

'''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       snake_case   (	snake_case      : Any					,		snake_case      : int			) ->  List[Any]:


    """simple docstring"""


    assert isinstance(snake_case					,		snake_case			)
    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       snake_case   (	snake_case      : Any					,		snake_case      : Dict					,		snake_case      : List[str]					,		snake_case      : Union[str, Any]			) ->  Optional[Any]:


    """simple docstring"""


    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=snake_case					,		keep_in_memory=snake_case			).read()
    _check_sql_dataset(snake_case					,		snake_case			)


@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       snake_case   (	snake_case      : Union[str, Any]					,		snake_case      : Union[str, Any]					,		snake_case      : List[Any]					,		snake_case      : Optional[Any]			) ->  Optional[int]:


    """simple docstring"""


    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(snake_case			) for feature, dtype in features.items()}			) if features is not None else None
    )
    lowerCAmelCase 						=							SqlDatasetReader('dataset'					,		'sqlite:///' + sqlite_path					,		features=snake_case					,		cache_dir=snake_case			).read()
    _check_sql_dataset(snake_case					,		snake_case			)


def       snake_case   (	snake_case      : Tuple			) ->  Optional[Any]:


    """simple docstring"""


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


@require_sqlalchemy
def       snake_case   (	snake_case      : Optional[int]					,		snake_case      : Optional[int]					,		snake_case      : List[str]			) ->  Optional[int]:


    """simple docstring"""


    lowerCAmelCase 						=							tmp_path / 'cache'
    lowerCAmelCase 						=							os.path.join(snake_case					,		'tmp.sql'			)
    lowerCAmelCase 						=							SqlDatasetReader('dataset'					,		'sqlite:///' + sqlite_path					,		cache_dir=snake_case			).read()
    SqlDatasetWriter(snake_case					,		'dataset'					,		'sqlite:///' + output_sqlite_path					,		num_proc=1			).write()

    lowerCAmelCase 						=							iter_sql_file(snake_case			)
    lowerCAmelCase 						=							iter_sql_file(snake_case			)

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


@require_sqlalchemy
def       snake_case   (	snake_case      : Dict					,		snake_case      : Tuple					,		snake_case      : Dict			) ->  Union[str, Any]:


    """simple docstring"""


    lowerCAmelCase 						=							tmp_path / 'cache'
    lowerCAmelCase 						=							os.path.join(snake_case					,		'tmp.sql'			)
    lowerCAmelCase 						=							SqlDatasetReader('dataset'					,		'sqlite:///' + sqlite_path					,		cache_dir=snake_case			).read()
    SqlDatasetWriter(snake_case					,		'dataset'					,		'sqlite:///' + output_sqlite_path					,		num_proc=2			).write()

    lowerCAmelCase 						=							iter_sql_file(snake_case			)
    lowerCAmelCase 						=							iter_sql_file(snake_case			)

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


@require_sqlalchemy
def       snake_case   (	snake_case      : Any					,		snake_case      : Optional[int]					,		snake_case      : Any			) ->  Any:


    """simple docstring"""


    lowerCAmelCase 						=							tmp_path / 'cache'
    lowerCAmelCase 						=							os.path.join(snake_case					,		'tmp.sql'			)
    lowerCAmelCase 						=							SqlDatasetReader('dataset'					,		'sqlite:///' + sqlite_path					,		cache_dir=snake_case			).read()
    with pytest.raises(snake_case			):
        SqlDatasetWriter(snake_case					,		'dataset'					,		'sqlite:///' + output_sqlite_path					,		num_proc=0			).write()








'''simple docstring'''



import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
     import jax
     import jax.numpy as jnp
     from flax.jax_utils import replicate
     from flax.training.common_utils import shard



@slow
@require_flax
class 				_snake_case					(   unittest.TestCase			):

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      super().tearDown()
      gc.collect()

