


"""simple docstring"""







from __future__ import annotations

import unittest
import warnings

from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import tensorflow as tf

       from transformers import TFAutoModelForSeqaSeqLM, TFMarianModel, TFMarianMTModel






@require_tf
class  __A							:






 """simple docstring"""




 __lowerCAmelCase					   =    MarianConfig
 __lowerCAmelCase					   =    {}
 __lowerCAmelCase					   =    'gelu'


 def __init__(						self					,       __A					,       __A=13					,       __A=7					,       __A=True					,       __A=False					,       __A=99					,       __A=32					,       __A=2					,       __A=4					,       __A=37					,       __A=0.1					,       __A=0.1					,       __A=20					,       __A=2					,       __A=1					,       __A=0					,       )   ->	Tuple:
     a											=parent
     a											=batch_size
     a											=seq_length
     a											=is_training
     a											=use_labels
     a											=vocab_size
     a											=hidden_size
     a											=num_hidden_layers
     a											=num_attention_heads
     a											=intermediate_size

     a											=hidden_dropout_prob
     a											=attention_probs_dropout_prob
     a											=max_position_embeddings
     a											=eos_token_id
     a											=pad_token_id
     a											=bos_token_id


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	str:
     a											=ids_tensor([self.batch_size, self.seq_length - 1]					,       self.vocab_size		)
     a											=tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size		)					,       1		)
     a											=tf.concat([input_ids, eos_tensor]					,       axis=1		)

     a											=ids_tensor([self.batch_size, self.seq_length]					,       self.vocab_size		)

     a											=self.config_cls(
         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_ids=[2]					,       bos_token_id=self.bos_token_id					,       pad_token_id=self.pad_token_id					,       decoder_start_token_id=self.pad_token_id					,       **self.config_updates					,       )
     a											=prepare_marian_inputs_dict(lowercase_					,       lowercase_					,       lowercase_		)
     return config, inputs_dict





 def  SCREAMING_SNAKE_CASE				(						self					,       __A					,       __A		)   ->	int:
     a											=TFMarianModel(config=lowercase_		).get_decoder()
     a											=inputs_dict['''input_ids''']

     a											=input_ids[:1, :]
     a											=inputs_dict['''attention_mask'''][:1, :]
     a											=inputs_dict['''head_mask''']
     a											=1

     # first forward pass
     a											=model(lowercase_					,       attention_mask=lowercase_					,       head_mask=lowercase_					,       use_cache=lowercase_		)

     a						, a											=outputs.to_tuple()

     # create hypothetical next token and extent to next_input_ids
     a											=ids_tensor((self.batch_size, 3)					,       config.vocab_size		)
     a											=tf.cast(ids_tensor((self.batch_size, 3)					,       2		)					,       tf.inta		)

     # append to next input_ids and
     a											=tf.concat([input_ids, next_tokens]					,       axis=-1		)
     a											=tf.concat([attention_mask, next_attn_mask]					,       axis=-1		)

     a											=model(lowercase_					,       attention_mask=lowercase_		)[0]
     a											=model(lowercase_					,       attention_mask=lowercase_					,       past_key_values=lowercase_		)[0]

     self.parent.assertEqual(next_tokens.shape[1]					,       output_from_past.shape[1]		)

     # select random slice
     a											=int(ids_tensor((1,)					,       output_from_past.shape[-1]		)		)
     a											=output_from_no_past[:, -3:, random_slice_idx]
     a											=output_from_past[:, :, random_slice_idx]

     # test that outputs are equal for slice
     tf.debugging.assert_near(lowercase_					,       lowercase_					,       rtol=1E-3		)

def _A		(		lowercase			,       lowercase			,       lowercase			,       lowercase=None			,       lowercase=None			,       lowercase=None			,       lowercase=None			,       lowercase=None			,       ):







    """simple docstring"""







    if attention_mask is None:
        a											=tf.cast(tf.math.not_equal(_A			,       config.pad_token_id						)			,       tf.inta						)
    if decoder_attention_mask is None:
        a											=tf.concat(
            [
                tf.ones(decoder_input_ids[:, :1].shape			,       dtype=tf.inta						),
                tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]			,       config.pad_token_id						)			,       tf.inta						),
            ]			,       axis=-1			,       )
    if head_mask is None:
        a											=tf.ones((config.encoder_layers, config.encoder_attention_heads)						)
    if decoder_head_mask is None:
        a											=tf.ones((config.decoder_layers, config.decoder_attention_heads)						)
    if cross_attn_head_mask is None:
        a											=tf.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": decoder_attention_mask,
        "head_mask": head_mask,
        "decoder_head_mask": decoder_head_mask,
        "cross_attn_head_mask": cross_attn_head_mask,
    }






@require_tf
class  __A							(			lowerCamelCase__,       lowerCamelCase__,       unittest.TestCase     ):






 """simple docstring"""




 __lowerCAmelCase					   =    (TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
 __lowerCAmelCase					   =    (TFMarianMTModel,) if is_tf_available() else ()
 __lowerCAmelCase					   =    (
     {
         'conversational': TFMarianMTModel,
         'feature-extraction': TFMarianModel,
         'summarization': TFMarianMTModel,
         'text2text-generation': TFMarianMTModel,
         'translation': TFMarianMTModel,
     }
     if is_tf_available()
     else {}
 )
 __lowerCAmelCase					   =    True
 __lowerCAmelCase					   =    False
 __lowerCAmelCase					   =    False


