


'''simple docstring'''


import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases




def      lowerCamelCase					(      lowerCamelCase  :      List[Any]							,						lowerCamelCase  :      Optional[Any]):
      assert isinstance(_lowerCamelCase							,						_lowerCamelCase)
      assert dataset.num_rows == 4
      assert dataset.num_columns == 3
      assert dataset.column_names == ["col_1", "col_2", "col_3"]
      for feature, expected_dtype in expected_features.items():
            assert dataset.features[feature].dtype == expected_dtype




@pytest.mark.parametrize("""keep_in_memory"""							,						[False, True])
def      lowerCamelCase					(      lowerCamelCase  :      Any							,						lowerCamelCase  :      Dict							,						lowerCamelCase  :      int):
      A_						:  Union[str, Any]          =				tmp_path / """cache"""
      A_						:  int          =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
      with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
            A_						:  Optional[Any]          =				ParquetDatasetReader(_lowerCamelCase							,						cache_dir=_lowerCamelCase							,						keep_in_memory=_lowerCamelCase).read()
      _check_parquet_dataset(_lowerCamelCase							,						_lowerCamelCase)




@pytest.mark.parametrize(
    """features"""							,						[
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]							,						)
def      lowerCamelCase					(      lowerCamelCase  :      Optional[Any]							,						lowerCamelCase  :      Any							,						lowerCamelCase  :      Tuple):
      A_						:  Optional[Any]          =				tmp_path / """cache"""
      A_						:  Optional[Any]          =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
      A_						:  Optional[Any]          =				features.copy() if features else default_expected_features
      A_						:  int          =				(
          Features({feature: Value(_lowerCamelCase) for feature, dtype in features.items()}) if features is not None else None
      )
      A_						:  Tuple          =				ParquetDatasetReader(_lowerCamelCase							,						features=_lowerCamelCase							,						cache_dir=_lowerCamelCase).read()
      _check_parquet_dataset(_lowerCamelCase							,						_lowerCamelCase)




@pytest.mark.parametrize("""split"""							,						[None, NamedSplit("""train"""), """train""", """test"""])
def      lowerCamelCase					(      lowerCamelCase  :      int							,						lowerCamelCase  :      Dict							,						lowerCamelCase  :      Union[str, Any]):
      A_						:  Optional[int]          =				tmp_path / """cache"""
      A_						:  Optional[Any]          =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
      A_						:  Optional[int]          =				ParquetDatasetReader(_lowerCamelCase							,						cache_dir=_lowerCamelCase							,						split=_lowerCamelCase).read()
      _check_parquet_dataset(_lowerCamelCase							,						_lowerCamelCase)
      assert dataset.split == split if split else "train"




@pytest.mark.parametrize("""path_type"""							,						[str, list])
def      lowerCamelCase					(      lowerCamelCase  :      List[str]							,						lowerCamelCase  :      str							,						lowerCamelCase  :      List[Any]):
      if issubclass(_lowerCamelCase							,						_lowerCamelCase):
            A_						:  List[Any]          =				parquet_path
      elif issubclass(_lowerCamelCase							,						_lowerCamelCase):
            A_						:  int          =				[parquet_path]
      A_						:  Union[str, Any]          =				tmp_path / """cache"""
      A_						:  int          =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
      A_						:  Union[str, Any]          =				ParquetDatasetReader(_lowerCamelCase							,						cache_dir=_lowerCamelCase).read()
      _check_parquet_dataset(_lowerCamelCase							,						_lowerCamelCase)




def      lowerCamelCase					(      lowerCamelCase  :      Union[str, Any]							,						lowerCamelCase  :      Tuple							,						lowerCamelCase  :      Optional[int]=("train",)):
      assert isinstance(_lowerCamelCase							,						_lowerCamelCase)
      for split in splits:
            A_						:  Union[str, Any]          =				dataset_dict[split]
            assert dataset.num_rows == 4
            assert dataset.num_columns == 3
            assert dataset.column_names == ["col_1", "col_2", "col_3"]
            for feature, expected_dtype in expected_features.items():
                  assert dataset.features[feature].dtype == expected_dtype




