

from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings



@maybe_allow_in_graph
class 						_A     (				nn.Module				):





  '''simple docstring'''



  def __init__(  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_=0.0   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = "geglu"   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = False   ,SCREAMING_SNAKE_CASE_ = False   ,SCREAMING_SNAKE_CASE_ = False   ,SCREAMING_SNAKE_CASE_ = False   ,SCREAMING_SNAKE_CASE_ = True   ,SCREAMING_SNAKE_CASE_ = "layer_norm"   ,SCREAMING_SNAKE_CASE_ = False   ,):

      '''simple docstring'''
      super().__init__()
      snake_case							:		Union[str, Any]        =		only_cross_attention

      snake_case							:		List[Any]        =		(num_embeds_ada_norm is not None) and norm_type == """ada_norm_zero"""
      snake_case							:		Optional[int]        =		(num_embeds_ada_norm is not None) and norm_type == """ada_norm"""

      if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
          raise ValueError(
              F"""`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"""
              F""" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}."""    )

      # Define 3 blocks. Each block has its own normalization layer.
      # 1. Self-Attn
      if self.use_ada_layer_norm:
          snake_case							:		Optional[int]        =		AdaLayerNorm(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )
      elif self.use_ada_layer_norm_zero:
          snake_case							:		str        =		AdaLayerNormZero(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )
      else:
          snake_case							:		List[Any]        =		nn.LayerNorm(SCREAMING_SNAKE_CASE_   ,elementwise_affine=SCREAMING_SNAKE_CASE_    )
      snake_case							:		Optional[int]        =		Attention(
          query_dim=SCREAMING_SNAKE_CASE_   ,heads=SCREAMING_SNAKE_CASE_   ,dim_head=SCREAMING_SNAKE_CASE_   ,dropout=SCREAMING_SNAKE_CASE_   ,bias=SCREAMING_SNAKE_CASE_   ,cross_attention_dim=cross_attention_dim if only_cross_attention else None   ,upcast_attention=SCREAMING_SNAKE_CASE_   ,)

      # 2. Cross-Attn
      if cross_attention_dim is not None or double_self_attention:
          # We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
          # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
          # the second cross attention block.
          snake_case							:		List[str]        =		(
              AdaLayerNorm(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )
              if self.use_ada_layer_norm
              else nn.LayerNorm(SCREAMING_SNAKE_CASE_   ,elementwise_affine=SCREAMING_SNAKE_CASE_    )
          )
          snake_case							:		Union[str, Any]        =		Attention(
              query_dim=SCREAMING_SNAKE_CASE_   ,cross_attention_dim=cross_attention_dim if not double_self_attention else None   ,heads=SCREAMING_SNAKE_CASE_   ,dim_head=SCREAMING_SNAKE_CASE_   ,dropout=SCREAMING_SNAKE_CASE_   ,bias=SCREAMING_SNAKE_CASE_   ,upcast_attention=SCREAMING_SNAKE_CASE_   ,)  # is self-attn if encoder_hidden_states is none
      else:
          snake_case							:		str        =		None
          snake_case							:		Any        =		None

      # 3. Feed-forward
      snake_case							:		Tuple        =		nn.LayerNorm(SCREAMING_SNAKE_CASE_   ,elementwise_affine=SCREAMING_SNAKE_CASE_    )
      snake_case							:		Dict        =		FeedForward(SCREAMING_SNAKE_CASE_   ,dropout=SCREAMING_SNAKE_CASE_   ,activation_fn=SCREAMING_SNAKE_CASE_   ,final_dropout=SCREAMING_SNAKE_CASE_    )

      # let chunk size default to None
      snake_case							:		Optional[int]        =		None
      snake_case							:		Optional[int]        =		0





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      # Sets chunk feed-forward
      snake_case							:		Any        =		chunk_size
      snake_case							:		List[Any]        =		dim





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = None   ,):

