



'''simple docstring'''






import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable

import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.training import train_state
from flax.training.common_utils import shard
from tqdm.auto import tqdm

from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule

class 							a__(      UpperCamelCase_			):
    lowercase__				=					42
    lowercase__				=					jnp.floataa
    lowercase__				=					True







    def     lowercase_ (   self				:						Union[str, Any]			):
      super().setup()
      a		:      str       =  nn.Dense(5		,    dtype=self.dtype			)







    def __call__(   self				:						int		,    *__snake_case				:						List[str]		,    **__snake_case				:						int			):
      a		:      Optional[Any]       =  super().__call__(*_a		,    **_a			)
      a		:      List[Any]       =  self.cls(outputs[2]			)
      return outputs[:2] + (cls_out,)

class 							a__(      UpperCamelCase_			):
    lowercase__				=					FlaxBigBirdForNaturalQuestionsModule
def    lowerCamelCase__ (		_A				,    _A				,    _A				,    _A				,    _A				,    _A       ):
  def cross_entropy(_A				,    _A				,    _A=None       ):
    a		:      Dict       =  logits.shape[-1]
    a		:      str       =  (labels[..., None] == jnp.arange(__lowerCAmelCase       )[None]).astype('f4'       )
    a		:      Optional[Any]       =  jax.nn.log_softmax(__lowerCAmelCase				,    axis=-1       )
    a		:      int       =  -jnp.sum(labels * logits				,    axis=-1       )
    if reduction is not None:
      a		:      str       =  reduction(__lowerCAmelCase       )
    return loss

  a		:      List[str]       =  partial(__lowerCAmelCase				,    reduction=jnp.mean       )
  a		:      str       =  cross_entropy(__lowerCAmelCase				,    __lowerCAmelCase       )
  a		:      Union[str, Any]       =  cross_entropy(__lowerCAmelCase				,    __lowerCAmelCase       )
  a		:      List[Any]       =  cross_entropy(__lowerCAmelCase				,    __lowerCAmelCase       )
  return (start_loss + end_loss + pooled_loss) / 3

@dataclass
class 							a__:
    lowercase__				=					"google/bigbird-roberta-base"
    lowercase__				=					30_00
    lowercase__				=					1_05_00

    lowercase__				=					1_28
    lowercase__				=					3

    lowercase__				=					1
    lowercase__				=					5

    # tx_args
    lowercase__				=					3E-5
    lowercase__				=					0.0
    lowercase__				=					2_00_00
    lowercase__				=					0.00_95

    lowercase__				=					"bigbird-roberta-natural-questions"
    lowercase__				=					"training-expt"
    lowercase__				=					"data/nq-training.jsonl"
    lowercase__				=					"data/nq-validation.jsonl"







    def     lowercase_ (   self				:						str			):
      os.makedirs(self.base_dir		,    exist_ok=_a			)
      a		:      Tuple       =  os.path.join(self.base_dir		,    self.save_dir			)
      a		:      Optional[int]       =  self.batch_size_per_device * jax.device_count()

@dataclass
class 							a__:
    lowercase__				=					42
    lowercase__				=					40_96  # no dynamic padding on TPUs







    def __call__(   self				:						Union[str, Any]		,    __snake_case				:						Union[str, Any]			):
      a		:      int       =  self.collate_fn(_a			)
      a		:      List[str]       =  jax.tree_util.tree_map(_a		,    _a			)
      return batch







    def     lowercase_ (   self				:						List[Any]		,    __snake_case				:						List[Any]			):
      a		:      int       =  self.fetch_inputs(features['input_ids']			)
      a		:      Tuple       =  {
          """input_ids""": jnp.array(_a		,    dtype=jnp.intaa			),
          """attention_mask""": jnp.array(_a		,    dtype=jnp.intaa			),
          """start_labels""": jnp.array(features['start_token']		,    dtype=jnp.intaa			),
          """end_labels""": jnp.array(features['end_token']		,    dtype=jnp.intaa			),
          """pooled_labels""": jnp.array(features['category']		,    dtype=jnp.intaa			),
      }
      return batch







    def     lowercase_ (   self				:						Tuple		,    __snake_case				:						List[Any]			):
      a		:      str       =  [self._fetch_inputs(_a			) for ids in input_ids]
      return zip(*_a			)







