





import unittest

from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
		from PIL import Image
else:




		class 				__lowercase			:







							'''simple docstring'''




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




@is_pipeline_test
@require_vision
@require_torch
class 				__lowercase			(			unittest.TestCase							):







					'''simple docstring'''




					__lowerCAmelCase  =  MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									__a :						List[Any]			       =  pipeline(
									    '''zero-shot-object-detection'''	,			model='''hf-internal-testing/tiny-random-owlvit-object-detection'''     )

									__a :						Union[str, Any]			       =  [
									    {
									        "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
									        "candidate_labels": ["cat", "remote", "couch"],
									    }
									]
									return object_detector, examples







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									__a :						Optional[Any]			       =  object_detector(examples[0]	,			threshold=0.0     )

									__a :						int			       =  len(_UpperCAmelCase     )
									self.assertGreater(_UpperCAmelCase	,			0     )
									self.assertEqual(
									    _UpperCAmelCase	,			[
									        {
									            '''score''': ANY(_UpperCAmelCase     ),
									            '''label''': ANY(_UpperCAmelCase     ),
									            '''box''': {'''xmin''': ANY(_UpperCAmelCase     ), '''ymin''': ANY(_UpperCAmelCase     ), '''xmax''': ANY(_UpperCAmelCase     ), '''ymax''': ANY(_UpperCAmelCase     )},
									        }
									        for i in range(_UpperCAmelCase     )
									    ]	,			)







					@require_tf
					@unittest.skip('''Zero Shot Object Detection not implemented in TF'''     )
					def  _lowerCamelCase		(						self     ):
									pass







					@require_torch
					def  _lowerCamelCase		(						self     ):
									__a :						List[str]			       =  pipeline(
									    '''zero-shot-object-detection'''	,			model='''hf-internal-testing/tiny-random-owlvit-object-detection'''     )

									__a :						Union[str, Any]			       =  object_detector(
									    '''./tests/fixtures/tests_samples/COCO/000000039769.png'''	,			candidate_labels=['''cat''', '''remote''', '''couch''']	,			threshold=0.6_4	,			)

									self.assertEqual(
									    nested_simplify(_UpperCAmelCase	,			decimals=4     )	,			[
									        {'''score''': 0.7_2_3_5, '''label''': '''cat''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
									        {'''score''': 0.7_2_1_8, '''label''': '''remote''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
									        {'''score''': 0.7_1_8_4, '''label''': '''couch''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
									        {'''score''': 0.6_7_4_8, '''label''': '''remote''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
									        {'''score''': 0.6_6_5_6, '''label''': '''cat''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
									        {'''score''': 0.6_6_1_4, '''label''': '''couch''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
									        {'''score''': 0.6_4_5_6, '''label''': '''remote''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
									        {'''score''': 0.6_4_2, '''label''': '''remote''', '''box''': {'''xmin''': 67, '''ymin''': 274, '''xmax''': 93, '''ymax''': 297}},
									        {'''score''': 0.6_4_1_9, '''label''': '''cat''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
									    ]	,			)

									__a :						List[Any]			       =  object_detector(
									    [
									        {
									            '''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
									            '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
									        }
									    ]	,			threshold=0.6_4	,			)

									self.assertEqual(
									    nested_simplify(_UpperCAmelCase	,			decimals=4     )	,			[
									        [
									            {'''score''': 0.7_2_3_5, '''label''': '''cat''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
									            {'''score''': 0.7_2_1_8, '''label''': '''remote''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
									            {'''score''': 0.7_1_8_4, '''label''': '''couch''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
									            {'''score''': 0.6_7_4_8, '''label''': '''remote''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
									            {'''score''': 0.6_6_5_6, '''label''': '''cat''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
									            {'''score''': 0.6_6_1_4, '''label''': '''couch''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
									            {'''score''': 0.6_4_5_6, '''label''': '''remote''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
									            {'''score''': 0.6_4_2, '''label''': '''remote''', '''box''': {'''xmin''': 67, '''ymin''': 274, '''xmax''': 93, '''ymax''': 297}},
									            {'''score''': 0.6_4_1_9, '''label''': '''cat''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
									        ]
									    ]	,			)







