
"""simple docstring"""




import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


A__   :						Optional[int] 							=     logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()



def 				a__			(					lowerCAmelCase						:							int		):






     '''simple docstring'''
     if "token" in model_name_or_path:
          return "rag_token"
     if "sequence" in model_name_or_path:
          return "rag_sequence"
     if "bart" in model_name_or_path:
          return "bart"
     return None



def 				a__			(					lowerCAmelCase						:							List[str]							,     lowerCAmelCase						:							str							,     lowerCAmelCase						:							Any		):






     '''simple docstring'''
     return max(metric_fn(lowerCAmelCase							,     lowerCAmelCase		) for gt in ground_truths		)



def 				a__			(					lowerCAmelCase						:							Union[str, Any]							,     lowerCAmelCase						:							Optional[Any]							,     lowerCAmelCase						:							str		):






     '''simple docstring'''
     UpperCAmelCase__				:			int							=    [line.strip() for line in open(lowerCAmelCase							,     "r"		).readlines()]
     UpperCAmelCase__				:			Optional[int]							=    []

     if args.gold_data_mode == "qa":
          UpperCAmelCase__				:			List[Any]							=    pd.read_csv(lowerCAmelCase							,     sep="\t"							,     header=lowerCAmelCase		)
          for answer_list in data[1]:
               UpperCAmelCase__				:			List[str]							=    ast.literal_eval(lowerCAmelCase		)
               answers.append(lowerCAmelCase		)
     else:
          UpperCAmelCase__				:			Union[str, Any]							=    [line.strip() for line in open(lowerCAmelCase							,     "r"		).readlines()]
          UpperCAmelCase__				:			Any							=    [[reference] for reference in references]

     UpperCAmelCase__				:			Any							=    0
     for prediction, ground_truths in zip(lowerCAmelCase							,     lowerCAmelCase		):
          total += 1
          em += metric_max_over_ground_truths(lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase		)
          fa += metric_max_over_ground_truths(lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase		)

     UpperCAmelCase__				:			Union[str, Any]							=    100.0 * em / total
     UpperCAmelCase__				:			int							=    100.0 * fa / total

     logger.info(F"F1: {fa:.2f}"		)
     logger.info(F"EM: {em:.2f}"		)



def 				a__			(					lowerCAmelCase						:							Union[str, Any]							,     lowerCAmelCase						:							str							,     lowerCAmelCase						:							Any		):






     '''simple docstring'''
     UpperCAmelCase__				:			str							=    args.k
     UpperCAmelCase__				:			str							=    [line.strip() for line in open(lowerCAmelCase							,     "r"		).readlines()]
     UpperCAmelCase__				:			Union[str, Any]							=    [line.strip() for line in open(lowerCAmelCase							,     "r"		).readlines()]

     UpperCAmelCase__				:			Dict							=    0
     for hypo, reference in zip(lowerCAmelCase							,     lowerCAmelCase		):
          UpperCAmelCase__				:			List[Any]							=    set(hypo.split("\t"		)[:k]		)
          UpperCAmelCase__				:			Any							=    set(reference.split("\t"		)		)
          total += 1
          em += len(hypo_provenance & ref_provenance		) / k

     UpperCAmelCase__				:			Optional[int]							=    100.0 * em / total
     logger.info(F"Precision@{k}: {em: .2f}"		)



def 				a__			(					lowerCAmelCase						:							Any							,     lowerCAmelCase						:							Tuple							,     lowerCAmelCase						:							Optional[int]		):






     '''simple docstring'''
     def strip_title(lowerCAmelCase						:							List[str]		):
          if title.startswith("\""		):
               UpperCAmelCase__				:			Optional[Any]							=    title[1:]
          if title.endswith("\""		):
               UpperCAmelCase__				:			int							=    title[:-1]
          return title

     UpperCAmelCase__				:			Dict							=    rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
         lowerCAmelCase							,     return_tensors="pt"							,     padding=lowerCAmelCase							,     truncation=lowerCAmelCase							,     )["input_ids"].to(args.device		)

     UpperCAmelCase__				:			Optional[Any]							=    rag_model.rag.question_encoder(lowerCAmelCase		)
     UpperCAmelCase__				:			Any							=    question_enc_outputs[0]

