





'''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     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase=82				,						__UpperCAmelCase=16   )    ->       Union[str, Any]:

   '''simple docstring'''







   set_seed(42   )
   __SCREAMING_SNAKE_CASE   				=							RegressionModel()
   __SCREAMING_SNAKE_CASE   				=							deepcopy(__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							RegressionDataset(length=__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							DataLoader(__UpperCAmelCase				,						batch_size=__UpperCAmelCase   )
   model.to(accelerator.device   )
   __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							accelerator.prepare(__UpperCAmelCase				,						__UpperCAmelCase   )
   return model, ddp_model, dataloader

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase=False   )    ->       Optional[int]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							AutoTokenizer.from_pretrained("""hf-internal-testing/mrpc-bert-base-cased"""   )
   __SCREAMING_SNAKE_CASE   				=							load_dataset("""glue"""				,						"""mrpc"""				,						split="""validation"""   )

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

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

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

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

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

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       int:

   '''simple docstring'''







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

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       int:

   '''simple docstring'''







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

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase=82				,						__UpperCAmelCase=False				,						__UpperCAmelCase=False				,						__UpperCAmelCase=16   )    ->       str:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							get_basic_setup(__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							generate_predictions(__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )
   assert (
       len(__UpperCAmelCase   ) == num_samples
   ), f"""Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(__UpperCAmelCase   )}"""

def     __magic_name__							(				__UpperCAmelCase = False				,						__UpperCAmelCase = False   )    ->       Dict:

   '''simple docstring'''







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

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

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							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]:
         __SCREAMING_SNAKE_CASE   				=							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**"""   )
   __SCREAMING_SNAKE_CASE   				=							Accelerator()
   test_torch_metrics(__UpperCAmelCase				,						512   )
   accelerator.state._reset_state()

def     __magic_name__							(				__UpperCAmelCase   )    ->       Dict:

   '''simple docstring'''







   main()


if __name__ == "__main__":
    main()












'''simple docstring'''

from pathlib import PurePosixPath
from typing import Optional

import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo

from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url







class __a			( _snake_case      ):
   __UpperCamelCase :			Any						   =						''
   __UpperCamelCase :			int						   =						'hf-legacy'  # "hf://"" is reserved for hffs





   def __init__(       self					:      Any					,lowerCamelCase					:      Optional[DatasetInfo] = None					,lowerCamelCase					:      Optional[str] = None					,**lowerCamelCase					:      Dict					,):

      '''simple docstring'''

      super().__init__(self					,**lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							repo_info
      __SCREAMING_SNAKE_CASE   				=							token
      __SCREAMING_SNAKE_CASE   				=							None





   def   UpperCAmelCase__  (       self					:      Optional[int]     ):

      '''simple docstring'''

      if self.dir_cache is None:
         __SCREAMING_SNAKE_CASE   				=							{}
         for hf_file in self.repo_info.siblings:
            # TODO(QL): add sizes
            __SCREAMING_SNAKE_CASE   				=							{
                """name""": hf_file.rfilename,
                """size""": None,
                """type""": """file""",
            }
            self.dir_cache.update(
                {
                    str(lowerCamelCase     ): {"""name""": str(lowerCamelCase     ), """size""": None, """type""": """directory"""}
                    for d in list(PurePosixPath(hf_file.rfilename     ).parents     )[:-1]
                }     )





   def   UpperCAmelCase__  (       self					:      List[Any]					,lowerCamelCase					:      str					,lowerCamelCase					:      str = "rb"					,**lowerCamelCase					:      Optional[Any]					,):

      '''simple docstring'''

      if not isinstance(self.repo_info					,lowerCamelCase     ):
         raise NotImplementedError(f"""Open is only implemented for dataset repositories, but got {self.repo_info}"""     )
      __SCREAMING_SNAKE_CASE   				=							hf_hub_url(self.repo_info.id					,lowerCamelCase					,revision=self.repo_info.sha     )
      return fsspec.open(
          lowerCamelCase					,mode=lowerCamelCase					,headers=get_authentication_headers_for_url(lowerCamelCase					,use_auth_token=self.token     )					,client_kwargs={"""trust_env""": True}					,).open()





   def   UpperCAmelCase__  (       self					:      Tuple					,lowerCamelCase					:      Any					,**lowerCamelCase					:      Optional[Any]     ):

      '''simple docstring'''

      self._get_dirs()
      __SCREAMING_SNAKE_CASE   				=							self._strip_protocol(lowerCamelCase     )
      if path in self.dir_cache:
         return self.dir_cache[path]
      else:
         raise FileNotFoundError(lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      str					,lowerCamelCase					:      Any					,lowerCamelCase					:      str=False					,**lowerCamelCase					:      Any     ):

      '''simple docstring'''

      self._get_dirs()
      __SCREAMING_SNAKE_CASE   				=							PurePosixPath(path.strip("""/"""     )     )
      __SCREAMING_SNAKE_CASE   				=							{}
      for p, f in self.dir_cache.items():
         __SCREAMING_SNAKE_CASE   				=							PurePosixPath(p.strip("""/"""     )     )
         __SCREAMING_SNAKE_CASE   				=							p.parent
         if root == path:
            __SCREAMING_SNAKE_CASE   				=							f
      __SCREAMING_SNAKE_CASE   				=							list(paths.values()     )
      if detail:
         return out
      else:
         return sorted(f["""name"""] for f in out     )





