
"""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					:	Dict							=		'true'


def  _UpperCamelCase	(						_A					,      _A=8_2					,      _A=1_6      )    ->  Tuple:
    set_seed(4_2      )
    _UpperCAmelCase          =   RegressionModel()
    _UpperCAmelCase          =   deepcopy(lowerCAmelCase_      )
    _UpperCAmelCase          =   RegressionDataset(length=lowerCAmelCase_      )
    _UpperCAmelCase          =   DataLoader(lowerCAmelCase_					,      batch_size=lowerCAmelCase_      )
    model.to(accelerator.device      )
    _UpperCAmelCase          =   accelerator.prepare(lowerCAmelCase_					,      lowerCAmelCase_      )
    return model, ddp_model, dataloader


def  _UpperCamelCase	(						_A					,      _A=False      )    ->  Union[str, Any]:
    _UpperCAmelCase          =   AutoTokenizer.from_pretrained("""hf-internal-testing/mrpc-bert-base-cased"""      )
    _UpperCAmelCase          =   load_dataset("""glue"""					,      """mrpc"""					,      split="""validation"""      )

    def tokenize_function(_A      ):
        _UpperCAmelCase          =   tokenizer(examples["""sentence1"""]					,      examples["""sentence2"""]					,      truncation=lowerCAmelCase_					,      max_length=lowerCAmelCase_      )
        return outputs

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

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

    def collate_fn(_A      ):
        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  _UpperCamelCase	(						_A					,      _A      )    ->  Union[str, Any]:
    _UpperCAmelCase          =   Accelerator(dispatch_batches=lowerCAmelCase_					,      split_batches=lowerCAmelCase_      )
    _UpperCAmelCase          =   get_dataloader(lowerCAmelCase_					,      not dispatch_batches      )
    _UpperCAmelCase          =   AutoModelForSequenceClassification.from_pretrained(
        """hf-internal-testing/mrpc-bert-base-cased"""					,      return_dict=lowerCAmelCase_      )
    _UpperCAmelCase          =   accelerator.prepare(lowerCAmelCase_					,      lowerCAmelCase_      )
    return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator


def  _UpperCamelCase	(						_A					,      _A					,      _A      )    ->  Tuple:
    _UpperCAmelCase          =   []
    for batch in dataloader:
        _UpperCAmelCase          =   batch.values()
        with torch.no_grad():
            _UpperCAmelCase          =   model(lowerCAmelCase_      )
            _UpperCAmelCase          =   accelerator.gather_for_metrics((logit, target)      )
            logits_and_targets.append((logit, target)      )
    _UpperCAmelCase          =   [], []
    for logit, targ in logits_and_targets:
        logits.append(lowerCAmelCase_      )
        targs.append(lowerCAmelCase_      )
    _UpperCAmelCase          =   torch.cat(lowerCAmelCase_      ), torch.cat(lowerCAmelCase_      )
    return logits, targs


def  _UpperCamelCase	(						_A					,      _A=8_2					,      _A=False					,      _A=False					,      _A=1_6      )    ->  str:
    _UpperCAmelCase          =   get_basic_setup(lowerCAmelCase_					,      lowerCAmelCase_					,      lowerCAmelCase_      )
    _UpperCAmelCase          =   generate_predictions(lowerCAmelCase_					,      lowerCAmelCase_					,      lowerCAmelCase_      )
    assert (
        len(lowerCAmelCase_      ) == num_samples
    ), F"""Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(lowerCAmelCase_      )}"""


def  _UpperCamelCase	(						_A = False					,      _A = False      )    ->  Optional[int]:
    _UpperCAmelCase          =   evaluate.load("""glue"""					,      """mrpc"""      )
    _UpperCAmelCase          =   get_mrpc_setup(lowerCAmelCase_					,      lowerCAmelCase_      )
    # First do baseline
    _UpperCAmelCase          =   setup['''no''']
    model.to(lowerCAmelCase_      )
    model.eval()
    for batch in dataloader:
        batch.to(lowerCAmelCase_      )
        with torch.inference_mode():
            _UpperCAmelCase          =   model(**lowerCAmelCase_      )
        _UpperCAmelCase          =   outputs.logits.argmax(dim=-1      )
        metric.add_batch(predictions=lowerCAmelCase_					,      references=batch["""labels"""]      )
    _UpperCAmelCase          =   metric.compute()

