

from __future__ import annotations

import unittest
import warnings

from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers import TFAutoModelForSeqaSeqLM, TFMarianModel, TFMarianMTModel






@require_tf
class 							A_				:
     _lowercase    :  str          =			MarianConfig
     _lowercase    :  Optional[int]          =			{}
     _lowercase    :  Optional[Any]          =			'gelu'


     def __init__(							self    :	Dict    ,						UpperCAmelCase    :	int    ,						UpperCAmelCase    :	List[Any]=1_3    ,						UpperCAmelCase    :	Optional[int]=7    ,						UpperCAmelCase    :	List[str]=True    ,						UpperCAmelCase    :	Dict=False    ,						UpperCAmelCase    :	Union[str, Any]=9_9    ,						UpperCAmelCase    :	Tuple=3_2    ,						UpperCAmelCase    :	List[str]=2    ,						UpperCAmelCase    :	Dict=4    ,						UpperCAmelCase    :	Tuple=3_7    ,						UpperCAmelCase    :	Union[str, Any]=0.1    ,						UpperCAmelCase    :	Dict=0.1    ,						UpperCAmelCase    :	Any=2_0    ,						UpperCAmelCase    :	Union[str, Any]=2    ,						UpperCAmelCase    :	Any=1    ,						UpperCAmelCase    :	str=0    ,						)							->			str:
      __lowerCAmelCase:     Tuple     			=   parent
      __lowerCAmelCase:     Any     			=   batch_size
      __lowerCAmelCase:     List[Any]     			=   seq_length
      __lowerCAmelCase:     Dict     			=   is_training
      __lowerCAmelCase:     List[Any]     			=   use_labels
      __lowerCAmelCase:     List[str]     			=   vocab_size
      __lowerCAmelCase:     Optional[Any]     			=   hidden_size
      __lowerCAmelCase:     Any     			=   num_hidden_layers
      __lowerCAmelCase:     Optional[Any]     			=   num_attention_heads
      __lowerCAmelCase:     int     			=   intermediate_size

      __lowerCAmelCase:     List[Any]     			=   hidden_dropout_prob
      __lowerCAmelCase:     List[str]     			=   attention_probs_dropout_prob
      __lowerCAmelCase:     Tuple     			=   max_position_embeddings
      __lowerCAmelCase:     Tuple     			=   eos_token_id
      __lowerCAmelCase:     List[Any]     			=   pad_token_id
      __lowerCAmelCase:     str     			=   bos_token_id


     def 						UpperCAmelCase						(							self    :	str      )							->			Optional[int]:
      __lowerCAmelCase:     int     			=   ids_tensor([self.batch_size, self.seq_length - 1]    ,						self.vocab_size      )
      __lowerCAmelCase:     Optional[int]     			=   tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size      )    ,						1      )
      __lowerCAmelCase:     List[Any]     			=   tf.concat([input_ids, eos_tensor]    ,						axis=1      )

      __lowerCAmelCase:     str     			=   ids_tensor([self.batch_size, self.seq_length]    ,						self.vocab_size      )

      __lowerCAmelCase:     Optional[int]     			=   self.config_cls(
          vocab_size=self.vocab_size    ,						d_model=self.hidden_size    ,						encoder_layers=self.num_hidden_layers    ,						decoder_layers=self.num_hidden_layers    ,						encoder_attention_heads=self.num_attention_heads    ,						decoder_attention_heads=self.num_attention_heads    ,						encoder_ffn_dim=self.intermediate_size    ,						decoder_ffn_dim=self.intermediate_size    ,						dropout=self.hidden_dropout_prob    ,						attention_dropout=self.attention_probs_dropout_prob    ,						max_position_embeddings=self.max_position_embeddings    ,						eos_token_ids=[2]    ,						bos_token_id=self.bos_token_id    ,						pad_token_id=self.pad_token_id    ,						decoder_start_token_id=self.pad_token_id    ,						**self.config_updates    ,						)
      __lowerCAmelCase:     Any     			=   prepare_marian_inputs_dict(UpperCAmelCase    ,						UpperCAmelCase    ,						UpperCAmelCase      )
      return config, inputs_dict


     def 						UpperCAmelCase						(							self    :	int    ,						UpperCAmelCase    :	Any    ,						UpperCAmelCase    :	int      )							->			Union[str, Any]:
      __lowerCAmelCase:     List[Any]     			=   TFMarianModel(config=UpperCAmelCase      ).get_decoder()
      __lowerCAmelCase:     str     			=   inputs_dict['input_ids']

