



'''simple docstring'''






import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


lowerCAmelCase:      List[str]		 =		logging.get_logger(__name__)
def    lowerCamelCase__ (		_A       ):
  a		:      Optional[int]       =  r'\w+[.]\d+'
  a		:      Optional[Any]       =  re.findall(_A				,    _A       )
  for pat in pats:
    a		:      Union[str, Any]       =  key.replace(_A				,    '_'.join(pat.split('.'       )       )       )
  return key
def    lowerCamelCase__ (		_A				,    _A				,    _A       ):
  a		:      List[Any]       =  pt_tuple_key[:-1] + ('scale',)
  if (
      any('norm' in str_ for str_ in pt_tuple_key       )
      and (pt_tuple_key[-1] == "bias")
      and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
      and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
  ):
    a		:      Dict       =  pt_tuple_key[:-1] + ('scale',)
    return renamed_pt_tuple_key, pt_tensor
  elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
    a		:      str       =  pt_tuple_key[:-1] + ('scale',)
    return renamed_pt_tuple_key, pt_tensor

  # embedding
  if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
    a		:      List[Any]       =  pt_tuple_key[:-1] + ('embedding',)
    return renamed_pt_tuple_key, pt_tensor

  # conv layer
  a		:      Dict       =  pt_tuple_key[:-1] + ('kernel',)
  if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
    a		:      Optional[int]       =  pt_tensor.transpose(2				,    3				,    1				,    0       )
    return renamed_pt_tuple_key, pt_tensor

  # linear layer
  a		:      str       =  pt_tuple_key[:-1] + ('kernel',)
  if pt_tuple_key[-1] == "weight":
    a		:      str       =  pt_tensor.T
    return renamed_pt_tuple_key, pt_tensor

  # old PyTorch layer norm weight
  a		:      Union[str, Any]       =  pt_tuple_key[:-1] + ('weight',)
  if pt_tuple_key[-1] == "gamma":
    return renamed_pt_tuple_key, pt_tensor

  # old PyTorch layer norm bias
  a		:      Any       =  pt_tuple_key[:-1] + ('bias',)
  if pt_tuple_key[-1] == "beta":
    return renamed_pt_tuple_key, pt_tensor

  return pt_tuple_key, pt_tensor

def    lowerCamelCase__ (		_A				,    _A				,    _A=42       ):
  # Step 1: Convert pytorch tensor to numpy
  a		:      Optional[int]       =  {k: v.numpy() for k, v in pt_state_dict.items()}

  # Step 2: Since the model is stateless, get random Flax params
  a		:      List[Any]       =  flax_model.init_weights(PRNGKey(_A       )       )

  a		:      Any       =  flatten_dict(_A       )
  a		:      List[str]       =  {}

  # Need to change some parameters name to match Flax names
  for pt_key, pt_tensor in pt_state_dict.items():
    a		:      Union[str, Any]       =  rename_key(_A       )
    a		:      Optional[int]       =  tuple(renamed_pt_key.split('.'       )       )

    # Correctly rename weight parameters
    a		:      List[Any]       =  rename_key_and_reshape_tensor(_A				,    _A				,    _A       )

    if flax_key in random_flax_state_dict:
      if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
        raise ValueError(
            f"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
            f"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""       )

        # also add unexpected weight so that warning is thrown
    a		:      Any       =  jnp.asarray(_A       )

  return unflatten_dict(_A       )




'''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}},
          ]		,    )