@pytest.mark.parametrize("""keep_in_memory"""							,						[False, True])
def      lowerCamelCase					(      lowerCamelCase  :      int							,						lowerCamelCase  :      Optional[Any]							,						lowerCamelCase  :      int):
      A_						:  Any          =				tmp_path / """cache"""
      A_						:  Any          =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
      with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
            A_						:  Any          =				ParquetDatasetReader(
                {"""train""": parquet_path}							,						cache_dir=_lowerCamelCase							,						keep_in_memory=_lowerCamelCase).read()
      _check_parquet_datasetdict(_lowerCamelCase							,						_lowerCamelCase)




@pytest.mark.parametrize(
    """features"""							,						[
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]							,						)
def      lowerCamelCase					(      lowerCamelCase  :      Union[str, Any]							,						lowerCamelCase  :      List[str]							,						lowerCamelCase  :      int):
      A_						:  Tuple          =				tmp_path / """cache"""
      A_						:  Dict          =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
      A_						:  Optional[int]          =				features.copy() if features else default_expected_features
      A_						:  str          =				(
          Features({feature: Value(_lowerCamelCase) for feature, dtype in features.items()}) if features is not None else None
      )
      A_						:  List[str]          =				ParquetDatasetReader({"""train""": parquet_path}							,						features=_lowerCamelCase							,						cache_dir=_lowerCamelCase).read()
      _check_parquet_datasetdict(_lowerCamelCase							,						_lowerCamelCase)




@pytest.mark.parametrize("""split"""							,						[None, NamedSplit("""train"""), """train""", """test"""])
def      lowerCamelCase					(      lowerCamelCase  :      Tuple							,						lowerCamelCase  :      int							,						lowerCamelCase  :      List[str]):
      if split:
            A_						:  List[Any]          =				{split: parquet_path}
      else:
            A_						:  List[str]          =				"""train"""
            A_						:  Optional[Any]          =				{"""train""": parquet_path, """test""": parquet_path}
      A_						:  List[str]          =				tmp_path / """cache"""
      A_						:  Dict          =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
      A_						:  List[str]          =				ParquetDatasetReader(_lowerCamelCase							,						cache_dir=_lowerCamelCase).read()
      _check_parquet_datasetdict(_lowerCamelCase							,						_lowerCamelCase							,						splits=list(path.keys()))
      assert all(dataset[split].split == split for split in path.keys())




def      lowerCamelCase					(      lowerCamelCase  :      Union[str, Any]							,						lowerCamelCase  :      str):
      A_						:  List[str]          =				ParquetDatasetWriter(_lowerCamelCase							,						tmp_path / """foo.parquet""")
      assert writer.write() > 0
      A_						:  Any          =				pq.ParquetFile(tmp_path / """foo.parquet""")
      A_						:  Dict          =				pf.read()
      assert dataset.data.table == output_table




def      lowerCamelCase					(      lowerCamelCase  :      List[Any]							,						lowerCamelCase  :      Dict):
      A_						:  Optional[int]          =				str(shared_datadir / """test_image_rgb.jpg""")
      A_						:  Optional[Any]          =				{"""image""": [image_path]}
      A_						:  List[str]          =				Features({"""image""": Image()})
      A_						:  int          =				Dataset.from_dict(_lowerCamelCase							,						features=_lowerCamelCase)
      A_						:  Tuple          =				ParquetDatasetWriter(_lowerCamelCase							,						tmp_path / """foo.parquet""")
      assert writer.write() > 0

      A_						:  List[str]          =				Dataset.from_parquet(str(tmp_path / """foo.parquet"""))
      assert dataset.features == reloaded_dataset.features

