


"""simple docstring"""



import argparse
import csv
import logging
import os
import random

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

from transformers import (
    CONFIG_NAME,
    WEIGHTS_NAME,
    AdamW,
    OpenAIGPTDoubleHeadsModel,
    OpenAIGPTTokenizer,
    get_linear_schedule_with_warmup,
)


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
__snake_case         =					logging.getLogger(__name__)







def 						__lowerCAmelCase		(		lowercase						:						List[str]		,							lowercase						:						Dict				)			->		str:


    """simple docstring"""





    snake_case					:   Dict         =					np.argmax(lowercase		,							axis=1				)
    return np.sum(outputs == labels				)







def 						__lowerCAmelCase		(		lowercase						:						Optional[Any]				)			->		List[Any]:


    """simple docstring"""





    with open(lowercase		,							encoding="utf_8"				) as f:
        snake_case					:   str         =					csv.reader(lowercase				)
        snake_case					:   Union[str, Any]         =					[]
        next(lowercase				)  # skip the first line
        for line in tqdm(lowercase				):
            output.append((" ".join(line[1:5]				), line[5], line[6], int(line[-1]				) - 1)				)
    return output







def 						__lowerCAmelCase		(		lowercase						:						Tuple		,							lowercase						:						Optional[int]		,							lowercase						:						List[str]		,							lowercase						:						int		,							lowercase						:						List[str]		,							lowercase						:						Tuple				)			->		Any:


    """simple docstring"""





    snake_case					:   Dict         =					[]
    for dataset in encoded_datasets:
        snake_case					:   int         =					len(lowercase				)
        snake_case					:   Optional[Any]         =					np.zeros((n_batch, 2, input_len)		,							dtype=np.intaa				)
        snake_case					:   Tuple         =					np.zeros((n_batch, 2)		,							dtype=np.intaa				)
        snake_case					:   List[Any]         =					np.full((n_batch, 2, input_len)		,							fill_value=-100		,							dtype=np.intaa				)
        snake_case					:   str         =					np.zeros((n_batch,)		,							dtype=np.intaa				)
        for (
            i,
            (story, conta, conta, mc_label),
        ) in enumerate(lowercase				):
            snake_case					:   int         =					[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
            snake_case					:   List[Any]         =					[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
            snake_case					:   Optional[Any]         =					with_conta
            snake_case					:   Tuple         =					with_conta
            snake_case					:   Tuple         =					len(lowercase				) - 1
            snake_case					:   Dict         =					len(lowercase				) - 1
            snake_case					:   Union[str, Any]         =					with_conta
            snake_case					:   Dict         =					with_conta
            snake_case					:   Union[str, Any]         =					mc_label
        snake_case					:   Optional[Any]         =					(input_ids, mc_token_ids, lm_labels, mc_labels)
        tensor_datasets.append(tuple(torch.tensor(lowercase				) for t in all_inputs				)				)
    return tensor_datasets







def 						__lowerCAmelCase		(		)			->		Optional[Any]:


    """simple docstring"""





    snake_case					:   List[str]         =					argparse.ArgumentParser()
    parser.add_argument("--model_name"		,							type=lowercase		,							default="openai-gpt"		,							help="pretrained model name"				)
    parser.add_argument("--do_train"		,							action="store_true"		,							help="Whether to run training."				)
    parser.add_argument("--do_eval"		,							action="store_true"		,							help="Whether to run eval on the dev set."				)
    parser.add_argument(
        "--output_dir"		,							default=lowercase		,							type=lowercase		,							required=lowercase		,							help="The output directory where the model predictions and checkpoints will be written."		,							)
    parser.add_argument("--train_dataset"		,							type=lowercase		,							default=""				)
    parser.add_argument("--eval_dataset"		,							type=lowercase		,							default=""				)
    parser.add_argument("--seed"		,							type=lowercase		,							default=42				)
    parser.add_argument("--num_train_epochs"		,							type=lowercase		,							default=3				)
    parser.add_argument("--train_batch_size"		,							type=lowercase		,							default=8				)
    parser.add_argument("--eval_batch_size"		,							type=lowercase		,							default=16				)
    parser.add_argument("--adam_epsilon"		,							default=1e-8		,							type=lowercase		,							help="Epsilon for Adam optimizer."				)
    parser.add_argument("--max_grad_norm"		,							type=lowercase		,							default=1				)
    parser.add_argument(
        "--max_steps"		,							default=-1		,							type=lowercase		,							help=(
            "If > 0: set total number of training                         steps to perform. Override num_train_epochs."
        )		,							)
    parser.add_argument(
        "--gradient_accumulation_steps"		,							type=lowercase		,							default=1		,							help="Number of updates steps to accumulate before                        performing a backward/update pass."		,							)
    parser.add_argument("--learning_rate"		,							type=lowercase		,							default=6.2_5e-5				)
    parser.add_argument("--warmup_steps"		,							default=0		,							type=lowercase		,							help="Linear warmup over warmup_steps."				)
    parser.add_argument("--lr_schedule"		,							type=lowercase		,							default="warmup_linear"				)
    parser.add_argument("--weight_decay"		,							type=lowercase		,							default=0.01				)
    parser.add_argument("--lm_coef"		,							type=lowercase		,							default=0.9				)
    parser.add_argument("--n_valid"		,							type=lowercase		,							default=374				)