      __lowerCAmelCase:     Tuple     			=   input_ids[:1, :]
      __lowerCAmelCase:     Optional[Any]     			=   inputs_dict['attention_mask'][:1, :]
      __lowerCAmelCase:     Dict     			=   inputs_dict['head_mask']
      __lowerCAmelCase:     List[Any]     			=   1

      # first forward pass
      __lowerCAmelCase:     Union[str, Any]     			=   model(UpperCAmelCase    ,						attention_mask=UpperCAmelCase    ,						head_mask=UpperCAmelCase    ,						use_cache=UpperCAmelCase      )

      __lowerCAmelCase:     int     			=   outputs.to_tuple()

      # create hypothetical next token and extent to next_input_ids
      __lowerCAmelCase:     int     			=   ids_tensor((self.batch_size, 3)    ,						config.vocab_size      )
      __lowerCAmelCase:     Tuple     			=   tf.cast(ids_tensor((self.batch_size, 3)    ,						2      )    ,						tf.inta      )

      # append to next input_ids and
      __lowerCAmelCase:     Any     			=   tf.concat([input_ids, next_tokens]    ,						axis=-1      )
      __lowerCAmelCase:     Optional[int]     			=   tf.concat([attention_mask, next_attn_mask]    ,						axis=-1      )

      __lowerCAmelCase:     Optional[int]     			=   model(UpperCAmelCase    ,						attention_mask=UpperCAmelCase      )[0]
      __lowerCAmelCase:     List[str]     			=   model(UpperCAmelCase    ,						attention_mask=UpperCAmelCase    ,						past_key_values=UpperCAmelCase      )[0]

      self.parent.assertEqual(next_tokens.shape[1]    ,						output_from_past.shape[1]      )

      # select random slice
      __lowerCAmelCase:     Optional[int]     			=   int(ids_tensor((1,)    ,						output_from_past.shape[-1]      )      )
      __lowerCAmelCase:     Tuple     			=   output_from_no_past[:, -3:, random_slice_idx]
      __lowerCAmelCase:     Dict     			=   output_from_past[:, :, random_slice_idx]

      # test that outputs are equal for slice
      tf.debugging.assert_near(UpperCAmelCase    ,						UpperCAmelCase    ,						rtol=1E-3      )







def _a				( SCREAMING_SNAKE_CASE	:    str					,      SCREAMING_SNAKE_CASE	:    Dict					,      SCREAMING_SNAKE_CASE	:    Dict					,      SCREAMING_SNAKE_CASE	:    Optional[int]=None					,      SCREAMING_SNAKE_CASE	:    Dict=None					,      SCREAMING_SNAKE_CASE	:    str=None					,      SCREAMING_SNAKE_CASE	:    List[str]=None					,      SCREAMING_SNAKE_CASE	:    str=None					,      )					->					Dict:

 """simple docstring"""






 if attention_mask is None:
  __lowerCAmelCase:     Dict     			=   tf.cast(tf.math.not_equal(SCREAMING_SNAKE_CASE					,      config.pad_token_id	)					,      tf.inta	)
 if decoder_attention_mask is None:
  __lowerCAmelCase:     Tuple     			=   tf.concat(
      [
          tf.ones(decoder_input_ids[:, :1].shape					,      dtype=tf.inta	),
          tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]					,      config.pad_token_id	)					,      tf.inta	),
      ]					,      axis=-1					,      )
 if head_mask is None:
  __lowerCAmelCase:     List[str]     			=   tf.ones((config.encoder_layers, config.encoder_attention_heads)	)
 if decoder_head_mask is None:
  __lowerCAmelCase:     List[Any]     			=   tf.ones((config.decoder_layers, config.decoder_attention_heads)	)
 if cross_attn_head_mask is None:
  __lowerCAmelCase:     Dict     			=   tf.ones((config.decoder_layers, config.decoder_attention_heads)	)
 return {
     "input_ids": input_ids,
     "decoder_input_ids": decoder_input_ids,
     "attention_mask": attention_mask,
     "decoder_attention_mask": decoder_attention_mask,
     "head_mask": head_mask,
     "decoder_head_mask": decoder_head_mask,
     "cross_attn_head_mask": cross_attn_head_mask,
 }






