



import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path

import pytest

import transformers
from transformers import (
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AutoTokenizer,
    BertConfig,
    BertTokenizer,
    BertTokenizerFast,
    CTRLTokenizer,
    GPTaTokenizer,
    GPTaTokenizerFast,
    PreTrainedTokenizerFast,
    RobertaTokenizer,
    RobertaTokenizerFast,
    is_tokenizers_available,
)
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.auto.tokenization_auto import (
    TOKENIZER_MAPPING,
    get_tokenizer_config,
    tokenizer_class_from_name,
)
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import (
    DUMMY_DIFF_TOKENIZER_IDENTIFIER,
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tokenizers,
    slow,
)


sys.path.append(str(Path(__file__).parent.parent.parent.parent / """utils"""))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
							from test_module.custom_tokenization_fast import CustomTokenizerFast

class 	lowerCamelCase_    ( unittest.TestCase						):






						'''simple docstring'''







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				Union[str, Any]:
									A							:					str              =						0







						@slow
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple )      ->				List[Any]:
									for model_name in (x for x in BERT_PRETRAINED_CONFIG_ARCHIVE_MAP.keys() if "japanese" not in x):
												A							:					Any              =						AutoTokenizer.from_pretrained(__lowerCamelCase )
												self.assertIsNotNone(__lowerCamelCase )
												self.assertIsInstance(__lowerCamelCase	,	(BertTokenizer, BertTokenizerFast) )
												self.assertGreater(len(__lowerCamelCase )	,	0 )

									for model_name in GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP.keys():
												A							:					Any              =						AutoTokenizer.from_pretrained(__lowerCamelCase )
												self.assertIsNotNone(__lowerCamelCase )
												self.assertIsInstance(__lowerCamelCase	,	(GPTaTokenizer, GPTaTokenizerFast) )
												self.assertGreater(len(__lowerCamelCase )	,	0 )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				List[str]:
									A							:					str              =						AutoTokenizer.from_pretrained(__lowerCamelCase )
									self.assertIsInstance(__lowerCamelCase	,	(BertTokenizer, BertTokenizerFast) )
									self.assertEqual(tokenizer.vocab_size	,	12 )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				List[Any]:
									A							:					Optional[int]              =						AutoTokenizer.from_pretrained(__lowerCamelCase )
									self.assertIsInstance(__lowerCamelCase	,	(RobertaTokenizer, RobertaTokenizerFast) )
									self.assertEqual(tokenizer.vocab_size	,	20 )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple )      ->				List[Any]:
									A							:					Optional[int]              =						AutoConfig.from_pretrained(__lowerCamelCase )
									self.assertIsInstance(__lowerCamelCase	,	__lowerCamelCase )
									# Check that tokenizer_type ≠ model_type
									A							:					Optional[Any]              =						AutoTokenizer.from_pretrained(__lowerCamelCase	,	config=__lowerCamelCase )
									self.assertIsInstance(__lowerCamelCase	,	(BertTokenizer, BertTokenizerFast) )
									self.assertEqual(tokenizer.vocab_size	,	12 )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict )      ->				int:
									with tempfile.TemporaryDirectory() as tmp_dir:
												shutil.copy("./tests/fixtures/vocab.txt"	,	os.path.join(__lowerCamelCase	,	"vocab.txt" ) )

												A							:					Optional[int]              =						AutoTokenizer.from_pretrained(__lowerCamelCase	,	tokenizer_type="bert"	,	use_fast=__lowerCamelCase )
												self.assertIsInstance(__lowerCamelCase	,	__lowerCamelCase )

									with tempfile.TemporaryDirectory() as tmp_dir:
												shutil.copy("./tests/fixtures/vocab.json"	,	os.path.join(__lowerCamelCase	,	"vocab.json" ) )
												shutil.copy("./tests/fixtures/merges.txt"	,	os.path.join(__lowerCamelCase	,	"merges.txt" ) )

												A							:					List[str]              =						AutoTokenizer.from_pretrained(__lowerCamelCase	,	tokenizer_type="gpt2"	,	use_fast=__lowerCamelCase )
												self.assertIsInstance(__lowerCamelCase	,	__lowerCamelCase )







						@require_tokenizers
						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				List[Any]:
									with tempfile.TemporaryDirectory() as tmp_dir:
												shutil.copy("./tests/fixtures/vocab.txt"	,	os.path.join(__lowerCamelCase	,	"vocab.txt" ) )

