
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 				lowercase		(					SCREAMING_SNAKE_CASE__		):
							lowercase_       :       jnp.ndarray
							lowercase_       :       jnp.ndarray







class 				lowercase		(					nn.Module		):
							lowercase_       :       int
							lowercase_       :       Tuple[int]       					=(16, 32, 96, 256)
							lowercase_       :       jnp.dtype       					=jnp.floataa




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

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

												lowercase							=						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__):
												lowercase							=						self.conv_in(A__)
												lowercase							=						nn.silu(A__)

												for block in self.blocks:
																	lowercase							=						block(A__)
																	lowercase							=						nn.silu(A__)

												lowercase							=						self.conv_out(A__)

												return embedding







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




							def  A__      (				self   ,A__):
												# init input tensors
												lowercase							=						(1, self.in_channels, self.sample_size, self.sample_size)
												lowercase							=						jnp.zeros(A__   ,dtype=jnp.floataa)
												lowercase							=						jnp.ones((1,)   ,dtype=jnp.intaa)
												lowercase							=						jnp.zeros((1, 1, self.cross_attention_dim)   ,dtype=jnp.floataa)
												lowercase							=						(1, 3, self.sample_size * 8, self.sample_size * 8)
												lowercase							=						jnp.zeros(A__   ,dtype=jnp.floataa)

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

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




							def  A__      (				self):
												lowercase							=						self.block_out_channels
												lowercase							=						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							=						self.num_attention_heads or self.attention_head_dim

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

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

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

												lowercase							=						self.only_cross_attention
												if isinstance(A__   ,A__):
																	lowercase							=						(only_cross_attention,) * len(self.down_block_types)

												if isinstance(A__   ,A__):
																	lowercase							=						(num_attention_heads,) * len(self.down_block_types)

												# down
												lowercase							=						[]
												lowercase							=						[]

												lowercase							=						block_out_channels[0]

												lowercase							=						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							=						output_channel
																	lowercase							=						block_out_channels[i]
																	lowercase							=						i == len(A__) - 1

																	if down_block_type == "CrossAttnDownBlock2D":
																						lowercase							=						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							=						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							=						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							=						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							=						down_blocks
												lowercase							=						controlnet_down_blocks

												# mid
												lowercase							=						block_out_channels[-1]
												lowercase							=						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							=						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   ,):
												lowercase							=						self.controlnet_conditioning_channel_order
												if channel_order == "bgr":
																	lowercase							=						jnp.flip(A__   ,axis=1)

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

												lowercase							=						self.time_proj(A__)
												lowercase							=						self.time_embedding(A__)

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

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

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

												# 4. mid
												lowercase							=						self.mid_block(A__   ,A__   ,A__   ,deterministic=not train)

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

												lowercase							=						controlnet_down_block_res_samples

												lowercase							=						self.controlnet_mid_block(A__)

												# 6. scaling
												lowercase							=						[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__)






import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__		):
					'''simple docstring'''
					lowercase							=						('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')

					lowercase							=						(
					    ('''layer.''', '''layer_'''),
					    ('''word_embeddings.weight''', '''word_embeddings'''),
					    ('''position_embeddings.weight''', '''position_embeddings'''),
					    ('''token_type_embeddings.weight''', '''token_type_embeddings'''),
					    ('''.''', '''/'''),
					    ('''LayerNorm/weight''', '''LayerNorm/gamma'''),
					    ('''LayerNorm/bias''', '''LayerNorm/beta'''),
					    ('''weight''', '''kernel'''),
					)

					if not os.path.isdir(lowerCAmelCase__		):
										os.makedirs(lowerCAmelCase__		)

					lowercase							=						model.state_dict()

					def to_tf_var_name(lowerCAmelCase__		):
										for patt, repl in iter(lowerCAmelCase__		):
															lowercase							=						name.replace(lowerCAmelCase__ ,			lowerCAmelCase__		)
										return f'bert/{name}'

					def create_tf_var(lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__		):
										lowercase							=						tf.dtypes.as_dtype(tensor.dtype		)
										lowercase							=						tf.get_variable(dtype=lowerCAmelCase__ ,			shape=tensor.shape ,			name=lowerCAmelCase__ ,			initializer=tf.zeros_initializer()		)
										session.run(tf.variables_initializer([tf_var]		)		)
										session.run(lowerCAmelCase__		)
										return tf_var

					tf.reset_default_graph()
					with tf.Session() as session:
										for var_name in state_dict:
															lowercase							=						to_tf_var_name(lowerCAmelCase__		)
															lowercase							=						state_dict[var_name].numpy()
															if any(x in var_name for x in tensors_to_transpose		):
																				lowercase							=						torch_tensor.T
															lowercase							=						create_tf_var(tensor=lowerCAmelCase__ ,			name=lowerCAmelCase__ ,			session=lowerCAmelCase__		)
															tf.keras.backend.set_value(lowerCAmelCase__ ,			lowerCAmelCase__		)
															lowercase							=						session.run(lowerCAmelCase__		)
															print(f'Successfully created {tf_name}: {np.allclose(lowerCAmelCase__ ,			lowerCAmelCase__		)}'		)

										lowercase							=						tf.train.Saver(tf.trainable_variables()		)
										saver.save(lowerCAmelCase__ ,			os.path.join(lowerCAmelCase__ ,			model_name.replace('''-''' ,			'''_'''		) + '''.ckpt'''		)		)



def        UpperCamelCase    (							lowerCAmelCase__=None		):
					'''simple docstring'''
					lowercase							=						argparse.ArgumentParser()
					parser.add_argument('''--model_name''' ,			type=lowerCAmelCase__ ,			required=lowerCAmelCase__ ,			help='''model name e.g. bert-base-uncased'''		)
					parser.add_argument(
					    '''--cache_dir''' ,			type=lowerCAmelCase__ ,			default=lowerCAmelCase__ ,			required=lowerCAmelCase__ ,			help='''Directory containing pytorch model'''		)
					parser.add_argument('''--pytorch_model_path''' ,			type=lowerCAmelCase__ ,			required=lowerCAmelCase__ ,			help='''/path/to/<pytorch-model-name>.bin'''		)
					parser.add_argument('''--tf_cache_dir''' ,			type=lowerCAmelCase__ ,			required=lowerCAmelCase__ ,			help='''Directory in which to save tensorflow model'''		)
					lowercase							=						parser.parse_args(lowerCAmelCase__		)

					lowercase							=						BertModel.from_pretrained(
					    pretrained_model_name_or_path=args.model_name ,			state_dict=torch.load(args.pytorch_model_path		) ,			cache_dir=args.cache_dir ,			)

					convert_pytorch_checkpoint_to_tf(model=lowerCAmelCase__ ,			ckpt_dir=args.tf_cache_dir ,			model_name=args.model_name		)


if __name__ == "__main__":
							main()




