
"""simple docstring"""




import argparse
import logging
import os
from datetime import datetime

import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm

from transformers import GPTaLMHeadModel


a		:       Dict     						=       logging.getLogger(__name__)



def     __magic_name__		(					UpperCamelCase     :		List[str]							,		UpperCamelCase     :		Tuple     )     ->						str:
    # save results
    if os.path.exists(UpperCamelCase     ):
        if os.path.exists(os.path.join(UpperCamelCase							,		'config.json'     )     ) and os.path.isfile(
            os.path.join(UpperCamelCase							,		'config.json'     )     ):
            os.remove(os.path.join(UpperCamelCase							,		'config.json'     )     )
        if os.path.exists(os.path.join(UpperCamelCase							,		'pytorch_model.bin'     )     ) and os.path.isfile(
            os.path.join(UpperCamelCase							,		'pytorch_model.bin'     )     ):
            os.remove(os.path.join(UpperCamelCase							,		'pytorch_model.bin'     )     )
    else:
        os.makedirs(UpperCamelCase     )
    model.save_pretrained(UpperCamelCase     )



def     __magic_name__		(					UpperCamelCase     :		Any							,		UpperCamelCase     :		Optional[int]=False     )     ->						Union[str, Any]:
    a__		=			2
    if unlogit:
        a__		=			torch.pow(UpperCamelCase							,		UpperCamelCase     )
    a__		=			p * torch.log(UpperCamelCase     )
    a__		=			0
    return -plogp.sum(dim=-1     )



def     __magic_name__		(					UpperCamelCase     :		Dict     )     ->						int:
    logger.info('lv, h >\t' + '\t'.join(f'{x + 1}' for x in range(len(UpperCamelCase     )     )     )     )
    for row in range(len(UpperCamelCase     )     ):
        if tensor.dtype != torch.long:
            logger.info(f'layer {row + 1}:\t' + '\t'.join(f'{x:.5f}' for x in tensor[row].cpu().data     )     )
        else:
            logger.info(f'layer {row + 1}:\t' + '\t'.join(f'{x:d}' for x in tensor[row].cpu().data     )     )



def     __magic_name__		(					UpperCamelCase     :		str							,		UpperCamelCase     :		Union[str, Any]							,		UpperCamelCase     :		Union[str, Any]							,		UpperCamelCase     :		Tuple=True							,		UpperCamelCase     :		int=True							,		UpperCamelCase     :		Tuple=None							,		UpperCamelCase     :		List[str]=False     )     ->						Union[str, Any]:
    a__	,     a__		=			model.config.num_hidden_layers, model.config.num_attention_heads
    a__		=			torch.zeros(UpperCamelCase							,		UpperCamelCase     ).to(args.device     )
    a__		=			torch.zeros(UpperCamelCase							,		UpperCamelCase     ).to(args.device     )

    if head_mask is None:
        a__		=			torch.ones(UpperCamelCase							,		UpperCamelCase     ).to(args.device     )

    head_mask.requires_grad_(requires_grad=UpperCamelCase     )
    # If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
    if actually_pruned:
        a__		=			None

    a__		=			0.0
    a__		=			0.0
    for step, inputs in enumerate(tqdm(UpperCamelCase							,		desc='Iteration'							,		disable=args.local_rank not in [-1, 0]     )     ):
        a__		=			tuple(t.to(args.device     ) for t in inputs     )
        ((a__)	,     )		=			inputs

        # Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
        a__		=			model(UpperCamelCase							,		labels=UpperCamelCase							,		head_mask=UpperCamelCase     )
        #  (loss), lm_logits, presents, (all hidden_states), (attentions)
        a__	,     a__	,     a__		=			(
            outputs[0],
            outputs[1],
            outputs[-1],
        )  # Loss and logits are the first, attention the last
        loss.backward()  # Backpropagate to populate the gradients in the head mask
        total_loss += loss.detach().cpu().numpy()
        if compute_entropy:
            for layer, attn in enumerate(UpperCamelCase     ):
                a__		=			entropy(attn.detach()							,		UpperCamelCase     )
                attn_entropy[layer] += masked_entropy.sum(-1     ).sum(0     ).sum(0     ).detach()

        if compute_importance:
            head_importance += head_mask.grad.abs().detach()
        tot_tokens += torch.ones_like(UpperCamelCase     ).float().detach().sum().data

