'''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


SCREAMING_SNAKE_CASE_:					int  ='true'





def      lowerCAmelCase_    ( snake_case_	:			Union[str, Any] ,   snake_case_	:			Tuple=82 ,   snake_case_	:			Tuple=16      )   ->				Union[str, Any]:


     '''simple docstring'''




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





def      lowerCAmelCase_    ( snake_case_	:			Accelerator ,   snake_case_	:			Dict=False      )   ->				str:


     '''simple docstring'''




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

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

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

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

     def collate_fn(snake_case_	:			Tuple      ):
          if use_longest:
               return tokenizer.pad(snake_case_ ,   padding="longest" ,   return_tensors="pt"      )
          return tokenizer.pad(snake_case_ ,   padding="max_length" ,   max_length=1_28 ,   return_tensors="pt"      )

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





def      lowerCAmelCase_    ( snake_case_	:			Any ,   snake_case_	:			Any      )   ->				Tuple:


     '''simple docstring'''




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





def      lowerCAmelCase_    ( snake_case_	:			str ,   snake_case_	:			Tuple ,   snake_case_	:			Any      )   ->				int:


     '''simple docstring'''




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





def      lowerCAmelCase_    ( snake_case_	:			Accelerator ,   snake_case_	:			Union[str, Any]=82 ,   snake_case_	:			Any=False ,   snake_case_	:			Union[str, Any]=False ,   snake_case_	:			str=16      )   ->				Any:


     '''simple docstring'''




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





def      lowerCAmelCase_    ( snake_case_	:			bool = False ,   snake_case_	:			bool = False      )   ->				Tuple:


     '''simple docstring'''




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

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


     '''simple docstring'''




     UpperCAmelCase_	   =       Accelerator(split_batches=snake_case_ ,   dispatch_batches=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(snake_case_ ,   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]:
               UpperCAmelCase_	   =       Accelerator(split_batches=snake_case_ ,   dispatch_batches=snake_case_      )
               if accelerator.is_local_main_process:
                    print(f"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"""      )
               test_torch_metrics(snake_case_ ,   99      )
               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(snake_case_ ,   5_12      )
     accelerator.state._reset_state()





def      lowerCAmelCase_    ( snake_case_	:			Union[str, Any]      )   ->				Optional[Any]:


     '''simple docstring'''




     main()


if __name__ == "__main__":
    main()





'''simple docstring'''





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


SCREAMING_SNAKE_CASE_:					Dict  =logging.get_logger(__name__)




class 	__A				(			UpperCamelCase__     ):
    a__	:						Any	       =  ["""pixel_values"""]



    def __init__(self   :   Any       ,      __a   :   bool = True       ,      __a   :   Optional[Dict[str, int]] = None       ,      __a   :   PILImageResampling = PILImageResampling.BICUBIC       ,      __a   :   bool = True       ,      __a   :   bool = True       ,      __a   :   Union[int, float] = 1 / 255       ,      __a   :   Dict[str, int] = None       ,      __a   :   bool = True       ,      __a   :   Optional[Union[float, List[float]]] = None       ,      __a   :   Optional[Union[float, List[float]]] = None       ,      **__a   :   Dict       ,      ):
         super().__init__(**__a			)
         UpperCAmelCase_	   =       size if size is not None else {"height": 224, "width": 224}
         UpperCAmelCase_	   =       get_size_dict(__a			)
         UpperCAmelCase_	   =       crop_size if crop_size is not None else {"height": 224, "width": 224}
         UpperCAmelCase_	   =       get_size_dict(__a       ,      default_to_square=__a       ,      param_name="crop_size"			)

         UpperCAmelCase_	   =       do_resize
         UpperCAmelCase_	   =       do_rescale
         UpperCAmelCase_	   =       do_normalize
         UpperCAmelCase_	   =       do_center_crop
         UpperCAmelCase_	   =       crop_size
         UpperCAmelCase_	   =       size
         UpperCAmelCase_	   =       resample
         UpperCAmelCase_	   =       rescale_factor
         UpperCAmelCase_	   =       image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
         UpperCAmelCase_	   =       image_std if image_std is not None else IMAGENET_DEFAULT_STD



    def  _lowercase						(self   :   Tuple       ,      __a   :   np.ndarray       ,      __a   :   Dict[str, int]       ,      __a   :   PILImageResampling = PILImageResampling.BILINEAR       ,      __a   :   Optional[Union[str, ChannelDimension]] = None       ,      **__a   :   Optional[Any]       ,      ):
         UpperCAmelCase_	   =       get_size_dict(__a			)