    def     lowercase_ (   self				:						List[str]		,    __snake_case				:						Union[str, Any]			):
      a		:      List[str]       =  [1 for _ in range(len(_a			)			)]
      while len(_a			) < self.max_length:
        input_ids.append(self.pad_id			)
        attention_mask.append(0			)
      return input_ids, attention_mask
def    lowerCamelCase__ (		_A				,    _A				,    _A=None       ):
  if seed is not None:
    a		:      Optional[int]       =  dataset.shuffle(seed=__lowerCAmelCase       )
  for i in range(len(__lowerCAmelCase       ) // batch_size       ):
    a		:      Optional[int]       =  dataset[i * batch_size : (i + 1) * batch_size]
    yield dict(__lowerCAmelCase       )
@partial(jax.pmap				,    axis_name='batch'       )
def    lowerCamelCase__ (		_A				,    _A				,    **_A       ):
  def loss_fn(_A       ):
    a		:      Any       =  model_inputs.pop('start_labels'       )
    a		:      Optional[int]       =  model_inputs.pop('end_labels'       )
    a		:      str       =  model_inputs.pop('pooled_labels'       )

    a		:      Dict       =  state.apply_fn(**__lowerCAmelCase				,    params=__lowerCAmelCase				,    dropout_rng=__lowerCAmelCase				,    train=__lowerCAmelCase       )
    a		:      List[str]       =  outputs

    return state.loss_fn(
        __lowerCAmelCase				,    __lowerCAmelCase				,    __lowerCAmelCase				,    __lowerCAmelCase				,    __lowerCAmelCase				,    __lowerCAmelCase				,    )

  a		:      Union[str, Any]       =  jax.random.split(__lowerCAmelCase       )
  a		:      int       =  jax.value_and_grad(__lowerCAmelCase       )
  a		:      Tuple       =  grad_fn(state.params       )
  a		:      Optional[int]       =  jax.lax.pmean({'loss': loss}				,    axis_name='batch'       )
  a		:      int       =  jax.lax.pmean(__lowerCAmelCase				,    'batch'       )

  a		:      Optional[int]       =  state.apply_gradients(grads=__lowerCAmelCase       )
  return state, metrics, new_drp_rng
@partial(jax.pmap				,    axis_name='batch'       )
def    lowerCamelCase__ (		_A				,    **_A       ):
  a		:      Dict       =  model_inputs.pop('start_labels'       )
  a		:      Optional[int]       =  model_inputs.pop('end_labels'       )
  a		:      List[str]       =  model_inputs.pop('pooled_labels'       )

  a		:      Dict       =  state.apply_fn(**__lowerCAmelCase				,    params=state.params				,    train=__lowerCAmelCase       )
  a		:      Optional[Any]       =  outputs

  a		:      Any       =  state.loss_fn(__lowerCAmelCase				,    __lowerCAmelCase				,    __lowerCAmelCase				,    __lowerCAmelCase				,    __lowerCAmelCase				,    __lowerCAmelCase       )
  a		:      List[str]       =  jax.lax.pmean({'loss': loss}				,    axis_name='batch'       )
  return metrics

class 							a__(      train_state.TrainState			):
    lowercase__				=					struct.field(pytree_node=UpperCamelCase_			)

@dataclass
class 							a__:
    lowercase__				=					42
    lowercase__				=					42
    lowercase__				=					42
    lowercase__				=					42
    lowercase__				=					42
    lowercase__				=					42
    lowercase__				=					None







    def     lowercase_ (   self				:						Dict		,    __snake_case				:						List[str]		,    __snake_case				:						str		,    __snake_case				:						List[str]		,    __snake_case				:						List[str]=None			):
      a		:      List[Any]       =  model.params
      a		:      Any       =  TrainState.create(
          apply_fn=model.__call__		,    params=_a		,    tx=_a		,    loss_fn=_a		,    )
      if ckpt_dir is not None:
        a		:      List[str]       =  restore_checkpoint(_a		,    _a			)
        a		:      str       =  {
            """lr""": args.lr,
            """init_lr""": args.init_lr,
            """warmup_steps""": args.warmup_steps,
            """num_train_steps""": num_train_steps,
            """weight_decay""": args.weight_decay,
        }
        a		:      Union[str, Any]       =  build_tx(**_a			)
        a		:      int       =  train_state.TrainState(
            step=_a		,    apply_fn=model.__call__		,    params=_a		,    tx=_a		,    opt_state=_a		,    )
        a		:      int       =  args
        a		:      Optional[Any]       =  data_collator
        a		:      Union[str, Any]       =  lr
        a		:      int       =  params
      a		:      str       =  jax_utils.replicate(_a			)
      return state