    parser.add_argument("--server_ip"		,							type=lowercase		,							default=""		,							help="Can be used for distant debugging."				)
    parser.add_argument("--server_port"		,							type=lowercase		,							default=""		,							help="Can be used for distant debugging."				)
    snake_case					:   Any         =					parser.parse_args()
    print(lowercase				)

    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=lowercase				)
        ptvsd.wait_for_attach()

    random.seed(args.seed				)
    np.random.seed(args.seed				)
    torch.manual_seed(args.seed				)
    torch.cuda.manual_seed_all(args.seed				)

    snake_case					:   int         =					torch.device("cuda" if torch.cuda.is_available() else "cpu"				)
    snake_case					:   Dict         =					torch.cuda.device_count()
    logger.info("device: {}, n_gpu {}".format(lowercase		,							lowercase				)				)

    if not args.do_train and not args.do_eval:
        raise ValueError("At least one of `do_train` or `do_eval` must be True."				)

    if not os.path.exists(args.output_dir				):
        os.makedirs(args.output_dir				)

    # Load tokenizer and model
    # This loading functions also add new tokens and embeddings called `special tokens`
    # These new embeddings will be fine-tuned on the RocStories dataset
    snake_case					:   Any         =					["_start_", "_delimiter_", "_classify_"]
    snake_case					:   Optional[int]         =					OpenAIGPTTokenizer.from_pretrained(args.model_name				)
    tokenizer.add_tokens(lowercase				)
    snake_case					:   List[Any]         =					tokenizer.convert_tokens_to_ids(lowercase				)
    snake_case					:   int         =					OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name				)
    model.resize_token_embeddings(len(lowercase				)				)
    model.to(lowercase				)

    # Load and encode the datasets
    def tokenize_and_encode(lowercase						:						Optional[int]				):
        if isinstance(lowercase		,							lowercase				):
            return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(lowercase				)				)
        elif isinstance(lowercase		,							lowercase				):
            return obj
        return [tokenize_and_encode(lowercase				) for o in obj]

    logger.info("Encoding dataset..."				)
    snake_case					:   Tuple         =					load_rocstories_dataset(args.train_dataset				)
    snake_case					:   Any         =					load_rocstories_dataset(args.eval_dataset				)
    snake_case					:   Optional[Any]         =					(train_dataset, eval_dataset)
    snake_case					:   Dict         =					tokenize_and_encode(lowercase				)

    # Compute the max input length for the Transformer
    snake_case					:   List[Any]         =					model.config.n_positions // 2 - 2
    snake_case					:   Dict         =					max(
        len(story[:max_length]				) + max(len(conta[:max_length]				)		,							len(conta[:max_length]				)				) + 3
        for dataset in encoded_datasets
        for story, conta, conta, _ in dataset				)
    snake_case					:   Optional[int]         =					min(lowercase		,							model.config.n_positions				)  # Max size of input for the pre-trained model