    # Normalize
    attn_entropy /= tot_tokens
    head_importance /= tot_tokens
    # Layerwise importance normalization
    if not args.dont_normalize_importance_by_layer:
        a__		=			2
        a__		=			torch.pow(torch.pow(UpperCamelCase							,		UpperCamelCase     ).sum(-1     )							,		1 / exponent     )
        head_importance /= norm_by_layer.unsqueeze(-1     ) + 1E-20

    if not args.dont_normalize_global_importance:
        a__		=			(head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

    # Print matrices
    if compute_entropy:
        logger.info('Attention entropies'     )
        print_ad_tensor(UpperCamelCase     )
    if compute_importance:
        logger.info('Head importance scores'     )
        print_ad_tensor(UpperCamelCase     )
    logger.info('Head ranked by importance scores'     )
    a__		=			torch.zeros(head_importance.numel()							,		dtype=torch.long							,		device=args.device     )
    a__		=			torch.arange(
        head_importance.numel()							,		device=args.device     )
    a__		=			head_ranks.view_as(UpperCamelCase     )
    print_ad_tensor(UpperCamelCase     )
    return attn_entropy, head_importance, total_loss



def     __magic_name__		(					UpperCamelCase     :		List[Any]							,		UpperCamelCase     :		int							,		UpperCamelCase     :		Dict     )     ->						Dict:
    a__	,     a__	,     a__		=			compute_heads_importance(UpperCamelCase							,		UpperCamelCase							,		UpperCamelCase							,		compute_entropy=UpperCamelCase     )
    a__		=			1 / loss  # instead of downsteam score use the LM loss
    logger.info('Pruning: original score: %f, threshold: %f'							,		UpperCamelCase							,		original_score * args.masking_threshold     )

    a__		=			torch.ones_like(UpperCamelCase     )
    a__		=			max(1							,		int(new_head_mask.numel() * args.masking_amount     )     )

    a__		=			original_score
    while current_score >= original_score * args.masking_threshold:
        a__		=			new_head_mask.clone().detach()  # save current head mask
        # heads from least important to most - keep only not-masked heads
        a__		=			float('Inf'     )
        a__		=			head_importance.view(-1     ).sort()[1]

        if len(UpperCamelCase     ) <= num_to_mask:
            print('BREAK BY num_to_mask'     )
            break

        # mask heads
        a__		=			current_heads_to_mask[:num_to_mask]
        logger.info('Heads to mask: %s'							,		str(current_heads_to_mask.tolist()     )     )
        a__		=			new_head_mask.view(-1     )
        a__		=			0.0
        a__		=			new_head_mask.view_as(UpperCamelCase     )
        a__		=			new_head_mask.clone().detach()
        print_ad_tensor(UpperCamelCase     )

        # Compute metric and head importance again
        a__	,     a__	,     a__		=			compute_heads_importance(
            UpperCamelCase							,		UpperCamelCase							,		UpperCamelCase							,		compute_entropy=UpperCamelCase							,		head_mask=UpperCamelCase     )
        a__		=			1 / loss
        logger.info(
            'Masking: current score: %f, remaining heads %d (%.1f percents)'							,		UpperCamelCase							,		new_head_mask.sum()							,		new_head_mask.sum() / new_head_mask.numel() * 100							,		)

    logger.info('Final head mask'     )
    print_ad_tensor(UpperCamelCase     )
    np.save(os.path.join(args.output_dir							,		'head_mask.npy'     )							,		head_mask.detach().cpu().numpy()     )