    # Then do distributed
    _UpperCAmelCase          =   setup['''ddp''']
    model.eval()
    for batch in dataloader:
        with torch.inference_mode():
            _UpperCAmelCase          =   model(**lowerCAmelCase_      )
        _UpperCAmelCase          =   outputs.logits.argmax(dim=-1      )
        _UpperCAmelCase          =   batch['''labels''']
        _UpperCAmelCase          =   accelerator.gather_for_metrics((preds, references)      )
        metric.add_batch(predictions=lowerCAmelCase_					,      references=lowerCAmelCase_      )
    _UpperCAmelCase          =   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	(						)    ->  Tuple:
    _UpperCAmelCase          =   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]:
            _UpperCAmelCase          =   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**"""      )
    _UpperCAmelCase          =   Accelerator()
    test_torch_metrics(lowerCAmelCase_					,      5_1_2      )
    accelerator.state._reset_state()


def  _UpperCamelCase	(						_A      )    ->  Any:
    main()


if __name__ == "__main__":
 main()

"""simple docstring"""



import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class 		a_						( _UpperCAmelCase	):
    a      :				Any     =				['image_processor', 'tokenizer']
    a      :				Optional[int]     =				'AutoImageProcessor'
    a      :				Any     =				'AutoTokenizer'







    def __init__(			self			:       List[str]     ,					__UpperCamelCase			:       List[Any]=None     ,					__UpperCamelCase			:       List[str]=None     ,					**__UpperCamelCase			:       List[Any]     )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase          =   None
        if "feature_extractor" in kwargs:
            warnings.warn(
                """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                """ instead."""     ,					__UpperCamelCase     ,					)
            _UpperCAmelCase          =   kwargs.pop("""feature_extractor"""     )

        _UpperCAmelCase          =   image_processor if image_processor is not None else feature_extractor
        if image_processor is None:
            raise ValueError("""You need to specify an `image_processor`."""     )
        if tokenizer is None:
            raise ValueError("""You need to specify a `tokenizer`."""     )

        super().__init__(__UpperCamelCase     ,					__UpperCamelCase     )
        _UpperCAmelCase          =   self.image_processor
        _UpperCAmelCase          =   False







    def __call__(			self			:       Union[str, Any]     ,					*__UpperCamelCase			:       str     ,					**__UpperCamelCase			:       Optional[Any]     )		->List[str]:






        '''simple docstring'''






        if self._in_target_context_manager:
            return self.current_processor(*__UpperCamelCase     ,					**__UpperCamelCase     )

        _UpperCAmelCase          =   kwargs.pop("""images"""     ,					__UpperCamelCase     )
        _UpperCAmelCase          =   kwargs.pop("""text"""     ,					__UpperCamelCase     )
        if len(__UpperCamelCase     ) > 0:
            _UpperCAmelCase          =   args[0]
            _UpperCAmelCase          =   args[1:]

        if images is None and text is None:
            raise ValueError("""You need to specify either an `images` or `text` input to process."""     )

        if images is not None:
            _UpperCAmelCase          =   self.image_processor(__UpperCamelCase     ,					*__UpperCamelCase     ,					**__UpperCamelCase     )
        if text is not None:
            _UpperCAmelCase          =   self.tokenizer(__UpperCamelCase     ,					**__UpperCamelCase     )

        if text is None:
            return inputs
        elif images is None:
            return encodings
        else:
            _UpperCAmelCase          =   encodings["""input_ids"""]
            return inputs







    def      _snake_case					(			self			:       Union[str, Any]     ,					*__UpperCamelCase			:       int     ,					**__UpperCamelCase			:       Tuple     )		->Tuple:






