

from dataclasses import dataclass
from typing import Optional

import numpy as np
import torch
import torch.nn as nn

from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block






@dataclass
class _UpperCamelCase							(							UpperCamelCase__      ):
			'''simple docstring'''







			lowerCamelCase__							=42






class _UpperCamelCase							(							nn.Module      ):
			'''simple docstring'''







			def __init__(	self							:       List[Any]					,					a							:       List[Any]=3					,					a							:       Dict=3					,					a							:       List[str]=("DownEncoderBlock2D",)					,					a							:       Union[str, Any]=(64,)					,					a							:       str=2					,					a							:       List[str]=32					,					a							:       Optional[int]="silu"					,					a							:       Union[str, Any]=True					,					)							->				Any:

				"""simple docstring"""

				super().__init__()
				SCREAMING_SNAKE_CASE      :					List[str]    =				layers_per_block

				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				torch.nn.Convad(
				    _a					,					block_out_channels[0]					,					kernel_size=3					,					stride=1					,					padding=1					,					)

				SCREAMING_SNAKE_CASE      :					str    =				None
				SCREAMING_SNAKE_CASE      :					int    =				nn.ModuleList([]		)

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

					SCREAMING_SNAKE_CASE      :					Tuple    =				get_down_block(
					    _a					,					num_layers=self.layers_per_block					,					in_channels=_a					,					out_channels=_a					,					add_downsample=not is_final_block					,					resnet_eps=1e-6					,					downsample_padding=0					,					resnet_act_fn=_a					,					resnet_groups=_a					,					attention_head_dim=_a					,					temb_channels=_a					,					)
					self.down_blocks.append(_a		)

				# mid
				SCREAMING_SNAKE_CASE      :					List[Any]    =				UNetMidBlockaD(
				    in_channels=block_out_channels[-1]					,					resnet_eps=1e-6					,					resnet_act_fn=_a					,					output_scale_factor=1					,					resnet_time_scale_shift="default"					,					attention_head_dim=block_out_channels[-1]					,					resnet_groups=_a					,					temb_channels=_a					,					)

				# out
				SCREAMING_SNAKE_CASE      :					Any    =				nn.GroupNorm(num_channels=block_out_channels[-1]					,					num_groups=_a					,					eps=1e-6		)
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				nn.SiLU()

				SCREAMING_SNAKE_CASE      :					List[str]    =				2 * out_channels if double_z else out_channels
				SCREAMING_SNAKE_CASE      :					List[Any]    =				nn.Convad(block_out_channels[-1]					,					_a					,					3					,					padding=1		)

				SCREAMING_SNAKE_CASE      :					Any    =				False






			def 							__UpperCamelCase       (	self							:       Dict					,					a							:       Dict		)							->				List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Any    =				x
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				self.conv_in(_a		)

				if self.training and self.gradient_checkpointing:

					def create_custom_forward(a							:       Any		):
						def custom_forward(*a							:       List[str]		):
							return module(*_a		)

						return custom_forward

					# down
					if is_torch_version(">="					,					"1.11.0"		):
						for down_block in self.down_blocks:
							SCREAMING_SNAKE_CASE      :					int    =				torch.utils.checkpoint.checkpoint(
							    create_custom_forward(_a		)					,					_a					,					use_reentrant=_a		)
						# middle
						SCREAMING_SNAKE_CASE      :					str    =				torch.utils.checkpoint.checkpoint(
						    create_custom_forward(self.mid_block		)					,					_a					,					use_reentrant=_a		)
					else:
						for down_block in self.down_blocks:
							SCREAMING_SNAKE_CASE      :					str    =				torch.utils.checkpoint.checkpoint(create_custom_forward(_a		)					,					_a		)
						# middle
						SCREAMING_SNAKE_CASE      :					Any    =				torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block		)					,					_a		)

				else:
					# down
					for down_block in self.down_blocks:
						SCREAMING_SNAKE_CASE      :					List[Any]    =				down_block(_a		)

