


"""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


a__    :							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(4_2)

a__    :							str            =					{'''base''': '''patrickvonplaten/wav2vec2_tiny_random''', '''robust''': '''patrickvonplaten/wav2vec2_tiny_random_robust'''}

a__    :							List[str]            =					'''zero2'''
a__    :							List[Any]            =					'''zero3'''
a__    :							Any            =					[ZEROa, ZEROa]




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    parameterized.to_safe_name("_".join(str(lowerCAmelCase_       ) for x in param.args       )       )
					return f"""{func.__name__}_{param_based_name}"""


# Cartesian-product of zero stages with models to test
a__    :							Optional[int]            =					list(itertools.product(stages, models.keys()))




@slow
@require_deepspeed
@require_torch_gpu
class 			UpperCamelCase_     (			UpperCamelCase):

	"""simple docstring"""





	@parameterized.expand(UpperCAmelCase__      ,      name_func=UpperCAmelCase__       )
	def   UpperCAmelCase_				(			self      :    Optional[int]      ,      UpperCAmelCase__      :    str      ,      UpperCAmelCase__      :    Optional[int]       )	->     Tuple:
						self.run_and_check(
						    stage=UpperCAmelCase__      ,      model=UpperCAmelCase__      ,      distributed=UpperCAmelCase__      ,      fpaa=UpperCAmelCase__      ,      )




	@require_torch_multi_gpu
	@parameterized.expand(UpperCAmelCase__      ,      name_func=UpperCAmelCase__       )
	def   UpperCAmelCase_				(			self      :    str      ,      UpperCAmelCase__      :    Tuple      ,      UpperCAmelCase__      :    str       )	->     Union[str, Any]:
						self.run_and_check(
						    stage=UpperCAmelCase__      ,      model=UpperCAmelCase__      ,      distributed=UpperCAmelCase__      ,      fpaa=UpperCAmelCase__      ,      )




	@parameterized.expand(UpperCAmelCase__      ,      name_func=UpperCAmelCase__       )
	def   UpperCAmelCase_				(			self      :    List[Any]      ,      UpperCAmelCase__      :    Union[str, Any]      ,      UpperCAmelCase__      :    Union[str, Any]       )	->     Union[str, Any]:
						self.run_and_check(
						    stage=UpperCAmelCase__      ,      model=UpperCAmelCase__      ,      distributed=UpperCAmelCase__      ,      fpaa=UpperCAmelCase__      ,      )




	@require_torch_multi_gpu
	@parameterized.expand(UpperCAmelCase__      ,      name_func=UpperCAmelCase__       )
	def   UpperCAmelCase_				(			self      :    Union[str, Any]      ,      UpperCAmelCase__      :    Dict      ,      UpperCAmelCase__      :    List[str]       )	->     Tuple:
						self.run_and_check(
						    stage=UpperCAmelCase__      ,      model=UpperCAmelCase__      ,      distributed=UpperCAmelCase__      ,      fpaa=UpperCAmelCase__      ,      )




	def   UpperCAmelCase_				(			self      :    Optional[int]      ,      UpperCAmelCase__      :    Dict       )	->     str:
						# 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   UpperCAmelCase_				(			self      :    str      ,      UpperCAmelCase__      :    str      ,      UpperCAmelCase__      :    str      ,      UpperCAmelCase__      :    int = 1_0      ,      UpperCAmelCase__      :    bool = True      ,      UpperCAmelCase__      :    bool = True      ,      UpperCAmelCase__      :    bool = True      ,      )	->     List[str]:
						__SCREAMING_SNAKE_CASE						       =    models[model]

						__SCREAMING_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   UpperCAmelCase_				(			self      :    Dict      ,      UpperCAmelCase__      :    str      ,      UpperCAmelCase__      :    str      ,      UpperCAmelCase__      :    int = 1_0      ,      UpperCAmelCase__      :    int = 1      ,      UpperCAmelCase__      :    bool = True      ,      UpperCAmelCase__      :    bool = True      ,      )	->     Optional[int]:
						__SCREAMING_SNAKE_CASE						       =    self.get_auto_remove_tmp_dir("./xxx"      ,      after=UpperCAmelCase__       )
						__SCREAMING_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
						__SCREAMING_SNAKE_CASE						       =    F"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split()
						__SCREAMING_SNAKE_CASE						       =    [F"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""]
						__SCREAMING_SNAKE_CASE						       =    self.get_launcher(UpperCAmelCase__       )

