





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  __lowerCAmelCase	:






				@staticmethod
				def       lowerCamelCase  (*__magic_name__ ,							**__magic_name__   )					->       Tuple:


								'''simple docstring'''



								pass



@is_pipeline_test
@require_vision
@require_timm
@require_torch
class  __lowerCAmelCase	(      unittest.TestCase					):
			lowerCamelCase_					:					Union[str, Any]	      =				MODEL_FOR_OBJECT_DETECTION_MAPPING






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )					->       Optional[Any]:


							'''simple docstring'''



							snake_case_						:   str           =  ObjectDetectionPipeline(model=__magic_name__ ,							image_processor=__magic_name__   )
							return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__   )					->       str:


							'''simple docstring'''



							snake_case_						:   Tuple           =  object_detector('''./tests/fixtures/tests_samples/COCO/000000039769.png''' ,							threshold=0.0   )

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

							import datasets

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

							snake_case_						:   Dict           =  [
							    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'''],
							]
							snake_case_						:   List[Any]           =  object_detector(__magic_name__ ,							threshold=0.0   )

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






			@require_tf
			@unittest.skip('''Object detection not implemented in TF'''   )
			def       lowerCamelCase  (self   )					->       Dict:


							'''simple docstring'''



							pass






			@require_torch
			def       lowerCamelCase  (self   )					->       str:


							'''simple docstring'''



							snake_case_						:   Tuple           =  '''hf-internal-testing/tiny-detr-mobilenetsv3'''

							snake_case_						:   Tuple           =  AutoModelForObjectDetection.from_pretrained(__magic_name__   )
							snake_case_						:   str           =  AutoFeatureExtractor.from_pretrained(__magic_name__   )
							snake_case_						:   Optional[Any]           =  ObjectDetectionPipeline(model=__magic_name__ ,							feature_extractor=__magic_name__   )

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

							self.assertEqual(
							    nested_simplify(__magic_name__ ,							decimals=4   ) ,							[
							        {'''score''': 0.3_376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
							        {'''score''': 0.3_376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
							    ] ,							)

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

							self.assertEqual(
							    nested_simplify(__magic_name__ ,							decimals=4   ) ,							[
							        [
							            {'''score''': 0.3_376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
							            {'''score''': 0.3_376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
							        ],
							        [
							            {'''score''': 0.3_376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
							            {'''score''': 0.3_376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
							        ],
							    ] ,							)






			@require_torch
			@slow
			def       lowerCamelCase  (self   )					->       List[str]:


							'''simple docstring'''



							snake_case_						:   Dict           =  '''facebook/detr-resnet-50'''

							snake_case_						:   int           =  AutoModelForObjectDetection.from_pretrained(__magic_name__   )
							snake_case_						:   Union[str, Any]           =  AutoFeatureExtractor.from_pretrained(__magic_name__   )
							snake_case_						:   List[Any]           =  ObjectDetectionPipeline(model=__magic_name__ ,							feature_extractor=__magic_name__   )

							snake_case_						:   Optional[Any]           =  object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''   )
							self.assertEqual(
							    nested_simplify(__magic_name__ ,							decimals=4   ) ,							[
							        {'''score''': 0.9_982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
							        {'''score''': 0.9_960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
							        {'''score''': 0.9_955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
							        {'''score''': 0.9_988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
							        {'''score''': 0.9_987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
							    ] ,							)

							snake_case_						:   Tuple           =  object_detector(
							    [
							        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
							        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
							    ]   )
							self.assertEqual(
							    nested_simplify(__magic_name__ ,							decimals=4   ) ,							[
							        [
							            {'''score''': 0.9_982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
							            {'''score''': 0.9_960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
							            {'''score''': 0.9_955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
							            {'''score''': 0.9_988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
							            {'''score''': 0.9_987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
							        ],
							        [
							            {'''score''': 0.9_982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
							            {'''score''': 0.9_960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
							            {'''score''': 0.9_955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
							            {'''score''': 0.9_988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
							            {'''score''': 0.9_987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
							        ],
							    ] ,							)






			@require_torch
			@slow
			def       lowerCamelCase  (self   )					->       Union[str, Any]:


							'''simple docstring'''



							snake_case_						:   List[str]           =  '''facebook/detr-resnet-50'''

							snake_case_						:   Optional[Any]           =  pipeline('''object-detection''' ,							model=__magic_name__   )

							snake_case_						:   Tuple           =  object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''   )
							self.assertEqual(
							    nested_simplify(__magic_name__ ,							decimals=4   ) ,							[
							        {'''score''': 0.9_982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
							        {'''score''': 0.9_960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
							        {'''score''': 0.9_955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
							        {'''score''': 0.9_988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
							        {'''score''': 0.9_987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
							    ] ,							)

							snake_case_						:   Union[str, Any]           =  object_detector(
							    [
							        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
							        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
							    ]   )
							self.assertEqual(
							    nested_simplify(__magic_name__ ,							decimals=4   ) ,							[
							        [
							            {'''score''': 0.9_982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
							            {'''score''': 0.9_960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
							            {'''score''': 0.9_955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
							            {'''score''': 0.9_988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
							            {'''score''': 0.9_987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
							        ],
							        [
							            {'''score''': 0.9_982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
							            {'''score''': 0.9_960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
							            {'''score''': 0.9_955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
							            {'''score''': 0.9_988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
							            {'''score''': 0.9_987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
							        ],
							    ] ,							)






			@require_torch
			@slow
			def       lowerCamelCase  (self   )					->       str:


							'''simple docstring'''



							snake_case_						:   Optional[Any]           =  0.9_985
							snake_case_						:   Any           =  '''facebook/detr-resnet-50'''

							snake_case_						:   List[str]           =  pipeline('''object-detection''' ,							model=__magic_name__   )

							snake_case_						:   int           =  object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg''' ,							threshold=__magic_name__   )
							self.assertEqual(
							    nested_simplify(__magic_name__ ,							decimals=4   ) ,							[
							        {'''score''': 0.9_988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
							        {'''score''': 0.9_987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
							    ] ,							)






			@require_torch
			@require_pytesseract
			@slow
			def       lowerCamelCase  (self   )					->       Dict:


							'''simple docstring'''



							snake_case_						:   Optional[int]           =  '''Narsil/layoutlmv3-finetuned-funsd'''
							snake_case_						:   Optional[Any]           =  0.9_993

							snake_case_						:   Any           =  pipeline('''object-detection''' ,							model=__magic_name__ ,							threshold=__magic_name__   )

							snake_case_						:   Dict           =  object_detector(
							    '''https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png'''   )
							self.assertEqual(
							    nested_simplify(__magic_name__ ,							decimals=4   ) ,							[
							        {'''score''': 0.9_993, '''label''': '''I-ANSWER''', '''box''': {'''xmin''': 294, '''ymin''': 254, '''xmax''': 343, '''ymax''': 264}},
							        {'''score''': 0.9_993, '''label''': '''I-ANSWER''', '''box''': {'''xmin''': 294, '''ymin''': 254, '''xmax''': 343, '''ymax''': 264}},
							    ] ,							)







from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


lowerCAmelCase_    =    logging.get_logger(__name__)

lowerCAmelCase_    =    {
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

lowerCAmelCase_    =    {
    '''vocab_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
    },
    '''tokenizer_config_file''': {
        '''facebook/blenderbot_small-90M''': (
            '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
        )
    },
}

lowerCAmelCase_    =    {
    '''facebook/blenderbot_small-90M''': 5_1_2,
}



class  __lowerCAmelCase	(      _a					):
			lowerCamelCase_					:					int	      =				VOCAB_FILES_NAMES
			lowerCamelCase_					:					List[Any]	      =				PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase_					:					int	      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase_					:					Optional[Any]	      =				BlenderbotSmallTokenizer






			def __init__(self ,							__magic_name__=None ,							__magic_name__=None ,							__magic_name__="<|endoftext|>" ,							__magic_name__="<|endoftext|>" ,							__magic_name__="<|endoftext|>" ,							__magic_name__=False ,							__magic_name__=True ,							**__magic_name__ ,							)					->       Union[str, Any]:


							'''simple docstring'''



							super().__init__(
							    ByteLevelBPETokenizer(
							        vocab=__magic_name__ ,							merges=__magic_name__ ,							add_prefix_space=__magic_name__ ,							trim_offsets=__magic_name__ ,							) ,							bos_token=__magic_name__ ,							eos_token=__magic_name__ ,							unk_token=__magic_name__ ,							**__magic_name__ ,							)
							snake_case_						:   Optional[int]           =  add_prefix_space






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__=None   )					->       Any:


							'''simple docstring'''



							snake_case_						:   Optional[int]           =  [self.bos_token_id] + token_ids_a + [self.eos_token_id]
							if token_ids_a is None:
											return output

							return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ = None   )					->       List[int]:


							'''simple docstring'''



							snake_case_						:   List[str]           =  [self.sep_token_id]
							snake_case_						:   Optional[Any]           =  [self.cls_token_id]

							if token_ids_a is None:
											return len(cls + token_ids_a + sep   ) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]
