


'''simple docstring'''







import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
   import torch


A      :     Dict										= random.Random()






def       lowerCAmelCase__       (				lowerCamelCase     :			str  ,lowerCamelCase     :			Optional[int]=1.0  ,lowerCamelCase     :			Dict=None  ,lowerCamelCase     :			List[Any]=None   ):
       if rng is None:
              _A     :	str      =					global_rng

       _A     :	Tuple      =					[]
       for batch_idx in range(shape[0]   ):
              values.append([]   )
              for _ in range(shape[1]   ):
                     values[-1].append(rng.random() * scale   )

       return values


@require_torch
class  __lowerCamelCase (       unittest.TestCase       ):
    """simple docstring"""


    def __init__(  self			:							List[str]      ,							SCREAMING_SNAKE_CASE			:							Union[str, Any]      ,							SCREAMING_SNAKE_CASE			:							List[str]=7      ,							SCREAMING_SNAKE_CASE			:							Tuple=400      ,							SCREAMING_SNAKE_CASE			:							Optional[int]=2000      ,							SCREAMING_SNAKE_CASE			:							Dict=1      ,							SCREAMING_SNAKE_CASE			:							List[str]=0.0      ,							SCREAMING_SNAKE_CASE			:							List[str]=24000      ,							SCREAMING_SNAKE_CASE			:							List[Any]=True      ,							):
           _A     :	List[str]      =					parent
           _A     :	Any      =					batch_size
           _A     :	int      =					min_seq_length
           _A     :	List[str]      =					max_seq_length
           _A     :	int      =					(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
           _A     :	Union[str, Any]      =					feature_size
           _A     :	Tuple      =					padding_value
           _A     :	Union[str, Any]      =					sampling_rate
           _A     :	List[str]      =					return_attention_mask


    def    A    (  self			:							str):
           return {
               "feature_size": self.feature_size,
               "padding_value": self.padding_value,
               "sampling_rate": self.sampling_rate,
               "return_attention_mask": self.return_attention_mask,
           }


    def    A    (  self			:							Any      ,							SCREAMING_SNAKE_CASE			:							Union[str, Any]=False      ,							SCREAMING_SNAKE_CASE			:							List[Any]=False):
           def _flatten(SCREAMING_SNAKE_CASE			:							Optional[Any]):
                  return list(itertools.chain(*SCREAMING_SNAKE_CASE))

           if equal_length:
                  _A     :	List[Any]      =					floats_list((self.batch_size, self.max_seq_length))
           else:
                  # make sure that inputs increase in size
                  _A     :	str      =					[
                      _flatten(floats_list((x, self.feature_size)))
                      for x in range(self.min_seq_length      ,							self.max_seq_length      ,							self.seq_length_diff)
                  ]

           if numpify:
                  _A     :	str      =					[np.asarray(SCREAMING_SNAKE_CASE) for x in audio_inputs]

           return audio_inputs




@require_torch
class  __lowerCamelCase (       a_				,					unittest.TestCase       ):
    """simple docstring"""
    a													=    EncodecFeatureExtractor


    def    A    (  self			:							List[Any]):
           _A     :	List[Any]      =					EnCodecFeatureExtractionTester(self)


    def    A    (  self			:							Tuple):
           # Tests that all call wrap to encode_plus and batch_encode_plus
           _A     :	Union[str, Any]      =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
           # create three inputs of length 800, 1000, and 1200
           _A     :	Tuple      =					[floats_list((1, x))[0] for x in range(800      ,							1400      ,							200)]
           _A     :	Any      =					[np.asarray(SCREAMING_SNAKE_CASE) for audio_input in audio_inputs]

           # Test not batched input
           _A     :	Tuple      =					feat_extract(audio_inputs[0]      ,							return_tensors='np').input_values
           _A     :	int      =					feat_extract(np_audio_inputs[0]      ,							return_tensors='np').input_values
           self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							atol=1e-3))

