
'''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 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE=8_2,		__SCREAMING_SNAKE_CASE=1_6       ):
    """simple docstring"""






    set_seed(4_2       )
    snake_case_ :    Optional[int]     			=						RegressionModel()
    snake_case_ :    List[str]     			=						deepcopy(__SCREAMING_SNAKE_CASE       )
    snake_case_ :    Optional[int]     			=						RegressionDataset(length=__SCREAMING_SNAKE_CASE       )
    snake_case_ :    str     			=						DataLoader(__SCREAMING_SNAKE_CASE,		batch_size=__SCREAMING_SNAKE_CASE       )
    model.to(accelerator.device       )
    snake_case_ :    Dict     			=						accelerator.prepare(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
    return model, ddp_model, dataloader

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE=False       ):
    """simple docstring"""






    snake_case_ :    Optional[int]     			=						AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased"       )
    snake_case_ :    Tuple     			=						load_dataset("glue",		"mrpc",		split="validation"       )

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

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

    snake_case_ :    Dict     			=						tokenized_datasets.rename_column("label",		"labels"       )

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

    return DataLoader(__SCREAMING_SNAKE_CASE,		shuffle=__SCREAMING_SNAKE_CASE,		collate_fn=__SCREAMING_SNAKE_CASE,		batch_size=1_6       )

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






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

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






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

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE=8_2,		__SCREAMING_SNAKE_CASE=False,		__SCREAMING_SNAKE_CASE=False,		__SCREAMING_SNAKE_CASE=1_6       ):
    """simple docstring"""






    snake_case_ :    List[str]     			=						get_basic_setup(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
    snake_case_ :    Union[str, Any]     			=						generate_predictions(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
    assert (
        len(__SCREAMING_SNAKE_CASE       ) == num_samples
    ), f'Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(__SCREAMING_SNAKE_CASE       )}'

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE = False,		__SCREAMING_SNAKE_CASE = False       ):
    """simple docstring"""






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

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






    snake_case_ :    Union[str, Any]     			=						Accelerator(split_batches=__SCREAMING_SNAKE_CASE,		dispatch_batches=__SCREAMING_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(__SCREAMING_SNAKE_CASE,		__SCREAMING_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]:
            snake_case_ :    int     			=						Accelerator(split_batches=__SCREAMING_SNAKE_CASE,		dispatch_batches=__SCREAMING_SNAKE_CASE       )
            if accelerator.is_local_main_process:
                print(f'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'       )
            test_torch_metrics(__SCREAMING_SNAKE_CASE,		9_9       )
            accelerator.state._reset_state()
    if accelerator.is_local_main_process:
        print("**Test last batch is not dropped when perfectly divisible**"       )
    snake_case_ :    Any     			=						Accelerator()
    test_torch_metrics(__SCREAMING_SNAKE_CASE,		5_1_2       )
    accelerator.state._reset_state()

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    main()


if __name__ == "__main__":
  main()






'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_pegasus import PegasusTokenizer
else:
  a_											=						None


a_											=						logging.get_logger(__name__)


a_											=						"▁"

a_											=						{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

a_											=						{
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"},
    "tokenizer_file": {
        "google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"
    },
}

a_											=						{
    "google/pegasus-xsum": 512,
}






class    UpperCAmelCase_				(	snake_case__					):
 UpperCAmelCase_		    =						VOCAB_FILES_NAMES
 UpperCAmelCase_		    =						PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase_		    =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase_		    =						PegasusTokenizer
 UpperCAmelCase_		    =						["""input_ids""", """attention_mask"""]





 def __init__(    self ,       lowercase_=None ,       lowercase_=None ,       lowercase_="<pad>" ,       lowercase_="</s>" ,       lowercase_="<unk>" ,       lowercase_="<mask_2>" ,       lowercase_="<mask_1>" ,       lowercase_=None ,       lowercase_=1_03 ,       **lowercase_ ,       ):
     snake_case_ :    List[Any]     			=						offset

     if additional_special_tokens is not None:
         if not isinstance(lowercase_ ,       lowercase_):
             raise TypeError(
                 F'additional_special_tokens should be of type {type(lowercase_)}, but is'
                 F' {type(lowercase_)}')

         snake_case_ :    Tuple     			=						(
             ([mask_token_sent] + additional_special_tokens)
             if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
             else additional_special_tokens
         )
         # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
         additional_special_tokens_extended += [
             F'<unk_{i}>' for i in range(len(lowercase_) ,       self.offset - 1)
         ]

         if len(set(lowercase_)) != len(lowercase_):
             raise ValueError(
                 "Please make sure that the provided additional_special_tokens do not contain an incorrectly"
                 F' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.')
         snake_case_ :    Optional[Any]     			=						additional_special_tokens_extended
     else:
         snake_case_ :    Tuple     			=						[mask_token_sent] if mask_token_sent is not None else []
         additional_special_tokens += [F'<unk_{i}>' for i in range(2 ,       self.offset)]

     super().__init__(
         lowercase_ ,       tokenizer_file=lowercase_ ,       pad_token=lowercase_ ,       eos_token=lowercase_ ,       unk_token=lowercase_ ,       mask_token=lowercase_ ,       mask_token_sent=lowercase_ ,       offset=lowercase_ ,       additional_special_tokens=lowercase_ ,       **lowercase_ ,       )
     snake_case_ :    int     			=						vocab_file
     snake_case_ :    str     			=						False if not self.vocab_file else True





 def        snake_case__		(    self ,       lowercase_):
     snake_case_ :    Union[str, Any]     			=						set(self.all_special_ids)  # call it once instead of inside list comp
     all_special_ids.remove(self.unk_token_id)  # <unk> is only sometimes special

     if all_special_ids != set(range(len(self.additional_special_tokens) + 3)):
         raise ValueError(
             "There should be 3 special tokens: mask_token, pad_token, and eos_token +"
             F' {len(self.additional_special_tokens)} additional_special_tokens, but got {all_special_ids}')

     return [1 if x in all_special_ids else 0 for x in seq]





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ = None ,       lowercase_ = False):
     if already_has_special_tokens:
         return self._special_token_mask(lowercase_)
     elif token_ids_a is None:
         return self._special_token_mask(lowercase_) + [1]
     else:
         return self._special_token_mask(token_ids_a + token_ids_a) + [1]





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_=None):
     if token_ids_a is None:
         return token_ids_a + [self.eos_token_id]
     # We don't expect to process pairs, but leave the pair logic for API consistency
     return token_ids_a + token_ids_a + [self.eos_token_id]





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ = None):
     if not self.can_save_slow_tokenizer:
         raise ValueError(
             "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
             "tokenizer.")

     if not os.path.isdir(lowercase_):
         logger.error(F'Vocabulary path ({save_directory}) should be a directory')
         return
     snake_case_ :    Dict     			=						os.path.join(
         lowercase_ ,       (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])

     if os.path.abspath(self.vocab_file) != os.path.abspath(lowercase_):
         copyfile(self.vocab_file ,       lowercase_)

     return (out_vocab_file,)




