





"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


__SCREAMING_SNAKE_CASE							:    Optional[int]		=    logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE							:    Tuple		=    {
    'tanreinama/GPTSAN-2.8B-spout_is_uniform': (
        'https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json'
    ),
}





class      __A     (__a):





							'''simple docstring'''

							__lowercase:		int							      =    """gptsan-japanese"""
							__lowercase:		Dict							      =    [
							    """past_key_values""",
							]
							__lowercase:		List[Any]							      =    {
							    """hidden_size""": """d_model""",
							    """num_attention_heads""": """num_heads""",
							    """num_hidden_layers""": """num_layers""",
							}



							def __init__(				self							:      Dict		,       UpperCAmelCase_							:      List[str]=36_000		,       UpperCAmelCase_							:      Any=1_280		,       UpperCAmelCase_							:      List[Any]=1_024		,       UpperCAmelCase_							:      Union[str, Any]=8_192		,       UpperCAmelCase_							:      List[Any]=4_096		,       UpperCAmelCase_							:      List[Any]=128		,       UpperCAmelCase_							:      Dict=10		,       UpperCAmelCase_							:      Tuple=0		,       UpperCAmelCase_							:      List[str]=16		,       UpperCAmelCase_							:      Any=16		,       UpperCAmelCase_							:      str=128		,       UpperCAmelCase_							:      Dict=0.0		,       UpperCAmelCase_							:      Optional[int]=1E-5		,       UpperCAmelCase_							:      int=False		,       UpperCAmelCase_							:      str=0.0		,       UpperCAmelCase_							:      Any="float32"		,       UpperCAmelCase_							:      Any=False		,       UpperCAmelCase_							:      int=False		,       UpperCAmelCase_							:      str=False		,       UpperCAmelCase_							:      List[Any]=0.002		,       UpperCAmelCase_							:      Tuple=False		,       UpperCAmelCase_							:      int=True		,       UpperCAmelCase_							:      int=35_998		,       UpperCAmelCase_							:      Optional[Any]=35_995		,       UpperCAmelCase_							:      List[Any]=35_999		,       **UpperCAmelCase_							:      Optional[Any]		,       )					->List[Any]:



														"""simple docstring"""



														snake_case_           =		vocab_size
														snake_case_           =		max_position_embeddings
														snake_case_           =		d_model
														snake_case_           =		d_ff
														snake_case_           =		d_ext
														snake_case_           =		d_spout
														snake_case_           =		num_switch_layers
														snake_case_           =		num_ext_layers
														snake_case_           =		num_switch_layers + num_ext_layers
														snake_case_           =		num_heads
														snake_case_           =		num_experts
														snake_case_           =		expert_capacity
														snake_case_           =		dropout_rate
														snake_case_           =		layer_norm_epsilon
														snake_case_           =		router_bias
														snake_case_           =		router_jitter_noise
														snake_case_           =		router_dtype
														snake_case_           =		router_ignore_padding_tokens
														snake_case_           =		output_hidden_states
														snake_case_           =		output_attentions
														snake_case_           =		initializer_factor
														snake_case_           =		output_router_logits
														snake_case_           =		use_cache

														super().__init__(
														    separator_token_id=A__		,       pad_token_id=A__		,       eos_token_id=A__		,       **A__		,       )







"""simple docstring"""
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


__SCREAMING_SNAKE_CASE							:    Union[str, Any]		=    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							:    Dict		=    {'base': 'patrickvonplaten/wav2vec2_tiny_random', 'robust': 'patrickvonplaten/wav2vec2_tiny_random_robust'}

__SCREAMING_SNAKE_CASE							:    Dict		=    'zero2'
__SCREAMING_SNAKE_CASE							:    List[Any]		=    'zero3'
__SCREAMING_SNAKE_CASE							:    int		=    [ZEROa, ZEROa]







def 						_a				(      _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
							snake_case_           =		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							:    Dict		=    list(itertools.product(stages, models.keys()))





@slow
@require_deepspeed
@require_torch_gpu
class      __A     (snake_case__):





							'''simple docstring'''