					# middle
					SCREAMING_SNAKE_CASE      :					Dict    =				self.mid_block(_a		)

				# post-process
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				self.conv_norm_out(_a		)
				SCREAMING_SNAKE_CASE      :					List[str]    =				self.conv_act(_a		)
				SCREAMING_SNAKE_CASE      :					str    =				self.conv_out(_a		)

				return sample






class _UpperCamelCase							(							nn.Module      ):
			'''simple docstring'''







			def __init__(	self							:       Dict					,					a							:       Union[str, Any]=3					,					a							:       Optional[Any]=3					,					a							:       str=("UpDecoderBlock2D",)					,					a							:       str=(64,)					,					a							:       Any=2					,					a							:       Union[str, Any]=32					,					a							:       Dict="silu"					,					a							:       int="group"					,					)							->				Optional[int]:

				"""simple docstring"""

				super().__init__()
				SCREAMING_SNAKE_CASE      :					Tuple    =				layers_per_block

				SCREAMING_SNAKE_CASE      :					Any    =				nn.Convad(
				    _a					,					block_out_channels[-1]					,					kernel_size=3					,					stride=1					,					padding=1					,					)

				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				None
				SCREAMING_SNAKE_CASE      :					str    =				nn.ModuleList([]		)

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				in_channels if norm_type == """spatial""" else None

				# mid
				SCREAMING_SNAKE_CASE      :					List[str]    =				UNetMidBlockaD(
				    in_channels=block_out_channels[-1]					,					resnet_eps=1e-6					,					resnet_act_fn=_a					,					output_scale_factor=1					,					resnet_time_scale_shift="default" if norm_type == "group" else norm_type					,					attention_head_dim=block_out_channels[-1]					,					resnet_groups=_a					,					temb_channels=_a					,					)

				# up
				SCREAMING_SNAKE_CASE      :					List[str]    =				list(reversed(_a		)		)
				SCREAMING_SNAKE_CASE      :					List[Any]    =				reversed_block_out_channels[0]
				for i, up_block_type in enumerate(_a		):
					SCREAMING_SNAKE_CASE      :					Any    =				output_channel
					SCREAMING_SNAKE_CASE      :					List[str]    =				reversed_block_out_channels[i]

					SCREAMING_SNAKE_CASE      :					Optional[Any]    =				i == len(_a		) - 1

					SCREAMING_SNAKE_CASE      :					List[str]    =				get_up_block(
					    _a					,					num_layers=self.layers_per_block + 1					,					in_channels=_a					,					out_channels=_a					,					prev_output_channel=_a					,					add_upsample=not is_final_block					,					resnet_eps=1e-6					,					resnet_act_fn=_a					,					resnet_groups=_a					,					attention_head_dim=_a					,					temb_channels=_a					,					resnet_time_scale_shift=_a					,					)
					self.up_blocks.append(_a		)
					SCREAMING_SNAKE_CASE      :					Optional[int]    =				output_channel

				# out
				if norm_type == "spatial":
					SCREAMING_SNAKE_CASE      :					Dict    =				SpatialNorm(block_out_channels[0]					,					_a		)
				else:
					SCREAMING_SNAKE_CASE      :					List[str]    =				nn.GroupNorm(num_channels=block_out_channels[0]					,					num_groups=_a					,					eps=1e-6		)
				SCREAMING_SNAKE_CASE      :					List[str]    =				nn.SiLU()
				SCREAMING_SNAKE_CASE      :					str    =				nn.Convad(block_out_channels[0]					,					_a					,					3					,					padding=1		)

				SCREAMING_SNAKE_CASE      :					int    =				False






			def 							__UpperCamelCase       (	self							:       List[str]					,					a							:       List[str]					,					a							:       Any=None		)							->				str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				z
				SCREAMING_SNAKE_CASE      :					Dict    =				self.conv_in(_a		)