@require_tf
class 							A_				(	snake_case__							,			snake_case__							,			unittest.TestCase      ):
     _lowercase    :  Optional[int]          =			(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
     _lowercase    :  int          =			(TFMarianMTModel,) if is_tf_available() else ()
     _lowercase    :  Optional[int]          =			(
         {
             'conversational': TFMarianMTModel,
             'feature-extraction': TFMarianModel,
             'summarization': TFMarianMTModel,
             'text2text-generation': TFMarianMTModel,
             'translation': TFMarianMTModel,
         }
         if is_tf_available()
         else {}
     )
     _lowercase    :  Dict          =			True
     _lowercase    :  List[Any]          =			False
     _lowercase    :  Optional[int]          =			False


     def 						UpperCAmelCase						(							self    :	Dict      )							->			Any:
      __lowerCAmelCase:     List[Any]     			=   TFMarianModelTester(self      )
      __lowerCAmelCase:     Any     			=   ConfigTester(self    ,						config_class=UpperCAmelCase      )


     def 						UpperCAmelCase						(							self    :	Optional[int]      )							->			Any:
      self.config_tester.run_common_tests()


     def 						UpperCAmelCase						(							self    :	Dict      )							->			Any:
      __lowerCAmelCase:     Tuple     			=   self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.check_decoder_model_past_large_inputs(*UpperCAmelCase      )






@require_tf
class 							A_				(	unittest.TestCase      ):
     _lowercase    :  int          =			1_0_0_0  # show more chars for failing integration tests


     @classmethod
     def 						UpperCAmelCase						(							cls    :	Dict      )							->			None:
      __lowerCAmelCase:     int     			=   F'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
      return cls


     @cached_property
     def 						UpperCAmelCase						(							self    :	Union[str, Any]      )							->			MarianTokenizer:
      return AutoTokenizer.from_pretrained(self.model_name      )


     @property
     def 						UpperCAmelCase						(							self    :	Any      )							->			int:
      return self.tokenizer.eos_token_id


     @cached_property
     def 						UpperCAmelCase						(							self    :	int      )							->			Optional[int]:
      warnings.simplefilter('error'      )
      __lowerCAmelCase:     TFMarianMTModel     			=   TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name      )
      assert isinstance(UpperCAmelCase    ,						UpperCAmelCase      )
      __lowerCAmelCase:     List[str]     			=   model.config
      self.assertListEqual(c.bad_words_ids    ,						[[c.pad_token_id]]      )
      self.assertEqual(c.max_length    ,						5_1_2      )
      self.assertEqual(c.decoder_start_token_id    ,						c.pad_token_id      )
      return model


     def 						UpperCAmelCase						(							self    :	List[str]    ,						**UpperCAmelCase    :	Dict      )							->			int:
      __lowerCAmelCase:     Optional[int]     			=   self.translate_src_text(**UpperCAmelCase      )
      self.assertListEqual(self.expected_text    ,						UpperCAmelCase      )


     def 						UpperCAmelCase						(							self    :	List[str]    ,						**UpperCAmelCase    :	Optional[int]      )							->			Optional[int]:
      __lowerCAmelCase:     Dict     			=   self.tokenizer(self.src_text    ,						**UpperCAmelCase    ,						padding=UpperCAmelCase    ,						return_tensors='tf'      )
      __lowerCAmelCase:     Tuple     			=   self.model.generate(
          model_inputs.input_ids    ,						attention_mask=model_inputs.attention_mask    ,						num_beams=2    ,						max_length=1_2_8      )
      __lowerCAmelCase:     Union[str, Any]     			=   self.tokenizer.batch_decode(generated_ids.numpy()    ,						skip_special_tokens=UpperCAmelCase      )
      return generated_words






