


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
					UpperCamelCase			:					Tuple         =							'''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 				UpperCamelCase_	(					__snake_case      :    Optional[Any]						,      __snake_case      :    Union[str, Any]						,      __snake_case      :    Union[str, Any]=None						,      __snake_case      :    Optional[Any]=None						,      __snake_case      :    Any=None						,      __snake_case      :    Tuple=None						,      __snake_case      :    Dict=None						,      __snake_case      :    List[Any]=None						,      )->   Tuple:




						'''simple docstring'''
						if attention_mask is None:
												__magic_name__					=      np.where(input_ids != config.pad_token_id						,      1						,      0  )
						if decoder_attention_mask is None:
												__magic_name__					=      np.where(decoder_input_ids != config.pad_token_id						,      1						,      0  )
						if head_mask is None:
												__magic_name__					=      np.ones((config.encoder_layers, config.encoder_attention_heads)  )
						if decoder_head_mask is None:
												__magic_name__					=      np.ones((config.decoder_layers, config.decoder_attention_heads)  )
						if cross_attn_head_mask is None:
												__magic_name__					=      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      __snake_case				:
					def __init__(						self       :						Union[str, Any]	,      _lowerCAmelCase       :						Dict	,      _lowerCAmelCase       :						Any=1_3	,      _lowerCAmelCase       :						Tuple=7	,      _lowerCAmelCase       :						Any=True	,      _lowerCAmelCase       :						str=False	,      _lowerCAmelCase       :						List[Any]=9_9	,      _lowerCAmelCase       :						Optional[int]=1_6	,      _lowerCAmelCase       :						Optional[Any]=2	,      _lowerCAmelCase       :						Optional[int]=4	,      _lowerCAmelCase       :						str=4	,      _lowerCAmelCase       :						Union[str, Any]="gelu"	,      _lowerCAmelCase       :						str=0.1	,      _lowerCAmelCase       :						Dict=0.1	,      _lowerCAmelCase       :						Tuple=3_2	,      _lowerCAmelCase       :						List[Any]=2	,      _lowerCAmelCase       :						Dict=1	,      _lowerCAmelCase       :						Optional[Any]=0	,      _lowerCAmelCase       :						List[Any]=0.02	,      )							->					List[str]:



											'''simple docstring'''





											__magic_name__					=      parent
											__magic_name__					=      batch_size
											__magic_name__					=      seq_length
											__magic_name__					=      is_training
											__magic_name__					=      use_labels
											__magic_name__					=      vocab_size
											__magic_name__					=      hidden_size
											__magic_name__					=      num_hidden_layers
											__magic_name__					=      num_attention_heads
											__magic_name__					=      intermediate_size
											__magic_name__					=      hidden_act
											__magic_name__					=      hidden_dropout_prob
											__magic_name__					=      attention_probs_dropout_prob
											__magic_name__					=      max_position_embeddings
											__magic_name__					=      eos_token_id
											__magic_name__					=      pad_token_id
											__magic_name__					=      bos_token_id
											__magic_name__					=      initializer_range
					def 	a			(						self       :						Optional[int]  )							->					Optional[int]:



											'''simple docstring'''





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

											__magic_name__					=      shift_tokens_right(_lowerCAmelCase	,      1	,      2  )

											__magic_name__					=      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=_lowerCAmelCase	,      )
											__magic_name__					=      prepare_marian_inputs_dict(_lowerCAmelCase	,      _lowerCAmelCase	,      _lowerCAmelCase  )
											return config, inputs_dict
					def 	a			(						self       :						Optional[Any]  )							->					List[str]:



											'''simple docstring'''





											__magic_name__		,      __magic_name__					=      self.prepare_config_and_inputs()
											return config, inputs_dict
					def 	a			(						self       :						List[Any]	,      _lowerCAmelCase       :						Any	,      _lowerCAmelCase       :						str	,      _lowerCAmelCase       :						int  )							->					Dict:



											'''simple docstring'''





											__magic_name__					=      2_0
											__magic_name__					=      model_class_name(_lowerCAmelCase  )

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

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

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

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

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

											__magic_name__					=      model.decode(_lowerCAmelCase	,      _lowerCAmelCase  )

											__magic_name__					=      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 	a			(						self       :						Dict	,      _lowerCAmelCase       :						str	,      _lowerCAmelCase       :						Union[str, Any]	,      _lowerCAmelCase       :						int  )							->					int:



											'''simple docstring'''





											__magic_name__					=      2_0
											__magic_name__					=      model_class_name(_lowerCAmelCase  )

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

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

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

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

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

											__magic_name__					=      model.decode(_lowerCAmelCase	,      _lowerCAmelCase	,      decoder_attention_mask=_lowerCAmelCase  )