    def     lowercase_ (   self				:						Tuple		,    __snake_case				:						List[Any]		,    __snake_case				:						str		,    __snake_case				:						Union[str, Any]			):
      a		:      int       =  self.args
      a		:      int       =  len(_a			) // args.batch_size

      a		:      Union[str, Any]       =  jax.random.PRNGKey(0			)
      a		:      Union[str, Any]       =  jax.random.split(_a		,    jax.device_count()			)
      for epoch in range(args.max_epochs			):
        a		:      Optional[int]       =  jnp.array(0		,    dtype=jnp.floataa			)
        a		:      int       =  get_batched_dataset(_a		,    args.batch_size		,    seed=_a			)
        a		:      int       =  0
        for batch in tqdm(_a		,    total=_a		,    desc=F"""Running EPOCH-{epoch}"""			):
          a		:      Union[str, Any]       =  self.data_collator(_a			)
          a		:      Dict       =  self.train_step_fn(_a		,    _a		,    **_a			)
          running_loss += jax_utils.unreplicate(metrics['loss']			)
          i += 1
          if i % args.logging_steps == 0:
            a		:      Any       =  jax_utils.unreplicate(state.step			)
            a		:      Optional[Any]       =  running_loss.item() / i
            a		:      Dict       =  self.scheduler_fn(state_step - 1			)

            a		:      Optional[Any]       =  self.evaluate(_a		,    _a			)
            a		:      Optional[int]       =  {
                """step""": state_step.item(),
                """eval_loss""": eval_loss.item(),
                """tr_loss""": tr_loss,
                """lr""": lr.item(),
            }
            tqdm.write(str(_a			)			)
            self.logger.log(_a		,    commit=_a			)

          if i % args.save_steps == 0:
            self.save_checkpoint(args.save_dir + F"""-e{epoch}-s{i}"""		,    state=_a			)







    def     lowercase_ (   self				:						Optional[int]		,    __snake_case				:						List[Any]		,    __snake_case				:						Optional[Any]			):
      a		:      Optional[Any]       =  get_batched_dataset(_a		,    self.args.batch_size			)
      a		:      Optional[Any]       =  len(_a			) // self.args.batch_size
      a		:      Optional[Any]       =  jnp.array(0		,    dtype=jnp.floataa			)
      a		:      Union[str, Any]       =  0
      for batch in tqdm(_a		,    total=_a		,    desc='Evaluating ... '			):
        a		:      Optional[Any]       =  self.data_collator(_a			)
        a		:      List[str]       =  self.val_step_fn(_a		,    **_a			)
        running_loss += jax_utils.unreplicate(metrics['loss']			)
        i += 1
      return running_loss / i







    def     lowercase_ (   self				:						Dict		,    __snake_case				:						Any		,    __snake_case				:						Dict			):
      a		:      Dict       =  jax_utils.unreplicate(_a			)
      print(F"""SAVING CHECKPOINT IN {save_dir}"""		,    end=' ... '			)
      self.model_save_fn(_a		,    params=state.params			)
      with open(os.path.join(_a		,    'opt_state.msgpack'			)		,    'wb'			) as f:
        f.write(to_bytes(state.opt_state			)			)
      joblib.dump(self.args		,    os.path.join(_a		,    'args.joblib'			)			)
      joblib.dump(self.data_collator		,    os.path.join(_a		,    'data_collator.joblib'			)			)
      with open(os.path.join(_a		,    'training_state.json'			)		,    'w'			) as f:
        json.dump({'step': state.step.item()}		,    _a			)
      print('DONE'			)
def    lowerCamelCase__ (		_A				,    _A       ):
  print(f"""RESTORING CHECKPOINT FROM {save_dir}"""				,    end=' ... '       )
  with open(os.path.join(__lowerCAmelCase				,    'flax_model.msgpack'       )				,    'rb'       ) as f:
    a		:      Optional[Any]       =  from_bytes(state.params				,    f.read()       )