@require_sentencepiece
@require_tokenizers
@require_tf
class 							A_				(	snake_case__      ):
     _lowercase    :  int          =			'mt'
     _lowercase    :  Union[str, Any]          =			'en'
     _lowercase    :  str          =			['Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.']
     _lowercase    :  str          =			['Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.']


     @unittest.skip('Skipping until #12647 is resolved.'      )
     @slow
     def 						UpperCAmelCase						(							self    :	Any      )							->			List[str]:
      self._assert_generated_batch_equal_expected()






@require_sentencepiece
@require_tokenizers
@require_tf
class 							A_				(	snake_case__      ):
     _lowercase    :  Optional[int]          =			'en'
     _lowercase    :  Optional[Any]          =			'zh'
     _lowercase    :  Tuple          =			['My name is Wolfgang and I live in Berlin']
     _lowercase    :  List[Any]          =			['我叫沃尔夫冈 我住在柏林']


     @unittest.skip('Skipping until #12647 is resolved.'      )
     @slow
     def 						UpperCAmelCase						(							self    :	List[str]      )							->			Optional[Any]:
      self._assert_generated_batch_equal_expected()






@require_sentencepiece
@require_tokenizers
@require_tf
class 							A_				(	snake_case__      ):
     _lowercase    :  Tuple          =			'en'
     _lowercase    :  Optional[int]          =			'ROMANCE'
     _lowercase    :  Any          =			[
         '>>fr<< Don\'t spend so much time watching TV.',
         '>>pt<< Your message has been sent.',
         '>>es<< He\'s two years older than me.',
     ]
     _lowercase    :  Tuple          =			[
         'Ne passez pas autant de temps à regarder la télé.',
         'A sua mensagem foi enviada.',
         'Es dos años más viejo que yo.',
     ]


     @unittest.skip('Skipping until #12647 is resolved.'      )
     @slow
     def 						UpperCAmelCase						(							self    :	Tuple      )							->			Dict:
      self._assert_generated_batch_equal_expected()


     @unittest.skip('Skipping until #12647 is resolved.'      )
     @slow
     def 						UpperCAmelCase						(							self    :	Union[str, Any]      )							->			List[str]:
      __lowerCAmelCase:     Optional[Any]     			=   TranslationPipeline(self.model    ,						self.tokenizer    ,						framework='tf'      )
      __lowerCAmelCase:     Tuple     			=   pipeline(self.src_text      )
      self.assertEqual(self.expected_text    ,						[x['translation_text'] for x in output]      )








import itertools
import os
import subprocess
from os.path import dirname

from parameterized import parameterized

from tests.trainer.test_trainer import TrainerIntegrationCommon  # noqa
from transformers import is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_tests_dir,
    require_deepspeed,
    require_torch_gpu,
    slow,
)
from transformers.trainer_utils import set_seed


if is_torch_available():
  from tests.trainer.test_trainer import (  # noqa
      RegressionModelConfig,
      RegressionPreTrainedModel,
      get_regression_trainer,
  )


set_seed(4_2)

_a	 =     get_tests_dir('''fixtures''')
_a	 =     os.path.join(dirname(get_tests_dir()))
_a	 =     dirname(os.path.abspath(__file__))

# default torch.distributed port
_a	 =     '''10999'''

_a	 =     '''t5-small'''

