

import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch

from parameterized import parameterized

from transformers.testing_utils import (
    CaptureStderr,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_torch_dist_unique_port,
    require_apex,
    require_bitsandbytes,
    require_fairscale,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    require_torch_non_multi_gpu,
    slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed


a_						     =    os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(F'''{bindir}/../../examples/pytorch/translation'''):
			from run_translation import main  # noqa


set_seed(42)
a_						     =    'sshleifer/student_marian_en_ro_6_1'
a_						     =    'sshleifer/tiny-mbart'





@require_torch
class _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			def 							__UpperCamelCase (			self							: Optional[int]      ,      a							: int=False      ,      a							: str=None      ,      a							: List[str]=True      ,      a							: int=True      ,      a							: Dict=True      ,      a							: List[str]=True      ,      )							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       self.run_trainer(
				    eval_steps=1      ,      max_len=12      ,      model_name=a      ,      num_train_epochs=1      ,      distributed=a      ,      extra_args_str=a      ,      predict_with_generate=a      ,      do_train=a      ,      do_eval=a      ,      do_predict=a      ,      )
				SCREAMING_SNAKE_CASE   :      Any				     =       TrainerState.load_from_json(os.path.join(a      ,      "trainer_state.json"	)	).log_history

				if not do_eval:
					return

				SCREAMING_SNAKE_CASE   :      int				     =       [log for log in logs if "eval_loss" in log.keys()]

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       eval_metrics[0]
				if predict_with_generate:
					assert "eval_bleu" in first_step_stats

					SCREAMING_SNAKE_CASE   :      List[str]				     =       eval_metrics[-1]
					assert isinstance(last_step_stats["eval_bleu"]      ,      a	)
					assert not math.isnan(float(last_step_stats["eval_loss"]	)	), "eval_loss must not be `nan`"
			@require_torch_non_multi_gpu
			def 							__UpperCamelCase (			self							: List[str]	)							->       int:

				"""simple docstring"""

				self.run_seqaseq_quick()
			@require_torch_multi_gpu
			def 							__UpperCamelCase (			self							: Any	)							->       Dict:

				"""simple docstring"""

				self.run_seqaseq_quick(distributed=a	)
			@require_torch_multi_gpu
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       int:

				"""simple docstring"""

				self.run_seqaseq_quick(distributed=a	)
			@unittest.skip("Requires an update of the env running those tests"	)
			@require_torch_multi_gpu
			@require_fairscale
			def 							__UpperCamelCase (			self							: List[Any]	)							->       Optional[Any]:

				"""simple docstring"""

				self.run_seqaseq_quick(distributed=a      ,      extra_args_str="--sharded_ddp simple"	)
			@unittest.skip("Requires an update of the env running those tests"	)
			@require_torch_multi_gpu
			@require_fairscale
			def 							__UpperCamelCase (			self							: int	)							->       List[Any]:

				"""simple docstring"""

				self.run_seqaseq_quick(distributed=a      ,      extra_args_str="--sharded_ddp simple --fp16"	)
			@unittest.skip("Requires an update of the env running those tests"	)
			@require_torch_multi_gpu
			@require_fairscale
			def 							__UpperCamelCase (			self							: Any	)							->       Union[str, Any]:

				"""simple docstring"""

				self.run_seqaseq_quick(distributed=a      ,      extra_args_str="--sharded_ddp zero_dp_2"      ,      predict_with_generate=a	)
			@unittest.skip("Requires an update of the env running those tests"	)
			@require_torch_multi_gpu
			@require_fairscale
			def 							__UpperCamelCase (			self							: str	)							->       int:

				"""simple docstring"""

				self.run_seqaseq_quick(
				    distributed=a      ,      extra_args_str="--sharded_ddp zero_dp_2 --fp16"      ,      predict_with_generate=a	)
			@require_apex
			@require_torch_gpu
			def 							__UpperCamelCase (			self							: List[Any]	)							->       List[Any]:

				"""simple docstring"""

				self.run_seqaseq_quick(distributed=a      ,      extra_args_str="--fp16 --fp16_backend=apex"	)
				# test 2nd time - was getting eval_loss': nan'
				# to reproduce the problem set distributed=False
				self.run_seqaseq_quick(distributed=a      ,      extra_args_str="--fp16 --fp16_backend=apex"	)
			@parameterized.expand(["base", "low", "high", "mixed"]	)
			@require_torch_multi_gpu
			def 							__UpperCamelCase (			self							: Tuple      ,      a							: List[str]	)							->       Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       {
				    # test with the default log_level - should be info and thus log info once
				    "base": {"extra_args_str": "", "n_matches": 1},
				    # test with low log_level and log_level_replica - should be noisy on all processes
				    # now the info string should appear twice on 2 processes
				    "low": {"extra_args_str": "--log_level debug --log_level_replica debug", "n_matches": 2},
				    # test with high log_level and low log_level_replica
				    # now the info string should appear once only on the replica
				    "high": {"extra_args_str": "--log_level error --log_level_replica debug", "n_matches": 1},
				    # test with high log_level and log_level_replica - should be quiet on all processes
				    "mixed": {"extra_args_str": "--log_level error --log_level_replica error", "n_matches": 0},
				}

