpperCamelCase			: str=False							):
   __lowerCamelCase													=    AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased'''							)
   __lowerCamelCase													=    load_dataset('''glue''' ,'''mrpc''' ,split='''validation'''							)

   def tokenize_function(_UpperCamelCase			: int							):
      __lowerCamelCase													=    tokenizer(examples['''sentence1'''] ,examples['''sentence2'''] ,truncation=_UpperCamelCase ,max_length=_UpperCamelCase							)
      return outputs

   with accelerator.main_process_first():
      __lowerCamelCase													=    dataset.map(
          _UpperCamelCase ,batched=_UpperCamelCase ,remove_columns=['''idx''', '''sentence1''', '''sentence2'''] ,)

   __lowerCamelCase													=    tokenized_datasets.rename_column('''label''' ,'''labels'''							)

   def collate_fn(_UpperCamelCase			: Any							):
      if use_longest:
         return tokenizer.pad(_UpperCamelCase ,padding='''longest''' ,return_tensors='''pt'''							)
      return tokenizer.pad(_UpperCamelCase ,padding='''max_length''' ,max_length=1_28 ,return_tensors='''pt'''							)

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





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





def 		a__			(		_UpperCamelCase			: Dict ,_UpperCamelCase			: Optional[Any] ,_UpperCamelCase			: Union[str, Any]							):
   __lowerCamelCase													=    []
   for batch in dataloader:
      __lowerCamelCase							,__lowerCamelCase													=    batch.values()
      with torch.no_grad():
         __lowerCamelCase													=    model(_UpperCamelCase							)
         __lowerCamelCase							,__lowerCamelCase													=    accelerator.gather_for_metrics((logit, target)							)
         logits_and_targets.append((logit, target)							)
   __lowerCamelCase							,__lowerCamelCase													=    [], []
   for logit, targ in logits_and_targets:
      logits.append(_UpperCamelCase							)
      targs.append(_UpperCamelCase							)
   __lowerCamelCase							,__lowerCamelCase													=    torch.cat(_UpperCamelCase							), torch.cat(_UpperCamelCase							)
   return logits, targs





def 		a__			(		_UpperCamelCase			: Accelerator ,_UpperCamelCase			: List[Any]=82 ,_UpperCamelCase			: str=False ,_UpperCamelCase			: List[str]=False ,_UpperCamelCase			: Optional[int]=16							):
   __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase													=    get_basic_setup(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase							)
   __lowerCamelCase							,__lowerCamelCase													=    generate_predictions(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase							)
   assert (
       len(_UpperCamelCase							) == num_samples
   ), F"""Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(_UpperCamelCase							)}"""





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

   # Then do distributed
   __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase													=    setup['''ddp''']
   model.eval()
   for batch in dataloader:
      with torch.inference_mode():
         __lowerCamelCase													=    model(**_UpperCamelCase							)
      __lowerCamelCase													=    outputs.logits.argmax(dim=-1							)
      __lowerCamelCase													=    batch['''labels''']
      __lowerCamelCase							,__lowerCamelCase													=    accelerator.gather_for_metrics((preds, references)							)
      metric.add_batch(predictions=_UpperCamelCase ,references=_UpperCamelCase							)
   __lowerCamelCase													=    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__			(		):
   __lowerCamelCase													=    Accelerator(split_batches=_UpperCamelCase ,dispatch_batches=_UpperCamelCase							)
   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(_UpperCamelCase ,_UpperCamelCase							)
            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]:
         __lowerCamelCase													=    Accelerator(split_batches=_UpperCamelCase ,dispatch_batches=_UpperCamelCase							)
         if accelerator.is_local_main_process:
            print(F"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"""							)
         test_torch_metrics(_UpperCamelCase ,99							)
         accelerator.state._reset_state()
   if accelerator.is_local_main_process:
      print('''**Test last batch is not dropped when perfectly divisible**'''							)
   __lowerCamelCase													=    Accelerator()
   test_torch_metrics(_UpperCamelCase ,5_12							)
   accelerator.state._reset_state()





def 		a__			(		_UpperCamelCase			: Optional[int]							):
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
  main()






