



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     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__=82   ,   UpperCAmelCase__=16 ):


      """simple docstring"""



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

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__=False ):


      """simple docstring"""



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

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

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

      lowerCamelCase								=		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     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



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

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



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

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__=82   ,   UpperCAmelCase__=False   ,   UpperCAmelCase__=False   ,   UpperCAmelCase__=16 ):


      """simple docstring"""



      lowerCamelCase			,							lowerCamelCase			,							lowerCamelCase								=		get_basic_setup(UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ )
      lowerCamelCase			,							lowerCamelCase								=		generate_predictions(UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ )
      assert (
          len(UpperCAmelCase__ ) == num_samples
      ), F"""Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(UpperCAmelCase__ )}"""

def     __lowercase(	UpperCAmelCase__ = False   ,   UpperCAmelCase__ = False ):


      """simple docstring"""



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

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


      """simple docstring"""



      lowerCamelCase								=		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]:
                  lowerCamelCase								=		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**" )
      lowerCamelCase								=		Accelerator()
      test_torch_metrics(UpperCAmelCase__   ,   512 )
      accelerator.state._reset_state()

def     __lowercase(	UpperCAmelCase__ ):


      """simple docstring"""



      main()


if __name__ == "__main__":
     main()




import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding





class    lowerCamelCase__  (						UpperCAmelCase_):
     """simple docstring"""



     _A       =			['image_processor', 'tokenizer']
     _A       =			'CLIPImageProcessor'
     _A       =			('CLIPTokenizer', 'CLIPTokenizerFast')







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






           '''simple docstring'''



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

           lowerCamelCase								=		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__(__a				,			__a    )







     def __call__(self				,			__a=None				,			__a=None				,			__a=None				,			**__a    ):






           '''simple docstring'''



           if text is None and images is None:
                 raise ValueError("You have to specify either text or images. Both cannot be none."    )

           if text is not None:
                 lowerCamelCase								=		self.tokenizer(__a				,			return_tensors=__a				,			**__a    )

           if images is not None:
                 lowerCamelCase								=		self.image_processor(__a				,			return_tensors=__a				,			**__a    )

           if text is not None and images is not None:
                 lowerCamelCase								=		image_features.pixel_values
                 return encoding
           elif text is not None:
                 return encoding
           else:
                 return BatchEncoding(data=dict(**__a    )				,			tensor_type=__a    )







     def 						_a      (self				,			*__a				,			**__a    ):






           '''simple docstring'''



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







     def 						_a      (self				,			*__a				,			**__a    ):






           '''simple docstring'''



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







     @property
     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.tokenizer.model_input_names
           lowerCamelCase								=		self.image_processor.model_input_names
           return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )







     @property
     def 						_a      (self    ):






           '''simple docstring'''



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







     @property
     def 						_a      (self    ):






           '''simple docstring'''



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