												A							:					str              =						AutoTokenizer.from_pretrained(__lowerCamelCase	,	tokenizer_type="bert" )
												self.assertIsInstance(__lowerCamelCase	,	__lowerCamelCase )

									with tempfile.TemporaryDirectory() as tmp_dir:
												shutil.copy("./tests/fixtures/vocab.json"	,	os.path.join(__lowerCamelCase	,	"vocab.json" ) )
												shutil.copy("./tests/fixtures/merges.txt"	,	os.path.join(__lowerCamelCase	,	"merges.txt" ) )

												A							:					Optional[int]              =						AutoTokenizer.from_pretrained(__lowerCamelCase	,	tokenizer_type="gpt2" )
												self.assertIsInstance(__lowerCamelCase	,	__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				Dict:
									with pytest.raises(__lowerCamelCase ):
												AutoTokenizer.from_pretrained("./"	,	tokenizer_type="xxx" )







						@require_tokenizers
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				Any:
									for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
												A							:					Optional[int]              =						tokenizer_class.from_pretrained("wietsedv/bert-base-dutch-cased" )
												self.assertIsInstance(__lowerCamelCase	,	(BertTokenizer, BertTokenizerFast) )

												if isinstance(__lowerCamelCase	,	__lowerCamelCase ):
															self.assertEqual(tokenizer.basic_tokenizer.do_lower_case	,	__lowerCamelCase )
												else:
															self.assertEqual(tokenizer.do_lower_case	,	__lowerCamelCase )

												self.assertEqual(tokenizer.model_max_length	,	5_12 )







						@require_tokenizers
						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				str:
									for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
												with self.assertRaisesRegex(
												    __lowerCamelCase	,	"julien-c/herlolip-not-exists is not a local folder and is not a valid model identifier"	,	):
															A							:					Dict              =						tokenizer_class.from_pretrained("julien-c/herlolip-not-exists" )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				str:
									# tests: https://github.com/huggingface/transformers/pull/13251
									# 1. models with `-`, e.g. xlm-roberta -> xlm_roberta
									# 2. models that don't remap 1-1 from model-name to model file, e.g., openai-gpt -> openai
									A							:					List[Any]              =						TOKENIZER_MAPPING.values()
									A							:					List[Any]              =						[]

									for slow_tok, fast_tok in tokenizers:
												if slow_tok is not None:
															tokenizer_names.append(slow_tok.__name__ )

												if fast_tok is not None:
															tokenizer_names.append(fast_tok.__name__ )

									for tokenizer_name in tokenizer_names:
												# must find the right class
												tokenizer_class_from_name(__lowerCamelCase )







						@require_tokenizers
						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				int:
									self.assertIsInstance(AutoTokenizer.from_pretrained("bert-base-cased"	,	use_fast=__lowerCamelCase )	,	__lowerCamelCase )
									self.assertIsInstance(AutoTokenizer.from_pretrained("bert-base-cased" )	,	__lowerCamelCase )







						@require_tokenizers
						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				Tuple:
									A							:					int              =						AutoTokenizer.from_pretrained("distilbert-base-uncased"	,	do_lower_case=__lowerCamelCase )
									A							:					List[str]              =						"Hello, world. How are you?"
									A							:					Optional[Any]              =						tokenizer.tokenize(__lowerCamelCase )
									self.assertEqual("[UNK]"	,	tokens[0] )

									A							:					Dict              =						AutoTokenizer.from_pretrained("microsoft/mpnet-base"	,	do_lower_case=__lowerCamelCase )
									A							:					Any              =						tokenizer.tokenize(__lowerCamelCase )
									self.assertEqual("[UNK]"	,	tokens[0] )







						@require_tokenizers
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				Tuple:
									A							:					List[str]              =						AutoTokenizer.from_pretrained("robot-test/dummy-tokenizer-fast-with-model-config" )
									self.assertEqual(type(__lowerCamelCase )	,	__lowerCamelCase )
									self.assertEqual(tokenizer.model_max_length	,	5_12 )
									self.assertEqual(tokenizer.vocab_size	,	3_00_00 )
									self.assertEqual(tokenizer.unk_token	,	"[UNK]" )
									self.assertEqual(tokenizer.padding_side	,	"right" )
									self.assertEqual(tokenizer.truncation_side	,	"right" )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				List[str]:
									A							:					Tuple              =						AutoTokenizer.from_pretrained(__lowerCamelCase )
									self.assertIsInstance(__lowerCamelCase	,	(BertTokenizer, BertTokenizerFast) )
									with tempfile.TemporaryDirectory() as tmp_dir:
												tokenizer.save_pretrained(__lowerCamelCase )
												A							:					Union[str, Any]              =						AutoTokenizer.from_pretrained(__lowerCamelCase )

