



import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MBartConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
    import os

    # The slow tests are often failing with OOM error on GPU
    # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
    # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
    __lowerCamelCase								=   """platform"""

    import jax
    import jax.numpy as jnp

    from transformers import AutoTokenizer
    from transformers.models.mbart.modeling_flax_mbart import (
        FlaxMBartForConditionalGeneration,
        FlaxMBartForQuestionAnswering,
        FlaxMBartForSequenceClassification,
        FlaxMBartModel,
        shift_tokens_right,
    )




def       UpperCAmelCase__	(							__snake_case  ,			__snake_case  ,			__snake_case=None  ,			__snake_case=None  ,			__snake_case=None  ,			__snake_case=None  ,			__snake_case=None  ,			__snake_case=None  ,			)				->	List[str]:
  if attention_mask is None:
    _A          =      np.where(input_ids != config.pad_token_id  ,			1  ,			0							)
  if decoder_attention_mask is None:
    _A          =      np.where(decoder_input_ids != config.pad_token_id  ,			1  ,			0							)
  if head_mask is None:
    _A          =      np.ones((config.encoder_layers, config.encoder_attention_heads)							)
  if decoder_head_mask is None:
    _A          =      np.ones((config.decoder_layers, config.decoder_attention_heads)							)
  if cross_attn_head_mask is None:
    _A          =      np.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,
  }



class 						_snake_case (     unittest.TestCase			):





 """simple docstring"""



 def __init__(  self		,      a		,      a=1_3		,      a=7		,      a=True		,      a=False		,      a=9_9		,      a=1_6		,      a=2		,      a=4		,      a=4		,      a="gelu"		,      a=0.1		,      a=0.1		,      a=3_2		,      a=2		,      a=1		,      a=0		,      a=2		,      a=0.02		,      )		->							Optional[int]:




   """simple docstring"""







   _A          =      parent
   _A          =      batch_size
   _A          =      seq_length
   _A          =      is_training
   _A          =      use_labels
   _A          =      vocab_size
   _A          =      hidden_size
   _A          =      num_hidden_layers
   _A          =      num_attention_heads
   _A          =      intermediate_size
   _A          =      hidden_act
   _A          =      hidden_dropout_prob
   _A          =      attention_probs_dropout_prob
   _A          =      max_position_embeddings
   _A          =      eos_token_id
   _A          =      pad_token_id
   _A          =      bos_token_id
   _A          =      decoder_start_token_id
   _A          =      initializer_range
 def lowercase_						(  self   )		->							List[Any]:




   """simple docstring"""







   _A          =      np.clip(ids_tensor([self.batch_size, self.seq_length - 1]		,      self.vocab_size   )		,      3		,      self.vocab_size   )
   _A          =      np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)		,      dtype=np.intaa   ))		,      -1   )

   _A          =      shift_tokens_right(a		,      1   )

   _A          =      MBartConfig(
       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_id=self.eos_token_id		,      bos_token_id=self.bos_token_id		,      pad_token_id=self.pad_token_id		,      decoder_start_token_id=self.decoder_start_token_id		,      initializer_range=self.initializer_range		,      use_cache=a		,      )
   _A          =      prepare_mbart_inputs_dict(a		,      a		,      a   )
   return config, inputs_dict
 def lowercase_						(  self   )		->							Optional[int]:




   """simple docstring"""







   _A      ,				_A          =      self.prepare_config_and_inputs()
   return config, inputs_dict
 def lowercase_						(  self		,      a		,      a		,      a   )		->							List[Any]:




   """simple docstring"""







   _A          =      2_0
   _A          =      model_class_name(a   )

   _A          =      model.encode(inputs_dict['''input_ids''']   )

   _A      ,				_A          =      (
       inputs_dict['''decoder_input_ids'''],
       inputs_dict['''decoder_attention_mask'''],
   )

   _A          =      model.init_cache(decoder_input_ids.shape[0]		,      a		,      a   )
   _A          =      jnp.ones((decoder_input_ids.shape[0], max_decoder_length)		,      dtype='''i4'''   )

   _A          =      jnp.broadcast_to(
       jnp.arange(decoder_input_ids.shape[-1] - 1   )[None, :]		,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		,      )
   _A          =      model.decode(
       decoder_input_ids[:, :-1]		,      a		,      decoder_attention_mask=a		,      past_key_values=a		,      decoder_position_ids=a		,      )

   _A          =      jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,      dtype='''i4'''   )
   _A          =      model.decode(
       decoder_input_ids[:, -1:]		,      a		,      decoder_attention_mask=a		,      past_key_values=outputs_cache.past_key_values		,      decoder_position_ids=a		,      )

   _A          =      model.decode(a		,      a   )

