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


_lowercase				:   int   				='''true'''



def A__	(      lowercase:  Any,		lowercase:  Union[str, Any]=82,		lowercase:  Optional[Any]=16       )					->		List[Any]:
       set_seed(42       )
       A    :  Any								=RegressionModel()
       A    :  Any								=deepcopy(__A       )
       A    :  Union[str, Any]								=RegressionDataset(length=__A       )
       A    :  List[Any]								=DataLoader(__A,		batch_size=__A       )
       model.to(accelerator.device       )
       A	,	A    :  Optional[int]								=accelerator.prepare(__A,		__A       )
       return model, ddp_model, dataloader



def A__	(      lowercase:  Accelerator,		lowercase:  Dict=False       )					->		List[Any]:
       A    :  Any								=AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased'       )
       A    :  List[Any]								=load_dataset('glue',		'mrpc',		split='validation'       )

       def tokenize_function(lowercase:  str       ):
              A    :  Union[str, Any]								=tokenizer(examples['sentence1'],		examples['sentence2'],		truncation=__A,		max_length=__A       )
              return outputs

       with accelerator.main_process_first():
              A    :  Union[str, Any]								=dataset.map(
                  __A,		batched=__A,		remove_columns=['idx', 'sentence1', 'sentence2'],		)

       A    :  Optional[Any]								=tokenized_datasets.rename_column('label',		'labels'       )

       def collate_fn(lowercase:  Optional[Any]       ):
              if use_longest:
                     return tokenizer.pad(__A,		padding='longest',		return_tensors='pt'       )
              return tokenizer.pad(__A,		padding='max_length',		max_length=128,		return_tensors='pt'       )

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



def A__	(      lowercase:  List[str],		lowercase:  Any       )					->		Tuple:
       A    :  Union[str, Any]								=Accelerator(dispatch_batches=__A,		split_batches=__A       )
       A    :  List[Any]								=get_dataloader(__A,		not dispatch_batches       )
       A    :  Union[str, Any]								=AutoModelForSequenceClassification.from_pretrained(
           'hf-internal-testing/mrpc-bert-base-cased',		return_dict=__A       )
       A	,	A    :  str								=accelerator.prepare(__A,		__A       )
       return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator



def A__	(      lowercase:  List[Any],		lowercase:  Optional[int],		lowercase:  List[Any]       )					->		str:
       A    :  List[str]								=[]
       for batch in dataloader:
              A	,	A    :  Optional[int]								=batch.values()
              with torch.no_grad():
                     A    :  Optional[Any]								=model(__A       )
                     A	,	A    :  Optional[Any]								=accelerator.gather_for_metrics((logit, target)       )
                     logits_and_targets.append((logit, target)       )
       A	,	A    :  List[Any]								=[], []
       for logit, targ in logits_and_targets:
              logits.append(__A       )
              targs.append(__A       )
       A	,	A    :  Dict								=torch.cat(__A       ), torch.cat(__A       )
       return logits, targs



def A__	(      lowercase:  Accelerator,		lowercase:  int=82,		lowercase:  str=False,		lowercase:  List[Any]=False,		lowercase:  str=16       )					->		Any:
       A	,	A	,	A    :  Union[str, Any]								=get_basic_setup(__A,		__A,		__A       )
       A	,	A    :  Optional[Any]								=generate_predictions(__A,		__A,		__A       )
       assert (
           len(__A       ) == num_samples
       ), F'Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(__A       )}'



def A__	(      lowercase:  bool = False,		lowercase:  bool = False       )					->		str:
       A    :  List[str]								=evaluate.load('glue',		'mrpc'       )
       A	,	A    :  str								=get_mrpc_setup(__A,		__A       )
       # First do baseline
       A	,	A	,	A    :  List[Any]								=setup['no']
       model.to(__A       )
       model.eval()
       for batch in dataloader:
              batch.to(__A       )
              with torch.inference_mode():
                     A    :  Union[str, Any]								=model(**__A       )
              A    :  Tuple								=outputs.logits.argmax(dim=-1       )
              metric.add_batch(predictions=__A,		references=batch['labels']       )
       A    :  Optional[int]								=metric.compute()

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



def A__	(      lowercase:  Dict       )					->		Optional[Any]:
       # For xla_spawn (TPUs)
       main()


if __name__ == "__main__":
    main()









from typing import Dict, List, Optional

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_lowerCamelCase      :					Any					    =      logging.get_logger(__name__)


_lowerCamelCase      :					Tuple					    =      {
    '''nielsr/canine-s''': 2048,
}

# Unicode defines 1,114,112 total “codepoints”
_lowerCamelCase      :					List[str]					    =      111_4112

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
_lowerCamelCase      :					List[str]					    =      0

