

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 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    lowerCamelCase		     =							42




@flax_register_to_config
class 			A						(							nn.Module   ,							_UpperCAmelCase   ,							_UpperCAmelCase			):







    """simple docstring"""





    lowerCamelCase		     =							32
    lowerCamelCase		     =							4
    lowerCamelCase		     =							4
    lowerCamelCase		     =							(
        "CrossAttnDownBlock2D",
        "CrossAttnDownBlock2D",
        "CrossAttnDownBlock2D",
        "DownBlock2D",
    )
    lowerCamelCase		     =							("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
    lowerCamelCase		     =							False
    lowerCamelCase		     =							(3_20, 6_40, 12_80, 12_80)
    lowerCamelCase		     =							2
    lowerCamelCase		     =							8
    lowerCamelCase		     =							None
    lowerCamelCase		     =							12_80
    lowerCamelCase		     =							0.0
    lowerCamelCase		     =							False
    lowerCamelCase		     =							jnp.floataa
    lowerCamelCase		     =							True
    lowerCamelCase		     =							0
    lowerCamelCase		     =							False


    def 						snake_case__					(				self			:	str,lowercase_			:	jax.random.KeyArray		)->   FrozenDict:





        '''simple docstring'''







        A__   				=       (1, self.in_channels, self.sample_size, self.sample_size)
        A__   				=       jnp.zeros(lowercase_,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(lowercase_		)
        A__   				=       {'params': params_rng, 'dropout': dropout_rng}

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


    def 						snake_case__					(				self			:	List[Any]		)->   Optional[int]:





        '''simple docstring'''







        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(lowercase_,dtype=self.dtype		)

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

        if isinstance(lowercase_,lowercase_		):
            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(lowercase_		) - 1

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

            down_blocks.append(lowercase_		)
        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(lowercase_		)		)
        A__   				=       list(reversed(lowercase_		)		)
        A__   				=       list(reversed(lowercase_		)		)
        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(lowercase_		) - 1		)]

            A__   				=       i == len(lowercase_		) - 1

            if up_block_type == "CrossAttnUpBlock2D":
                A__   				=       FlaxCrossAttnUpBlockaD(
                    in_channels=lowercase_,out_channels=lowercase_,prev_output_channel=lowercase_,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=lowercase_,out_channels=lowercase_,prev_output_channel=lowercase_,num_layers=self.layers_per_block + 1,add_upsample=not is_final_block,dropout=self.dropout,dtype=self.dtype,)

            up_blocks.append(lowercase_		)
            A__   				=       output_channel
        A__   				=       up_blocks

        # out
        A__   				=       nn.GroupNorm(num_groups=3_2,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			:	Dict,lowercase_			:	Any,lowercase_			:	Optional[int],lowercase_			:	Tuple,lowercase_			:	Tuple=None,lowercase_			:	Tuple=None,lowercase_			:	bool = True,lowercase_			:	bool = False,)->   Union[FlaxUNetaDConditionOutput, Tuple]:





        '''simple docstring'''







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

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

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

        # 3. down
        A__   				=       (sample,)
        for down_block in self.down_blocks:
            if isinstance(lowercase_,lowercase_		):
                A__				,						A__   				=       down_block(lowercase_,lowercase_,lowercase_,deterministic=not train		)
            else:
                A__				,						A__   				=       down_block(lowercase_,lowercase_,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(
                lowercase_,lowercase_		):
                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(lowercase_,lowercase_,lowercase_,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(lowercase_,lowercase_		):
                A__   				=       up_block(
                    lowercase_,temb=lowercase_,encoder_hidden_states=lowercase_,res_hidden_states_tuple=lowercase_,deterministic=not train,)
            else:
                A__   				=       up_block(lowercase_,temb=lowercase_,res_hidden_states_tuple=lowercase_,deterministic=not train		)

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

        if not return_dict:
            return (sample,)

        return FlaxUNetaDConditionOutput(sample=lowercase_		)




import numpy as np

from transformers import Pipeline
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int				)							->				int:





    '''simple docstring'''






    A__   				=       np.max(SCREAMING_SNAKE_CASE__   ,      axis=-1   ,      keepdims=SCREAMING_SNAKE_CASE__				)
    A__   				=       np.exp(outputs - maxes				)
    return shifted_exp / shifted_exp.sum(axis=-1   ,      keepdims=SCREAMING_SNAKE_CASE__				)


class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    def 						snake_case__					(				self			:	Dict,**lowercase_			:	Tuple		)->   Tuple:





        '''simple docstring'''







        A__   				=       {}
        if "second_text" in kwargs:
            A__   				=       kwargs['second_text']
        return preprocess_kwargs, {}, {}


    def 						snake_case__					(				self			:	List[Any],lowercase_			:	int,lowercase_			:	Optional[int]=None		)->   List[str]:





        '''simple docstring'''







        return self.tokenizer(lowercase_,text_pair=lowercase_,return_tensors=self.framework		)


    def 						snake_case__					(				self			:	str,lowercase_			:	Dict		)->   List[str]:





        '''simple docstring'''







        return self.model(**lowercase_		)


    def 						snake_case__					(				self			:	Dict,lowercase_			:	Optional[int]		)->   Dict:





        '''simple docstring'''







        A__   				=       model_outputs.logits[0].numpy()
        A__   				=       softmax(lowercase_		)

        A__   				=       np.argmax(lowercase_		)
        A__   				=       self.model.config.idalabel[best_class]
        A__   				=       probabilities[best_class].item()
        A__   				=       logits.tolist()
        return {"label": label, "score": score, "logits": logits}

