

'''simple docstring'''

from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_vision_available():
    from PIL import Image

    from ..image_utils import load_image

if is_torch_available():
    import torch

    from transformers.modeling_outputs import BaseModelOutput

    from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

A_       						=							logging.get_logger(__name__)





@add_end_docstrings(_a						)
class 							_snake_case (       _a						):
   def __init__( self			:      Union[str, Any]     ,**SCREAMING_SNAKE_CASE__			:      Optional[int]							):
     super().__init__(**SCREAMING_SNAKE_CASE__							)

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

     requires_backends(self     ,"vision"							)
     self.check_model_type(SCREAMING_SNAKE_CASE__							)
   def __call__( self			:      str     ,SCREAMING_SNAKE_CASE__			:      Union[str, "Image.Image", List[Dict[str, Any]]]     ,SCREAMING_SNAKE_CASE__			:      Union[str, List[str]] = None     ,**SCREAMING_SNAKE_CASE__			:      str     ,):
     if "text_queries" in kwargs:
       SCREAMING_SNAKE_CASE:str						      =					kwargs.pop("text_queries"							)

     if isinstance(SCREAMING_SNAKE_CASE__     ,(str, Image.Image)							):
       SCREAMING_SNAKE_CASE:Dict						      =					{"image": image, "candidate_labels": candidate_labels}
     else:
       SCREAMING_SNAKE_CASE:Optional[int]						      =					image
     SCREAMING_SNAKE_CASE:int						      =					super().__call__(SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)
     return results
   def     __UpperCamelCase						( self			:      Dict     ,**SCREAMING_SNAKE_CASE__			:      str							):
     SCREAMING_SNAKE_CASE:List[str]						      =					{}
     if "threshold" in kwargs:
       SCREAMING_SNAKE_CASE:Tuple						      =					kwargs["threshold"]
     if "top_k" in kwargs:
       SCREAMING_SNAKE_CASE:List[str]						      =					kwargs["top_k"]
     return {}, {}, postprocess_params
   def     __UpperCamelCase						( self			:      int     ,SCREAMING_SNAKE_CASE__			:      List[Any]							):
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					load_image(inputs["image"]							)
     SCREAMING_SNAKE_CASE:int						      =					inputs["candidate_labels"]
     if isinstance(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							):
       SCREAMING_SNAKE_CASE:List[Any]						      =					candidate_labels.split(","							)

     SCREAMING_SNAKE_CASE:int						      =					torch.tensor([[image.height, image.width]]     ,dtype=torch.intaa							)
     for i, candidate_label in enumerate(SCREAMING_SNAKE_CASE__							):
       SCREAMING_SNAKE_CASE:List[Any]						      =					self.tokenizer(SCREAMING_SNAKE_CASE__     ,return_tensors=self.framework							)
       SCREAMING_SNAKE_CASE:List[str]						      =					self.image_processor(SCREAMING_SNAKE_CASE__     ,return_tensors=self.framework							)
       yield {
           "is_last": i == len(SCREAMING_SNAKE_CASE__							) - 1,
           "target_size": target_size,
           "candidate_label": candidate_label,
           **text_inputs,
           **image_features,
       }
   def     __UpperCamelCase						( self			:      Union[str, Any]     ,SCREAMING_SNAKE_CASE__			:      Tuple							):
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					model_inputs.pop("target_size"							)
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					model_inputs.pop("candidate_label"							)
     SCREAMING_SNAKE_CASE:Dict						      =					model_inputs.pop("is_last"							)

     SCREAMING_SNAKE_CASE:int						      =					self.model(**SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:List[Any]						      =					{"target_size": target_size, "candidate_label": candidate_label, "is_last": is_last, **outputs}
     return model_outputs
   def     __UpperCamelCase						( self			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      List[Any]     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]=0.1     ,SCREAMING_SNAKE_CASE__			:      List[Any]=None							):
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					[]
     for model_output in model_outputs:
       SCREAMING_SNAKE_CASE:Optional[int]						      =					model_output["candidate_label"]
       SCREAMING_SNAKE_CASE:Tuple						      =					BaseModelOutput(SCREAMING_SNAKE_CASE__							)
       SCREAMING_SNAKE_CASE:int						      =					self.image_processor.post_process_object_detection(
           outputs=SCREAMING_SNAKE_CASE__     ,threshold=SCREAMING_SNAKE_CASE__     ,target_sizes=model_output["target_size"]							)[0]

       for index in outputs["scores"].nonzero():
         SCREAMING_SNAKE_CASE:Optional[Any]						      =					outputs["scores"][index].item()
         SCREAMING_SNAKE_CASE:List[str]						      =					self._get_bounding_box(outputs["boxes"][index][0]							)

