
"""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 							_SCREAMING_SNAKE_CASE	(	UpperCamelCase__				):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:				List[Any]				=       4_2
				SCREAMING_SNAKE_CASE_:				int				=       4_2






class 							_SCREAMING_SNAKE_CASE	(	nn.Module				):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:				List[str]				=       4_2
				SCREAMING_SNAKE_CASE_:				Any				=       (1_6, 3_2, 9_6, 2_5_6)
				SCREAMING_SNAKE_CASE_:				Any				=       jnp.floataa


				def 					__lowerCamelCase						(       self						:      Optional[Any]	)		->	Tuple:







											"""simple docstring"""




											_lowerCAmelCase							=      nn.Conv(
											    self.block_out_channels[0]					,  kernel_size=(3, 3)					,  padding=((1, 1), (1, 1))					,  dtype=self.dtype					,  )

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

											_lowerCAmelCase							=      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						:      int					,  UpperCAmelCase_						:      Dict	)		->	int:







											"""simple docstring"""




											_lowerCAmelCase							=      self.conv_in(__lowerCamelCase	)
											_lowerCAmelCase							=      nn.silu(__lowerCamelCase	)

											for block in self.blocks:
																		_lowerCAmelCase							=      block(__lowerCamelCase	)
																		_lowerCAmelCase							=      nn.silu(__lowerCamelCase	)

											_lowerCAmelCase							=      self.conv_out(__lowerCamelCase	)

											return embedding






@flax_register_to_config
class 							_SCREAMING_SNAKE_CASE	(	nn.Module   ,					UpperCamelCase__   ,					UpperCamelCase__				):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:				int				=       3_2
				SCREAMING_SNAKE_CASE_:				List[str]				=       4
				SCREAMING_SNAKE_CASE_:				Union[str, Any]				=       (
				    "CrossAttnDownBlock2D",
				    "CrossAttnDownBlock2D",
				    "CrossAttnDownBlock2D",
				    "DownBlock2D",
				)
				SCREAMING_SNAKE_CASE_:				str				=       False
				SCREAMING_SNAKE_CASE_:				Any				=       (3_2_0, 6_4_0, 1_2_8_0, 1_2_8_0)
				SCREAMING_SNAKE_CASE_:				str				=       2
				SCREAMING_SNAKE_CASE_:				Dict				=       8
				SCREAMING_SNAKE_CASE_:				str				=       None
				SCREAMING_SNAKE_CASE_:				List[Any]				=       1_2_8_0
				SCREAMING_SNAKE_CASE_:				Union[str, Any]				=       0.0
				SCREAMING_SNAKE_CASE_:				Any				=       False
				SCREAMING_SNAKE_CASE_:				List[Any]				=       jnp.floataa
				SCREAMING_SNAKE_CASE_:				Tuple				=       True
				SCREAMING_SNAKE_CASE_:				List[Any]				=       0
				SCREAMING_SNAKE_CASE_:				Optional[int]				=       "rgb"
				SCREAMING_SNAKE_CASE_:				Optional[int]				=       (1_6, 3_2, 9_6, 2_5_6)


				def 					__lowerCamelCase						(       self						:      Any					,  UpperCAmelCase_						:      Dict	)		->	Tuple:







											"""simple docstring"""




											_lowerCAmelCase							=      (1, self.in_channels, self.sample_size, self.sample_size)
											_lowerCAmelCase							=      jnp.zeros(__lowerCamelCase					,  dtype=jnp.floataa	)
											_lowerCAmelCase							=      jnp.ones((1,)					,  dtype=jnp.intaa	)
											_lowerCAmelCase							=      jnp.zeros((1, 1, self.cross_attention_dim)					,  dtype=jnp.floataa	)
											_lowerCAmelCase							=      (1, 3, self.sample_size * 8, self.sample_size * 8)
											_lowerCAmelCase							=      jnp.zeros(__lowerCamelCase					,  dtype=jnp.floataa	)