     UpperCAmelCase__				:			Any							=    rag_model.retriever(
         lowerCAmelCase							,     question_enc_pool_output.cpu().detach().to(torch.floataa		).numpy()							,     prefix=rag_model.rag.generator.config.prefix							,     n_docs=rag_model.config.n_docs							,     return_tensors="pt"							,     )
     UpperCAmelCase__				:			Optional[int]							=    rag_model.retriever.index.get_doc_dicts(result.doc_ids		)
     UpperCAmelCase__				:			Union[str, Any]							=    []
     for docs in all_docs:
          UpperCAmelCase__				:			int							=    [strip_title(lowerCAmelCase		) for title in docs["title"]]
          provenance_strings.append("\t".join(lowerCAmelCase		)		)
     return provenance_strings



def 				a__			(					lowerCAmelCase						:							List[str]							,     lowerCAmelCase						:							Optional[Any]							,     lowerCAmelCase						:							List[str]		):






     '''simple docstring'''
     with torch.no_grad():
          UpperCAmelCase__				:			Tuple							=    rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
              lowerCAmelCase							,     return_tensors="pt"							,     padding=lowerCAmelCase							,     truncation=lowerCAmelCase		)

          UpperCAmelCase__				:			Any							=    inputs_dict.input_ids.to(args.device		)
          UpperCAmelCase__				:			Optional[int]							=    inputs_dict.attention_mask.to(args.device		)
          UpperCAmelCase__				:			int							=    rag_model.generate(  # rag_model overwrites generate
              lowerCAmelCase							,     attention_mask=lowerCAmelCase							,     num_beams=args.num_beams							,     min_length=args.min_length							,     max_length=args.max_length							,     early_stopping=lowerCAmelCase							,     num_return_sequences=1							,     bad_words_ids=[[0, 0]]							,     )
          UpperCAmelCase__				:			Any							=    rag_model.retriever.generator_tokenizer.batch_decode(lowerCAmelCase							,     skip_special_tokens=lowerCAmelCase		)

          if args.print_predictions:
               for q, a in zip(lowerCAmelCase							,     lowerCAmelCase		):
                    logger.info("Q: {} - A: {}".format(lowerCAmelCase							,     lowerCAmelCase		)		)

          return answers



def 				a__			(					):






     '''simple docstring'''
     UpperCAmelCase__				:			Any							=    argparse.ArgumentParser()
     parser.add_argument(
         "--model_type"							,     choices=["rag_sequence", "rag_token", "bart"]							,     type=lowerCAmelCase							,     help=(
             "RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the"
             " model_name_or_path"
         )							,     )
     parser.add_argument(
         "--index_name"							,     default=lowerCAmelCase							,     choices=["exact", "compressed", "legacy"]							,     type=lowerCAmelCase							,     help="RAG model retriever type"							,     )
     parser.add_argument(
         "--index_path"							,     default=lowerCAmelCase							,     type=lowerCAmelCase							,     help="Path to the retrieval index"							,     )
     parser.add_argument("--n_docs"							,     default=5							,     type=lowerCAmelCase							,     help="Number of retrieved docs"		)
     parser.add_argument(
         "--model_name_or_path"							,     default=lowerCAmelCase							,     type=lowerCAmelCase							,     required=lowerCAmelCase							,     help="Path to pretrained checkpoints or model identifier from huggingface.co/models"							,     )
     parser.add_argument(
         "--eval_mode"							,     choices=["e2e", "retrieval"]							,     default="e2e"							,     type=lowerCAmelCase							,     help=(
             "Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"
             " precision@k."
         )							,     )
     parser.add_argument("--k"							,     default=1							,     type=lowerCAmelCase							,     help="k for the precision@k calculation"		)
     parser.add_argument(
         "--evaluation_set"							,     default=lowerCAmelCase							,     type=lowerCAmelCase							,     required=lowerCAmelCase							,     help="Path to a file containing evaluation samples"							,     )
     parser.add_argument(
         "--gold_data_path"							,     default=lowerCAmelCase							,     type=lowerCAmelCase							,     required=lowerCAmelCase							,     help="Path to a tab-separated file with gold samples"							,     )
     parser.add_argument(
         "--gold_data_mode"							,     default="qa"							,     type=lowerCAmelCase							,     choices=["qa", "ans"]							,     help=(
             "Format of the gold data file"
             "qa - a single line in the following format: question [tab] answer_list"
             "ans - a single line of the gold file contains the expected answer string"
         )							,     )
     parser.add_argument(
         "--predictions_path"							,     type=lowerCAmelCase							,     default="predictions.txt"							,     help="Name of the predictions file, to be stored in the checkpoints directory"							,     )
     parser.add_argument(
         "--eval_all_checkpoints"							,     action="store_true"							,     help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number"							,     )
     parser.add_argument(
         "--eval_batch_size"							,     default=8							,     type=lowerCAmelCase							,     help="Batch size per GPU/CPU for evaluation."							,     )
     parser.add_argument(
         "--recalculate"							,     help="Recalculate predictions even if the prediction file exists"							,     action="store_true"							,     )
     parser.add_argument(
         "--num_beams"							,     default=4							,     type=lowerCAmelCase							,     help="Number of beams to be used when generating answers"							,     )
     parser.add_argument("--min_length"							,     default=1							,     type=lowerCAmelCase							,     help="Min length of the generated answers"		)
     parser.add_argument("--max_length"							,     default=50							,     type=lowerCAmelCase							,     help="Max length of the generated answers"		)

