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 __snake_case    :




      @staticmethod
      def      __a						(   *_lowercase      :    List[Any]	,     **_lowercase      :    List[Any]				):



        """simple docstring"""






        pass







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class __snake_case    (						unittest.TestCase		):
   lowerCAmelCase_														=							MODEL_FOR_OBJECT_DETECTION_MAPPING




   def      __a						(   self      :    Union[str, Any]	,     _lowercase      :    Union[str, Any]	,     _lowercase      :    Dict	,     _lowercase      :    Optional[int]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				ObjectDetectionPipeline(model=_lowercase	,     image_processor=_lowercase				)
     return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]




   def      __a						(   self      :    List[str]	,     _lowercase      :    Optional[int]	,     _lowercase      :    int				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				object_detector("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	,     threshold=0.0				)

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

     import datasets

     SCREAMING_SNAKE_CASE__      							=				datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""	,     """image"""	,     split="""test"""				)

     SCREAMING_SNAKE_CASE__      							=				[
         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"""],
     ]
     SCREAMING_SNAKE_CASE__      							=				object_detector(_lowercase	,     threshold=0.0				)

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




   @require_tf
   @unittest.skip("""Object detection not implemented in TF"""				)
   def      __a						(   self      :    List[Any]				):



     """simple docstring"""






     pass




   @require_torch
   def      __a						(   self      :    Optional[int]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				"""hf-internal-testing/tiny-detr-mobilenetsv3"""

     SCREAMING_SNAKE_CASE__      							=				AutoModelForObjectDetection.from_pretrained(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				ObjectDetectionPipeline(model=_lowercase	,     feature_extractor=_lowercase				)

     SCREAMING_SNAKE_CASE__      							=				object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""	,     threshold=0.0				)

     self.assertEqual(
         nested_simplify(_lowercase	,     decimals=4				)	,     [
             {"""score""": 0.33_76, """label""": """LABEL_0""", """box""": {"""xmin""": 1_59, """ymin""": 1_20, """xmax""": 4_80, """ymax""": 3_59}},
             {"""score""": 0.33_76, """label""": """LABEL_0""", """box""": {"""xmin""": 1_59, """ymin""": 1_20, """xmax""": 4_80, """ymax""": 3_59}},
         ]	,     )

     SCREAMING_SNAKE_CASE__      							=				object_detector(
         [
             """http://images.cocodataset.org/val2017/000000039769.jpg""",
             """http://images.cocodataset.org/val2017/000000039769.jpg""",
         ]	,     threshold=0.0	,     )

     self.assertEqual(
         nested_simplify(_lowercase	,     decimals=4				)	,     [
             [
                 {"""score""": 0.33_76, """label""": """LABEL_0""", """box""": {"""xmin""": 1_59, """ymin""": 1_20, """xmax""": 4_80, """ymax""": 3_59}},
                 {"""score""": 0.33_76, """label""": """LABEL_0""", """box""": {"""xmin""": 1_59, """ymin""": 1_20, """xmax""": 4_80, """ymax""": 3_59}},
             ],
             [
                 {"""score""": 0.33_76, """label""": """LABEL_0""", """box""": {"""xmin""": 1_59, """ymin""": 1_20, """xmax""": 4_80, """ymax""": 3_59}},
                 {"""score""": 0.33_76, """label""": """LABEL_0""", """box""": {"""xmin""": 1_59, """ymin""": 1_20, """xmax""": 4_80, """ymax""": 3_59}},
             ],
         ]	,     )




   @require_torch
   @slow
   def      __a						(   self      :    Tuple				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				"""facebook/detr-resnet-50"""

     SCREAMING_SNAKE_CASE__      							=				AutoModelForObjectDetection.from_pretrained(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				ObjectDetectionPipeline(model=_lowercase	,     feature_extractor=_lowercase				)

     SCREAMING_SNAKE_CASE__      							=				object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""				)
     self.assertEqual(
         nested_simplify(_lowercase	,     decimals=4				)	,     [
             {"""score""": 0.99_82, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 1_75, """ymax""": 1_17}},
             {"""score""": 0.99_60, """label""": """remote""", """box""": {"""xmin""": 3_33, """ymin""": 72, """xmax""": 3_68, """ymax""": 1_87}},
             {"""score""": 0.99_55, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 6_39, """ymax""": 4_73}},
             {"""score""": 0.99_88, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 3_14, """ymax""": 4_70}},
             {"""score""": 0.99_87, """label""": """cat""", """box""": {"""xmin""": 3_45, """ymin""": 23, """xmax""": 6_40, """ymax""": 3_68}},
         ]	,     )

