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							 =					'''true'''



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE=82  ,			SCREAMING_SNAKE_CASE=16	):
    '''simple docstring'''







    set_seed(42	)
    __UpperCamelCase     :List[str]			  =		RegressionModel()
    __UpperCamelCase     :List[Any]			  =		deepcopy(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :Optional[int]			  =		RegressionDataset(length=SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :List[Any]			  =		DataLoader(SCREAMING_SNAKE_CASE  ,			batch_size=SCREAMING_SNAKE_CASE	)
    model.to(accelerator.device	)
    __UpperCamelCase  ,       __UpperCamelCase     :Optional[Any]			  =		accelerator.prepare(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
    return model, ddp_model, dataloader



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE=False	):
    '''simple docstring'''







    __UpperCamelCase     :List[str]			  =		AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased'''	)
    __UpperCamelCase     :Optional[int]			  =		load_dataset('''glue'''  ,			'''mrpc'''  ,			split='''validation'''	)

    def tokenize_function(SCREAMING_SNAKE_CASE	):
        __UpperCamelCase     :Optional[Any]			  =		tokenizer(examples['''sentence1''']  ,			examples['''sentence2''']  ,			truncation=SCREAMING_SNAKE_CASE  ,			max_length=SCREAMING_SNAKE_CASE	)
        return outputs

    with accelerator.main_process_first():
        __UpperCamelCase     :Any			  =		dataset.map(
            SCREAMING_SNAKE_CASE  ,			batched=SCREAMING_SNAKE_CASE  ,			remove_columns=['''idx''', '''sentence1''', '''sentence2''']  ,			)

    __UpperCamelCase     :Union[str, Any]			  =		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=128  ,			return_tensors='''pt'''	)

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



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







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



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :List[str]			  =		[]
    for batch in dataloader:
        __UpperCamelCase  ,       __UpperCamelCase     :Tuple			  =		batch.values()
        with torch.no_grad():
            __UpperCamelCase     :Any			  =		model(SCREAMING_SNAKE_CASE	)
            __UpperCamelCase  ,       __UpperCamelCase     :Any			  =		accelerator.gather_for_metrics((logit, target)	)
            logits_and_targets.append((logit, target)	)
    __UpperCamelCase  ,       __UpperCamelCase     :List[Any]			  =		[], []
    for logit, targ in logits_and_targets:
        logits.append(SCREAMING_SNAKE_CASE	)
        targs.append(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase  ,       __UpperCamelCase     :str			  =		torch.cat(SCREAMING_SNAKE_CASE	), torch.cat(SCREAMING_SNAKE_CASE	)
    return logits, targs



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE=82  ,			SCREAMING_SNAKE_CASE=False  ,			SCREAMING_SNAKE_CASE=False  ,			SCREAMING_SNAKE_CASE=16	):
    '''simple docstring'''







    __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase     :Tuple			  =		get_basic_setup(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
    __UpperCamelCase  ,       __UpperCamelCase     :List[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 lowerCamelCase  (   SCREAMING_SNAKE_CASE = False  ,			SCREAMING_SNAKE_CASE = False	):
    '''simple docstring'''







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

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







    __UpperCamelCase     :str			  =		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]:
            __UpperCamelCase     :Union[str, Any]			  =		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  ,			99	)
            accelerator.state._reset_state()
    if accelerator.is_local_main_process:
        print('''**Test last batch is not dropped when perfectly divisible**'''	)
    __UpperCamelCase     :Union[str, Any]			  =		Accelerator()
    test_torch_metrics(SCREAMING_SNAKE_CASE  ,			512	)
    accelerator.state._reset_state()



def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    main()


if __name__ == "__main__":
    main()








from collections import defaultdict
from typing import Optional

from ..image_utils import load_image
from ..utils import (
    add_end_docstrings,
    is_torch_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_torch_available():
       import torch

       from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING

lowerCamelCase           =					logging.get_logger(__name__)







@add_end_docstrings(lowerCamelCase__							)
class 						__magic_name__			(							lowerCamelCase__							):

   '''simple docstring'''




   def __init__(				self,    **lowercase_       )							->    Optional[int]:






     """simple docstring"""






     super().__init__(**lowercase_       )
     requires_backends(self,    '''vision'''       )
     requires_backends(self,    '''torch'''       )

     if self.framework != "pt":
       raise ValueError(F"""The {self.__class__} is only available in PyTorch."""       )

     self.check_model_type(lowercase_       )



   def  _UpperCAmelCase (				self,    **lowercase_       )							->    Optional[Any]:






