'''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			(							UpperCamelCase__    ,  UpperCamelCase__    )     ->   Tuple:
    assert isinstance(UpperCamelCase__    ,  UpperCamelCase__    )
    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			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    )     ->   Any:
    __lowerCamelCase          =  tmp_path / '''cache'''
    __lowerCamelCase          =  {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
    with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
        __lowerCamelCase          =  SqlDatasetReader(
            '''dataset'''    ,  '''sqlite:///''' + sqlite_path    ,  cache_dir=UpperCamelCase__    ,  keep_in_memory=UpperCamelCase__    ).read()
    _check_sql_dataset(UpperCamelCase__    ,  UpperCamelCase__    )






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






def __lowerCAmelCase			(							UpperCamelCase__    )     ->   Any:




    with contextlib.closing(sqlitea.connect(UpperCamelCase__    )    ) as con:
        __lowerCamelCase          =  con.cursor()
        cur.execute('''SELECT * FROM dataset'''    )
        for row in cur:
            yield row






@require_sqlalchemy
def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    )     ->   List[Any]:
    __lowerCamelCase          =  tmp_path / '''cache'''
    __lowerCamelCase          =  os.path.join(UpperCamelCase__    ,  '''tmp.sql'''    )
    __lowerCamelCase          =  SqlDatasetReader('''dataset'''    ,  '''sqlite:///''' + sqlite_path    ,  cache_dir=UpperCamelCase__    ).read()
    SqlDatasetWriter(UpperCamelCase__    ,  '''dataset'''    ,  '''sqlite:///''' + output_sqlite_path    ,  num_proc=1    ).write()

    __lowerCamelCase          =  iter_sql_file(UpperCamelCase__    )
    __lowerCamelCase          =  iter_sql_file(UpperCamelCase__    )

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






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

    __lowerCamelCase          =  iter_sql_file(UpperCamelCase__    )
    __lowerCamelCase          =  iter_sql_file(UpperCamelCase__    )

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






@require_sqlalchemy
def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    )     ->   List[Any]:
    __lowerCamelCase          =  tmp_path / '''cache'''
    __lowerCamelCase          =  os.path.join(UpperCamelCase__    ,  '''tmp.sql'''    )
    __lowerCamelCase          =  SqlDatasetReader('''dataset'''    ,  '''sqlite:///''' + sqlite_path    ,  cache_dir=UpperCamelCase__    ).read()
    with pytest.raises(UpperCamelCase__    ):
        SqlDatasetWriter(UpperCamelCase__    ,  '''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    a__				(       UpperCAmelCase__							,       UpperCAmelCase__			):






     @register_to_config
     def __init__(			self		:		Any  ,    *,
         a		:		int = 4  ,    a		:		int = 7_68  ,    a		:		int  ,    a		:		Optional[int]  ,    ):





         """simple docstring"""



         super().__init__()

         __lowerCamelCase          =  nn.Parameter(torch.zeros(a   )   )

         # parameters for additional clip time embeddings
         __lowerCamelCase          =  nn.Linear(a  ,    a   )
         __lowerCamelCase          =  nn.Linear(a  ,    a   )

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






     def        SCREAMING_SNAKE_CASE__       (			self		:		int  ,    *, a		:		Union[str, Any]  ,    a		:		Any  ,    a		:		str  ,    a		:		Any   ):





         """simple docstring"""



         if do_classifier_free_guidance:
             # Add the classifier free guidance embeddings to the image embeddings
             __lowerCamelCase          =  image_embeddings.shape[0]
             __lowerCamelCase          =  self.learned_classifier_free_guidance_embeddings.unsqueeze(0   )
             __lowerCamelCase          =  classifier_free_guidance_embeddings.expand(
                 a  ,    -1   )
             __lowerCamelCase          =  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]

         __lowerCamelCase          =  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, ...
         __lowerCamelCase          =  self.embedding_proj(a   )
         __lowerCamelCase          =  self.clip_image_embeddings_project_to_time_embeddings(a   )
         __lowerCamelCase          =  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"
         __lowerCamelCase          =  self.clip_extra_context_tokens_proj(a   )
         __lowerCamelCase          =  clip_extra_context_tokens.reshape(a  ,    -1  ,    self.clip_extra_context_tokens   )
         __lowerCamelCase          =  clip_extra_context_tokens.permute(0  ,    2  ,    1   )

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

         return text_encoder_hidden_states, additive_clip_time_embeddings