     SCREAMING_SNAKE_CASE__      							=				object_detector(
         [
             """http://images.cocodataset.org/val2017/000000039769.jpg""",
             """http://images.cocodataset.org/val2017/000000039769.jpg""",
         ]				)
     self.assertEqual(
         nested_simplify(_lowercase	,     decimals=4				)	,     [
             [
                 {"""score""": 0.99_82, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 1_75, """ymax""": 1_17}},
                 {"""score""": 0.99_60, """label""": """remote""", """box""": {"""xmin""": 3_33, """ymin""": 72, """xmax""": 3_68, """ymax""": 1_87}},
                 {"""score""": 0.99_55, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 6_39, """ymax""": 4_73}},
                 {"""score""": 0.99_88, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 3_14, """ymax""": 4_70}},
                 {"""score""": 0.99_87, """label""": """cat""", """box""": {"""xmin""": 3_45, """ymin""": 23, """xmax""": 6_40, """ymax""": 3_68}},
             ],
             [
                 {"""score""": 0.99_82, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 1_75, """ymax""": 1_17}},
                 {"""score""": 0.99_60, """label""": """remote""", """box""": {"""xmin""": 3_33, """ymin""": 72, """xmax""": 3_68, """ymax""": 1_87}},
                 {"""score""": 0.99_55, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 6_39, """ymax""": 4_73}},
                 {"""score""": 0.99_88, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 3_14, """ymax""": 4_70}},
                 {"""score""": 0.99_87, """label""": """cat""", """box""": {"""xmin""": 3_45, """ymin""": 23, """xmax""": 6_40, """ymax""": 3_68}},
             ],
         ]	,     )




   @require_torch
   @slow
   def      __a						(   self      :    Dict				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				"""facebook/detr-resnet-50"""

     SCREAMING_SNAKE_CASE__      							=				pipeline("""object-detection"""	,     model=_lowercase				)

     SCREAMING_SNAKE_CASE__      							=				object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""				)
     self.assertEqual(
         nested_simplify(_lowercase	,     decimals=4				)	,     [
             {"""score""": 0.99_82, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 1_75, """ymax""": 1_17}},
             {"""score""": 0.99_60, """label""": """remote""", """box""": {"""xmin""": 3_33, """ymin""": 72, """xmax""": 3_68, """ymax""": 1_87}},
             {"""score""": 0.99_55, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 6_39, """ymax""": 4_73}},
             {"""score""": 0.99_88, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 3_14, """ymax""": 4_70}},
             {"""score""": 0.99_87, """label""": """cat""", """box""": {"""xmin""": 3_45, """ymin""": 23, """xmax""": 6_40, """ymax""": 3_68}},
         ]	,     )

     SCREAMING_SNAKE_CASE__      							=				object_detector(
         [
             """http://images.cocodataset.org/val2017/000000039769.jpg""",
             """http://images.cocodataset.org/val2017/000000039769.jpg""",
         ]				)
     self.assertEqual(
         nested_simplify(_lowercase	,     decimals=4				)	,     [
             [
                 {"""score""": 0.99_82, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 1_75, """ymax""": 1_17}},
                 {"""score""": 0.99_60, """label""": """remote""", """box""": {"""xmin""": 3_33, """ymin""": 72, """xmax""": 3_68, """ymax""": 1_87}},
                 {"""score""": 0.99_55, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 6_39, """ymax""": 4_73}},
                 {"""score""": 0.99_88, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 3_14, """ymax""": 4_70}},
                 {"""score""": 0.99_87, """label""": """cat""", """box""": {"""xmin""": 3_45, """ymin""": 23, """xmax""": 6_40, """ymax""": 3_68}},
             ],
             [
                 {"""score""": 0.99_82, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 1_75, """ymax""": 1_17}},
                 {"""score""": 0.99_60, """label""": """remote""", """box""": {"""xmin""": 3_33, """ymin""": 72, """xmax""": 3_68, """ymax""": 1_87}},
                 {"""score""": 0.99_55, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 6_39, """ymax""": 4_73}},
                 {"""score""": 0.99_88, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 3_14, """ymax""": 4_70}},
                 {"""score""": 0.99_87, """label""": """cat""", """box""": {"""xmin""": 3_45, """ymin""": 23, """xmax""": 6_40, """ymax""": 3_68}},
             ],
         ]	,     )




