

'''simple docstring'''

# 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    A_  ( snake_case      ,       snake_case      ,       snake_case = None      ,       ):
		SCREAMING_SNAKE_CASE:Tuple						      =					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__})''' )
		SCREAMING_SNAKE_CASE:int						      =					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})''' )
				SCREAMING_SNAKE_CASE:Tuple						      =					format_type





def    A_  ( snake_case      ,       snake_case      ,       snake_case = None ):
		SCREAMING_SNAKE_CASE:int						      =					aliases if aliases is not None else []
		for alias in set(aliases + [format_type] ):
				SCREAMING_SNAKE_CASE:Union[str, Any]						      =					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    A_  ( snake_case ):
		if format_type in _FORMAT_TYPES_ALIASES:
				return _FORMAT_TYPES_ALIASES[format_type]
		else:
				return format_type





def    A_  ( snake_case      ,       **snake_case ):
		SCREAMING_SNAKE_CASE:List[str]						      =					get_format_type_from_alias(snake_case__ )
		if format_type in _FORMAT_TYPES:
				return _FORMAT_TYPES[format_type](**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}\'''' )









'''simple docstring'''

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_  ( snake_case      ,       snake_case=82      ,       snake_case=16 ):
  set_seed(42 )
  SCREAMING_SNAKE_CASE:Optional[int]						      =					RegressionModel()
  SCREAMING_SNAKE_CASE:int						      =					deepcopy(snake_case )
  SCREAMING_SNAKE_CASE:List[str]						      =					RegressionDataset(length=snake_case )
  SCREAMING_SNAKE_CASE:int						      =					DataLoader(snake_case      ,       batch_size=snake_case )
  model.to(accelerator.device )
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Dict						      =					accelerator.prepare(snake_case      ,       snake_case )
  return model, ddp_model, dataloader





def    A_  ( snake_case      ,       snake_case=False ):
  SCREAMING_SNAKE_CASE:List[Any]						      =					AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased" )
  SCREAMING_SNAKE_CASE:Dict						      =					load_dataset("glue"      ,       "mrpc"      ,       split="validation" )

  def tokenize_function(snake_case ):
    SCREAMING_SNAKE_CASE:Optional[int]						      =					tokenizer(examples["sentence1"]      ,       examples["sentence2"]      ,       truncation=snake_case      ,       max_length=snake_case )
    return outputs

  with accelerator.main_process_first():
    SCREAMING_SNAKE_CASE:int						      =					dataset.map(
        snake_case      ,       batched=snake_case      ,       remove_columns=["idx", "sentence1", "sentence2"]      ,       )

  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					tokenized_datasets.rename_column("label"      ,       "labels" )

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

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





def    A_  ( snake_case      ,       snake_case ):
  SCREAMING_SNAKE_CASE:Optional[int]						      =					Accelerator(dispatch_batches=snake_case      ,       split_batches=snake_case )
  SCREAMING_SNAKE_CASE:List[Any]						      =					get_dataloader(snake_case      ,       not dispatch_batches )
  SCREAMING_SNAKE_CASE:Tuple						      =					AutoModelForSequenceClassification.from_pretrained(
      "hf-internal-testing/mrpc-bert-base-cased"      ,       return_dict=snake_case )
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:List[str]						      =					accelerator.prepare(snake_case      ,       snake_case )
  return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator





def    A_  ( snake_case      ,       snake_case      ,       snake_case ):
  SCREAMING_SNAKE_CASE:str						      =					[]
  for batch in dataloader:
    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Tuple						      =					batch.values()
    with torch.no_grad():
      SCREAMING_SNAKE_CASE:int						      =					model(snake_case )
      SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:List[str]						      =					accelerator.gather_for_metrics((logit, target) )
      logits_and_targets.append((logit, target) )
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Union[str, Any]						      =					[], []
  for logit, targ in logits_and_targets:
    logits.append(snake_case )
    targs.append(snake_case )
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Optional[Any]						      =					torch.cat(snake_case ), torch.cat(snake_case )
  return logits, targs





def    A_  ( snake_case      ,       snake_case=82      ,       snake_case=False      ,       snake_case=False      ,       snake_case=16 ):
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:int						      =					get_basic_setup(snake_case      ,       snake_case      ,       snake_case )
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Optional[Any]						      =					generate_predictions(snake_case      ,       snake_case      ,       snake_case )
  assert (
      len(snake_case ) == num_samples
  ), F'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(snake_case )}'''





def    A_  ( snake_case = False      ,       snake_case = False ):
  SCREAMING_SNAKE_CASE:List[Any]						      =					evaluate.load("glue"      ,       "mrpc" )
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Dict						      =					get_mrpc_setup(snake_case      ,       snake_case )
  # First do baseline
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Tuple						      =					setup["no"]
  model.to(snake_case )
  model.eval()
  for batch in dataloader:
    batch.to(snake_case )
    with torch.inference_mode():
      SCREAMING_SNAKE_CASE:Dict						      =					model(**snake_case )
    SCREAMING_SNAKE_CASE:Any						      =					outputs.logits.argmax(dim=-1 )
    metric.add_batch(predictions=snake_case      ,       references=batch["labels"] )
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					metric.compute()

  # Then do distributed
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Tuple						      =					setup["ddp"]
  model.eval()
  for batch in dataloader:
    with torch.inference_mode():
      SCREAMING_SNAKE_CASE:Any						      =					model(**snake_case )
    SCREAMING_SNAKE_CASE:Optional[Any]						      =					outputs.logits.argmax(dim=-1 )
    SCREAMING_SNAKE_CASE:List[str]						      =					batch["labels"]
    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:int						      =					accelerator.gather_for_metrics((preds, references) )
    metric.add_batch(predictions=snake_case      ,       references=snake_case )
  SCREAMING_SNAKE_CASE:Any						      =					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_  ( ):
  SCREAMING_SNAKE_CASE:Optional[int]						      =					Accelerator(split_batches=snake_case      ,       dispatch_batches=snake_case )
  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(snake_case      ,       snake_case )
        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]:
      SCREAMING_SNAKE_CASE:Optional[Any]						      =					Accelerator(split_batches=snake_case      ,       dispatch_batches=snake_case )
      if accelerator.is_local_main_process:
        print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99''' )
      test_torch_metrics(snake_case      ,       99 )
      accelerator.state._reset_state()
  if accelerator.is_local_main_process:
    print("**Test last batch is not dropped when perfectly divisible**" )
  SCREAMING_SNAKE_CASE:List[str]						      =					Accelerator()
  test_torch_metrics(snake_case      ,       512 )
  accelerator.state._reset_state()





def    A_  ( snake_case ):
  # For xla_spawn (TPUs)
  main()


if __name__ == "__main__":
    main()