_lowerCamelCase      :					Any					    =      0xe_0_0_0
_lowerCamelCase      :					Union[str, Any]					    =      0xe_0_0_1
_lowerCamelCase      :					Any					    =      0xe_0_0_2
_lowerCamelCase      :					List[str]					    =      0xe_0_0_3
_lowerCamelCase      :					Any					    =      0xe_0_0_4

# Maps special codepoints to human-readable names.
_lowerCamelCase      :					Dict[int, str]					    =      {
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: "[CLS]",
    SEP: "[SEP]",
    BOS: "[BOS]",
    MASK: "[MASK]",
    PAD: "[PAD]",
    RESERVED: "[RESERVED]",
}

# Maps special codepoint human-readable names to their codepoint values.
_lowerCamelCase      :					Dict[str, int]					    =      {name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}





class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       lowercase_						=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES

       def __init__(							self ,   lowercase__=chr(lowercase__     ) ,   lowercase__=chr(lowercase__     ) ,   lowercase__=chr(lowercase__     ) ,   lowercase__=chr(lowercase__     ) ,   lowercase__=chr(lowercase__     ) ,   lowercase__=chr(lowercase__     ) ,   lowercase__=False ,   lowercase__=2_0_4_8 ,   **lowercase__ ,   ):







             '''simple docstring'''



             __A    =AddedToken(lowercase__ ,   lstrip=lowercase__ ,   rstrip=lowercase__     ) if isinstance(lowercase__ ,   lowercase__     ) else bos_token
             __A    =AddedToken(lowercase__ ,   lstrip=lowercase__ ,   rstrip=lowercase__     ) if isinstance(lowercase__ ,   lowercase__     ) else eos_token
             __A    =AddedToken(lowercase__ ,   lstrip=lowercase__ ,   rstrip=lowercase__     ) if isinstance(lowercase__ ,   lowercase__     ) else sep_token
             __A    =AddedToken(lowercase__ ,   lstrip=lowercase__ ,   rstrip=lowercase__     ) if isinstance(lowercase__ ,   lowercase__     ) else cls_token
             __A    =AddedToken(lowercase__ ,   lstrip=lowercase__ ,   rstrip=lowercase__     ) if isinstance(lowercase__ ,   lowercase__     ) else pad_token

             # Mask token behave like a normal word, i.e. include the space before it
             __A    =AddedToken(lowercase__ ,   lstrip=lowercase__ ,   rstrip=lowercase__     ) if isinstance(lowercase__ ,   lowercase__     ) else mask_token

             super().__init__(
                 bos_token=lowercase__ ,   eos_token=lowercase__ ,   sep_token=lowercase__ ,   cls_token=lowercase__ ,   pad_token=lowercase__ ,   mask_token=lowercase__ ,   add_prefix_space=lowercase__ ,   model_max_length=lowercase__ ,   **lowercase__ ,   )

             # Creates a mapping for looking up the IDs of special symbols.
             __A    ={}
             for codepoint, name in SPECIAL_CODEPOINTS.items():
                   __A    =codepoint

             # Creates a mapping for looking up the string forms of special symbol IDs.
             __A    ={
                 codepoint: name for name, codepoint in self._special_codepoints.items()
             }

             __A    =UNICODE_VOCAB_SIZE
             __A    =len(self._special_codepoints     )

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return self._unicode_vocab_size

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             return list(lowercase__     )

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             try:
                   return ord(lowercase__     )
             except TypeError:
                   raise ValueError(f'''invalid token: \'{token}\''''     )

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             try:
                   if index in SPECIAL_CODEPOINTS:
                         return SPECIAL_CODEPOINTS[index]
                   return chr(lowercase__     )
             except TypeError:
                   raise ValueError(f'''invalid id: {index}'''     )

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             return "".join(lowercase__     )

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = None     ):







             '''simple docstring'''



             __A    =[self.sep_token_id]
             __A    =[self.cls_token_id]

             __A    =cls + token_ids_a + sep
             if token_ids_a is not None:
                   result += token_ids_a + sep
             return result

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = None ,   lowercase__ = False     ):







             '''simple docstring'''



             if already_has_special_tokens:
                   return super().get_special_tokens_mask(
                       token_ids_a=lowercase__ ,   token_ids_a=lowercase__ ,   already_has_special_tokens=lowercase__     )

             __A    =[1] + ([0] * len(lowercase__     )) + [1]
             if token_ids_a is not None:
                   result += ([0] * len(lowercase__     )) + [1]
             return result

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = None     ):







             '''simple docstring'''



             __A    =[self.sep_token_id]
             __A    =[self.cls_token_id]

             __A    =len(cls + token_ids_a + sep     ) * [0]
             if token_ids_a is not None:
                   result += len(token_ids_a + sep     ) * [1]
             return result


       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = None     ):







             '''simple docstring'''



             return ()


