




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        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				int:
						_UpperCAmelCase      =			parameterized.to_safe_name("""_""".join(str(__UpperCAmelCase       ) 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       _A				(		_snake_case     ):






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






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






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






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






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								pass






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE = 10						,		_SCREAMING_SNAKE_CASE = True						,		_SCREAMING_SNAKE_CASE = True						,		_SCREAMING_SNAKE_CASE = True						,		):
								_UpperCAmelCase      =			models[model]

								_UpperCAmelCase      =			self.run_trainer(
								    stage=_SCREAMING_SNAKE_CASE						,		model_name=_SCREAMING_SNAKE_CASE						,		eval_steps=_SCREAMING_SNAKE_CASE						,		num_train_epochs=1						,		distributed=_SCREAMING_SNAKE_CASE						,		fpaa=_SCREAMING_SNAKE_CASE						,		)

								self.do_checks(_SCREAMING_SNAKE_CASE			)

								return output_dir






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE = 10						,		_SCREAMING_SNAKE_CASE = 1						,		_SCREAMING_SNAKE_CASE = True						,		_SCREAMING_SNAKE_CASE = True						,		):
								_UpperCAmelCase      =			self.get_auto_remove_tmp_dir("""./xxx"""						,		after=_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			F"\n            --model_name_or_path {model_name}\n            --dataset_name hf-internal-testing/librispeech_asr_dummy\n            --dataset_config_name clean\n            --train_split_name validation\n            --validation_split_name validation\n            --output_dir {output_dir}\n            --num_train_epochs {str(_SCREAMING_SNAKE_CASE			)}\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 2\n            --evaluation_strategy steps\n            --learning_rate 5e-4\n            --warmup_steps 8\n            --orthography timit\n            --preprocessing_num_workers 1\n            --group_by_length\n            --freeze_feature_extractor\n            --report_to none\n            --save_steps 0\n            --eval_steps {eval_steps}\n            --report_to none\n        ".split()

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

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

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

								return output_dir






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE=False			):
								_UpperCAmelCase      =			min(2						,		get_gpu_count()			) if distributed else 1
								return F"deepspeed --num_nodes 1 --num_gpus {num_gpus}".split()





import jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


a													=			logging.get_logger(__name__)

a													=			"T5Config"




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				jnp.ndarray:
						_UpperCAmelCase      =			jnp.zeros_like(snake_case       )
						_UpperCAmelCase      =			shifted_input_ids.at[:, 1:].set(input_ids[:, :-1]       )
						_UpperCAmelCase      =			shifted_input_ids.at[:, 0].set(snake_case       )

						_UpperCAmelCase      =			jnp.where(shifted_input_ids == -1_0_0			,     snake_case			,     snake_case       )
						return shifted_input_ids




class       _A				(		__lowercase     ):
		__a					    =       """mt5"""
		__a					    =       MTaConfig




class       _A				(		__lowercase     ):
		__a					    =       """mt5"""
		__a					    =       MTaConfig




class       _A				(		__lowercase     ):
		__a					    =       """mt5"""
		__a					    =       MTaConfig