"""simple docstring"""





from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


_UpperCamelCase     :		str											=					logging.get_logger(__name__)


_UpperCamelCase     :		Optional[int]											=					TypeVar("DatasetType", Dataset, IterableDataset)





def        a_  (   _lowerCAmelCase						:				List[DatasetType]     ,				_lowerCAmelCase						:				Optional[List[float]] = None     ,				_lowerCAmelCase						:				Optional[int] = None     ,				_lowerCAmelCase						:				Optional[DatasetInfo] = None     ,				_lowerCAmelCase						:				Optional[NamedSplit] = None     ,				_lowerCAmelCase						:				Literal["first_exhausted", "all_exhausted"] = "first_exhausted"     ,				):





       '''simple docstring'''

       from .arrow_dataset import Dataset
       from .iterable_dataset import IterableDataset

       if not datasets:
              raise ValueError('Unable to interleave an empty list of datasets.'  )
       for i, dataset in enumerate(_lowerCAmelCase  ):
              if not isinstance(_lowerCAmelCase     ,				(Dataset, IterableDataset)  ):
                     if isinstance(_lowerCAmelCase     ,				(DatasetDict, IterableDatasetDict)  ):
                            if not dataset:
                                   raise ValueError(
                                       f"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
                                       'is an empty dataset dictionary.'  )
                            raise ValueError(
                                f"""Dataset at position {i} has at least one split: {list(_lowerCAmelCase  )}\n"""
                                f"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(_lowerCAmelCase  )  )}']"""  )
                     raise ValueError(
                         f"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_lowerCAmelCase  ).__name__}."""  )
              if i == 0:
                     lowercase__					,   lowercase__    :	Union[str, Any]     					=			(
                         (Dataset, IterableDataset) if isinstance(_lowerCAmelCase     ,				_lowerCAmelCase  ) else (IterableDataset, Dataset)
                     )
              elif not isinstance(_lowerCAmelCase     ,				_lowerCAmelCase  ):
                     raise ValueError(
                         f"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."""  )
       if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
              raise ValueError(f"""{stopping_strategy} is not supported. Please enter a valid stopping_strategy."""  )
       if dataset_type is Dataset:
              return _interleave_map_style_datasets(
                  _lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase     ,				info=_lowerCAmelCase     ,				split=_lowerCAmelCase     ,				stopping_strategy=_lowerCAmelCase  )
       else:
              return _interleave_iterable_datasets(
                  _lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase     ,				info=_lowerCAmelCase     ,				split=_lowerCAmelCase     ,				stopping_strategy=_lowerCAmelCase  )






def        a_  (   _lowerCAmelCase						:				List[DatasetType]     ,				_lowerCAmelCase						:				Optional[DatasetInfo] = None     ,				_lowerCAmelCase						:				Optional[NamedSplit] = None     ,				_lowerCAmelCase						:				int = 0     ,				):





       '''simple docstring'''

       if not dsets:
              raise ValueError('Unable to concatenate an empty list of datasets.'  )
       for i, dataset in enumerate(_lowerCAmelCase  ):
              if not isinstance(_lowerCAmelCase     ,				(Dataset, IterableDataset)  ):
                     if isinstance(_lowerCAmelCase     ,				(DatasetDict, IterableDatasetDict)  ):
                            if not dataset:
                                   raise ValueError(
                                       f"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
                                       'is an empty dataset dictionary.'  )
                            raise ValueError(
                                f"""Dataset at position {i} has at least one split: {list(_lowerCAmelCase  )}\n"""
                                f"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(_lowerCAmelCase  )  )}']"""  )
                     raise ValueError(
                         f"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_lowerCAmelCase  ).__name__}."""  )
              if i == 0:
                     lowercase__					,   lowercase__    :	Dict     					=			(
                         (Dataset, IterableDataset) if isinstance(_lowerCAmelCase     ,				_lowerCAmelCase  ) else (IterableDataset, Dataset)
                     )
              elif not isinstance(_lowerCAmelCase     ,				_lowerCAmelCase  ):
                     raise ValueError(
                         f"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."""  )
       if dataset_type is Dataset:
              return _concatenate_map_style_datasets(_lowerCAmelCase     ,				info=_lowerCAmelCase     ,				split=_lowerCAmelCase     ,				axis=_lowerCAmelCase  )
       else:
              return _concatenate_iterable_datasets(_lowerCAmelCase     ,				info=_lowerCAmelCase     ,				split=_lowerCAmelCase     ,				axis=_lowerCAmelCase  )


