


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_						:

    '''simple docstring'''

    @staticmethod
    def 						UpperCamelCase__					(     *_UpperCAmelCase		,				**_UpperCAmelCase ):
          pass







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

 '''simple docstring'''

 __snake_case												=  MODEL_FOR_OBJECT_DETECTION_MAPPING



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       snake_case_	=				ObjectDetectionPipeline(model=_UpperCAmelCase		,				image_processor=_UpperCAmelCase )
       return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       snake_case_	=				object_detector('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		,				threshold=0.0 )

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

       import datasets

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

       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'''],
       ]
       snake_case_	=				object_detector(_UpperCAmelCase		,				threshold=0.0 )

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



 @require_tf
 @unittest.skip('''Object detection not implemented in TF''' )
 def 						UpperCamelCase__					(     self ):
       pass



 @require_torch
 def 						UpperCamelCase__					(     self ):
       snake_case_	=				'''hf-internal-testing/tiny-detr-mobilenetsv3'''

       snake_case_	=				AutoModelForObjectDetection.from_pretrained(_UpperCAmelCase )
       snake_case_	=				AutoFeatureExtractor.from_pretrained(_UpperCAmelCase )
       snake_case_	=				ObjectDetectionPipeline(model=_UpperCAmelCase		,				feature_extractor=_UpperCAmelCase )

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

       self.assertEqual(
           nested_simplify(_UpperCAmelCase		,				decimals=4 )		,				[
               {'''score''': 0.3_376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_59, '''ymin''': 1_20, '''xmax''': 4_80, '''ymax''': 3_59}},
               {'''score''': 0.3_376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_59, '''ymin''': 1_20, '''xmax''': 4_80, '''ymax''': 3_59}},
           ]		,				)

       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(_UpperCAmelCase		,				decimals=4 )		,				[
               [
                   {'''score''': 0.3_376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_59, '''ymin''': 1_20, '''xmax''': 4_80, '''ymax''': 3_59}},
                   {'''score''': 0.3_376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_59, '''ymin''': 1_20, '''xmax''': 4_80, '''ymax''': 3_59}},
               ],
               [
                   {'''score''': 0.3_376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_59, '''ymin''': 1_20, '''xmax''': 4_80, '''ymax''': 3_59}},
                   {'''score''': 0.3_376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_59, '''ymin''': 1_20, '''xmax''': 4_80, '''ymax''': 3_59}},
               ],
           ]		,				)



 @require_torch
 @slow
 def 						UpperCamelCase__					(     self ):
       snake_case_	=				'''facebook/detr-resnet-50'''

       snake_case_	=				AutoModelForObjectDetection.from_pretrained(_UpperCAmelCase )
       snake_case_	=				AutoFeatureExtractor.from_pretrained(_UpperCAmelCase )
       snake_case_	=				ObjectDetectionPipeline(model=_UpperCAmelCase		,				feature_extractor=_UpperCAmelCase )

       snake_case_	=				object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg''' )
       self.assertEqual(
           nested_simplify(_UpperCAmelCase		,				decimals=4 )		,				[
               {'''score''': 0.9_982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 1_75, '''ymax''': 1_17}},
               {'''score''': 0.9_960, '''label''': '''remote''', '''box''': {'''xmin''': 3_33, '''ymin''': 72, '''xmax''': 3_68, '''ymax''': 1_87}},
               {'''score''': 0.9_955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_39, '''ymax''': 4_73}},
               {'''score''': 0.9_988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 3_14, '''ymax''': 4_70}},
               {'''score''': 0.9_987, '''label''': '''cat''', '''box''': {'''xmin''': 3_45, '''ymin''': 23, '''xmax''': 6_40, '''ymax''': 3_68}},
           ]		,				)