					@require_torch
					@slow
					def  _lowerCamelCase		(						self     ):
									__a :						List[str]			       =  pipeline('''zero-shot-object-detection'''     )

									__a :						Dict			       =  object_detector(
									    '''http://images.cocodataset.org/val2017/000000039769.jpg'''	,			candidate_labels=['''cat''', '''remote''', '''couch''']	,			)
									self.assertEqual(
									    nested_simplify(_UpperCAmelCase	,			decimals=4     )	,			[
									        {'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
									        {'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
									        {'''score''': 0.2_5_3_7, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
									        {'''score''': 0.1_4_7_4, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
									        {'''score''': 0.1_2_0_8, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
									    ]	,			)

									__a :						Union[str, Any]			       =  object_detector(
									    [
									        {
									            '''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''',
									            '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
									        },
									        {
									            '''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''',
									            '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
									        },
									    ]	,			)
									self.assertEqual(
									    nested_simplify(_UpperCAmelCase	,			decimals=4     )	,			[
									        [
									            {'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
									            {'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
									            {'''score''': 0.2_5_3_7, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
									            {'''score''': 0.1_4_7_4, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
									            {'''score''': 0.1_2_0_8, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
									        ],
									        [
									            {'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
									            {'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
									            {'''score''': 0.2_5_3_7, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
									            {'''score''': 0.1_4_7_4, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
									            {'''score''': 0.1_2_0_8, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
									        ],
									    ]	,			)







					@require_tf
					@unittest.skip('''Zero Shot Object Detection not implemented in TF'''     )
					def  _lowerCamelCase		(						self     ):
									pass







					@require_torch
					@slow
					def  _lowerCamelCase		(						self     ):
									__a :						Tuple			       =  0.2
									__a :						Tuple			       =  pipeline('''zero-shot-object-detection'''     )

									__a :						Dict			       =  object_detector(
									    '''http://images.cocodataset.org/val2017/000000039769.jpg'''	,			candidate_labels=['''cat''', '''remote''', '''couch''']	,			threshold=_UpperCAmelCase	,			)
									self.assertEqual(
									    nested_simplify(_UpperCAmelCase	,			decimals=4     )	,			[
									        {'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
									        {'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
									        {'''score''': 0.2_5_3_7, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
									    ]	,			)







					@require_torch
					@slow
					def  _lowerCamelCase		(						self     ):
									__a :						Dict			       =  2
									__a :						List[Any]			       =  pipeline('''zero-shot-object-detection'''     )

									__a :						Tuple			       =  object_detector(
									    '''http://images.cocodataset.org/val2017/000000039769.jpg'''	,			candidate_labels=['''cat''', '''remote''', '''couch''']	,			top_k=_UpperCAmelCase	,			)
									self.assertEqual(
									    nested_simplify(_UpperCAmelCase	,			decimals=4     )	,			[
									        {'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
									        {'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
									    ]	,			)






"""simple docstring"""






import copy
import os
from typing import Union

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


A 			=  logging.get_logger(__name__)

A 			=  {
    '''microsoft/git-base''': '''https://huggingface.co/microsoft/git-base/resolve/main/config.json''',
}




class 				__lowercase			(			_UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase  =  '''git_vision_model'''







					def __init__(						self	,			_UpperCAmelCase=768	,			_UpperCAmelCase=3072	,			_UpperCAmelCase=12	,			_UpperCAmelCase=12	,			_UpperCAmelCase=3	,			_UpperCAmelCase=224	,			_UpperCAmelCase=16	,			_UpperCAmelCase="quick_gelu"	,			_UpperCAmelCase=1e-5	,			_UpperCAmelCase=0.0	,			_UpperCAmelCase=0.0_2	,			**_UpperCAmelCase	,			):
									super().__init__(**_UpperCAmelCase     )

