






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     _SCREAMING_SNAKE_CASE       (			nn.Module      ):





	def __init__(		self				:					Optional[Any] ,  __lowerCamelCase				:					int ,  __lowerCamelCase				:					int ,  __lowerCamelCase				:					int ,  __lowerCamelCase				:					Any=0.0 ,  __lowerCamelCase				:					Optional[int] = None ,  __lowerCamelCase				:					str = "geglu" ,  __lowerCamelCase				:					Optional[int] = None ,  __lowerCamelCase				:					bool = False ,  __lowerCamelCase				:					bool = False ,  __lowerCamelCase				:					bool = False ,  __lowerCamelCase				:					bool = False ,  __lowerCamelCase				:					bool = True ,  __lowerCamelCase				:					str = "layer_norm" ,  __lowerCamelCase				:					bool = False ,  ):
		super().__init__()
		UpperCamelCase     :List[Any]          =	only_cross_attention

		UpperCamelCase     :Dict          =	(num_embeds_ada_norm is not None) and norm_type == '''ada_norm_zero'''
		UpperCamelCase     :Optional[Any]          =	(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:
			UpperCamelCase     :Tuple          =	AdaLayerNorm(snake_case_ ,  snake_case_	)
		elif self.use_ada_layer_norm_zero:
			UpperCamelCase     :Union[str, Any]          =	AdaLayerNormZero(snake_case_ ,  snake_case_	)
		else:
			UpperCamelCase     :Optional[int]          =	nn.LayerNorm(snake_case_ ,  elementwise_affine=snake_case_	)
		UpperCamelCase     :List[Any]          =	Attention(
		    query_dim=snake_case_ ,  heads=snake_case_ ,  dim_head=snake_case_ ,  dropout=snake_case_ ,  bias=snake_case_ ,  cross_attention_dim=cross_attention_dim if only_cross_attention else None ,  upcast_attention=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.
			UpperCamelCase     :List[str]          =	(
			    AdaLayerNorm(snake_case_ ,  snake_case_	)
			    if self.use_ada_layer_norm
			    else nn.LayerNorm(snake_case_ ,  elementwise_affine=snake_case_	)
			)
			UpperCamelCase     :int          =	Attention(
			    query_dim=snake_case_ ,  cross_attention_dim=cross_attention_dim if not double_self_attention else None ,  heads=snake_case_ ,  dim_head=snake_case_ ,  dropout=snake_case_ ,  bias=snake_case_ ,  upcast_attention=snake_case_ ,  )  # is self-attn if encoder_hidden_states is none
		else:
			UpperCamelCase     :Dict          =	None
			UpperCamelCase     :Tuple          =	None

		# 3. Feed-forward
		UpperCamelCase     :int          =	nn.LayerNorm(snake_case_ ,  elementwise_affine=snake_case_	)
		UpperCamelCase     :Any          =	FeedForward(snake_case_ ,  dropout=snake_case_ ,  activation_fn=snake_case_ ,  final_dropout=snake_case_	)

		# let chunk size default to None
		UpperCamelCase     :Any          =	None
		UpperCamelCase     :List[str]          =	0





	def 						_A (		self				:					List[Any] ,  __lowerCamelCase				:					Optional[int] ,  __lowerCamelCase				:					int	):
		# Sets chunk feed-forward
		UpperCamelCase     :Dict          =	chunk_size
		UpperCamelCase     :List[Any]          =	dim





	def 						_A (		self				:					Optional[int] ,  __lowerCamelCase				:					torch.FloatTensor ,  __lowerCamelCase				:					Optional[torch.FloatTensor] = None ,  __lowerCamelCase				:					Optional[torch.FloatTensor] = None ,  __lowerCamelCase				:					Optional[torch.FloatTensor] = None ,  __lowerCamelCase				:					Optional[torch.LongTensor] = None ,  __lowerCamelCase				:					Dict[str, Any] = None ,  __lowerCamelCase				:					Optional[torch.LongTensor] = None ,  ):
		# Notice that normalization is always applied before the real computation in the following blocks.
		# 1. Self-Attention
		if self.use_ada_layer_norm:
			UpperCamelCase     :List[str]          =	self.norma(snake_case_ ,  snake_case_	)
		elif self.use_ada_layer_norm_zero:
			UpperCamelCase     :Tuple          =	self.norma(
			    snake_case_ ,  snake_case_ ,  snake_case_ ,  hidden_dtype=hidden_states.dtype	)
		else:
			UpperCamelCase     :str          =	self.norma(snake_case_	)