           # Test batched
           _A     :	Any      =					feat_extract(SCREAMING_SNAKE_CASE      ,							padding=SCREAMING_SNAKE_CASE      ,							return_tensors='np').input_values
           _A     :	Optional[int]      =					feat_extract(SCREAMING_SNAKE_CASE      ,							padding=SCREAMING_SNAKE_CASE      ,							return_tensors='np').input_values
           for enc_seq_a, enc_seq_a in zip(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE):
                  self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							atol=1e-3))


    def    A    (  self			:							Any):
           _A     :	Dict      =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
           _A     :	Any      =					np.random.rand(100).astype(np.floataa)
           _A     :	Optional[int]      =					np_audio_inputs.tolist()

           for inputs in [py_audio_inputs, np_audio_inputs]:
                  _A     :	List[str]      =					feature_extractor.pad([{'input_values': inputs}]      ,							return_tensors='np')
                  self.assertTrue(np_processed.input_values.dtype == np.floataa)
                  _A     :	int      =					feature_extractor.pad([{'input_values': inputs}]      ,							return_tensors='pt')
                  self.assertTrue(pt_processed.input_values.dtype == torch.floataa)


    def    A    (  self			:							Any      ,							SCREAMING_SNAKE_CASE			:							List[Any]):
           from datasets import load_dataset

           _A     :	int      =					load_dataset('hf-internal-testing/librispeech_asr_dummy'      ,							'clean'      ,							split='validation')
           # automatic decoding with librispeech
           _A     :	str      =					ds.sort('id').select(range(SCREAMING_SNAKE_CASE))[:num_samples]['audio']

           return [x["array"] for x in audio_samples]


    def    A    (  self			:							List[str]):
           # fmt: off
           _A     :	Tuple      =					torch.tensor(
               [2.38_04e-03, 2.07_52e-03, 1.98_36e-03, 2.10_57e-03, 1.61_74e-03,
                3.05_18e-04, 9.15_53e-05, 3.35_69e-04, 9.76_56e-04, 1.83_11e-03,
                2.01_42e-03, 2.10_57e-03, 1.73_95e-03, 4.57_76e-04, -3.96_73e-04,
                4.57_76e-04, 1.00_71e-03, 9.15_53e-05, 4.88_28e-04, 1.15_97e-03,
                7.32_42e-04, 9.46_04e-04, 1.80_05e-03, 1.83_11e-03, 8.85_01e-04,
                4.27_25e-04, 4.88_28e-04, 7.32_42e-04, 1.09_86e-03, 2.10_57e-03])
           # fmt: on
           _A     :	Dict      =					self._load_datasamples(1)
           _A     :	str      =					EncodecFeatureExtractor()
           _A     :	Any      =					feature_extractor(SCREAMING_SNAKE_CASE      ,							return_tensors='pt').input_values
           self.assertEquals(input_values.shape      ,							(1, 1, 93680))
           self.assertTrue(torch.allclose(input_values[0, 0, :30]      ,							SCREAMING_SNAKE_CASE      ,							atol=1e-6))


    def    A    (  self			:							Dict):
           # fmt: off
           _A     :	List[Any]      =					torch.tensor(
               [2.38_04e-03, 2.07_52e-03, 1.98_36e-03, 2.10_57e-03, 1.61_74e-03,
                3.05_18e-04, 9.15_53e-05, 3.35_69e-04, 9.76_56e-04, 1.83_11e-03,
                2.01_42e-03, 2.10_57e-03, 1.73_95e-03, 4.57_76e-04, -3.96_73e-04,
                4.57_76e-04, 1.00_71e-03, 9.15_53e-05, 4.88_28e-04, 1.15_97e-03,
                7.32_42e-04, 9.46_04e-04, 1.80_05e-03, 1.83_11e-03, 8.85_01e-04,
                4.27_25e-04, 4.88_28e-04, 7.32_42e-04, 1.09_86e-03, 2.10_57e-03])
           # fmt: on
           _A     :	int      =					self._load_datasamples(1)
           _A     :	int      =					[np.tile(input_audio[0][None]      ,							reps=(2, 1))]
           input_audio[0][1] *= 0.5
           _A     :	List[str]      =					EncodecFeatureExtractor(feature_size=2)
           _A     :	Optional[Any]      =					feature_extractor(SCREAMING_SNAKE_CASE      ,							return_tensors='pt').input_values
           self.assertEquals(input_values.shape      ,							(1, 2, 93680))
           self.assertTrue(torch.allclose(input_values[0, 0, :30]      ,							SCREAMING_SNAKE_CASE      ,							atol=1e-6))
           self.assertTrue(torch.allclose(input_values[0, 1, :30]      ,							EXPECTED_INPUT_VALUES * 0.5      ,							atol=1e-6))