				SCREAMING_SNAKE_CASE      :					str    =				next(iter(self.up_blocks.parameters()		)		).dtype
				if self.training and self.gradient_checkpointing:

					def create_custom_forward(a							:       Optional[Any]		):
						def custom_forward(*a							:       Optional[int]		):
							return module(*_a		)

						return custom_forward

					if is_torch_version(">="					,					"1.11.0"		):
						# middle
						SCREAMING_SNAKE_CASE      :					str    =				torch.utils.checkpoint.checkpoint(
						    create_custom_forward(self.mid_block		)					,					_a					,					_a					,					use_reentrant=_a		)
						SCREAMING_SNAKE_CASE      :					Dict    =				sample.to(_a		)

						# up
						for up_block in self.up_blocks:
							SCREAMING_SNAKE_CASE      :					Tuple    =				torch.utils.checkpoint.checkpoint(
							    create_custom_forward(_a		)					,					_a					,					_a					,					use_reentrant=_a		)
					else:
						# middle
						SCREAMING_SNAKE_CASE      :					Optional[int]    =				torch.utils.checkpoint.checkpoint(
						    create_custom_forward(self.mid_block		)					,					_a					,					_a		)
						SCREAMING_SNAKE_CASE      :					Optional[int]    =				sample.to(_a		)

						# up
						for up_block in self.up_blocks:
							SCREAMING_SNAKE_CASE      :					Tuple    =				torch.utils.checkpoint.checkpoint(create_custom_forward(_a		)					,					_a					,					_a		)
				else:
					# middle
					SCREAMING_SNAKE_CASE      :					Tuple    =				self.mid_block(_a					,					_a		)
					SCREAMING_SNAKE_CASE      :					Dict    =				sample.to(_a		)

					# up
					for up_block in self.up_blocks:
						SCREAMING_SNAKE_CASE      :					Dict    =				up_block(_a					,					_a		)

        # post-process
				if latent_embeds is None:
					SCREAMING_SNAKE_CASE      :					List[Any]    =				self.conv_norm_out(_a		)
				else:
					SCREAMING_SNAKE_CASE      :					int    =				self.conv_norm_out(_a					,					_a		)
				SCREAMING_SNAKE_CASE      :					Tuple    =				self.conv_act(_a		)
				SCREAMING_SNAKE_CASE      :					Tuple    =				self.conv_out(_a		)

				return sample






class _UpperCamelCase							(							nn.Module      ):
			'''simple docstring'''







			def __init__(	self							:       Tuple					,					a							:       Any					,					a							:       int					,					a							:       Optional[Any]					,					a							:       int=None					,					a							:       Union[str, Any]="random"					,					a							:       str=False					,					a							:       Any=True		)							->				Union[str, Any]:

				"""simple docstring"""

				super().__init__()
				SCREAMING_SNAKE_CASE      :					List[str]    =				n_e
				SCREAMING_SNAKE_CASE      :					List[str]    =				vq_embed_dim
				SCREAMING_SNAKE_CASE      :					int    =				beta
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				legacy

				SCREAMING_SNAKE_CASE      :					int    =				nn.Embedding(self.n_e					,					self.vq_embed_dim		)
				self.embedding.weight.data.uniform_(-1.0 / self.n_e					,					1.0 / self.n_e		)

				SCREAMING_SNAKE_CASE      :					Tuple    =				remap
				if self.remap is not None:
					self.register_buffer("used"					,					torch.tensor(np.load(self.remap		)		)		)
					SCREAMING_SNAKE_CASE      :					Optional[Any]    =				self.used.shape[0]
					SCREAMING_SNAKE_CASE      :					Dict    =				unknown_index  # "random" or "extra" or integer
					if self.unknown_index == "extra":
						SCREAMING_SNAKE_CASE      :					List[str]    =				self.re_embed
						SCREAMING_SNAKE_CASE      :					str    =				self.re_embed + 1
					print(
					    F"Remapping {self.n_e} indices to {self.re_embed} indices. "
					    F"Using {self.unknown_index} for unknown indices."		)
				else:
					SCREAMING_SNAKE_CASE      :					Optional[Any]    =				n_e