		UpperCamelCase     :int          =	cross_attention_kwargs if cross_attention_kwargs is not None else {}

		UpperCamelCase     :List[str]          =	self.attna(
		    snake_case_ ,  encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None ,  attention_mask=snake_case_ ,  **snake_case_ ,  )
		if self.use_ada_layer_norm_zero:
			UpperCamelCase     :Any          =	gate_msa.unsqueeze(1	) * attn_output
		UpperCamelCase     :Optional[int]          =	attn_output + hidden_states

		# 2. Cross-Attention
		if self.attna is not None:
			UpperCamelCase     :List[Any]          =	(
			    self.norma(snake_case_ ,  snake_case_	) if self.use_ada_layer_norm else self.norma(snake_case_	)
			)

			UpperCamelCase     :Optional[Any]          =	self.attna(
			    snake_case_ ,  encoder_hidden_states=snake_case_ ,  attention_mask=snake_case_ ,  **snake_case_ ,  )
			UpperCamelCase     :str          =	attn_output + hidden_states

		# 3. Feed-forward
		UpperCamelCase     :Tuple          =	self.norma(snake_case_	)

		if self.use_ada_layer_norm_zero:
			UpperCamelCase     :List[Any]          =	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`."""	)

			UpperCamelCase     :Optional[Any]          =	norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
			UpperCamelCase     :int          =	torch.cat(
			    [self.ff(snake_case_	) for hid_slice in norm_hidden_states.chunk(snake_case_ ,  dim=self._chunk_dim	)] ,  dim=self._chunk_dim ,  )
		else:
			UpperCamelCase     :int          =	self.ff(snake_case_	)

		if self.use_ada_layer_norm_zero:
			UpperCamelCase     :Union[str, Any]          =	gate_mlp.unsqueeze(1	) * ff_output

		UpperCamelCase     :Any          =	ff_output + hidden_states

		return hidden_states

class     _SCREAMING_SNAKE_CASE       (			nn.Module      ):





	def __init__(		self				:					Optional[int] ,  __lowerCamelCase				:					int ,  __lowerCamelCase				:					Optional[int] = None ,  __lowerCamelCase				:					int = 4 ,  __lowerCamelCase				:					float = 0.0 ,  __lowerCamelCase				:					str = "geglu" ,  __lowerCamelCase				:					bool = False ,  ):
		super().__init__()
		UpperCamelCase     :Optional[Any]          =	int(dim * mult	)
		UpperCamelCase     :Optional[int]          =	dim_out if dim_out is not None else dim

		if activation_fn == "gelu":
			UpperCamelCase     :Optional[Any]          =	GELU(snake_case_ ,  snake_case_	)
		if activation_fn == "gelu-approximate":
			UpperCamelCase     :Dict          =	GELU(snake_case_ ,  snake_case_ ,  approximate="""tanh"""	)
		elif activation_fn == "geglu":
			UpperCamelCase     :int          =	GEGLU(snake_case_ ,  snake_case_	)
		elif activation_fn == "geglu-approximate":
			UpperCamelCase     :Tuple          =	ApproximateGELU(snake_case_ ,  snake_case_	)

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





	def 						_A (		self				:					Tuple ,  __lowerCamelCase				:					Optional[Any]	):
		for module in self.net:
			UpperCamelCase     :List[Any]          =	module(snake_case_	)
		return hidden_states

class     _SCREAMING_SNAKE_CASE       (			nn.Module      ):





	def __init__(		self				:					Dict ,  __lowerCamelCase				:					int ,  __lowerCamelCase				:					int ,  __lowerCamelCase				:					str = "none"	):
		super().__init__()
		UpperCamelCase     :Optional[int]          =	nn.Linear(snake_case_ ,  snake_case_	)
		UpperCamelCase     :Union[str, Any]          =	approximate





	def 						_A (		self				:					Union[str, Any] ,  __lowerCamelCase				:					Union[str, Any]	):
		if gate.device.type != "mps":
			return F.gelu(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 						_A (		self				:					Union[str, Any] ,  __lowerCamelCase				:					Dict	):
		UpperCamelCase     :Tuple          =	self.proj(snake_case_	)
		UpperCamelCase     :Tuple          =	self.gelu(snake_case_	)
		return hidden_states

class     _SCREAMING_SNAKE_CASE       (			nn.Module      ):





	def __init__(		self				:					int ,  __lowerCamelCase				:					int ,  __lowerCamelCase				:					int	):
		super().__init__()
		UpperCamelCase     :Optional[Any]          =	nn.Linear(snake_case_ ,  dim_out * 2	)





