


import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MarianConfig, 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 MarianTokenizer
		from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right






def 	lowerCamelCase_       (			_a      ,  _a      ,  _a=None      ,  _a=None      ,  _a=None      ,  _a=None      ,  _a=None      ,  _a=None      ,  ):




		"""simple docstring"""






		if attention_mask is None:
				lowerCAmelCase__  :     List[Any]       	=   np.where(input_ids != config.pad_token_id      ,  1      ,  0			)
		if decoder_attention_mask is None:
				lowerCAmelCase__  :     List[Any]       	=   np.where(decoder_input_ids != config.pad_token_id      ,  1      ,  0			)
		if head_mask is None:
				lowerCAmelCase__  :     Dict       	=   np.ones((config.encoder_layers, config.encoder_attention_heads)			)
		if decoder_head_mask is None:
				lowerCAmelCase__  :     Tuple       	=   np.ones((config.decoder_layers, config.decoder_attention_heads)			)
		if cross_attn_head_mask is None:
				lowerCAmelCase__  :     Any       	=   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    _a				:




	def __init__(   self		:						Any						,   _SCREAMING_SNAKE_CASE		:						Optional[Any]						,   _SCREAMING_SNAKE_CASE		:						List[str]=13						,   _SCREAMING_SNAKE_CASE		:						List[Any]=7						,   _SCREAMING_SNAKE_CASE		:						Any=True						,   _SCREAMING_SNAKE_CASE		:						Tuple=False						,   _SCREAMING_SNAKE_CASE		:						int=99						,   _SCREAMING_SNAKE_CASE		:						List[Any]=16						,   _SCREAMING_SNAKE_CASE		:						List[str]=2						,   _SCREAMING_SNAKE_CASE		:						Any=4						,   _SCREAMING_SNAKE_CASE		:						Any=4						,   _SCREAMING_SNAKE_CASE		:						int="gelu"						,   _SCREAMING_SNAKE_CASE		:						List[str]=0.1						,   _SCREAMING_SNAKE_CASE		:						int=0.1						,   _SCREAMING_SNAKE_CASE		:						List[Any]=32						,   _SCREAMING_SNAKE_CASE		:						Union[str, Any]=2						,   _SCREAMING_SNAKE_CASE		:						Dict=1						,   _SCREAMING_SNAKE_CASE		:						List[Any]=0						,   _SCREAMING_SNAKE_CASE		:						str=0.02						,   )->			int:
			lowerCAmelCase__  :     Dict       	=   parent
			lowerCAmelCase__  :     Union[str, Any]       	=   batch_size
			lowerCAmelCase__  :     Optional[Any]       	=   seq_length
			lowerCAmelCase__  :     List[str]       	=   is_training
			lowerCAmelCase__  :     Union[str, Any]       	=   use_labels
			lowerCAmelCase__  :     Optional[int]       	=   vocab_size
			lowerCAmelCase__  :     Any       	=   hidden_size
			lowerCAmelCase__  :     List[str]       	=   num_hidden_layers
			lowerCAmelCase__  :     Optional[int]       	=   num_attention_heads
			lowerCAmelCase__  :     Dict       	=   intermediate_size
			lowerCAmelCase__  :     Any       	=   hidden_act
			lowerCAmelCase__  :     int       	=   hidden_dropout_prob
			lowerCAmelCase__  :     List[Any]       	=   attention_probs_dropout_prob
			lowerCAmelCase__  :     Tuple       	=   max_position_embeddings
			lowerCAmelCase__  :     Any       	=   eos_token_id
			lowerCAmelCase__  :     str       	=   pad_token_id
			lowerCAmelCase__  :     Tuple       	=   bos_token_id
			lowerCAmelCase__  :     List[Any]       	=   initializer_range




