

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 	__A    (				__lowerCamelCase							):


		"""simple docstring"""




		a__					       =  42
		a__					       =  42







class 	__A    (				nn.Module							):


		"""simple docstring"""




		a__					       =  42
		a__					       =  (1_6, 3_2, 9_6, 2_5_6)
		a__					       =  jnp.floataa


		def 		A__					(				self):
			_UpperCamelCase   :		Union[str, Any]			=						nn.Conv(
			    self.block_out_channels[0]	,       kernel_size=(3, 3)	,       padding=((1, 1), (1, 1))	,       dtype=self.dtype	,       )

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

			_UpperCamelCase   :		int			=						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	,       __snake_case):
			_UpperCamelCase   :		Optional[Any]			=						self.conv_in(a_)
			_UpperCamelCase   :		Optional[Any]			=						nn.silu(a_)

			for block in self.blocks:
				_UpperCamelCase   :		str			=						block(a_)
				_UpperCamelCase   :		str			=						nn.silu(a_)

			_UpperCamelCase   :		Any			=						self.conv_out(a_)

			return embedding







@flax_register_to_config
class 	__A    (				nn.Module    ,							__lowerCamelCase    ,							__lowerCamelCase							):


		"""simple docstring"""




		a__					       =  3_2
		a__					       =  4
		a__					       =  (
		    "CrossAttnDownBlock2D",
		    "CrossAttnDownBlock2D",
		    "CrossAttnDownBlock2D",
		    "DownBlock2D",
		)
		a__					       =  False
		a__					       =  (3_2_0, 6_4_0, 1_2_8_0, 1_2_8_0)
		a__					       =  2
		a__					       =  8
		a__					       =  None
		a__					       =  1_2_8_0
		a__					       =  0.0
		a__					       =  False
		a__					       =  jnp.floataa
		a__					       =  True
		a__					       =  0
		a__					       =  "rgb"
		a__					       =  (1_6, 3_2, 9_6, 2_5_6)


		def 		A__					(				self	,       __snake_case):
			# init input tensors
			_UpperCamelCase   :		Tuple			=						(1, self.in_channels, self.sample_size, self.sample_size)
			_UpperCamelCase   :		Union[str, Any]			=						jnp.zeros(a_	,       dtype=jnp.floataa)
			_UpperCamelCase   :		int			=						jnp.ones((1,)	,       dtype=jnp.intaa)
			_UpperCamelCase   :		int			=						jnp.zeros((1, 1, self.cross_attention_dim)	,       dtype=jnp.floataa)
			_UpperCamelCase   :		Union[str, Any]			=						(1, 3, self.sample_size * 8, self.sample_size * 8)
			_UpperCamelCase   :		List[str]			=						jnp.zeros(a_	,       dtype=jnp.floataa)

			_UpperCamelCase   :		List[str]			=						jax.random.split(a_)
			_UpperCamelCase   :		Dict			=						{"params": params_rng, "dropout": dropout_rng}

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


		def 		A__					(				self):
			_UpperCamelCase   :		Tuple			=						self.block_out_channels
			_UpperCamelCase   :		Optional[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.
			_UpperCamelCase   :		List[str]			=						self.num_attention_heads or self.attention_head_dim

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

			# time
			_UpperCamelCase   :		Union[str, Any]			=						FlaxTimesteps(
			    block_out_channels[0]	,       flip_sin_to_cos=self.flip_sin_to_cos	,       freq_shift=self.config.freq_shift)
			_UpperCamelCase   :		int			=						FlaxTimestepEmbedding(a_	,       dtype=self.dtype)

			_UpperCamelCase   :		List[str]			=						FlaxControlNetConditioningEmbedding(
			    conditioning_embedding_channels=block_out_channels[0]	,       block_out_channels=self.conditioning_embedding_out_channels	,       )

			_UpperCamelCase   :		Dict			=						self.only_cross_attention
			if isinstance(a_	,       a_):
				_UpperCamelCase   :		List[Any]			=						(only_cross_attention,) * len(self.down_block_types)

			if isinstance(a_	,       a_):
				_UpperCamelCase   :		Optional[Any]			=						(num_attention_heads,) * len(self.down_block_types)

			# down
			_UpperCamelCase   :		Tuple			=						[]
			_UpperCamelCase   :		List[str]			=						[]

			_UpperCamelCase   :		Dict			=						block_out_channels[0]