        '''simple docstring'''






        return self.tokenizer.batch_decode(*__UpperCamelCase     ,					**__UpperCamelCase     )







    def      _snake_case					(			self			:       Tuple     ,					*__UpperCamelCase			:       int     ,					**__UpperCamelCase			:       Union[str, Any]     )		->int:






        '''simple docstring'''






        return self.tokenizer.decode(*__UpperCamelCase     ,					**__UpperCamelCase     )







    @contextmanager
    def      _snake_case					(			self			:       Tuple     )		->Union[str, Any]:






        '''simple docstring'''






        warnings.warn(
            """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
            """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
            """your images inputs, or in a separate call."""     )
        _UpperCAmelCase          =   True
        _UpperCAmelCase          =   self.tokenizer
        yield
        _UpperCAmelCase          =   self.image_processor
        _UpperCAmelCase          =   False







    def      _snake_case					(			self			:       str     ,					__UpperCamelCase			:       Dict     ,					__UpperCamelCase			:       Optional[Any]=False     ,					__UpperCamelCase			:       Union[str, Any]=None     )		->List[str]:






        '''simple docstring'''






        if added_vocab is None:
            _UpperCAmelCase          =   self.tokenizer.get_added_vocab()

        _UpperCAmelCase          =   {}

        while tokens:
            _UpperCAmelCase          =   re.search(r"""<s_(.*?)>"""     ,					__UpperCamelCase     ,					re.IGNORECASE     )
            if start_token is None:
                break
            _UpperCAmelCase          =   start_token.group(1     )
            _UpperCAmelCase          =   re.search(rf"""</s_{key}>"""     ,					__UpperCamelCase     ,					re.IGNORECASE     )
            _UpperCAmelCase          =   start_token.group()
            if end_token is None:
                _UpperCAmelCase          =   tokens.replace(__UpperCamelCase     ,					""""""     )
            else:
                _UpperCAmelCase          =   end_token.group()
                _UpperCAmelCase          =   re.escape(__UpperCamelCase     )
                _UpperCAmelCase          =   re.escape(__UpperCamelCase     )
                _UpperCAmelCase          =   re.search(f"""{start_token_escaped}(.*?){end_token_escaped}"""     ,					__UpperCamelCase     ,					re.IGNORECASE     )
                if content is not None:
                    _UpperCAmelCase          =   content.group(1     ).strip()
                    if r"<s_" in content and r"</s_" in content:  # non-leaf node
                        _UpperCAmelCase          =   self.tokenajson(__UpperCamelCase     ,					is_inner_value=__UpperCamelCase     ,					added_vocab=__UpperCamelCase     )
                        if value:
                            if len(__UpperCamelCase     ) == 1:
                                _UpperCAmelCase          =   value[0]
                            _UpperCAmelCase          =   value
                    else:  # leaf nodes
                        _UpperCAmelCase          =   []
                        for leaf in content.split(r"""<sep/>"""     ):
                            _UpperCAmelCase          =   leaf.strip()
                            if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
                                _UpperCAmelCase          =   leaf[1:-2]  # for categorical special tokens
                            output[key].append(__UpperCamelCase     )
                        if len(output[key]     ) == 1:
                            _UpperCAmelCase          =   output[key][0]

                _UpperCAmelCase          =   tokens[tokens.find(__UpperCamelCase     ) + len(__UpperCamelCase     ) :].strip()
                if tokens[:6] == r"<sep/>":  # non-leaf nodes
                    return [output] + self.tokenajson(tokens[6:]     ,					is_inner_value=__UpperCamelCase     ,					added_vocab=__UpperCamelCase     )

        if len(__UpperCamelCase     ):
            return [output] if is_inner_value else output
        else:
            return [] if is_inner_value else {"text_sequence": tokens}







    @property
    def      _snake_case					(			self			:       Tuple     )		->Optional[int]:






        '''simple docstring'''






        warnings.warn(
            """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""     ,					__UpperCamelCase     ,					)
        return self.image_processor_class







    @property
    def      _snake_case					(			self			:       List[str]     )		->Dict:






        '''simple docstring'''






        warnings.warn(
            """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""     ,					__UpperCamelCase     ,					)
        return self.image_processor