    return head_mask



def     __magic_name__		(					UpperCamelCase     :		Optional[Any]							,		UpperCamelCase     :		List[Any]							,		UpperCamelCase     :		str							,		UpperCamelCase     :		Union[str, Any]     )     ->						Optional[int]:
    a__		=			datetime.now()
    a__	,     a__	,     a__		=			compute_heads_importance(
        UpperCamelCase							,		UpperCamelCase							,		UpperCamelCase							,		compute_entropy=UpperCamelCase							,		compute_importance=UpperCamelCase							,		head_mask=UpperCamelCase     )
    a__		=			1 / loss
    a__		=			datetime.now() - before_time

    a__		=			sum(p.numel() for p in model.parameters()     )
    a__		=			{
        layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(UpperCamelCase     )     )
    }

    for k, v in heads_to_prune.items():
        if isinstance(UpperCamelCase							,		UpperCamelCase     ):
            a__		=			[
                v,
            ]

    assert sum(len(UpperCamelCase     ) for h in heads_to_prune.values()     ) == (1 - head_mask.long()).sum().item()
    model.prune_heads(UpperCamelCase     )
    a__		=			sum(p.numel() for p in model.parameters()     )

    a__		=			datetime.now()
    a__	,     a__	,     a__		=			compute_heads_importance(
        UpperCamelCase							,		UpperCamelCase							,		UpperCamelCase							,		compute_entropy=UpperCamelCase							,		compute_importance=UpperCamelCase							,		head_mask=UpperCamelCase							,		actually_pruned=UpperCamelCase							,		)

    a__		=			1 / loss
    a__		=			datetime.now() - before_time

    logger.info(
        'Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)'							,		UpperCamelCase							,		UpperCamelCase							,		pruned_num_params / original_num_params * 100							,		)
    logger.info('Pruning: score with masking: %f score with pruning: %f'							,		UpperCamelCase							,		UpperCamelCase     )
    logger.info('Pruning: speed ratio (original timing / new timing): %f percents'							,		original_time / new_time * 100     )
    save_model(UpperCamelCase							,		args.output_dir     )



def     __magic_name__		(					)     ->						Union[str, Any]:
    a__		=			argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--data_dir'							,		default=UpperCamelCase							,		type=UpperCamelCase							,		required=UpperCamelCase							,		help='The input data dir. Should contain the .tsv files (or other data files) for the task.'							,		)
    parser.add_argument(
        '--model_name_or_path'							,		default=UpperCamelCase							,		type=UpperCamelCase							,		required=UpperCamelCase							,		help='Path to pretrained model or model identifier from huggingface.co/models'							,		)
    parser.add_argument(
        '--output_dir'							,		default=UpperCamelCase							,		type=UpperCamelCase							,		required=UpperCamelCase							,		help='The output directory where the model predictions and checkpoints will be written.'							,		)

    # Other parameters
    parser.add_argument(
        '--config_name'							,		default=''							,		type=UpperCamelCase							,		help='Pretrained config name or path if not the same as model_name_or_path'							,		)
    parser.add_argument(
        '--tokenizer_name'							,		default=''							,		type=UpperCamelCase							,		help='Pretrained tokenizer name or path if not the same as model_name_or_path'							,		)
    parser.add_argument(
        '--cache_dir'							,		default=UpperCamelCase							,		type=UpperCamelCase							,		help='Where do you want to store the pre-trained models downloaded from s3'							,		)
    parser.add_argument(
        '--data_subset'							,		type=UpperCamelCase							,		default=-1							,		help='If > 0: limit the data to a subset of data_subset instances.'     )
    parser.add_argument(
        '--overwrite_output_dir'							,		action='store_true'							,		help='Whether to overwrite data in output directory'     )
    parser.add_argument(
        '--overwrite_cache'							,		action='store_true'							,		help='Overwrite the cached training and evaluation sets'     )

    parser.add_argument(
        '--dont_normalize_importance_by_layer'							,		action='store_true'							,		help='Don\'t normalize importance score by layers'     )
    parser.add_argument(
        '--dont_normalize_global_importance'							,		action='store_true'							,		help='Don\'t normalize all importance scores between 0 and 1'							,		)