# *** Working Models ***
_a	 =     '''hf-internal-testing/tiny-albert'''
_a	 =     '''sshleifer/bart-tiny-random'''
_a	 =     '''hf-internal-testing/tiny-bert'''
_a	 =     '''hf-internal-testing/tiny-random-bigbird_pegasus'''
_a	 =     '''hf-internal-testing/tiny-random-big_bird'''
_a	 =     '''hf-internal-testing/tiny-random-blenderbot'''
_a	 =     '''bigscience/bigscience-small-testing'''
_a	 =     '''hf-internal-testing/tiny-random-deberta'''
_a	 =     '''hf-internal-testing/tiny-random-deberta-v2'''
_a	 =     '''sshleifer/tiny-distilbert-base-cased'''
_a	 =     '''hf-internal-testing/tiny-electra'''
_a	 =     '''hf-internal-testing/tiny-random-flaubert'''
_a	 =     '''stas/tiny-wmt19-en-de'''
_a	 =     '''hf-internal-testing/tiny-random-funnel'''
_a	 =     '''sshleifer/tiny-gpt2'''
_a	 =     '''hf-internal-testing/tiny-random-gptj'''
_a	 =     '''hf-internal-testing/tiny-random-gpt_neo'''
_a	 =     '''hf-internal-testing/tiny-layoutlm'''
_a	 =     '''hf-internal-testing/tiny-random-led'''
_a	 =     '''hf-internal-testing/tiny-random-longformer'''
_a	 =     '''stas/tiny-m2m_100'''  # hf tiny model is unsuitable
_a	 =     '''sshleifer/tiny-marian-en-de'''
_a	 =     '''sshleifer/tiny-mbart'''
_a	 =     '''hf-internal-testing/tiny-random-mobilebert'''
_a	 =     '''hf-internal-testing/tiny-random-mpnet'''
_a	 =     '''stas/pegasus-cnn_dailymail-tiny-random'''
_a	 =     '''hf-internal-testing/tiny-random-prophetnet'''
_a	 =     '''sshleifer/tiny-distilroberta-base'''
_a	 =     '''hf-internal-testing/tiny-random-squeezebert'''
_a	 =     '''patrickvonplaten/t5-tiny-random'''
_a	 =     '''hf-internal-testing/tiny-random-t5-v1.1'''
_a	 =     '''hf-internal-testing/tiny-random-vit'''
_a	 =     '''hf-internal-testing/tiny-xlm-roberta'''
_a	 =     '''sshleifer/tiny-xlnet-base-cased'''


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
_a	 =     '''hf-internal-testing/tiny-random-mt5'''
_a	 =     '''hf-internal-testing/tiny-random-camembert'''
_a	 =     '''hf-internal-testing/tiny-random-openai-gpt'''

# missing tokenizer files
_a	 =     '''hf-internal-testing/tiny-random-convbert'''
_a	 =     '''hf-internal-testing/tiny-random-layoutlmv2'''
_a	 =     '''hf-internal-testing/tiny-random-hubert'''

# issues with tokenizer
_a	 =     '''hf-internal-testing/tiny-random-ctrl'''
_a	 =     '''hf-internal-testing/tiny-random-transfo-xl'''  # same as ctrl

# other issues with tiny models
_a	 =     '''hf-internal-testing/tiny-random-ibert'''  # multiple issues with either mlm/qa/clas
_a	 =     '''hf-internal-testing/tiny-random-reformer'''  # multiple issues with either mlm/qa/clas

# *** Lacking official examples to test with ***
# or not working with examples
_a	 =     '''hf-internal-testing/tiny-random-dpr'''
# - "dpr"  examples/research_projects/rag-end2end-retriever/
_a	 =     '''hf-internal-testing/tiny-random-rag'''
# - "rag" research_projects
_a	 =     ''''''
# - "luke" Entities classes - no plan to make such example
_a	 =     '''hf-internal-testing/tiny-random-lxmert'''
# - "lxmert" doesn't work with run_qa.py
_a	 =     '''hf-internal-testing/tiny-random-clip'''
# - "clip" nothing under pytorch examples - XXX: Suraj is working on adding some - check by end of Sep
_a	 =     '''hf-internal-testing/tiny-random-speech_to_text'''
# - "speech_to_text", nothing under pytorch examples


# *** Reactive mode ***
# models with low usage, unstable API, things about to change - do nothing about the following until someone runs into a problem
_a	 =     '''hf-internal-testing/tiny-random-tapas'''







def _a				( SCREAMING_SNAKE_CASE	:    List[str]=False	)					->					Union[str, Any]:

 """simple docstring"""






 __lowerCAmelCase:     Union[str, Any]     			=   min(2					,      get_gpu_count()	) if distributed else 1
 __lowerCAmelCase:     Any     			=   os.environ.get('DS_TEST_PORT'					,      SCREAMING_SNAKE_CASE	)
 return f'''deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'''.split()







def _a				( )					->					Optional[Any]:

 """simple docstring"""






 __lowerCAmelCase:     int     			=   f'''{FIXTURE_DIRECTORY}/tests_samples'''
 __lowerCAmelCase:     List[str]     			=   f'''{data_dir_samples}/wmt_en_ro'''
 __lowerCAmelCase:     str     			=   f'''{data_dir_samples}/xsum'''
 __lowerCAmelCase:     int     			=   '\n        --do_train\n        --max_train_samples 4\n        --per_device_train_batch_size 2\n        --num_train_epochs 1\n        --fp16\n        --report_to none\n        --overwrite_output_dir\n        '.split()

 # try to cover as many models as possible once (it's enough to run on one task per model)
 # but need a tiny model for each
 #
 # should have "{model_type.upper()}_TINY" corresponding vars defined, e.g., T5_TINY, etc.
 __lowerCAmelCase:     Dict     			=   {
     'trans': [
         'bart',
         'fsmt',
         'm2m_100',
         'marian',
         'mbart',
         't5',
         't5_v1',
         # "mt5", missing model files
     ],
     'sum': [
         'pegasus',
     ],
     'clm': [
         'big_bird',
         'bigbird_pegasus',
         'blenderbot',
         'bloom',
         'gpt2',
         'gpt_neo',
         'gptj',
         'xlm-roberta',
         'prophetnet',
         # "camembert", missing model files
     ],
     'mlm': [
         'albert',
         'deberta',
         'deberta-v2',
         'distilbert',
         'electra',
         'flaubert',
         'funnel',
         'layoutlm',
         # "reformer", # multiple issues with either mlm/qa/clas
     ],
     'qa': [
         'led',
         'longformer',
         'mobilebert',
         'mpnet',
         'roberta',
         'squeezebert',
         # "convbert", # missing tokenizer files
         # "layoutlmv2", missing model files
     ],
     'clas': [
         'bert',
         'xlnet',
         # "hubert", # missing tokenizer files
         # "ibert", # multiple issues with either mlm/qa/clas
         # "transfo-xl", # tokenizer issues as ctrl
         # "ctrl", # tokenizer issues
         # "openai-gpt", missing model files
         # "tapas", multiple issues
     ],
     'img_clas': [
         'vit',
     ],
 }

 __lowerCAmelCase:     Optional[Any]     			=   f'''{ROOT_DIRECTORY}/examples/pytorch'''

 __lowerCAmelCase:     Optional[int]     			=   {
     'trans': f'''
        {scripts_dir}/translation/run_translation.py
        --train_file {data_dir_wmt}/train.json
        --source_lang en
        --target_lang ro
        ''',
     'sum': f'''
        {scripts_dir}/summarization/run_summarization.py
        --train_file {data_dir_xsum}/sample.json
        --max_source_length 12
        --max_target_length 12
        --lang en
        ''',
     'clm': f'''
        {scripts_dir}/language-modeling/run_clm.py
        --train_file {FIXTURE_DIRECTORY}/sample_text.txt
        --block_size 8
        ''',
     'mlm': f'''
        {scripts_dir}/language-modeling/run_mlm.py
        --train_file {FIXTURE_DIRECTORY}/sample_text.txt
        ''',
     'qa': f'''
        {scripts_dir}/question-answering/run_qa.py
        --train_file {data_dir_samples}/SQUAD/sample.json
        ''',
     'clas': f'''
        {scripts_dir}/text-classification/run_glue.py
        --train_file {data_dir_samples}/MRPC/train.csv
        --max_seq_length 12
        --task_name MRPC
        ''',
     'img_clas': f'''
        {scripts_dir}/image-classification/run_image_classification.py
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --remove_unused_columns False
            --max_steps 10
            --image_processor_name {DS_TESTS_DIRECTORY}/vit_feature_extractor.json
        ''',
 }