     """simple docstring"""






     a__  ={}
     a__  ={}
     a__  ={}
     # preprocess args
     if "points_per_batch" in kwargs:
       a__  =kwargs['''points_per_batch''']
     if "points_per_crop" in kwargs:
       a__  =kwargs['''points_per_crop''']
     if "crops_n_layers" in kwargs:
       a__  =kwargs['''crops_n_layers''']
     if "crop_overlap_ratio" in kwargs:
       a__  =kwargs['''crop_overlap_ratio''']
     if "crop_n_points_downscale_factor" in kwargs:
       a__  =kwargs['''crop_n_points_downscale_factor''']
     # postprocess args
     if "pred_iou_thresh" in kwargs:
       a__  =kwargs['''pred_iou_thresh''']
     if "stability_score_offset" in kwargs:
       a__  =kwargs['''stability_score_offset''']
     if "mask_threshold" in kwargs:
       a__  =kwargs['''mask_threshold''']
     if "stability_score_thresh" in kwargs:
       a__  =kwargs['''stability_score_thresh''']
     if "crops_nms_thresh" in kwargs:
       a__  =kwargs['''crops_nms_thresh''']
     if "output_rle_mask" in kwargs:
       a__  =kwargs['''output_rle_mask''']
     if "output_bboxes_mask" in kwargs:
       a__  =kwargs['''output_bboxes_mask''']
     return preprocess_kwargs, forward_params, postprocess_kwargs



   def __call__(				self,    lowercase_,    *lowercase_,    lowercase_=None,    lowercase_=None,    **lowercase_       )							->    List[Any]:






     """simple docstring"""






     return super().__call__(lowercase_,    *lowercase_,    num_workers=lowercase_,    batch_size=lowercase_,    **lowercase_       )



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_=64,    lowercase_ = 0,    lowercase_ = 512 / 1500,    lowercase_ = 32,    lowercase_ = 1,    )							->    Any:






     """simple docstring"""






     a__  =load_image(lowercase_       )
     a__  =self.image_processor.size['''longest_edge''']
     a__,      a__,      a__,      a__  =self.image_processor.generate_crop_boxes(
         lowercase_,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    lowercase_       )
     a__  =self.image_processor(images=lowercase_,    return_tensors='''pt'''       )

     with self.device_placement():
       if self.framework == "pt":
         a__  =self.get_inference_context()
         with inference_context():
           a__  =self._ensure_tensor_on_device(lowercase_,    device=self.device       )
           a__  =self.model.get_image_embeddings(model_inputs.pop('''pixel_values'''       )       )
           a__  =image_embeddings

     a__  =grid_points.shape[1]
     a__  =points_per_batch if points_per_batch is not None else n_points

     if points_per_batch <= 0:
       raise ValueError(
           '''Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. '''
           '''To return all points at once, set points_per_batch to None'''       )

     for i in range(0,    lowercase_,    lowercase_       ):
       a__  =grid_points[:, i : i + points_per_batch, :, :]
       a__  =input_labels[:, i : i + points_per_batch]
       a__  =i == n_points - points_per_batch
       yield {
           "input_points": batched_points,
           "input_labels": labels,
           "input_boxes": crop_boxes,
           "is_last": is_last,
           **model_inputs,
       }



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_=0.88,    lowercase_=0.95,    lowercase_=0,    lowercase_=1,    )							->    int:






     """simple docstring"""






     a__  =model_inputs.pop('''input_boxes'''       )
     a__  =model_inputs.pop('''is_last'''       )
     a__  =model_inputs.pop('''original_sizes'''       ).tolist()
     a__  =model_inputs.pop('''reshaped_input_sizes'''       ).tolist()

     a__  =self.model(**lowercase_       )

     # post processing happens here in order to avoid CPU GPU copies of ALL the masks
     a__  =model_outputs['''pred_masks''']
     a__  =self.image_processor.post_process_masks(
         lowercase_,    lowercase_,    lowercase_,    lowercase_,    binarize=lowercase_       )
     a__  =model_outputs['''iou_scores''']
     a__,      a__,      a__  =self.image_processor.filter_masks(
         masks[0],    iou_scores[0],    original_sizes[0],    input_boxes[0],    lowercase_,    lowercase_,    lowercase_,    lowercase_,    )
     return {
         "masks": masks,
         "is_last": is_last,
         "boxes": boxes,
         "iou_scores": iou_scores,
     }



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_=False,    lowercase_=False,    lowercase_=0.7,    )							->    Any:






     """simple docstring"""






     a__  =[]
     a__  =[]
     a__  =[]
     for model_output in model_outputs:
       all_scores.append(model_output.pop('''iou_scores'''       )       )
       all_masks.extend(model_output.pop('''masks'''       )       )
       all_boxes.append(model_output.pop('''boxes'''       )       )

     a__  =torch.cat(lowercase_       )
     a__  =torch.cat(lowercase_       )
     a__,      a__,      a__,      a__  =self.image_processor.post_process_for_mask_generation(
         lowercase_,    lowercase_,    lowercase_,    lowercase_       )

     a__  =defaultdict(lowercase_       )
     for output in model_outputs:
       for k, v in output.items():
         extra[k].append(lowercase_       )

     a__  ={}
     if output_rle_mask:
       a__  =rle_mask

     if output_bboxes_mask:
       a__  =bounding_boxes

     return {"masks": output_masks, "scores": iou_scores, **optional, **extra}
