
"""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


UpperCAmelCase__          = 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)

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

UpperCAmelCase__          = 'zero2'
UpperCAmelCase__          = 'zero3'
UpperCAmelCase__          = [ZEROa, ZEROa]
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Optional[int] ,					__lowerCamelCase   :       List[str] ,					__lowerCamelCase   :       str		)     ->					int:
  # 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(__lowerCamelCase		) for x in param.args		)		)
  return f'''{func.__name__}_{param_based_name}'''


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

@slow
@require_deepspeed
@require_torch_gpu
class       lowerCAmelCase__				(						A_	):







     @parameterized.expand(_lowerCamelCase    ,      name_func=_lowerCamelCase			)
     def      lowercase	(							self			:			Optional[int]    ,      _lowerCamelCase			:			Union[str, Any]    ,      _lowerCamelCase			:			List[str]			):
       self.run_and_check(
           stage=_lowerCamelCase    ,      model=_lowerCamelCase    ,      distributed=_lowerCamelCase    ,      fpaa=_lowerCamelCase    ,      )







     @require_torch_multi_gpu
     @parameterized.expand(_lowerCamelCase    ,      name_func=_lowerCamelCase			)
     def      lowercase	(							self			:			Optional[Any]    ,      _lowerCamelCase			:			Tuple    ,      _lowerCamelCase			:			Union[str, Any]			):
       self.run_and_check(
           stage=_lowerCamelCase    ,      model=_lowerCamelCase    ,      distributed=_lowerCamelCase    ,      fpaa=_lowerCamelCase    ,      )







     @parameterized.expand(_lowerCamelCase    ,      name_func=_lowerCamelCase			)
     def      lowercase	(							self			:			Optional[int]    ,      _lowerCamelCase			:			Optional[Any]    ,      _lowerCamelCase			:			List[Any]			):
       self.run_and_check(
           stage=_lowerCamelCase    ,      model=_lowerCamelCase    ,      distributed=_lowerCamelCase    ,      fpaa=_lowerCamelCase    ,      )







     @require_torch_multi_gpu
     @parameterized.expand(_lowerCamelCase    ,      name_func=_lowerCamelCase			)
     def      lowercase	(							self			:			str    ,      _lowerCamelCase			:			Union[str, Any]    ,      _lowerCamelCase			:			Any			):
       self.run_and_check(
           stage=_lowerCamelCase    ,      model=_lowerCamelCase    ,      distributed=_lowerCamelCase    ,      fpaa=_lowerCamelCase    ,      )







     def      lowercase	(							self			:			int    ,      _lowerCamelCase			:			int			):
       # 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      lowercase	(							self			:			List[Any]    ,      _lowerCamelCase			:			str    ,      _lowerCamelCase			:			str    ,      _lowerCamelCase			:			int = 10    ,      _lowerCamelCase			:			bool = True    ,      _lowerCamelCase			:			bool = True    ,      _lowerCamelCase			:			bool = True    ,      ):
       _snake_case					     =       models[model]

       _snake_case					     =       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      lowercase	(							self			:			Tuple    ,      _lowerCamelCase			:			str    ,      _lowerCamelCase			:			str    ,      _lowerCamelCase			:			int = 10    ,      _lowerCamelCase			:			int = 1    ,      _lowerCamelCase			:			bool = True    ,      _lowerCamelCase			:			bool = True    ,      ):
       _snake_case					     =       self.get_auto_remove_tmp_dir('''./xxx'''    ,      after=_lowerCamelCase			)
       _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(_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
       _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(_lowerCamelCase			)

       _snake_case					     =       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      lowercase	(							self			:			List[str]    ,      _lowerCamelCase			:			str=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)
       _snake_case					     =       min(2    ,      get_gpu_count()			) if distributed else 1
       return f'''deepspeed --num_nodes 1 --num_gpus {num_gpus}'''.split()





"""simple docstring"""



# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

UpperCAmelCase__          = 'tiny-wmt19-en-ru'

# Build

# borrowed from a test
UpperCAmelCase__          = [
    'l',
    'o',
    'w',
    'e',
    'r',
    's',
    't',
    'i',
    'd',
    'n',
    'w</w>',
    'r</w>',
    't</w>',
    'lo',
    'low',
    'er</w>',
    'low</w>',
    'lowest</w>',
    'newer</w>',
    'wider</w>',
    '<unk>',
]
UpperCAmelCase__          = dict(zip(vocab, range(len(vocab))))
UpperCAmelCase__          = ['l o 123', 'lo w 1456', 'e r</w> 1789', '']

with tempfile.TemporaryDirectory() as tmpdirname:
 UpperCAmelCase__          = Path(tmpdirname)
 UpperCAmelCase__          = build_dir / VOCAB_FILES_NAMES['src_vocab_file']
 UpperCAmelCase__          = build_dir / VOCAB_FILES_NAMES['tgt_vocab_file']
 UpperCAmelCase__          = build_dir / VOCAB_FILES_NAMES['merges_file']
 with open(src_vocab_file, 'w') as fp:
  fp.write(json.dumps(vocab_tokens))
 with open(tgt_vocab_file, 'w') as fp:
  fp.write(json.dumps(vocab_tokens))
 with open(merges_file, 'w') as fp:
  fp.write('\n'.join(merges))

 UpperCAmelCase__          = FSMTTokenizer(
     langs=['en', 'ru'],
     src_vocab_size=len(vocab),
     tgt_vocab_size=len(vocab),
     src_vocab_file=src_vocab_file,
     tgt_vocab_file=tgt_vocab_file,
     merges_file=merges_file,
 )

UpperCAmelCase__          = FSMTConfig(
    langs=['ru', 'en'],
    src_vocab_size=1000,
    tgt_vocab_size=1000,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

UpperCAmelCase__          = FSMTForConditionalGeneration(config)
print(F"num of params {tiny_model.num_parameters()}")

# Test
UpperCAmelCase__          = tokenizer(['Making tiny model'], return_tensors='pt')
UpperCAmelCase__          = tiny_model(**batch)

print('test output:', len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F"Generated {mname_tiny}")

# Upload
# transformers-cli upload tiny-wmt19-en-ru