				SCREAMING_SNAKE_CASE      :					str    =				sane_index_shape
			def 							__UpperCamelCase       (	self							:       int					,					a							:       List[Any]		)							->				List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				inds.shape
				assert len(_a		) > 1
				SCREAMING_SNAKE_CASE      :					Dict    =				inds.reshape(ishape[0]					,					-1		)
				SCREAMING_SNAKE_CASE      :					Any    =				self.used.to(_a		)
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				(inds[:, :, None] == used[None, None, ...]).long()
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				match.argmax(-1		)
				SCREAMING_SNAKE_CASE      :					Dict    =				match.sum(2		) < 1
				if self.unknown_index == "random":
					SCREAMING_SNAKE_CASE      :					Dict    =				torch.randint(0					,					self.re_embed					,					size=new[unknown].shape		).to(device=new.device		)
				else:
					SCREAMING_SNAKE_CASE      :					Tuple    =				self.unknown_index
				return new.reshape(_a		)
			def 							__UpperCamelCase       (	self							:       int					,					a							:       str		)							->				Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Dict    =				inds.shape
				assert len(_a		) > 1
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				inds.reshape(ishape[0]					,					-1		)
				SCREAMING_SNAKE_CASE      :					List[Any]    =				self.used.to(_a		)
				if self.re_embed > self.used.shape[0]:  # extra token
					SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				0  # simply set to zero
				SCREAMING_SNAKE_CASE      :					int    =				torch.gather(used[None, :][inds.shape[0] * [0], :]					,					1					,					_a		)
				return back.reshape(_a		)
			def 							__UpperCamelCase       (	self							:       Dict					,					a							:       List[str]		)							->				str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Dict    =				z.permute(0					,					2					,					3					,					1		).contiguous()
				SCREAMING_SNAKE_CASE      :					List[str]    =				z.view(-1					,					self.vq_embed_dim		)

				# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
				SCREAMING_SNAKE_CASE      :					Tuple    =				torch.argmin(torch.cdist(_a					,					self.embedding.weight		)					,					dim=1		)

				SCREAMING_SNAKE_CASE      :					List[Any]    =				self.embedding(_a		).view(z.shape		)
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				None
				SCREAMING_SNAKE_CASE      :					int    =				None

				# compute loss for embedding
				if not self.legacy:
					SCREAMING_SNAKE_CASE      :					Any    =				self.beta * torch.mean((z_q.detach() - z) ** 2		) + torch.mean((z_q - z.detach()) ** 2		)
				else:
					SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				torch.mean((z_q.detach() - z) ** 2		) + self.beta * torch.mean((z_q - z.detach()) ** 2		)

				# preserve gradients
				SCREAMING_SNAKE_CASE      :					int    =				z + (z_q - z).detach()

				# reshape back to match original input shape
				SCREAMING_SNAKE_CASE      :					Dict    =				z_q.permute(0					,					3					,					1					,					2		).contiguous()

				if self.remap is not None:
					SCREAMING_SNAKE_CASE      :					Dict    =				min_encoding_indices.reshape(z.shape[0]					,					-1		)  # add batch axis
					SCREAMING_SNAKE_CASE      :					List[Any]    =				self.remap_to_used(_a		)
					SCREAMING_SNAKE_CASE      :					str    =				min_encoding_indices.reshape(-1					,					1		)  # flatten

				if self.sane_index_shape:
					SCREAMING_SNAKE_CASE      :					List[Any]    =				min_encoding_indices.reshape(z_q.shape[0]					,					z_q.shape[2]					,					z_q.shape[3]		)

				return z_q, loss, (perplexity, min_encodings, min_encoding_indices)






			def 							__UpperCamelCase       (	self							:       Tuple					,					a							:       int					,					a							:       Tuple		)							->				Dict:

				"""simple docstring"""

				if self.remap is not None:
					SCREAMING_SNAKE_CASE      :					Optional[int]    =				indices.reshape(shape[0]					,					-1		)  # add batch axis
					SCREAMING_SNAKE_CASE      :					str    =				self.unmap_to_all(_a		)
					SCREAMING_SNAKE_CASE      :					Optional[Any]    =				indices.reshape(-1		)  # flatten again