         if "shortest_edge" in size:
              UpperCAmelCase_	   =       get_resize_output_image_size(__a       ,      size=size["shortest_edge"]       ,      default_to_square=__a			)
              # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
         elif "height" in size and "width" in size:
              UpperCAmelCase_	   =       (size["height"], size["width"])
         else:
              raise ValueError(f"""Size must contain 'height' and 'width' keys or 'shortest_edge' key. Got {size.keys()}"""			)
         return resize(__a       ,      size=__a       ,      resample=__a       ,      data_format=__a       ,      **__a			)



    def  _lowercase						(self   :   List[Any]       ,      __a   :   np.ndarray       ,      __a   :   Dict[str, int]       ,      __a   :   Optional[Union[str, ChannelDimension]] = None       ,      **__a   :   int       ,      ):
         UpperCAmelCase_	   =       get_size_dict(__a			)
         if "height" not in size or "width" not in size:
              raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""			)
         return center_crop(__a       ,      size=(size["height"], size["width"])       ,      data_format=__a       ,      **__a			)



    def  _lowercase						(self   :   str       ,      __a   :   np.ndarray       ,      __a   :   float       ,      __a   :   Optional[Union[str, ChannelDimension]] = None       ,      **__a   :   List[str]			):
         return rescale(__a       ,      scale=__a       ,      data_format=__a       ,      **__a			)



    def  _lowercase						(self   :   Any       ,      __a   :   np.ndarray       ,      __a   :   Union[float, List[float]]       ,      __a   :   Union[float, List[float]]       ,      __a   :   Optional[Union[str, ChannelDimension]] = None       ,      **__a   :   Dict       ,      ):
         return normalize(__a       ,      mean=__a       ,      std=__a       ,      data_format=__a       ,      **__a			)






    def  _lowercase						(self   :   Dict       ,      __a   :   ImageInput       ,      __a   :   Optional[bool] = None       ,      __a   :   Dict[str, int] = None       ,      __a   :   PILImageResampling = None       ,      __a   :   bool = None       ,      __a   :   int = None       ,      __a   :   Optional[bool] = None       ,      __a   :   Optional[float] = None       ,      __a   :   Optional[bool] = None       ,      __a   :   Optional[Union[float, List[float]]] = None       ,      __a   :   Optional[Union[float, List[float]]] = None       ,      __a   :   Optional[Union[str, TensorType]] = None       ,      __a   :   Union[str, ChannelDimension] = ChannelDimension.FIRST       ,      **__a   :   Dict       ,      ):
         UpperCAmelCase_	   =       do_resize if do_resize is not None else self.do_resize
         UpperCAmelCase_	   =       do_rescale if do_rescale is not None else self.do_rescale
         UpperCAmelCase_	   =       do_normalize if do_normalize is not None else self.do_normalize
         UpperCAmelCase_	   =       do_center_crop if do_center_crop is not None else self.do_center_crop
         UpperCAmelCase_	   =       crop_size if crop_size is not None else self.crop_size
         UpperCAmelCase_	   =       get_size_dict(__a       ,      param_name="crop_size"       ,      default_to_square=__a			)
         UpperCAmelCase_	   =       resample if resample is not None else self.resample
         UpperCAmelCase_	   =       rescale_factor if rescale_factor is not None else self.rescale_factor
         UpperCAmelCase_	   =       image_mean if image_mean is not None else self.image_mean
         UpperCAmelCase_	   =       image_std if image_std is not None else self.image_std

         UpperCAmelCase_	   =       size if size is not None else self.size
         UpperCAmelCase_	   =       get_size_dict(__a			)

         if not is_batched(__a			):
              UpperCAmelCase_	   =       [images]

         if not valid_images(__a			):
              raise ValueError(
                  "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                  "torch.Tensor, tf.Tensor or jax.ndarray."			)

         if do_resize and size is None:
              raise ValueError("Size must be specified if do_resize is True."			)

         if do_center_crop and crop_size is None:
              raise ValueError("Crop size must be specified if do_center_crop is True."			)

         if do_rescale and rescale_factor is None:
              raise ValueError("Rescale factor must be specified if do_rescale is True."			)

         # All transformations expect numpy arrays.
         UpperCAmelCase_	   =       [to_numpy_array(__a			) for image in images]

         if do_resize:
              UpperCAmelCase_	   =       [self.resize(image=__a       ,      size=__a       ,      resample=__a			) for image in images]

         if do_center_crop:
              UpperCAmelCase_	   =       [self.center_crop(image=__a       ,      size=__a			) for image in images]

         if do_rescale:
              UpperCAmelCase_	   =       [self.rescale(image=__a       ,      scale=__a			) for image in images]

         if do_normalize:
              UpperCAmelCase_	   =       [self.normalize(image=__a       ,      mean=__a       ,      std=__a			) for image in images]

         UpperCAmelCase_	   =       [to_channel_dimension_format(__a       ,      __a			) for image in images]

         UpperCAmelCase_	   =       {"pixel_values": images}
         return BatchFeature(data=__a       ,      tensor_type=__a			)