   _A          =      np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])   )   )
   self.parent.assertTrue(diff < 1e-3		,      msg=f'''Max diff is {diff}'''   )



 def lowercase_						(  self		,      a		,      a		,      a   )		->							int:




   """simple docstring"""







   _A          =      2_0
   _A          =      model_class_name(a   )

   _A          =      model.encode(inputs_dict['''input_ids''']   )

   _A      ,				_A          =      (
       inputs_dict['''decoder_input_ids'''],
       inputs_dict['''decoder_attention_mask'''],
   )

   _A          =      jnp.concatenate(
       [
           decoder_attention_mask,
           jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])   ),
       ]		,      axis=-1		,      )

   _A          =      model.init_cache(decoder_input_ids.shape[0]		,      a		,      a   )
   _A          =      jnp.broadcast_to(
       jnp.arange(decoder_input_ids.shape[-1] - 1   )[None, :]		,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		,      )

   _A          =      model.decode(
       decoder_input_ids[:, :-1]		,      a		,      decoder_attention_mask=a		,      past_key_values=a		,      decoder_position_ids=a		,      )
   _A          =      jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,      dtype='''i4'''   )
   _A          =      model.decode(
       decoder_input_ids[:, -1:]		,      a		,      past_key_values=outputs_cache.past_key_values		,      decoder_attention_mask=a		,      decoder_position_ids=a		,      )

   _A          =      model.decode(a		,      a		,      decoder_attention_mask=a   )

   _A          =      np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])   )   )
   self.parent.assertTrue(diff < 1e-3		,      msg=f'''Max diff is {diff}'''   )







@require_flax
class 						_snake_case (     unittest.TestCase			):





 """simple docstring"""



 lowerCamelCase_       =     9_9
 def lowercase_						(  self   )		->							int:




   """simple docstring"""







   _A          =      np.array(
       [
           [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
           [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
           [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
           [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
           [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
           [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
           [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
           [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
           [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
           [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
           [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
           [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
           [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
       ]		,      dtype=np.intaa		,      )

   _A          =      input_ids.shape[0]
   _A          =      MBartConfig(
       vocab_size=self.vocab_size		,      d_model=2_4		,      encoder_layers=2		,      decoder_layers=2		,      encoder_attention_heads=2		,      decoder_attention_heads=2		,      encoder_ffn_dim=3_2		,      decoder_ffn_dim=3_2		,      max_position_embeddings=4_8		,      eos_token_id=2		,      pad_token_id=1		,      bos_token_id=0		,      )
   return config, input_ids, batch_size
 def lowercase_						(  self   )		->							List[str]:




   """simple docstring"""







   _A      ,				_A      ,				_A          =      self._get_config_and_data()
   _A          =      FlaxMBartForSequenceClassification(a   )
   _A          =      model(input_ids=a		,      decoder_input_ids=a   )
   _A          =      (batch_size, config.num_labels)
   self.assertEqual(outputs['''logits'''].shape		,      a   )
 def lowercase_						(  self   )		->							Union[str, Any]:




   """simple docstring"""







   _A      ,				_A      ,				_A          =      self._get_config_and_data()
   _A          =      FlaxMBartForQuestionAnswering(a   )
   _A          =      model(input_ids=a   )

   self.assertEqual(outputs['''start_logits'''].shape		,      input_ids.shape   )
   self.assertEqual(outputs['''end_logits'''].shape		,      input_ids.shape   )
 def lowercase_						(  self   )		->							List[str]:




   """simple docstring"""







   _A      ,				_A      ,				_A          =      self._get_config_and_data()
   _A          =      FlaxMBartForConditionalGeneration(a   )
   _A          =      lm_model(input_ids=a   )
   _A          =      (batch_size, input_ids.shape[1], config.vocab_size)
   self.assertEqual(outputs['''logits'''].shape		,      a   )
 def lowercase_						(  self   )		->							int:




   """simple docstring"""







   _A          =      MBartConfig(
       vocab_size=self.vocab_size		,      d_model=1_4		,      encoder_layers=2		,      decoder_layers=2		,      encoder_attention_heads=2		,      decoder_attention_heads=2		,      encoder_ffn_dim=8		,      decoder_ffn_dim=8		,      max_position_embeddings=4_8		,      )
   _A          =      FlaxMBartForConditionalGeneration(a   )
   _A          =      np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]]		,      dtype=np.intaa   )
   _A          =      np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]		,      dtype=np.intaa   )
   _A          =      lm_model(input_ids=a		,      decoder_input_ids=a   )
   _A          =      (*summary.shape, config.vocab_size)
   self.assertEqual(outputs['''logits'''].shape		,      a   )



 def lowercase_						(  self   )		->							Optional[int]:




   """simple docstring"""