				# get quantized latent vectors
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				self.embedding(_a		)

				if shape is not None:
					SCREAMING_SNAKE_CASE      :					Tuple    =				z_q.view(_a		)
					# reshape back to match original input shape
					SCREAMING_SNAKE_CASE      :					str    =				z_q.permute(0					,					3					,					1					,					2		).contiguous()

				return z_q






class _UpperCamelCase							(							UpperCamelCase__      ):
			'''simple docstring'''







			def __init__(	self							:       Optional[int]					,					a							:       Optional[int]					,					a							:       Any=False		)							->				List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					str    =				parameters
				SCREAMING_SNAKE_CASE      :					Dict    =				torch.chunk(_a					,					2					,					dim=1		)
				SCREAMING_SNAKE_CASE      :					List[Any]    =				torch.clamp(self.logvar					,					-30.0					,					20.0		)
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				deterministic
				SCREAMING_SNAKE_CASE      :					List[str]    =				torch.exp(0.5 * self.logvar		)
				SCREAMING_SNAKE_CASE      :					Dict    =				torch.exp(self.logvar		)
				if self.deterministic:
					SCREAMING_SNAKE_CASE      :					str    =				torch.zeros_like(
					    self.mean					,					device=self.parameters.device					,					dtype=self.parameters.dtype		)
			def 							__UpperCamelCase       (	self							:       List[Any]					,					a							:       List[Any] = None		)							->				torch.FloatTensor:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				randn_tensor(
				    self.mean.shape					,					generator=_a					,					device=self.parameters.device					,					dtype=self.parameters.dtype		)
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				self.mean + self.std * sample
				return x
			def 							__UpperCamelCase       (	self							:       List[str]					,					a							:       Any=None		)							->				int:

				"""simple docstring"""

				if self.deterministic:
					return torch.Tensor([0.0]		)
				else:
					if other is None:
						return 0.5 * torch.sum(torch.pow(self.mean					,					2		) + self.var - 1.0 - self.logvar					,					dim=[1, 2, 3]		)
					else:
						return 0.5 * torch.sum(
						    torch.pow(self.mean - other.mean					,					2		) / other.var
						    + self.var / other.var
						    - 1.0
						    - self.logvar
						    + other.logvar					,					dim=[1, 2, 3]					,					)
			def 							__UpperCamelCase       (	self							:       List[str]					,					a							:       List[str]					,					a							:       Tuple=[1, 2, 3]		)							->				Dict:

				"""simple docstring"""

				if self.deterministic:
					return torch.Tensor([0.0]		)
				SCREAMING_SNAKE_CASE      :					Dict    =				np.log(2.0 * np.pi		)
				return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean					,					2		) / self.var					,					dim=_a		)






			def 							__UpperCamelCase       (	self							:       Dict		)							->				int:

				"""simple docstring"""

				return self.mean





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,
)


_snake_case										=       getLogger(__name__)







def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_,snake_case_ = 8,snake_case_ = 1024,snake_case_="val",snake_case_=None,snake_case_=False,snake_case_="summarization",snake_case_=None,snake_case_=1,snake_case_ = None,snake_case_="",**snake_case_,):
      _A							:     Dict			=						str(snake_case_     )
      assert local_rank is not None
      torch.distributed.init_process_group(backend="""nccl""",rank=snake_case_     )