									self.assertIsInstance(__lowerCamelCase	,	tokenizer.__class__ )
									self.assertEqual(tokenizera.vocab_size	,	12 )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict )      ->				Any:
									A							:					int              =						AutoTokenizer.from_pretrained("ctrl" )
									# There is no fast CTRL so this always gives us a slow tokenizer.
									self.assertIsInstance(__lowerCamelCase	,	__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict )      ->				Optional[Any]:
									# Check we can load the tokenizer config of an online model.
									A							:					List[Any]              =						get_tokenizer_config("bert-base-cased" )
									A							:					str              =						config.pop("_commit_hash"	,	__lowerCamelCase )
									# If we ever update bert-base-cased tokenizer config, this dict here will need to be updated.
									self.assertEqual(__lowerCamelCase	,	{"do_lower_case": False} )

									# This model does not have a tokenizer_config so we get back an empty dict.
									A							:					Optional[Any]              =						get_tokenizer_config(__lowerCamelCase )
									self.assertDictEqual(__lowerCamelCase	,	{} )

									# A tokenizer saved with `save_pretrained` always creates a tokenizer config.
									A							:					Optional[int]              =						AutoTokenizer.from_pretrained(__lowerCamelCase )
									with tempfile.TemporaryDirectory() as tmp_dir:
												tokenizer.save_pretrained(__lowerCamelCase )
												A							:					Dict              =						get_tokenizer_config(__lowerCamelCase )

									# Check the class of the tokenizer was properly saved (note that it always saves the slow class).
									self.assertEqual(config["tokenizer_class"]	,	"BertTokenizer" )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple )      ->				Optional[Any]:
									try:
												AutoConfig.register("custom"	,	__lowerCamelCase )

												AutoTokenizer.register(__lowerCamelCase	,	slow_tokenizer_class=__lowerCamelCase )
												# Trying to register something existing in the Transformers library will raise an error
												with self.assertRaises(__lowerCamelCase ):
															AutoTokenizer.register(__lowerCamelCase	,	slow_tokenizer_class=__lowerCamelCase )

												A							:					Dict              =						CustomTokenizer.from_pretrained(__lowerCamelCase )
												with tempfile.TemporaryDirectory() as tmp_dir:
															tokenizer.save_pretrained(__lowerCamelCase )

															A							:					List[Any]              =						AutoTokenizer.from_pretrained(__lowerCamelCase )
															self.assertIsInstance(__lowerCamelCase	,	__lowerCamelCase )

									finally:
												if "custom" in CONFIG_MAPPING._extra_content:
															del CONFIG_MAPPING._extra_content["custom"]
												if CustomConfig in TOKENIZER_MAPPING._extra_content:
															del TOKENIZER_MAPPING._extra_content[CustomConfig]







						@require_tokenizers
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple )      ->				Tuple:
									try:
												AutoConfig.register("custom"	,	__lowerCamelCase )

												# Can register in two steps
												AutoTokenizer.register(__lowerCamelCase	,	slow_tokenizer_class=__lowerCamelCase )
												self.assertEqual(TOKENIZER_MAPPING[CustomConfig]	,	(CustomTokenizer, None) )
												AutoTokenizer.register(__lowerCamelCase	,	fast_tokenizer_class=__lowerCamelCase )
												self.assertEqual(TOKENIZER_MAPPING[CustomConfig]	,	(CustomTokenizer, CustomTokenizerFast) )

												del TOKENIZER_MAPPING._extra_content[CustomConfig]
												# Can register in one step
												AutoTokenizer.register(
												    __lowerCamelCase	,	slow_tokenizer_class=__lowerCamelCase	,	fast_tokenizer_class=__lowerCamelCase )
												self.assertEqual(TOKENIZER_MAPPING[CustomConfig]	,	(CustomTokenizer, CustomTokenizerFast) )

												# Trying to register something existing in the Transformers library will raise an error
												with self.assertRaises(__lowerCamelCase ):
															AutoTokenizer.register(__lowerCamelCase	,	fast_tokenizer_class=__lowerCamelCase )