    def    A    (  self			:							Optional[Any]):
           _A     :	Optional[int]      =					self._load_datasamples(2)
           # would be easier if the stride was like
           _A     :	Any      =					EncodecFeatureExtractor(feature_size=1      ,							chunk_length_s=1      ,							overlap=0.01)

           # pad and trunc raise an error ?
           with self.assertRaisesRegex(
               SCREAMING_SNAKE_CASE      ,							'^Both padding and truncation were set. Make sure you only set one.$'      ,							):
                  _A     :	int      =					feature_extractor(
                      SCREAMING_SNAKE_CASE      ,							padding='max_length'      ,							truncation=SCREAMING_SNAKE_CASE      ,							return_tensors='pt').input_values

           # truncate to chunk
           _A     :	int      =					feature_extractor(SCREAMING_SNAKE_CASE      ,							truncation=SCREAMING_SNAKE_CASE      ,							return_tensors='pt').input_values
           self.assertEquals(truncated_outputs.shape      ,							(2, 1, 71520))  # 2 chunks

           # force truncate to max_length
           _A     :	List[str]      =					feature_extractor(
               SCREAMING_SNAKE_CASE      ,							truncation=SCREAMING_SNAKE_CASE      ,							max_length=48000      ,							return_tensors='pt').input_values
           self.assertEquals(truncated_outputs.shape      ,							(2, 1, 48000))

           # pad to chunk
           _A     :	Optional[Any]      =					feature_extractor(SCREAMING_SNAKE_CASE      ,							padding=SCREAMING_SNAKE_CASE      ,							return_tensors='pt').input_values
           self.assertEquals(padded_outputs.shape      ,							(2, 1, 95280))

           # pad to chunk
           _A     :	List[Any]      =					feature_extractor(SCREAMING_SNAKE_CASE      ,							return_tensors='pt').input_values
           self.assertEquals(truncated_outputs.shape      ,							(2, 1, 95280))

           # force pad to max length
           _A     :	Optional[int]      =					feature_extractor(
               SCREAMING_SNAKE_CASE      ,							padding='max_length'      ,							max_length=100000      ,							return_tensors='pt').input_values
           self.assertEquals(truncated_outputs.shape      ,							(2, 1, 100000))

           # force no pad
           with self.assertRaisesRegex(
               SCREAMING_SNAKE_CASE      ,							'^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'      ,							):
                  _A     :	Optional[int]      =					feature_extractor(SCREAMING_SNAKE_CASE      ,							padding=SCREAMING_SNAKE_CASE      ,							return_tensors='pt').input_values

           _A     :	Optional[int]      =					feature_extractor(input_audio[0]      ,							padding=SCREAMING_SNAKE_CASE      ,							return_tensors='pt').input_values
           self.assertEquals(truncated_outputs.shape      ,							(1, 1, 93680))

           # no pad if no chunk_length_s
           _A     :	Any      =					None
           with self.assertRaisesRegex(
               SCREAMING_SNAKE_CASE      ,							'^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'      ,							):
                  _A     :	Optional[Any]      =					feature_extractor(SCREAMING_SNAKE_CASE      ,							padding=SCREAMING_SNAKE_CASE      ,							return_tensors='pt').input_values

           _A     :	Dict      =					feature_extractor(input_audio[0]      ,							padding=SCREAMING_SNAKE_CASE      ,							return_tensors='pt').input_values
           self.assertEquals(truncated_outputs.shape      ,							(1, 1, 93680))