      '''simple docstring'''
      # Notice that normalization is always applied before the real computation in the following blocks.
      # 1. Self-Attention
      if self.use_ada_layer_norm:
          snake_case							:		List[Any]        =		self.norma(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )
      elif self.use_ada_layer_norm_zero:
          snake_case      ,		snake_case      ,		snake_case      ,		snake_case      ,		snake_case							:		int        =		self.norma(
              SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,hidden_dtype=hidden_states.dtype    )
      else:
          snake_case							:		str        =		self.norma(SCREAMING_SNAKE_CASE_    )

      snake_case							:		Dict        =		cross_attention_kwargs if cross_attention_kwargs is not None else {}

      snake_case							:		Tuple        =		self.attna(
          SCREAMING_SNAKE_CASE_   ,encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None   ,attention_mask=SCREAMING_SNAKE_CASE_   ,**SCREAMING_SNAKE_CASE_   ,)
      if self.use_ada_layer_norm_zero:
          snake_case							:		Tuple        =		gate_msa.unsqueeze(1    ) * attn_output
      snake_case							:		Dict        =		attn_output + hidden_states

      # 2. Cross-Attention
      if self.attna is not None:
          snake_case							:		int        =		(
              self.norma(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ) if self.use_ada_layer_norm else self.norma(SCREAMING_SNAKE_CASE_    )
          )

          snake_case							:		Any        =		self.attna(
              SCREAMING_SNAKE_CASE_   ,encoder_hidden_states=SCREAMING_SNAKE_CASE_   ,attention_mask=SCREAMING_SNAKE_CASE_   ,**SCREAMING_SNAKE_CASE_   ,)
          snake_case							:		Optional[Any]        =		attn_output + hidden_states

      # 3. Feed-forward
      snake_case							:		Dict        =		self.norma(SCREAMING_SNAKE_CASE_    )

      if self.use_ada_layer_norm_zero:
          snake_case							:		int        =		norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]

      if self._chunk_size is not None:
          # "feed_forward_chunk_size" can be used to save memory
          if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
              raise ValueError(
                  F"""`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`."""    )

          snake_case							:		Dict        =		norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
          snake_case							:		Union[str, Any]        =		torch.cat(
              [self.ff(SCREAMING_SNAKE_CASE_    ) for hid_slice in norm_hidden_states.chunk(SCREAMING_SNAKE_CASE_   ,dim=self._chunk_dim    )]   ,dim=self._chunk_dim   ,)
      else:
          snake_case							:		int        =		self.ff(SCREAMING_SNAKE_CASE_    )

      if self.use_ada_layer_norm_zero:
          snake_case							:		List[Any]        =		gate_mlp.unsqueeze(1    ) * ff_output

      snake_case							:		Tuple        =		ff_output + hidden_states

      return hidden_states







class 						_A     (				nn.Module				):





  '''simple docstring'''



  def __init__(  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = 4   ,SCREAMING_SNAKE_CASE_ = 0.0   ,SCREAMING_SNAKE_CASE_ = "geglu"   ,SCREAMING_SNAKE_CASE_ = False   ,):

      '''simple docstring'''
      super().__init__()
      snake_case							:		List[Any]        =		int(dim * mult    )
      snake_case							:		int        =		dim_out if dim_out is not None else dim

      if activation_fn == "gelu":
          snake_case							:		Optional[Any]        =		GELU(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )
      if activation_fn == "gelu-approximate":
          snake_case							:		List[str]        =		GELU(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,approximate="""tanh"""    )
      elif activation_fn == "geglu":
          snake_case							:		str        =		GEGLU(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )
      elif activation_fn == "geglu-approximate":
          snake_case							:		str        =		ApproximateGELU(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )

      snake_case							:		List[Any]        =		nn.ModuleList([]    )
      # project in
      self.net.append(SCREAMING_SNAKE_CASE_    )
      # project dropout
      self.net.append(nn.Dropout(SCREAMING_SNAKE_CASE_    )    )
      # project out
      self.net.append(nn.Linear(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )    )
      # FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
      if final_dropout:
          self.net.append(nn.Dropout(SCREAMING_SNAKE_CASE_    )    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      for module in self.net:
          snake_case							:		str        =		module(SCREAMING_SNAKE_CASE_    )
      return hidden_states







class 						_A     (				nn.Module				):





  '''simple docstring'''



  def __init__(  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_ = "none"    ):

