
"""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


lowerCAmelCase__				    =       '''true'''
def  a__      (    SCREAMING_SNAKE_CASE   :   Union[str, Any]    ,				SCREAMING_SNAKE_CASE   :   str=8_2    ,				SCREAMING_SNAKE_CASE   :   Any=1_6    ):


  '''simple docstring'''







  set_seed(4_2    )
  lowerCAmelCase					:					int								=							RegressionModel()
  lowerCAmelCase					:					Dict								=							deepcopy(_lowerCAmelCase    )
  lowerCAmelCase					:					Optional[int]								=							RegressionDataset(length=_lowerCAmelCase    )
  lowerCAmelCase					:					List[Any]								=							DataLoader(_lowerCAmelCase    ,				batch_size=_lowerCAmelCase    )
  model.to(accelerator.device    )
  lowerCAmelCase					:					List[Any]								=							accelerator.prepare(_lowerCAmelCase    ,				_lowerCAmelCase    )
  return model, ddp_model, dataloader
def  a__      (    SCREAMING_SNAKE_CASE   :   str    ,				SCREAMING_SNAKE_CASE   :   int=False    ):


  '''simple docstring'''







  lowerCAmelCase					:					Optional[Any]								=							AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased"    )
  lowerCAmelCase					:					List[str]								=							load_dataset("glue"    ,				"mrpc"    ,				split="validation"    )

  def tokenize_function(SCREAMING_SNAKE_CASE   :   Union[str, Any]    ):
    lowerCAmelCase					:					Tuple								=							tokenizer(examples["sentence1"]    ,				examples["sentence2"]    ,				truncation=_lowerCAmelCase    ,				max_length=_lowerCAmelCase    )
    return outputs

  with accelerator.main_process_first():
    lowerCAmelCase					:					Any								=							dataset.map(
        _lowerCAmelCase    ,				batched=_lowerCAmelCase    ,				remove_columns=["idx", "sentence1", "sentence2"]    ,				)

  lowerCAmelCase					:					List[str]								=							tokenized_datasets.rename_column("label"    ,				"labels"    )

  def collate_fn(SCREAMING_SNAKE_CASE   :   Any    ):
    if use_longest:
      return tokenizer.pad(_lowerCAmelCase    ,				padding="longest"    ,				return_tensors="pt"    )
    return tokenizer.pad(_lowerCAmelCase    ,				padding="max_length"    ,				max_length=1_2_8    ,				return_tensors="pt"    )

  return DataLoader(_lowerCAmelCase    ,				shuffle=_lowerCAmelCase    ,				collate_fn=_lowerCAmelCase    ,				batch_size=1_6    )
def  a__      (    SCREAMING_SNAKE_CASE   :   Optional[int]    ,				SCREAMING_SNAKE_CASE   :   Optional[Any]    ):


  '''simple docstring'''







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


  '''simple docstring'''







  lowerCAmelCase					:					int								=							[]
  for batch in dataloader:
    lowerCAmelCase					:					Dict								=							batch.values()
    with torch.no_grad():
      lowerCAmelCase					:					Any								=							model(_lowerCAmelCase    )
      lowerCAmelCase					:					Dict								=							accelerator.gather_for_metrics((logit, target)    )
      logits_and_targets.append((logit, target)    )
  lowerCAmelCase					:					Dict								=							[], []
  for logit, targ in logits_and_targets:
    logits.append(_lowerCAmelCase    )
    targs.append(_lowerCAmelCase    )
  lowerCAmelCase					:					str								=							torch.cat(_lowerCAmelCase    ), torch.cat(_lowerCAmelCase    )
  return logits, targs
def  a__      (    SCREAMING_SNAKE_CASE   :   int    ,				SCREAMING_SNAKE_CASE   :   List[str]=8_2    ,				SCREAMING_SNAKE_CASE   :   List[Any]=False    ,				SCREAMING_SNAKE_CASE   :   str=False    ,				SCREAMING_SNAKE_CASE   :   Optional[int]=1_6    ):


  '''simple docstring'''







  lowerCAmelCase					:					str								=							get_basic_setup(_lowerCAmelCase    ,				_lowerCAmelCase    ,				_lowerCAmelCase    )
  lowerCAmelCase					:					List[Any]								=							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__      (    SCREAMING_SNAKE_CASE   :   str = False    ,				SCREAMING_SNAKE_CASE   :   str = False    ):