												# We pass through a bert tokenizer fast cause there is no converter slow to fast for our new toknizer
												# and that model does not have a tokenizer.json
												with tempfile.TemporaryDirectory() as tmp_dir:
															A							:					Dict              =						BertTokenizerFast.from_pretrained(__lowerCamelCase )
															bert_tokenizer.save_pretrained(__lowerCamelCase )
															A							:					List[str]              =						CustomTokenizerFast.from_pretrained(__lowerCamelCase )

												with tempfile.TemporaryDirectory() as tmp_dir:
															tokenizer.save_pretrained(__lowerCamelCase )

															A							:					Union[str, Any]              =						AutoTokenizer.from_pretrained(__lowerCamelCase )
															self.assertIsInstance(__lowerCamelCase	,	__lowerCamelCase )

															A							:					Tuple              =						AutoTokenizer.from_pretrained(__lowerCamelCase	,	use_fast=__lowerCamelCase )
															self.assertIsInstance(__lowerCamelCase	,	__lowerCamelCase )

									finally:
												if "custom" in CONFIG_MAPPING._extra_content:
															del CONFIG_MAPPING._extra_content["custom"]
												if CustomConfig in TOKENIZER_MAPPING._extra_content:
															del TOKENIZER_MAPPING._extra_content[CustomConfig]







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any] )      ->				List[str]:
									# If remote code is not set, we will time out when asking whether to load the model.
									with self.assertRaises(__lowerCamelCase ):
												A							:					Optional[int]              =						AutoTokenizer.from_pretrained("hf-internal-testing/test_dynamic_tokenizer" )
									# If remote code is disabled, we can't load this config.
									with self.assertRaises(__lowerCamelCase ):
												A							:					Dict              =						AutoTokenizer.from_pretrained(
												    "hf-internal-testing/test_dynamic_tokenizer"	,	trust_remote_code=__lowerCamelCase )

									A							:					Optional[Any]              =						AutoTokenizer.from_pretrained("hf-internal-testing/test_dynamic_tokenizer"	,	trust_remote_code=__lowerCamelCase )
									self.assertTrue(tokenizer.special_attribute_present )
									# Test tokenizer can be reloaded.
									with tempfile.TemporaryDirectory() as tmp_dir:
												tokenizer.save_pretrained(__lowerCamelCase )
												A							:					str              =						AutoTokenizer.from_pretrained(__lowerCamelCase	,	trust_remote_code=__lowerCamelCase )
									self.assertTrue(reloaded_tokenizer.special_attribute_present )

									if is_tokenizers_available():
												self.assertEqual(tokenizer.__class__.__name__	,	"NewTokenizerFast" )
												self.assertEqual(reloaded_tokenizer.__class__.__name__	,	"NewTokenizerFast" )

												# Test we can also load the slow version
												A							:					int              =						AutoTokenizer.from_pretrained(
												    "hf-internal-testing/test_dynamic_tokenizer"	,	trust_remote_code=__lowerCamelCase	,	use_fast=__lowerCamelCase )
												self.assertTrue(tokenizer.special_attribute_present )
												self.assertEqual(tokenizer.__class__.__name__	,	"NewTokenizer" )
												# Test tokenizer can be reloaded.
												with tempfile.TemporaryDirectory() as tmp_dir:
															tokenizer.save_pretrained(__lowerCamelCase )
															A							:					List[str]              =						AutoTokenizer.from_pretrained(__lowerCamelCase	,	trust_remote_code=__lowerCamelCase	,	use_fast=__lowerCamelCase )
												self.assertEqual(reloaded_tokenizer.__class__.__name__	,	"NewTokenizer" )
												self.assertTrue(reloaded_tokenizer.special_attribute_present )
									else:
												self.assertEqual(tokenizer.__class__.__name__	,	"NewTokenizer" )
												self.assertEqual(reloaded_tokenizer.__class__.__name__	,	"NewTokenizer" )







						@require_tokenizers
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict )      ->				Any:

									class 	lowerCamelCase_    ( _A						):






															'''simple docstring'''
															a__   =  False







									class 	lowerCamelCase_    ( _A						):