      A_						:  List[str]          =				ParquetDatasetReader(str(tmp_path / """foo.parquet""")							,						streaming=_lowerCamelCase).read()
      assert dataset.features == reloaded_iterable_dataset.features






@pytest.mark.parametrize(
    """feature, expected"""							,						[
        (Features({"""foo""": Value("""int32""")}), None),
        (Features({"""image""": Image(), """foo""": Value("""int32""")}), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({"""nested""": Sequence(Audio())}), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ]							,						)
def      lowerCamelCase					(      lowerCamelCase  :      int							,						lowerCamelCase  :      int):
      assert get_writer_batch_size(_lowerCamelCase) == expected




'''simple docstring'''


import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union


if TYPE_CHECKING:
     from ...processing_utils import ProcessorMixin
     from ...utils import TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__magic_name__			=      logging.get_logger(__name__)

__magic_name__			=      {
    'google/owlvit-base-patch32': 'https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json',
    'google/owlvit-base-patch16': 'https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json',
    'google/owlvit-large-patch14': 'https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json',
}



class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							"""owlvit_text_model"""






  def __init__(							self :       Union[str, Any]	,_a :       Any=49408	,_a :       Any=512	,_a :       Tuple=2048	,_a :       Dict=12	,_a :       Optional[int]=8	,_a :       Tuple=16	,_a :       Tuple="quick_gelu"	,_a :       Optional[Any]=1e-5	,_a :       List[Any]=0.0	,_a :       Optional[int]=0.02	,_a :       Dict=1.0	,_a :       Dict=0	,_a :       Any=49406	,_a :       Tuple=49407	,**_a :       List[Any]	,):

        '''simple docstring'''




        super().__init__(pad_token_id=_a	,bos_token_id=_a	,eos_token_id=_a	,**_a )

        A_						:  Tuple          =				vocab_size
        A_						:  int          =				hidden_size
        A_						:  Optional[int]          =				intermediate_size
        A_						:  Optional[int]          =				num_hidden_layers
        A_						:  Union[str, Any]          =				num_attention_heads
        A_						:  int          =				max_position_embeddings
        A_						:  str          =				hidden_act
        A_						:  Union[str, Any]          =				layer_norm_eps
        A_						:  Tuple          =				attention_dropout
        A_						:  Union[str, Any]          =				initializer_range
        A_						:  List[Any]          =				initializer_factor






  @classmethod
  def        _a							(							cls :       List[str]	,_a :       Union[str, os.PathLike]	,**_a :       str ):

        '''simple docstring'''




        cls._set_token_in_kwargs(_a )

        A_   ,					A_						:  int          =				cls.get_config_dict(_a	,**_a )

        # get the text config dict if we are loading from OwlViTConfig
        if config_dict.get("""model_type""" ) == "owlvit":
              A_						:  Union[str, Any]          =				config_dict["""text_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(_a	,**_a )







class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							"""owlvit_vision_model"""






  def __init__(							self :       List[Any]	,_a :       Optional[Any]=768	,_a :       Tuple=3072	,_a :       Dict=12	,_a :       int=12	,_a :       Dict=3	,_a :       Tuple=768	,_a :       int=32	,_a :       int="quick_gelu"	,_a :       List[Any]=1e-5	,_a :       Tuple=0.0	,_a :       List[Any]=0.02	,_a :       str=1.0	,**_a :       int	,):

        '''simple docstring'''




        super().__init__(**_a )

        A_						:  List[str]          =				hidden_size
        A_						:  Union[str, Any]          =				intermediate_size
        A_						:  Union[str, Any]          =				num_hidden_layers
        A_						:  Optional[Any]          =				num_attention_heads
        A_						:  int          =				num_channels
        A_						:  str          =				image_size
        A_						:  List[Any]          =				patch_size
        A_						:  int          =				hidden_act
        A_						:  List[Any]          =				layer_norm_eps
        A_						:  List[str]          =				attention_dropout
        A_						:  str          =				initializer_range
        A_						:  str          =				initializer_factor