       snake_case_	=				object_detector(
           [
               '''http://images.cocodataset.org/val2017/000000039769.jpg''',
               '''http://images.cocodataset.org/val2017/000000039769.jpg''',
           ] )
       self.assertEqual(
           nested_simplify(_UpperCAmelCase		,				decimals=4 )		,				[
               [
                   {'''score''': 0.9_982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 1_75, '''ymax''': 1_17}},
                   {'''score''': 0.9_960, '''label''': '''remote''', '''box''': {'''xmin''': 3_33, '''ymin''': 72, '''xmax''': 3_68, '''ymax''': 1_87}},
                   {'''score''': 0.9_955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_39, '''ymax''': 4_73}},
                   {'''score''': 0.9_988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 3_14, '''ymax''': 4_70}},
                   {'''score''': 0.9_987, '''label''': '''cat''', '''box''': {'''xmin''': 3_45, '''ymin''': 23, '''xmax''': 6_40, '''ymax''': 3_68}},
               ],
               [
                   {'''score''': 0.9_982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 1_75, '''ymax''': 1_17}},
                   {'''score''': 0.9_960, '''label''': '''remote''', '''box''': {'''xmin''': 3_33, '''ymin''': 72, '''xmax''': 3_68, '''ymax''': 1_87}},
                   {'''score''': 0.9_955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_39, '''ymax''': 4_73}},
                   {'''score''': 0.9_988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 3_14, '''ymax''': 4_70}},
                   {'''score''': 0.9_987, '''label''': '''cat''', '''box''': {'''xmin''': 3_45, '''ymin''': 23, '''xmax''': 6_40, '''ymax''': 3_68}},
               ],
           ]		,				)



 @require_torch
 @slow
 def 						UpperCamelCase__					(     self ):
       snake_case_	=				'''facebook/detr-resnet-50'''

       snake_case_	=				pipeline('''object-detection'''		,				model=_UpperCAmelCase )

       snake_case_	=				object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg''' )
       self.assertEqual(
           nested_simplify(_UpperCAmelCase		,				decimals=4 )		,				[
               {'''score''': 0.9_982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 1_75, '''ymax''': 1_17}},
               {'''score''': 0.9_960, '''label''': '''remote''', '''box''': {'''xmin''': 3_33, '''ymin''': 72, '''xmax''': 3_68, '''ymax''': 1_87}},
               {'''score''': 0.9_955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_39, '''ymax''': 4_73}},
               {'''score''': 0.9_988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 3_14, '''ymax''': 4_70}},
               {'''score''': 0.9_987, '''label''': '''cat''', '''box''': {'''xmin''': 3_45, '''ymin''': 23, '''xmax''': 6_40, '''ymax''': 3_68}},
           ]		,				)

       snake_case_	=				object_detector(
           [
               '''http://images.cocodataset.org/val2017/000000039769.jpg''',
               '''http://images.cocodataset.org/val2017/000000039769.jpg''',
           ] )
       self.assertEqual(
           nested_simplify(_UpperCAmelCase		,				decimals=4 )		,				[
               [
                   {'''score''': 0.9_982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 1_75, '''ymax''': 1_17}},
                   {'''score''': 0.9_960, '''label''': '''remote''', '''box''': {'''xmin''': 3_33, '''ymin''': 72, '''xmax''': 3_68, '''ymax''': 1_87}},
                   {'''score''': 0.9_955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_39, '''ymax''': 4_73}},
                   {'''score''': 0.9_988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 3_14, '''ymax''': 4_70}},
                   {'''score''': 0.9_987, '''label''': '''cat''', '''box''': {'''xmin''': 3_45, '''ymin''': 23, '''xmax''': 6_40, '''ymax''': 3_68}},
               ],
               [
                   {'''score''': 0.9_982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 1_75, '''ymax''': 1_17}},
                   {'''score''': 0.9_960, '''label''': '''remote''', '''box''': {'''xmin''': 3_33, '''ymin''': 72, '''xmax''': 3_68, '''ymax''': 1_87}},
                   {'''score''': 0.9_955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_39, '''ymax''': 4_73}},
                   {'''score''': 0.9_988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 3_14, '''ymax''': 4_70}},
                   {'''score''': 0.9_987, '''label''': '''cat''', '''box''': {'''xmin''': 3_45, '''ymin''': 23, '''xmax''': 6_40, '''ymax''': 3_68}},
               ],
           ]		,				)