           # no pad if no overlap
           _A     :	Optional[int]      =					2
           _A     :	Optional[Any]      =					None
           with self.assertRaisesRegex(
               SCREAMING_SNAKE_CASE      ,							'^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'      ,							):
                  _A     :	Tuple      =					feature_extractor(SCREAMING_SNAKE_CASE      ,							padding=SCREAMING_SNAKE_CASE      ,							return_tensors='pt').input_values

           _A     :	Tuple      =					feature_extractor(input_audio[0]      ,							padding=SCREAMING_SNAKE_CASE      ,							return_tensors='pt').input_values
           self.assertEquals(truncated_outputs.shape      ,							(1, 1, 93680))










'''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
   A      :     Union[str, 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       lowerCAmelCase__       (				lowerCamelCase     :			int  ,lowerCamelCase     :			List[Any]  ,lowerCamelCase     :			List[Any]=None  ,lowerCamelCase     :			Union[str, Any]=None  ,lowerCamelCase     :			int=None  ,lowerCamelCase     :			List[str]=None  ,lowerCamelCase     :			Optional[int]=None  ,lowerCamelCase     :			Dict=None  ,):
       if attention_mask is None:
              _A     :	str      =					np.where(input_ids != config.pad_token_id  ,1  ,0   )
       if decoder_attention_mask is None:
              _A     :	List[str]      =					np.where(decoder_input_ids != config.pad_token_id  ,1  ,0   )
       if head_mask is None:
              _A     :	List[str]      =					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     :	Tuple      =					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			:							List[str]      ,							SCREAMING_SNAKE_CASE			:							Dict      ,							SCREAMING_SNAKE_CASE			:							Tuple=13      ,							SCREAMING_SNAKE_CASE			:							Optional[Any]=7      ,							SCREAMING_SNAKE_CASE			:							List[str]=True      ,							SCREAMING_SNAKE_CASE			:							Any=False      ,							SCREAMING_SNAKE_CASE			:							Union[str, Any]=99      ,							SCREAMING_SNAKE_CASE			:							List[str]=16      ,							SCREAMING_SNAKE_CASE			:							Dict=2      ,							SCREAMING_SNAKE_CASE			:							Tuple=4      ,							SCREAMING_SNAKE_CASE			:							str=4      ,							SCREAMING_SNAKE_CASE			:							Tuple="gelu"      ,							SCREAMING_SNAKE_CASE			:							int=0.1      ,							SCREAMING_SNAKE_CASE			:							Tuple=0.1      ,							SCREAMING_SNAKE_CASE			:							int=32      ,							SCREAMING_SNAKE_CASE			:							Union[str, Any]=2      ,							SCREAMING_SNAKE_CASE			:							Union[str, Any]=1      ,							SCREAMING_SNAKE_CASE			:							str=0      ,							SCREAMING_SNAKE_CASE			:							Any=0.02      ,							):
           _A     :	Tuple      =					parent
           _A     :	Union[str, Any]      =					batch_size
           _A     :	List[str]      =					seq_length
           _A     :	Optional[int]      =					is_training
           _A     :	Optional[int]      =					use_labels
           _A     :	List[str]      =					vocab_size
           _A     :	Tuple      =					hidden_size
           _A     :	List[str]      =					num_hidden_layers
           _A     :	int      =					num_attention_heads
           _A     :	List[Any]      =					intermediate_size
           _A     :	Optional[int]      =					hidden_act
           _A     :	str      =					hidden_dropout_prob
           _A     :	Tuple      =					attention_probs_dropout_prob
           _A     :	Optional[Any]      =					max_position_embeddings
           _A     :	str      =					eos_token_id
           _A     :	Tuple      =					pad_token_id
           _A     :	Union[str, Any]      =					bos_token_id
           _A     :	Any      =					initializer_range


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

           _A     :	str      =					shift_tokens_right(SCREAMING_SNAKE_CASE      ,							1      ,							2)

           _A     :	List[Any]      =					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=SCREAMING_SNAKE_CASE      ,							)
           _A     :	List[Any]      =					prepare_marian_inputs_dict(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)
           return config, inputs_dict


    def    A    (  self			:							List[str]):
           _A  , _A     :	Tuple      =					self.prepare_config_and_inputs()
           return config, inputs_dict


    def    A    (  self			:							int      ,							SCREAMING_SNAKE_CASE			:							List[str]      ,							SCREAMING_SNAKE_CASE			:							Any      ,							SCREAMING_SNAKE_CASE			:							List[Any]):
           _A     :	Optional[Any]      =					20
           _A     :	Any      =					model_class_name(SCREAMING_SNAKE_CASE)