    parser.add_argument(
        '--try_masking'							,		action='store_true'							,		help='Whether to try to mask head until a threshold of accuracy.'     )
    parser.add_argument(
        '--masking_threshold'							,		default=0.9							,		type=UpperCamelCase							,		help='masking threshold in term of metrics (stop masking when metric < threshold * original metric value).'							,		)
    parser.add_argument(
        '--masking_amount'							,		default=0.1							,		type=UpperCamelCase							,		help='Amount to heads to masking at each masking step.'     )
    parser.add_argument('--metric_name'							,		default='acc'							,		type=UpperCamelCase							,		help='Metric to use for head masking.'     )

    parser.add_argument(
        '--max_seq_length'							,		default=128							,		type=UpperCamelCase							,		help=(
            'The maximum total input sequence length after WordPiece tokenization. \n'
            'Sequences longer than this will be truncated, sequences shorter padded.'
        )							,		)
    parser.add_argument('--batch_size'							,		default=1							,		type=UpperCamelCase							,		help='Batch size.'     )

    parser.add_argument('--seed'							,		type=UpperCamelCase							,		default=42     )
    parser.add_argument('--local_rank'							,		type=UpperCamelCase							,		default=-1							,		help='local_rank for distributed training on gpus'     )
    parser.add_argument('--no_cuda'							,		action='store_true'							,		help='Whether not to use CUDA when available'     )
    parser.add_argument('--server_ip'							,		type=UpperCamelCase							,		default=''							,		help='Can be used for distant debugging.'     )
    parser.add_argument('--server_port'							,		type=UpperCamelCase							,		default=''							,		help='Can be used for distant debugging.'     )
    a__		=			parser.parse_args()

    if args.server_ip and args.server_port:
        # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
        import ptvsd

        print('Waiting for debugger attach'     )
        ptvsd.enable_attach(address=(args.server_ip, args.server_port)							,		redirect_output=UpperCamelCase     )
        ptvsd.wait_for_attach()

    # Setup devices and distributed training
    if args.local_rank == -1 or args.no_cuda:
        a__		=			torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'     )
        a__		=			0 if args.no_cuda else torch.cuda.device_count()
    else:
        torch.cuda.set_device(args.local_rank     )
        a__		=			torch.device('cuda'							,		args.local_rank     )
        a__		=			1
        torch.distributed.init_process_group(backend='nccl'     )  # Initializes the distributed backend

    # Setup logging
    logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN     )
    logger.info('device: {} n_gpu: {}, distributed: {}'.format(args.device							,		args.n_gpu							,		bool(args.local_rank != -1     )     )     )

    a__		=			GPTaLMHeadModel.from_pretrained(args.model_name_or_path     )

    # Distributed and parallel training
    model.to(args.device     )
    if args.local_rank != -1:
        a__		=			nn.parallel.DistributedDataParallel(
            UpperCamelCase							,		device_ids=[args.local_rank]							,		output_device=args.local_rank							,		find_unused_parameters=UpperCamelCase     )
    elif args.n_gpu > 1:
        a__		=			nn.DataParallel(UpperCamelCase     )

    # Print/save training arguments
    os.makedirs(args.output_dir							,		exist_ok=UpperCamelCase     )
    torch.save(UpperCamelCase							,		os.path.join(args.output_dir							,		'run_args.bin'     )     )
    logger.info('Training/evaluation parameters %s'							,		UpperCamelCase     )

    # Prepare dataset
    a__		=			np.concatenate(
        [
            np.loadtxt(args.data_dir							,		dtype=np.intaa     ),
        ]     )
    a__		=			(torch.from_numpy(UpperCamelCase     ),)
    a__		=			TensorDataset(*UpperCamelCase     )
    a__		=			RandomSampler(UpperCamelCase     )
    a__		=			DataLoader(UpperCamelCase							,		sampler=UpperCamelCase							,		batch_size=args.batch_size     )

