





'''simple docstring'''

import io
import json

import fsspec
import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases





def lowerCamelCase						(     __lowerCamelCase :    Dict					,       __lowerCamelCase :    Optional[Any]    )						->Union[str, 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 :    Dict					,       __lowerCamelCase :    int					,       __lowerCamelCase :    Optional[Any]    )						->Optional[int]:
    _SCREAMING_SNAKE_CASE									=			tmp_path / """cache"""
    _SCREAMING_SNAKE_CASE									=			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
    with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
        _SCREAMING_SNAKE_CASE									=			JsonDatasetReader(__lowerCamelCase					,       cache_dir=__lowerCamelCase					,       keep_in_memory=__lowerCamelCase    ).read()
    _check_json_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 :    Dict					,       __lowerCamelCase :    str					,       __lowerCamelCase :    str    )						->Dict:
    _SCREAMING_SNAKE_CASE									=			tmp_path / """cache"""
    _SCREAMING_SNAKE_CASE									=			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
    _SCREAMING_SNAKE_CASE									=			features.copy() if features else default_expected_features
    _SCREAMING_SNAKE_CASE									=			(
        Features({feature: Value(__lowerCamelCase    ) for feature, dtype in features.items()}    ) if features is not None else None
    )
    _SCREAMING_SNAKE_CASE									=			JsonDatasetReader(__lowerCamelCase					,       features=__lowerCamelCase					,       cache_dir=__lowerCamelCase    ).read()
    _check_json_dataset(__lowerCamelCase					,       __lowerCamelCase    )





@pytest.mark.parametrize(
    """features"""					,       [
        None,
        {"""col_3""": """float64""", """col_1""": """string""", """col_2""": """int64"""},
    ]					,       )
def lowerCamelCase						(     __lowerCamelCase :    Union[str, Any]					,       __lowerCamelCase :    Optional[Any]					,       __lowerCamelCase :    Dict    )						->Union[str, Any]:
    _SCREAMING_SNAKE_CASE									=			tmp_path / """cache"""
    _SCREAMING_SNAKE_CASE									=			{"""col_3""": """float64""", """col_1""": """string""", """col_2""": """int64"""}
    _SCREAMING_SNAKE_CASE									=			features.copy() if features else default_expected_features
    _SCREAMING_SNAKE_CASE									=			(
        Features({feature: Value(__lowerCamelCase    ) for feature, dtype in features.items()}    ) if features is not None else None
    )
    _SCREAMING_SNAKE_CASE									=			JsonDatasetReader(__lowerCamelCase					,       features=__lowerCamelCase					,       cache_dir=__lowerCamelCase    ).read()
    assert isinstance(__lowerCamelCase					,       __lowerCamelCase    )
    assert dataset.num_rows == 2
    assert dataset.num_columns == 3
    assert dataset.column_names == ["col_3", "col_1", "col_2"]
    for feature, expected_dtype in expected_features.items():
        assert dataset.features[feature].dtype == expected_dtype





def lowerCamelCase						(     __lowerCamelCase :    Optional[Any]					,       __lowerCamelCase :    Tuple    )						->Tuple:
    # jsonl_312_path features are {"col_3": "float64", "col_1": "string", "col_2": "int64"}
    _SCREAMING_SNAKE_CASE									=			{"""col_2""": """int64""", """col_3""": """float64""", """col_1""": """string"""}
    _SCREAMING_SNAKE_CASE									=			features.copy()
    _SCREAMING_SNAKE_CASE									=			(
        Features({feature: Value(__lowerCamelCase    ) for feature, dtype in features.items()}    ) if features is not None else None
    )
    _SCREAMING_SNAKE_CASE									=			tmp_path / """cache"""
    _SCREAMING_SNAKE_CASE									=			JsonDatasetReader(__lowerCamelCase					,       features=__lowerCamelCase					,       cache_dir=__lowerCamelCase    ).read()
    assert isinstance(__lowerCamelCase					,       __lowerCamelCase    )
    assert dataset.num_rows == 2
    assert dataset.num_columns == 3
    assert dataset.column_names == ["col_2", "col_3", "col_1"]
    for feature, expected_dtype in expected_features.items():
        assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize("""split"""					,       [None, NamedSplit("""train"""    ), """train""", """test"""]    )
def lowerCamelCase						(     __lowerCamelCase :    Any					,       __lowerCamelCase :    Optional[Any]					,       __lowerCamelCase :    Optional[int]    )						->Dict:
    _SCREAMING_SNAKE_CASE									=			tmp_path / """cache"""
    _SCREAMING_SNAKE_CASE									=			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
    _SCREAMING_SNAKE_CASE									=			JsonDatasetReader(__lowerCamelCase					,       cache_dir=__lowerCamelCase					,       split=__lowerCamelCase    ).read()
    _check_json_dataset(__lowerCamelCase					,       __lowerCamelCase    )
    assert dataset.split == split if split else "train"