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

			lowerCAmelCase__  :     Optional[int]       	=   shift_tokens_right(A_						,   1						,   2						)

			lowerCAmelCase__  :     Any       	=   MarianConfig(
			    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_						,   )
			lowerCAmelCase__  :     List[str]       	=   prepare_marian_inputs_dict(A_						,   A_						,   A_						)
			return config, inputs_dict




	def 				UpperCAmelCase__(   self		:						List[Any]						)->			int:
			lowerCAmelCase__	,      lowerCAmelCase__  :     List[Any]       	=   self.prepare_config_and_inputs()
			return config, inputs_dict




	def 				UpperCAmelCase__(   self		:						List[str]						,   _SCREAMING_SNAKE_CASE		:						Any						,   _SCREAMING_SNAKE_CASE		:						int						,   _SCREAMING_SNAKE_CASE		:						Optional[int]						)->			Dict:
			lowerCAmelCase__  :     Optional[Any]       	=   20
			lowerCAmelCase__  :     str       	=   model_class_name(A_						)

			lowerCAmelCase__  :     Optional[Any]       	=   model.encode(inputs_dict['''input_ids''']						)

			lowerCAmelCase__	,      lowerCAmelCase__  :     int       	=   (
			    inputs_dict['''decoder_input_ids'''],
			    inputs_dict['''decoder_attention_mask'''],
			)

			lowerCAmelCase__  :     str       	=   model.init_cache(decoder_input_ids.shape[0]						,   A_						,   A_						)
			lowerCAmelCase__  :     Optional[int]       	=   jnp.ones((decoder_input_ids.shape[0], max_decoder_length)						,   dtype='''i4'''						)

			lowerCAmelCase__  :     Tuple       	=   jnp.broadcast_to(
			    jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :]						,   (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)						,   )
			lowerCAmelCase__  :     Optional[int]       	=   model.decode(
			    decoder_input_ids[:, :-1]						,   A_						,   decoder_attention_mask=A_						,   past_key_values=A_						,   decoder_position_ids=A_						,   )

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

			lowerCAmelCase__  :     Union[str, Any]       	=   model.decode(A_						,   A_						)

			lowerCAmelCase__  :     str       	=   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		:						Union[str, Any]						,   _SCREAMING_SNAKE_CASE		:						Optional[int]						,   _SCREAMING_SNAKE_CASE		:						str						,   _SCREAMING_SNAKE_CASE		:						str						)->			Union[str, Any]:
			lowerCAmelCase__  :     Union[str, Any]       	=   20
			lowerCAmelCase__  :     str       	=   model_class_name(A_						)

			lowerCAmelCase__  :     Optional[Any]       	=   model.encode(inputs_dict['''input_ids''']						)

			lowerCAmelCase__	,      lowerCAmelCase__  :     Dict       	=   (
			    inputs_dict['''decoder_input_ids'''],
			    inputs_dict['''decoder_attention_mask'''],
			)

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

			lowerCAmelCase__  :     List[Any]       	=   model.init_cache(decoder_input_ids.shape[0]						,   A_						,   A_						)
			lowerCAmelCase__  :     Optional[int]       	=   jnp.broadcast_to(
			    jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :]						,   (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)						,   )

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

			lowerCAmelCase__  :     Union[str, Any]       	=   model.decode(A_						,   A_						,   decoder_attention_mask=A_						)

			lowerCAmelCase__  :     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    _a				(							__A	,		unittest.TestCase	,		__A):
	_a    :    Tuple        =				True
	_a    :    Optional[Any]        =				(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
	_a    :    Tuple        =				(FlaxMarianMTModel,) if is_flax_available() else ()




	def 				UpperCAmelCase__(   self		:						Optional[Any]						)->			int:
			lowerCAmelCase__  :     Any       	=   FlaxMarianModelTester(self						)




	def 				UpperCAmelCase__(   self		:						Tuple						)->			List[str]:
			lowerCAmelCase__	,      lowerCAmelCase__  :     Optional[int]       	=   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		:						List[Any]						)->			Union[str, Any]:
			lowerCAmelCase__	,      lowerCAmelCase__  :     Optional[Any]       	=   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		:						Dict						)->			Optional[int]:
			lowerCAmelCase__	,      lowerCAmelCase__  :     Dict       	=   self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					with self.subTest(model_class.__name__						):
							lowerCAmelCase__  :     List[str]       	=   self._prepare_for_class(A_						,   A_						)
							lowerCAmelCase__  :     str       	=   model_class(A_						)

