

import sys
from pathlib import Path


lowerCAmelCase						     =  Path(__file__).resolve().parents[3] / """src"""
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

lowerCAmelCase						     =  {"""base""": """patrickvonplaten/wav2vec2_tiny_random""", """robust""": """patrickvonplaten/wav2vec2_tiny_random_robust"""}

lowerCAmelCase						     =  """zero2"""
lowerCAmelCase						     =  """zero3"""
lowerCAmelCase						     =  [ZEROa, ZEROa]

def    A_  (							a					,   a					,   a  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  int           =     parameterized.to_safe_name('_'.join(str(a  ) for x in param.args  )  )
      return f"{func.__name__}_{param_based_name}"


# Cartesian-product of zero stages with models to test
lowerCAmelCase						     =  list(itertools.product(stages, models.keys()))



@slow
@require_deepspeed
@require_torch_gpu
class  _A		(  _snake_case):

  @parameterized.expand(snake_case_ ,					name_func=snake_case_					)
  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        self.run_and_check(
            stage=snake_case_ ,					model=snake_case_ ,					distributed=snake_case_ ,					fpaa=snake_case_ ,					)

  @require_torch_multi_gpu
  @parameterized.expand(snake_case_ ,					name_func=snake_case_					)
  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        self.run_and_check(
            stage=snake_case_ ,					model=snake_case_ ,					distributed=snake_case_ ,					fpaa=snake_case_ ,					)

  @parameterized.expand(snake_case_ ,					name_func=snake_case_					)
  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        self.run_and_check(
            stage=snake_case_ ,					model=snake_case_ ,					distributed=snake_case_ ,					fpaa=snake_case_ ,					)

  @require_torch_multi_gpu
  @parameterized.expand(snake_case_ ,					name_func=snake_case_					)
  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        self.run_and_check(
            stage=snake_case_ ,					model=snake_case_ ,					distributed=snake_case_ ,					fpaa=snake_case_ ,					)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        pass

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE = 10 ,					_SCREAMING_SNAKE_CASE = True ,					_SCREAMING_SNAKE_CASE = True ,					_SCREAMING_SNAKE_CASE = True ,					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[str]           =     models[model]

        SCREAMING_SNAKE_CASE_				:  Any           =     self.run_trainer(
            stage=snake_case_ ,					model_name=snake_case_ ,					eval_steps=snake_case_ ,					num_train_epochs=1 ,					distributed=snake_case_ ,					fpaa=snake_case_ ,					)

        self.do_checks(snake_case_					)

        return output_dir

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE = 10 ,					_SCREAMING_SNAKE_CASE = 1 ,					_SCREAMING_SNAKE_CASE = True ,					_SCREAMING_SNAKE_CASE = True ,					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  str           =     self.get_auto_remove_tmp_dir('./xxx' ,					after=snake_case_					)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     f"\n            --model_name_or_path {model_name}\n            --dataset_name hf-internal-testing/librispeech_asr_dummy\n            --dataset_config_name clean\n            --train_split_name validation\n            --validation_split_name validation\n            --output_dir {output_dir}\n            --num_train_epochs {str(snake_case_					)}\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 2\n            --evaluation_strategy steps\n            --learning_rate 5e-4\n            --warmup_steps 8\n            --orthography timit\n            --preprocessing_num_workers 1\n            --group_by_length\n            --freeze_feature_extractor\n            --report_to none\n            --save_steps 0\n            --eval_steps {eval_steps}\n            --report_to none\n        ".split()

        if fpaa:
              args.extend(['--fp16']					)

        # currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
        # hence the separate config files
        SCREAMING_SNAKE_CASE_				:  str           =     f"--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json".split()
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     [f"{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"]
        SCREAMING_SNAKE_CASE_				:  List[str]           =     self.get_launcher(snake_case_					)

        SCREAMING_SNAKE_CASE_				:  int           =     launcher + script + args + ds_args
        # keep for quick debug
        # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
        execute_subprocess_async(snake_case_ ,					env=self.get_env()					)

        return output_dir



  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE=False					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     min(2 ,					get_gpu_count()					) if distributed else 1
        return f"deepspeed --num_nodes 1 --num_gpus {num_gpus}".split()