@pytest.mark.parametrize("""path_type"""					,       [str, list]    )
def lowerCamelCase						(     __lowerCamelCase :    Optional[Any]					,       __lowerCamelCase :    Union[str, Any]					,       __lowerCamelCase :    str    )						->Any:
    if issubclass(__lowerCamelCase					,       __lowerCamelCase    ):
        _SCREAMING_SNAKE_CASE									=			jsonl_path
    elif issubclass(__lowerCamelCase					,       __lowerCamelCase    ):
        _SCREAMING_SNAKE_CASE									=			[jsonl_path]
    _SCREAMING_SNAKE_CASE									=			tmp_path / """cache"""
    _SCREAMING_SNAKE_CASE									=			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
    _SCREAMING_SNAKE_CASE									=			JsonDatasetReader(__lowerCamelCase					,       cache_dir=__lowerCamelCase    ).read()
    _check_json_dataset(__lowerCamelCase					,       __lowerCamelCase    )





def lowerCamelCase						(     __lowerCamelCase :    Tuple					,       __lowerCamelCase :    List[str]					,       __lowerCamelCase :    int=("train",)    )						->Optional[int]:
    assert isinstance(__lowerCamelCase					,       __lowerCamelCase    )
    for split in splits:
        _SCREAMING_SNAKE_CASE									=			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 :    str					,       __lowerCamelCase :    Any					,       __lowerCamelCase :    int    )						->List[Any]:
    _SCREAMING_SNAKE_CASE									=			tmp_path / """cache"""
    _SCREAMING_SNAKE_CASE									=			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
    with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
        _SCREAMING_SNAKE_CASE									=			JsonDatasetReader({"""train""": jsonl_path}					,       cache_dir=__lowerCamelCase					,       keep_in_memory=__lowerCamelCase    ).read()
    _check_json_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 :    Tuple					,       __lowerCamelCase :    Union[str, Any]					,       __lowerCamelCase :    Any    )						->str:
    _SCREAMING_SNAKE_CASE									=			tmp_path / """cache"""
    _SCREAMING_SNAKE_CASE									=			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
    _SCREAMING_SNAKE_CASE									=			features.copy() if features else default_expected_features
    _SCREAMING_SNAKE_CASE									=			(
        Features({feature: Value(__lowerCamelCase    ) for feature, dtype in features.items()}    ) if features is not None else None
    )
    _SCREAMING_SNAKE_CASE									=			JsonDatasetReader({"""train""": jsonl_path}					,       features=__lowerCamelCase					,       cache_dir=__lowerCamelCase    ).read()
    _check_json_datasetdict(__lowerCamelCase					,       __lowerCamelCase    )





