"""simple docstring"""





from typing import Optional, Tuple, Union

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict

from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
    FlaxCrossAttnDownBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
)


@flax.struct.dataclass
class   UpperCAmelCase_			(					_a):
      lowerCamelCase__	:					jnp.ndarray
      lowerCamelCase__	:					jnp.ndarray





class   UpperCAmelCase_			(					nn.Module):
      lowerCamelCase__	:					int
      lowerCamelCase__	:					Tuple[int]										=						(1_6, 3_2, 9_6, 2_5_6)
      lowerCamelCase__	:					jnp.dtype										=						jnp.floataa


      def 	_UpperCAmelCase (     self							) ->      str:
             lowercase__    :	Dict     					=			nn.Conv(
                 self.block_out_channels[0]	,    kernel_size=(3, 3)	,    padding=((1, 1), (1, 1))	,    dtype=self.dtype	,    )

             lowercase__    :	str     					=			[]
             for i in range(len(self.block_out_channels							) - 1							):
                    lowercase__    :	str     					=			self.block_out_channels[i]
                    lowercase__    :	Optional[Any]     					=			self.block_out_channels[i + 1]
                    lowercase__    :	str     					=			nn.Conv(
                        a	,    kernel_size=(3, 3)	,    padding=((1, 1), (1, 1))	,    dtype=self.dtype	,    )
                    blocks.append(a							)
                    lowercase__    :	Optional[int]     					=			nn.Conv(
                        a	,    kernel_size=(3, 3)	,    strides=(2, 2)	,    padding=((1, 1), (1, 1))	,    dtype=self.dtype	,    )
                    blocks.append(a							)
             lowercase__    :	Optional[Any]     					=			blocks

             lowercase__    :	Any     					=			nn.Conv(
                 self.conditioning_embedding_channels	,    kernel_size=(3, 3)	,    padding=((1, 1), (1, 1))	,    kernel_init=nn.initializers.zeros_init()	,    bias_init=nn.initializers.zeros_init()	,    dtype=self.dtype	,    )


      def __call__(     self	,    a							) ->      str:
             lowercase__    :	Optional[Any]     					=			self.conv_in(a							)
             lowercase__    :	Tuple     					=			nn.silu(a							)

             for block in self.blocks:
                    lowercase__    :	Dict     					=			block(a							)
                    lowercase__    :	int     					=			nn.silu(a							)

             lowercase__    :	Tuple     					=			self.conv_out(a							)

             return embedding





@flax_register_to_config
class   UpperCAmelCase_			(					nn.Module				,							_a				,							_a):
      lowerCamelCase__	:					int										=						3_2
      lowerCamelCase__	:					int										=						4
      lowerCamelCase__	:					Tuple[str]										=						(
          "CrossAttnDownBlock2D",
          "CrossAttnDownBlock2D",
          "CrossAttnDownBlock2D",
          "DownBlock2D",
      )
      lowerCamelCase__	:					Union[bool, Tuple[bool]]										=						False
      lowerCamelCase__	:					Tuple[int]										=						(3_2_0, 6_4_0, 1_2_8_0, 1_2_8_0)
      lowerCamelCase__	:					int										=						2
      lowerCamelCase__	:					Union[int, Tuple[int]]										=						8
      lowerCamelCase__	:					Optional[Union[int, Tuple[int]]]										=						None
      lowerCamelCase__	:					int										=						1_2_8_0
      lowerCamelCase__	:					float										=						0.0
      lowerCamelCase__	:					bool										=						False
      lowerCamelCase__	:					jnp.dtype										=						jnp.floataa
      lowerCamelCase__	:					bool										=						True
      lowerCamelCase__	:					int										=						0
      lowerCamelCase__	:					str										=						"rgb"
      lowerCamelCase__	:					Tuple[int]										=						(1_6, 3_2, 9_6, 2_5_6)