      '''simple docstring'''
      super().__init__()
      snake_case							:		Optional[Any]        =		nn.Linear(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )
      snake_case							:		Optional[int]        =		approximate





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      if gate.device.type != "mps":
          return F.gelu(SCREAMING_SNAKE_CASE_   ,approximate=self.approximate    )
      # mps: gelu is not implemented for float16
      return F.gelu(gate.to(dtype=torch.floataa    )   ,approximate=self.approximate    ).to(dtype=gate.dtype    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		str        =		self.proj(SCREAMING_SNAKE_CASE_    )
      snake_case							:		Dict        =		self.gelu(SCREAMING_SNAKE_CASE_    )
      return hidden_states







class 						_A     (				nn.Module				):





  '''simple docstring'''



  def __init__(  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      super().__init__()
      snake_case							:		Dict        =		nn.Linear(SCREAMING_SNAKE_CASE_   ,dim_out * 2    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      if gate.device.type != "mps":
          return F.gelu(SCREAMING_SNAKE_CASE_    )
      # mps: gelu is not implemented for float16
      return F.gelu(gate.to(dtype=torch.floataa    )    ).to(dtype=gate.dtype    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case      ,		snake_case							:		Optional[int]        =		self.proj(SCREAMING_SNAKE_CASE_    ).chunk(2   ,dim=-1    )
      return hidden_states * self.gelu(SCREAMING_SNAKE_CASE_    )







class 						_A     (				nn.Module				):





  '''simple docstring'''



  def __init__(  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      super().__init__()
      snake_case							:		str        =		nn.Linear(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		Dict        =		self.proj(SCREAMING_SNAKE_CASE_    )
      return x * torch.sigmoid(1.7_02 * x    )







class 						_A     (				nn.Module				):





  '''simple docstring'''



  def __init__(  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      super().__init__()
      snake_case							:		List[Any]        =		nn.Embedding(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )
      snake_case							:		List[str]        =		nn.SiLU()
      snake_case							:		str        =		nn.Linear(SCREAMING_SNAKE_CASE_   ,embedding_dim * 2    )
      snake_case							:		str        =		nn.LayerNorm(SCREAMING_SNAKE_CASE_   ,elementwise_affine=SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		int        =		self.linear(self.silu(self.emb(SCREAMING_SNAKE_CASE_    )    )    )
      snake_case      ,		snake_case							:		Tuple        =		torch.chunk(SCREAMING_SNAKE_CASE_   ,2    )
      snake_case							:		int        =		self.norm(SCREAMING_SNAKE_CASE_    ) * (1 + scale) + shift
      return x







class 						_A     (				nn.Module				):





  '''simple docstring'''



  def __init__(  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      super().__init__()

      snake_case							:		int        =		CombinedTimestepLabelEmbeddings(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )

      snake_case							:		Dict        =		nn.SiLU()
      snake_case							:		Dict        =		nn.Linear(SCREAMING_SNAKE_CASE_   ,6 * embedding_dim   ,bias=SCREAMING_SNAKE_CASE_    )
      snake_case							:		Optional[Any]        =		nn.LayerNorm(SCREAMING_SNAKE_CASE_   ,elementwise_affine=SCREAMING_SNAKE_CASE_   ,eps=1E-6    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_=None    ):

      '''simple docstring'''
      snake_case							:		Any        =		self.linear(self.silu(self.emb(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,hidden_dtype=SCREAMING_SNAKE_CASE_    )    )    )
      snake_case      ,		snake_case      ,		snake_case      ,		snake_case      ,		snake_case      ,		snake_case							:		Optional[Any]        =		emb.chunk(6   ,dim=1    )
      snake_case							:		int        =		self.norm(SCREAMING_SNAKE_CASE_    ) * (1 + scale_msa[:, None]) + shift_msa[:, None]
      return x, gate_msa, shift_mlp, scale_mlp, gate_mlp







class 						_A     (				nn.Module				):





  '''simple docstring'''



  def __init__(  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = 1E-5    ):