     parser.add_argument(
         "--print_predictions"							,     action="store_true"							,     help="If True, prints predictions while evaluating."							,     )
     parser.add_argument(
         "--print_docs"							,     action="store_true"							,     help="If True, prints docs retried while generating."							,     )
     UpperCAmelCase__				:			str							=    parser.parse_args()
     UpperCAmelCase__				:			Union[str, Any]							=    torch.device("cuda" if torch.cuda.is_available() else "cpu"		)
     return args



def 				a__			(					lowerCAmelCase						:							Dict		):






     '''simple docstring'''
     UpperCAmelCase__				:			Optional[int]							=    {}
     if args.model_type is None:
          UpperCAmelCase__				:			Optional[int]							=    infer_model_type(args.model_name_or_path		)
          assert args.model_type is not None
     if args.model_type.startswith("rag"		):
          UpperCAmelCase__				:			Dict							=    RagTokenForGeneration if args.model_type == "rag_token" else RagSequenceForGeneration
          UpperCAmelCase__				:			List[str]							=    args.n_docs
          if args.index_name is not None:
               UpperCAmelCase__				:			Any							=    args.index_name
          if args.index_path is not None:
               UpperCAmelCase__				:			Dict							=    args.index_path
     else:
          UpperCAmelCase__				:			str							=    BartForConditionalGeneration

     UpperCAmelCase__				:			Dict							=    (
         [f.path for f in os.scandir(args.model_name_or_path		) if f.is_dir()]
         if args.eval_all_checkpoints
         else [args.model_name_or_path]
     )

     logger.info("Evaluate the following checkpoints: %s"							,     lowerCAmelCase		)

     UpperCAmelCase__				:			Tuple							=    get_scores if args.eval_mode == "e2e" else get_precision_at_k
     UpperCAmelCase__				:			List[Any]							=    evaluate_batch_eae if args.eval_mode == "e2e" else evaluate_batch_retrieval

     for checkpoint in checkpoints:
          if os.path.exists(args.predictions_path		) and (not args.recalculate):
               logger.info("Calculating metrics based on an existing predictions file: {}".format(args.predictions_path		)		)
               score_fn(lowerCAmelCase							,     args.predictions_path							,     args.gold_data_path		)
               continue

          logger.info("***** Running evaluation for {} *****".format(lowerCAmelCase		)		)
          logger.info("  Batch size = %d"							,     args.eval_batch_size		)
          logger.info("  Predictions will be stored under {}".format(args.predictions_path		)		)

          if args.model_type.startswith("rag"		):
               UpperCAmelCase__				:			Optional[int]							=    RagRetriever.from_pretrained(lowerCAmelCase							,     **lowerCAmelCase		)
               UpperCAmelCase__				:			List[Any]							=    model_class.from_pretrained(lowerCAmelCase							,     retriever=lowerCAmelCase							,     **lowerCAmelCase		)
               model.retriever.init_retrieval()
          else:
               UpperCAmelCase__				:			int							=    model_class.from_pretrained(lowerCAmelCase							,     **lowerCAmelCase		)
          model.to(args.device		)

