
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,
    FlaxCrossAttnUpBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
    FlaxUpBlockaD,
)





@flax.struct.dataclass
class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     42





@flax_register_to_config
class 							UpperCAmelCase      (  nn.Module			,			__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     32
	snake_case_					    =     4
	snake_case_					    =     4
	snake_case_					    =     (
	    "CrossAttnDownBlock2D",
	    "CrossAttnDownBlock2D",
	    "CrossAttnDownBlock2D",
	    "DownBlock2D",
	)
	snake_case_					    =     ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
	snake_case_					    =     False
	snake_case_					    =     (320, 640, 1280, 1280)
	snake_case_					    =     2
	snake_case_					    =     8
	snake_case_					    =     None
	snake_case_					    =     1280
	snake_case_					    =     0.0
	snake_case_					    =     False
	snake_case_					    =     jnp.floataa
	snake_case_					    =     True
	snake_case_					    =     0
	snake_case_					    =     False






	def 				UpperCamelCase_  (       self					: Tuple							,A					: jax.random.KeyArray ):
					# init input tensors
					__A   			= (1, self.in_channels, self.sample_size, self.sample_size)
					__A   			= jnp.zeros(A							,dtype=jnp.floataa )
					__A   			= jnp.ones((1,)							,dtype=jnp.intaa )
					__A   			= jnp.zeros((1, 1, self.cross_attention_dim)							,dtype=jnp.floataa )

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

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






	def 				UpperCamelCase_  (       self					: Dict ):
					__A   			= self.block_out_channels
					__A   			= block_out_channels[0] * 4

					if self.num_attention_heads is not None:
									raise ValueError(
									    "At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19." )

					# 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(A							,dtype=self.dtype )

					__A   			= self.only_cross_attention
					if isinstance(A							,A ):
									__A   			= (only_cross_attention,) * len(self.down_block_types )

					if isinstance(A							,A ):
									__A   			= (num_attention_heads,) * len(self.down_block_types )

					# down
					__A   			= []
					__A   			= block_out_channels[0]
					for i, down_block_type in enumerate(self.down_block_types ):
									__A   			= output_channel
									__A   			= block_out_channels[i]
									__A   			= i == len(A ) - 1

									if down_block_type == "CrossAttnDownBlock2D":
													__A   			= 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]							,use_memory_efficient_attention=self.use_memory_efficient_attention							,dtype=self.dtype							,)
									else:
													__A   			= 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 )
					__A   			= down_blocks

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

					# up
					__A   			= []
					__A   			= list(reversed(A ) )
					__A   			= list(reversed(A ) )
					__A   			= list(reversed(A ) )
					__A   			= reversed_block_out_channels[0]
					for i, up_block_type in enumerate(self.up_block_types ):
									__A   			= output_channel
									__A   			= reversed_block_out_channels[i]
									__A   			= reversed_block_out_channels[min(i + 1							,len(A ) - 1 )]

									__A   			= i == len(A ) - 1

									if up_block_type == "CrossAttnUpBlock2D":
													__A   			= FlaxCrossAttnUpBlockaD(
													    in_channels=A							,out_channels=A							,prev_output_channel=A							,num_layers=self.layers_per_block + 1							,num_attention_heads=reversed_num_attention_heads[i]							,add_upsample=not is_final_block							,dropout=self.dropout							,use_linear_projection=self.use_linear_projection							,only_cross_attention=only_cross_attention[i]							,use_memory_efficient_attention=self.use_memory_efficient_attention							,dtype=self.dtype							,)
									else:
													__A   			= FlaxUpBlockaD(
													    in_channels=A							,out_channels=A							,prev_output_channel=A							,num_layers=self.layers_per_block + 1							,add_upsample=not is_final_block							,dropout=self.dropout							,dtype=self.dtype							,)

									up_blocks.append(A )
									__A   			= output_channel
					__A   			= up_blocks

					# out
					__A   			= nn.GroupNorm(num_groups=32							,epsilon=1E-5 )
					__A   			= nn.Conv(
					    self.out_channels							,kernel_size=(3, 3)							,strides=(1, 1)							,padding=((1, 1), (1, 1))							,dtype=self.dtype							,)







	def __call__(       self					: Optional[Any]							,A					: Tuple							,A					: List[Any]							,A					: List[str]							,A					: Union[str, Any]=None							,A					: Union[str, Any]=None							,A					: bool = True							,A					: bool = False							,):
					# 1. time
					if not isinstance(A							,jnp.ndarray ):
									__A   			= jnp.array([timesteps]							,dtype=jnp.intaa )
					elif isinstance(A							,jnp.ndarray ) and len(timesteps.shape ) == 0:
									__A   			= timesteps.astype(dtype=jnp.floataa )
									__A   			= jnp.expand_dims(A							,0 )