      '''simple docstring'''
      super().__init__()
      snake_case							:		Optional[Any]        =		num_groups
      snake_case							:		Dict        =		eps

      if act_fn is None:
          snake_case							:		Tuple        =		None
      else:
          snake_case							:		Tuple        =		get_activation(SCREAMING_SNAKE_CASE_    )

      snake_case							:		Tuple        =		nn.Linear(SCREAMING_SNAKE_CASE_   ,out_dim * 2    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      if self.act:
          snake_case							:		int        =		self.act(SCREAMING_SNAKE_CASE_    )
      snake_case							:		List[Any]        =		self.linear(SCREAMING_SNAKE_CASE_    )
      snake_case							:		Optional[int]        =		emb[:, :, None, None]
      snake_case      ,		snake_case							:		Optional[int]        =		emb.chunk(2   ,dim=1    )

      snake_case							:		Union[str, Any]        =		F.group_norm(SCREAMING_SNAKE_CASE_   ,self.num_groups   ,eps=self.eps    )
      snake_case							:		List[Any]        =		x * (1 + scale) + shift
      return x








import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
    SeqaSeqDataset,
    calculate_bleu,
    calculate_rouge,
    chunks,
    lmap,
    load_json,
    parse_numeric_n_bool_cl_kwargs,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


lowerCamelCase		       =      getLogger(__name__)






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase = 8	,				__UpperCamelCase = 10_24	,				__UpperCamelCase="val"	,				__UpperCamelCase=None	,				__UpperCamelCase=False	,				__UpperCamelCase="summarization"	,				__UpperCamelCase=None	,				__UpperCamelCase=1	,				__UpperCamelCase = None	,				__UpperCamelCase=""	,				**__UpperCamelCase	,				)	->						Dict:
				a__	:							Any      						= str(__UpperCamelCase )
				assert local_rank is not None
				torch.distributed.init_process_group(backend="nccl"	,				rank=__UpperCamelCase )

				a__	:							int      						= Path(__UpperCamelCase )
				a__	:							List[Any]      						= save_dir.joinpath(F'rank_{local_rank}_output.json' )
				torch.cuda.set_device(__UpperCamelCase )
				a__	:							Any      						= AutoModelForSeqaSeqLM.from_pretrained(__UpperCamelCase ).cuda()
				if fpaa:
								a__	:							Optional[int]      						= model.half()
				# determine if we need to increase num_beams
				use_task_specific_params(__UpperCamelCase	,				__UpperCamelCase )  # update config with task specific params
				a__	:							Optional[Any]      						= generate_kwargs.pop("num_beams"	,				model.config.num_beams )  # AttributeError risk?
				if num_return_sequences > num_beams:
								a__	:							Optional[Any]      						= num_return_sequences

				a__	:							Any      						= AutoTokenizer.from_pretrained(__UpperCamelCase )
				logger.info(F'Inferred tokenizer type: {tokenizer.__class__}' )  # if this is wrong, check config.model_type.

				if max_source_length is None:
								a__	:							Optional[Any]      						= tokenizer.model_max_length
				if prefix is None:
								a__	:							str      						= prefix or getattr(model.config	,				"prefix"	,				"" ) or ""
				a__	:							str      						= SeqaSeqDataset(
				    __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				max_target_length=10_24	,				type_path=__UpperCamelCase	,				n_obs=__UpperCamelCase	,				prefix=__UpperCamelCase	,				**__UpperCamelCase	,				)
				# I set shuffle=True for a more accurate progress bar.
				# If all the longest samples are first, the prog bar estimate is too high at the beginning.
				a__	:							Any      						= ds.make_sortish_sampler(__UpperCamelCase	,				distributed=__UpperCamelCase	,				add_extra_examples=__UpperCamelCase	,				shuffle=__UpperCamelCase )
				a__	:							Optional[int]      						= DataLoader(__UpperCamelCase	,				sampler=__UpperCamelCase	,				batch_size=__UpperCamelCase	,				collate_fn=ds.collate_fn )
				a__	:							Optional[int]      						= []
				for batch in tqdm(__UpperCamelCase ):
								a__	:							Optional[Any]      						= model.generate(
								    input_ids=batch["input_ids"].to(model.device )	,				attention_mask=batch["attention_mask"].to(model.device )	,				num_return_sequences=__UpperCamelCase	,				num_beams=__UpperCamelCase	,				**__UpperCamelCase	,				)
								a__	:							Any      						= tokenizer.batch_decode(__UpperCamelCase	,				skip_special_tokens=__UpperCamelCase	,				clean_up_tokenization_spaces=__UpperCamelCase )
								a__	:							Dict      						= batch["ids"]
								if num_return_sequences > 1:
												a__	:							int      						= chunks(__UpperCamelCase	,				__UpperCamelCase )  # batch size chunks, each of size num_return_seq
								for i, pred in enumerate(__UpperCamelCase ):
												results.append({"pred": pred, "id": ids[i].item()} )
				save_json(__UpperCamelCase	,				__UpperCamelCase )
				return results, sampler.num_replicas