	def 						_A (		self				:					Union[str, Any] ,  __lowerCamelCase				:					int	):
		if gate.device.type != "mps":
			return F.gelu(snake_case_	)
		# mps: gelu is not implemented for float16
		return F.gelu(gate.to(dtype=torch.floataa	)	).to(dtype=gate.dtype	)





	def 						_A (		self				:					List[str] ,  __lowerCamelCase				:					Dict	):
		UpperCamelCase     :List[str]          =	self.proj(snake_case_	).chunk(2 ,  dim=-1	)
		return hidden_states * self.gelu(snake_case_	)

class     _SCREAMING_SNAKE_CASE       (			nn.Module      ):





	def __init__(		self				:					Union[str, Any] ,  __lowerCamelCase				:					int ,  __lowerCamelCase				:					int	):
		super().__init__()
		UpperCamelCase     :List[str]          =	nn.Linear(snake_case_ ,  snake_case_	)





	def 						_A (		self				:					Any ,  __lowerCamelCase				:					Optional[int]	):
		UpperCamelCase     :Tuple          =	self.proj(snake_case_	)
		return x * torch.sigmoid(1.702 * x	)

class     _SCREAMING_SNAKE_CASE       (			nn.Module      ):





	def __init__(		self				:					Union[str, Any] ,  __lowerCamelCase				:					Union[str, Any] ,  __lowerCamelCase				:					Tuple	):
		super().__init__()
		UpperCamelCase     :Dict          =	nn.Embedding(snake_case_ ,  snake_case_	)
		UpperCamelCase     :Union[str, Any]          =	nn.SiLU()
		UpperCamelCase     :List[Any]          =	nn.Linear(snake_case_ ,  embedding_dim * 2	)
		UpperCamelCase     :Tuple          =	nn.LayerNorm(snake_case_ ,  elementwise_affine=snake_case_	)





	def 						_A (		self				:					int ,  __lowerCamelCase				:					Dict ,  __lowerCamelCase				:					List[Any]	):
		UpperCamelCase     :Optional[int]          =	self.linear(self.silu(self.emb(snake_case_	)	)	)
		UpperCamelCase     :Any          =	torch.chunk(snake_case_ ,  2	)
		UpperCamelCase     :str          =	self.norm(snake_case_	) * (1 + scale) + shift
		return x

class     _SCREAMING_SNAKE_CASE       (			nn.Module      ):





	def __init__(		self				:					Optional[int] ,  __lowerCamelCase				:					Tuple ,  __lowerCamelCase				:					Dict	):
		super().__init__()

		UpperCamelCase     :Union[str, Any]          =	CombinedTimestepLabelEmbeddings(snake_case_ ,  snake_case_	)

		UpperCamelCase     :str          =	nn.SiLU()
		UpperCamelCase     :Optional[int]          =	nn.Linear(snake_case_ ,  6 * embedding_dim ,  bias=snake_case_	)
		UpperCamelCase     :Tuple          =	nn.LayerNorm(snake_case_ ,  elementwise_affine=snake_case_ ,  eps=1E-6	)





	def 						_A (		self				:					Union[str, Any] ,  __lowerCamelCase				:					Any ,  __lowerCamelCase				:					Union[str, Any] ,  __lowerCamelCase				:					Optional[Any] ,  __lowerCamelCase				:					Optional[int]=None	):
		UpperCamelCase     :Union[str, Any]          =	self.linear(self.silu(self.emb(snake_case_ ,  snake_case_ ,  hidden_dtype=snake_case_	)	)	)
		UpperCamelCase     :Optional[int]          =	emb.chunk(6 ,  dim=1	)
		UpperCamelCase     :Optional[Any]          =	self.norm(snake_case_	) * (1 + scale_msa[:, None]) + shift_msa[:, None]
		return x, gate_msa, shift_mlp, scale_mlp, gate_mlp

class     _SCREAMING_SNAKE_CASE       (			nn.Module      ):





	def __init__(		self				:					Union[str, Any] ,  __lowerCamelCase				:					int ,  __lowerCamelCase				:					int ,  __lowerCamelCase				:					int ,  __lowerCamelCase				:					Optional[str] = None ,  __lowerCamelCase				:					float = 1E-5	):
		super().__init__()
		UpperCamelCase     :Optional[int]          =	num_groups
		UpperCamelCase     :List[str]          =	eps

		if act_fn is None:
			UpperCamelCase     :List[Any]          =	None
		else:
			UpperCamelCase     :Tuple          =	get_activation(snake_case_	)

