
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


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


set_seed(42)
lowercase__		:Any   =    "sshleifer/student_marian_en_ro_6_1"
lowercase__		:Optional[Any]   =    "sshleifer/tiny-mbart"







@require_torch
class 				lowercase		(					SCREAMING_SNAKE_CASE__		):




							def  A__      (				self   ,A__=False   ,A__=None   ,A__=True   ,A__=True   ,A__=True   ,A__=True   ,):
												lowercase							=						self.run_trainer(
												    eval_steps=1   ,max_len=1_2   ,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__   ,)
												lowercase							=						TrainerState.load_from_json(os.path.join(A__   ,'''trainer_state.json''')).log_history

												if not do_eval:
																	return

												lowercase							=						[log for log in logs if '''eval_loss''' in log.keys()]

												lowercase							=						eval_metrics[0]
												if predict_with_generate:
																	assert "eval_bleu" in first_step_stats

																	lowercase							=						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  A__      (				self):
												self.run_seqaseq_quick()




							@require_torch_multi_gpu
							def  A__      (				self):
												self.run_seqaseq_quick(distributed=A__)




							@require_torch_multi_gpu
							def  A__      (				self):
												self.run_seqaseq_quick(distributed=A__)




							@unittest.skip('''Requires an update of the env running those tests''')
							@require_torch_multi_gpu
							@require_fairscale
							def  A__      (				self):
												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  A__      (				self):
												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  A__      (				self):
												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  A__      (				self):
												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  A__      (				self):
												# XXX: apex breaks the trainer if it's run twice e.g. run_seq2seq.main() from the same
												# program and it breaks other tests that run from the same pytest worker, therefore until this is
												# sorted out it must be run only in an external program, that is distributed=True in this
												# test and only under one or more gpus - if we want cpu will need to make a special test
												#
												# specifically to the problem traced it to self.optimizer.step() - if it's run 2nd time via
												# 2nd main() call it botches the future eval.
												#
												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  A__      (				self   ,A__):
												# as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
												lowercase							=						{
												    # 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},
												}

												lowercase							=						experiments[experiment_id]
												lowercase							=						{'''distributed''': True, '''predict_with_generate''': False, '''do_eval''': False, '''do_predict''': False}
												lowercase							=						'''Running training'''
												with CaptureStderr() as cl:
																	self.run_seqaseq_quick(**A__   ,extra_args_str=data['''extra_args_str'''])
												lowercase							=						len(re.findall(A__   ,cl.err))
												self.assertEqual(A__   ,data['''n_matches'''])




							@slow
							def  A__      (				self):
												lowercase							=						self.run_trainer(
												    eval_steps=2   ,max_len=1_2_8   ,model_name=A__   ,learning_rate=3E-4   ,num_train_epochs=1_0   ,distributed=A__   ,)

												# Check metrics
												lowercase							=						TrainerState.load_from_json(os.path.join(A__   ,'''trainer_state.json''')).log_history
												lowercase							=						[log for log in logs if '''eval_loss''' in log.keys()]
												lowercase							=						eval_metrics[0]
												lowercase							=						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
												lowercase							=						os.listdir(A__)
												lowercase							=						{os.path.basename(A__) for p in contents}
												assert "generated_predictions.txt" in contents
												assert "predict_results.json" in contents




							@slow
							@require_bitsandbytes
							def  A__      (				self):
												from transformers.training_args import OptimizerNames

												def train_and_return_metrics(A__) -> Tuple[int, float]:
																	lowercase							=						'''--skip_memory_metrics 0'''

																	lowercase							=						self.run_trainer(
																	    max_len=1_2_8   ,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
																	lowercase							=						TrainerState.load_from_json(Path(A__   ,'''trainer_state.json''')).log_history
																	lowercase							=						int(logs[0]['''train_mem_gpu_peaked_delta'''] / 2**2_0)
																	lowercase							=						int(logs[0]['''train_mem_gpu_alloc_delta'''] / 2**2_0)