							@jax.jit
							def encode_jitted(_SCREAMING_SNAKE_CASE		:						Optional[int]						,   _SCREAMING_SNAKE_CASE		:						List[str]=None						,   **_SCREAMING_SNAKE_CASE		:						Optional[Any]						):
									return model.encode(input_ids=A_						,   attention_mask=A_						)

							with self.subTest('''JIT Enabled'''						):
									lowerCAmelCase__  :     Dict       	=   encode_jitted(**A_						).to_tuple()

							with self.subTest('''JIT Disabled'''						):
									with jax.disable_jit():
											lowerCAmelCase__  :     int       	=   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		:						List[str]						)->			int:
			lowerCAmelCase__	,      lowerCAmelCase__  :     Dict       	=   self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					with self.subTest(model_class.__name__						):
							lowerCAmelCase__  :     Tuple       	=   model_class(A_						)
							lowerCAmelCase__  :     str       	=   model.encode(inputs_dict['''input_ids''']						,   inputs_dict['''attention_mask''']						)

							lowerCAmelCase__  :     Optional[int]       	=   {
							    '''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(_SCREAMING_SNAKE_CASE		:						Tuple						,   _SCREAMING_SNAKE_CASE		:						Union[str, Any]						,   _SCREAMING_SNAKE_CASE		:						str						):
									return model.decode(
									    decoder_input_ids=A_						,   decoder_attention_mask=A_						,   encoder_outputs=A_						,   )

							with self.subTest('''JIT Enabled'''						):
									lowerCAmelCase__  :     Any       	=   decode_jitted(**A_						).to_tuple()

							with self.subTest('''JIT Disabled'''						):
									with jax.disable_jit():
											lowerCAmelCase__  :     Tuple       	=   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		:						str						)->			Any:



			for model_class_name in self.all_model_classes:
					lowerCAmelCase__  :     int       	=   model_class_name.from_pretrained('''Helsinki-NLP/opus-mt-en-de'''						)
					# FlaxMarianForSequenceClassification expects eos token in input_ids
					lowerCAmelCase__  :     Optional[int]       	=   np.ones((1, 1)						) * model.config.eos_token_id
					lowerCAmelCase__  :     Any       	=   model(A_						)
					self.assertIsNotNone(A_						)





@require_flax
@require_sentencepiece
@require_tokenizers
class    _a				(							unittest.TestCase):
	_a    :    Dict        =				None
	_a    :    List[Any]        =				None




	@classmethod
	def 				UpperCAmelCase__(   cls		:						Any						)->			Dict:
			lowerCAmelCase__  :     Optional[Any]       	=   F'Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'
			return cls




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




	@property
	def 				UpperCAmelCase__(   self		:						Union[str, Any]						)->			List[Any]:
			return self.tokenizer.eos_token_id




	@cached_property
	def 				UpperCAmelCase__(   self		:						Dict						)->			str:
			lowerCAmelCase__  :     Optional[Any]       	=   FlaxMarianMTModel.from_pretrained(self.model_name						)
			self.assertEqual(model.config.decoder_start_token_id						,   model.config.pad_token_id						)
			return model




	def 				UpperCAmelCase__(   self		:						Optional[Any]						,   **_SCREAMING_SNAKE_CASE		:						List[Any]						)->			List[str]:
			lowerCAmelCase__  :     Tuple       	=   self.translate_src_text(**A_						)
			self.assertListEqual(self.expected_text						,   A_						)




	def 				UpperCAmelCase__(   self		:						int						,   **_SCREAMING_SNAKE_CASE		:						List[str]						)->			Optional[int]:
			lowerCAmelCase__  :     List[Any]       	=   self.tokenizer(self.src_text						,   padding=A_						,   return_tensors='''np'''						,   **A_						)
			lowerCAmelCase__  :     Optional[int]       	=   self.model.generate(
			    model_inputs.input_ids						,   attention_mask=model_inputs.attention_mask						,   num_beams=2						,   max_length=128						,   ).sequences
			lowerCAmelCase__  :     Dict       	=   self.tokenizer.batch_decode(A_						,   skip_special_tokens=A_						)
			return generated_words