  with open(os.path.join(__lowerCAmelCase				,    'opt_state.msgpack'       )				,    'rb'       ) as f:
    a		:      Dict       =  from_bytes(state.opt_state				,    f.read()       )

  a		:      Any       =  joblib.load(os.path.join(__lowerCAmelCase				,    'args.joblib'       )       )
  a		:      Dict       =  joblib.load(os.path.join(__lowerCAmelCase				,    'data_collator.joblib'       )       )

  with open(os.path.join(__lowerCAmelCase				,    'training_state.json'       )				,    'r'       ) as f:
    a		:      str       =  json.load(__lowerCAmelCase       )
  a		:      Optional[Any]       =  training_state["""step"""]

  print('DONE'       )
  return params, opt_state, step, args, data_collator
def    lowerCamelCase__ (		_A				,    _A				,    _A				,    _A       ):
  a		:      int       =  num_train_steps - warmup_steps
  a		:      Union[str, Any]       =  optax.linear_schedule(init_value=__lowerCAmelCase				,    end_value=__lowerCAmelCase				,    transition_steps=__lowerCAmelCase       )
  a		:      int       =  optax.linear_schedule(init_value=__lowerCAmelCase				,    end_value=1E-7				,    transition_steps=__lowerCAmelCase       )
  a		:      Dict       =  optax.join_schedules(schedules=[warmup_fn, decay_fn]				,    boundaries=[warmup_steps]       )
  return lr

def    lowerCamelCase__ (		_A				,    _A				,    _A				,    _A				,    _A       ):
  def weight_decay_mask(_A       ):
    a		:      List[str]       =  traverse_util.flatten_dict(__lowerCAmelCase       )
    a		:      Dict       =  {k: (v[-1] != """bias""" and v[-2:] != ("""LayerNorm""", """scale""")) for k, v in params.items()}
    return traverse_util.unflatten_dict(__lowerCAmelCase       )

  a		:      List[Any]       =  scheduler_fn(__lowerCAmelCase				,    __lowerCAmelCase				,    __lowerCAmelCase				,    __lowerCAmelCase       )

  a		:      List[str]       =  optax.adamw(learning_rate=__lowerCAmelCase				,    weight_decay=__lowerCAmelCase				,    mask=__lowerCAmelCase       )
  return tx, lr




'''simple docstring'''






import unittest

from transformers import (
    MODEL_FOR_OBJECT_DETECTION_MAPPING,
    AutoFeatureExtractor,
    AutoModelForObjectDetection,
    ObjectDetectionPipeline,
    is_vision_available,
    pipeline,
)
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_pytesseract,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
     from PIL import Image
else:

     class 							a__:







         @staticmethod
         def     lowercase_ (   *__snake_case				:						int		,    **__snake_case				:						str			):
           pass

@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 							a__(      unittest.TestCase			):
    lowercase__				=					MODEL_FOR_OBJECT_DETECTION_MAPPING







    def     lowercase_ (   self				:						Optional[Any]		,    __snake_case				:						Union[str, Any]		,    __snake_case				:						int		,    __snake_case				:						Tuple			):
      a		:      Tuple       =  ObjectDetectionPipeline(model=__snake_case		,    image_processor=__snake_case			)
      return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]







    def     lowercase_ (   self				:						Optional[Any]		,    __snake_case				:						str		,    __snake_case				:						Optional[Any]			):
      a		:      Any       =  object_detector('./tests/fixtures/tests_samples/COCO/000000039769.png'		,    threshold=0.0			)

      self.assertGreater(len(__snake_case			)		,    0			)
      for detected_object in outputs:
        self.assertEqual(
            __snake_case		,    {
                'score': ANY(__snake_case			),
                'label': ANY(__snake_case			),
                'box': {'xmin': ANY(__snake_case			), 'ymin': ANY(__snake_case			), 'xmax': ANY(__snake_case			), 'ymax': ANY(__snake_case			)},
            }		,    )