 __lowerCAmelCase:     List[Any]     			=   get_launcher(distributed=SCREAMING_SNAKE_CASE	)

 __lowerCAmelCase:     List[str]     			=   {}
 for task, args in tasks.items():
  __lowerCAmelCase:     Tuple     			=   args.split()
  for model in tasksamodels[task]:
   __lowerCAmelCase:     Dict     			=   globals()[f'''{model.upper().replace('-'					,      '_'	)}_TINY''']
   __lowerCAmelCase:     List[Any]     			=   f'''--model_name_or_path {model_name}'''.split()
   __lowerCAmelCase:     str     			=   launcher + args + args_model + args_main

   # # generation special case
   # if task == "gen":
   #     launcher = f"deepspeed --num_nodes 1 --num_gpus 1".split()
   #     args_model += f"--model_type {model}".split()
   #     cmds[f"{task}_{model}"] = launcher + args + args_model
   # else:

 return cmds


_a	 =     make_task_cmds()

_a	 =     '''zero2'''
_a	 =     '''zero3'''

_a	 =     [ZEROa, ZEROa]







def _a				( SCREAMING_SNAKE_CASE	:    Any					,      SCREAMING_SNAKE_CASE	:    List[Any]					,      SCREAMING_SNAKE_CASE	:    Optional[int]	)					->					Dict:

 """simple docstring"""






 __lowerCAmelCase:     Any     			=   parameterized.to_safe_name('_'.join(str(SCREAMING_SNAKE_CASE	) 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, task_cmds.keys()))






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


     def 						UpperCAmelCase						(							self    :	int    ,						UpperCAmelCase    :	Any    ,						UpperCAmelCase    :	Optional[Any]      )							->			List[Any]:
      # return a ready to run train cmd
      if task not in task_cmds:
       raise ValueError(F'''don\'t know of task {task}, have {task_cmds.keys()}'''      )

      __lowerCAmelCase:     List[str]     			=   task_cmds[task]
      __lowerCAmelCase:     str     			=   F'''--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json'''.split()

      __lowerCAmelCase:     Dict     			=   self.get_auto_remove_tmp_dir()
      __lowerCAmelCase:     Optional[Any]     			=   F'''--output_dir {output_dir}'''.split()

      cmd += args_ds + args_out

      return cmd, output_dir


     @parameterized.expand(UpperCAmelCase    ,						name_func=UpperCAmelCase      )
     def 						UpperCAmelCase						(							self    :	Any    ,						UpperCAmelCase    :	Optional[Any]    ,						UpperCAmelCase    :	Dict      )							->			List[str]:
      # testing the ability to do a run followed by recovery of full fp32 weights

      __lowerCAmelCase			,							__lowerCAmelCase:     str     			=   self.get_task_cmd(UpperCAmelCase    ,						UpperCAmelCase      )

      # 1. generate the checkpoint
      cmd += "--save_steps 1".split()
      # keep for quick debug
      # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] + cmd)); die
      execute_subprocess_async(UpperCAmelCase    ,						env=self.get_env()      )

      # 2. test that the fp32 weights get reconsolidated
      __lowerCAmelCase:     Optional[Any]     			=   F'''{output_dir}/checkpoint-1'''
      __lowerCAmelCase:     Dict     			=   F'''{chkpt_dir}/out.bin'''
      __lowerCAmelCase:     Optional[Any]     			=   F'''{chkpt_dir}/zero_to_fp32.py {chkpt_dir} {recovered_model_path}'''
      # keep for quick debug
      # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
      subprocess.check_call(UpperCAmelCase    ,						shell=UpperCAmelCase      )
      assert os.path.exists(UpperCAmelCase      ), F'''{recovered_model_path} was not found'''

      # possibly could also test that the resulting saved model is usable but given that we use
      # random models we won't know if it's any good