  '''simple docstring'''







  lowerCAmelCase					:					List[Any]								=							evaluate.load("glue"    ,				"mrpc"    )
  lowerCAmelCase					:					Optional[int]								=							get_mrpc_setup(_lowerCAmelCase    ,				_lowerCAmelCase    )
  # First do baseline
  lowerCAmelCase					:					List[Any]								=							setup["""no"""]
  model.to(_lowerCAmelCase    )
  model.eval()
  for batch in dataloader:
    batch.to(_lowerCAmelCase    )
    with torch.inference_mode():
      lowerCAmelCase					:					Dict								=							model(**_lowerCAmelCase    )
    lowerCAmelCase					:					List[Any]								=							outputs.logits.argmax(dim=-1    )
    metric.add_batch(predictions=_lowerCAmelCase    ,				references=batch["labels"]    )
  lowerCAmelCase					:					int								=							metric.compute()

  # Then do distributed
  lowerCAmelCase					:					int								=							setup["""ddp"""]
  model.eval()
  for batch in dataloader:
    with torch.inference_mode():
      lowerCAmelCase					:					Optional[int]								=							model(**_lowerCAmelCase    )
    lowerCAmelCase					:					Union[str, Any]								=							outputs.logits.argmax(dim=-1    )
    lowerCAmelCase					:					Optional[Any]								=							batch["""labels"""]
    lowerCAmelCase					:					List[str]								=							accelerator.gather_for_metrics((preds, references)    )
    metric.add_batch(predictions=_lowerCAmelCase    ,				references=_lowerCAmelCase    )
  lowerCAmelCase					:					Dict								=							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__      (    ):


  '''simple docstring'''







  lowerCAmelCase					:					List[Any]								=							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]:
      lowerCAmelCase					:					Union[str, Any]								=							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    ,				9_9    )
      accelerator.state._reset_state()
  if accelerator.is_local_main_process:
    print("**Test last batch is not dropped when perfectly divisible**"    )
  lowerCAmelCase					:					List[Any]								=							Accelerator()
  test_torch_metrics(_lowerCAmelCase    ,				5_1_2    )
  accelerator.state._reset_state()
def  a__      (    SCREAMING_SNAKE_CASE   :   Any    ):


  '''simple docstring'''







  main()


if __name__ == "__main__":
     main()









"""simple docstring"""







# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
     import jax
     import jaxlib

lowerCAmelCase__				    =       get_logger()

lowerCAmelCase__				    =       None


class 				SCREAMING_SNAKE_CASE__    (		TensorFormatter[Mapping, "jax.Array", Mapping]    ):
       """simple docstring"""


       def __init__(						self		,					snake_case__=None		,					snake_case__=None		,					**snake_case__						):






         """simple docstring"""





         super().__init__(features=snake_case__						)
         import jax
         from jaxlib.xla_client import Device

         if isinstance(snake_case__		,					snake_case__						):
           raise ValueError(
               f"""Expected {device} to be a `str` not {type(snake_case__						)}, as `jaxlib.xla_extension.Device` """
               "is not serializable neither with `pickle` nor with `dill`. Instead you can surround "
               "the device with `str()` to get its string identifier that will be internally mapped "
               "to the actual `jaxlib.xla_extension.Device`."						)
         lowerCAmelCase					:					List[str]								=							device if isinstance(snake_case__		,					snake_case__						) else str(jax.devices()[0]						)
         # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
         # with `pickle` nor with `dill`, so we need to use a global variable instead
         global DEVICE_MAPPING
         if DEVICE_MAPPING is None:
           lowerCAmelCase					:					Optional[Any]								=							self._map_devices_to_str()
         if self.device not in list(DEVICE_MAPPING.keys()						):
           logger.warning(
               f"""Device with string identifier {self.device} not listed among the available """
               f"""devices: {list(DEVICE_MAPPING.keys()						)}, so falling back to the default """
               f"""device: {str(jax.devices()[0]						)}."""						)
           lowerCAmelCase					:					Dict								=							str(jax.devices()[0]						)
         lowerCAmelCase					:					Union[str, Any]								=							jnp_array_kwargs