    # Compute head entropy and importance score
    compute_heads_importance(UpperCamelCase							,		UpperCamelCase							,		UpperCamelCase     )

    # Try head masking (set heads to zero until the score goes under a threshole)
    # and head pruning (remove masked heads and see the effect on the network)
    if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
        a__		=			mask_heads(UpperCamelCase							,		UpperCamelCase							,		UpperCamelCase     )
        prune_heads(UpperCamelCase							,		UpperCamelCase							,		UpperCamelCase							,		UpperCamelCase     )


if __name__ == "__main__":
    main()




"""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     lowercase(_lowercase       ):
     __snake_case:	jnp.ndarray
     __snake_case:	jnp.ndarray







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



     def 			lowercase__  (							self   )		->     Dict:







         """simple docstring"""



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

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

         a__		=			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     ,				__SCREAMING_SNAKE_CASE   )		->     Any:







         """simple docstring"""



         a__		=			self.conv_in(__SCREAMING_SNAKE_CASE   )
         a__		=			nn.silu(__SCREAMING_SNAKE_CASE   )

         for block in self.blocks:
             a__		=			block(__SCREAMING_SNAKE_CASE   )
             a__		=			nn.silu(__SCREAMING_SNAKE_CASE   )

         a__		=			self.conv_out(__SCREAMING_SNAKE_CASE   )

         return embedding







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



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     FrozenDict:







         """simple docstring"""



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

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

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



     def 			lowercase__  (							self   )		->     str:







         """simple docstring"""



         a__		=			self.block_out_channels
         a__		=			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.
         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(__SCREAMING_SNAKE_CASE     ,				dtype=self.dtype   )

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

         a__		=			self.only_cross_attention
         if isinstance(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   ):
             a__		=			(only_cross_attention,) * len(self.down_block_types   )

         if isinstance(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   ):
             a__		=			(num_attention_heads,) * len(self.down_block_types   )

         # down
         a__		=			[]
         a__		=			[]

         a__		=			block_out_channels[0]

         a__		=			nn.Conv(
             __SCREAMING_SNAKE_CASE     ,				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(__SCREAMING_SNAKE_CASE   )

         for i, down_block_type in enumerate(self.down_block_types   ):
             a__		=			output_channel
             a__		=			block_out_channels[i]
             a__		=			i == len(__SCREAMING_SNAKE_CASE   ) - 1

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

             down_blocks.append(__SCREAMING_SNAKE_CASE   )

             for _ in range(self.layers_per_block   ):
                 a__		=			nn.Conv(
                     __SCREAMING_SNAKE_CASE     ,				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(__SCREAMING_SNAKE_CASE   )

             if not is_final_block:
                 a__		=			nn.Conv(
                     __SCREAMING_SNAKE_CASE     ,				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(__SCREAMING_SNAKE_CASE   )

         a__		=			down_blocks
         a__		=			controlnet_down_blocks

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

         a__		=			nn.Conv(
             __SCREAMING_SNAKE_CASE     ,				kernel_size=(1, 1)     ,				padding='VALID'     ,				kernel_init=nn.initializers.zeros_init()     ,				bias_init=nn.initializers.zeros_init()     ,				dtype=self.dtype     ,				)




     def __call__(							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE = 1.0     ,				__SCREAMING_SNAKE_CASE = True     ,				__SCREAMING_SNAKE_CASE = False     ,				)		->     Union[FlaxControlNetOutput, Tuple]:







         """simple docstring"""



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

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

         a__		=			self.time_proj(__SCREAMING_SNAKE_CASE   )
         a__		=			self.time_embedding(__SCREAMING_SNAKE_CASE   )

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

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

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

         # 4. mid
         a__		=			self.mid_block(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				deterministic=not train   )

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

         a__		=			controlnet_down_block_res_samples

         a__		=			self.controlnet_mid_block(__SCREAMING_SNAKE_CASE   )

         # 6. scaling
         a__		=			[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=__SCREAMING_SNAKE_CASE     ,				mid_block_res_sample=__SCREAMING_SNAKE_CASE   )