															'''simple docstring'''
															a__   =  NewTokenizer
															a__   =  False

									try:
												AutoConfig.register("custom"	,	__lowerCamelCase )
												AutoTokenizer.register(__lowerCamelCase	,	slow_tokenizer_class=__lowerCamelCase )
												AutoTokenizer.register(__lowerCamelCase	,	fast_tokenizer_class=__lowerCamelCase )
												# If remote code is not set, the default is to use local
												A							:					List[str]              =						AutoTokenizer.from_pretrained("hf-internal-testing/test_dynamic_tokenizer" )
												self.assertEqual(tokenizer.__class__.__name__	,	"NewTokenizerFast" )
												self.assertFalse(tokenizer.special_attribute_present )
												A							:					List[Any]              =						AutoTokenizer.from_pretrained("hf-internal-testing/test_dynamic_tokenizer"	,	use_fast=__lowerCamelCase )
												self.assertEqual(tokenizer.__class__.__name__	,	"NewTokenizer" )
												self.assertFalse(tokenizer.special_attribute_present )

												# If remote code is disabled, we load the local one.
												A							:					Optional[int]              =						AutoTokenizer.from_pretrained(
												    "hf-internal-testing/test_dynamic_tokenizer"	,	trust_remote_code=__lowerCamelCase )
												self.assertEqual(tokenizer.__class__.__name__	,	"NewTokenizerFast" )
												self.assertFalse(tokenizer.special_attribute_present )
												A							:					List[str]              =						AutoTokenizer.from_pretrained(
												    "hf-internal-testing/test_dynamic_tokenizer"	,	trust_remote_code=__lowerCamelCase	,	use_fast=__lowerCamelCase )
												self.assertEqual(tokenizer.__class__.__name__	,	"NewTokenizer" )
												self.assertFalse(tokenizer.special_attribute_present )

												# If remote is enabled, we load from the Hub
												A							:					Any              =						AutoTokenizer.from_pretrained(
												    "hf-internal-testing/test_dynamic_tokenizer"	,	trust_remote_code=__lowerCamelCase )
												self.assertEqual(tokenizer.__class__.__name__	,	"NewTokenizerFast" )
												self.assertTrue(tokenizer.special_attribute_present )
												A							:					List[str]              =						AutoTokenizer.from_pretrained(
												    "hf-internal-testing/test_dynamic_tokenizer"	,	trust_remote_code=__lowerCamelCase	,	use_fast=__lowerCamelCase )
												self.assertEqual(tokenizer.__class__.__name__	,	"NewTokenizer" )
												self.assertTrue(tokenizer.special_attribute_present )

									finally:
												if "custom" in CONFIG_MAPPING._extra_content:
															del CONFIG_MAPPING._extra_content["custom"]
												if CustomConfig in TOKENIZER_MAPPING._extra_content:
															del TOKENIZER_MAPPING._extra_content[CustomConfig]







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict )      ->				int:
									A							:					Any              =						AutoTokenizer.from_pretrained(
									    "hf-internal-testing/test_dynamic_tokenizer_legacy"	,	trust_remote_code=__lowerCamelCase )
									self.assertTrue(tokenizer.special_attribute_present )
									if is_tokenizers_available():
												self.assertEqual(tokenizer.__class__.__name__	,	"NewTokenizerFast" )

												# Test we can also load the slow version
												A							:					Optional[Any]              =						AutoTokenizer.from_pretrained(
												    "hf-internal-testing/test_dynamic_tokenizer_legacy"	,	trust_remote_code=__lowerCamelCase	,	use_fast=__lowerCamelCase )
												self.assertTrue(tokenizer.special_attribute_present )
												self.assertEqual(tokenizer.__class__.__name__	,	"NewTokenizer" )
									else:
												self.assertEqual(tokenizer.__class__.__name__	,	"NewTokenizer" )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				Tuple:
									with self.assertRaisesRegex(
									    __lowerCamelCase	,	"bert-base is not a local folder and is not a valid model identifier" ):
												A							:					Optional[int]              =						AutoTokenizer.from_pretrained("bert-base" )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict )      ->				Optional[int]:
									with self.assertRaisesRegex(
									    __lowerCamelCase	,	r"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)" ):
												A							:					List[str]              =						AutoTokenizer.from_pretrained(__lowerCamelCase	,	revision="aaaaaa" )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				Dict:
									# Make sure we have cached the tokenizer.
									A							:					List[Any]              =						AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert" )
									with RequestCounter() as counter:
												A							:					Dict              =						AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert" )
												self.assertEqual(counter.get_request_count	,	0 )
												self.assertEqual(counter.head_request_count	,	1 )
												self.assertEqual(counter.other_request_count	,	0 )