		UpperCamelCase     :int          =	nn.Linear(snake_case_ ,  out_dim * 2	)





	def 						_A (		self				:					List[str] ,  __lowerCamelCase				:					Union[str, Any] ,  __lowerCamelCase				:					List[str]	):
		if self.act:
			UpperCamelCase     :List[str]          =	self.act(snake_case_	)
		UpperCamelCase     :Any          =	self.linear(snake_case_	)
		UpperCamelCase     :Tuple          =	emb[:, :, None, None]
		UpperCamelCase     :Optional[int]          =	emb.chunk(2 ,  dim=1	)

		UpperCamelCase     :Dict          =	F.group_norm(snake_case_ ,  self.num_groups ,  eps=self.eps	)
		UpperCamelCase     :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,
)


lowercase__       =getLogger(__name__)




def    __UpperCamelCase     (				lowerCAmelCase__							:				Tuple  ,   lowerCAmelCase__							:				str  ,   lowerCAmelCase__							:				str  ,   lowerCAmelCase__							:				int = 8  ,   lowerCAmelCase__							:				int = 1_0_2_4  ,   lowerCAmelCase__							:				Union[str, Any]="val"  ,   lowerCAmelCase__							:				int=None  ,   lowerCAmelCase__							:				Any=False  ,   lowerCAmelCase__							:				Union[str, Any]="summarization"  ,   lowerCAmelCase__							:				Tuple=None  ,   lowerCAmelCase__							:				Optional[int]=1  ,   lowerCAmelCase__							:				Dict = None  ,   lowerCAmelCase__							:				int=""  ,   **lowerCAmelCase__							:				int  ,   ):
 __a :  List[Any]						=  str(lowerCAmelCase__  )
 assert local_rank is not None
 torch.distributed.init_process_group(backend='''nccl'''  ,   rank=lowerCAmelCase__  )

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

 __a :  Optional[Any]						=  AutoTokenizer.from_pretrained(lowerCAmelCase__  )
 logger.info(f"Inferred tokenizer type: {tokenizer.__class__}"  )  # if this is wrong, check config.model_type.

 if max_source_length is None:
  __a :  Dict						=  tokenizer.model_max_length
 if prefix is None:
  __a :  Dict						=  prefix or getattr(model.config  ,   '''prefix'''  ,   ''''''  ) or ''''''
 __a :  List[Any]						=  SeqaSeqDataset(
     lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   max_target_length=1_0_2_4  ,   type_path=lowerCAmelCase__  ,   n_obs=lowerCAmelCase__  ,   prefix=lowerCAmelCase__  ,   **lowerCAmelCase__  ,   )
 # 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 :  Tuple						=  ds.make_sortish_sampler(lowerCAmelCase__  ,   distributed=lowerCAmelCase__  ,   add_extra_examples=lowerCAmelCase__  ,   shuffle=lowerCAmelCase__  )
 __a :  List[Any]						=  DataLoader(lowerCAmelCase__  ,   sampler=lowerCAmelCase__  ,   batch_size=lowerCAmelCase__  ,   collate_fn=ds.collate_fn  )
 __a :  List[Any]						=  []
 for batch in tqdm(lowerCAmelCase__  ):
  __a :  Any						=  model.generate(
      input_ids=batch['''input_ids'''].to(model.device  )  ,   attention_mask=batch['''attention_mask'''].to(model.device  )  ,   num_return_sequences=lowerCAmelCase__  ,   num_beams=lowerCAmelCase__  ,   **lowerCAmelCase__  ,   )
  __a :  List[Any]						=  tokenizer.batch_decode(lowerCAmelCase__  ,   skip_special_tokens=lowerCAmelCase__  ,   clean_up_tokenization_spaces=lowerCAmelCase__  )
  __a :  int						=  batch['''ids''']
  if num_return_sequences > 1:
   __a :  List[str]						=  chunks(lowerCAmelCase__  ,   lowerCAmelCase__  )  # batch size chunks, each of size num_return_seq
  for i, pred in enumerate(lowerCAmelCase__  ):
   results.append({'''pred''': pred, '''id''': ids[i].item()}  )
 save_json(lowerCAmelCase__  ,   lowerCAmelCase__  )
 return results, sampler.num_replicas