							@parameterized.expand(UpperCAmelCase_		,       name_func=UpperCAmelCase_       )
							def 		lowerCAmelCase       (				self							:      Union[str, Any]		,       UpperCAmelCase_							:      Dict		,       UpperCAmelCase_							:      Union[str, Any]       )					->Any:



														"""simple docstring"""



														self.run_and_check(
														    stage=UpperCAmelCase_		,       model=UpperCAmelCase_		,       distributed=UpperCAmelCase_		,       fpaa=UpperCAmelCase_		,       )



							@require_torch_multi_gpu
							@parameterized.expand(UpperCAmelCase_		,       name_func=UpperCAmelCase_       )
							def 		lowerCAmelCase       (				self							:      Optional[int]		,       UpperCAmelCase_							:      List[Any]		,       UpperCAmelCase_							:      Optional[Any]       )					->Optional[Any]:



														"""simple docstring"""



														self.run_and_check(
														    stage=UpperCAmelCase_		,       model=UpperCAmelCase_		,       distributed=UpperCAmelCase_		,       fpaa=UpperCAmelCase_		,       )



							@parameterized.expand(UpperCAmelCase_		,       name_func=UpperCAmelCase_       )
							def 		lowerCAmelCase       (				self							:      Tuple		,       UpperCAmelCase_							:      Dict		,       UpperCAmelCase_							:      Optional[int]       )					->List[str]:



														"""simple docstring"""



														self.run_and_check(
														    stage=UpperCAmelCase_		,       model=UpperCAmelCase_		,       distributed=UpperCAmelCase_		,       fpaa=UpperCAmelCase_		,       )



							@require_torch_multi_gpu
							@parameterized.expand(UpperCAmelCase_		,       name_func=UpperCAmelCase_       )
							def 		lowerCAmelCase       (				self							:      Dict		,       UpperCAmelCase_							:      str		,       UpperCAmelCase_							:      List[Any]       )					->Optional[int]:



														"""simple docstring"""



														self.run_and_check(
														    stage=UpperCAmelCase_		,       model=UpperCAmelCase_		,       distributed=UpperCAmelCase_		,       fpaa=UpperCAmelCase_		,       )



							def 		lowerCAmelCase       (				self							:      Optional[int]		,       UpperCAmelCase_							:      Union[str, Any]       )					->Optional[int]:



														"""simple docstring"""



														pass



							def 		lowerCAmelCase       (				self							:      Any		,       UpperCAmelCase_							:      str		,       UpperCAmelCase_							:      str		,       UpperCAmelCase_							:      int = 10		,       UpperCAmelCase_							:      bool = True		,       UpperCAmelCase_							:      bool = True		,       UpperCAmelCase_							:      bool = True		,       )					->List[str]:



														"""simple docstring"""



														snake_case_           =		models[model]

														snake_case_           =		self.run_trainer(
														    stage=UpperCAmelCase_		,       model_name=UpperCAmelCase_		,       eval_steps=UpperCAmelCase_		,       num_train_epochs=1		,       distributed=UpperCAmelCase_		,       fpaa=UpperCAmelCase_		,       )

														self.do_checks(UpperCAmelCase_       )

														return output_dir



							def 		lowerCAmelCase       (				self							:      Union[str, Any]		,       UpperCAmelCase_							:      str		,       UpperCAmelCase_							:      str		,       UpperCAmelCase_							:      int = 10		,       UpperCAmelCase_							:      int = 1		,       UpperCAmelCase_							:      bool = True		,       UpperCAmelCase_							:      bool = True		,       )					->List[str]:



														"""simple docstring"""



														snake_case_           =		self.get_auto_remove_tmp_dir("""./xxx"""		,       after=UpperCAmelCase_       )
														snake_case_           =		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(UpperCAmelCase_       )}
            --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
														snake_case_           =		F"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split()
														snake_case_           =		[F"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""]
														snake_case_           =		self.get_launcher(UpperCAmelCase_       )

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

														return output_dir



							def 		lowerCAmelCase       (				self							:      Union[str, Any]		,       UpperCAmelCase_							:      Any=False       )					->Tuple:



														"""simple docstring"""



														snake_case_           =		min(2		,       get_gpu_count()       ) if distributed else 1
														return F"""deepspeed --num_nodes 1 --num_gpus {num_gpus}""".split()