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase						,   lowerCAmelCase 						=							FlaxControlNetModel.from_pretrained(
          'lllyasviel/sd-controlnet-canny'      , from_pt=_SCREAMING_SNAKE_CASE      , dtype=jnp.bfloataa	)
      lowerCAmelCase						,   lowerCAmelCase 						=							FlaxStableDiffusionControlNetPipeline.from_pretrained(
          'runwayml/stable-diffusion-v1-5'      , controlnet=_SCREAMING_SNAKE_CASE      , from_pt=_SCREAMING_SNAKE_CASE      , dtype=jnp.bfloataa	)
      lowerCAmelCase 						=							controlnet_params

      lowerCAmelCase 						=							'bird'
      lowerCAmelCase 						=							jax.device_count()
      lowerCAmelCase 						=							pipe.prepare_text_inputs([prompts] * num_samples	)

      lowerCAmelCase 						=							load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'	)
      lowerCAmelCase 						=							pipe.prepare_image_inputs([canny_image] * num_samples	)

      lowerCAmelCase 						=							jax.random.PRNGKey(0	)
      lowerCAmelCase 						=							jax.random.split(_SCREAMING_SNAKE_CASE      , jax.device_count()	)

      lowerCAmelCase 						=							replicate(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							shard(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							shard(_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							pipe(
          prompt_ids=_SCREAMING_SNAKE_CASE      , image=_SCREAMING_SNAKE_CASE      , params=_SCREAMING_SNAKE_CASE      , prng_seed=_SCREAMING_SNAKE_CASE      , num_inference_steps=50      , jit=_SCREAMING_SNAKE_CASE      , ).images
      assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

      lowerCAmelCase 						=							images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]	)
      lowerCAmelCase 						=							images[0, 2_53:2_56, 2_53:2_56, -1]

      lowerCAmelCase 						=							jnp.asarray(jax.device_get(image_slice.flatten()	)	)
      lowerCAmelCase 						=							jnp.array(
          [0.167_969, 0.116_699, 0.081_543, 0.154_297, 0.132_812, 0.108_887, 0.169_922, 0.169_922, 0.205_078]	)
      print(F'output_slice: {output_slice}'	)
      assert jnp.abs(output_slice - expected_slice	).max() < 1e-2


  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase						,   lowerCAmelCase 						=							FlaxControlNetModel.from_pretrained(
          'lllyasviel/sd-controlnet-openpose'      , from_pt=_SCREAMING_SNAKE_CASE      , dtype=jnp.bfloataa	)
      lowerCAmelCase						,   lowerCAmelCase 						=							FlaxStableDiffusionControlNetPipeline.from_pretrained(
          'runwayml/stable-diffusion-v1-5'      , controlnet=_SCREAMING_SNAKE_CASE      , from_pt=_SCREAMING_SNAKE_CASE      , dtype=jnp.bfloataa	)
      lowerCAmelCase 						=							controlnet_params

      lowerCAmelCase 						=							'Chef in the kitchen'
      lowerCAmelCase 						=							jax.device_count()
      lowerCAmelCase 						=							pipe.prepare_text_inputs([prompts] * num_samples	)

      lowerCAmelCase 						=							load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png'	)
      lowerCAmelCase 						=							pipe.prepare_image_inputs([pose_image] * num_samples	)

      lowerCAmelCase 						=							jax.random.PRNGKey(0	)
      lowerCAmelCase 						=							jax.random.split(_SCREAMING_SNAKE_CASE      , jax.device_count()	)

      lowerCAmelCase 						=							replicate(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							shard(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							shard(_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							pipe(
          prompt_ids=_SCREAMING_SNAKE_CASE      , image=_SCREAMING_SNAKE_CASE      , params=_SCREAMING_SNAKE_CASE      , prng_seed=_SCREAMING_SNAKE_CASE      , num_inference_steps=50      , jit=_SCREAMING_SNAKE_CASE      , ).images
      assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

      lowerCAmelCase 						=							images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]	)
      lowerCAmelCase 						=							images[0, 2_53:2_56, 2_53:2_56, -1]

      lowerCAmelCase 						=							jnp.asarray(jax.device_get(image_slice.flatten()	)	)
      lowerCAmelCase 						=							jnp.array(
          [[0.271_484, 0.261_719, 0.275_391, 0.277_344, 0.279_297, 0.291_016, 0.294_922, 0.302_734, 0.302_734]]	)
      print(F'output_slice: {output_slice}'	)
      assert jnp.abs(output_slice - expected_slice	).max() < 1e-2