@pytest.mark.parametrize("""split"""					,       [None, NamedSplit("""train"""    ), """train""", """test"""]    )
def lowerCamelCase						(     __lowerCamelCase :    str					,       __lowerCamelCase :    str					,       __lowerCamelCase :    int    )						->int:
    if split:
        _SCREAMING_SNAKE_CASE									=			{split: jsonl_path}
    else:
        _SCREAMING_SNAKE_CASE									=			"""train"""
        _SCREAMING_SNAKE_CASE									=			{"""train""": jsonl_path, """test""": jsonl_path}
    _SCREAMING_SNAKE_CASE									=			tmp_path / """cache"""
    _SCREAMING_SNAKE_CASE									=			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
    _SCREAMING_SNAKE_CASE									=			JsonDatasetReader(__lowerCamelCase					,       cache_dir=__lowerCamelCase    ).read()
    _check_json_datasetdict(__lowerCamelCase					,       __lowerCamelCase					,       splits=list(path.keys()    )    )
    assert all(dataset[split].split == split for split in path.keys()    )





def lowerCamelCase						(     __lowerCamelCase :    List[str]    )						->Optional[Any]:
    return json.load(__lowerCamelCase    )





def lowerCamelCase						(     __lowerCamelCase :    int    )						->str:
    return [json.loads(__lowerCamelCase    ) for line in buffer]






class 					a_     :

    '''simple docstring'''


    @pytest.mark.parametrize("""lines, load_json_function""" ,		[(True, load_json_lines), (False, load_json)]       )
    def 			snake_case_(		self ,		A ,		A ,		A       )		->   Dict:
        with io.BytesIO() as buffer:
            JsonDatasetWriter(A ,		A ,		lines=A       ).write()
            buffer.seek(0       )
            _SCREAMING_SNAKE_CASE									=			load_json_function(A       )
        assert isinstance(A ,		A       )
        assert isinstance(exported_content[0] ,		A       )
        assert len(A       ) == 10


    @pytest.mark.parametrize(
        """orient, container, keys, len_at""" ,		[
            ("""records""", list, {"""tokens""", """labels""", """answers""", """id"""}, None),
            ("""split""", dict, {"""columns""", """data"""}, """data"""),
            ("""index""", dict, set("""0123456789"""       ), None),
            ("""columns""", dict, {"""tokens""", """labels""", """answers""", """id"""}, """tokens"""),
            ("""values""", list, None, None),
            ("""table""", dict, {"""schema""", """data"""}, """data"""),
        ] ,		)
    def 			snake_case_(		self ,		A ,		A ,		A ,		A ,		A       )		->   Union[str, Any]:
        with io.BytesIO() as buffer:
            JsonDatasetWriter(A ,		A ,		lines=A ,		orient=A       ).write()
            buffer.seek(0       )
            _SCREAMING_SNAKE_CASE									=			load_json(A       )
        assert isinstance(A ,		A       )
        if keys:
            if container is dict:
                assert exported_content.keys() == keys
            else:
                assert exported_content[0].keys() == keys
        else:
            assert not hasattr(A ,		"""keys"""       ) and not hasattr(exported_content[0] ,		"""keys"""       )
        if len_at:
            assert len(exported_content[len_at]       ) == 10
        else:
            assert len(A       ) == 10


    @pytest.mark.parametrize("""lines, load_json_function""" ,		[(True, load_json_lines), (False, load_json)]       )
    def 			snake_case_(		self ,		A ,		A ,		A       )		->   Tuple:
        with io.BytesIO() as buffer:
            JsonDatasetWriter(A ,		A ,		lines=A ,		num_proc=2       ).write()
            buffer.seek(0       )
            _SCREAMING_SNAKE_CASE									=			load_json_function(A       )
        assert isinstance(A ,		A       )
        assert isinstance(exported_content[0] ,		A       )
        assert len(A       ) == 10