    # Prepare inputs tensors and dataloaders
    snake_case					:   Tuple         =					pre_process_datasets(lowercase		,							lowercase		,							lowercase		,							*lowercase				)
    snake_case      ,snake_case					:   List[Any]         =					tensor_datasets[0], tensor_datasets[1]

    snake_case					:   str         =					TensorDataset(*lowercase				)
    snake_case					:   Tuple         =					RandomSampler(lowercase				)
    snake_case					:   Dict         =					DataLoader(lowercase		,							sampler=lowercase		,							batch_size=args.train_batch_size				)

    snake_case					:   Optional[Any]         =					TensorDataset(*lowercase				)
    snake_case					:   int         =					SequentialSampler(lowercase				)
    snake_case					:   Any         =					DataLoader(lowercase		,							sampler=lowercase		,							batch_size=args.eval_batch_size				)

    # Prepare optimizer
    if args.do_train:
        if args.max_steps > 0:
            snake_case					:   Optional[int]         =					args.max_steps
            snake_case					:   str         =					args.max_steps // (len(lowercase				) // args.gradient_accumulation_steps) + 1
        else:
            snake_case					:   List[str]         =					len(lowercase				) // args.gradient_accumulation_steps * args.num_train_epochs

        snake_case					:   List[Any]         =					list(model.named_parameters()				)
        snake_case					:   str         =					["bias", "LayerNorm.bias", "LayerNorm.weight"]
        snake_case					:   Union[str, Any]         =					[
            {
                "params": [p for n, p in param_optimizer if not any(nd in n for nd in no_decay				)],
                "weight_decay": args.weight_decay,
            },
            {"params": [p for n, p in param_optimizer if any(nd in n for nd in no_decay				)], "weight_decay": 0.0},
        ]
        snake_case					:   Optional[Any]         =					AdamW(lowercase		,							lr=args.learning_rate		,							eps=args.adam_epsilon				)
        snake_case					:   Optional[int]         =					get_linear_schedule_with_warmup(
            lowercase		,							num_warmup_steps=args.warmup_steps		,							num_training_steps=lowercase				)

    if args.do_train:
        snake_case      ,snake_case      ,snake_case					:   int         =					0, 0, None
        model.train()
        for _ in trange(int(args.num_train_epochs				)		,							desc="Epoch"				):
            snake_case					:   int         =					0
            snake_case					:   int         =					0
            snake_case					:   Union[str, Any]         =					tqdm(lowercase		,							desc="Training"				)
            for step, batch in enumerate(lowercase				):
                snake_case					:   Optional[Any]         =					tuple(t.to(lowercase				) for t in batch				)
                snake_case      ,snake_case      ,snake_case      ,snake_case					:   List[Any]         =					batch
                snake_case					:   Dict         =					model(lowercase		,							mc_token_ids=lowercase		,							lm_labels=lowercase		,							mc_labels=lowercase				)
                snake_case					:   Union[str, Any]         =					args.lm_coef * losses[0] + losses[1]
                loss.backward()
                optimizer.step()
                scheduler.step()
                optimizer.zero_grad()
                tr_loss += loss.item()
                snake_case					:   List[Any]         =					(
                    loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
                )
                nb_tr_steps += 1
                snake_case					:   Optional[Any]         =					"Training loss: {:.2e} lr: {:.2e}".format(lowercase		,							scheduler.get_lr()[0]				)

    # Save a trained model
    if args.do_train:
        # Save a trained model, configuration and tokenizer
        snake_case					:   List[str]         =					model.module if hasattr(lowercase		,							"module"				) else model  # Only save the model itself

        # If we save using the predefined names, we can load using `from_pretrained`
        snake_case					:   List[Any]         =					os.path.join(args.output_dir		,							lowercase				)
        snake_case					:   Dict         =					os.path.join(args.output_dir		,							lowercase				)

        torch.save(model_to_save.state_dict()		,							lowercase				)
        model_to_save.config.to_json_file(lowercase				)
        tokenizer.save_vocabulary(args.output_dir				)

        # Load a trained model and vocabulary that you have fine-tuned
        snake_case					:   Optional[int]         =					OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir				)
        snake_case					:   Dict         =					OpenAIGPTTokenizer.from_pretrained(args.output_dir				)
        model.to(lowercase				)

    if args.do_eval:
        model.eval()
        snake_case      ,snake_case					:   Dict         =					0, 0
        snake_case      ,snake_case					:   Union[str, Any]         =					0, 0
        for batch in tqdm(lowercase		,							desc="Evaluating"				):
            snake_case					:   Dict         =					tuple(t.to(lowercase				) for t in batch				)
            snake_case      ,snake_case      ,snake_case      ,snake_case					:   str         =					batch
            with torch.no_grad():
                snake_case      ,snake_case      ,snake_case      ,snake_case					:   int         =					model(
                    lowercase		,							mc_token_ids=lowercase		,							lm_labels=lowercase		,							mc_labels=lowercase				)