      def 	_UpperCAmelCase (     self	,    a							) ->      FrozenDict:
             # init input tensors
             lowercase__    :	Optional[int]     					=			(1, self.in_channels, self.sample_size, self.sample_size)
             lowercase__    :	Union[str, Any]     					=			jnp.zeros(a	,    dtype=jnp.floataa							)
             lowercase__    :	Dict     					=			jnp.ones((1,)	,    dtype=jnp.intaa							)
             lowercase__    :	Dict     					=			jnp.zeros((1, 1, self.cross_attention_dim)	,    dtype=jnp.floataa							)
             lowercase__    :	str     					=			(1, 3, self.sample_size * 8, self.sample_size * 8)
             lowercase__    :	str     					=			jnp.zeros(a	,    dtype=jnp.floataa							)

             lowercase__					,   lowercase__    :	Union[str, Any]     					=			jax.random.split(a							)
             lowercase__    :	List[str]     					=			{'params': params_rng, 'dropout': dropout_rng}

             return self.init(a	,    a	,    a	,    a	,    a							)["params"]


      def 	_UpperCAmelCase (     self							) ->      Any:
             lowercase__    :	Tuple     					=			self.block_out_channels
             lowercase__    :	Any     					=			block_out_channels[0] * 4

             # If `num_attention_heads` is not defined (which is the case for most models)
             # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
             # The reason for this behavior is to correct for incorrectly named variables that were introduced
             # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
             # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
             # which is why we correct for the naming here.
             lowercase__    :	List[str]     					=			self.num_attention_heads or self.attention_head_dim

             # input
             lowercase__    :	int     					=			nn.Conv(
                 block_out_channels[0]	,    kernel_size=(3, 3)	,    strides=(1, 1)	,    padding=((1, 1), (1, 1))	,    dtype=self.dtype	,    )

             # time
             lowercase__    :	str     					=			FlaxTimesteps(
                 block_out_channels[0]	,    flip_sin_to_cos=self.flip_sin_to_cos	,    freq_shift=self.config.freq_shift							)
             lowercase__    :	Any     					=			FlaxTimestepEmbedding(a	,    dtype=self.dtype							)

             lowercase__    :	Union[str, Any]     					=			FlaxControlNetConditioningEmbedding(
                 conditioning_embedding_channels=block_out_channels[0]	,    block_out_channels=self.conditioning_embedding_out_channels	,    )

             lowercase__    :	Dict     					=			self.only_cross_attention
             if isinstance(a	,    a							):
                    lowercase__    :	int     					=			(only_cross_attention,) * len(self.down_block_types							)

             if isinstance(a	,    a							):
                    lowercase__    :	Tuple     					=			(num_attention_heads,) * len(self.down_block_types							)

             # down
             lowercase__    :	int     					=			[]
             lowercase__    :	List[Any]     					=			[]

             lowercase__    :	Union[str, Any]     					=			block_out_channels[0]

             lowercase__    :	Dict     					=			nn.Conv(
                 a	,    kernel_size=(1, 1)	,    padding='VALID'	,    kernel_init=nn.initializers.zeros_init()	,    bias_init=nn.initializers.zeros_init()	,    dtype=self.dtype	,    )
             controlnet_down_blocks.append(a							)

             for i, down_block_type in enumerate(self.down_block_types							):
                    lowercase__    :	List[Any]     					=			output_channel
                    lowercase__    :	Tuple     					=			block_out_channels[i]
                    lowercase__    :	List[str]     					=			i == len(a							) - 1

                    if down_block_type == "CrossAttnDownBlock2D":
                           lowercase__    :	Optional[Any]     					=			FlaxCrossAttnDownBlockaD(
                               in_channels=a	,    out_channels=a	,    dropout=self.dropout	,    num_layers=self.layers_per_block	,    num_attention_heads=num_attention_heads[i]	,    add_downsample=not is_final_block	,    use_linear_projection=self.use_linear_projection	,    only_cross_attention=only_cross_attention[i]	,    dtype=self.dtype	,    )
                    else:
                           lowercase__    :	Tuple     					=			FlaxDownBlockaD(
                               in_channels=a	,    out_channels=a	,    dropout=self.dropout	,    num_layers=self.layers_per_block	,    add_downsample=not is_final_block	,    dtype=self.dtype	,    )

                    down_blocks.append(a							)

                    for _ in range(self.layers_per_block							):
                           lowercase__    :	Tuple     					=			nn.Conv(
                               a	,    kernel_size=(1, 1)	,    padding='VALID'	,    kernel_init=nn.initializers.zeros_init()	,    bias_init=nn.initializers.zeros_init()	,    dtype=self.dtype	,    )
                           controlnet_down_blocks.append(a							)

                    if not is_final_block:
                           lowercase__    :	Dict     					=			nn.Conv(
                               a	,    kernel_size=(1, 1)	,    padding='VALID'	,    kernel_init=nn.initializers.zeros_init()	,    bias_init=nn.initializers.zeros_init()	,    dtype=self.dtype	,    )
                           controlnet_down_blocks.append(a							)

             lowercase__    :	int     					=			down_blocks
             lowercase__    :	Any     					=			controlnet_down_blocks

