

# 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											=     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)

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

A											=     'zero2'
A											=     'zero3'
A											=     [ZEROa, ZEROa]





def a(lowercase__			, lowercase__			, lowercase__	):





	'''simple docstring'''
	# 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(_lowercase	) for x in param.args	)	)
	return f"""{func.__name__}_{param_based_name}"""


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






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






			"""simple docstring"""




			@parameterized.expand(UpperCamelCase__ ,				name_func=UpperCamelCase__							)
			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""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 ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


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


			@parameterized.expand(UpperCamelCase__ ,				name_func=UpperCamelCase__							)
			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""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 ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


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


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


				pass


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase = 10 ,				__UpperCamelCase = True ,				__UpperCamelCase = True ,				__UpperCamelCase = True ,				):

				"""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 ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase = 10 ,				__UpperCamelCase = 1 ,				__UpperCamelCase = True ,				__UpperCamelCase = True ,				):

				"""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 ,				__UpperCamelCase=False							):

				"""simple docstring"""


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



from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


A											=     logging.get_logger(__name__)

A											=     {
    'google/bit-50': 'https://huggingface.co/google/bit-50/resolve/main/config.json',
}


class SCREAMING_SNAKE_CASE  (		__snake_case							,						__snake_case							):






			"""simple docstring"""




			__A						  =  """bit"""
			__A						  =  ["""preactivation""", """bottleneck"""]
			__A						  =  ["""SAME""", """VALID"""]


			def __init__(    self ,				__UpperCamelCase=3 ,				__UpperCamelCase=64 ,				__UpperCamelCase=[2_56, 5_12, 10_24, 20_48] ,				__UpperCamelCase=[3, 4, 6, 3] ,				__UpperCamelCase="preactivation" ,				__UpperCamelCase="relu" ,				__UpperCamelCase=None ,				__UpperCamelCase=32 ,				__UpperCamelCase=0.0 ,				__UpperCamelCase=False ,				__UpperCamelCase=32 ,				__UpperCamelCase=1 ,				__UpperCamelCase=None ,				__UpperCamelCase=None ,				**__UpperCamelCase ,				):

				"""simple docstring"""


				super().__init__(**__UpperCamelCase							)
				if layer_type not in self.layer_types:
					raise ValueError(f"""layer_type={layer_type} is not one of {','.join(self.layer_types							)}"""							)
				if global_padding is not None:
					if global_padding.upper() in self.supported_padding:
						snake_case_      		= global_padding.upper()
					else:
						raise ValueError(f"""Padding strategy {global_padding} not supported"""							)
				snake_case_      		= num_channels
				snake_case_      		= embedding_size
				snake_case_      		= hidden_sizes
				snake_case_      		= depths
				snake_case_      		= layer_type
				snake_case_      		= hidden_act
				snake_case_      		= global_padding
				snake_case_      		= num_groups
				snake_case_      		= drop_path_rate
				snake_case_      		= embedding_dynamic_padding
				snake_case_      		= output_stride
				snake_case_      		= width_factor

				snake_case_      		= ['stem'] + [f"""stage{idx}""" for idx in range(1 ,				len(__UpperCamelCase							) + 1							)]
				snake_case_		,       snake_case_      		= get_aligned_output_features_output_indices(
				    out_features=__UpperCamelCase ,				out_indices=__UpperCamelCase ,				stage_names=self.stage_names							)
