



"""simple docstring"""





from random import shuffle

import tensorflow as tf
from numpy import array
def 						__lowerCAmelCase    (		__UpperCamelCase   :      Union[str, Any]       ,			__UpperCamelCase   :      str     ):


						'''simple docstring'''






						snake_case_ :			Optional[Any]            =      int(__UpperCamelCase     )
						assert noofclusters < len(__UpperCamelCase     )

						# Find out the dimensionality
						snake_case_ :			List[str]            =      len(vectors[0]     )

						# Will help select random centroids from among the available vectors
						snake_case_ :			Union[str, Any]            =      list(range(len(__UpperCamelCase     )     )     )
						shuffle(__UpperCamelCase     )

						# GRAPH OF COMPUTATION
						# We initialize a new graph and set it as the default during each run
						# of this algorithm. This ensures that as this function is called
						# multiple times, the default graph doesn't keep getting crowded with
						# unused ops and Variables from previous function calls.

						snake_case_ :			Optional[Any]            =      tf.Graph()

						with graph.as_default():
												# SESSION OF COMPUTATION

												snake_case_ :			Tuple            =      tf.Session()

												##CONSTRUCTING THE ELEMENTS OF COMPUTATION

												##First lets ensure we have a Variable vector for each centroid,
												##initialized to one of the vectors from the available data points
												snake_case_ :			int            =      [
												    tf.Variable(vectors[vector_indices[i]]     ) for i in range(__UpperCamelCase     )
												]
												##These nodes will assign the centroid Variables the appropriate
												##values
												snake_case_ :			Any            =      tf.placeholder("""float64"""       ,			[dim]     )
												snake_case_ :			int            =      []
												for centroid in centroids:
																		cent_assigns.append(tf.assign(__UpperCamelCase       ,			__UpperCamelCase     )     )

												##Variables for cluster assignments of individual vectors(initialized
												##to 0 at first)
												snake_case_ :			Tuple            =      [tf.Variable(0     ) for i in range(len(__UpperCamelCase     )     )]
												##These nodes will assign an assignment Variable the appropriate
												##value
												snake_case_ :			Dict            =      tf.placeholder("""int32"""     )
												snake_case_ :			List[Any]            =      []
												for assignment in assignments:
																		cluster_assigns.append(tf.assign(__UpperCamelCase       ,			__UpperCamelCase     )     )

												##Now lets construct the node that will compute the mean
												# The placeholder for the input
												snake_case_ :			Union[str, Any]            =      tf.placeholder("""float"""       ,			[None, dim]     )
												# The Node/op takes the input and computes a mean along the 0th
												# dimension, i.e. the list of input vectors
												snake_case_ :			str            =      tf.reduce_mean(__UpperCamelCase       ,			0     )

												##Node for computing Euclidean distances
												# Placeholders for input
												snake_case_ :			Union[str, Any]            =      tf.placeholder("""float"""       ,			[dim]     )
												snake_case_ :			Any            =      tf.placeholder("""float"""       ,			[dim]     )
												snake_case_ :			Optional[Any]            =      tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(__UpperCamelCase       ,			__UpperCamelCase     )       ,			2     )     )     )

												##This node will figure out which cluster to assign a vector to,
												##based on Euclidean distances of the vector from the centroids.
												# Placeholder for input
												snake_case_ :			Union[str, Any]            =      tf.placeholder("""float"""       ,			[noofclusters]     )
												snake_case_ :			List[Any]            =      tf.argmin(__UpperCamelCase       ,			0     )

												##INITIALIZING STATE VARIABLES

												##This will help initialization of all Variables defined with respect
												##to the graph. The Variable-initializer should be defined after
												##all the Variables have been constructed, so that each of them
												##will be included in the initialization.
												snake_case_ :			str            =      tf.initialize_all_variables()

												# Initialize all variables
												sess.run(__UpperCamelCase     )

												##CLUSTERING ITERATIONS

												# Now perform the Expectation-Maximization steps of K-Means clustering
												# iterations. To keep things simple, we will only do a set number of
												# iterations, instead of using a Stopping Criterion.
												snake_case_ :			Union[str, Any]            =      1_0_0
												for _ in range(__UpperCamelCase     ):
																		##EXPECTATION STEP
																		##Based on the centroid locations till last iteration, compute
																		##the _expected_ centroid assignments.
																		# Iterate over each vector
																		for vector_n in range(len(__UpperCamelCase     )     ):
																								snake_case_ :			Optional[int]            =      vectors[vector_n]
																								# Compute Euclidean distance between this vector and each
																								# centroid. Remember that this list cannot be named
																								#'centroid_distances', since that is the input to the
																								# cluster assignment node.
																								snake_case_ :			Dict            =      [
																								    sess.run(__UpperCamelCase       ,			feed_dict={va: vect, va: sess.run(__UpperCamelCase     )}     )
																								    for centroid in centroids
																								]
																								# Now use the cluster assignment node, with the distances
																								# as the input
																								snake_case_ :			List[str]            =      sess.run(
																								    __UpperCamelCase       ,			feed_dict={centroid_distances: distances}     )
																								# Now assign the value to the appropriate state variable
																								sess.run(
																								    cluster_assigns[vector_n]       ,			feed_dict={assignment_value: assignment}     )

																		##MAXIMIZATION STEP
																		# Based on the expected state computed from the Expectation Step,
																		# compute the locations of the centroids so as to maximize the
																		# overall objective of minimizing within-cluster Sum-of-Squares
																		for cluster_n in range(__UpperCamelCase     ):
																								# Collect all the vectors assigned to this cluster
																								snake_case_ :			Any            =      [
																								    vectors[i]
																								    for i in range(len(__UpperCamelCase     )     )
																								    if sess.run(assignments[i]     ) == cluster_n
																								]
																								# Compute new centroid location
																								snake_case_ :			Dict            =      sess.run(
																								    __UpperCamelCase       ,			feed_dict={mean_input: array(__UpperCamelCase     )}     )
																								# Assign value to appropriate variable
																								sess.run(
																								    cent_assigns[cluster_n]       ,			feed_dict={centroid_value: new_location}     )

        # Return centroids and assignments
												snake_case_ :			List[str]            =      sess.run(__UpperCamelCase     )
												snake_case_ :			List[str]            =      sess.run(__UpperCamelCase     )
												return centroids, assignments






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()