  @classmethod
  def        _a							(							cls :       List[Any]	,_a :       Union[str, os.PathLike]	,**_a :       str ):

        '''simple docstring'''




        cls._set_token_in_kwargs(_a )

        A_   ,					A_						:  Optional[int]          =				cls.get_config_dict(_a	,**_a )

        # get the vision config dict if we are loading from OwlViTConfig
        if config_dict.get("""model_type""" ) == "owlvit":
              A_						:  List[str]          =				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(_a	,**_a )







class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							"""owlvit"""
  a_  					=							True






  def __init__(							self :       Union[str, Any]	,_a :       List[str]=None	,_a :       List[str]=None	,_a :       Dict=512	,_a :       List[Any]=2.6592	,_a :       Optional[Any]=True	,**_a :       Optional[int]	,):

        '''simple docstring'''




        super().__init__(**_a )

        if text_config is None:
              A_						:  List[Any]          =				{}
              logger.info("""text_config is None. Initializing the OwlViTTextConfig with default values.""" )

        if vision_config is None:
              A_						:  Tuple          =				{}
              logger.info("""vision_config is None. initializing the OwlViTVisionConfig with default values.""" )

        A_						:  Dict          =				OwlViTTextConfig(**_a )
        A_						:  Dict          =				OwlViTVisionConfig(**_a )

        A_						:  Any          =				projection_dim
        A_						:  Optional[int]          =				logit_scale_init_value
        A_						:  Optional[int]          =				return_dict
        A_						:  Dict          =				1.0






  @classmethod
  def        _a							(							cls :       Union[str, Any]	,_a :       Union[str, os.PathLike]	,**_a :       Optional[int] ):

        '''simple docstring'''




        cls._set_token_in_kwargs(_a )

        A_   ,					A_						:  List[Any]          =				cls.get_config_dict(_a	,**_a )

        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(_a	,**_a )






  @classmethod
  def        _a							(							cls :       int	,_a :       Dict	,_a :       Dict	,**_a :       List[str] ):

        '''simple docstring'''




        A_						:  str          =				{}
        A_						:  int          =				text_config
        A_						:  Union[str, Any]          =				vision_config

        return cls.from_dict(_a	,**_a )






  def        _a							(							self :       Optional[int] ):

        '''simple docstring'''




        A_						:  Dict          =				copy.deepcopy(self.__dict__ )
        A_						:  str          =				self.text_config.to_dict()
        A_						:  Optional[int]          =				self.vision_config.to_dict()
        A_						:  List[Any]          =				self.__class__.model_type
        return output







class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  @property
  def        _a							(							self :       int ):

        '''simple docstring'''




        return OrderedDict(
            [
                ("""input_ids""", {0: """batch""", 1: """sequence"""}),
                ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
                ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
            ] )






  @property
  def        _a							(							self :       str ):

        '''simple docstring'''




        return OrderedDict(
            [
                ("""logits_per_image""", {0: """batch"""}),
                ("""logits_per_text""", {0: """batch"""}),
                ("""text_embeds""", {0: """batch"""}),
                ("""image_embeds""", {0: """batch"""}),
            ] )






  @property
  def        _a							(							self :       Optional[Any] ):

        '''simple docstring'''




        return 1e-4






  def        _a							(							self :       int	,_a :       "ProcessorMixin"	,_a :       int = -1	,_a :       int = -1	,_a :       Optional["TensorType"] = None	,):

        '''simple docstring'''




        A_						:  Any          =				super().generate_dummy_inputs(
            processor.tokenizer	,batch_size=_a	,seq_length=_a	,framework=_a )
        A_						:  Any          =				super().generate_dummy_inputs(
            processor.image_processor	,batch_size=_a	,framework=_a )
        return {**text_input_dict, **image_input_dict}






  @property
  def        _a							(							self :       Optional[Any] ):

        '''simple docstring'''




        return 14