       @staticmethod
       def 	lowercase__							(						):






         """simple docstring"""





         import jax

         return {str(snake_case__						): device for device in jax.devices()}

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         import jax
         import jax.numpy as jnp

         if isinstance(snake_case__		,					snake_case__						) and column:
           if all(
               isinstance(snake_case__		,					jax.Array						) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column						):
             return jnp.stack(snake_case__		,					axis=0						)
         return column

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         import jax
         import jax.numpy as jnp

         if isinstance(snake_case__		,					(str, bytes, type(snake_case__						))						):
           return value
         elif isinstance(snake_case__		,					(np.character, np.ndarray)						) and np.issubdtype(value.dtype		,					np.character						):
           return value.tolist()

         lowerCAmelCase					:					Tuple								=							{}

         if isinstance(snake_case__		,					(np.number, np.ndarray)						) and np.issubdtype(value.dtype		,					np.integer						):
           # the default int precision depends on the jax config
           # see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
           if jax.config.jax_enable_xaa:
             lowerCAmelCase					:					List[Any]								=							{"dtype": jnp.intaa}
           else:
             lowerCAmelCase					:					Optional[Any]								=							{"dtype": jnp.intaa}
         elif isinstance(snake_case__		,					(np.number, np.ndarray)						) and np.issubdtype(value.dtype		,					np.floating						):
           lowerCAmelCase					:					int								=							{"dtype": jnp.floataa}
         elif config.PIL_AVAILABLE and "PIL" in sys.modules:
           import PIL.Image

           if isinstance(snake_case__		,					PIL.Image.Image						):
             lowerCAmelCase					:					Dict								=							np.asarray(snake_case__						)

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
         global DEVICE_MAPPING
         if DEVICE_MAPPING is None:
           lowerCAmelCase					:					str								=							self._map_devices_to_str()

         with jax.default_device(DEVICE_MAPPING[self.device]						):
           # calling jnp.array on a np.ndarray does copy the data
           # see https://github.com/google/jax/issues/4486
           return jnp.array(snake_case__		,					**{**default_dtype, **self.jnp_array_kwargs}						)

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         import jax

         # support for torch, tf, jax etc.
         if config.TORCH_AVAILABLE and "torch" in sys.modules:
           import torch

           if isinstance(snake_case__		,					torch.Tensor						):
             return self._tensorize(data_struct.detach().cpu().numpy()[()]						)
         if hasattr(snake_case__		,					"__array__"						) and not isinstance(snake_case__		,					jax.Array						):
           lowerCAmelCase					:					Dict								=							data_struct.__array__()
         # support for nested types like struct of list of struct
         if isinstance(snake_case__		,					np.ndarray						):
           if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
             return self._consolidate([self.recursive_tensorize(snake_case__						) for substruct in data_struct]						)
         elif isinstance(snake_case__		,					(list, tuple)						):
           return self._consolidate([self.recursive_tensorize(snake_case__						) for substruct in data_struct]						)
         return self._tensorize(snake_case__						)

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         return map_nested(self._recursive_tensorize		,					snake_case__		,					map_list=snake_case__						)

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					List[Any]								=							self.numpy_arrow_extractor().extract_row(snake_case__						)
         lowerCAmelCase					:					Tuple								=							self.python_features_decoder.decode_row(snake_case__						)
         return self.recursive_tensorize(snake_case__						)

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[int]								=							self.numpy_arrow_extractor().extract_column(snake_case__						)
         lowerCAmelCase					:					Dict								=							self.python_features_decoder.decode_column(snake_case__		,					pa_table.column_names[0]						)
         lowerCAmelCase					:					Optional[Any]								=							self.recursive_tensorize(snake_case__						)
         lowerCAmelCase					:					Dict								=							self._consolidate(snake_case__						)
         return column







       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[Any]								=							self.numpy_arrow_extractor().extract_batch(snake_case__						)
         lowerCAmelCase					:					str								=							self.python_features_decoder.decode_batch(snake_case__						)
         lowerCAmelCase					:					Tuple								=							self.recursive_tensorize(snake_case__						)
         for column_name in batch:
           lowerCAmelCase					:					Optional[int]								=							self._consolidate(batch[column_name]						)
         return batch