			_UpperCamelCase   :		Any			=						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):
				_UpperCamelCase   :		List[str]			=						output_channel
				_UpperCamelCase   :		Any			=						block_out_channels[i]
				_UpperCamelCase   :		Optional[Any]			=						i == len(a_) - 1

				if down_block_type == "CrossAttnDownBlock2D":
					_UpperCamelCase   :		Dict			=						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:
					_UpperCamelCase   :		Dict			=						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):
					_UpperCamelCase   :		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_)

				if not is_final_block:
					_UpperCamelCase   :		Union[str, Any]			=						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_)

			_UpperCamelCase   :		List[str]			=						down_blocks
			_UpperCamelCase   :		Union[str, Any]			=						controlnet_down_blocks

			# mid
			_UpperCamelCase   :		Optional[int]			=						block_out_channels[-1]
			_UpperCamelCase   :		List[str]			=						FlaxUNetMidBlockaDCrossAttn(
			    in_channels=a_	,       dropout=self.dropout	,       num_attention_heads=num_attention_heads[-1]	,       use_linear_projection=self.use_linear_projection	,       dtype=self.dtype	,       )

			_UpperCamelCase   :		str			=						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	,       __snake_case	,       __snake_case	,       __snake_case	,       __snake_case	,       __snake_case = 1.0	,       __snake_case = True	,       __snake_case = False	,       ):
			_UpperCamelCase   :		int			=						self.controlnet_conditioning_channel_order
			if channel_order == "bgr":
				_UpperCamelCase   :		int			=						jnp.flip(a_	,       axis=1)

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

			_UpperCamelCase   :		Optional[int]			=						self.time_proj(a_)
			_UpperCamelCase   :		Any			=						self.time_embedding(a_)

			# 2. pre-process
			_UpperCamelCase   :		Union[str, Any]			=						jnp.transpose(a_	,       (0, 2, 3, 1))
			_UpperCamelCase   :		Tuple			=						self.conv_in(a_)

			_UpperCamelCase   :		Tuple			=						jnp.transpose(a_	,       (0, 2, 3, 1))
			_UpperCamelCase   :		int			=						self.controlnet_cond_embedding(a_)
			sample += controlnet_cond

			# 3. down
			_UpperCamelCase   :		str			=						(sample,)
			for down_block in self.down_blocks:
				if isinstance(a_	,       a_):
					_UpperCamelCase   :		Dict			=						down_block(a_	,       a_	,       a_	,       deterministic=not train)
				else:
					_UpperCamelCase   :		Tuple			=						down_block(a_	,       a_	,       deterministic=not train)
				down_block_res_samples += res_samples

			# 4. mid
			_UpperCamelCase   :		List[str]			=						self.mid_block(a_	,       a_	,       a_	,       deterministic=not train)

			# 5. contronet blocks
			_UpperCamelCase   :		Union[str, Any]			=						()
			for down_block_res_sample, controlnet_block in zip(a_	,       self.controlnet_down_blocks):
				_UpperCamelCase   :		Optional[int]			=						controlnet_block(a_)
				controlnet_down_block_res_samples += (down_block_res_sample,)

			_UpperCamelCase   :		List[Any]			=						controlnet_down_block_res_samples

			_UpperCamelCase   :		Any			=						self.controlnet_mid_block(a_)

			# 6. scaling
			_UpperCamelCase   :		Optional[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_)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCAmelCase__								=		{
    """configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
    """tokenization_canine""": ["""CanineTokenizer"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase__								=		[
							    """CANINE_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """CanineForMultipleChoice""",
							    """CanineForQuestionAnswering""",
							    """CanineForSequenceClassification""",
							    """CanineForTokenClassification""",
							    """CanineLayer""",
							    """CanineModel""",
							    """CaninePreTrainedModel""",
							    """load_tf_weights_in_canine""",
							]


if TYPE_CHECKING:
							from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
							from .tokenization_canine import CanineTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_canine import (
														    CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
														    CanineForMultipleChoice,
														    CanineForQuestionAnswering,
														    CanineForSequenceClassification,
														    CanineForTokenClassification,
														    CanineLayer,
														    CanineModel,
														    CaninePreTrainedModel,
														    load_tf_weights_in_canine,
														)


else:
							import sys

							lowerCAmelCase__								=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