											__magic_name__					=      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				(   _UpperCAmelCase	,      unittest.TestCase	,      _UpperCAmelCase):
					SCREAMING_SNAKE_CASE_		:       Dict								=			True
					SCREAMING_SNAKE_CASE_		:       Optional[Any]								=			(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
					SCREAMING_SNAKE_CASE_		:       Dict								=			(FlaxMarianMTModel,) if is_flax_available() else ()
					def 	a			(						self       :						Dict  )							->					Union[str, Any]:



											'''simple docstring'''





											__magic_name__					=      FlaxMarianModelTester(self  )
					def 	a			(						self       :						str  )							->					Union[str, Any]:



											'''simple docstring'''





											__magic_name__		,      __magic_name__					=      self.model_tester.prepare_config_and_inputs()
											for model_class in self.all_model_classes:
																	self.model_tester.check_use_cache_forward(_lowerCAmelCase	,      _lowerCAmelCase	,      _lowerCAmelCase  )
					def 	a			(						self       :						List[Any]  )							->					str:



											'''simple docstring'''





											__magic_name__		,      __magic_name__					=      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(_lowerCAmelCase	,      _lowerCAmelCase	,      _lowerCAmelCase  )
					def 	a			(						self       :						Optional[Any]  )							->					List[Any]:



											'''simple docstring'''





											__magic_name__		,      __magic_name__					=      self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																	with self.subTest(model_class.__name__  ):
																							__magic_name__					=      self._prepare_for_class(_lowerCAmelCase	,      _lowerCAmelCase  )
																							__magic_name__					=      model_class(_lowerCAmelCase  )

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

																							with self.subTest('JIT Enabled'  ):
																													__magic_name__					=      encode_jitted(**_lowerCAmelCase  ).to_tuple()

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

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



											'''simple docstring'''





											__magic_name__		,      __magic_name__					=      self.model_tester.prepare_config_and_inputs_for_common()

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

																							__magic_name__					=      {
																							    '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(_lowerCAmelCase       :						int	,      _lowerCAmelCase       :						Tuple	,      _lowerCAmelCase       :						Optional[Any]  ):
																													return model.decode(
																													    decoder_input_ids=_lowerCAmelCase	,      decoder_attention_mask=_lowerCAmelCase	,      encoder_outputs=_lowerCAmelCase	,      )

																							with self.subTest('JIT Enabled'  ):
																													__magic_name__					=      decode_jitted(**_lowerCAmelCase  ).to_tuple()

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

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







					@slow
					def 	a			(						self       :						Any  )							->					Dict:



											'''simple docstring'''





											for model_class_name in self.all_model_classes:
																	__magic_name__					=      model_class_name.from_pretrained('Helsinki-NLP/opus-mt-en-de'  )
																	# FlaxMarianForSequenceClassification expects eos token in input_ids
																	__magic_name__					=      np.ones((1, 1)  ) * model.config.eos_token_id
																	__magic_name__					=      model(_lowerCAmelCase  )
																	self.assertIsNotNone(_lowerCAmelCase  )







@require_flax
@require_sentencepiece
@require_tokenizers
class      __snake_case				(   unittest.TestCase):
					SCREAMING_SNAKE_CASE_		:       str								=			None
					SCREAMING_SNAKE_CASE_		:       Dict								=			None
					@classmethod
					def 	a			(						cls       :						List[str]  )							->					None:



											'''simple docstring'''





											__magic_name__					=      F'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
											return cls
					@cached_property
					def 	a			(						self       :						List[Any]  )							->					Dict:



											'''simple docstring'''





											return MarianTokenizer.from_pretrained(self.model_name  )
					@property
					def 	a			(						self       :						Union[str, Any]  )							->					int:



											'''simple docstring'''





											return self.tokenizer.eos_token_id
					@cached_property
					def 	a			(						self       :						Optional[int]  )							->					Any:



											'''simple docstring'''





											__magic_name__					=      FlaxMarianMTModel.from_pretrained(self.model_name  )
											self.assertEqual(model.config.decoder_start_token_id	,      model.config.pad_token_id  )
											return model
					def 	a			(						self       :						int	,      **_lowerCAmelCase       :						Union[str, Any]  )							->					Any:



											'''simple docstring'''





											__magic_name__					=      self.translate_src_text(**_lowerCAmelCase  )
											self.assertListEqual(self.expected_text	,      _lowerCAmelCase  )







					def 	a			(						self       :						Tuple	,      **_lowerCAmelCase       :						List[Any]  )							->					str:



											'''simple docstring'''