         SCREAMING_SNAKE_CASE:Tuple						      =					{"score": score, "label": label, "box": box}
         results.append(SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					sorted(SCREAMING_SNAKE_CASE__     ,key=lambda SCREAMING_SNAKE_CASE__							: x["score"]     ,reverse=SCREAMING_SNAKE_CASE__							)
     if top_k:
       SCREAMING_SNAKE_CASE:List[Any]						      =					results[:top_k]

     return results







   def     __UpperCamelCase						( self			:      int     ,SCREAMING_SNAKE_CASE__			:      "torch.Tensor"							):
     if self.framework != "pt":
       raise ValueError("The ZeroShotObjectDetectionPipeline is only available in PyTorch."							)
     SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Tuple						      =					box.int().tolist()
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					{
         "xmin": xmin,
         "ymin": ymin,
         "xmax": xmax,
         "ymax": ymax,
     }
     return bbox









'''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    A_  ( snake_case      ,       snake_case=82      ,       snake_case=16 ):
  set_seed(42 )
  SCREAMING_SNAKE_CASE:Optional[int]						      =					RegressionModel()
  SCREAMING_SNAKE_CASE:int						      =					deepcopy(snake_case )
  SCREAMING_SNAKE_CASE:List[str]						      =					RegressionDataset(length=snake_case )
  SCREAMING_SNAKE_CASE:int						      =					DataLoader(snake_case      ,       batch_size=snake_case )
  model.to(accelerator.device )
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Dict						      =					accelerator.prepare(snake_case      ,       snake_case )
  return model, ddp_model, dataloader





def    A_  ( snake_case      ,       snake_case=False ):
  SCREAMING_SNAKE_CASE:List[Any]						      =					AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased" )
  SCREAMING_SNAKE_CASE:Dict						      =					load_dataset("glue"      ,       "mrpc"      ,       split="validation" )

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

  with accelerator.main_process_first():
    SCREAMING_SNAKE_CASE:int						      =					dataset.map(
        snake_case      ,       batched=snake_case      ,       remove_columns=["idx", "sentence1", "sentence2"]      ,       )

  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					tokenized_datasets.rename_column("label"      ,       "labels" )

  def collate_fn(snake_case ):
    if use_longest:
      return tokenizer.pad(snake_case      ,       padding="longest"      ,       return_tensors="pt" )
    return tokenizer.pad(snake_case      ,       padding="max_length"      ,       max_length=128      ,       return_tensors="pt" )

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





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





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





def    A_  ( snake_case      ,       snake_case=82      ,       snake_case=False      ,       snake_case=False      ,       snake_case=16 ):
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:int						      =					get_basic_setup(snake_case      ,       snake_case      ,       snake_case )
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Optional[Any]						      =					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    A_  ( snake_case = False      ,       snake_case = False ):
  SCREAMING_SNAKE_CASE:List[Any]						      =					evaluate.load("glue"      ,       "mrpc" )
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Dict						      =					get_mrpc_setup(snake_case      ,       snake_case )
  # First do baseline
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Tuple						      =					setup["no"]
  model.to(snake_case )
  model.eval()
  for batch in dataloader:
    batch.to(snake_case )
    with torch.inference_mode():
      SCREAMING_SNAKE_CASE:Dict						      =					model(**snake_case )
    SCREAMING_SNAKE_CASE:Any						      =					outputs.logits.argmax(dim=-1 )
    metric.add_batch(predictions=snake_case      ,       references=batch["labels"] )
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					metric.compute()

  # Then do distributed
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Tuple						      =					setup["ddp"]
  model.eval()
  for batch in dataloader:
    with torch.inference_mode():
      SCREAMING_SNAKE_CASE:Any						      =					model(**snake_case )
    SCREAMING_SNAKE_CASE:Optional[Any]						      =					outputs.logits.argmax(dim=-1 )
    SCREAMING_SNAKE_CASE:List[str]						      =					batch["labels"]
    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:int						      =					accelerator.gather_for_metrics((preds, references) )
    metric.add_batch(predictions=snake_case      ,       references=snake_case )
  SCREAMING_SNAKE_CASE: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    A_  ( ):
  SCREAMING_SNAKE_CASE:Optional[int]						      =					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]:
      SCREAMING_SNAKE_CASE:Optional[Any]						      =					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**" )
  SCREAMING_SNAKE_CASE:List[str]						      =					Accelerator()
  test_torch_metrics(snake_case      ,       512 )
  accelerator.state._reset_state()





def    A_  ( snake_case ):
  # For xla_spawn (TPUs)
  main()


if __name__ == "__main__":
    main()