@require_flax
@require_sentencepiece
@require_tokenizers
class    _a				(							__A):
	_a    :    Any        =				'''en'''
	_a    :    Optional[Any]        =				'''fr'''
	_a    :    Union[str, Any]        =				[
	    '''I am a small frog.''',
	    '''Now I can forget the 100 words of german that I know.''',
	]
	_a    :    Union[str, Any]        =				[
	    '''Je suis une petite grenouille.''',
	    '''Maintenant, je peux oublier les 100 mots d\'allemand que je connais.''',
	]




	@slow
	def 				UpperCAmelCase__(   self		:						List[str]						)->			str:
			self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class    _a				(							__A):
	_a    :    List[Any]        =				'''fr'''
	_a    :    Dict        =				'''en'''
	_a    :    Optional[Any]        =				[
	    '''Donnez moi le micro.''',
	    '''Tom et Mary étaient assis à une table.''',  # Accents
	]
	_a    :    List[Any]        =				[
	    '''Give me the microphone.''',
	    '''Tom and Mary were sitting at a table.''',
	]




	@slow
	def 				UpperCAmelCase__(   self		:						Union[str, Any]						)->			List[Any]:
			self._assert_generated_batch_equal_expected()





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




	@slow
	def 				UpperCAmelCase__(   self		:						str						)->			str:
			self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class    _a				(							__A):
	_a    :    int        =				'''en'''
	_a    :    str        =				'''de'''
	_a    :    List[str]        =				[
	    '''I am a small frog.''',
	    '''Now I can forget the 100 words of german that I know.''',
	    '''Tom asked his teacher for advice.''',
	    '''That\'s how I would do it.''',
	    '''Tom really admired Mary\'s courage.''',
	    '''Turn around and close your eyes.''',
	]
	_a    :    Any        =				[
	    '''Ich bin ein kleiner Frosch.''',
	    '''Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.''',
	    '''Tom bat seinen Lehrer um Rat.''',
	    '''So würde ich das machen.''',
	    '''Tom bewunderte Marias Mut wirklich.''',
	    '''Drehen Sie sich um und schließen Sie die Augen.''',
	]




	@slow
	def 				UpperCAmelCase__(   self		:						Optional[Any]						)->			Dict:
			self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class    _a				(							__A):
	_a    :    Tuple        =				'''en'''
	_a    :    Optional[int]        =				'''zh'''
	_a    :    int        =				['''My name is Wolfgang and I live in Berlin''']
	_a    :    str        =				['''我叫沃尔夫冈 我住在柏林''']




	@slow
	def 				UpperCAmelCase__(   self		:						Union[str, Any]						)->			Tuple:
			self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class    _a				(							__A):
	_a    :    Tuple        =				'''ru'''
	_a    :    str        =				'''fr'''
	_a    :    Optional[Any]        =				['''Он показал мне рукопись своей новой пьесы.''']
	_a    :    List[Any]        =				['''Il m\'a montré le manuscrit de sa nouvelle pièce.''']




	@slow
	def 				UpperCAmelCase__(   self		:						List[str]						)->			Union[str, Any]:
			self._assert_generated_batch_equal_expected()





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




	@slow
	def 				UpperCAmelCase__(   self		:						Optional[Any]						)->			Tuple:
			self._assert_generated_batch_equal_expected()









"""simple docstring"""






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				:     Union[str, Any]								=  get_tests_dir("""fixtures""")
lowerCamelCase				:     str								=  os.path.join(dirname(get_tests_dir()))
lowerCamelCase				:     Union[str, Any]								=  dirname(os.path.abspath(__file__))

# default torch.distributed port
lowerCamelCase				:     Optional[Any]								=  """10999"""

lowerCamelCase				:     Optional[Any]								=  """t5-small"""

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


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
lowerCamelCase				:     Optional[int]								=  """hf-internal-testing/tiny-random-mt5"""
lowerCamelCase				:     Optional[int]								=  """hf-internal-testing/tiny-random-camembert"""
lowerCamelCase				:     Any								=  """hf-internal-testing/tiny-random-openai-gpt"""