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


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


set_seed(42)
__SCREAMING_SNAKE_CASE           =  """sshleifer/student_marian_en_ro_6_1"""
__SCREAMING_SNAKE_CASE           =  """sshleifer/tiny-mbart"""

@require_torch
class 	lowerCamelCase_    ( _A						):






						'''simple docstring'''







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int]	,	__lowerCamelCase      :			Optional[Any]=False	,	__lowerCamelCase      :			str=None	,	__lowerCamelCase      :			List[str]=True	,	__lowerCamelCase      :			str=True	,	__lowerCamelCase      :			List[Any]=True	,	__lowerCamelCase      :			Optional[int]=True	,	)      ->				Dict:
									A							:					str              =						self.run_trainer(
									    eval_steps=1	,	max_len=12	,	model_name=__lowerCamelCase	,	num_train_epochs=1	,	distributed=__lowerCamelCase	,	extra_args_str=__lowerCamelCase	,	predict_with_generate=__lowerCamelCase	,	do_train=__lowerCamelCase	,	do_eval=__lowerCamelCase	,	do_predict=__lowerCamelCase	,	)
									A							:					Dict              =						TrainerState.load_from_json(os.path.join(__lowerCamelCase	,	"trainer_state.json" ) ).log_history

									if not do_eval:
												return

									A							:					List[Any]              =						[log for log in logs if "eval_loss" in log.keys()]

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

												A							:					List[str]              =						eval_metrics[-1]
												assert isinstance(last_step_stats["eval_bleu"]	,	__lowerCamelCase )
												assert not math.isnan(float(last_step_stats["eval_loss"] ) ), "eval_loss must not be `nan`"







						@require_torch_non_multi_gpu
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple )      ->				List[Any]:
									self.run_seqaseq_quick()







						@require_torch_multi_gpu
						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				int:
									self.run_seqaseq_quick(distributed=__lowerCamelCase )







						@require_torch_multi_gpu
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				str:
									self.run_seqaseq_quick(distributed=__lowerCamelCase )







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







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







						@unittest.skip("Requires an update of the env running those tests" )
						@require_torch_multi_gpu
						@require_fairscale
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any] )      ->				List[str]:
									self.run_seqaseq_quick(
									    distributed=__lowerCamelCase	,	extra_args_str="--sharded_ddp zero_dp_2 --fp16"	,	predict_with_generate=__lowerCamelCase )







						@require_apex
						@require_torch_gpu
						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				Dict:
									# 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=__lowerCamelCase	,	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=__lowerCamelCase	,	extra_args_str="--fp16 --fp16_backend=apex" )







						@parameterized.expand(["base", "low", "high", "mixed"] )
						@require_torch_multi_gpu
						def 			SCREAMING_SNAKE_CASE__		(	self      :			str	,	__lowerCamelCase      :			List[str] )      ->				Tuple:
									# as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
									A							:					Dict              =						{
									    # 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},
									}

									A							:					List[str]              =						experiments[experiment_id]
									A							:					Union[str, Any]              =						{"distributed": True, "predict_with_generate": False, "do_eval": False, "do_predict": False}
									A							:					Union[str, Any]              =						"Running training"
									with CaptureStderr() as cl:
												self.run_seqaseq_quick(**__lowerCamelCase	,	extra_args_str=data["extra_args_str"] )
									A							:					Dict              =						len(re.findall(__lowerCamelCase	,	cl.err ) )
									self.assertEqual(__lowerCamelCase	,	data["n_matches"] )







						@slow
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				Optional[int]:
									A							:					int              =						self.run_trainer(
									    eval_steps=2	,	max_len=1_28	,	model_name=__lowerCamelCase	,	learning_rate=3e-4	,	num_train_epochs=10	,	distributed=__lowerCamelCase	,	)

									# Check metrics
									A							:					str              =						TrainerState.load_from_json(os.path.join(__lowerCamelCase	,	"trainer_state.json" ) ).log_history
									A							:					Dict              =						[log for log in logs if "eval_loss" in log.keys()]
									A							:					Dict              =						eval_metrics[0]
									A							:					int              =						eval_metrics[-1]

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

									# test if do_predict saves generations and metrics
									A							:					Optional[Any]              =						os.listdir(__lowerCamelCase )
									A							:					Any              =						{os.path.basename(__lowerCamelCase ) for p in contents}
									assert "generated_predictions.txt" in contents
									assert "predict_results.json" in contents







