




'''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
     UpperCAmelCase_				:     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 	_UpperCamelCase       (_lowerCamelCase							:   Union[str, Any]      ,		_lowerCamelCase							:   str      ,		_lowerCamelCase							:   Optional[int]=None      ,		_lowerCamelCase							:   Dict=None      ,		_lowerCamelCase							:   Union[str, Any]=None      ,		_lowerCamelCase							:   Tuple=None      ,		_lowerCamelCase							:   Optional[int]=None      ,		_lowerCamelCase							:   List[str]=None      ,		)->      Optional[int]:




     '''simple docstring'''





     if attention_mask is None:
          __snake_case			    =     np.where(input_ids != config.pad_token_id      ,		1      ,		0		)
     if decoder_attention_mask is None:
          __snake_case			    =     np.where(decoder_input_ids != config.pad_token_id      ,		1      ,		0		)
     if head_mask is None:
          __snake_case			    =     np.ones((config.encoder_layers, config.encoder_attention_heads)		)
     if decoder_head_mask is None:
          __snake_case			    =     np.ones((config.decoder_layers, config.decoder_attention_heads)		)
     if cross_attn_head_mask is None:
          __snake_case			    =     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      :


       def __init__(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE=13			,     __SCREAMING_SNAKE_CASE=7			,     __SCREAMING_SNAKE_CASE=True			,     __SCREAMING_SNAKE_CASE=False			,     __SCREAMING_SNAKE_CASE=99			,     __SCREAMING_SNAKE_CASE=16			,     __SCREAMING_SNAKE_CASE=2			,     __SCREAMING_SNAKE_CASE=4			,     __SCREAMING_SNAKE_CASE=4			,     __SCREAMING_SNAKE_CASE="gelu"			,     __SCREAMING_SNAKE_CASE=0.1			,     __SCREAMING_SNAKE_CASE=0.1			,     __SCREAMING_SNAKE_CASE=32			,     __SCREAMING_SNAKE_CASE=2			,     __SCREAMING_SNAKE_CASE=1			,     __SCREAMING_SNAKE_CASE=0			,     __SCREAMING_SNAKE_CASE=0.02			,     )					->     int:



            '''simple docstring'''




            __snake_case			    =     parent
            __snake_case			    =     batch_size
            __snake_case			    =     seq_length
            __snake_case			    =     is_training
            __snake_case			    =     use_labels
            __snake_case			    =     vocab_size
            __snake_case			    =     hidden_size
            __snake_case			    =     num_hidden_layers
            __snake_case			    =     num_attention_heads
            __snake_case			    =     intermediate_size
            __snake_case			    =     hidden_act
            __snake_case			    =     hidden_dropout_prob
            __snake_case			    =     attention_probs_dropout_prob
            __snake_case			    =     max_position_embeddings
            __snake_case			    =     eos_token_id
            __snake_case			    =     pad_token_id
            __snake_case			    =     bos_token_id
            __snake_case			    =     initializer_range


       def 					lowerCAmelCase					(      self     )					->     int:



            '''simple docstring'''




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

            __snake_case			    =     shift_tokens_right(_lowercase			,     1			,     2     )

            __snake_case			    =     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=_lowercase			,     )
            __snake_case			    =     prepare_marian_inputs_dict(_lowercase			,     _lowercase			,     _lowercase     )
            return config, inputs_dict


       def 					lowerCAmelCase					(      self     )					->     Optional[Any]:



            '''simple docstring'''




            __snake_case    ,			__snake_case			    =     self.prepare_config_and_inputs()
            return config, inputs_dict


       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     )					->     List[Any]:



            '''simple docstring'''




            __snake_case			    =     20
            __snake_case			    =     model_class_name(_lowercase     )

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

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

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

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

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

            __snake_case			    =     model.decode(_lowercase			,     _lowercase     )

            __snake_case			    =     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			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     )					->     List[Any]:



            '''simple docstring'''




