



import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

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
						_SCREAMING_SNAKE_CASE      	=		"""platform"""

						import jax
						import jax.numpy as jnp

						from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
						    FlaxBlenderbotSmallForConditionalGeneration,
						    FlaxBlenderbotSmallModel,
						    shift_tokens_right,
						)


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a=None   , __a=None   , __a=None   , __a=None   , __a=None   , __a=None   , ):
	if attention_mask is None:
		snake_case_		:			Optional[int]											=	np.where(input_ids != config.pad_token_id   , 1   , 0   )
	if decoder_attention_mask is None:
		snake_case_		:			Dict											=	np.where(decoder_input_ids != config.pad_token_id   , 1   , 0   )
	if head_mask is None:
		snake_case_		:			List[str]											=	np.ones((config.encoder_layers, config.encoder_attention_heads)   )
	if decoder_head_mask is None:
		snake_case_		:			List[Any]											=	np.ones((config.decoder_layers, config.decoder_attention_heads)   )
	if cross_attn_head_mask is None:
		snake_case_		:			List[str]											=	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": attention_mask,
	}
class SCREAMING_SNAKE_CASE_						:






						def __init__(      self      :      Optional[int]		,   _A      :      List[str]		,   _A      :      Union[str, Any]=13		,   _A      :      Union[str, Any]=7		,   _A      :      Union[str, Any]=True		,   _A      :      List[Any]=False		,   _A      :      Dict=99		,   _A      :      Any=16		,   _A      :      Tuple=2		,   _A      :      Dict=4		,   _A      :      str=4		,   _A      :      List[Any]="gelu"		,   _A      :      int=0.1		,   _A      :      Optional[Any]=0.1		,   _A      :      List[str]=32		,   _A      :      List[str]=2		,   _A      :      List[Any]=1		,   _A      :      List[str]=0		,   _A      :      List[Any]=0.0_2		,   )							->    Dict:







							"""simple docstring"""


							snake_case_		:			Any											=	parent
							snake_case_		:			Tuple											=	batch_size
							snake_case_		:			Optional[Any]											=	seq_length
							snake_case_		:			Dict											=	is_training
							snake_case_		:			Optional[Any]											=	use_labels
							snake_case_		:			int											=	vocab_size
							snake_case_		:			Optional[Any]											=	hidden_size
							snake_case_		:			Optional[Any]											=	num_hidden_layers
							snake_case_		:			Dict											=	num_attention_heads
							snake_case_		:			Tuple											=	intermediate_size
							snake_case_		:			Optional[Any]											=	hidden_act
							snake_case_		:			Optional[Any]											=	hidden_dropout_prob
							snake_case_		:			Dict											=	attention_probs_dropout_prob
							snake_case_		:			Optional[int]											=	max_position_embeddings
							snake_case_		:			List[Any]											=	eos_token_id
							snake_case_		:			List[str]											=	pad_token_id
							snake_case_		:			int											=	bos_token_id
							snake_case_		:			List[Any]											=	initializer_range






						def UpperCAmelCase_				(      self      :      Dict	)							->    int:







							"""simple docstring"""


							snake_case_		:			Optional[Any]											=	np.clip(ids_tensor([self.batch_size, self.seq_length - 1]		,   self.vocab_size	)		,   3		,   self.vocab_size	)
							snake_case_		:			List[str]											=	np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)		,   dtype=np.intaa	))		,   -1	)

							snake_case_		:			List[str]											=	shift_tokens_right(_A		,   1		,   2	)

							snake_case_		:			Optional[Any]											=	BlenderbotSmallConfig(
							    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		,   initializer_range=self.initializer_range		,   use_cache=_A		,   )
							snake_case_		:			Optional[int]											=	prepare_blenderbot_inputs_dict(_A		,   _A		,   _A	)
							return config, inputs_dict






						def UpperCAmelCase_				(      self      :      Optional[Any]	)							->    Any:







							"""simple docstring"""


							snake_case_		:			int											=	self.prepare_config_and_inputs()
							return config, inputs_dict






						def UpperCAmelCase_				(      self      :      List[str]		,   _A      :      List[Any]		,   _A      :      Any		,   _A      :      Union[str, Any]	)							->    Any:







