



"""simple docstring"""
import sys
from pathlib import Path


SCREAMING_SNAKE_CASE_				= Path(__file__).resolve().parents[3] / """src"""
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

SCREAMING_SNAKE_CASE_				= {"""base""": """patrickvonplaten/wav2vec2_tiny_random""", """robust""": """patrickvonplaten/wav2vec2_tiny_random_robust"""}

SCREAMING_SNAKE_CASE_				= """zero2"""
SCREAMING_SNAKE_CASE_				= """zero3"""
SCREAMING_SNAKE_CASE_				= [ZEROa, ZEROa]



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      Union[str, Any]:
		# customize the test name generator function as we want both params to appear in the sub-test
		# name, as by default it shows only the first param
		a_     :      List[str] 						=     parameterized.to_safe_name("_".join(str(SCREAMING_SNAKE_CASE__	) for x in param.args	)	)
		return F"""{func.__name__}_{param_based_name}"""


# Cartesian-product of zero stages with models to test
SCREAMING_SNAKE_CASE_				= list(itertools.product(stages, models.keys()))






@slow
@require_deepspeed
@require_torch_gpu
class 			snake_case_						(						a_				):






							@parameterized.expand(a_			,     name_func=a_      )
							def snake_case_  (   self			,     a_			,     a_      ):
									self.run_and_check(
									    stage=a_			,     model=a_			,     distributed=a_			,     fpaa=a_			,     )






							@require_torch_multi_gpu
							@parameterized.expand(a_			,     name_func=a_      )
							def snake_case_  (   self			,     a_			,     a_      ):
									self.run_and_check(
									    stage=a_			,     model=a_			,     distributed=a_			,     fpaa=a_			,     )






							@parameterized.expand(a_			,     name_func=a_      )
							def snake_case_  (   self			,     a_			,     a_      ):
									self.run_and_check(
									    stage=a_			,     model=a_			,     distributed=a_			,     fpaa=a_			,     )






							@require_torch_multi_gpu
							@parameterized.expand(a_			,     name_func=a_      )
							def snake_case_  (   self			,     a_			,     a_      ):
									self.run_and_check(
									    stage=a_			,     model=a_			,     distributed=a_			,     fpaa=a_			,     )






							def snake_case_  (   self			,     a_      ):
									# XXX: run_asr is premature and doesn't save any results
									# so all we check for now is that the process didn't fail
									pass






							def snake_case_  (   self			,     a_			,     a_			,     a_ = 1_0			,     a_ = True			,     a_ = True			,     a_ = True			,     ):
									a_     :      int 						=     models[model]

									a_     :      Tuple 						=     self.run_trainer(
									    stage=a_			,     model_name=a_			,     eval_steps=a_			,     num_train_epochs=1			,     distributed=a_			,     fpaa=a_			,     )

									self.do_checks(a_      )

									return output_dir






							def snake_case_  (   self			,     a_			,     a_			,     a_ = 1_0			,     a_ = 1			,     a_ = True			,     a_ = True			,     ):
									a_     :      Any 						=     self.get_auto_remove_tmp_dir("./xxx"			,     after=a_      )
									a_     :      Dict 						=     F"""
            --model_name_or_path {model_name}
            --dataset_name hf-internal-testing/librispeech_asr_dummy
            --dataset_config_name clean
            --train_split_name validation
            --validation_split_name validation
            --output_dir {output_dir}
            --num_train_epochs {str(a_      )}
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 2
            --evaluation_strategy steps
            --learning_rate 5e-4
            --warmup_steps 8
            --orthography timit
            --preprocessing_num_workers 1
            --group_by_length
            --freeze_feature_extractor
            --report_to none
            --save_steps 0
            --eval_steps {eval_steps}
            --report_to none
        """.split()

									if fpaa:
											args.extend(["--fp16"]      )

									# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
									# hence the separate config files
									a_     :      Any 						=     F"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split()
									a_     :      int 						=     [F"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""]
									a_     :      List[str] 						=     self.get_launcher(a_      )

									a_     :      Union[str, Any] 						=     launcher + script + args + ds_args
									# keep for quick debug
									# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
									execute_subprocess_async(a_			,     env=self.get_env()      )

									return output_dir






							def snake_case_  (   self			,     a_=False      ):
									# 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup
									# - it won't be able to handle that
									# 2. for now testing with just 2 gpus max (since some quality tests may give different
									# results with mode gpus because we use very little data)
									a_     :      Optional[Any] 						=     min(2			,     get_gpu_count()      ) if distributed else 1
									return F"""deepspeed --num_nodes 1 --num_gpus {num_gpus}""".split()




"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_				= {
    """configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LlamaConfig"""],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_				= ["""LlamaTokenizer"""]

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_				= ["""LlamaTokenizerFast"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_				= [
					    """LlamaForCausalLM""",
					    """LlamaModel""",
					    """LlamaPreTrainedModel""",
					    """LlamaForSequenceClassification""",
					]


if TYPE_CHECKING:
					from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_llama import LlamaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_llama_fast import LlamaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
					import sys

					SCREAMING_SNAKE_CASE_				= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)