





'''simple docstring'''







import contextlib
import os
import sqlitea

import pytest

from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy






def  _lowerCamelCase				( lowercase						:      Dict    ,							lowercase						:      Optional[Any]						)	->	str:
				assert isinstance(lowercase    ,							lowercase						)
				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






@require_sqlalchemy
@pytest.mark.parametrize("keep_in_memory"    ,							[False, True]						)
def  _lowerCamelCase				( lowercase						:      Optional[Any]    ,							lowercase						:      List[Any]    ,							lowercase						:      Optional[Any]    ,							lowercase						:      Any						)	->	Optional[Any]:
				_a								= tmp_path / "cache"
				_a								= {"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								= SqlDatasetReader(
								    "dataset"    ,							"sqlite:///" + sqlite_path    ,							cache_dir=lowercase    ,							keep_in_memory=lowercase						).read()
				_check_sql_dataset(lowercase    ,							lowercase						)






@require_sqlalchemy
@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				( lowercase						:      Any    ,							lowercase						:      Optional[int]    ,							lowercase						:      Optional[int]    ,							lowercase						:      int						)	->	Optional[int]:
				_a								= tmp_path / "cache"
				_a								= {"col_1": "string", "col_2": "int64", "col_3": "float64"}
				_a								= features.copy() if features else default_expected_features
				_a								= (
				    Features({feature: Value(lowercase						) for feature, dtype in features.items()}						) if features is not None else None
				)
				_a								= SqlDatasetReader("dataset"    ,							"sqlite:///" + sqlite_path    ,							features=lowercase    ,							cache_dir=lowercase						).read()
				_check_sql_dataset(lowercase    ,							lowercase						)






def  _lowerCamelCase				( lowercase						:      List[str]						)	->	Optional[Any]:
				with contextlib.closing(sqlitea.connect(lowercase						)						) as con:
								_a								= con.cursor()
								cur.execute("SELECT * FROM dataset"						)
								for row in cur:
												yield row






@require_sqlalchemy
def  _lowerCamelCase				( lowercase						:      List[Any]    ,							lowercase						:      Tuple    ,							lowercase						:      Optional[int]						)	->	Optional[Any]:
				_a								= tmp_path / "cache"
				_a								= os.path.join(lowercase    ,							"tmp.sql"						)
				_a								= SqlDatasetReader("dataset"    ,							"sqlite:///" + sqlite_path    ,							cache_dir=lowercase						).read()
				SqlDatasetWriter(lowercase    ,							"dataset"    ,							"sqlite:///" + output_sqlite_path    ,							num_proc=1						).write()

				_a								= iter_sql_file(lowercase						)
				_a								= iter_sql_file(lowercase						)

				for rowa, rowa in zip(lowercase    ,							lowercase						):
								assert rowa == rowa






@require_sqlalchemy
def  _lowerCamelCase				( lowercase						:      Any    ,							lowercase						:      Any    ,							lowercase						:      Union[str, Any]						)	->	List[Any]:
				_a								= tmp_path / "cache"
				_a								= os.path.join(lowercase    ,							"tmp.sql"						)
				_a								= SqlDatasetReader("dataset"    ,							"sqlite:///" + sqlite_path    ,							cache_dir=lowercase						).read()
				SqlDatasetWriter(lowercase    ,							"dataset"    ,							"sqlite:///" + output_sqlite_path    ,							num_proc=2						).write()

				_a								= iter_sql_file(lowercase						)
				_a								= iter_sql_file(lowercase						)

				for rowa, rowa in zip(lowercase    ,							lowercase						):
								assert rowa == rowa






@require_sqlalchemy
def  _lowerCamelCase				( lowercase						:      Optional[Any]    ,							lowercase						:      Optional[int]    ,							lowercase						:      Optional[Any]						)	->	Tuple:
				_a								= tmp_path / "cache"
				_a								= os.path.join(lowercase    ,							"tmp.sql"						)
				_a								= SqlDatasetReader("dataset"    ,							"sqlite:///" + sqlite_path    ,							cache_dir=lowercase						).read()
				with pytest.raises(lowercase						):
								SqlDatasetWriter(lowercase    ,							"dataset"    ,							"sqlite:///" + output_sqlite_path    ,							num_proc=0						).write()







'''simple docstring'''







import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_       ,							lowerCamelCase_      ):






				"""simple docstring"""







				@register_to_config
				def __init__(  self     :					Dict					,				*,
				    __a     :					int = 4					,				__a     :					int = 7_68					,				__a     :					int					,				__a     :					int					,				):
								super().__init__()

								_a								= nn.Parameter(torch.zeros(__a					)					)

								# parameters for additional clip time embeddings
								_a								= nn.Linear(__a					,				__a					)
								_a								= nn.Linear(__a					,				__a					)

								# parameters for encoder hidden states
								_a								= clip_extra_context_tokens
								_a								= nn.Linear(
								    __a					,				self.clip_extra_context_tokens * cross_attention_dim					)
								_a								= nn.Linear(__a					,				__a					)
								_a								= nn.LayerNorm(__a					)




				def   UpperCamelCase__   (  self     :					Optional[Any]					,				*, __a     :					Tuple					,				__a     :					Union[str, Any]					,				__a     :					Any					,				__a     :					List[Any]					):
								if do_classifier_free_guidance:
												# Add the classifier free guidance embeddings to the image embeddings
												_a								= image_embeddings.shape[0]
												_a								= self.learned_classifier_free_guidance_embeddings.unsqueeze(0					)
												_a								= classifier_free_guidance_embeddings.expand(
												    __a					,				-1					)
												_a								= torch.cat([classifier_free_guidance_embeddings, image_embeddings]					,				dim=0					)

								# The image embeddings batch size and the text embeddings batch size are equal
								assert image_embeddings.shape[0] == prompt_embeds.shape[0]

								_a								= prompt_embeds.shape[0]

								# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
								# adding CLIP embeddings to the existing timestep embedding, ...
								_a								= self.embedding_proj(__a					)
								_a								= self.clip_image_embeddings_project_to_time_embeddings(__a					)
								_a								= time_projected_image_embeddings + time_projected_prompt_embeds

								# ... and by projecting CLIP embeddings into four
								# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
								_a								= self.clip_extra_context_tokens_proj(__a					)
								_a								= clip_extra_context_tokens.reshape(__a					,				-1					,				self.clip_extra_context_tokens					)
								_a								= clip_extra_context_tokens.permute(0					,				2					,				1					)

								_a								= self.encoder_hidden_states_proj(__a					)
								_a								= self.text_encoder_hidden_states_norm(__a					)
								_a								= torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]					,				dim=1					)

								return text_encoder_hidden_states, additive_clip_time_embeddings