def     SCREAMING_SNAKE_CASE(      )	->						List[str]:
				a__	:							List[str]      						= argparse.ArgumentParser(
				    epilog="Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate" )
				parser.add_argument("--data_dir"	,				type=__UpperCamelCase	,				help="like cnn_dm/test.source" )
				parser.add_argument(
				    "--model_name"	,				type=__UpperCamelCase	,				help="like facebook/bart-large-cnn,t5-base, etc."	,				default="sshleifer/distilbart-xsum-12-3"	,				)
				parser.add_argument("--save_dir"	,				type=__UpperCamelCase	,				help="where to save"	,				default="tmp_gen" )
				parser.add_argument("--max_source_length"	,				type=__UpperCamelCase	,				default=__UpperCamelCase )
				parser.add_argument(
				    "--type_path"	,				type=__UpperCamelCase	,				default="test"	,				help="which subset to evaluate typically train/val/test" )
				parser.add_argument("--task"	,				type=__UpperCamelCase	,				default="summarization"	,				help="used for task_specific_params + metrics" )
				parser.add_argument("--bs"	,				type=__UpperCamelCase	,				default=8	,				required=__UpperCamelCase	,				help="batch size" )
				parser.add_argument(
				    "--local_rank"	,				type=__UpperCamelCase	,				default=-1	,				required=__UpperCamelCase	,				help="should be passed by distributed.launch" )

				parser.add_argument(
				    "--n_obs"	,				type=__UpperCamelCase	,				default=__UpperCamelCase	,				required=__UpperCamelCase	,				help="How many observations. Defaults to all." )
				parser.add_argument(
				    "--num_return_sequences"	,				type=__UpperCamelCase	,				default=1	,				required=__UpperCamelCase	,				help="How many sequences to return" )
				parser.add_argument(
				    "--sync_timeout"	,				type=__UpperCamelCase	,				default=6_00	,				required=__UpperCamelCase	,				help="How long should master process wait for other processes to finish."	,				)
				parser.add_argument("--src_lang"	,				type=__UpperCamelCase	,				default=__UpperCamelCase	,				required=__UpperCamelCase )
				parser.add_argument("--tgt_lang"	,				type=__UpperCamelCase	,				default=__UpperCamelCase	,				required=__UpperCamelCase )
				parser.add_argument(
				    "--prefix"	,				type=__UpperCamelCase	,				required=__UpperCamelCase	,				default=__UpperCamelCase	,				help="will be added to the begininng of src examples" )
				parser.add_argument("--fp16"	,				action="store_true" )
				parser.add_argument("--debug"	,				action="store_true" )
				a__	:							str      						= time.time()
				a__						,   a__	:							Dict      						= parser.parse_known_args()
				a__	:							Optional[Any]      						= parse_numeric_n_bool_cl_kwargs(__UpperCamelCase )
				if generate_kwargs and args.local_rank <= 0:
								print(F'parsed the following generate kwargs: {generate_kwargs}' )
				a__	:							List[str]      						= Path(args.save_dir + "_tmp" )
				Path(__UpperCamelCase ).mkdir(exist_ok=__UpperCamelCase )  # this handles locking.
				a__	:							List[Any]      						= list(json_save_dir.glob("rank_*.json" ) )
				if intermediate_files:
								raise ValueError(F'Found files at {json_save_dir} please move or remove them.' )
								# In theory, a node could finish and save before another node hits this. If this happens, we can address later.
				a__	:							List[Any]      						= {}
				if args.src_lang is not None:
								a__	:							str      						= args.src_lang
				if args.tgt_lang is not None:
								a__	:							int      						= args.tgt_lang

