






# 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_		:		Dict		     =			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_		:		int		     =			{'base': 'patrickvonplaten/wav2vec2_tiny_random', 'robust': 'patrickvonplaten/wav2vec2_tiny_random_robust'}

A_		:		Tuple		     =			'zero2'
A_		:		int		     =			'zero3'
A_		:		Union[str, Any]		     =			[ZEROa, ZEROa]

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )	->    List[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
  UpperCamelCase_: List[Any]   				=    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_		:		str		     =			list(itertools.product(stages, models.keys()))




@slow
@require_deepspeed
@require_torch_gpu
class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







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





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





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





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





       def       _a   ( self  ,      _lowerCamelCase							):
         # 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       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = 1_0  ,      _lowerCamelCase = True  ,      _lowerCamelCase = True  ,      _lowerCamelCase = True  ,      ):
         UpperCamelCase_: Dict   				=    models[model]

         UpperCamelCase_: str   				=    self.run_trainer(
             stage=_lowerCamelCase  ,      model_name=_lowerCamelCase  ,      eval_steps=_lowerCamelCase  ,      num_train_epochs=1  ,      distributed=_lowerCamelCase  ,      fpaa=_lowerCamelCase  ,      )

         self.do_checks(_lowerCamelCase							)

         return output_dir





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = 1_0  ,      _lowerCamelCase = 1  ,      _lowerCamelCase = True  ,      _lowerCamelCase = True  ,      ):
         UpperCamelCase_: List[str]   				=    self.get_auto_remove_tmp_dir('./xxx'  ,      after=_lowerCamelCase							)
         UpperCamelCase_: List[Any]   				=    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(_lowerCamelCase							)}
            --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
         UpperCamelCase_: List[Any]   				=    f'''--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'''.split()
         UpperCamelCase_: Optional[Any]   				=    [f'''{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py''']
         UpperCamelCase_: Optional[Any]   				=    self.get_launcher(_lowerCamelCase							)

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

         return output_dir





       def       _a   ( self  ,      _lowerCamelCase=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)
         UpperCamelCase_: Union[str, Any]   				=    min(2  ,      get_gpu_count()							) if distributed else 1
         return f'''deepspeed --num_nodes 1 --num_gpus {num_gpus}'''.split()







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
   from transformers import AutoModelForSeqaSeqLM, AutoTokenizer




@require_torch
@require_sentencepiece
@require_tokenizers
class     _lowerCAmelCase(	unittest.TestCase ):





       """simple docstring"""







       @slow
       def       _a   ( self							):
         UpperCamelCase_: Optional[int]   				=    AutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'  ,      return_dict=_lowerCamelCase							).to(_lowerCamelCase							)
         UpperCamelCase_: Dict   				=    AutoTokenizer.from_pretrained('google/mt5-small'							)

         UpperCamelCase_: Dict   				=    tokenizer('Hello there'  ,      return_tensors='pt'							).input_ids
         UpperCamelCase_: Optional[Any]   				=    tokenizer('Hi I am'  ,      return_tensors='pt'							).input_ids

         UpperCamelCase_: int   				=    model(input_ids.to(_lowerCamelCase							)  ,      labels=labels.to(_lowerCamelCase							)							).loss
         UpperCamelCase_: Tuple   				=    -(labels.shape[-1] * loss.item())

         UpperCamelCase_: Any   				=    -8_4.9_1_2_7
         self.assertTrue(abs(mtf_score - EXPECTED_SCORE							) < 1e-4							)