						@slow
						@require_bitsandbytes
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict )      ->				List[str]:
									from transformers.training_args import OptimizerNames

									def train_and_return_metrics(__lowerCamelCase      :			str ) -> Tuple[int, float]:
												A							:					Optional[int]              =						"--skip_memory_metrics 0"

												A							:					str              =						self.run_trainer(
												    max_len=1_28	,	model_name=__lowerCamelCase	,	learning_rate=3e-4	,	num_train_epochs=1	,	optim=__lowerCamelCase	,	distributed=__lowerCamelCase	,	extra_args_str=__lowerCamelCase	,	do_eval=__lowerCamelCase	,	do_predict=__lowerCamelCase	,	n_gpus_to_use=1	,	)

												# Check metrics
												A							:					Union[str, Any]              =						TrainerState.load_from_json(Path(__lowerCamelCase	,	"trainer_state.json" ) ).log_history
												A							:					str              =						int(logs[0]["train_mem_gpu_peaked_delta"] / 2**20 )
												A							:					List[Any]              =						int(logs[0]["train_mem_gpu_alloc_delta"] / 2**20 )

												A							:					int              =						logs[0]["train_loss"]
												return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

									A       , A       , A							:					int              =						train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value )
									A       , A       , A							:					Optional[int]              =						train_and_return_metrics(OptimizerNames.ADAMW_BNB.value )

									A							:					Tuple              =						gpu_alloc_mem_orig - gpu_alloc_mem_bnb

									A							:					Dict              =						gpu_peak_mem_orig + gpu_alloc_mem_orig
									A							:					Dict              =						gpu_peak_mem_bnb + gpu_alloc_mem_bnb
									A							:					int              =						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
									A							:					Tuple              =						1_20

									# 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(
									    __lowerCamelCase	,	__lowerCamelCase	,	"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(
									    __lowerCamelCase	,	__lowerCamelCase	,	"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(
									    __lowerCamelCase	,	__lowerCamelCase	,	F"""loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}""" )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str	,	__lowerCamelCase      :			int	,	__lowerCamelCase      :			str	,	__lowerCamelCase      :			int	,	__lowerCamelCase      :			float = 3e-3	,	__lowerCamelCase      :			str = "adafactor"	,	__lowerCamelCase      :			bool = False	,	__lowerCamelCase      :			str = None	,	__lowerCamelCase      :			int = 0	,	__lowerCamelCase      :			bool = True	,	__lowerCamelCase      :			bool = True	,	__lowerCamelCase      :			bool = True	,	__lowerCamelCase      :			bool = True	,	__lowerCamelCase      :			int = None	,	)      ->				List[str]:
									A							:					Optional[int]              =						self.test_file_dir / "../fixtures/tests_samples/wmt_en_ro"
									A							:					Optional[int]              =						self.get_auto_remove_tmp_dir()
									A							:					int              =						F"""
            --model_name_or_path {model_name}
            --train_file {data_dir}/train.json
            --validation_file {data_dir}/val.json
            --test_file {data_dir}/test.json
            --output_dir {output_dir}
            --overwrite_output_dir
            --max_train_samples 8
            --max_source_length {max_len}
            --max_target_length {max_len}
            --do_train
            --num_train_epochs {str(__lowerCamelCase )}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(__lowerCamelCase )}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        """.split()

									A							:					Optional[Any]              =						F"""
            --do_eval
            --per_device_eval_batch_size 4
            --max_eval_samples 8
            --val_max_target_length {max_len}
            --evaluation_strategy steps
            --eval_steps {str(__lowerCamelCase )}
        """.split()

									A							:					Optional[Any]              =						"\n            --do_predict\n        ".split()

									A							:					Optional[int]              =						[]
									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:
															A							:					Dict              =						get_gpu_count()
												A							:					Any              =						get_torch_dist_unique_port()
												A							:					Optional[Any]              =						F"""
                -m torch.distributed.run
                --nproc_per_node={n_gpus_to_use}
                --master_port={master_port}
                {self.examples_dir_str}/pytorch/translation/run_translation.py
            """.split()
												A							:					Any              =						[sys.executable] + distributed_args + args
												# keep for quick debug
												# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
												execute_subprocess_async(__lowerCamelCase	,	env=self.get_env() )
									else:
												A							:					List[Any]              =						["run_translation.py"] + args
												with patch.object(__lowerCamelCase	,	"argv"	,	__lowerCamelCase ):
															main()

									return output_dir