				Path(args.save_dir ).mkdir(exist_ok=__UpperCamelCase )
				a__						,   a__	:							Tuple      						= eval_data_dir(
				    args.data_dir	,				__UpperCamelCase	,				args.model_name	,				type_path=args.type_path	,				bs=args.bs	,				fpaa=args.fpaa	,				task=args.task	,				local_rank=args.local_rank	,				n_obs=args.n_obs	,				max_source_length=args.max_source_length	,				num_return_sequences=args.num_return_sequences	,				prefix=args.prefix	,				dataset_kwargs=__UpperCamelCase	,				**__UpperCamelCase	,				)

				if args.local_rank <= 0:
								a__	:							str      						= Path(args.save_dir )
								save_dir.mkdir(exist_ok=__UpperCamelCase )
								a__	:							Optional[int]      						= gather_results_from_each_node(__UpperCamelCase	,				__UpperCamelCase	,				args.sync_timeout )
								a__	:							Tuple      						= combine_partial_results(__UpperCamelCase )
								if args.num_return_sequences > 1:
												a__	:							Optional[int]      						= save_dir.joinpath("pseudolabel_results.json" )
												print(F'Saving aggregated results at {save_path}, intermediate in {json_save_dir}/' )
												save_json(__UpperCamelCase	,				__UpperCamelCase )
												return
								a__	:							Any      						= Path(args.data_dir ).joinpath(args.type_path + ".target" )
								with open(__UpperCamelCase ) as f:
												a__	:							Optional[int]      						= [x.rstrip() for x in f.readlines()][: len(__UpperCamelCase )]

								# Calculate metrics, save metrics,  and save _generations.txt
								a__	:							Any      						= "translation" in args.task
								a__	:							List[Any]      						= calculate_bleu if calc_bleu else calculate_rouge
								a__	:							List[Any]      						= "bleu" if calc_bleu else "rouge"
								a__	:							Dict      						= score_fn(__UpperCamelCase	,				__UpperCamelCase )
								a__	:							int      						= len(__UpperCamelCase )
								a__	:							Union[str, Any]      						= time.time() - start_time
								a__	:							List[Any]      						= round(runtime / metrics["n_obs"]	,				4 )
								a__	:							List[str]      						= num_replicas
								# TODO(@stas00): add whatever metadata to metrics
								a__	:							int      						= save_dir.joinpath(F'{args.type_path}_{metric_name}.json' )
								save_json(__UpperCamelCase	,				__UpperCamelCase	,				indent=__UpperCamelCase )
								print(__UpperCamelCase )
								write_txt_file(__UpperCamelCase	,				save_dir.joinpath(F'{args.type_path}_generations.txt' ) )
								if args.debug:
												write_txt_file(__UpperCamelCase	,				save_dir.joinpath(F'{args.type_path}.target' ) )
								else:
												shutil.rmtree(__UpperCamelCase )






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						List:
				a__	:							Optional[Any]      						= []
				for partial_result in partial_results:
								records.extend(__UpperCamelCase )
				a__	:							Tuple      						= sorted(__UpperCamelCase	,				key=lambda __UpperCamelCase : x["id"] )
				a__	:							Tuple      						= [x["pred"] for x in records]
				return preds






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )	->						List[Dict[str, List]]:
				# WAIT FOR lots of .json files
				a__	:							Optional[Any]      						= time.time()
				logger.info("waiting for all nodes to finish" )
				a__	:							int      						= None
				while (time.time() - start_wait) < timeout:
								a__	:							List[str]      						= list(save_dir.glob("rank_*.json" ) )
								if len(__UpperCamelCase ) < num_replicas:
												continue
								try:
												# make sure all json files are fully saved
												a__	:							int      						= lmap(__UpperCamelCase	,				__UpperCamelCase )
												return json_data
								except JSONDecodeError:
												continue
				else:
								raise TimeoutError("Rank 0 gave up on waiting for other processes" )
				# Unreachable


if __name__ == "__main__":
						# Usage for MT:
						run_generate()