					__A   			= self.time_proj(A )
					__A   			= self.time_embedding(A )

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

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

					if down_block_additional_residuals is not None:
									__A   			= ()

									for down_block_res_sample, down_block_additional_residual in zip(
									    A							,A ):
													down_block_res_sample += down_block_additional_residual
													new_down_block_res_samples += (down_block_res_sample,)

									__A   			= new_down_block_res_samples

					# 4. mid
					__A   			= self.mid_block(A							,A							,A							,deterministic=not train )

					if mid_block_additional_residual is not None:
									sample += mid_block_additional_residual

					# 5. up
					for up_block in self.up_blocks:
									__A   			= down_block_res_samples[-(self.layers_per_block + 1) :]
									__A   			= down_block_res_samples[: -(self.layers_per_block + 1)]
									if isinstance(A							,A ):
													__A   			= up_block(
													    A							,temb=A							,encoder_hidden_states=A							,res_hidden_states_tuple=A							,deterministic=not train							,)
									else:
													__A   			= up_block(A							,temb=A							,res_hidden_states_tuple=A							,deterministic=not train )

        # 6. post-process
					__A   			= self.conv_norm_out(A )
					__A   			= nn.silu(A )
					__A   			= self.conv_out(A )
					__A   			= jnp.transpose(A							,(0, 3, 1, 2) )

					if not return_dict:
									return (sample,)

					return FlaxUNetaDConditionOutput(sample=A )


import argparse
import os

import torch

from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint


def 						UpperCAmelCase			(	a_						)		->	List[str]:







				"""simple docstring"""



				return sum(param.float().sum() if "encoder.embeddings" not in key else 0 for key, param in state_dict.items()						)


def 						UpperCAmelCase			(	a_		,   a_						)		->	Tuple:







				"""simple docstring"""



				__A   			= {}

				for key, value in state_dict.items():
								if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
												continue

								__A   			= key.replace("heads.cmd.mim_head.cls.predictions"		,   "mmm_image_head"						)
								__A   			= key.replace("heads.cmd.mlm_head.cls.predictions"		,   "mmm_text_head"						)
								__A   			= key.replace("heads.cmd.itm_head.cls"		,   "itm_head"						)
								__A   			= key.replace("heads.cmd.itm_head.pooler"		,   "itm_head.pooler"						)
								__A   			= key.replace("heads.cmd.clip_head.logit_scale"		,   "flava.logit_scale"						)
								__A   			= key.replace("heads.fairseq_mlm.cls.predictions"		,   "mlm_head"						)
								__A   			= key.replace("heads.imagenet.mim_head.cls.predictions"		,   "mim_head"						)
								__A   			= key.replace("mm_text_projection"		,   "flava.text_to_mm_projection"						)
								__A   			= key.replace("mm_image_projection"		,   "flava.image_to_mm_projection"						)
								__A   			= key.replace("image_encoder.module"		,   "flava.image_model"						)
								__A   			= key.replace("text_encoder.module"		,   "flava.text_model"						)
								__A   			= key.replace("mm_encoder.module.encoder.cls_token"		,   "flava.multimodal_model.cls_token"						)
								__A   			= key.replace("mm_encoder.module"		,   "flava.multimodal_model"						)
								__A   			= key.replace("text_projection"		,   "flava.text_projection"						)
								__A   			= key.replace("image_projection"		,   "flava.image_projection"						)

								__A   			= value.float()

				for key, value in codebook_state_dict.items():
								__A   			= value

				return upgrade


@torch.no_grad()
def 						UpperCAmelCase			(	a_		,   a_		,   a_		,   a_=None						)		->	Tuple:







				"""simple docstring"""



				if config_path is not None:
								__A   			= FlavaConfig.from_pretrained(a_						)
				else:
								__A   			= FlavaConfig()

				__A   			= FlavaForPreTraining(a_						).eval()

				__A   			= convert_dalle_checkpoint(a_		,   a_		,   save_checkpoint=a_						)

				if os.path.exists(a_						):
								__A   			= torch.load(a_		,   map_location="cpu"						)
				else:
								__A   			= torch.hub.load_state_dict_from_url(a_		,   map_location="cpu"						)

				__A   			= upgrade_state_dict(a_		,   a_						)
				hf_model.load_state_dict(a_						)
				__A   			= hf_model.state_dict()
				__A   			= count_parameters(a_						)
				__A   			= count_parameters(a_						) + count_parameters(a_						)

				assert torch.allclose(a_		,   a_		,   atol=1E-3						)

				hf_model.save_pretrained(a_						)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Any							 =				argparse.ArgumentParser()
					parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
					parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to flava checkpoint')
					parser.add_argument('--codebook_path', default=None, type=str, help='Path to flava codebook checkpoint')
					parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
					SCREAMING_SNAKE_CASE							:Optional[int]							 =				parser.parse_args()

					convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)