# missing tokenizer files
lowerCamelCase				:     Optional[int]								=  """hf-internal-testing/tiny-random-convbert"""
lowerCamelCase				:     str								=  """hf-internal-testing/tiny-random-layoutlmv2"""
lowerCamelCase				:     List[Any]								=  """hf-internal-testing/tiny-random-hubert"""

# issues with tokenizer
lowerCamelCase				:     List[str]								=  """hf-internal-testing/tiny-random-ctrl"""
lowerCamelCase				:     Optional[int]								=  """hf-internal-testing/tiny-random-transfo-xl"""  # same as ctrl

# other issues with tiny models
lowerCamelCase				:     Tuple								=  """hf-internal-testing/tiny-random-ibert"""  # multiple issues with either mlm/qa/clas
lowerCamelCase				:     Dict								=  """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				:     List[str]								=  """hf-internal-testing/tiny-random-dpr"""
# - "dpr"  examples/research_projects/rag-end2end-retriever/
lowerCamelCase				:     str								=  """hf-internal-testing/tiny-random-rag"""
# - "rag" research_projects
lowerCamelCase				:     List[Any]								=  """"""
# - "luke" Entities classes - no plan to make such example
lowerCamelCase				:     Optional[int]								=  """hf-internal-testing/tiny-random-lxmert"""
# - "lxmert" doesn't work with run_qa.py
lowerCamelCase				:     Dict								=  """hf-internal-testing/tiny-random-clip"""
# - "clip" nothing under pytorch examples - XXX: Suraj is working on adding some - check by end of Sep
lowerCamelCase				:     Optional[Any]								=  """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				:     Union[str, Any]								=  """hf-internal-testing/tiny-random-tapas"""


def 							A__ (     UpperCamelCase__=False    ):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE        =			min(2       ,							get_gpu_count()    ) if distributed else 1
  _SCREAMING_SNAKE_CASE        =			os.environ.get('''DS_TEST_PORT'''       ,							UpperCamelCase__    )
  return F'''deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'''.split()


def 							A__ (     ):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE        =			F'''{FIXTURE_DIRECTORY}/tests_samples'''
  _SCREAMING_SNAKE_CASE        =			F'''{data_dir_samples}/wmt_en_ro'''
  _SCREAMING_SNAKE_CASE        =			F'''{data_dir_samples}/xsum'''
  _SCREAMING_SNAKE_CASE        =			'''
        --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.
  _SCREAMING_SNAKE_CASE        =			{
      '''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''',
      ],
  }

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

  _SCREAMING_SNAKE_CASE        =			{
      '''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
        ''',
  }

  _SCREAMING_SNAKE_CASE        =			get_launcher(distributed=UpperCamelCase__    )

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

lowerCamelCase				:     Any								=  """zero2"""
lowerCamelCase				:     List[Any]								=  """zero3"""

lowerCamelCase				:     int								=  [ZEROa, ZEROa]


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__    ):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE        =			parameterized.to_safe_name('''_'''.join(str(UpperCamelCase__    ) for x in param.args    )    )
  return F'''{func.__name__}_{param_based_name}'''


# Cartesian-product of zero stages with models to test
lowerCamelCase				:     Optional[Any]								=  list(itertools.product(stages, task_cmds.keys()))







@slow
@require_deepspeed
@require_torch_gpu
class    __snake_case(					__A       ):




    def   A   (   self    ,							A_    ,							A_			):





      '''simple docstring'''



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

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

      _SCREAMING_SNAKE_CASE        =			self.get_auto_remove_tmp_dir()
      _SCREAMING_SNAKE_CASE        =			F'''--output_dir {output_dir}'''.split()

      cmd += args_ds + args_out

      return cmd, output_dir




    @parameterized.expand(A_    ,							name_func=A_			)
    def   A   (   self    ,							A_    ,							A_			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE        =			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
      _SCREAMING_SNAKE_CASE        =			F'''{output_dir}/checkpoint-1'''
      _SCREAMING_SNAKE_CASE        =			F'''{chkpt_dir}/out.bin'''
      _SCREAMING_SNAKE_CASE        =			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