   _A          =      np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]]		,      dtype=np.intaa   )
   _A          =      shift_tokens_right(a		,      1   )
   _A          =      np.equal(a		,      1   ).astype(np.floataa   ).sum()
   _A          =      np.equal(a		,      1   ).astype(np.floataa   ).sum()
   self.assertEqual(shifted.shape		,      input_ids.shape   )
   self.assertEqual(a		,      n_pad_before - 1   )
   self.assertTrue(np.equal(shifted[:, 0]		,      2   ).all()   )







@require_flax
class 						_snake_case (     lowerCamelCase   ,unittest.TestCase   ,lowerCamelCase			):





 """simple docstring"""



 lowerCamelCase_       =     True
 lowerCamelCase_       =     (
     (
         FlaxMBartModel,
         FlaxMBartForConditionalGeneration,
         FlaxMBartForSequenceClassification,
         FlaxMBartForQuestionAnswering,
     )
     if is_flax_available()
     else ()
 )
 lowerCamelCase_       =     (FlaxMBartForConditionalGeneration,) if is_flax_available() else ()
 def lowercase_						(  self   )		->							Tuple:




   """simple docstring"""







   _A          =      FlaxMBartModelTester(self   )
 def lowercase_						(  self   )		->							Union[str, Any]:




   """simple docstring"""







   _A      ,				_A          =      self.model_tester.prepare_config_and_inputs()
   for model_class in self.all_model_classes:
     self.model_tester.check_use_cache_forward(a		,      a		,      a   )
 def lowercase_						(  self   )		->							int:




   """simple docstring"""







   _A      ,				_A          =      self.model_tester.prepare_config_and_inputs()
   for model_class in self.all_model_classes:
     self.model_tester.check_use_cache_forward_with_attn_mask(a		,      a		,      a   )
 def lowercase_						(  self   )		->							List[Any]:




   """simple docstring"""







   _A      ,				_A          =      self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     with self.subTest(model_class.__name__   ):
       _A          =      self._prepare_for_class(a		,      a   )
       _A          =      model_class(a   )

       @jax.jit
       def encode_jitted(a		,      a=None		,      **a   ):
         return model.encode(input_ids=a		,      attention_mask=a   )

       with self.subTest('''JIT Enabled'''   ):
         _A          =      encode_jitted(**a   ).to_tuple()

       with self.subTest('''JIT Disabled'''   ):
         with jax.disable_jit():
           _A          =      encode_jitted(**a   ).to_tuple()

       self.assertEqual(len(a   )		,      len(a   )   )
       for jitted_output, output in zip(a		,      a   ):
         self.assertEqual(jitted_output.shape		,      output.shape   )
 def lowercase_						(  self   )		->							Optional[Any]:




   """simple docstring"""







   _A      ,				_A          =      self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     with self.subTest(model_class.__name__   ):
       _A          =      model_class(a   )
       _A          =      model.encode(inputs_dict['''input_ids''']		,      inputs_dict['''attention_mask''']   )

       _A          =      {
           '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
           '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
           '''encoder_outputs''': encoder_outputs,
       }

       @jax.jit
       def decode_jitted(a		,      a		,      a   ):
         return model.decode(
             decoder_input_ids=a		,      decoder_attention_mask=a		,      encoder_outputs=a		,      )

       with self.subTest('''JIT Enabled'''   ):
         _A          =      decode_jitted(**a   ).to_tuple()

       with self.subTest('''JIT Disabled'''   ):
         with jax.disable_jit():
           _A          =      decode_jitted(**a   ).to_tuple()

       self.assertEqual(len(a   )		,      len(a   )   )
       for jitted_output, output in zip(a		,      a   ):
         self.assertEqual(jitted_output.shape		,      output.shape   )



 @slow
 def lowercase_						(  self   )		->							int:




   """simple docstring"""







   for model_class_name in self.all_model_classes:
     _A          =      model_class_name.from_pretrained('''facebook/mbart-large-cc25'''		,      from_pt=a   )
     # FlaxMBartForSequenceClassification expects eos token in input_ids
     _A          =      np.ones((1, 1)   ) * model.config.eos_token_id
     _A          =      model(a   )
     self.assertIsNotNone(a   )







@require_flax
@require_sentencepiece
@require_tokenizers
class 						_snake_case (     unittest.TestCase			):





 """simple docstring"""



 lowerCamelCase_       =     [
     ''' UN Chief Says There Is No Military Solution in Syria''',
 ]
 lowerCamelCase_       =     [
     '''Şeful ONU declară că nu există o soluţie militară în Siria''',
 ]
 lowerCamelCase_       =     '''facebook/mbart-large-en-ro'''
 @cached_property
 def lowercase_						(  self   )		->							List[str]:




   """simple docstring"""







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




   """simple docstring"""