   @require_torch
   @slow
   def      __a						(   self      :    str				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				0.99_85
     SCREAMING_SNAKE_CASE__      							=				"""facebook/detr-resnet-50"""

     SCREAMING_SNAKE_CASE__      							=				pipeline("""object-detection"""	,     model=_lowercase				)

     SCREAMING_SNAKE_CASE__      							=				object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""	,     threshold=_lowercase				)
     self.assertEqual(
         nested_simplify(_lowercase	,     decimals=4				)	,     [
             {"""score""": 0.99_88, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 3_14, """ymax""": 4_70}},
             {"""score""": 0.99_87, """label""": """cat""", """box""": {"""xmin""": 3_45, """ymin""": 23, """xmax""": 6_40, """ymax""": 3_68}},
         ]	,     )




   @require_torch
   @require_pytesseract
   @slow
   def      __a						(   self      :    Any				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				"""Narsil/layoutlmv3-finetuned-funsd"""
     SCREAMING_SNAKE_CASE__      							=				0.99_93

     SCREAMING_SNAKE_CASE__      							=				pipeline("""object-detection"""	,     model=_lowercase	,     threshold=_lowercase				)

     SCREAMING_SNAKE_CASE__      							=				object_detector(
         """https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png"""				)
     self.assertEqual(
         nested_simplify(_lowercase	,     decimals=4				)	,     [
             {"""score""": 0.99_93, """label""": """I-ANSWER""", """box""": {"""xmin""": 2_94, """ymin""": 2_54, """xmax""": 3_43, """ymax""": 2_64}},
             {"""score""": 0.99_93, """label""": """I-ANSWER""", """box""": {"""xmin""": 2_94, """ymin""": 2_54, """xmax""": 3_43, """ymax""": 2_64}},
         ]	,     )

import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


__lowerCamelCase      :			int							       =		4
__lowerCamelCase      :			Dict							       =		3







class __snake_case    (						lowerCamelCase_		):
   pass





def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	List[str]     )							-> List[str]:







  """simple docstring"""





  for shard in shards:
    for i in range(__UpperCamelCase     ):
      yield {"i": i, "shard": shard}



def      __SCREAMING_SNAKE_CASE  ( )							-> Union[str, Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				int(os.environ["""RANK"""]     )
  SCREAMING_SNAKE_CASE__      							=				int(os.environ["""WORLD_SIZE"""]     )

  SCREAMING_SNAKE_CASE__      							=				ArgumentParser()
  parser.add_argument("""--streaming"""			,					type=__UpperCamelCase     )
  parser.add_argument("""--local_rank"""			,					type=__UpperCamelCase     )
  parser.add_argument("""--num_workers"""			,					type=__UpperCamelCase			,					default=0     )
  SCREAMING_SNAKE_CASE__      							=				parser.parse_args()
  SCREAMING_SNAKE_CASE__      							=				args.streaming
  SCREAMING_SNAKE_CASE__      							=				args.num_workers

  SCREAMING_SNAKE_CASE__      							=				{"""shards""": [f"""shard_{shard_idx}""" for shard_idx in range(__UpperCamelCase     )]}
  SCREAMING_SNAKE_CASE__      							=				IterableDataset.from_generator(__UpperCamelCase			,					gen_kwargs=__UpperCamelCase     )
  if not streaming:
    SCREAMING_SNAKE_CASE__      							=				Dataset.from_list(list(__UpperCamelCase     )     )

  SCREAMING_SNAKE_CASE__      							=				split_dataset_by_node(__UpperCamelCase			,					rank=__UpperCamelCase			,					world_size=__UpperCamelCase     )
  SCREAMING_SNAKE_CASE__      							=				torch.utils.data.DataLoader(__UpperCamelCase			,					num_workers=__UpperCamelCase     )

  SCREAMING_SNAKE_CASE__      							=				NUM_SHARDS * NUM_ITEMS_PER_SHARD
  SCREAMING_SNAKE_CASE__      							=				full_size // world_size
  expected_local_size += int(rank < (full_size % world_size)     )

  SCREAMING_SNAKE_CASE__      							=				sum(1 for _ in dataloader     )
  if local_size != expected_local_size:
    raise FailedTestError(f"""local_size {local_size} != expected_local_size {expected_local_size}"""     )


if __name__ == "__main__":
   main()