          with open(args.evaluation_set							,     "r"		) as eval_file, open(args.predictions_path							,     "w"		) as preds_file:
               UpperCAmelCase__				:			int							=    []
               for line in tqdm(lowerCAmelCase		):
                    questions.append(line.strip()		)
                    if len(lowerCAmelCase		) == args.eval_batch_size:
                         UpperCAmelCase__				:			Dict							=    evaluate_batch_fn(lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase		)
                         preds_file.write("\n".join(lowerCAmelCase		) + "\n"		)
                         preds_file.flush()
                         UpperCAmelCase__				:			List[Any]							=    []
               if len(lowerCAmelCase		) > 0:
                    UpperCAmelCase__				:			Any							=    evaluate_batch_fn(lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase		)
                    preds_file.write("\n".join(lowerCAmelCase		)		)
                    preds_file.flush()

               score_fn(lowerCAmelCase							,     args.predictions_path							,     args.gold_data_path		)


if __name__ == "__main__":
    A__   :						str 							=     get_args()
    main(args)





"""simple docstring"""




from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block







@dataclass
class 	_lowercase    (							lowerCAmelCase_					):


     '''simple docstring'''





     _A		    =				42







class 	_lowercase    (							lowerCAmelCase_						, lowerCAmelCase_					):


     '''simple docstring'''





     @register_to_config
     def __init__(   self			,      __UpperCamelCase = None			,      __UpperCamelCase = 3			,      __UpperCamelCase = 3			,      __UpperCamelCase = False			,      __UpperCamelCase = "positional"			,      __UpperCamelCase = 0			,      __UpperCamelCase = True			,      __UpperCamelCase = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")			,      __UpperCamelCase = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")			,      __UpperCamelCase = (2_24, 4_48, 6_72, 8_96)			,      __UpperCamelCase = 2			,      __UpperCamelCase = 1			,      __UpperCamelCase = 1			,      __UpperCamelCase = "conv"			,      __UpperCamelCase = "conv"			,      __UpperCamelCase = "silu"			,      __UpperCamelCase = 8			,      __UpperCamelCase = 32			,      __UpperCamelCase = 1E-5			,      __UpperCamelCase = "default"			,      __UpperCamelCase = True			,      __UpperCamelCase = None			,      __UpperCamelCase = None			,      )->       Any:
          super().__init__()

          UpperCAmelCase__				:			Optional[int]							=    sample_size
          UpperCAmelCase__				:			Any							=    block_out_channels[0] * 4

          # Check inputs
          if len(__UpperCamelCase	) != len(__UpperCamelCase	):
               raise ValueError(
                   F"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."	)

          if len(__UpperCamelCase	) != len(__UpperCamelCase	):
               raise ValueError(
                   F"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."	)

          # input
          UpperCAmelCase__				:			List[Any]							=    nn.Convad(__UpperCamelCase			,      block_out_channels[0]			,      kernel_size=3			,      padding=(1, 1)	)

          # time
          if time_embedding_type == "fourier":
               UpperCAmelCase__				:			int							=    GaussianFourierProjection(embedding_size=block_out_channels[0]			,      scale=16	)
               UpperCAmelCase__				:			Dict							=    2 * block_out_channels[0]
          elif time_embedding_type == "positional":
               UpperCAmelCase__				:			List[str]							=    Timesteps(block_out_channels[0]			,      __UpperCamelCase			,      __UpperCamelCase	)
               UpperCAmelCase__				:			Any							=    block_out_channels[0]

          UpperCAmelCase__				:			List[str]							=    TimestepEmbedding(__UpperCamelCase			,      __UpperCamelCase	)

          # class embedding
          if class_embed_type is None and num_class_embeds is not None:
               UpperCAmelCase__				:			Optional[Any]							=    nn.Embedding(__UpperCamelCase			,      __UpperCamelCase	)
          elif class_embed_type == "timestep":
               UpperCAmelCase__				:			str							=    TimestepEmbedding(__UpperCamelCase			,      __UpperCamelCase	)
          elif class_embed_type == "identity":
               UpperCAmelCase__				:			Union[str, Any]							=    nn.Identity(__UpperCamelCase			,      __UpperCamelCase	)
          else:
               UpperCAmelCase__				:			Optional[int]							=    None