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

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

           _A     :	Any      =					model.init_cache(decoder_input_ids.shape[0]      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)
           _A     :	Tuple      =					jnp.ones((decoder_input_ids.shape[0], max_decoder_length)      ,							dtype='i4')

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

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

           _A     :	List[Any]      =					model.decode(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)

           _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}')


    def    A    (  self			:							List[Any]      ,							SCREAMING_SNAKE_CASE			:							Tuple      ,							SCREAMING_SNAKE_CASE			:							Optional[int]      ,							SCREAMING_SNAKE_CASE			:							Tuple):
           _A     :	List[Any]      =					20
           _A     :	str      =					model_class_name(SCREAMING_SNAKE_CASE)

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

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

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

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

           _A     :	Tuple      =					model.decode(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							decoder_attention_mask=SCREAMING_SNAKE_CASE)

           _A     :	int      =					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 (       a_				,					unittest.TestCase				,					a_       ):
    """simple docstring"""
    a													=    True
    a													=    (FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
    a													=    (FlaxMarianMTModel,) if is_flax_available() else ()


    def    A    (  self			:							str):
           _A     :	Any      =					FlaxMarianModelTester(self)


    def    A    (  self			:							Optional[Any]):
           _A  , _A     :	int      =					self.model_tester.prepare_config_and_inputs()
           for model_class in self.all_model_classes:
                  self.model_tester.check_use_cache_forward(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)


    def    A    (  self			:							int):
           _A  , _A     :	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(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)


    def    A    (  self			:							Tuple):
           _A  , _A     :	Any      =					self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  with self.subTest(model_class.__name__):
                         _A     :	int      =					self._prepare_for_class(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)
                         _A     :	int      =					model_class(SCREAMING_SNAKE_CASE)

                         @jax.jit
                         def encode_jitted(SCREAMING_SNAKE_CASE			:							Union[str, Any]      ,							SCREAMING_SNAKE_CASE			:							Optional[int]=None      ,							**SCREAMING_SNAKE_CASE			:							int):
                                return model.encode(input_ids=SCREAMING_SNAKE_CASE      ,							attention_mask=SCREAMING_SNAKE_CASE)

                         with self.subTest('JIT Enabled'):
                                _A     :	str      =					encode_jitted(**SCREAMING_SNAKE_CASE).to_tuple()

                         with self.subTest('JIT Disabled'):
                                with jax.disable_jit():
                                       _A     :	List[str]      =					encode_jitted(**SCREAMING_SNAKE_CASE).to_tuple()

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


    def    A    (  self			:							Tuple):
           _A  , _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     :	List[Any]      =					model_class(SCREAMING_SNAKE_CASE)
                         _A     :	Tuple      =					model.encode(inputs_dict['input_ids']      ,							inputs_dict['attention_mask'])

                         _A     :	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(SCREAMING_SNAKE_CASE			:							Optional[Any]      ,							SCREAMING_SNAKE_CASE			:							Any      ,							SCREAMING_SNAKE_CASE			:							List[Any]):
                                return model.decode(
                                    decoder_input_ids=SCREAMING_SNAKE_CASE      ,							decoder_attention_mask=SCREAMING_SNAKE_CASE      ,							encoder_outputs=SCREAMING_SNAKE_CASE      ,							)

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

                         with self.subTest('JIT Disabled'):
                                with jax.disable_jit():
                                       _A     :	List[str]      =					decode_jitted(**SCREAMING_SNAKE_CASE).to_tuple()

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


    @slow
    def    A    (  self			:							Optional[Any]):
           for model_class_name in self.all_model_classes:
                  _A     :	str      =					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     :	str      =					model(SCREAMING_SNAKE_CASE)
                  self.assertIsNotNone(SCREAMING_SNAKE_CASE)