"""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   UpperCAmelCase_			(					unittest.TestCase):


      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             # clean up the VRAM after each test
             super().tearDown()
             gc.collect()


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             lowercase__					,   lowercase__    :	str     					=			FlaxControlNetModel.from_pretrained(
                 'lllyasviel/sd-controlnet-canny'	,    from_pt=a	,    dtype=jnp.bfloataa							)
             lowercase__					,   lowercase__    :	List[str]     					=			FlaxStableDiffusionControlNetPipeline.from_pretrained(
                 'runwayml/stable-diffusion-v1-5'	,    controlnet=a	,    from_pt=a	,    dtype=jnp.bfloataa							)
             lowercase__    :	List[Any]     					=			controlnet_params

             lowercase__    :	int     					=			'bird'
             lowercase__    :	List[Any]     					=			jax.device_count()
             lowercase__    :	Dict     					=			pipe.prepare_text_inputs([prompts] * num_samples							)

             lowercase__    :	Union[str, Any]     					=			load_image(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'							)
             lowercase__    :	Optional[int]     					=			pipe.prepare_image_inputs([canny_image] * num_samples							)

             lowercase__    :	List[Any]     					=			jax.random.PRNGKey(0							)
             lowercase__    :	Tuple     					=			jax.random.split(a	,    jax.device_count()							)

             lowercase__    :	str     					=			replicate(a							)
             lowercase__    :	List[str]     					=			shard(a							)
             lowercase__    :	Dict     					=			shard(a							)

             lowercase__    :	List[Any]     					=			pipe(
                 prompt_ids=a	,    image=a	,    params=a	,    prng_seed=a	,    num_inference_steps=5_0	,    jit=a	,    ).images
             assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)

             lowercase__    :	Any     					=			images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
             lowercase__    :	Tuple     					=			images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

             lowercase__    :	int     					=			jnp.asarray(jax.device_get(image_slice.flatten()							)							)
             lowercase__    :	Optional[Any]     					=			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 	_UpperCAmelCase (     self							) ->      List[str]:
             lowercase__					,   lowercase__    :	int     					=			FlaxControlNetModel.from_pretrained(
                 'lllyasviel/sd-controlnet-openpose'	,    from_pt=a	,    dtype=jnp.bfloataa							)
             lowercase__					,   lowercase__    :	Optional[Any]     					=			FlaxStableDiffusionControlNetPipeline.from_pretrained(
                 'runwayml/stable-diffusion-v1-5'	,    controlnet=a	,    from_pt=a	,    dtype=jnp.bfloataa							)
             lowercase__    :	Optional[Any]     					=			controlnet_params

             lowercase__    :	List[Any]     					=			'Chef in the kitchen'
             lowercase__    :	List[str]     					=			jax.device_count()
             lowercase__    :	Dict     					=			pipe.prepare_text_inputs([prompts] * num_samples							)

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

             lowercase__    :	List[str]     					=			jax.random.PRNGKey(0							)
             lowercase__    :	str     					=			jax.random.split(a	,    jax.device_count()							)

             lowercase__    :	Optional[Any]     					=			replicate(a							)
             lowercase__    :	Optional[Any]     					=			shard(a							)
             lowercase__    :	List[Any]     					=			shard(a							)

             lowercase__    :	List[Any]     					=			pipe(
                 prompt_ids=a	,    image=a	,    params=a	,    prng_seed=a	,    num_inference_steps=5_0	,    jit=a	,    ).images
             assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)

             lowercase__    :	Union[str, Any]     					=			images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
             lowercase__    :	List[str]     					=			images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

             lowercase__    :	Optional[int]     					=			jnp.asarray(jax.device_get(image_slice.flatten()							)							)
             lowercase__    :	str     					=			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