          UpperCAmelCase__				:			str							=    nn.ModuleList([]	)
          UpperCAmelCase__				:			Any							=    None
          UpperCAmelCase__				:			Tuple							=    nn.ModuleList([]	)

          # down
          UpperCAmelCase__				:			Any							=    block_out_channels[0]
          for i, down_block_type in enumerate(__UpperCamelCase	):
               UpperCAmelCase__				:			List[Any]							=    output_channel
               UpperCAmelCase__				:			Optional[int]							=    block_out_channels[i]
               UpperCAmelCase__				:			Any							=    i == len(__UpperCamelCase	) - 1

               UpperCAmelCase__				:			List[Any]							=    get_down_block(
                   __UpperCamelCase			,      num_layers=__UpperCamelCase			,      in_channels=__UpperCamelCase			,      out_channels=__UpperCamelCase			,      temb_channels=__UpperCamelCase			,      add_downsample=not is_final_block			,      resnet_eps=__UpperCamelCase			,      resnet_act_fn=__UpperCamelCase			,      resnet_groups=__UpperCamelCase			,      attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel			,      downsample_padding=__UpperCamelCase			,      resnet_time_scale_shift=__UpperCamelCase			,      downsample_type=__UpperCamelCase			,      )
               self.down_blocks.append(__UpperCamelCase	)

          # mid
          UpperCAmelCase__				:			List[str]							=    UNetMidBlockaD(
              in_channels=block_out_channels[-1]			,      temb_channels=__UpperCamelCase			,      resnet_eps=__UpperCamelCase			,      resnet_act_fn=__UpperCamelCase			,      output_scale_factor=__UpperCamelCase			,      resnet_time_scale_shift=__UpperCamelCase			,      attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1]			,      resnet_groups=__UpperCamelCase			,      add_attention=__UpperCamelCase			,      )

          # up
          UpperCAmelCase__				:			Tuple							=    list(reversed(__UpperCamelCase	)	)
          UpperCAmelCase__				:			Optional[Any]							=    reversed_block_out_channels[0]
          for i, up_block_type in enumerate(__UpperCamelCase	):
               UpperCAmelCase__				:			int							=    output_channel
               UpperCAmelCase__				:			List[Any]							=    reversed_block_out_channels[i]
               UpperCAmelCase__				:			Dict							=    reversed_block_out_channels[min(i + 1			,      len(__UpperCamelCase	) - 1	)]

               UpperCAmelCase__				:			int							=    i == len(__UpperCamelCase	) - 1

               UpperCAmelCase__				:			Dict							=    get_up_block(
                   __UpperCamelCase			,      num_layers=layers_per_block + 1			,      in_channels=__UpperCamelCase			,      out_channels=__UpperCamelCase			,      prev_output_channel=__UpperCamelCase			,      temb_channels=__UpperCamelCase			,      add_upsample=not is_final_block			,      resnet_eps=__UpperCamelCase			,      resnet_act_fn=__UpperCamelCase			,      resnet_groups=__UpperCamelCase			,      attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel			,      resnet_time_scale_shift=__UpperCamelCase			,      upsample_type=__UpperCamelCase			,      )
               self.up_blocks.append(__UpperCamelCase	)
               UpperCAmelCase__				:			Dict							=    output_channel

          # out
          UpperCAmelCase__				:			str							=    norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4			,      32	)
          UpperCAmelCase__				:			Tuple							=    nn.GroupNorm(num_channels=block_out_channels[0]			,      num_groups=__UpperCamelCase			,      eps=__UpperCamelCase	)
          UpperCAmelCase__				:			Any							=    nn.SiLU()
          UpperCAmelCase__				:			Optional[int]							=    nn.Convad(block_out_channels[0]			,      __UpperCamelCase			,      kernel_size=3			,      padding=1	)



     def 			lowerCAmelCase__							(   self			,      __UpperCamelCase			,      __UpperCamelCase			,      __UpperCamelCase = None			,      __UpperCamelCase = True			,      )->       Union[UNetaDOutput, Tuple]:
          # 0. center input if necessary
          if self.config.center_input_sample:
               UpperCAmelCase__				:			Any							=    2 * sample - 1.0