																	lowercase							=						logs[0]['''train_loss''']
																	return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

												lowercase	,						lowercase	,						lowercase							=						train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value)
												lowercase	,						lowercase	,						lowercase							=						train_and_return_metrics(OptimizerNames.ADAMW_BNB.value)

												lowercase							=						gpu_alloc_mem_orig - gpu_alloc_mem_bnb

												lowercase							=						gpu_peak_mem_orig + gpu_alloc_mem_orig
												lowercase							=						gpu_peak_mem_bnb + gpu_alloc_mem_bnb
												lowercase							=						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
												lowercase							=						1_2_0

												# 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  A__      (				self   ,A__   ,A__   ,A__   ,A__ = 3E-3   ,A__ = "adafactor"   ,A__ = False   ,A__ = None   ,A__ = 0   ,A__ = True   ,A__ = True   ,A__ = True   ,A__ = True   ,A__ = None   ,):
												lowercase							=						self.test_file_dir / '''../fixtures/tests_samples/wmt_en_ro'''
												lowercase							=						self.get_auto_remove_tmp_dir()
												lowercase							=						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()

												lowercase							=						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()

												lowercase							=						'''
            --do_predict
        '''.split()

												lowercase							=						[]
												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:
																						lowercase							=						get_gpu_count()
																	lowercase							=						get_torch_dist_unique_port()
																	lowercase							=						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()
																	lowercase							=						[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:
																	lowercase							=						['''run_translation.py'''] + args
																	with patch.object(A__   ,'''argv'''   ,A__):
																						main()

												return output_dir






import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pytest

from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex

from .utils import require_elasticsearch, require_faiss


lowercase__		:Optional[int]   =    pytest.mark.integration







@require_faiss
class 				lowercase		(					SCREAMING_SNAKE_CASE__		):




							def  A__      (				self):
												lowercase							=						Dataset.from_dict({'''filename''': ['''my_name-train''' + '''_''' + str(A__) for x in np.arange(3_0).tolist()]})
												return dset




							def  A__      (				self):
												import faiss

												lowercase							=						self._create_dummy_dataset()
												lowercase							=						dset.map(
												    lambda A__   ,A__: {"vecs": i * np.ones(5   ,dtype=np.floataa)}   ,with_indices=A__   ,keep_in_memory=A__)
												lowercase							=						dset.add_faiss_index('''vecs'''   ,batch_size=1_0_0   ,metric_type=faiss.METRIC_INNER_PRODUCT)
												lowercase	,						lowercase							=						dset.get_nearest_examples('''vecs'''   ,np.ones(5   ,dtype=np.floataa))
												self.assertEqual(examples['''filename'''][0]   ,'''my_name-train_29''')
												dset.drop_index('''vecs''')




							def  A__      (				self):
												import faiss

												lowercase							=						self._create_dummy_dataset()
												dset.add_faiss_index_from_external_arrays(
												    external_arrays=np.ones((3_0, 5)) * np.arange(3_0).reshape(-1   ,1)   ,index_name='''vecs'''   ,batch_size=1_0_0   ,metric_type=faiss.METRIC_INNER_PRODUCT   ,)
												lowercase	,						lowercase							=						dset.get_nearest_examples('''vecs'''   ,np.ones(5   ,dtype=np.floataa))
												self.assertEqual(examples['''filename'''][0]   ,'''my_name-train_29''')




							def  A__      (				self):
												import faiss

												lowercase							=						self._create_dummy_dataset()
												dset.add_faiss_index_from_external_arrays(
												    external_arrays=np.ones((3_0, 5)) * np.arange(3_0).reshape(-1   ,1)   ,index_name='''vecs'''   ,metric_type=faiss.METRIC_INNER_PRODUCT   ,)

												# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
												# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
												# see https://bugs.python.org/issue14243 and
												# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
												with tempfile.NamedTemporaryFile(delete=A__) as tmp_file:
																	dset.save_faiss_index('''vecs'''   ,tmp_file.name)
																	dset.load_faiss_index('''vecs2'''   ,tmp_file.name)
												os.unlink(tmp_file.name)

												lowercase	,						lowercase							=						dset.get_nearest_examples('''vecs2'''   ,np.ones(5   ,dtype=np.floataa))
												self.assertEqual(examples['''filename'''][0]   ,'''my_name-train_29''')




							def  A__      (				self):
												lowercase							=						self._create_dummy_dataset()
												dset.add_faiss_index_from_external_arrays(
												    external_arrays=np.ones((3_0, 5)) * np.arange(3_0).reshape(-1   ,1)   ,index_name='''vecs''')
												dset.drop_index('''vecs''')
												self.assertRaises(A__   ,partial(dset.get_nearest_examples   ,'''vecs2'''   ,np.ones(5   ,dtype=np.floataa)))





							def  A__      (				self):
												from elasticsearch import Elasticsearch

												lowercase							=						self._create_dummy_dataset()
												with patch('''elasticsearch.Elasticsearch.search''') as mocked_search, patch(
												    '''elasticsearch.client.IndicesClient.create''') as mocked_index_create, patch('''elasticsearch.helpers.streaming_bulk''') as mocked_bulk:
																	lowercase							=						{'''acknowledged''': True}
																	mocked_bulk.return_value([(True, None)] * 3_0)
																	lowercase							=						{'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 2_9}]}}
																	lowercase							=						Elasticsearch()

																	dset.add_elasticsearch_index('''filename'''   ,es_client=A__)
																	lowercase	,						lowercase							=						dset.get_nearest_examples('''filename'''   ,'''my_name-train_29''')
																	self.assertEqual(examples['''filename'''][0]   ,'''my_name-train_29''')







@require_faiss
class 				lowercase		(					SCREAMING_SNAKE_CASE__		):




							def  A__      (				self):
												import faiss

												lowercase							=						FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT)

												# add vectors
												index.add_vectors(np.eye(5   ,dtype=np.floataa))
												self.assertIsNotNone(index.faiss_index)
												self.assertEqual(index.faiss_index.ntotal   ,5)
												index.add_vectors(np.zeros((5, 5)   ,dtype=np.floataa))
												self.assertEqual(index.faiss_index.ntotal   ,1_0)

												# single query
												lowercase							=						np.zeros(5   ,dtype=np.floataa)
												lowercase							=						1
												lowercase	,						lowercase							=						index.search(A__)
												self.assertRaises(A__   ,index.search   ,query.reshape(-1   ,1))
												self.assertGreater(scores[0]   ,0)
												self.assertEqual(indices[0]   ,1)

												# batched queries
												lowercase							=						np.eye(5   ,dtype=np.floataa)[::-1]
												lowercase	,						lowercase							=						index.search_batch(A__)
												self.assertRaises(A__   ,index.search_batch   ,queries[0])
												lowercase							=						[scores[0] for scores in total_scores]
												lowercase							=						[indices[0] for indices in total_indices]
												self.assertGreater(np.min(A__)   ,0)
												self.assertListEqual([4, 3, 2, 1, 0]   ,A__)




							def  A__      (				self):
												import faiss

												lowercase							=						FaissIndex(string_factory='''Flat''')
												index.add_vectors(np.eye(5   ,dtype=np.floataa))
												self.assertIsInstance(index.faiss_index   ,faiss.IndexFlat)
												lowercase							=						FaissIndex(string_factory='''LSH''')
												index.add_vectors(np.eye(5   ,dtype=np.floataa))
												self.assertIsInstance(index.faiss_index   ,faiss.IndexLSH)
												with self.assertRaises(A__):
																	lowercase							=						FaissIndex(string_factory='''Flat'''   ,custom_index=faiss.IndexFlat(5))




							def  A__      (				self):
												import faiss

												lowercase							=						faiss.IndexFlat(5)
												lowercase							=						FaissIndex(custom_index=A__)
												index.add_vectors(np.eye(5   ,dtype=np.floataa))
												self.assertIsInstance(index.faiss_index   ,faiss.IndexFlat)





							def  A__      (				self):
												import faiss

												lowercase							=						FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT)
												index.add_vectors(np.eye(5   ,dtype=np.floataa))

												# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
												# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
												# see https://bugs.python.org/issue14243 and
												# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
												with tempfile.NamedTemporaryFile(delete=A__) as tmp_file:
																	index.save(tmp_file.name)
																	lowercase							=						FaissIndex.load(tmp_file.name)
												os.unlink(tmp_file.name)

												lowercase							=						np.zeros(5   ,dtype=np.floataa)
												lowercase							=						1
												lowercase	,						lowercase							=						index.search(A__)
												self.assertGreater(scores[0]   ,0)
												self.assertEqual(indices[0]   ,1)



@require_faiss
def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					import faiss

					lowercase							=						FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT		)
					index.add_vectors(np.eye(5 ,			dtype=np.floataa		)		)

					lowercase							=						'''index.faiss'''
					lowercase							=						f'mock://{index_name}'
					index.save(lowerCAmelCase__ ,			storage_options=mockfs.storage_options		)
					lowercase							=						FaissIndex.load(lowerCAmelCase__ ,			storage_options=mockfs.storage_options		)

					lowercase							=						np.zeros(5 ,			dtype=np.floataa		)
					lowercase							=						1
					lowercase	,						lowercase							=						index.search(lowerCAmelCase__		)
					assert scores[0] > 0
					assert indices[0] == 1







@require_elasticsearch
class 				lowercase		(					SCREAMING_SNAKE_CASE__		):




							def  A__      (				self):
												from elasticsearch import Elasticsearch

												with patch('''elasticsearch.Elasticsearch.search''') as mocked_search, patch(
												    '''elasticsearch.client.IndicesClient.create''') as mocked_index_create, patch('''elasticsearch.helpers.streaming_bulk''') as mocked_bulk:
																	lowercase							=						Elasticsearch()
																	lowercase							=						{'''acknowledged''': True}
																	lowercase							=						ElasticSearchIndex(es_client=A__)
																	mocked_bulk.return_value([(True, None)] * 3)
																	index.add_documents(['''foo''', '''bar''', '''foobar'''])

																	# single query
																	lowercase							=						'''foo'''
																	lowercase							=						{'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 0}]}}
																	lowercase	,						lowercase							=						index.search(A__)
																	self.assertEqual(scores[0]   ,1)
																	self.assertEqual(indices[0]   ,0)

																	# single query with timeout
																	lowercase							=						'''foo'''
																	lowercase							=						{'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 0}]}}
																	lowercase	,						lowercase							=						index.search(A__   ,request_timeout=3_0)
																	self.assertEqual(scores[0]   ,1)
																	self.assertEqual(indices[0]   ,0)

																	# batched queries
																	lowercase							=						['''foo''', '''bar''', '''foobar''']
																	lowercase							=						{'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 1}]}}
																	lowercase	,						lowercase							=						index.search_batch(A__)
																	lowercase							=						[scores[0] for scores in total_scores]
																	lowercase							=						[indices[0] for indices in total_indices]
																	self.assertGreater(np.min(A__)   ,0)
																	self.assertListEqual([1, 1, 1]   ,A__)

																	# batched queries with timeout
																	lowercase							=						['''foo''', '''bar''', '''foobar''']
																	lowercase							=						{'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 1}]}}
																	lowercase	,						lowercase							=						index.search_batch(A__   ,request_timeout=3_0)
																	lowercase							=						[scores[0] for scores in total_scores]
																	lowercase							=						[indices[0] for indices in total_indices]
																	self.assertGreater(np.min(A__)   ,0)
																	self.assertListEqual([1, 1, 1]   ,A__)