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, Pipeline


if is_vision_available():
    from ..image_utils import load_image


if is_torch_available():
    import torch

    from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

lowerCAmelCase     :   Optional[int]						     =  logging.get_logger(__name__)


lowerCAmelCase     :   Optional[int]						     =  Dict[str, Any]
lowerCAmelCase     :   Any						     =  List[Prediction]
@add_end_docstrings(__magic_name__)
class  _A		(  __magic_name__):

  def __init__(				self ,					*_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        super().__init__(*_SCREAMING_SNAKE_CASE ,					**_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(
            dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()					)					)

  def  UpperCAmelCase    (				self ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Any           =     {}
        if "threshold" in kwargs:
              SCREAMING_SNAKE_CASE_				:  List[str]           =     kwargs['threshold']
        return {}, {}, postprocess_kwargs

  def __call__(				self ,					*_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        return super().__call__(*_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     load_image(_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     torch.IntTensor([[image.height, image.width]]					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     self.image_processor(images=[image] ,					return_tensors='pt'					)
        if self.tokenizer is not None:
              SCREAMING_SNAKE_CASE_				:  Tuple           =     self.tokenizer(text=inputs['words'] ,					boxes=inputs['boxes'] ,					return_tensors='pt'					)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     target_size
        return inputs

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     model_inputs.pop('target_size'					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     self.model(**_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     outputs.__class__({'target_size': target_size, **outputs}					)
        if self.tokenizer is not None:
              SCREAMING_SNAKE_CASE_				:  str           =     model_inputs['bbox']
        return model_outputs

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE=0.9					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[Any]           =     model_outputs['target_size']
        if self.tokenizer is not None:
              # This is a LayoutLMForTokenClassification variant.
              # The OCR got the boxes and the model classified the words.
              SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Any           =     target_size[0].tolist()

              def unnormalize(_SCREAMING_SNAKE_CASE					):
                    return self._get_bounding_box(
                        torch.Tensor(
                            [
                                (width * bbox[0] / 1000),
                                (height * bbox[1] / 1000),
                                (width * bbox[2] / 1000),
                                (height * bbox[3] / 1000),
                            ]					)					)

              SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     model_outputs['logits'].squeeze(0					).softmax(dim=-1					).max(dim=-1					)
              SCREAMING_SNAKE_CASE_				:  str           =     [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
              SCREAMING_SNAKE_CASE_				:  List[Any]           =     [unnormalize(_SCREAMING_SNAKE_CASE					) for bbox in model_outputs['bbox'].squeeze(0					)]
              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     ['score', 'label', 'box']
              SCREAMING_SNAKE_CASE_				:  Tuple           =     [dict(zip(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)					) for vals in zip(scores.tolist() ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					) if vals[0] > threshold]
        else:
              # This is a regular ForObjectDetectionModel
              SCREAMING_SNAKE_CASE_				:  Dict           =     self.image_processor.post_process_object_detection(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)
              SCREAMING_SNAKE_CASE_				:  int           =     raw_annotations[0]
              SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     raw_annotation['scores']
              SCREAMING_SNAKE_CASE_				:  Any           =     raw_annotation['labels']
              SCREAMING_SNAKE_CASE_				:  Tuple           =     raw_annotation['boxes']

              SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     scores.tolist()
              SCREAMING_SNAKE_CASE_				:  Any           =     [self.model.config.idalabel[label.item()] for label in labels]
              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     [self._get_bounding_box(_SCREAMING_SNAKE_CASE					) for box in boxes]

              # {"scores": [...], ...} --> [{"score":x, ...}, ...]
              SCREAMING_SNAKE_CASE_				:  str           =     ['score', 'label', 'box']
              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     [
                  dict(zip(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)					)
                  for vals in zip(raw_annotation['scores'] ,					raw_annotation['labels'] ,					raw_annotation['boxes']					)
              ]

        return annotation



  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        if self.framework != "pt":
              raise ValueError('The ObjectDetectionPipeline is only available in PyTorch.'					)
        SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  List[Any]           =     box.int().tolist()
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     {
            'xmin': xmin,
            'ymin': ymin,
            'xmax': xmax,
            'ymax': ymax,
        }
        return bbox






