






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 		_snake_case	(	A__       ):
			_lowercase		:     jnp.ndarray
			_lowercase		:     jnp.ndarray



class 		_snake_case	(	nn.Module       ):
			_lowercase		:     int
			_lowercase		:     Tuple[int]        =				(16, 32, 96, 2_56)
			_lowercase		:     jnp.dtype        =				jnp.floataa






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Optional[int]:
						SCREAMING_SNAKE_CASE							=			nn.Conv(
						    self.block_out_channels[0]		,       kernel_size=(3, 3)		,       padding=((1, 1), (1, 1))		,       dtype=self.dtype		,       )

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

						SCREAMING_SNAKE_CASE							=			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)   ->							Any:
						SCREAMING_SNAKE_CASE							=			self.conv_in(a)
						SCREAMING_SNAKE_CASE							=			nn.silu(a)

						for block in self.blocks:
									SCREAMING_SNAKE_CASE							=			block(a)
									SCREAMING_SNAKE_CASE							=			nn.silu(a)

						SCREAMING_SNAKE_CASE							=			self.conv_out(a)

						return embedding



@flax_register_to_config
class 		_snake_case	(	nn.Module ,     A__ ,     A__       ):
			_lowercase		:     int        =				32
			_lowercase		:     int        =				4
			_lowercase		:     Tuple[str]        =				(
			    "CrossAttnDownBlock2D",
			    "CrossAttnDownBlock2D",
			    "CrossAttnDownBlock2D",
			    "DownBlock2D",
			)
			_lowercase		:     Union[bool, Tuple[bool]]        =				False
			_lowercase		:     Tuple[int]        =				(3_20, 6_40, 12_80, 12_80)
			_lowercase		:     int        =				2
			_lowercase		:     Union[int, Tuple[int]]        =				8
			_lowercase		:     Optional[Union[int, Tuple[int]]]        =				None
			_lowercase		:     int        =				12_80
			_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, 2_56)






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

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

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






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							str:
						SCREAMING_SNAKE_CASE							=			self.block_out_channels
						SCREAMING_SNAKE_CASE							=			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.
						SCREAMING_SNAKE_CASE							=			self.num_attention_heads or self.attention_head_dim

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

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

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

						SCREAMING_SNAKE_CASE							=			self.only_cross_attention
						if isinstance(a		,       a):
									SCREAMING_SNAKE_CASE							=			(only_cross_attention,) * len(self.down_block_types)

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

						# down
						SCREAMING_SNAKE_CASE							=			[]
						SCREAMING_SNAKE_CASE							=			[]

						SCREAMING_SNAKE_CASE							=			block_out_channels[0]

						SCREAMING_SNAKE_CASE							=			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):
									SCREAMING_SNAKE_CASE							=			output_channel
									SCREAMING_SNAKE_CASE							=			block_out_channels[i]
									SCREAMING_SNAKE_CASE							=			i == len(a) - 1

									if down_block_type == "CrossAttnDownBlock2D":
												SCREAMING_SNAKE_CASE							=			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:
												SCREAMING_SNAKE_CASE							=			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):
												SCREAMING_SNAKE_CASE							=			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:
												SCREAMING_SNAKE_CASE							=			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)

						SCREAMING_SNAKE_CASE							=			down_blocks
						SCREAMING_SNAKE_CASE							=			controlnet_down_blocks

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

						SCREAMING_SNAKE_CASE							=			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]:
						SCREAMING_SNAKE_CASE							=			self.controlnet_conditioning_channel_order
						if channel_order == "bgr":
									SCREAMING_SNAKE_CASE							=			jnp.flip(a		,       axis=1)

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

						SCREAMING_SNAKE_CASE							=			self.time_proj(a)
						SCREAMING_SNAKE_CASE							=			self.time_embedding(a)

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

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

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

						# 4. mid
						SCREAMING_SNAKE_CASE							=			self.mid_block(a		,       a		,       a		,       deterministic=not train)

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

						SCREAMING_SNAKE_CASE							=			controlnet_down_block_res_samples

						SCREAMING_SNAKE_CASE							=			self.controlnet_mid_block(a)

						# 6. scaling
						SCREAMING_SNAKE_CASE							=			[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 torch

from transformers.utils import WEIGHTS_NAME


a__		     =     ['''small''', '''medium''', '''large''']

a__		     =     '''lm_head.decoder.weight'''
a__		     =     '''lm_head.weight'''





def 				A__ (snake_case   :		str		,	snake_case   :		str				)     ->						Union[str, Any]:
					__UpperCamelCase					:	Union[str, Any]             =      torch.load(snake_case				)
					__UpperCamelCase					:	int             =      d.pop(snake_case				)
					os.makedirs(snake_case		,	exist_ok=snake_case				)
					torch.save(snake_case		,	os.path.join(snake_case		,	snake_case				)				)


if __name__ == "__main__":
	a__		     =     argparse.ArgumentParser()
	parser.add_argument('''--dialogpt_path''', default='''.''', type=str)
	a__		     =     parser.parse_args()
	for MODEL in DIALOGPT_MODELS:
		a__		     =     os.path.join(args.dialogpt_path, f"{MODEL}_ft.pkl")
		a__		     =     f"./DialoGPT-{MODEL}"
		convert_dialogpt_checkpoint(
		    checkpoint_path,
		    pytorch_dump_folder_path,
		)






