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"""






 lowerCamelCase	:jnp.ndarray
 lowerCamelCase	:jnp.ndarray



class 				a							(			nn.Module       ):







 """simple docstring"""






 lowerCamelCase	:int
 lowerCamelCase	:Tuple[int]    			=		(16, 32, 96, 256)
 lowerCamelCase	:jnp.dtype    			=		jnp.floataa



 def      UpperCAmelCase							(					self							)  ->					Optional[Any]:
        _A        =  nn.Conv(
            self.block_out_channels[0]		,				kernel_size=(3, 3)		,				padding=((1, 1), (1, 1))		,				dtype=self.dtype		,				)

        _A        =  []
        for i in range(len(self.block_out_channels							) - 1							):
               _A        =  self.block_out_channels[i]
               _A        =  self.block_out_channels[i + 1]
               _A        =  nn.Conv(
                   lowerCAmelCase_		,				kernel_size=(3, 3)		,				padding=((1, 1), (1, 1))		,				dtype=self.dtype		,				)
               blocks.append(lowerCAmelCase_							)
               _A        =  nn.Conv(
                   lowerCAmelCase_		,				kernel_size=(3, 3)		,				strides=(2, 2)		,				padding=((1, 1), (1, 1))		,				dtype=self.dtype		,				)
               blocks.append(lowerCAmelCase_							)
        _A        =  blocks

        _A        =  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		,				lowerCAmelCase_							)  ->					int:
        _A        =  self.conv_in(lowerCAmelCase_							)
        _A        =  nn.silu(lowerCAmelCase_							)

        for block in self.blocks:
               _A        =  block(lowerCAmelCase_							)
               _A        =  nn.silu(lowerCAmelCase_							)

        _A        =  self.conv_out(lowerCAmelCase_							)

        return embedding



@flax_register_to_config
class 				a							(			nn.Module       ,			__lowerCAmelCase       ,			__lowerCAmelCase       ):







 """simple docstring"""






 lowerCamelCase	:int    			=		32
 lowerCamelCase	:int    			=		4
 lowerCamelCase	:Tuple[str]    			=		(
     "CrossAttnDownBlock2D",
     "CrossAttnDownBlock2D",
     "CrossAttnDownBlock2D",
     "DownBlock2D",
 )
 lowerCamelCase	:Union[bool, Tuple[bool]]    			=		False
 lowerCamelCase	:Tuple[int]    			=		(320, 640, 1280, 1280)
 lowerCamelCase	:int    			=		2
 lowerCamelCase	:Union[int, Tuple[int]]    			=		8
 lowerCamelCase	:Optional[Union[int, Tuple[int]]]    			=		None
 lowerCamelCase	:int    			=		1280
 lowerCamelCase	:float    			=		0.0
 lowerCamelCase	:bool    			=		False
 lowerCamelCase	:jnp.dtype    			=		jnp.floataa
 lowerCamelCase	:bool    			=		True
 lowerCamelCase	:int    			=		0
 lowerCamelCase	:str    			=		"rgb"
 lowerCamelCase	:Tuple[int]    			=		(16, 32, 96, 256)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_							)  ->					FrozenDict:
        # init input tensors
        _A        =  (1, self.in_channels, self.sample_size, self.sample_size)
        _A        =  jnp.zeros(lowerCAmelCase_		,				dtype=jnp.floataa							)
        _A        =  jnp.ones((1,)		,				dtype=jnp.intaa							)
        _A        =  jnp.zeros((1, 1, self.cross_attention_dim)		,				dtype=jnp.floataa							)
        _A        =  (1, 3, self.sample_size * 8, self.sample_size * 8)
        _A        =  jnp.zeros(lowerCAmelCase_		,				dtype=jnp.floataa							)

        _A   ,  _A        =  jax.random.split(lowerCAmelCase_							)
        _A        =  {"""params""": params_rng, """dropout""": dropout_rng}

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



 def      UpperCAmelCase							(					self							)  ->					Any:
        _A        =  self.block_out_channels
        _A        =  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.
        _A        =  self.num_attention_heads or self.attention_head_dim

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

        # time
        _A        =  FlaxTimesteps(
            block_out_channels[0]		,				flip_sin_to_cos=self.flip_sin_to_cos		,				freq_shift=self.config.freq_shift							)
        _A        =  FlaxTimestepEmbedding(lowerCAmelCase_		,				dtype=self.dtype							)

        _A        =  FlaxControlNetConditioningEmbedding(
            conditioning_embedding_channels=block_out_channels[0]		,				block_out_channels=self.conditioning_embedding_out_channels		,				)

        _A        =  self.only_cross_attention
        if isinstance(lowerCAmelCase_		,				lowerCAmelCase_							):
               _A        =  (only_cross_attention,) * len(self.down_block_types							)

        if isinstance(lowerCAmelCase_		,				lowerCAmelCase_							):
               _A        =  (num_attention_heads,) * len(self.down_block_types							)

        # down
        _A        =  []
        _A        =  []

        _A        =  block_out_channels[0]

