


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




     '''simple docstring'''


     def __init__(						self			,						__lowerCamelCase			,						__lowerCamelCase=1_3			,						__lowerCamelCase=7			,						__lowerCamelCase=True			,						__lowerCamelCase=False			,						__lowerCamelCase=9_9			,						__lowerCamelCase=1_6			,						__lowerCamelCase=2			,						__lowerCamelCase=4			,						__lowerCamelCase=4			,						__lowerCamelCase="gelu"			,						__lowerCamelCase=0.1			,						__lowerCamelCase=0.1			,						__lowerCamelCase=3_2			,						__lowerCamelCase=2			,						__lowerCamelCase=1			,						__lowerCamelCase=0			,						__lowerCamelCase=0.0_2			,						)      ->    List[str]:
            _A				:	Tuple       =			parent
            _A				:	Optional[Any]       =			batch_size
            _A				:	Any       =			seq_length
            _A				:	Dict       =			is_training
            _A				:	List[Any]       =			use_labels
            _A				:	List[Any]       =			vocab_size
            _A				:	str       =			hidden_size
            _A				:	int       =			num_hidden_layers
            _A				:	List[str]       =			num_attention_heads
            _A				:	Union[str, Any]       =			intermediate_size
            _A				:	Optional[Any]       =			hidden_act
            _A				:	str       =			hidden_dropout_prob
            _A				:	Optional[int]       =			attention_probs_dropout_prob
            _A				:	Tuple       =			max_position_embeddings
            _A				:	Union[str, Any]       =			eos_token_id
            _A				:	Optional[int]       =			pad_token_id
            _A				:	str       =			bos_token_id
            _A				:	Any       =			initializer_range


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

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

            _A				:	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			,						)
            _A				:	Union[str, Any]       =			prepare_marian_inputs_dict(_A			,						_A			,						_A)
            return config, inputs_dict


     def 			_lowerCamelCase			(						self)      ->    Dict:
            _A				:	Union[str, Any]       =			self.prepare_config_and_inputs()
            return config, inputs_dict


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase)      ->    Dict:
            _A				:	str       =			2_0
            _A				:	Any       =			model_class_name(_A)

            _A				:	List[Any]       =			model.encode(inputs_dict["input_ids"])

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

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

            _A				:	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)			,						)
            _A				:	Tuple       =			model.decode(
                decoder_input_ids[:, :-1]			,						_A			,						decoder_attention_mask=_A			,						past_key_values=_A			,						decoder_position_ids=_A			,						)

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

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

            _A				:	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 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase)      ->    int:
            _A				:	Union[str, Any]       =			2_0
            _A				:	Optional[int]       =			model_class_name(_A)

            _A				:	Union[str, Any]       =			model.encode(inputs_dict["input_ids"])

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

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

            _A				:	int       =			model.init_cache(decoder_input_ids.shape[0]			,						_A			,						_A)
            _A				:	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)			,						)

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

            _A				:	Any       =			model.decode(_A			,						_A			,						decoder_attention_mask=_A)

            _A				:	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 					lowerCAmelCase__	(    lowerCAmelCase__					,   unittest.TestCase					,   lowerCAmelCase__):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE        =			True
     __SCREAMING_SNAKE_CASE        =			(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
     __SCREAMING_SNAKE_CASE        =			(FlaxMarianMTModel,) if is_flax_available() else ()


     def 			_lowerCamelCase			(						self)      ->    Any:
            _A				:	Optional[int]       =			FlaxMarianModelTester(self)


     def 			_lowerCamelCase			(						self)      ->    Any:
            _A				:	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 			_lowerCamelCase			(						self)      ->    List[Any]:
            _A				:	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 			_lowerCamelCase			(						self)      ->    Any:
            _A				:	Tuple       =			self.model_tester.prepare_config_and_inputs_for_common()

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

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

                          with self.subTest("JIT Enabled"):
                                 _A				:	Tuple       =			encode_jitted(**_A).to_tuple()

                          with self.subTest("JIT Disabled"):
                                 with jax.disable_jit():
                                        _A				:	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 			_lowerCamelCase			(						self)      ->    List[str]:
            _A				:	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__):
                          _A				:	Optional[int]       =			model_class(_A)
                          _A				:	Dict       =			model.encode(inputs_dict["input_ids"]			,						inputs_dict["attention_mask"])

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

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

                          with self.subTest("JIT Disabled"):
                                 with jax.disable_jit():
                                        _A				:	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 			_lowerCamelCase			(						self)      ->    Optional[int]:




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







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




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE        =			None
     __SCREAMING_SNAKE_CASE        =			None


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


     @cached_property
     def 			_lowerCamelCase			(						self)      ->    List[str]:
            return MarianTokenizer.from_pretrained(self.model_name)


     @property
     def 			_lowerCamelCase			(						self)      ->    List[str]:
            return self.tokenizer.eos_token_id


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


     def 			_lowerCamelCase			(						self			,						**__lowerCamelCase)      ->    Dict:
            _A				:	Any       =			self.translate_src_text(**_A)
            self.assertListEqual(self.expected_text			,						_A)


     def 			_lowerCamelCase			(						self			,						**__lowerCamelCase)      ->    Tuple:
            _A				:	List[Any]       =			self.tokenizer(self.src_text			,						padding=_A			,						return_tensors="np"			,						**_A)
            _A				:	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
            _A				:	List[str]       =			self.tokenizer.batch_decode(_A			,						skip_special_tokens=_A)
            return generated_words







@require_flax
@require_sentencepiece
@require_tokenizers
class 					lowerCAmelCase__	(    lowerCAmelCase__):




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


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







@require_flax
@require_sentencepiece
@require_tokenizers
class 					lowerCAmelCase__	(    lowerCAmelCase__):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE        =			"fr"
     __SCREAMING_SNAKE_CASE        =			"en"
     __SCREAMING_SNAKE_CASE        =			[
         "Donnez moi le micro.",
         "Tom et Mary étaient assis à une table.",  # Accents
     ]
     __SCREAMING_SNAKE_CASE        =			[
         "Give me the microphone.",
         "Tom and Mary were sitting at a table.",
     ]


     @slow
     def 			_lowerCamelCase			(						self)      ->    Optional[int]:
            self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 					lowerCAmelCase__	(    lowerCAmelCase__):




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


     @slow
     def 			_lowerCamelCase			(						self)      ->    Optional[int]:
            self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 					lowerCAmelCase__	(    lowerCAmelCase__):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE        =			"en"
     __SCREAMING_SNAKE_CASE        =			"de"
     __SCREAMING_SNAKE_CASE        =			[
         "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        =			[
         "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 			_lowerCamelCase			(						self)      ->    Any:
            self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 					lowerCAmelCase__	(    lowerCAmelCase__):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE        =			"en"
     __SCREAMING_SNAKE_CASE        =			"zh"
     __SCREAMING_SNAKE_CASE        =			["My name is Wolfgang and I live in Berlin"]
     __SCREAMING_SNAKE_CASE        =			["我叫沃尔夫冈 我住在柏林"]


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







@require_flax
@require_sentencepiece
@require_tokenizers
class 					lowerCAmelCase__	(    lowerCAmelCase__):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE        =			"ru"
     __SCREAMING_SNAKE_CASE        =			"fr"
     __SCREAMING_SNAKE_CASE        =			["Он показал мне рукопись своей новой пьесы."]
     __SCREAMING_SNAKE_CASE        =			["Il m\'a montré le manuscrit de sa nouvelle pièce."]


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







@require_flax
@require_sentencepiece
@require_tokenizers
class 					lowerCAmelCase__	(    lowerCAmelCase__):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE        =			"en"
     __SCREAMING_SNAKE_CASE        =			"ROMANCE"
     __SCREAMING_SNAKE_CASE        =			[
         ">>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        =			[
         "Ne passez pas autant de temps à regarder la télé.",
         "A sua mensagem foi enviada.",
         "Es dos años más viejo que yo.",
     ]


     @slow
     def 			_lowerCamelCase			(						self)      ->    Optional[int]:
            self._assert_generated_batch_equal_expected()




def 							_UpperCAmelCase    (UpperCamelCase__					:  str						):
       _A				:	Tuple       =			str(UpperCamelCase__						).strip()
       if not bin_string:
              raise ValueError("Empty string was passed to the function"						)
       _A				:	Dict       =			bin_string[0] == "-"
       if is_negative:
              _A				:	Any       =			bin_string[1:]
       if not all(char in "01" for char in bin_string						):
              raise ValueError("Non-binary value was passed to the function"						)
       _A				:	Union[str, Any]       =			0
       for char in bin_string:
              _A				:	Union[str, Any]       =			2 * decimal_number + int(UpperCamelCase__						)
       return -decimal_number if is_negative else decimal_number


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

    testmod()