				SCREAMING_SNAKE_CASE   :      str				     =       experiments[experiment_id]
				SCREAMING_SNAKE_CASE   :      int				     =       {"distributed": True, "predict_with_generate": False, "do_eval": False, "do_predict": False}
				SCREAMING_SNAKE_CASE   :      Dict				     =       "Running training"
				with CaptureStderr() as cl:
					self.run_seqaseq_quick(**a      ,      extra_args_str=data["extra_args_str"]	)
				SCREAMING_SNAKE_CASE   :      Dict				     =       len(re.findall(a      ,      cl.err	)	)
				self.assertEqual(a      ,      data["n_matches"]	)
			@slow
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       self.run_trainer(
				    eval_steps=2      ,      max_len=128      ,      model_name=a      ,      learning_rate=3e-4      ,      num_train_epochs=10      ,      distributed=a      ,      )

				# Check metrics
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       TrainerState.load_from_json(os.path.join(a      ,      "trainer_state.json"	)	).log_history
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       [log for log in logs if "eval_loss" in log.keys()]
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       eval_metrics[0]
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       eval_metrics[-1]

				assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
				assert isinstance(last_step_stats["eval_bleu"]      ,      a	)

				# test if do_predict saves generations and metrics
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       os.listdir(a	)
				SCREAMING_SNAKE_CASE   :      str				     =       {os.path.basename(a	) for p in contents}
				assert "generated_predictions.txt" in contents
				assert "predict_results.json" in contents
			@slow
			@require_bitsandbytes
			def 							__UpperCamelCase (			self							: Dict	)							->       Optional[Any]:

				"""simple docstring"""

				from transformers.training_args import OptimizerNames

				def train_and_return_metrics(a							: str	) -> Tuple[int, float]:
					SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       "--skip_memory_metrics 0"

					SCREAMING_SNAKE_CASE   :      List[Any]				     =       self.run_trainer(
					    max_len=128      ,      model_name=a      ,      learning_rate=3e-4      ,      num_train_epochs=1      ,      optim=a      ,      distributed=a      ,      extra_args_str=a      ,      do_eval=a      ,      do_predict=a      ,      n_gpus_to_use=1      ,      )

					# Check metrics
					SCREAMING_SNAKE_CASE   :      Tuple				     =       TrainerState.load_from_json(Path(a      ,      "trainer_state.json"	)	).log_history
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       int(logs[0]["train_mem_gpu_peaked_delta"] / 2**20	)
					SCREAMING_SNAKE_CASE   :      str				     =       int(logs[0]["train_mem_gpu_alloc_delta"] / 2**20	)

					SCREAMING_SNAKE_CASE   :      str				     =       logs[0]["train_loss"]
					return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

				SCREAMING_SNAKE_CASE   :      Tuple				     =       train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value	)
				SCREAMING_SNAKE_CASE   :      List[str]				     =       train_and_return_metrics(OptimizerNames.ADAMW_BNB.value	)

				SCREAMING_SNAKE_CASE   :      List[str]				     =       gpu_alloc_mem_orig - gpu_alloc_mem_bnb

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       gpu_peak_mem_orig + gpu_alloc_mem_orig
				SCREAMING_SNAKE_CASE   :      str				     =       gpu_peak_mem_bnb + gpu_alloc_mem_bnb
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       gpu_total_mem_orig - gpu_total_mem_bnb

				# sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
				# doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
				# in 2 bytes and the diff in optim memory usage is derived as so:
				#
				# - normal 25*8=~200MB (8 bytes per param)
				# - bnb    25*2= ~50MB (2 bytes per param)
				#
				# Thus we should expect ~150MB total memory saved.
				#
				# Peak memory should be the same - the total should be different by about that same margin
				#
				# After leaving a small margin to accommodate for differences between gpus let's check
				# that we have at least 120MB in savings
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       120

