
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
						__snake_case					:							List[str]           =							'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 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE=None     ,						SCREAMING_SNAKE_CASE=None     ,						SCREAMING_SNAKE_CASE=None     ,						SCREAMING_SNAKE_CASE=None     ,						SCREAMING_SNAKE_CASE=None     ,						SCREAMING_SNAKE_CASE=None     ,						):




			"""simple docstring"""


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

						'''simple docstring'''



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


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

									UpperCAmelCase__		:str 		=    shift_tokens_right(lowerCAmelCase_  , 1  , 2	)

									UpperCAmelCase__		:Tuple 		=    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_  , )
									UpperCAmelCase__		:Optional[int] 		=    prepare_marian_inputs_dict(lowerCAmelCase_  , lowerCAmelCase_  , lowerCAmelCase_	)
									return config, inputs_dict


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


						def    A__       (  self  , A  , A  , A	)  ->Optional[Any]:
									UpperCAmelCase__		:Optional[int] 		=    20
									UpperCAmelCase__		:Any 		=    model_class_name(lowerCAmelCase_	)

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

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

									UpperCAmelCase__		:Optional[Any] 		=    model.init_cache(decoder_input_ids.shape[0]  , lowerCAmelCase_  , lowerCAmelCase_	)
									UpperCAmelCase__		:Dict 		=    jnp.ones((decoder_input_ids.shape[0], max_decoder_length)  , dtype='i4'	)

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

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

									UpperCAmelCase__		:List[Any] 		=    model.decode(lowerCAmelCase_  , lowerCAmelCase_	)

									UpperCAmelCase__		:List[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}"""	)


						def    A__       (  self  , A  , A  , A	)  ->Dict:
									UpperCAmelCase__		:Any 		=    20
									UpperCAmelCase__		:Dict 		=    model_class_name(lowerCAmelCase_	)

									UpperCAmelCase__		:Optional[int] 		=    model.encode(inputs_dict['input_ids']	)

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

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

									UpperCAmelCase__		:List[str] 		=    model.init_cache(decoder_input_ids.shape[0]  , lowerCAmelCase_  , lowerCAmelCase_	)
									UpperCAmelCase__		:int 		=    jnp.broadcast_to(
									    jnp.arange(decoder_input_ids.shape[-1] - 1	)[None, :]  , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)  , )

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

									UpperCAmelCase__		:Optional[int] 		=    model.decode(lowerCAmelCase_  , lowerCAmelCase_  , decoder_attention_mask=lowerCAmelCase_	)

									UpperCAmelCase__		: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 	UpperCamelCase__       (  _lowerCamelCase						,			unittest.TestCase						,			_lowerCamelCase):

						'''simple docstring'''



						__a     :    str												=			True
						__a     :    Dict												=			(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
						__a     :    Tuple												=			(FlaxMarianMTModel,) if is_flax_available() else ()


						def    A__       (  self	)  ->List[Any]:
									UpperCAmelCase__		:Union[str, Any] 		=    FlaxMarianModelTester(self	)


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

									for model_class in self.all_model_classes:
												with self.subTest(model_class.__name__	):
															UpperCAmelCase__		:Optional[int] 		=    self._prepare_for_class(lowerCAmelCase_  , lowerCAmelCase_	)
															UpperCAmelCase__		:Dict 		=    model_class(lowerCAmelCase_	)

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

															with self.subTest('JIT Enabled'	):
																		UpperCAmelCase__		:str 		=    encode_jitted(**lowerCAmelCase_	).to_tuple()

															with self.subTest('JIT Disabled'	):
																		with jax.disable_jit():
																					UpperCAmelCase__		:Tuple 		=    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	)  ->str:
									UpperCAmelCase__		,       UpperCAmelCase__		:Union[str, Any] 		=    self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
												with self.subTest(model_class.__name__	):
															UpperCAmelCase__		:str 		=    model_class(lowerCAmelCase_	)
															UpperCAmelCase__		:List[Any] 		=    model.encode(inputs_dict['input_ids']  , inputs_dict['attention_mask']	)

															UpperCAmelCase__		: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(A  , A  , A	):
																		return model.decode(
																		    decoder_input_ids=lowerCAmelCase_  , decoder_attention_mask=lowerCAmelCase_  , encoder_outputs=lowerCAmelCase_  , )

															with self.subTest('JIT Enabled'	):
																		UpperCAmelCase__		:Optional[int] 		=    decode_jitted(**lowerCAmelCase_	).to_tuple()

															with self.subTest('JIT Disabled'	):
																		with jax.disable_jit():
																					UpperCAmelCase__		:Any 		=    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:
									for model_class_name in self.all_model_classes:
												UpperCAmelCase__		:Optional[int] 		=    model_class_name.from_pretrained('Helsinki-NLP/opus-mt-en-de'	)
												# FlaxMarianForSequenceClassification expects eos token in input_ids
												UpperCAmelCase__		:List[str] 		=    np.ones((1, 1)	) * model.config.eos_token_id
												UpperCAmelCase__		:int 		=    model(lowerCAmelCase_	)
												self.assertIsNotNone(lowerCAmelCase_	)


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

						'''simple docstring'''



						__a     :    Optional[int]												=			None
						__a     :    List[str]												=			None


						@classmethod
						def    A__       (  cls	)  ->None:
									UpperCAmelCase__		:int 		=    F"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
									return cls


						@cached_property
						def    A__       (  self	)  ->int:
									return MarianTokenizer.from_pretrained(self.model_name	)