@require_flax
@require_sentencepiece
@require_tokenizers
class  __lowerCamelCase (       unittest.TestCase       ):
    """simple docstring"""
    a													=    None
    a													=    None


    @classmethod
    def    A    (  cls			:							List[Any]):
           _A     :	Optional[int]      =					F'Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'
           return cls


    @cached_property
    def    A    (  self			:							Optional[Any]):
           return MarianTokenizer.from_pretrained(self.model_name)


    @property
    def    A    (  self			:							Dict):
           return self.tokenizer.eos_token_id


    @cached_property
    def    A    (  self			:							int):
           _A     :	FlaxMarianMTModel      =					FlaxMarianMTModel.from_pretrained(self.model_name)
           self.assertEqual(model.config.decoder_start_token_id      ,							model.config.pad_token_id)
           return model


    def    A    (  self			:							Union[str, Any]      ,							**SCREAMING_SNAKE_CASE			:							int):
           _A     :	str      =					self.translate_src_text(**SCREAMING_SNAKE_CASE)
           self.assertListEqual(self.expected_text      ,							SCREAMING_SNAKE_CASE)


    def    A    (  self			:							str      ,							**SCREAMING_SNAKE_CASE			:							List[Any]):
           _A     :	int      =					self.tokenizer(self.src_text      ,							padding=SCREAMING_SNAKE_CASE      ,							return_tensors='np'      ,							**SCREAMING_SNAKE_CASE)
           _A     :	Union[str, Any]      =					self.model.generate(
               model_inputs.input_ids      ,							attention_mask=model_inputs.attention_mask      ,							num_beams=2      ,							max_length=128      ,							).sequences
           _A     :	int      =					self.tokenizer.batch_decode(SCREAMING_SNAKE_CASE      ,							skip_special_tokens=SCREAMING_SNAKE_CASE)
           return generated_words




@require_flax
@require_sentencepiece
@require_tokenizers
class  __lowerCamelCase (       a_       ):
    """simple docstring"""
    a													=    "en"
    a													=    "fr"
    a													=    [
        "I am a small frog.",
        "Now I can forget the 100 words of german that I know.",
    ]
    a													=    [
        "Je suis une petite grenouille.",
        "Maintenant, je peux oublier les 100 mots d'allemand que je connais.",
    ]


    @slow
    def    A    (  self			:							Any):
           self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class  __lowerCamelCase (       a_       ):
    """simple docstring"""
    a													=    "fr"
    a													=    "en"
    a													=    [
        "Donnez moi le micro.",
        "Tom et Mary étaient assis à une table.",  # Accents
    ]
    a													=    [
        "Give me the microphone.",
        "Tom and Mary were sitting at a table.",
    ]


    @slow
    def    A    (  self			:							Any):
           self._assert_generated_batch_equal_expected()




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


    @slow
    def    A    (  self			:							List[Any]):
           self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class  __lowerCamelCase (       a_       ):
    """simple docstring"""
    a													=    "en"
    a													=    "de"
    a													=    [
        "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													=    [
        "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			:							Optional[Any]):
           self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class  __lowerCamelCase (       a_       ):
    """simple docstring"""
    a													=    "en"
    a													=    "zh"
    a													=    ["My name is Wolfgang and I live in Berlin"]
    a													=    ["我叫沃尔夫冈 我住在柏林"]


    @slow
    def    A    (  self			:							Optional[Any]):
           self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class  __lowerCamelCase (       a_       ):
    """simple docstring"""
    a													=    "ru"
    a													=    "fr"
    a													=    ["Он показал мне рукопись своей новой пьесы."]
    a													=    ["Il m'a montré le manuscrit de sa nouvelle pièce."]


    @slow
    def    A    (  self			:							Optional[int]):
           self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class  __lowerCamelCase (       a_       ):
    """simple docstring"""
    a													=    "en"
    a													=    "ROMANCE"
    a													=    [
        ">>fr<< Don't spend so much time watching TV.",
        ">>pt<< Your message has been sent.",
        ">>es<< He's two years older than me.",
    ]
    a													=    [
        "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			:							Any):
           self._assert_generated_batch_equal_expected()