						__SCREAMING_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   UpperCAmelCase_				(			self      :    List[str]      ,      UpperCAmelCase__      :    Dict=False       )	->     Union[str, Any]:
						# 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)
						__SCREAMING_SNAKE_CASE						       =    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 List

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a__    :							Tuple            =					logging.get_logger(__name__)

a__    :							List[Any]            =					{
    '''snap-research/efficientformer-l1-300''': (
        '''https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json'''
    ),
}




class 			UpperCamelCase_     (			UpperCamelCase):

	"""simple docstring"""





	snake_case__    :  Any    =      "efficientformer"




	def __init__(			self      :    Any      ,      UpperCAmelCase__      :    List[int] = [3, 2, 6, 4]      ,      UpperCAmelCase__      :    List[int] = [4_8, 9_6, 2_2_4, 4_4_8]      ,      UpperCAmelCase__      :    List[bool] = [True, True, True, True]      ,      UpperCAmelCase__      :    int = 4_4_8      ,      UpperCAmelCase__      :    int = 3_2      ,      UpperCAmelCase__      :    int = 4      ,      UpperCAmelCase__      :    int = 7      ,      UpperCAmelCase__      :    int = 5      ,      UpperCAmelCase__      :    int = 8      ,      UpperCAmelCase__      :    int = 4      ,      UpperCAmelCase__      :    float = 0.0      ,      UpperCAmelCase__      :    int = 1_6      ,      UpperCAmelCase__      :    int = 3      ,      UpperCAmelCase__      :    int = 3      ,      UpperCAmelCase__      :    int = 3      ,      UpperCAmelCase__      :    int = 2      ,      UpperCAmelCase__      :    int = 1      ,      UpperCAmelCase__      :    float = 0.0      ,      UpperCAmelCase__      :    int = 1      ,      UpperCAmelCase__      :    bool = True      ,      UpperCAmelCase__      :    bool = True      ,      UpperCAmelCase__      :    float = 1E-5      ,      UpperCAmelCase__      :    str = "gelu"      ,      UpperCAmelCase__      :    float = 0.02      ,      UpperCAmelCase__      :    float = 1E-12      ,      UpperCAmelCase__      :    int = 2_2_4      ,      UpperCAmelCase__      :    float = 1E-05      ,      **UpperCAmelCase__      :    Tuple      ,      )	->     None:
						super().__init__(**UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    hidden_act
						__SCREAMING_SNAKE_CASE						       =    hidden_dropout_prob
						__SCREAMING_SNAKE_CASE						       =    hidden_sizes
						__SCREAMING_SNAKE_CASE						       =    num_hidden_layers
						__SCREAMING_SNAKE_CASE						       =    num_attention_heads
						__SCREAMING_SNAKE_CASE						       =    initializer_range
						__SCREAMING_SNAKE_CASE						       =    layer_norm_eps
						__SCREAMING_SNAKE_CASE						       =    patch_size
						__SCREAMING_SNAKE_CASE						       =    num_channels
						__SCREAMING_SNAKE_CASE						       =    depths
						__SCREAMING_SNAKE_CASE						       =    mlp_expansion_ratio
						__SCREAMING_SNAKE_CASE						       =    downsamples
						__SCREAMING_SNAKE_CASE						       =    dim
						__SCREAMING_SNAKE_CASE						       =    key_dim
						__SCREAMING_SNAKE_CASE						       =    attention_ratio
						__SCREAMING_SNAKE_CASE						       =    resolution
						__SCREAMING_SNAKE_CASE						       =    pool_size
						__SCREAMING_SNAKE_CASE						       =    downsample_patch_size
						__SCREAMING_SNAKE_CASE						       =    downsample_stride
						__SCREAMING_SNAKE_CASE						       =    downsample_pad
						__SCREAMING_SNAKE_CASE						       =    drop_path_rate
						__SCREAMING_SNAKE_CASE						       =    num_metaad_blocks
						__SCREAMING_SNAKE_CASE						       =    distillation
						__SCREAMING_SNAKE_CASE						       =    use_layer_scale
						__SCREAMING_SNAKE_CASE						       =    layer_scale_init_value
						__SCREAMING_SNAKE_CASE						       =    image_size
						__SCREAMING_SNAKE_CASE						       =    batch_norm_eps