            __snake_case			    =     20
            __snake_case			    =     model_class_name(_lowercase     )

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

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

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

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

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

            __snake_case			    =     model.decode(_lowercase			,     _lowercase			,     decoder_attention_mask=_lowercase     )

            __snake_case			    =     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      (       _snake_case ,      unittest.TestCase ,      _snake_case):
       __lowercase     :					Optional[Any]       				=	True
       __lowercase     :					str       				=	(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
       __lowercase     :					Any       				=	(FlaxMarianMTModel,) if is_flax_available() else ()


       def 					lowerCAmelCase					(      self     )					->     Tuple:



            '''simple docstring'''




            __snake_case			    =     FlaxMarianModelTester(self     )


       def 					lowerCAmelCase					(      self     )					->     Optional[Any]:



            '''simple docstring'''




            __snake_case    ,			__snake_case			    =     self.model_tester.prepare_config_and_inputs()
            for model_class in self.all_model_classes:
                 self.model_tester.check_use_cache_forward(_lowercase			,     _lowercase			,     _lowercase     )


       def 					lowerCAmelCase					(      self     )					->     str:



            '''simple docstring'''




            __snake_case    ,			__snake_case			    =     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(_lowercase			,     _lowercase			,     _lowercase     )


       def 					lowerCAmelCase					(      self     )					->     str:



            '''simple docstring'''




            __snake_case    ,			__snake_case			    =     self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__     ):
                      __snake_case			    =     self._prepare_for_class(_lowercase			,     _lowercase     )
                      __snake_case			    =     model_class(_lowercase     )

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

                      with self.subTest('''JIT Enabled'''     ):
                           __snake_case			    =     encode_jitted(**_lowercase     ).to_tuple()

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

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


       def 					lowerCAmelCase					(      self     )					->     str:



            '''simple docstring'''




            __snake_case    ,			__snake_case			    =     self.model_tester.prepare_config_and_inputs_for_common()

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

                      __snake_case			    =     {
                          '''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			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     ):
                           return model.decode(
                               decoder_input_ids=_lowercase			,     decoder_attention_mask=_lowercase			,     encoder_outputs=_lowercase			,     )

                      with self.subTest('''JIT Enabled'''     ):
                           __snake_case			    =     decode_jitted(**_lowercase     ).to_tuple()

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

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




       @slow
       def 					lowerCAmelCase					(      self     )					->     Any:



            '''simple docstring'''




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





@require_flax
@require_sentencepiece
@require_tokenizers
class 			lowerCAmelCase      (       unittest.TestCase):
       __lowercase     :					str       				=	None
       __lowercase     :					List[str]       				=	None


       @classmethod
       def 					lowerCAmelCase					(      cls     )					->     None:



            '''simple docstring'''




            __snake_case			    =     F'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
            return cls


       @cached_property
       def 					lowerCAmelCase					(      self     )					->     str:



            '''simple docstring'''




            return MarianTokenizer.from_pretrained(self.model_name     )


       @property
       def 					lowerCAmelCase					(      self     )					->     int:



            '''simple docstring'''




            return self.tokenizer.eos_token_id


       @cached_property
       def 					lowerCAmelCase					(      self     )					->     Optional[int]:



            '''simple docstring'''




            __snake_case			    =     FlaxMarianMTModel.from_pretrained(self.model_name     )
            self.assertEqual(model.config.decoder_start_token_id			,     model.config.pad_token_id     )
            return model


       def 					lowerCAmelCase					(      self			,     **__SCREAMING_SNAKE_CASE     )					->     Union[str, Any]:



            '''simple docstring'''




            __snake_case			    =     self.translate_src_text(**_lowercase     )
            self.assertListEqual(self.expected_text			,     _lowercase     )




       def 					lowerCAmelCase					(      self			,     **__SCREAMING_SNAKE_CASE     )					->     Any:



            '''simple docstring'''