				# uncomment the following if this test starts failing - requires py38 for a new print feature
				# gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
				# print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
				# print(f" {gpu_alloc_mem_bnb=}MB  {gpu_peak_mem_bnb=}MB  {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
				# print(f"{gpu_alloc_mem_diff=}MB")
				# print(f"{gpu_peak_mem_diff=}MB")
				# print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
				# print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")

				self.assertGreater(
				    a      ,      a      ,      "should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got"
				    F" a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and"
				    F" gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB"      ,      )

				self.assertGreater(
				    a      ,      a      ,      "should use ~150MB less total gpu memory with BNB, compared to without it for this model but got"
				    F" a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and"
				    F" gpu_total_mem_bnb={gpu_total_mem_bnb}MB"      ,      )

				self.assertEqual(
				    a      ,      a      ,      F"loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}"	)






			def 							__UpperCamelCase (			self							: Any      ,      a							: int      ,      a							: str      ,      a							: int      ,      a							: float = 3e-3      ,      a							: str = "adafactor"      ,      a							: bool = False      ,      a							: str = None      ,      a							: int = 0      ,      a							: bool = True      ,      a							: bool = True      ,      a							: bool = True      ,      a							: bool = True      ,      a							: int = None      ,      )							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       self.test_file_dir / "../fixtures/tests_samples/wmt_en_ro"
				SCREAMING_SNAKE_CASE   :      Dict				     =       self.get_auto_remove_tmp_dir()
				SCREAMING_SNAKE_CASE   :      Dict				     =       F"\n            --model_name_or_path {model_name}\n            --train_file {data_dir}/train.json\n            --validation_file {data_dir}/val.json\n            --test_file {data_dir}/test.json\n            --output_dir {output_dir}\n            --overwrite_output_dir\n            --max_train_samples 8\n            --max_source_length {max_len}\n            --max_target_length {max_len}\n            --do_train\n            --num_train_epochs {str(a	)}\n            --per_device_train_batch_size 4\n            --learning_rate {learning_rate}\n            --warmup_steps 8\n            --logging_steps 0\n            --logging_strategy no\n            --save_steps {str(a	)}\n            --group_by_length\n            --label_smoothing_factor 0.1\n            --target_lang ro_RO\n            --source_lang en_XX\n        ".split()

				SCREAMING_SNAKE_CASE   :      Tuple				     =       F"\n            --do_eval\n            --per_device_eval_batch_size 4\n            --max_eval_samples 8\n            --val_max_target_length {max_len}\n            --evaluation_strategy steps\n            --eval_steps {str(a	)}\n        ".split()

				SCREAMING_SNAKE_CASE   :      List[str]				     =       "\n            --do_predict\n        ".split()

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       []
				if do_train:
					args += args_train

				if do_eval:
					args += args_eval

				if do_predict:
					args += args_predict

				if predict_with_generate:
					args += "--predict_with_generate".split()

				if do_train:
					if optim == "adafactor":
						args += "--adafactor".split()
					else:
						args += F"--optim {optim}".split()

				if extra_args_str is not None:
					args += extra_args_str.split()

				if distributed:
					if n_gpus_to_use is None:
						SCREAMING_SNAKE_CASE   :      Tuple				     =       get_gpu_count()
					SCREAMING_SNAKE_CASE   :      int				     =       get_torch_dist_unique_port()
					SCREAMING_SNAKE_CASE   :      str				     =       F"\n                -m torch.distributed.run\n                --nproc_per_node={n_gpus_to_use}\n                --master_port={master_port}\n                {self.examples_dir_str}/pytorch/translation/run_translation.py\n            ".split()
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       [sys.executable] + distributed_args + args
					# keep for quick debug
					# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
					execute_subprocess_async(a      ,      env=self.get_env()	)
				else:
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       ["run_translation.py"] + args
					with patch.object(a      ,      "argv"      ,      a	):
						main()

				return output_dir


import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type

from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm


a_						     =    re.compile('[^A-Za-z_0-9]')
# parameters used in DuplicationIndex
a_						     =    10
a_						     =    256

def  lowerCamelCase__ (					_a):
	if len(_a) < MIN_NUM_TOKENS:
		return None
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       MinHash(num_perm=_a)
	for token in set(_a):
		min_hash.update(token.encode())
	return min_hash

def  lowerCamelCase__ (					_a):
	return {t for t in NON_ALPHA.split(_a) if len(t.strip()) > 0}






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: Optional[Any]      ,      *,
			    a							: float = 0.85      ,      )							->       List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       duplication_jaccard_threshold
				SCREAMING_SNAKE_CASE   :      Any				     =       NUM_PERM
				SCREAMING_SNAKE_CASE   :      int				     =       MinHashLSH(threshold=self._duplication_jaccard_threshold      ,      num_perm=self._num_perm	)