						@property
						def    A__       (  self	)  ->int:
									return self.tokenizer.eos_token_id


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


						def    A__       (  self  , **A	)  ->List[str]:
									UpperCAmelCase__		:Union[str, Any] 		=    self.translate_src_text(**lowerCAmelCase_	)
									self.assertListEqual(self.expected_text  , lowerCAmelCase_	)


						def    A__       (  self  , **A	)  ->int:
									UpperCAmelCase__		:int 		=    self.tokenizer(self.src_text  , padding=lowerCAmelCase_  , return_tensors='np'  , **lowerCAmelCase_	)
									UpperCAmelCase__		:str 		=    self.model.generate(
									    model_inputs.input_ids  , attention_mask=model_inputs.attention_mask  , num_beams=2  , max_length=1_28  , ).sequences
									UpperCAmelCase__		:Dict 		=    self.tokenizer.batch_decode(lowerCAmelCase_  , skip_special_tokens=lowerCAmelCase_	)
									return generated_words


@require_flax
@require_sentencepiece
@require_tokenizers
class 	UpperCamelCase__       (  _lowerCamelCase):

						'''simple docstring'''



						__a     :    List[str]												=			"""en"""
						__a     :    List[Any]												=			"""fr"""
						__a     :    List[Any]												=			[
						    """I am a small frog.""",
						    """Now I can forget the 100 words of german that I know.""",
						]
						__a     :    Optional[Any]												=			[
						    """Je suis une petite grenouille.""",
						    """Maintenant, je peux oublier les 100 mots d\'allemand que je connais.""",
						]


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


@require_flax
@require_sentencepiece
@require_tokenizers
class 	UpperCamelCase__       (  _lowerCamelCase):

						'''simple docstring'''



						__a     :    List[str]												=			"""fr"""
						__a     :    Any												=			"""en"""
						__a     :    List[Any]												=			[
						    """Donnez moi le micro.""",
						    """Tom et Mary étaient assis à une table.""",  # Accents
						]
						__a     :    Dict												=			[
						    """Give me the microphone.""",
						    """Tom and Mary were sitting at a table.""",
						]


						@slow
						def    A__       (  self	)  ->Dict:
									self._assert_generated_batch_equal_expected()


@require_flax
@require_sentencepiece
@require_tokenizers
class 	UpperCamelCase__       (  _lowerCamelCase):

						'''simple docstring'''



						__a     :    Optional[Any]												=			"""mt"""
						__a     :    Optional[int]												=			"""en"""
						__a     :    Optional[int]												=			["""Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."""]
						__a     :    Any												=			["""Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."""]


						@slow
						def    A__       (  self	)  ->int:
									self._assert_generated_batch_equal_expected()


@require_flax
@require_sentencepiece
@require_tokenizers
class 	UpperCamelCase__       (  _lowerCamelCase):

						'''simple docstring'''



						__a     :    Optional[Any]												=			"""en"""
						__a     :    int												=			"""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     :    Optional[int]												=			[
						    """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	)  ->List[str]:
									self._assert_generated_batch_equal_expected()


@require_flax
@require_sentencepiece
@require_tokenizers
class 	UpperCamelCase__       (  _lowerCamelCase):

						'''simple docstring'''



						__a     :    List[Any]												=			"""en"""
						__a     :    Optional[int]												=			"""zh"""
						__a     :    Tuple												=			["""My name is Wolfgang and I live in Berlin"""]
						__a     :    Dict												=			["""我叫沃尔夫冈 我住在柏林"""]


						@slow
						def    A__       (  self	)  ->Tuple:
									self._assert_generated_batch_equal_expected()


@require_flax
@require_sentencepiece
@require_tokenizers
class 	UpperCamelCase__       (  _lowerCamelCase):

						'''simple docstring'''



						__a     :    Dict												=			"""ru"""
						__a     :    str												=			"""fr"""
						__a     :    List[str]												=			["""Он показал мне рукопись своей новой пьесы."""]
						__a     :    Optional[int]												=			["""Il m\'a montré le manuscrit de sa nouvelle pièce."""]


						@slow
						def    A__       (  self	)  ->Dict:
									self._assert_generated_batch_equal_expected()


@require_flax
@require_sentencepiece
@require_tokenizers
class 	UpperCamelCase__       (  _lowerCamelCase):

						'''simple docstring'''



						__a     :    Optional[Any]												=			"""en"""
						__a     :    Union[str, Any]												=			"""ROMANCE"""
						__a     :    Union[str, 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     :    Dict												=			[
						    """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	)  ->Tuple:
									self._assert_generated_batch_equal_expected()












import unittest

from transformers import is_torch_available, load_tool

from .test_tools_common import ToolTesterMixin


if is_torch_available():
					import torch
class       UpperCamelCase_		(	unittest.TestCase				,	_lowerCamelCase       ):




					def 						lowerCAmelCase      (		self	)		->				str:
										_snake_case						=					load_tool('speech-to-text'	)
										self.tool.setup()




					def 						lowerCAmelCase      (		self	)		->				Optional[Any]:
										_snake_case						=					self.tool(torch.ones(3000	)	)
										self.assertEqual(lowerCAmelCase_					,    ' you'	)






					def 						lowerCAmelCase      (		self	)		->				Optional[int]:
										_snake_case						=					self.tool(audio=torch.ones(3000	)	)
										self.assertEqual(lowerCAmelCase_					,    ' you'	)