    @pytest.mark.parametrize(
        """orient, container, keys, len_at""" ,		[
            ("""records""", list, {"""tokens""", """labels""", """answers""", """id"""}, None),
            ("""split""", dict, {"""columns""", """data"""}, """data"""),
            ("""index""", dict, set("""0123456789"""       ), None),
            ("""columns""", dict, {"""tokens""", """labels""", """answers""", """id"""}, """tokens"""),
            ("""values""", list, None, None),
            ("""table""", dict, {"""schema""", """data"""}, """data"""),
        ] ,		)
    def 			snake_case_(		self ,		A ,		A ,		A ,		A ,		A       )		->   List[Any]:
        with io.BytesIO() as buffer:
            JsonDatasetWriter(A ,		A ,		lines=A ,		orient=A ,		num_proc=2       ).write()
            buffer.seek(0       )
            _SCREAMING_SNAKE_CASE									=			load_json(A       )
        assert isinstance(A ,		A       )
        if keys:
            if container is dict:
                assert exported_content.keys() == keys
            else:
                assert exported_content[0].keys() == keys
        else:
            assert not hasattr(A ,		"""keys"""       ) and not hasattr(exported_content[0] ,		"""keys"""       )
        if len_at:
            assert len(exported_content[len_at]       ) == 10
        else:
            assert len(A       ) == 10


    def 			snake_case_(		self ,		A       )		->   int:
        with pytest.raises(A       ):
            with io.BytesIO() as buffer:
                JsonDatasetWriter(A ,		A ,		num_proc=0       )





    @pytest.mark.parametrize("""compression, extension""" ,		[("""gzip""", """gz"""), ("""bz2""", """bz2"""), ("""xz""", """xz""")]       )
    def 			snake_case_(		self ,		A ,		A ,		A ,		A ,		A       )		->   Any:
        _SCREAMING_SNAKE_CASE									=			tmp_path_factory.mktemp("""data"""       ) / f'test.json.{extension}'
        _SCREAMING_SNAKE_CASE									=			str(shared_datadir / f'test_file.json.{extension}'       )
        JsonDatasetWriter(A ,		A ,		compression=A       ).write()

        with fsspec.open(A ,		"""rb""" ,		compression="""infer"""       ) as f:
            _SCREAMING_SNAKE_CASE									=			f.read()
        with fsspec.open(A ,		"""rb""" ,		compression="""infer"""       ) as f:
            _SCREAMING_SNAKE_CASE									=			f.read()
        assert exported_content == original_content










'''simple docstring'''

import copy
import os
from typing import Union

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


lowercase_					= logging.get_logger(__name__)

lowercase_					= {
    """BridgeTower/bridgetower-base""": """https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json""",
    """BridgeTower/bridgetower-base-itm-mlm""": (
        """https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json"""
    ),
}


class 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = '''bridgetower_vision_model'''


    def __init__(		self ,		A=768 ,		A=12 ,		A=3 ,		A=16 ,		A=288 ,		A=1 ,		A=1e-05 ,		A=False ,		A=True ,		A=False ,		**A ,		)		->   Dict:
        super().__init__(**A       )
        _SCREAMING_SNAKE_CASE									=			hidden_size
        _SCREAMING_SNAKE_CASE									=			num_hidden_layers
        _SCREAMING_SNAKE_CASE									=			num_channels
        _SCREAMING_SNAKE_CASE									=			patch_size
        _SCREAMING_SNAKE_CASE									=			image_size
        _SCREAMING_SNAKE_CASE									=			initializer_factor
        _SCREAMING_SNAKE_CASE									=			layer_norm_eps
        _SCREAMING_SNAKE_CASE									=			stop_gradient
        _SCREAMING_SNAKE_CASE									=			share_layernorm
        _SCREAMING_SNAKE_CASE									=			remove_last_layer





    @classmethod
    def 			snake_case_(		cls ,		A ,		**A       )		->   "PretrainedConfig":
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			cls.get_config_dict(A ,		**A       )

        if config_dict.get("""model_type"""       ) == "bridgetower":
            _SCREAMING_SNAKE_CASE									=			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 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = '''bridgetower_text_model'''


    def __init__(		self ,		A=5_0265 ,		A=768 ,		A=12 ,		A=12 ,		A=1 ,		A=3072 ,		A="gelu" ,		A=0.1 ,		A=0.1 ,		A=514 ,		A=1 ,		A=1e-05 ,		A=1 ,		A=0 ,		A=2 ,		A="absolute" ,		A=True ,		**A ,		)		->   Union[str, Any]:
        super().__init__(**A       )