											__magic_name__					=      self.tokenizer(self.src_text	,      padding=_lowerCAmelCase	,      return_tensors='np'	,      **_lowerCAmelCase  )
											__magic_name__					=      self.model.generate(
											    model_inputs.input_ids	,      attention_mask=model_inputs.attention_mask	,      num_beams=2	,      max_length=1_2_8	,      ).sequences
											__magic_name__					=      self.tokenizer.batch_decode(_lowerCAmelCase	,      skip_special_tokens=_lowerCAmelCase  )
											return generated_words







@require_flax
@require_sentencepiece
@require_tokenizers
class      __snake_case				(   _UpperCAmelCase):
					SCREAMING_SNAKE_CASE_		:       Dict								=			"en"
					SCREAMING_SNAKE_CASE_		:       Tuple								=			"fr"
					SCREAMING_SNAKE_CASE_		:       Any								=			[
					    "I am a small frog.",
					    "Now I can forget the 100 words of german that I know.",
					]
					SCREAMING_SNAKE_CASE_		:       Any								=			[
					    "Je suis une petite grenouille.",
					    "Maintenant, je peux oublier les 100 mots d'allemand que je connais.",
					]
					@slow
					def 	a			(						self       :						Tuple  )							->					Dict:



											'''simple docstring'''





											self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class      __snake_case				(   _UpperCAmelCase):
					SCREAMING_SNAKE_CASE_		:       int								=			"fr"
					SCREAMING_SNAKE_CASE_		:       int								=			"en"
					SCREAMING_SNAKE_CASE_		:       Union[str, Any]								=			[
					    "Donnez moi le micro.",
					    "Tom et Mary étaient assis à une table.",  # Accents
					]
					SCREAMING_SNAKE_CASE_		:       Dict								=			[
					    "Give me the microphone.",
					    "Tom and Mary were sitting at a table.",
					]
					@slow
					def 	a			(						self       :						Optional[int]  )							->					Tuple:



											'''simple docstring'''





											self._assert_generated_batch_equal_expected()







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



											'''simple docstring'''





											self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class      __snake_case				(   _UpperCAmelCase):
					SCREAMING_SNAKE_CASE_		:       List[str]								=			"en"
					SCREAMING_SNAKE_CASE_		:       Optional[int]								=			"de"
					SCREAMING_SNAKE_CASE_		:       Optional[Any]								=			[
					    "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.",
					]
					SCREAMING_SNAKE_CASE_		:       str								=			[
					    "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 	a			(						self       :						int  )							->					Any:



											'''simple docstring'''





											self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class      __snake_case				(   _UpperCAmelCase):
					SCREAMING_SNAKE_CASE_		:       Optional[int]								=			"en"
					SCREAMING_SNAKE_CASE_		:       Optional[Any]								=			"zh"
					SCREAMING_SNAKE_CASE_		:       Optional[Any]								=			["My name is Wolfgang and I live in Berlin"]
					SCREAMING_SNAKE_CASE_		:       str								=			["我叫沃尔夫冈 我住在柏林"]
					@slow
					def 	a			(						self       :						Any  )							->					Tuple:



											'''simple docstring'''





											self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class      __snake_case				(   _UpperCAmelCase):
					SCREAMING_SNAKE_CASE_		:       str								=			"ru"
					SCREAMING_SNAKE_CASE_		:       List[Any]								=			"fr"
					SCREAMING_SNAKE_CASE_		:       List[Any]								=			["Он показал мне рукопись своей новой пьесы."]
					SCREAMING_SNAKE_CASE_		:       Tuple								=			["Il m'a montré le manuscrit de sa nouvelle pièce."]
					@slow
					def 	a			(						self       :						Dict  )							->					Any:



											'''simple docstring'''





											self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class      __snake_case				(   _UpperCAmelCase):
					SCREAMING_SNAKE_CASE_		:       Optional[Any]								=			"en"
					SCREAMING_SNAKE_CASE_		:       Tuple								=			"ROMANCE"
					SCREAMING_SNAKE_CASE_		:       Optional[Any]								=			[
					    ">>fr<< Don't spend so much time watching TV.",
					    ">>pt<< Your message has been sent.",
					    ">>es<< He's two years older than me.",
					]
					SCREAMING_SNAKE_CASE_		:       Optional[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 	a			(						self       :						int  )							->					Any:



											'''simple docstring'''





											self._assert_generated_batch_equal_expected()





def 				UpperCamelCase_	(					__snake_case      :    set						,      __snake_case      :    set  )->   set:




						'''simple docstring'''
						assert isinstance(__snake_case						,      __snake_case  ), F'''The input value of [set_a={set_a}] is not a set'''
						assert isinstance(__snake_case						,      __snake_case  ), F'''The input value of [set_b={set_b}] is not a set'''

						return {a + b for a in set_a for b in set_b}


if __name__ == "__main__":
					from doctest import testmod

					testmod()