            __snake_case			    =     self.tokenizer(self.src_text			,     padding=_lowercase			,     return_tensors='''np'''			,     **_lowercase     )
            __snake_case			    =     self.model.generate(
                model_inputs.input_ids			,     attention_mask=model_inputs.attention_mask			,     num_beams=2			,     max_length=128			,     ).sequences
            __snake_case			    =     self.tokenizer.batch_decode(_lowercase			,     skip_special_tokens=_lowercase     )
            return generated_words





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


       @slow
       def 					lowerCAmelCase					(      self     )					->     Dict:



            '''simple docstring'''




            self._assert_generated_batch_equal_expected()





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


       @slow
       def 					lowerCAmelCase					(      self     )					->     int:



            '''simple docstring'''




            self._assert_generated_batch_equal_expected()





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


       @slow
       def 					lowerCAmelCase					(      self     )					->     Union[str, Any]:



            '''simple docstring'''




            self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class 			lowerCAmelCase      (       _snake_case):
       __lowercase     :					str       				=	"en"
       __lowercase     :					List[str]       				=	"de"
       __lowercase     :					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.",
       ]
       __lowercase     :					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 					lowerCAmelCase					(      self     )					->     Dict:



            '''simple docstring'''




            self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class 			lowerCAmelCase      (       _snake_case):
       __lowercase     :					List[Any]       				=	"en"
       __lowercase     :					Union[str, Any]       				=	"zh"
       __lowercase     :					Optional[Any]       				=	["My name is Wolfgang and I live in Berlin"]
       __lowercase     :					List[Any]       				=	["我叫沃尔夫冈 我住在柏林"]


       @slow
       def 					lowerCAmelCase					(      self     )					->     Optional[Any]:



            '''simple docstring'''




            self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class 			lowerCAmelCase      (       _snake_case):
       __lowercase     :					Dict       				=	"ru"
       __lowercase     :					Optional[int]       				=	"fr"
       __lowercase     :					Optional[Any]       				=	["Он показал мне рукопись своей новой пьесы."]
       __lowercase     :					int       				=	["Il m'a montré le manuscrit de sa nouvelle pièce."]


       @slow
       def 					lowerCAmelCase					(      self     )					->     Union[str, Any]:



            '''simple docstring'''




            self._assert_generated_batch_equal_expected()





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



            '''simple docstring'''




            self._assert_generated_batch_equal_expected()











import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
							from PIL import Image







class 							a_  (		unittest.TestCase					, _snake_case				):

						def 	__a       (    self	:Union[str, Any])		->					Optional[Any]:
												UpperCAmelCase_     							=     load_tool('''image-question-answering''')
												self.tool.setup()
												UpperCAmelCase_     							=     load_tool('''image-question-answering'''   ,						remote=_lowercase)

						def 	__a       (    self	:int)		->					Union[str, Any]:
												UpperCAmelCase_     							=     Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO''')) / '''000000039769.png''')
												UpperCAmelCase_     							=     self.tool(_lowercase   ,						'''How many cats are sleeping on the couch?''')
												self.assertEqual(_lowercase   ,						'''2''')

						def 	__a       (    self	:int)		->					Dict:
												UpperCAmelCase_     							=     Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO''')) / '''000000039769.png''')
												UpperCAmelCase_     							=     self.remote_tool(_lowercase   ,						'''How many cats are sleeping on the couch?''')
												self.assertEqual(_lowercase   ,						'''2''')

						def 	__a       (    self	:Optional[int])		->					int:
												UpperCAmelCase_     							=     Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO''')) / '''000000039769.png''')
												UpperCAmelCase_     							=     self.tool(image=_lowercase   ,						question='''How many cats are sleeping on the couch?''')
												self.assertEqual(_lowercase   ,						'''2''')




						def 	__a       (    self	:Optional[Any])		->					List[str]:
												UpperCAmelCase_     							=     Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO''')) / '''000000039769.png''')
												UpperCAmelCase_     							=     self.remote_tool(image=_lowercase   ,						question='''How many cats are sleeping on the couch?''')
												self.assertEqual(_lowercase   ,						'''2''')