									__a :						Union[str, Any]			       =  hidden_size
									__a :						Optional[int]			       =  intermediate_size
									__a :						Tuple			       =  num_hidden_layers
									__a :						Tuple			       =  num_attention_heads
									__a :						Optional[int]			       =  num_channels
									__a :						Tuple			       =  patch_size
									__a :						Any			       =  image_size
									__a :						Optional[Any]			       =  initializer_range
									__a :						Optional[int]			       =  attention_dropout
									__a :						Any			       =  layer_norm_eps
									__a :						Dict			       =  hidden_act







					@classmethod
					def  _lowerCamelCase		(						cls	,			_UpperCAmelCase	,			**_UpperCAmelCase     ):
									cls._set_token_in_kwargs(_UpperCAmelCase     )

									__a       ,						__a :						Dict			       =  cls.get_config_dict(_UpperCAmelCase	,			**_UpperCAmelCase     )

									# get the vision config dict if we are loading from GITConfig
									if config_dict.get('''model_type'''     ) == "git":
													__a :						int			       =  config_dict['''vision_config''']

									if "model_type" in config_dict and hasattr(cls	,			'''model_type'''     ) and config_dict["model_type"] != cls.model_type:
													logger.warning(
													    f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
													    f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""     )

									return cls.from_dict(_UpperCAmelCase	,			**_UpperCAmelCase     )






class 				__lowercase			(			_UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase  =  '''git'''







					def __init__(						self	,			_UpperCAmelCase=None	,			_UpperCAmelCase=30522	,			_UpperCAmelCase=768	,			_UpperCAmelCase=6	,			_UpperCAmelCase=12	,			_UpperCAmelCase=3072	,			_UpperCAmelCase="gelu"	,			_UpperCAmelCase=0.1	,			_UpperCAmelCase=0.1	,			_UpperCAmelCase=1024	,			_UpperCAmelCase=0.0_2	,			_UpperCAmelCase=1e-1_2	,			_UpperCAmelCase=0	,			_UpperCAmelCase="absolute"	,			_UpperCAmelCase=True	,			_UpperCAmelCase=False	,			_UpperCAmelCase=101	,			_UpperCAmelCase=102	,			_UpperCAmelCase=None	,			**_UpperCAmelCase	,			):
									super().__init__(bos_token_id=_UpperCAmelCase	,			eos_token_id=_UpperCAmelCase	,			pad_token_id=_UpperCAmelCase	,			**_UpperCAmelCase     )

									if vision_config is None:
													__a :						Union[str, Any]			       =  {}
													logger.info('''vision_config is None. initializing the GitVisionConfig with default values.'''     )

									__a :						Union[str, Any]			       =  GitVisionConfig(**_UpperCAmelCase     )
									__a :						List[str]			       =  vocab_size
									__a :						Any			       =  hidden_size
									__a :						Dict			       =  num_hidden_layers
									__a :						Tuple			       =  num_attention_heads
									__a :						List[str]			       =  hidden_act
									__a :						Any			       =  intermediate_size
									__a :						int			       =  hidden_dropout_prob
									__a :						Dict			       =  attention_probs_dropout_prob
									__a :						Tuple			       =  max_position_embeddings
									__a :						int			       =  initializer_range
									__a :						List[str]			       =  layer_norm_eps
									__a :						Tuple			       =  position_embedding_type
									__a :						Optional[int]			       =  use_cache
									__a :						str			       =  tie_word_embeddings
									__a :						Optional[Any]			       =  num_image_with_embedding

									__a :						str			       =  bos_token_id
									__a :						Dict			       =  eos_token_id







					def  _lowerCamelCase		(						self     ):
									__a :						Optional[int]			       =  copy.deepcopy(self.__dict__     )
									__a :						int			       =  self.vision_config.to_dict()
									__a :						Tuple			       =  self.__class__.model_type
									return output