        _A        =  nn.Conv(
            lowerCAmelCase_		,				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(lowerCAmelCase_							)

        for i, down_block_type in enumerate(self.down_block_types							):
               _A        =  output_channel
               _A        =  block_out_channels[i]
               _A        =  i == len(lowerCAmelCase_							) - 1

               if down_block_type == "CrossAttnDownBlock2D":
                      _A        =  FlaxCrossAttnDownBlockaD(
                          in_channels=lowerCAmelCase_		,				out_channels=lowerCAmelCase_		,				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:
                      _A        =  FlaxDownBlockaD(
                          in_channels=lowerCAmelCase_		,				out_channels=lowerCAmelCase_		,				dropout=self.dropout		,				num_layers=self.layers_per_block		,				add_downsample=not is_final_block		,				dtype=self.dtype		,				)

               down_blocks.append(lowerCAmelCase_							)

               for _ in range(self.layers_per_block							):
                      _A        =  nn.Conv(
                          lowerCAmelCase_		,				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(lowerCAmelCase_							)

               if not is_final_block:
                      _A        =  nn.Conv(
                          lowerCAmelCase_		,				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(lowerCAmelCase_							)

        _A        =  down_blocks
        _A        =  controlnet_down_blocks

        # mid
        _A        =  block_out_channels[-1]
        _A        =  FlaxUNetMidBlockaDCrossAttn(
            in_channels=lowerCAmelCase_		,				dropout=self.dropout		,				num_attention_heads=num_attention_heads[-1]		,				use_linear_projection=self.use_linear_projection		,				dtype=self.dtype		,				)

        _A        =  nn.Conv(
            lowerCAmelCase_		,				kernel_size=(1, 1)		,				padding="""VALID"""		,				kernel_init=nn.initializers.zeros_init()		,				bias_init=nn.initializers.zeros_init()		,				dtype=self.dtype		,				)



 def __call__(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_ = 1.0		,				lowerCAmelCase_ = True		,				lowerCAmelCase_ = False		,				)  ->					Union[FlaxControlNetOutput, Tuple]:
        _A        =  self.controlnet_conditioning_channel_order
        if channel_order == "bgr":
               _A        =  jnp.flip(lowerCAmelCase_		,				axis=1							)

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

        _A        =  self.time_proj(lowerCAmelCase_							)
        _A        =  self.time_embedding(lowerCAmelCase_							)

        # 2. pre-process
        _A        =  jnp.transpose(lowerCAmelCase_		,				(0, 2, 3, 1)							)
        _A        =  self.conv_in(lowerCAmelCase_							)

        _A        =  jnp.transpose(lowerCAmelCase_		,				(0, 2, 3, 1)							)
        _A        =  self.controlnet_cond_embedding(lowerCAmelCase_							)
        sample += controlnet_cond

        # 3. down
        _A        =  (sample,)
        for down_block in self.down_blocks:
               if isinstance(lowerCAmelCase_		,				lowerCAmelCase_							):
                      _A   ,  _A        =  down_block(lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				deterministic=not train							)
               else:
                      _A   ,  _A        =  down_block(lowerCAmelCase_		,				lowerCAmelCase_		,				deterministic=not train							)
               down_block_res_samples += res_samples

        # 4. mid
        _A        =  self.mid_block(lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				deterministic=not train							)

        # 5. contronet blocks
        _A        =  ()
        for down_block_res_sample, controlnet_block in zip(lowerCAmelCase_		,				self.controlnet_down_blocks							):
               _A        =  controlnet_block(lowerCAmelCase_							)
               controlnet_down_block_res_samples += (down_block_res_sample,)

        _A        =  controlnet_down_block_res_samples

        _A        =  self.controlnet_mid_block(lowerCAmelCase_							)

        # 6. scaling
        _A        =  [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=lowerCAmelCase_		,				mid_block_res_sample=lowerCAmelCase_							)





import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()






def  snake_case  (       snake_case__			:int   ,	snake_case__			:List[str]   ,	snake_case__			:Union[str, Any])						->   str:
       # Initialise PyTorch model
       _A        =  AlbertConfig.from_json_file(snake_case__)
       print(F'''Building PyTorch model from configuration: {config}''')
       _A        =  AlbertForPreTraining(snake_case__)

       # Load weights from tf checkpoint
       load_tf_weights_in_albert(snake_case__   ,	snake_case__   ,	snake_case__)

       # Save pytorch-model
       print(F'''Save PyTorch model to {pytorch_dump_path}''')
       torch.save(model.state_dict()   ,	snake_case__)


if __name__ == "__main__":
    _SCREAMING_SNAKE_CASE										=						argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
    )
    parser.add_argument(
        '--albert_config_file',
        default=None,
        type=str,
        required=True,
        help=(
            'The config json file corresponding to the pre-trained ALBERT model. \n'
            'This specifies the model architecture.'
        ),
    )
    parser.add_argument(
        '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
    )
    _SCREAMING_SNAKE_CASE										=						parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)




