





'''simple docstring'''


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
					__lowercase							:				List[Any]					=       '''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 lowercase_		(				_lowercase     ,    _lowercase     ,    _lowercase=None     ,    _lowercase=None     ,    _lowercase=None     ,    _lowercase=None     ,    _lowercase=None     ,    _lowercase=None     ,    )       ->							Any:
					'''simple docstring'''

					if attention_mask is None:
										lowerCamelCase_   : Optional[Any]       =						np.where(input_ids != config.pad_token_id     ,    1     ,    0			)
					if decoder_attention_mask is None:
										lowerCamelCase_   : str       =						np.where(decoder_input_ids != config.pad_token_id     ,    1     ,    0			)
					if head_mask is None:
										lowerCamelCase_   : Any       =						np.ones((config.encoder_layers, config.encoder_attention_heads)			)
					if decoder_head_mask is None:
										lowerCamelCase_   : Any       =						np.ones((config.decoder_layers, config.decoder_attention_heads)			)
					if cross_attn_head_mask is None:
										lowerCamelCase_   : int       =						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 		__lowercase			:

			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=0.02     ,				):
								lowerCamelCase_   : Tuple       =						parent
								lowerCamelCase_   : Optional[Any]       =						batch_size
								lowerCamelCase_   : Any       =						seq_length
								lowerCamelCase_   : Dict       =						is_training
								lowerCamelCase_   : List[Any]       =						use_labels
								lowerCamelCase_   : List[Any]       =						vocab_size
								lowerCamelCase_   : str       =						hidden_size
								lowerCamelCase_   : int       =						num_hidden_layers
								lowerCamelCase_   : List[str]       =						num_attention_heads
								lowerCamelCase_   : Union[str, Any]       =						intermediate_size
								lowerCamelCase_   : Optional[Any]       =						hidden_act
								lowerCamelCase_   : str       =						hidden_dropout_prob
								lowerCamelCase_   : Optional[int]       =						attention_probs_dropout_prob
								lowerCamelCase_   : Tuple       =						max_position_embeddings
								lowerCamelCase_   : Union[str, Any]       =						eos_token_id
								lowerCamelCase_   : Optional[int]       =						pad_token_id
								lowerCamelCase_   : str       =						bos_token_id
								lowerCamelCase_   : Any       =						initializer_range

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

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

								lowerCamelCase_   : Optional[int]       =						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_   : Union[str, Any]       =						prepare_marian_inputs_dict(_A     ,				_A     ,				_A )
								return config, inputs_dict

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : Union[str, Any]       =						self.prepare_config_and_inputs()
								return config, inputs_dict

			def     UpperCAmelCase__	(self     ,				A     ,				A     ,				A ):
								lowerCamelCase_   : str       =						2_0
								lowerCamelCase_   : Any       =						model_class_name(_A )

								lowerCamelCase_   : List[Any]       =						model.encode(inputs_dict['''input_ids'''] )

								lowerCamelCase_   : Tuple       =						(
								    inputs_dict['''decoder_input_ids'''],
								    inputs_dict['''decoder_attention_mask'''],
								)

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

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

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

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

								lowerCamelCase_   : 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     ,				A     ,				A     ,				A ):
								lowerCamelCase_   : Union[str, Any]       =						2_0
								lowerCamelCase_   : Optional[int]       =						model_class_name(_A )

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

								lowerCamelCase_   : List[Any]       =						(
								    inputs_dict['''decoder_input_ids'''],
								    inputs_dict['''decoder_attention_mask'''],
								)

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

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

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

								lowerCamelCase_   : 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 		__lowercase			(					lowerCAmelCase__ ,       unittest.TestCase ,       lowerCAmelCase__      ):
			lowerCamelCase     :						List[Any]		 =     True
			lowerCamelCase     :						List[Any]		 =     (FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
			lowerCamelCase     :						Union[str, Any]		 =     (FlaxMarianMTModel,) if is_flax_available() else ()

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : Optional[int]       =						FlaxMarianModelTester(self )

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : 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 ):
								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 ):
								lowerCamelCase_   : Tuple       =						self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__ ):
																		lowerCamelCase_   : Dict       =						self._prepare_for_class(_A     ,				_A )
																		lowerCamelCase_   : Optional[int]       =						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''' ):
																							lowerCamelCase_   : Tuple       =						encode_jitted(**_A ).to_tuple()

																		with self.subTest('''JIT Disabled''' ):
																							with jax.disable_jit():
																												lowerCamelCase_   : List[Any]       =						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 ):
								lowerCamelCase_   : Optional[int]       =						self.model_tester.prepare_config_and_inputs_for_common()

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

																		lowerCamelCase_   : 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     ,				A     ,				A ):
																							return model.decode(
																							    decoder_input_ids=_A     ,				decoder_attention_mask=_A     ,				encoder_outputs=_A     ,				)

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

																		with self.subTest('''JIT Disabled''' ):
																							with jax.disable_jit():
																												lowerCamelCase_   : Union[str, 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 ):




								for model_class_name in self.all_model_classes:
													lowerCamelCase_   : Dict       =						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_   : Optional[int]       =						model(_A )
													self.assertIsNotNone(_A )

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

			@classmethod
			def     UpperCAmelCase__	(cls ):
								lowerCamelCase_   : Optional[int]       =						F"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
								return cls

			@cached_property
			def     UpperCAmelCase__	(self ):
								return MarianTokenizer.from_pretrained(self.model_name )