      import datasets

      a		:      Any       =  datasets.load_dataset('hf-internal-testing/fixtures_image_utils'		,    'image'		,    split='test'			)

      a		:      Tuple       =  [
          Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			),
          'http://images.cocodataset.org/val2017/000000039769.jpg',
          # RGBA
          dataset[0]['file'],
          # LA
          dataset[1]['file'],
          # L
          dataset[2]['file'],
      ]
      a		:      List[Any]       =  object_detector(__snake_case		,    threshold=0.0			)

      self.assertEqual(len(__snake_case			)		,    len(__snake_case			)			)
      for outputs in batch_outputs:
        self.assertGreater(len(__snake_case			)		,    0			)
        for detected_object in outputs:
          self.assertEqual(
              __snake_case		,    {
                  'score': ANY(__snake_case			),
                  'label': ANY(__snake_case			),
                  'box': {'xmin': ANY(__snake_case			), 'ymin': ANY(__snake_case			), 'xmax': ANY(__snake_case			), 'ymax': ANY(__snake_case			)},
              }		,    )







    @require_tf
    @unittest.skip('Object detection not implemented in TF'			)
    def     lowercase_ (   self				:						List[str]			):
      pass







    @require_torch
    def     lowercase_ (   self				:						Tuple			):
      a		:      Union[str, Any]       =  'hf-internal-testing/tiny-detr-mobilenetsv3'

      a		:      str       =  AutoModelForObjectDetection.from_pretrained(__snake_case			)
      a		:      str       =  AutoFeatureExtractor.from_pretrained(__snake_case			)
      a		:      Any       =  ObjectDetectionPipeline(model=__snake_case		,    feature_extractor=__snake_case			)

      a		:      str       =  object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'		,    threshold=0.0			)

      self.assertEqual(
          nested_simplify(__snake_case		,    decimals=4			)		,    [
              {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 1_59, 'ymin': 1_20, 'xmax': 4_80, 'ymax': 3_59}},
              {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 1_59, 'ymin': 1_20, 'xmax': 4_80, 'ymax': 3_59}},
          ]		,    )

      a		:      Dict       =  object_detector(
          [
              'http://images.cocodataset.org/val2017/000000039769.jpg',
              'http://images.cocodataset.org/val2017/000000039769.jpg',
          ]		,    threshold=0.0		,    )

      self.assertEqual(
          nested_simplify(__snake_case		,    decimals=4			)		,    [
              [
                  {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 1_59, 'ymin': 1_20, 'xmax': 4_80, 'ymax': 3_59}},
                  {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 1_59, 'ymin': 1_20, 'xmax': 4_80, 'ymax': 3_59}},
              ],
              [
                  {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 1_59, 'ymin': 1_20, 'xmax': 4_80, 'ymax': 3_59}},
                  {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 1_59, 'ymin': 1_20, 'xmax': 4_80, 'ymax': 3_59}},
              ],
          ]		,    )







    @require_torch
    @slow
    def     lowercase_ (   self				:						Optional[int]			):
      a		:      Union[str, Any]       =  'facebook/detr-resnet-50'

      a		:      str       =  AutoModelForObjectDetection.from_pretrained(__snake_case			)
      a		:      List[Any]       =  AutoFeatureExtractor.from_pretrained(__snake_case			)
      a		:      str       =  ObjectDetectionPipeline(model=__snake_case		,    feature_extractor=__snake_case			)

      a		:      List[Any]       =  object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'			)
      self.assertEqual(
          nested_simplify(__snake_case		,    decimals=4			)		,    [
              {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 1_75, 'ymax': 1_17}},
              {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 3_33, 'ymin': 72, 'xmax': 3_68, 'ymax': 1_87}},
              {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 6_39, 'ymax': 4_73}},
              {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 3_14, 'ymax': 4_70}},
              {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 3_45, 'ymin': 23, 'xmax': 6_40, 'ymax': 3_68}},
          ]		,    )