							"""simple docstring"""


							snake_case_		:			List[str]											=	20
							snake_case_		:			Union[str, Any]											=	model_class_name(_A	)

							snake_case_		:			Union[str, Any]											=	model.encode(inputs_dict['input_ids']	)

							snake_case_		:			Optional[Any]											=	(
							    inputs_dict['decoder_input_ids'],
							    inputs_dict['decoder_attention_mask'],
							)

							snake_case_		:			List[str]											=	model.init_cache(decoder_input_ids.shape[0]		,   _A		,   _A	)
							snake_case_		:			List[str]											=	jnp.ones((decoder_input_ids.shape[0], max_decoder_length)		,   dtype='i4'	)

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

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

							snake_case_		:			Dict											=	model.decode(_A		,   _A	)

							snake_case_		:			Dict											=	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 UpperCAmelCase_				(      self      :      Optional[int]		,   _A      :      Tuple		,   _A      :      int		,   _A      :      int	)							->    List[Any]:







							"""simple docstring"""


							snake_case_		:			str											=	20
							snake_case_		:			Any											=	model_class_name(_A	)

							snake_case_		:			Union[str, Any]											=	model.encode(inputs_dict['input_ids']	)

							snake_case_		:			str											=	(
							    inputs_dict['decoder_input_ids'],
							    inputs_dict['decoder_attention_mask'],
							)

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

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

							snake_case_		:			List[Any]											=	model.decode(
							    decoder_input_ids[:, :-1]		,   _A		,   decoder_attention_mask=_A		,   past_key_values=_A		,   decoder_position_ids=_A		,   )
							snake_case_		:			Optional[int]											=	jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,   dtype='i4'	)
							snake_case_		:			Dict											=	model.decode(
							    decoder_input_ids[:, -1:]		,   _A		,   past_key_values=outputs_cache.past_key_values		,   decoder_attention_mask=_A		,   decoder_position_ids=_A		,   )

							snake_case_		:			Optional[Any]											=	model.decode(_A		,   _A		,   decoder_attention_mask=_A	)

							snake_case_		:			Optional[Any]											=	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 SCREAMING_SNAKE_CASE_						(					unittest.TestCase					):
						__magic_name__:			Union[str, Any]       =       99






						def UpperCAmelCase_				(      self      :      int	)							->    Any:







							"""simple docstring"""


							snake_case_		:			Optional[Any]											=	np.array(
							    [
							        [71, 82, 18, 33, 46, 91, 2],
							        [68, 34, 26, 58, 30, 82, 2],
							        [5, 97, 17, 39, 94, 40, 2],
							        [76, 83, 94, 25, 70, 78, 2],
							        [87, 59, 41, 35, 48, 66, 2],
							        [55, 13, 16, 58, 5, 2, 1],  # note padding
							        [64, 27, 31, 51, 12, 75, 2],
							        [52, 64, 86, 17, 83, 39, 2],
							        [48, 61, 9, 24, 71, 82, 2],
							        [26, 1, 60, 48, 22, 13, 2],
							        [21, 5, 62, 28, 14, 76, 2],
							        [45, 98, 37, 86, 59, 48, 2],
							        [70, 70, 50, 9, 28, 0, 2],
							    ]		,   dtype=np.intaa		,   )

							snake_case_		:			Optional[int]											=	input_ids.shape[0]
							snake_case_		:			int											=	BlenderbotSmallConfig(
							    vocab_size=self.vocab_size		,   d_model=24		,   encoder_layers=2		,   decoder_layers=2		,   encoder_attention_heads=2		,   decoder_attention_heads=2		,   encoder_ffn_dim=32		,   decoder_ffn_dim=32		,   max_position_embeddings=48		,   eos_token_id=2		,   pad_token_id=1		,   bos_token_id=0		,   )
							return config, input_ids, batch_size






						def UpperCAmelCase_				(      self      :      Optional[Any]	)							->    int:







							"""simple docstring"""


							snake_case_		:			List[str]											=	self._get_config_and_data()
							snake_case_		:			Tuple											=	FlaxBlenderbotSmallForConditionalGeneration(_A	)
							snake_case_		:			Union[str, Any]											=	lm_model(input_ids=_A	)
							snake_case_		:			int											=	(batch_size, input_ids.shape[1], config.vocab_size)
							self.assertEqual(outputs['logits'].shape		,   _A	)






						def UpperCAmelCase_				(      self      :      Union[str, Any]	)							->    Dict:







							"""simple docstring"""


							snake_case_		:			int											=	BlenderbotSmallConfig(
							    vocab_size=self.vocab_size		,   d_model=14		,   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=48		,   )
							snake_case_		:			Dict											=	FlaxBlenderbotSmallForConditionalGeneration(_A	)
							snake_case_		:			Optional[Any]											=	np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]		,   dtype=np.intaa	)
							snake_case_		:			List[Any]											=	np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]		,   dtype=np.intaa	)
							snake_case_		:			List[str]											=	lm_model(input_ids=_A		,   decoder_input_ids=_A	)
							snake_case_		:			Optional[Any]											=	(*summary.shape, config.vocab_size)
							self.assertEqual(outputs['logits'].shape		,   _A	)






						def UpperCAmelCase_				(      self      :      Union[str, Any]	)							->    Tuple:







							"""simple docstring"""


							snake_case_		:			Union[str, Any]											=	np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]		,   dtype=np.intaa	)
							snake_case_		:			str											=	shift_tokens_right(_A		,   1		,   2	)
							snake_case_		:			Dict											=	np.equal(_A		,   1	).astype(np.floataa	).sum()
							snake_case_		:			int											=	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 SCREAMING_SNAKE_CASE_						(					snake_case_       ,				unittest.TestCase       ,				snake_case_					):
						__magic_name__:			Dict       =       True
						__magic_name__:			List[Any]       =       (
						    (
						        FlaxBlenderbotSmallModel,
						        FlaxBlenderbotSmallForConditionalGeneration,
						    )
						    if is_flax_available()
						    else ()
						)
						__magic_name__:			Optional[int]       =       (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()






						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    List[Any]:







							"""simple docstring"""


							snake_case_		:			Any											=	FlaxBlenderbotSmallModelTester(self	)






						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    List[str]:







							"""simple docstring"""


							snake_case_		:			Union[str, Any]											=	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 UpperCAmelCase_				(      self      :      str	)							->    List[Any]:







							"""simple docstring"""


							snake_case_		:			Tuple											=	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 UpperCAmelCase_				(      self      :      Optional[Any]	)							->    Dict:







							"""simple docstring"""


							snake_case_		:			List[str]											=	self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								with self.subTest(model_class.__name__	):
									snake_case_		:			Union[str, Any]											=	self._prepare_for_class(_A		,   _A	)
									snake_case_		:			List[Any]											=	model_class(_A	)

									@jax.jit
									def encode_jitted(_A      :      Any		,   _A      :      Any=None		,   **_A      :      Union[str, Any]	):
										return model.encode(input_ids=_A		,   attention_mask=_A	)

									with self.subTest('JIT Enabled'	):
										snake_case_		:			Optional[Any]											=	encode_jitted(**_A	).to_tuple()

									with self.subTest('JIT Disabled'	):
										with jax.disable_jit():
											snake_case_		:			Dict											=	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 UpperCAmelCase_				(      self      :      Optional[Any]	)							->    List[str]:







							"""simple docstring"""


							snake_case_		:			Optional[Any]											=	self.model_tester.prepare_config_and_inputs_for_common()

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

									snake_case_		:			List[Any]											=	{
									    '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      :      Tuple		,   _A      :      List[Any]		,   _A      :      List[str]	):
										return model.decode(
										    decoder_input_ids=_A		,   decoder_attention_mask=_A		,   encoder_outputs=_A		,   )

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

									with self.subTest('JIT Disabled'	):
										with jax.disable_jit():
											snake_case_		:			List[Any]											=	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 UpperCAmelCase_				(      self      :      Any	)							->    int:







							"""simple docstring"""


							for model_class_name in self.all_model_classes:
								snake_case_		:			Tuple											=	model_class_name.from_pretrained('facebook/blenderbot_small-90M'	)
								# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
								snake_case_		:			Tuple											=	np.ones((1, 1)	) * model.config.eos_token_id
								snake_case_		:			Tuple											=	model(_A	)
								self.assertIsNotNone(_A	)








# 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


_SCREAMING_SNAKE_CASE      	=		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)

_SCREAMING_SNAKE_CASE      	=		{"""base""": """patrickvonplaten/wav2vec2_tiny_random""", """robust""": """patrickvonplaten/wav2vec2_tiny_random_robust"""}

_SCREAMING_SNAKE_CASE      	=		"""zero2"""
_SCREAMING_SNAKE_CASE      	=		"""zero3"""
_SCREAMING_SNAKE_CASE      	=		[ZEROa, ZEROa]


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   ):
	# 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_		:			Tuple											=	parameterized.to_safe_name('_'.join(str(__a   ) for x in param.args   )   )
	return f"""{func.__name__}_{param_based_name}"""


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


@slow
@require_deepspeed
@require_torch_gpu
class SCREAMING_SNAKE_CASE_						(					snake_case_					):






						@parameterized.expand(_A		,   name_func=_A	)
						def UpperCAmelCase_				(      self      :      Tuple		,   _A      :      Optional[Any]		,   _A      :      Optional[int]	)							->    Dict:







							"""simple docstring"""


							self.run_and_check(
							    stage=_A		,   model=_A		,   distributed=_A		,   fpaa=_A		,   )






						@require_torch_multi_gpu
						@parameterized.expand(_A		,   name_func=_A	)
						def UpperCAmelCase_				(      self      :      int		,   _A      :      Any		,   _A      :      int	)							->    Dict:







							"""simple docstring"""


							self.run_and_check(
							    stage=_A		,   model=_A		,   distributed=_A		,   fpaa=_A		,   )






						@parameterized.expand(_A		,   name_func=_A	)
						def UpperCAmelCase_				(      self      :      str		,   _A      :      List[str]		,   _A      :      str	)							->    Tuple:







							"""simple docstring"""


							self.run_and_check(
							    stage=_A		,   model=_A		,   distributed=_A		,   fpaa=_A		,   )






						@require_torch_multi_gpu
						@parameterized.expand(_A		,   name_func=_A	)
						def UpperCAmelCase_				(      self      :      Optional[int]		,   _A      :      int		,   _A      :      Union[str, Any]	)							->    List[Any]:







							"""simple docstring"""


							self.run_and_check(
							    stage=_A		,   model=_A		,   distributed=_A		,   fpaa=_A		,   )






						def UpperCAmelCase_				(      self      :      Any		,   _A      :      Union[str, Any]	)							->    Any:







							"""simple docstring"""


							pass






						def UpperCAmelCase_				(      self      :      List[str]		,   _A      :      str		,   _A      :      str		,   _A      :      int = 10		,   _A      :      bool = True		,   _A      :      bool = True		,   _A      :      bool = True		,   )							->    Any:







							"""simple docstring"""


							snake_case_		:			Dict											=	models[model]

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

							self.do_checks(_A	)

							return output_dir






						def UpperCAmelCase_				(      self      :      Any		,   _A      :      str		,   _A      :      str		,   _A      :      int = 10		,   _A      :      int = 1		,   _A      :      bool = True		,   _A      :      bool = True		,   )							->    Dict:







							"""simple docstring"""


							snake_case_		:			str											=	self.get_auto_remove_tmp_dir('./xxx'		,   after=_A	)
							snake_case_		:			Tuple											=	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(_A	)}
            --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_		:			Any											=	F"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split()
							snake_case_		:			str											=	[F"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""]
							snake_case_		:			int											=	self.get_launcher(_A	)

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

							return output_dir






						def UpperCAmelCase_				(      self      :      Optional[Any]		,   _A      :      Optional[Any]=False	)							->    List[str]:







							"""simple docstring"""


							snake_case_		:			str											=	min(2		,   get_gpu_count()	) if distributed else 1
							return F"""deepspeed --num_nodes 1 --num_gpus {num_gpus}""".split()

