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       _snake_case	(   lowerCAmelCase							:   Dict   , lowerCAmelCase							:   List[str]	):
		"""simple docstring"""







		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       _snake_case	(   lowerCAmelCase							:   Dict   , lowerCAmelCase							:   Union[str, Any]   , lowerCAmelCase							:   List[str]	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    tmp_path / "cache"
		SCREAMING_SNAKE_CASE_  :		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():
				SCREAMING_SNAKE_CASE_  :		int				      =    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       _snake_case	(   lowerCAmelCase							:   Union[str, Any]   , lowerCAmelCase							:   Optional[int]   , lowerCAmelCase							:   Optional[Any]	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    tmp_path / "cache"
		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    {"col_1": "string", "col_2": "int64", "col_3": "float64"}
		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    features.copy() if features else default_expected_features
		SCREAMING_SNAKE_CASE_  :		int				      =    (
		    Features({feature: Value(lowerCAmelCase	) for feature, dtype in features.items()}	) if features is not None else None
		)
		SCREAMING_SNAKE_CASE_  :		Dict				      =    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       _snake_case	(   lowerCAmelCase							:   str   , lowerCAmelCase							:   int   , lowerCAmelCase							:   List[str]	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    tmp_path / "cache"
		SCREAMING_SNAKE_CASE_  :		Any				      =    {"col_3": "float64", "col_1": "string", "col_2": "int64"}
		SCREAMING_SNAKE_CASE_  :		Dict				      =    features.copy() if features else default_expected_features
		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    (
		    Features({feature: Value(lowerCAmelCase	) for feature, dtype in features.items()}	) if features is not None else None
		)
		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    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       _snake_case	(   lowerCAmelCase							:   List[Any]   , lowerCAmelCase							:   Optional[Any]	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Dict				      =    {"col_2": "int64", "col_3": "float64", "col_1": "string"}
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    features.copy()
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    (
		    Features({feature: Value(lowerCAmelCase	) for feature, dtype in features.items()}	) if features is not None else None
		)
		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    tmp_path / "cache"
		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    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       _snake_case	(   lowerCAmelCase							:   Tuple   , lowerCAmelCase							:   Optional[int]   , lowerCAmelCase							:   Any	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    tmp_path / "cache"
		SCREAMING_SNAKE_CASE_  :		Any				      =    {"col_1": "string", "col_2": "int64", "col_3": "float64"}
		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    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       _snake_case	(   lowerCAmelCase							:   List[Any]   , lowerCAmelCase							:   Optional[int]   , lowerCAmelCase							:   str	):
		"""simple docstring"""







		if issubclass(lowerCAmelCase   , lowerCAmelCase	):
				SCREAMING_SNAKE_CASE_  :		Tuple				      =    jsonl_path
		elif issubclass(lowerCAmelCase   , lowerCAmelCase	):
				SCREAMING_SNAKE_CASE_  :		List[str]				      =    [jsonl_path]
		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    tmp_path / "cache"
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    {"col_1": "string", "col_2": "int64", "col_3": "float64"}
		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    JsonDatasetReader(lowerCAmelCase   , cache_dir=lowerCAmelCase	).read()
		_check_json_dataset(lowerCAmelCase   , lowerCAmelCase	)




def       _snake_case	(   lowerCAmelCase							:   str   , lowerCAmelCase							:   int   , lowerCAmelCase							:   Union[str, Any]=("train",)	):
		"""simple docstring"""







		assert isinstance(lowerCAmelCase   , lowerCAmelCase	)
		for split in splits:
				SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    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       _snake_case	(   lowerCAmelCase							:   Any   , lowerCAmelCase							:   int   , lowerCAmelCase							:   Any	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		str				      =    tmp_path / "cache"
		SCREAMING_SNAKE_CASE_  :		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():
				SCREAMING_SNAKE_CASE_  :		List[str]				      =    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       _snake_case	(   lowerCAmelCase							:   Dict   , lowerCAmelCase							:   Optional[int]   , lowerCAmelCase							:   Dict	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		int				      =    tmp_path / "cache"
		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    {"col_1": "string", "col_2": "int64", "col_3": "float64"}
		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    features.copy() if features else default_expected_features
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    (
		    Features({feature: Value(lowerCAmelCase	) for feature, dtype in features.items()}	) if features is not None else None
		)
		SCREAMING_SNAKE_CASE_  :		Dict				      =    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       _snake_case	(   lowerCAmelCase							:   str   , lowerCAmelCase							:   Any   , lowerCAmelCase							:   Union[str, Any]	):
		"""simple docstring"""







		if split:
				SCREAMING_SNAKE_CASE_  :		Any				      =    {split: jsonl_path}
		else:
				SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    "train"
				SCREAMING_SNAKE_CASE_  :		List[Any]				      =    {"train": jsonl_path, "test": jsonl_path}
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    tmp_path / "cache"
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    {"col_1": "string", "col_2": "int64", "col_3": "float64"}
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    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       _snake_case	(   lowerCAmelCase							:   str	):
		"""simple docstring"""







		return json.load(lowerCAmelCase	)




def       _snake_case	(   lowerCAmelCase							:   Optional[int]	):
		"""simple docstring"""







		return [json.loads(lowerCAmelCase	) for line in buffer]







class 		a__						:

					@pytest.mark.parametrize("lines, load_json_function",[(True, load_json_lines), (False, load_json)]    )
					def 				__UpperCamelCase   (    self      :       Optional[Any],_A      :       Optional[Any],_A      :       str,_A      :       Union[str, Any]    ):







							"""simple docstring"""






							with io.BytesIO() as buffer:
									JsonDatasetWriter(_A,_A,lines=_A    ).write()
									buffer.seek(0    )
									SCREAMING_SNAKE_CASE_  :		Dict				      =    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 				__UpperCamelCase   (    self      :       str,_A      :       Any,_A      :       List[str],_A      :       Union[str, Any],_A      :       Union[str, Any],_A      :       List[Any]    ):







							"""simple docstring"""






							with io.BytesIO() as buffer:
									JsonDatasetWriter(_A,_A,lines=_A,orient=_A    ).write()
									buffer.seek(0    )
									SCREAMING_SNAKE_CASE_  :		str				      =    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 				__UpperCamelCase   (    self      :       Tuple,_A      :       int,_A      :       Any,_A      :       Optional[int]    ):







							"""simple docstring"""






							with io.BytesIO() as buffer:
									JsonDatasetWriter(_A,_A,lines=_A,num_proc=2    ).write()
									buffer.seek(0    )
									SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    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 				__UpperCamelCase   (    self      :       Union[str, Any],_A      :       Dict,_A      :       Optional[int],_A      :       Optional[int],_A      :       Dict,_A      :       int    ):







							"""simple docstring"""






							with io.BytesIO() as buffer:
									JsonDatasetWriter(_A,_A,lines=_A,orient=_A,num_proc=2    ).write()
									buffer.seek(0    )
									SCREAMING_SNAKE_CASE_  :		Any				      =    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 				__UpperCamelCase   (    self      :       str,_A      :       List[str]    ):







							"""simple docstring"""






							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 				__UpperCamelCase   (    self      :       Optional[int],_A      :       str,_A      :       Union[str, Any],_A      :       Optional[Any],_A      :       List[Any],_A      :       Optional[int]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str				      =    tmp_path_factory.mktemp("data"    ) / F'test.json.{extension}'
							SCREAMING_SNAKE_CASE_  :		Any				      =    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_  :		Union[str, Any]				      =    f.read()
							with fsspec.open(_A,"rb",compression="infer"    ) as f:
									SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    f.read()
							assert exported_content == original_content








"""simple docstring"""
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


lowerCamelCase_					=      logging.get_logger(__name__)  # pylint: disable=invalid-name







class 			UpperCamelCase_       (__A     ):





	def __init__(      self					: Optional[int]	,	lowerCAmelCase_					: List[Any]	,	lowerCAmelCase_					: Dict=768						)						->			List[Any]:
				super().__init__(lowerCAmelCase_						)
				UpperCAmelCase_     : Dict			     =					proj_size

				UpperCAmelCase_     : Optional[Any]			     =					CLIPVisionModel(lowerCAmelCase_						)
				UpperCAmelCase_     : Tuple			     =					PaintByExampleMapper(lowerCAmelCase_						)
				UpperCAmelCase_     : str			     =					nn.LayerNorm(config.hidden_size						)
				UpperCAmelCase_     : List[Any]			     =					nn.Linear(config.hidden_size	,	self.proj_size						)

				# uncondition for scaling
				UpperCAmelCase_     : Optional[int]			     =					nn.Parameter(torch.randn((1, 1, self.proj_size)						)						)





	def      _SCREAMING_SNAKE_CASE      (      self					: int	,	lowerCAmelCase_					: int	,	lowerCAmelCase_					: Dict=False						)						->			Union[str, Any]:
				UpperCAmelCase_     : Optional[int]			     =					self.model(pixel_values=lowerCAmelCase_						)
				UpperCAmelCase_     : Optional[Any]			     =					clip_output.pooler_output
				UpperCAmelCase_     : List[Any]			     =					self.mapper(latent_states[:, None]						)
				UpperCAmelCase_     : List[str]			     =					self.final_layer_norm(lowerCAmelCase_						)
				UpperCAmelCase_     : Union[str, Any]			     =					self.proj_out(lowerCAmelCase_						)
				if return_uncond_vector:
							return latent_states, self.uncond_vector

				return latent_states







class 			UpperCamelCase_       (nn.Module     ):





	def __init__(      self					: Dict	,	lowerCAmelCase_					: Union[str, Any]						)						->			Tuple:
				super().__init__()
				UpperCAmelCase_     : List[Any]			     =					(config.num_hidden_layers + 1) // 5
				UpperCAmelCase_     : Optional[Any]			     =					config.hidden_size
				UpperCAmelCase_     : List[str]			     =					1
				UpperCAmelCase_     : Union[str, Any]			     =					nn.ModuleList(
				    [
				        BasicTransformerBlock(lowerCAmelCase_	,	lowerCAmelCase_	,	lowerCAmelCase_	,	activation_fn="gelu"	,	attention_bias=lowerCAmelCase_						)
				        for _ in range(lowerCAmelCase_						)
				    ]						)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]	,	lowerCAmelCase_					: List[str]						)						->			str:
				for block in self.blocks:
							UpperCAmelCase_     : int			     =					block(lowerCAmelCase_						)

				return hidden_states