            snake_case					:   Tuple         =					mc_logits.detach().cpu().numpy()
            snake_case					:   Optional[int]         =					mc_labels.to("cpu"				).numpy()
            snake_case					:   str         =					accuracy(lowercase		,							lowercase				)

            eval_loss += mc_loss.mean().item()
            eval_accuracy += tmp_eval_accuracy

            nb_eval_examples += input_ids.size(0				)
            nb_eval_steps += 1

        snake_case					:   Optional[Any]         =					eval_loss / nb_eval_steps
        snake_case					:   Tuple         =					eval_accuracy / nb_eval_examples
        snake_case					:   Union[str, Any]         =					tr_loss / nb_tr_steps if args.do_train else None
        snake_case					:   Tuple         =					{"eval_loss": eval_loss, "eval_accuracy": eval_accuracy, "train_loss": train_loss}

        snake_case					:   Union[str, Any]         =					os.path.join(args.output_dir		,							"eval_results.txt"				)
        with open(lowercase		,							"w"				) as writer:
            logger.info("***** Eval results *****"				)
            for key in sorted(result.keys()				):
                logger.info("  %s = %s"		,							lowercase		,							str(result[key]				)				)
                writer.write("%s = %s\n" % (key, str(result[key]				))				)


if __name__ == "__main__":
  main()









"""simple docstring"""



import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD







class _lowerCAmelCase       (   nn.Module			):
   __UpperCAmelCase				: int
   __UpperCAmelCase				: int
   __UpperCAmelCase				: float													=						0.0
   __UpperCAmelCase				: int													=						1
   __UpperCAmelCase				: int													=						1
   __UpperCAmelCase				: bool													=						True
   __UpperCAmelCase				: bool													=						False
   __UpperCAmelCase				: bool													=						False
   __UpperCAmelCase				: bool													=						False
   __UpperCAmelCase				: jnp.dtype													=						jnp.floataa







   def 				lowerCamelCase	(			self		)			->	List[str]:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					[]
       snake_case					:   Tuple         =					[]

       for i in range(self.num_layers		):
           snake_case					:   Any         =					self.in_channels if i == 0 else self.out_channels

           snake_case					:   Optional[int]         =					FlaxResnetBlockaD(
               in_channels=UpperCamelCase__		,			out_channels=self.out_channels		,			dropout_prob=self.dropout		,			dtype=self.dtype		,			)
           resnets.append(UpperCamelCase__		)

           snake_case					:   Dict         =					FlaxTransformeraDModel(
               in_channels=self.out_channels		,			n_heads=self.num_attention_heads		,			d_head=self.out_channels // self.num_attention_heads		,			depth=1		,			use_linear_projection=self.use_linear_projection		,			only_cross_attention=self.only_cross_attention		,			use_memory_efficient_attention=self.use_memory_efficient_attention		,			dtype=self.dtype		,			)
           attentions.append(UpperCamelCase__		)

       snake_case					:   List[Any]         =					resnets
       snake_case					:   List[Any]         =					attentions

       if self.add_downsample:
           snake_case					:   Optional[Any]         =					FlaxDownsampleaD(self.out_channels		,			dtype=self.dtype		)







   def __call__(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__=True		)			->	List[str]:

       '''simple docstring'''




       snake_case					:   Tuple         =					()

       for resnet, attn in zip(self.resnets		,			self.attentions		):
           snake_case					:   str         =					resnet(UpperCamelCase__		,			UpperCamelCase__		,			deterministic=UpperCamelCase__		)
           snake_case					:   int         =					attn(UpperCamelCase__		,			UpperCamelCase__		,			deterministic=UpperCamelCase__		)
           output_states += (hidden_states,)

       if self.add_downsample:
           snake_case					:   Any         =					self.downsamplers_a(UpperCamelCase__		)
           output_states += (hidden_states,)

       return hidden_states, output_states







class _lowerCAmelCase       (   nn.Module			):
   __UpperCAmelCase				: int
   __UpperCAmelCase				: int
   __UpperCAmelCase				: float													=						0.0
   __UpperCAmelCase				: int													=						1
   __UpperCAmelCase				: bool													=						True
   __UpperCAmelCase				: jnp.dtype													=						jnp.floataa







   def 				lowerCamelCase	(			self		)			->	List[str]:

       '''simple docstring'''




       snake_case					:   Dict         =					[]

       for i in range(self.num_layers		):
           snake_case					:   List[str]         =					self.in_channels if i == 0 else self.out_channels

           snake_case					:   str         =					FlaxResnetBlockaD(
               in_channels=UpperCamelCase__		,			out_channels=self.out_channels		,			dropout_prob=self.dropout		,			dtype=self.dtype		,			)
           resnets.append(UpperCamelCase__		)
       snake_case					:   str         =					resnets

       if self.add_downsample:
           snake_case					:   str         =					FlaxDownsampleaD(self.out_channels		,			dtype=self.dtype		)







   def __call__(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__=True		)			->	List[Any]:

       '''simple docstring'''




       snake_case					:   Tuple         =					()

       for resnet in self.resnets:
           snake_case					:   Optional[Any]         =					resnet(UpperCamelCase__		,			UpperCamelCase__		,			deterministic=UpperCamelCase__		)
           output_states += (hidden_states,)

       if self.add_downsample:
           snake_case					:   Tuple         =					self.downsamplers_a(UpperCamelCase__		)
           output_states += (hidden_states,)

       return hidden_states, output_states







class _lowerCAmelCase       (   nn.Module			):
   __UpperCAmelCase				: int
   __UpperCAmelCase				: int
   __UpperCAmelCase				: int
   __UpperCAmelCase				: float													=						0.0
   __UpperCAmelCase				: int													=						1
   __UpperCAmelCase				: int													=						1
   __UpperCAmelCase				: bool													=						True
   __UpperCAmelCase				: bool													=						False
   __UpperCAmelCase				: bool													=						False
   __UpperCAmelCase				: bool													=						False
   __UpperCAmelCase				: jnp.dtype													=						jnp.floataa







   def 				lowerCamelCase	(			self		)			->	Optional[int]:

       '''simple docstring'''




       snake_case					:   Dict         =					[]
       snake_case					:   Union[str, Any]         =					[]

       for i in range(self.num_layers		):
           snake_case					:   Optional[Any]         =					self.in_channels if (i == self.num_layers - 1) else self.out_channels
           snake_case					:   Union[str, Any]         =					self.prev_output_channel if i == 0 else self.out_channels

           snake_case					:   Optional[Any]         =					FlaxResnetBlockaD(
               in_channels=resnet_in_channels + res_skip_channels		,			out_channels=self.out_channels		,			dropout_prob=self.dropout		,			dtype=self.dtype		,			)
           resnets.append(UpperCamelCase__		)

           snake_case					:   int         =					FlaxTransformeraDModel(
               in_channels=self.out_channels		,			n_heads=self.num_attention_heads		,			d_head=self.out_channels // self.num_attention_heads		,			depth=1		,			use_linear_projection=self.use_linear_projection		,			only_cross_attention=self.only_cross_attention		,			use_memory_efficient_attention=self.use_memory_efficient_attention		,			dtype=self.dtype		,			)
           attentions.append(UpperCamelCase__		)

       snake_case					:   str         =					resnets
       snake_case					:   List[Any]         =					attentions

       if self.add_upsample:
           snake_case					:   str         =					FlaxUpsampleaD(self.out_channels		,			dtype=self.dtype		)







   def __call__(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__=True		)			->	Optional[int]:

       '''simple docstring'''




       for resnet, attn in zip(self.resnets		,			self.attentions		):
           # pop res hidden states
           snake_case					:   Dict         =					res_hidden_states_tuple[-1]
           snake_case					:   Optional[int]         =					res_hidden_states_tuple[:-1]
           snake_case					:   List[Any]         =					jnp.concatenate((hidden_states, res_hidden_states)		,			axis=-1		)

           snake_case					:   List[Any]         =					resnet(UpperCamelCase__		,			UpperCamelCase__		,			deterministic=UpperCamelCase__		)
           snake_case					:   Any         =					attn(UpperCamelCase__		,			UpperCamelCase__		,			deterministic=UpperCamelCase__		)

       if self.add_upsample:
           snake_case					:   int         =					self.upsamplers_a(UpperCamelCase__		)

       return hidden_states







class _lowerCAmelCase       (   nn.Module			):
   __UpperCAmelCase				: int
   __UpperCAmelCase				: int
   __UpperCAmelCase				: int
   __UpperCAmelCase				: float													=						0.0
   __UpperCAmelCase				: int													=						1
   __UpperCAmelCase				: bool													=						True
   __UpperCAmelCase				: jnp.dtype													=						jnp.floataa







   def 				lowerCamelCase	(			self		)			->	Union[str, Any]:

       '''simple docstring'''




       snake_case					:   str         =					[]

       for i in range(self.num_layers		):
           snake_case					:   Union[str, Any]         =					self.in_channels if (i == self.num_layers - 1) else self.out_channels
           snake_case					:   Optional[Any]         =					self.prev_output_channel if i == 0 else self.out_channels

           snake_case					:   Dict         =					FlaxResnetBlockaD(
               in_channels=resnet_in_channels + res_skip_channels		,			out_channels=self.out_channels		,			dropout_prob=self.dropout		,			dtype=self.dtype		,			)
           resnets.append(UpperCamelCase__		)

       snake_case					:   Optional[int]         =					resnets

       if self.add_upsample:
           snake_case					:   Dict         =					FlaxUpsampleaD(self.out_channels		,			dtype=self.dtype		)







   def __call__(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__=True		)			->	int:

       '''simple docstring'''




       for resnet in self.resnets:
           # pop res hidden states
           snake_case					:   int         =					res_hidden_states_tuple[-1]
           snake_case					:   List[Any]         =					res_hidden_states_tuple[:-1]
           snake_case					:   Optional[int]         =					jnp.concatenate((hidden_states, res_hidden_states)		,			axis=-1		)

           snake_case					:   Dict         =					resnet(UpperCamelCase__		,			UpperCamelCase__		,			deterministic=UpperCamelCase__		)

       if self.add_upsample:
           snake_case					:   Optional[int]         =					self.upsamplers_a(UpperCamelCase__		)

       return hidden_states







class _lowerCAmelCase       (   nn.Module			):
   __UpperCAmelCase				: int
   __UpperCAmelCase				: float													=						0.0
   __UpperCAmelCase				: int													=						1
   __UpperCAmelCase				: int													=						1
   __UpperCAmelCase				: bool													=						False
   __UpperCAmelCase				: bool													=						False
   __UpperCAmelCase				: jnp.dtype													=						jnp.floataa







   def 				lowerCamelCase	(			self		)			->	Tuple:

       '''simple docstring'''




       snake_case					:   Dict         =					[
           FlaxResnetBlockaD(
               in_channels=self.in_channels		,			out_channels=self.in_channels		,			dropout_prob=self.dropout		,			dtype=self.dtype		,			)
       ]

       snake_case					:   Any         =					[]

       for _ in range(self.num_layers		):
           snake_case					:   Union[str, Any]         =					FlaxTransformeraDModel(
               in_channels=self.in_channels		,			n_heads=self.num_attention_heads		,			d_head=self.in_channels // self.num_attention_heads		,			depth=1		,			use_linear_projection=self.use_linear_projection		,			use_memory_efficient_attention=self.use_memory_efficient_attention		,			dtype=self.dtype		,			)
           attentions.append(UpperCamelCase__		)

           snake_case					:   List[Any]         =					FlaxResnetBlockaD(
               in_channels=self.in_channels		,			out_channels=self.in_channels		,			dropout_prob=self.dropout		,			dtype=self.dtype		,			)
           resnets.append(UpperCamelCase__		)

       snake_case					:   Union[str, Any]         =					resnets
       snake_case					:   str         =					attentions







   def __call__(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__=True		)			->	Any:

       '''simple docstring'''




       snake_case					:   Optional[Any]         =					self.resnets[0](UpperCamelCase__		,			UpperCamelCase__		)
       for attn, resnet in zip(self.attentions		,			self.resnets[1:]		):
           snake_case					:   Union[str, Any]         =					attn(UpperCamelCase__		,			UpperCamelCase__		,			deterministic=UpperCamelCase__		)
           snake_case					:   Optional[Any]         =					resnet(UpperCamelCase__		,			UpperCamelCase__		,			deterministic=UpperCamelCase__		)

       return hidden_states