   _A          =      FlaxMBartForConditionalGeneration.from_pretrained(self.model_name		,      from_pt=a   )
   return model
 def lowercase_						(  self		,      **a   )		->							Any:




   """simple docstring"""







   _A          =      self.translate_src_text(**a   )
   self.assertListEqual(self.expected_text		,      a   )
 def lowercase_						(  self		,      **a   )		->							Optional[int]:




   """simple docstring"""







   _A          =      self.tokenizer(self.src_text		,      **a		,      return_tensors='''np'''   )
   _A          =      self.model.generate(
       model_inputs.input_ids		,      attention_mask=model_inputs.attention_mask		,      decoder_start_token_id=self.tokenizer.lang_code_to_id['''ro_RO''']		,      early_stopping=a		,      num_beams=2		,      ).sequences
   _A          =      self.tokenizer.batch_decode(a		,      skip_special_tokens=a   )
   return generated_words



 @slow
 def lowercase_						(  self   )		->							Union[str, Any]:




   """simple docstring"""







   self._assert_generated_batch_equal_expected()




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)

__lowerCamelCase								=   get_tests_dir("""fixtures""")
__lowerCamelCase								=   os.path.join(dirname(get_tests_dir()))
__lowerCamelCase								=   dirname(os.path.abspath(__file__))

# default torch.distributed port
__lowerCamelCase								=   """10999"""

__lowerCamelCase								=   """t5-small"""

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


# *** To Fix ***


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

# missing tokenizer files
__lowerCamelCase								=   """hf-internal-testing/tiny-random-convbert"""
__lowerCamelCase								=   """hf-internal-testing/tiny-random-layoutlmv2"""
__lowerCamelCase								=   """hf-internal-testing/tiny-random-hubert"""

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

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

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




def       UpperCAmelCase__	(							__snake_case=False							)				->	List[Any]:
  # 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)
  _A          =      min(2  ,			get_gpu_count()							) if distributed else 1
  _A          =      os.environ.get('''DS_TEST_PORT'''  ,			__snake_case							)
  return F'''deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'''.split()




def       UpperCAmelCase__	(							)				->	Dict:
  _A          =      F'''{FIXTURE_DIRECTORY}/tests_samples'''
  _A          =      F'''{data_dir_samples}/wmt_en_ro'''
  _A          =      F'''{data_dir_samples}/xsum'''
  _A          =      '''
        --do_train
        --max_train_samples 4
        --per_device_train_batch_size 2
        --num_train_epochs 1
        --fp16
        --report_to none
        --overwrite_output_dir
        '''.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.
  _A          =      {
      '''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''',
      ],
  }

  _A          =      F'''{ROOT_DIRECTORY}/examples/pytorch'''

  _A          =      {
      '''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
        ''',
  }

  _A          =      get_launcher(distributed=__snake_case							)

  _A          =      {}
  for task, args in tasks.items():
    _A          =      args.split()
    for model in tasksamodels[task]:
      _A          =      globals()[F'''{model.upper().replace("-"  ,			"_"							)}_TINY''']
      _A          =      F'''--model_name_or_path {model_name}'''.split()
      _A          =      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


__lowerCamelCase								=   make_task_cmds()

__lowerCamelCase								=   """zero2"""
__lowerCamelCase								=   """zero3"""

__lowerCamelCase								=   [ZEROa, ZEROa]




def       UpperCAmelCase__	(							__snake_case  ,			__snake_case  ,			__snake_case							)				->	str:
  # 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
  _A          =      parameterized.to_safe_name('''_'''.join(str(__snake_case							) for x in param.args							)							)
  return F'''{func.__name__}_{param_based_name}'''


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



@slow
@require_deepspeed
@require_torch_gpu
class 						_snake_case (     lowerCamelCase			):





 """simple docstring"""



 def lowercase_						(  self		,      a		,      a   )		->							Dict:




   """simple docstring"""







   if task not in task_cmds:
     raise ValueError(f'''don\'t know of task {task}, have {task_cmds.keys()}'''   )

   _A          =      task_cmds[task]
   _A          =      f'''--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json'''.split()

   _A          =      self.get_auto_remove_tmp_dir()
   _A          =      f'''--output_dir {output_dir}'''.split()

   cmd += args_ds + args_out

   return cmd, output_dir



 @parameterized.expand(a		,      name_func=a   )
 def lowercase_						(  self		,      a		,      a   )		->							Optional[Any]:




   """simple docstring"""







   _A      ,				_A          =      self.get_task_cmd(a		,      a   )

   # 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(a		,      env=self.get_env()   )

   # 2. test that the fp32 weights get reconsolidated
   _A          =      f'''{output_dir}/checkpoint-1'''
   _A          =      f'''{chkpt_dir}/out.bin'''
   _A          =      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(a		,      shell=a   )
   assert os.path.exists(a   ), 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