def    __UpperCamelCase     (				):
 __a :  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=lowerCAmelCase__  ,   help='''like cnn_dm/test.source'''  )
 parser.add_argument(
     '''--model_name'''  ,   type=lowerCAmelCase__  ,   help='''like facebook/bart-large-cnn,t5-base, etc.'''  ,   default='''sshleifer/distilbart-xsum-12-3'''  ,   )
 parser.add_argument('''--save_dir'''  ,   type=lowerCAmelCase__  ,   help='''where to save'''  ,   default='''tmp_gen'''  )
 parser.add_argument('''--max_source_length'''  ,   type=lowerCAmelCase__  ,   default=lowerCAmelCase__  )
 parser.add_argument(
     '''--type_path'''  ,   type=lowerCAmelCase__  ,   default='''test'''  ,   help='''which subset to evaluate typically train/val/test'''  )
 parser.add_argument('''--task'''  ,   type=lowerCAmelCase__  ,   default='''summarization'''  ,   help='''used for task_specific_params + metrics'''  )
 parser.add_argument('''--bs'''  ,   type=lowerCAmelCase__  ,   default=8  ,   required=lowerCAmelCase__  ,   help='''batch size'''  )
 parser.add_argument(
     '''--local_rank'''  ,   type=lowerCAmelCase__  ,   default=-1  ,   required=lowerCAmelCase__  ,   help='''should be passed by distributed.launch'''  )

 parser.add_argument(
     '''--n_obs'''  ,   type=lowerCAmelCase__  ,   default=lowerCAmelCase__  ,   required=lowerCAmelCase__  ,   help='''How many observations. Defaults to all.'''  )
 parser.add_argument(
     '''--num_return_sequences'''  ,   type=lowerCAmelCase__  ,   default=1  ,   required=lowerCAmelCase__  ,   help='''How many sequences to return'''  )
 parser.add_argument(
     '''--sync_timeout'''  ,   type=lowerCAmelCase__  ,   default=6_0_0  ,   required=lowerCAmelCase__  ,   help='''How long should master process wait for other processes to finish.'''  ,   )
 parser.add_argument('''--src_lang'''  ,   type=lowerCAmelCase__  ,   default=lowerCAmelCase__  ,   required=lowerCAmelCase__  )
 parser.add_argument('''--tgt_lang'''  ,   type=lowerCAmelCase__  ,   default=lowerCAmelCase__  ,   required=lowerCAmelCase__  )
 parser.add_argument(
     '''--prefix'''  ,   type=lowerCAmelCase__  ,   required=lowerCAmelCase__  ,   default=lowerCAmelCase__  ,   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 :  int						=  time.time()
 __a				,       __a :  Tuple						=  parser.parse_known_args()
 __a :  Optional[int]						=  parse_numeric_n_bool_cl_kwargs(lowerCAmelCase__  )
 if generate_kwargs and args.local_rank <= 0:
  print(f"parsed the following generate kwargs: {generate_kwargs}"  )
 __a :  Union[str, Any]						=  Path(args.save_dir + '''_tmp'''  )
 Path(lowerCAmelCase__  ).mkdir(exist_ok=lowerCAmelCase__  )  # this handles locking.
 __a :  Dict						=  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 :  Optional[Any]						=  {}
 if args.src_lang is not None:
  __a :  int						=  args.src_lang
 if args.tgt_lang is not None:
  __a :  Optional[Any]						=  args.tgt_lang

 Path(args.save_dir  ).mkdir(exist_ok=lowerCAmelCase__  )
 __a				,       __a :  Tuple						=  eval_data_dir(
     args.data_dir  ,   lowerCAmelCase__  ,   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=lowerCAmelCase__  ,   **lowerCAmelCase__  ,   )

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

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




def    __UpperCamelCase     (				lowerCAmelCase__							:				Optional[int]  ):
 __a :  Optional[int]						=  []
 for partial_result in partial_results:
  records.extend(lowerCAmelCase__  )
 __a :  Tuple						=  sorted(lowerCAmelCase__  ,   key=lambda lowerCAmelCase__  : x["id"]  )
 __a :  Tuple						=  [x['''pred'''] for x in records]
 return preds




def    __UpperCamelCase     (				lowerCAmelCase__							:				Any  ,   lowerCAmelCase__							:				Optional[Any]  ,   lowerCAmelCase__							:				Dict  ):
 # WAIT FOR lots of .json files
 __a :  Tuple						=  time.time()
 logger.info('''waiting for all nodes to finish'''  )
 __a :  Optional[int]						=  None
 while (time.time() - start_wait) < timeout:
  __a :  Optional[int]						=  list(save_dir.glob('''rank_*.json'''  )  )
  if len(lowerCAmelCase__  ) < num_replicas:
   continue
  try:
   # make sure all json files are fully saved
   __a :  Tuple						=  lmap(lowerCAmelCase__  ,   lowerCAmelCase__  )
   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()