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       defaultdict(a	)
			def 							__UpperCamelCase (			self							: List[str]      ,      a							: Tuple      ,      a							: MinHash	)							->       None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       self._index.query(a	)
				if code_key in self._index.keys:
					print(F"Duplicate key {code_key}"	)
					return

				self._index.insert(a      ,      a	)
				if len(a	) > 0:
					for base_duplicate in close_duplicates:
						if base_duplicate in self._duplicate_clusters:
							self._duplicate_clusters[base_duplicate].add(a	)
							break
					else:
						self._duplicate_clusters[close_duplicates[0]].add(a	)
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       List[List[Dict]]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       []
				for base, duplicates in self._duplicate_clusters.items():
					SCREAMING_SNAKE_CASE   :      int				     =       [base] + list(a	)
					# reformat the cluster to be a list of dict
					SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       [{"base_index": el[0], "repo_name": el[1], "path": el[2]} for el in cluster]
					duplicate_clusters.append(a	)
				return duplicate_clusters






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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       self.get_duplicate_clusters()
				with open(a      ,      "w"	) as f:
					json.dump(a      ,      a	)

def  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      List[str]				     =       element
	SCREAMING_SNAKE_CASE   :      Any				     =       get_min_hash([t for t in NON_ALPHA.split(data["content"]) if len(t.strip()) > 0])
	if min_hash is not None:
		return (index, data["repo_name"], data["path"]), min_hash

def  lowerCamelCase__ (					_a):
	with mp.Pool() as pool:
		for data in pool.imap_unordered(
		    _compute_min_hash							,			ThreadedIterator(_a							,			max_queue_size=10000)							,			chunksize=100							,			):
			if data is not None:
				yield data

def  lowerCamelCase__ (					_a							,			_a):
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       DuplicationIndex(duplication_jaccard_threshold=_a)

	for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(_a))							,			max_queue_size=100)):
		di.add(_a							,			_a)

	# Returns a List[Cluster] where Cluster is List[str] with the filenames.
	return di.get_duplicate_clusters()

def  lowerCamelCase__ (					_a							,			_a):
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       get_tokens(_a)
	SCREAMING_SNAKE_CASE   :      List[str]				     =       get_tokens(_a)
	return len(tokensa & tokensa) / len(tokensa | tokensa)


a_						     =    None

def  lowerCamelCase__ (					_a							,			_a):
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       []
	for elementa in cluster:
		SCREAMING_SNAKE_CASE   :      int				     =       _shared_dataset[elementa["base_index"]]["content"]
		for elementa in extremes:
			SCREAMING_SNAKE_CASE   :      int				     =       _shared_dataset[elementa["base_index"]]["content"]
			if jaccard_similarity(_a							,			_a) >= jaccard_threshold:
				elementa["copies"] += 1
				break
		else:
			SCREAMING_SNAKE_CASE   :      Optional[int]				     =       1
			extremes.append(_a)
	return extremes

def  lowerCamelCase__ (					_a							,			_a							,			_a):
	global _shared_dataset
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       dataset
	SCREAMING_SNAKE_CASE   :      str				     =       []
	SCREAMING_SNAKE_CASE   :      Dict				     =       partial(_find_cluster_extremes_shared							,			jaccard_threshold=_a)
	with mp.Pool() as pool:
		for extremes in tqdm(
		    pool.imap_unordered(
		        _a							,			_a							,			)							,			total=len(_a)							,			):
			extremes_list.append(_a)
	return extremes_list


def  lowerCamelCase__ (					_a							,			_a = 0.85):
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       make_duplicate_clusters(_a							,			_a)
	SCREAMING_SNAKE_CASE   :      Dict				     =       {x["base_index"] for cluster in duplicate_clusters for x in cluster}
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       {}
	SCREAMING_SNAKE_CASE   :      List[str]				     =       find_extremes(_a							,			_a							,			_a)
	for extremes in extremes_clusters:
		for element in extremes:
			SCREAMING_SNAKE_CASE   :      str				     =       element
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       duplicate_indices - set(extreme_dict.keys())
	SCREAMING_SNAKE_CASE   :      str				     =       dataset.filter(lambda _a							,			_a: idx not in remove_indices							,			with_indices=_a)

	# update duplicate_clusters
	for cluster in duplicate_clusters:
		for element in cluster:
			SCREAMING_SNAKE_CASE   :      Dict				     =       element["base_index"] in extreme_dict
			if element["is_extreme"]:
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       extreme_dict[element["base_index"]]["copies"]

	print(f"Original dataset size: {len(_a)}")
	print(f"Number of duplicate clusters: {len(_a)}")
	print(f"Files in duplicate cluster: {len(_a)}")
	print(f"Unique files in duplicate cluster: {len(_a)}")
	print(f"Filtered dataset size: {len(_a)}")

	return ds_filter, duplicate_clusters