             # mid
             lowercase__    :	str     					=			block_out_channels[-1]
             lowercase__    :	Tuple     					=			FlaxUNetMidBlockaDCrossAttn(
                 in_channels=a	,    dropout=self.dropout	,    num_attention_heads=num_attention_heads[-1]	,    use_linear_projection=self.use_linear_projection	,    dtype=self.dtype	,    )

             lowercase__    :	int     					=			nn.Conv(
                 a	,    kernel_size=(1, 1)	,    padding='VALID'	,    kernel_init=nn.initializers.zeros_init()	,    bias_init=nn.initializers.zeros_init()	,    dtype=self.dtype	,    )


      def __call__(     self	,    a	,    a	,    a	,    a	,    a = 1.0	,    a = True	,    a = False	,    ) ->      Union[FlaxControlNetOutput, Tuple]:
             lowercase__    :	Optional[int]     					=			self.controlnet_conditioning_channel_order
             if channel_order == "bgr":
                    lowercase__    :	List[Any]     					=			jnp.flip(a	,    axis=1							)

             # 1. time
             if not isinstance(a	,    jnp.ndarray							):
                    lowercase__    :	Union[str, Any]     					=			jnp.array([timesteps]	,    dtype=jnp.intaa							)
             elif isinstance(a	,    jnp.ndarray							) and len(timesteps.shape							) == 0:
                    lowercase__    :	Optional[Any]     					=			timesteps.astype(dtype=jnp.floataa							)
                    lowercase__    :	str     					=			jnp.expand_dims(a	,    0							)

             lowercase__    :	Tuple     					=			self.time_proj(a							)
             lowercase__    :	List[str]     					=			self.time_embedding(a							)

             # 2. pre-process
             lowercase__    :	Optional[Any]     					=			jnp.transpose(a	,    (0, 2, 3, 1)							)
             lowercase__    :	List[Any]     					=			self.conv_in(a							)

             lowercase__    :	Optional[int]     					=			jnp.transpose(a	,    (0, 2, 3, 1)							)
             lowercase__    :	Optional[Any]     					=			self.controlnet_cond_embedding(a							)
             sample += controlnet_cond

             # 3. down
             lowercase__    :	Union[str, Any]     					=			(sample,)
             for down_block in self.down_blocks:
                    if isinstance(a	,    a							):
                           lowercase__					,   lowercase__    :	Any     					=			down_block(a	,    a	,    a	,    deterministic=not train							)
                    else:
                           lowercase__					,   lowercase__    :	Tuple     					=			down_block(a	,    a	,    deterministic=not train							)
                    down_block_res_samples += res_samples

             # 4. mid
             lowercase__    :	Dict     					=			self.mid_block(a	,    a	,    a	,    deterministic=not train							)

             # 5. contronet blocks
             lowercase__    :	Dict     					=			()
             for down_block_res_sample, controlnet_block in zip(a	,    self.controlnet_down_blocks							):
                    lowercase__    :	List[str]     					=			controlnet_block(a							)
                    controlnet_down_block_res_samples += (down_block_res_sample,)

             lowercase__    :	Dict     					=			controlnet_down_block_res_samples

             lowercase__    :	Union[str, Any]     					=			self.controlnet_mid_block(a							)

             # 6. scaling
             lowercase__    :	int     					=			[sample * conditioning_scale for sample in down_block_res_samples]
             mid_block_res_sample *= conditioning_scale

             if not return_dict:
                    return (down_block_res_samples, mid_block_res_sample)

             return FlaxControlNetOutput(
                 down_block_res_samples=a	,    mid_block_res_sample=a							)


"""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