											_lowerCAmelCase						,		_lowerCAmelCase							=      jax.random.split(__lowerCamelCase	)
											_lowerCAmelCase							=      {'params': params_rng, 'dropout': dropout_rng}

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


				def 					__lowerCamelCase						(       self						:      Dict	)		->	Optional[int]:







											"""simple docstring"""




											_lowerCAmelCase							=      self.block_out_channels
											_lowerCAmelCase							=      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.
											_lowerCAmelCase							=      self.num_attention_heads or self.attention_head_dim

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

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

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

											_lowerCAmelCase							=      self.only_cross_attention
											if isinstance(__lowerCamelCase					,  __lowerCamelCase	):
																		_lowerCAmelCase							=      (only_cross_attention,) * len(self.down_block_types	)

											if isinstance(__lowerCamelCase					,  __lowerCamelCase	):
																		_lowerCAmelCase							=      (num_attention_heads,) * len(self.down_block_types	)

											# down
											_lowerCAmelCase							=      []
											_lowerCAmelCase							=      []

											_lowerCAmelCase							=      block_out_channels[0]

											_lowerCAmelCase							=      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	):
																		_lowerCAmelCase							=      output_channel
																		_lowerCAmelCase							=      block_out_channels[i]
																		_lowerCAmelCase							=      i == len(__lowerCamelCase	) - 1

																		if down_block_type == "CrossAttnDownBlock2D":
																									_lowerCAmelCase							=      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:
																									_lowerCAmelCase							=      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	):
																									_lowerCAmelCase							=      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:
																									_lowerCAmelCase							=      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	)

											_lowerCAmelCase							=      down_blocks
											_lowerCAmelCase							=      controlnet_down_blocks

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

											_lowerCAmelCase							=      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						:      Union[str, Any]					,  UpperCAmelCase_						:      Optional[int]					,  UpperCAmelCase_						:      Any					,  UpperCAmelCase_						:      Dict					,  UpperCAmelCase_						:      List[str]					,  UpperCAmelCase_						:      Optional[Any] = 1.0					,  UpperCAmelCase_						:      Optional[Any] = True					,  UpperCAmelCase_						:      Union[str, Any] = False					,  )		->	List[Any]:







											"""simple docstring"""




											_lowerCAmelCase							=      self.controlnet_conditioning_channel_order
											if channel_order == "bgr":
																		_lowerCAmelCase							=      jnp.flip(__lowerCamelCase					,  axis=1	)

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

											_lowerCAmelCase							=      self.time_proj(__lowerCamelCase	)
											_lowerCAmelCase							=      self.time_embedding(__lowerCamelCase	)

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

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

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

											# 4. mid
											_lowerCAmelCase							=      self.mid_block(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  deterministic=not train	)

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

											_lowerCAmelCase							=      controlnet_down_block_res_samples

											_lowerCAmelCase							=      self.controlnet_mid_block(__lowerCamelCase	)

											# 6. scaling
											_lowerCAmelCase							=      [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 transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()


def       UpperCamelCase__(			UpperCamelCase__     :			Any					,	UpperCamelCase__     :			int					,	UpperCamelCase__     :			List[Any]    )->Optional[int]:
       # Initialise PyTorch model
       A__		   =		RemBertConfig.from_json_file(UpperCamelCase__    )
       print('''Building PyTorch model from configuration: {}'''.format(str(UpperCamelCase__    )    )    )
       A__		   =		RemBertModel(UpperCamelCase__    )

       # Load weights from tf checkpoint
       load_tf_weights_in_rembert(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__    )

       # Save pytorch-model
       print('''Save PyTorch model to {}'''.format(UpperCamelCase__    )    )
       torch.save(model.state_dict()					,	UpperCamelCase__    )


if __name__ == "__main__":
       a__: List[Any]							   =						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(
           '--rembert_config_file',
           default=None,
           type=str,
           required=True,
           help=(
               'The config json file corresponding to the pre-trained RemBERT 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.'
       )
       a__: Union[str, Any]							   =						parser.parse_args()
       convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)