      a		:      List[str]       =  object_detector(
          [
              'http://images.cocodataset.org/val2017/000000039769.jpg',
              'http://images.cocodataset.org/val2017/000000039769.jpg',
          ]			)
      self.assertEqual(
          nested_simplify(__snake_case		,    decimals=4			)		,    [
              [
                  {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 1_75, 'ymax': 1_17}},
                  {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 3_33, 'ymin': 72, 'xmax': 3_68, 'ymax': 1_87}},
                  {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 6_39, 'ymax': 4_73}},
                  {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 3_14, 'ymax': 4_70}},
                  {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 3_45, 'ymin': 23, 'xmax': 6_40, 'ymax': 3_68}},
              ],
              [
                  {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 1_75, 'ymax': 1_17}},
                  {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 3_33, 'ymin': 72, 'xmax': 3_68, 'ymax': 1_87}},
                  {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 6_39, 'ymax': 4_73}},
                  {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 3_14, 'ymax': 4_70}},
                  {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 3_45, 'ymin': 23, 'xmax': 6_40, 'ymax': 3_68}},
              ],
          ]		,    )







    @require_torch
    @slow
    def     lowercase_ (   self				:						Any			):
      a		:      Any       =  'facebook/detr-resnet-50'

      a		:      int       =  pipeline('object-detection'		,    model=__snake_case			)

      a		:      Dict       =  object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'			)
      self.assertEqual(
          nested_simplify(__snake_case		,    decimals=4			)		,    [
              {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 1_75, 'ymax': 1_17}},
              {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 3_33, 'ymin': 72, 'xmax': 3_68, 'ymax': 1_87}},
              {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 6_39, 'ymax': 4_73}},
              {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 3_14, 'ymax': 4_70}},
              {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 3_45, 'ymin': 23, 'xmax': 6_40, 'ymax': 3_68}},
          ]		,    )

      a		:      int       =  object_detector(
          [
              'http://images.cocodataset.org/val2017/000000039769.jpg',
              'http://images.cocodataset.org/val2017/000000039769.jpg',
          ]			)
      self.assertEqual(
          nested_simplify(__snake_case		,    decimals=4			)		,    [
              [
                  {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 1_75, 'ymax': 1_17}},
                  {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 3_33, 'ymin': 72, 'xmax': 3_68, 'ymax': 1_87}},
                  {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 6_39, 'ymax': 4_73}},
                  {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 3_14, 'ymax': 4_70}},
                  {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 3_45, 'ymin': 23, 'xmax': 6_40, 'ymax': 3_68}},
              ],
              [
                  {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 1_75, 'ymax': 1_17}},
                  {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 3_33, 'ymin': 72, 'xmax': 3_68, 'ymax': 1_87}},
                  {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 6_39, 'ymax': 4_73}},
                  {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 3_14, 'ymax': 4_70}},
                  {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 3_45, 'ymin': 23, 'xmax': 6_40, 'ymax': 3_68}},
              ],
          ]		,    )







    @require_torch
    @slow
    def     lowercase_ (   self				:						Optional[int]			):
      a		:      Optional[Any]       =  0.9985
      a		:      Optional[int]       =  'facebook/detr-resnet-50'

      a		:      List[Any]       =  pipeline('object-detection'		,    model=__snake_case			)

      a		:      Any       =  object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'		,    threshold=__snake_case			)
      self.assertEqual(
          nested_simplify(__snake_case		,    decimals=4			)		,    [
              {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 3_14, 'ymax': 4_70}},
              {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 3_45, 'ymin': 23, 'xmax': 6_40, 'ymax': 3_68}},
          ]		,    )







    @require_torch
    @require_pytesseract
    @slow
    def     lowercase_ (   self				:						Dict			):
      a		:      Optional[int]       =  'Narsil/layoutlmv3-finetuned-funsd'
      a		:      Optional[int]       =  0.9993

      a		:      List[Any]       =  pipeline('object-detection'		,    model=__snake_case		,    threshold=__snake_case			)

      a		:      Union[str, Any]       =  object_detector(
          'https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png'			)
      self.assertEqual(
          nested_simplify(__snake_case		,    decimals=4			)		,    [
              {'score': 0.9993, 'label': 'I-ANSWER', 'box': {'xmin': 2_94, 'ymin': 2_54, 'xmax': 3_43, 'ymax': 2_64}},
              {'score': 0.9993, 'label': 'I-ANSWER', 'box': {'xmin': 2_94, 'ymin': 2_54, 'xmax': 3_43, 'ymax': 2_64}},
          ]		,    )