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

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

      if max_source_length is None:
            _A							:     Optional[int]			=						tokenizer.model_max_length
      if prefix is None:
            _A							:     Tuple			=						prefix or getattr(model.config,"""prefix""",""""""     ) or """"""
      _A							:     Optional[int]			=						SeqaSeqDataset(
          snake_case_,snake_case_,snake_case_,max_target_length=1024,type_path=snake_case_,n_obs=snake_case_,prefix=snake_case_,**snake_case_,)
      # 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							:     Optional[int]			=						ds.make_sortish_sampler(snake_case_,distributed=snake_case_,add_extra_examples=snake_case_,shuffle=snake_case_     )
      _A							:     Dict			=						DataLoader(snake_case_,sampler=snake_case_,batch_size=snake_case_,collate_fn=ds.collate_fn     )
      _A							:     Optional[Any]			=						[]
      for batch in tqdm(snake_case_     ):
            _A							:     Tuple			=						model.generate(
                input_ids=batch["""input_ids"""].to(model.device     ),attention_mask=batch["""attention_mask"""].to(model.device     ),num_return_sequences=snake_case_,num_beams=snake_case_,**snake_case_,)
            _A							:     Any			=						tokenizer.batch_decode(snake_case_,skip_special_tokens=snake_case_,clean_up_tokenization_spaces=snake_case_     )
            _A							:     Dict			=						batch["""ids"""]
            if num_return_sequences > 1:
                  _A							:     Any			=						chunks(snake_case_,snake_case_     )  # batch size chunks, each of size num_return_seq
            for i, pred in enumerate(snake_case_     ):
                  results.append({"""pred""": pred, """id""": ids[i].item()}     )
      save_json(snake_case_,snake_case_     )
      return results, sampler.num_replicas







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

      parser.add_argument(
          """--n_obs""",type=snake_case_,default=snake_case_,required=snake_case_,help="""How many observations. Defaults to all."""     )
      parser.add_argument(
          """--num_return_sequences""",type=snake_case_,default=1,required=snake_case_,help="""How many sequences to return"""     )
      parser.add_argument(
          """--sync_timeout""",type=snake_case_,default=600,required=snake_case_,help="""How long should master process wait for other processes to finish.""",)
      parser.add_argument("""--src_lang""",type=snake_case_,default=snake_case_,required=snake_case_     )
      parser.add_argument("""--tgt_lang""",type=snake_case_,default=snake_case_,required=snake_case_     )
      parser.add_argument(
          """--prefix""",type=snake_case_,required=snake_case_,default=snake_case_,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							:     Union[str, Any]			=						time.time()
      _A			,   _A							:     List[str]			=						parser.parse_known_args()
      _A							:     List[str]			=						parse_numeric_n_bool_cl_kwargs(snake_case_     )
      if generate_kwargs and args.local_rank <= 0:
            print(f'''parsed the following generate kwargs: {generate_kwargs}'''     )
      _A							:     Dict			=						Path(args.save_dir + """_tmp"""     )
      Path(snake_case_     ).mkdir(exist_ok=snake_case_     )  # this handles locking.
      _A							:     int			=						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							:     Any			=						{}
      if args.src_lang is not None:
            _A							:     int			=						args.src_lang
      if args.tgt_lang is not None:
            _A							:     Dict			=						args.tgt_lang

      Path(args.save_dir     ).mkdir(exist_ok=snake_case_     )
      _A			,   _A							:     str			=						eval_data_dir(
          args.data_dir,snake_case_,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=snake_case_,**snake_case_,)

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

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







def 				lowerCAmelCase_	(     snake_case_     ):
      _A							:     Dict			=						[]
      for partial_result in partial_results:
            records.extend(snake_case_     )
      _A							:     Optional[Any]			=						sorted(snake_case_,key=lambda snake_case_     : x["id"]     )
      _A							:     List[str]			=						[x["""pred"""] for x in records]
      return preds







def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_     ):
      # WAIT FOR lots of .json files
      _A							:     Optional[Any]			=						time.time()
      logger.info("""waiting for all nodes to finish"""     )
      _A							:     List[str]			=						None
      while (time.time() - start_wait) < timeout:
            _A							:     str			=						list(save_dir.glob("""rank_*.json"""     )     )
            if len(snake_case_     ) < num_replicas:
                  continue
            try:
                  # make sure all json files are fully saved
                  _A							:     List[str]			=						lmap(snake_case_,snake_case_     )
                  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()