 @require_torch
 @slow
 def 						UpperCamelCase__					(     self ):
       snake_case_	=				0.9_985
       snake_case_	=				'''facebook/detr-resnet-50'''

       snake_case_	=				pipeline('''object-detection'''		,				model=_UpperCAmelCase )

       snake_case_	=				object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''		,				threshold=_UpperCAmelCase )
       self.assertEqual(
           nested_simplify(_UpperCAmelCase		,				decimals=4 )		,				[
               {'''score''': 0.9_988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 3_14, '''ymax''': 4_70}},
               {'''score''': 0.9_987, '''label''': '''cat''', '''box''': {'''xmin''': 3_45, '''ymin''': 23, '''xmax''': 6_40, '''ymax''': 3_68}},
           ]		,				)





 @require_torch
 @require_pytesseract
 @slow
 def 						UpperCamelCase__					(     self ):
       snake_case_	=				'''Narsil/layoutlmv3-finetuned-funsd'''
       snake_case_	=				0.9_993

       snake_case_	=				pipeline('''object-detection'''		,				model=_UpperCAmelCase		,				threshold=_UpperCAmelCase )

       snake_case_	=				object_detector(
           '''https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png''' )
       self.assertEqual(
           nested_simplify(_UpperCAmelCase		,				decimals=4 )		,				[
               {'''score''': 0.9_993, '''label''': '''I-ANSWER''', '''box''': {'''xmin''': 2_94, '''ymin''': 2_54, '''xmax''': 3_43, '''ymax''': 2_64}},
               {'''score''': 0.9_993, '''label''': '''I-ANSWER''', '''box''': {'''xmin''': 2_94, '''ymin''': 2_54, '''xmax''': 3_43, '''ymax''': 2_64}},
           ]		,				)



import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase      =   logging.get_logger(__name__)

UpperCAmelCase      =   {
    """microsoft/wavlm-base""": """https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json""",
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}







class lowerCAmelCase_						(			lowerCamelCase__	):

 '''simple docstring'''