        _SCREAMING_SNAKE_CASE									=			vocab_size
        _SCREAMING_SNAKE_CASE									=			hidden_size
        _SCREAMING_SNAKE_CASE									=			num_hidden_layers
        _SCREAMING_SNAKE_CASE									=			num_attention_heads
        _SCREAMING_SNAKE_CASE									=			hidden_act
        _SCREAMING_SNAKE_CASE									=			initializer_factor
        _SCREAMING_SNAKE_CASE									=			intermediate_size
        _SCREAMING_SNAKE_CASE									=			hidden_dropout_prob
        _SCREAMING_SNAKE_CASE									=			attention_probs_dropout_prob
        _SCREAMING_SNAKE_CASE									=			max_position_embeddings
        _SCREAMING_SNAKE_CASE									=			type_vocab_size
        _SCREAMING_SNAKE_CASE									=			layer_norm_eps
        _SCREAMING_SNAKE_CASE									=			position_embedding_type
        _SCREAMING_SNAKE_CASE									=			use_cache
        _SCREAMING_SNAKE_CASE									=			pad_token_id
        _SCREAMING_SNAKE_CASE									=			bos_token_id
        _SCREAMING_SNAKE_CASE									=			eos_token_id





    @classmethod
    def 			snake_case_(		cls ,		A ,		**A       )		->   "PretrainedConfig":
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			cls.get_config_dict(A ,		**A       )

        if config_dict.get("""model_type"""       ) == "bridgetower":
            _SCREAMING_SNAKE_CASE									=			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 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = '''bridgetower'''


    def __init__(		self ,		A=True ,		A="gelu" ,		A=768 ,		A=1 ,		A=1e-05 ,		A=False ,		A="add" ,		A=12 ,		A=6 ,		A=False ,		A=False ,		A=None ,		A=None ,		**A ,		)		->   Tuple:
        # TODO: remove this once the Hub files are updated.
        _SCREAMING_SNAKE_CASE									=			kwargs.pop("""text_config_dict""" ,		A       )
        _SCREAMING_SNAKE_CASE									=			kwargs.pop("""vision_config_dict""" ,		A       )

        super().__init__(**A       )
        _SCREAMING_SNAKE_CASE									=			share_cross_modal_transformer_layers
        _SCREAMING_SNAKE_CASE									=			hidden_act
        _SCREAMING_SNAKE_CASE									=			hidden_size
        _SCREAMING_SNAKE_CASE									=			initializer_factor
        _SCREAMING_SNAKE_CASE									=			layer_norm_eps
        _SCREAMING_SNAKE_CASE									=			share_link_tower_layers
        _SCREAMING_SNAKE_CASE									=			link_tower_type
        _SCREAMING_SNAKE_CASE									=			num_attention_heads
        _SCREAMING_SNAKE_CASE									=			num_hidden_layers
        _SCREAMING_SNAKE_CASE									=			tie_word_embeddings
        _SCREAMING_SNAKE_CASE									=			init_layernorm_from_vision_encoder

        if text_config is None:
            _SCREAMING_SNAKE_CASE									=			{}
            logger.info("""`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values."""       )

        if vision_config is None:
            _SCREAMING_SNAKE_CASE									=			{}
            logger.info("""`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values."""       )

        _SCREAMING_SNAKE_CASE									=			BridgeTowerTextConfig(**A       )
        _SCREAMING_SNAKE_CASE									=			BridgeTowerVisionConfig(**A       )


    @classmethod
    def 			snake_case_(		cls ,		A ,		A ,		**A       )		->   int:
        return cls(text_config=text_config.to_dict() ,		vision_config=vision_config.to_dict() ,		**A       )





    def 			snake_case_(		self       )		->   List[Any]:
        _SCREAMING_SNAKE_CASE									=			copy.deepcopy(self.__dict__       )
        _SCREAMING_SNAKE_CASE									=			self.text_config.to_dict()
        _SCREAMING_SNAKE_CASE									=			self.vision_config.to_dict()
        _SCREAMING_SNAKE_CASE									=			self.__class__.model_type
        return output