          # 1. time
          UpperCAmelCase__				:			Tuple							=    timestep
          if not torch.is_tensor(__UpperCamelCase	):
               UpperCAmelCase__				:			Optional[int]							=    torch.tensor([timesteps]			,      dtype=torch.long			,      device=sample.device	)
          elif torch.is_tensor(__UpperCamelCase	) and len(timesteps.shape	) == 0:
               UpperCAmelCase__				:			List[Any]							=    timesteps[None].to(sample.device	)

          # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
          UpperCAmelCase__				:			Dict							=    timesteps * torch.ones(sample.shape[0]			,      dtype=timesteps.dtype			,      device=timesteps.device	)

          UpperCAmelCase__				:			List[str]							=    self.time_proj(__UpperCamelCase	)

          # timesteps does not contain any weights and will always return f32 tensors
          # but time_embedding might actually be running in fp16. so we need to cast here.
          # there might be better ways to encapsulate this.
          UpperCAmelCase__				:			Dict							=    t_emb.to(dtype=self.dtype	)
          UpperCAmelCase__				:			Optional[int]							=    self.time_embedding(__UpperCamelCase	)

          if self.class_embedding is not None:
               if class_labels is None:
                    raise ValueError("class_labels should be provided when doing class conditioning"	)

               if self.config.class_embed_type == "timestep":
                    UpperCAmelCase__				:			List[Any]							=    self.time_proj(__UpperCamelCase	)

               UpperCAmelCase__				:			List[str]							=    self.class_embedding(__UpperCamelCase	).to(dtype=self.dtype	)
               UpperCAmelCase__				:			Optional[int]							=    emb + class_emb

          # 2. pre-process
          UpperCAmelCase__				:			List[str]							=    sample
          UpperCAmelCase__				:			str							=    self.conv_in(__UpperCamelCase	)

          # 3. down
          UpperCAmelCase__				:			List[str]							=    (sample,)
          for downsample_block in self.down_blocks:
               if hasattr(__UpperCamelCase			,      "skip_conv"	):
                    UpperCAmelCase__				,						UpperCAmelCase__				,						UpperCAmelCase__				:			Optional[Any]							=    downsample_block(
                        hidden_states=__UpperCamelCase			,      temb=__UpperCamelCase			,      skip_sample=__UpperCamelCase	)
               else:
                    UpperCAmelCase__				,						UpperCAmelCase__				:			Optional[Any]							=    downsample_block(hidden_states=__UpperCamelCase			,      temb=__UpperCamelCase	)

               down_block_res_samples += res_samples

          # 4. mid
          UpperCAmelCase__				:			Union[str, Any]							=    self.mid_block(__UpperCamelCase			,      __UpperCamelCase	)

          # 5. up
          UpperCAmelCase__				:			Dict							=    None
          for upsample_block in self.up_blocks:
               UpperCAmelCase__				:			Union[str, Any]							=    down_block_res_samples[-len(upsample_block.resnets	) :]
               UpperCAmelCase__				:			Optional[Any]							=    down_block_res_samples[: -len(upsample_block.resnets	)]

               if hasattr(__UpperCamelCase			,      "skip_conv"	):
                    UpperCAmelCase__				,						UpperCAmelCase__				:			str							=    upsample_block(__UpperCamelCase			,      __UpperCamelCase			,      __UpperCamelCase			,      __UpperCamelCase	)
               else:
                    UpperCAmelCase__				:			Tuple							=    upsample_block(__UpperCamelCase			,      __UpperCamelCase			,      __UpperCamelCase	)

        # 6. post-process
          UpperCAmelCase__				:			List[Any]							=    self.conv_norm_out(__UpperCamelCase	)
          UpperCAmelCase__				:			Any							=    self.conv_act(__UpperCamelCase	)
          UpperCAmelCase__				:			Optional[Any]							=    self.conv_out(__UpperCamelCase	)

          if skip_sample is not None:
               sample += skip_sample

          if self.config.time_embedding_type == "fourier":
               UpperCAmelCase__				:			str							=    timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]	)))	)
               UpperCAmelCase__				:			int							=    sample / timesteps

          if not return_dict:
               return (sample,)

          return UNetaDOutput(sample=__UpperCamelCase	)