			@property
			def     UpperCAmelCase__	(self ):
								return self.tokenizer.eos_token_id

			@cached_property
			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : FlaxMarianMTModel       =						FlaxMarianMTModel.from_pretrained(self.model_name )
								self.assertEqual(model.config.decoder_start_token_id     ,				model.config.pad_token_id )
								return model

			def     UpperCAmelCase__	(self     ,				**A ):
								lowerCamelCase_   : Any       =						self.translate_src_text(**_A )
								self.assertListEqual(self.expected_text     ,				_A )






			def     UpperCAmelCase__	(self     ,				**A ):
								lowerCamelCase_   : List[Any]       =						self.tokenizer(self.src_text     ,				padding=_A     ,				return_tensors='''np'''     ,				**_A )
								lowerCamelCase_   : Union[str, Any]       =						self.model.generate(
								    model_inputs.input_ids     ,				attention_mask=model_inputs.attention_mask     ,				num_beams=2     ,				max_length=1_2_8     ,				).sequences
								lowerCamelCase_   : List[str]       =						self.tokenizer.batch_decode(_A     ,				skip_special_tokens=_A )
								return generated_words

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

			@slow
			def     UpperCAmelCase__	(self ):
								self._assert_generated_batch_equal_expected()

@require_flax
@require_sentencepiece
@require_tokenizers
class 		__lowercase			(					lowerCAmelCase__      ):
			lowerCamelCase     :						int		 =     "fr"
			lowerCamelCase     :						Optional[int]		 =     "en"
			lowerCamelCase     :						Any		 =     [
			    "Donnez moi le micro.",
			    "Tom et Mary étaient assis à une table.",  # Accents
			]
			lowerCamelCase     :						Union[str, Any]		 =     [
			    "Give me the microphone.",
			    "Tom and Mary were sitting at a table.",
			]

			@slow
			def     UpperCAmelCase__	(self ):
								self._assert_generated_batch_equal_expected()

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

			@slow
			def     UpperCAmelCase__	(self ):
								self._assert_generated_batch_equal_expected()

@require_flax
@require_sentencepiece
@require_tokenizers
class 		__lowercase			(					lowerCAmelCase__      ):
			lowerCamelCase     :						Dict		 =     "en"
			lowerCamelCase     :						str		 =     "de"
			lowerCamelCase     :						Tuple		 =     [
			    "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.",
			]
			lowerCamelCase     :						List[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 ):
								self._assert_generated_batch_equal_expected()

@require_flax
@require_sentencepiece
@require_tokenizers
class 		__lowercase			(					lowerCAmelCase__      ):
			lowerCamelCase     :						Tuple		 =     "en"
			lowerCamelCase     :						Optional[Any]		 =     "zh"
			lowerCamelCase     :						int		 =     ["My name is Wolfgang and I live in Berlin"]
			lowerCamelCase     :						Optional[Any]		 =     ["我叫沃尔夫冈 我住在柏林"]

			@slow
			def     UpperCAmelCase__	(self ):
								self._assert_generated_batch_equal_expected()

@require_flax
@require_sentencepiece
@require_tokenizers
class 		__lowercase			(					lowerCAmelCase__      ):
			lowerCamelCase     :						int		 =     "ru"
			lowerCamelCase     :						Union[str, Any]		 =     "fr"
			lowerCamelCase     :						Any		 =     ["Он показал мне рукопись своей новой пьесы."]
			lowerCamelCase     :						int		 =     ["Il m\'a montré le manuscrit de sa nouvelle pièce."]

			@slow
			def     UpperCAmelCase__	(self ):
								self._assert_generated_batch_equal_expected()

@require_flax
@require_sentencepiece
@require_tokenizers
class 		__lowercase			(					lowerCAmelCase__      ):
			lowerCamelCase     :						Optional[int]		 =     "en"
			lowerCamelCase     :						str		 =     "ROMANCE"
			lowerCamelCase     :						Dict		 =     [
			    ">>fr<< Don\'t spend so much time watching TV.",
			    ">>pt<< Your message has been sent.",
			    ">>es<< He\'s two years older than me.",
			]
			lowerCamelCase     :						Union[str, 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 ):
								self._assert_generated_batch_equal_expected()




'''simple docstring'''


import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_with_pabee

from transformers.testing_utils import TestCasePlus


logging.basicConfig(level=logging.DEBUG)

__lowercase							:				Optional[Any]					=       logging.getLogger()




def lowercase_		(				)       ->							Union[str, Any]:
					'''simple docstring'''

					lowerCamelCase_   : Any       =						argparse.ArgumentParser()
					parser.add_argument('''-f'''			)
					lowerCamelCase_   : Optional[int]       =						parser.parse_args()
					return args.f






class 		__lowercase			(					_lowercase      ):

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : Dict       =						logging.StreamHandler(sys.stdout )
								logger.addHandler(A )

								lowerCamelCase_   : int       =						self.get_auto_remove_tmp_dir()
								lowerCamelCase_   : Dict       =						F"""
            run_glue_with_pabee.py
            --model_type albert
            --model_name_or_path albert-base-v2
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --task_name mrpc
            --do_train
            --do_eval
            --per_gpu_train_batch_size=2
            --per_gpu_eval_batch_size=1
            --learning_rate=2e-5
            --max_steps=50
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

								with patch.object(A     ,				'''argv'''     ,				A ):
													lowerCamelCase_   : Optional[Any]       =						run_glue_with_pabee.main()
													for value in result.values():
																		self.assertGreaterEqual(A     ,				0.75 )