 __snake_case												=  "wavlm"



 def __init__(     self		,				_UpperCAmelCase=32		,				_UpperCAmelCase=7_68		,				_UpperCAmelCase=12		,				_UpperCAmelCase=12		,				_UpperCAmelCase=30_72		,				_UpperCAmelCase="gelu"		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=0.0		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=0.02		,				_UpperCAmelCase=1E-5		,				_UpperCAmelCase="group"		,				_UpperCAmelCase="gelu"		,				_UpperCAmelCase=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)		,				_UpperCAmelCase=(5, 2, 2, 2, 2, 2, 2)		,				_UpperCAmelCase=(10, 3, 3, 3, 3, 2, 2)		,				_UpperCAmelCase=False		,				_UpperCAmelCase=1_28		,				_UpperCAmelCase=16		,				_UpperCAmelCase=3_20		,				_UpperCAmelCase=8_00		,				_UpperCAmelCase=False		,				_UpperCAmelCase=True		,				_UpperCAmelCase=0.05		,				_UpperCAmelCase=10		,				_UpperCAmelCase=2		,				_UpperCAmelCase=0.0		,				_UpperCAmelCase=10		,				_UpperCAmelCase=3_20		,				_UpperCAmelCase=2		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=1_00		,				_UpperCAmelCase=2_56		,				_UpperCAmelCase=2_56		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase="mean"		,				_UpperCAmelCase=False		,				_UpperCAmelCase=False		,				_UpperCAmelCase=2_56		,				_UpperCAmelCase=(5_12, 5_12, 5_12, 5_12, 15_00)		,				_UpperCAmelCase=(5, 3, 3, 1, 1)		,				_UpperCAmelCase=(1, 2, 3, 1, 1)		,				_UpperCAmelCase=5_12		,				_UpperCAmelCase=80		,				_UpperCAmelCase=0		,				_UpperCAmelCase=1		,				_UpperCAmelCase=2		,				_UpperCAmelCase=False		,				_UpperCAmelCase=3		,				_UpperCAmelCase=2		,				_UpperCAmelCase=3		,				_UpperCAmelCase=None		,				**_UpperCAmelCase		,				):
       super().__init__(**_UpperCAmelCase		,				pad_token_id=_UpperCAmelCase		,				bos_token_id=_UpperCAmelCase		,				eos_token_id=_UpperCAmelCase )
       snake_case_	=				hidden_size
       snake_case_	=				feat_extract_norm
       snake_case_	=				feat_extract_activation
       snake_case_	=				list(_UpperCAmelCase )
       snake_case_	=				list(_UpperCAmelCase )
       snake_case_	=				list(_UpperCAmelCase )
       snake_case_	=				conv_bias
       snake_case_	=				num_buckets
       snake_case_	=				max_bucket_distance
       snake_case_	=				num_conv_pos_embeddings
       snake_case_	=				num_conv_pos_embedding_groups
       snake_case_	=				len(self.conv_dim )
       snake_case_	=				num_hidden_layers
       snake_case_	=				intermediate_size
       snake_case_	=				hidden_act
       snake_case_	=				num_attention_heads
       snake_case_	=				hidden_dropout
       snake_case_	=				attention_dropout
       snake_case_	=				activation_dropout
       snake_case_	=				feat_proj_dropout
       snake_case_	=				final_dropout
       snake_case_	=				layerdrop
       snake_case_	=				layer_norm_eps
       snake_case_	=				initializer_range
       snake_case_	=				num_ctc_classes
       snake_case_	=				vocab_size
       snake_case_	=				do_stable_layer_norm
       snake_case_	=				use_weighted_layer_sum
       snake_case_	=				classifier_proj_size

       if (
           (len(self.conv_stride ) != self.num_feat_extract_layers)
           or (len(self.conv_kernel ) != self.num_feat_extract_layers)
           or (len(self.conv_dim ) != self.num_feat_extract_layers)
       ):
             raise ValueError(
                 '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
                 ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
                 F''' {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,'''
                 F''' `len(config.conv_kernel) = {len(self.conv_kernel )}`.''' )

       # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
       snake_case_	=				apply_spec_augment
       snake_case_	=				mask_time_prob
       snake_case_	=				mask_time_length
       snake_case_	=				mask_time_min_masks
       snake_case_	=				mask_feature_prob
       snake_case_	=				mask_feature_length

       # parameters for pretraining with codevector quantized representations
       snake_case_	=				num_codevectors_per_group
       snake_case_	=				num_codevector_groups
       snake_case_	=				contrastive_logits_temperature
       snake_case_	=				num_negatives
       snake_case_	=				codevector_dim
       snake_case_	=				proj_codevector_dim
       snake_case_	=				diversity_loss_weight

       # ctc loss
       snake_case_	=				ctc_loss_reduction
       snake_case_	=				ctc_zero_infinity

       # adapter
       snake_case_	=				add_adapter
       snake_case_	=				adapter_kernel_size
       snake_case_	=				adapter_stride
       snake_case_	=				num_adapter_layers
       snake_case_	=				output_hidden_size or hidden_size

       # SequenceClassification-specific parameter. Feel free to ignore for other classes.
       snake_case_	=				classifier_proj_size

       # XVector-specific parameters. Feel free to ignore for other classes.
       snake_case_	=				list(_UpperCAmelCase )
       snake_case_	=				list(_UpperCAmelCase )
       snake_case_	=				list(_UpperCAmelCase )
       snake_case_	=				xvector_output_dim





 @property
 def 						UpperCamelCase__					(     self ):
       return functools.reduce(operator.mul		,				self.conv_stride		,				1 )