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Dict:
     a											=TFMarianModelTester(self		)
     a											=ConfigTester(self					,       config_class=lowercase_		)


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	str:
     self.config_tester.run_common_tests()





 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Tuple:
     a											=self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.check_decoder_model_past_large_inputs(*lowercase_		)






@require_tf
class  __A							(			unittest.TestCase     ):






 """simple docstring"""




 __lowerCAmelCase					   =    1000  # show more chars for failing integration tests


 @classmethod
 def  SCREAMING_SNAKE_CASE				(						cls		)   ->	None:
     a											=f'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
     return cls


 @cached_property
 def  SCREAMING_SNAKE_CASE				(						self		)   ->	MarianTokenizer:
     return AutoTokenizer.from_pretrained(self.model_name		)


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


 @cached_property
 def  SCREAMING_SNAKE_CASE				(						self		)   ->	int:
     warnings.simplefilter('''error'''		)
     a											=TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name		)
     assert isinstance(lowercase_					,       lowercase_		)
     a											=model.config
     self.assertListEqual(c.bad_words_ids					,       [[c.pad_token_id]]		)
     self.assertEqual(c.max_length					,       512		)
     self.assertEqual(c.decoder_start_token_id					,       c.pad_token_id		)
     return model


 def  SCREAMING_SNAKE_CASE				(						self					,       **__A		)   ->	str:
     a											=self.translate_src_text(**lowercase_		)
     self.assertListEqual(self.expected_text					,       lowercase_		)





 def  SCREAMING_SNAKE_CASE				(						self					,       **__A		)   ->	Any:
     a											=self.tokenizer(self.src_text					,       **lowercase_					,       padding=lowercase_					,       return_tensors='''tf'''		)
     a											=self.model.generate(
         model_inputs.input_ids					,       attention_mask=model_inputs.attention_mask					,       num_beams=2					,       max_length=128		)
     a											=self.tokenizer.batch_decode(generated_ids.numpy()					,       skip_special_tokens=lowercase_		)
     return generated_words






@require_sentencepiece
@require_tokenizers
@require_tf
class  __A							(			lowerCamelCase__     ):






 """simple docstring"""




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


 @unittest.skip('''Skipping until #12647 is resolved.'''		)
 @slow
 def  SCREAMING_SNAKE_CASE				(						self		)   ->	List[Any]:
     self._assert_generated_batch_equal_expected()






@require_sentencepiece
@require_tokenizers
@require_tf
class  __A							(			lowerCamelCase__     ):






 """simple docstring"""




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


 @unittest.skip('''Skipping until #12647 is resolved.'''		)
 @slow
 def  SCREAMING_SNAKE_CASE				(						self		)   ->	str:
     self._assert_generated_batch_equal_expected()






@require_sentencepiece
@require_tokenizers
@require_tf
class  __A							(			lowerCamelCase__     ):






 """simple docstring"""




 __lowerCAmelCase					   =    'en'
 __lowerCAmelCase					   =    'ROMANCE'
 __lowerCAmelCase					   =    [
     '>>fr<< Don\'t spend so much time watching TV.',
     '>>pt<< Your message has been sent.',
     '>>es<< He\'s two years older than me.',
 ]
 __lowerCAmelCase					   =    [
     'Ne passez pas autant de temps à regarder la télé.',
     'A sua mensagem foi enviada.',
     'Es dos años más viejo que yo.',
 ]


 @unittest.skip('''Skipping until #12647 is resolved.'''		)
 @slow
 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Optional[int]:
     self._assert_generated_batch_equal_expected()





 @unittest.skip('''Skipping until #12647 is resolved.'''		)
 @slow
 def  SCREAMING_SNAKE_CASE				(						self		)   ->	List[str]:
     a											=TranslationPipeline(self.model					,       self.tokenizer					,       framework='''tf'''		)
     a											=pipeline(self.src_text		)
     self.assertEqual(self.expected_text					,       [x['''translation_text'''] for x in output]		)


import os
import sys
from pathlib import Path

import pytest

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node

from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch




def      UpperCAmelCase__							(     ):





  '''simple docstring'''







  a__    =Dataset.from_dict({'''i''': range(17				)}				)
  a__    =len(_A				)
  a__    =3
  a__    =[
      split_dataset_by_node(_A      , rank=_A      , world_size=_A				) for rank in range(_A				)
  ]
  assert sum(len(_A				) for ds in datasets_per_rank				) == full_size
  assert len({tuple(x.values()				) for ds in datasets_per_rank for x in ds}				) == full_size




def      UpperCAmelCase__							(     ):





  '''simple docstring'''







  def gen():
    return ({"i": i} for i in range(17				))

  a__    =3
  a__    =IterableDataset.from_generator(_A				)
  a__    =len(list(_A				)				)
  a__    =[
      split_dataset_by_node(_A      , rank=_A      , world_size=_A				) for rank in range(_A				)
  ]
  assert sum(len(list(_A				)				) for ds in datasets_per_rank				) == full_size
  assert len({tuple(x.values()				) for ds in datasets_per_rank for x in ds}				) == full_size




@pytest.mark.parametrize('''shards_per_node'''      , [1, 2, 3]				)
def      UpperCAmelCase__							(     _A :					Dict				):





  '''simple docstring'''







  def gen(_A :					List[str]				):
    for shard in shards:
      yield from ({"i": i, "shard": shard} for i in range(17				))

  a__    =3
  a__    =shards_per_node * world_size
  a__    ={'''shards''': [F"""shard_{shard_idx}.txt""" for shard_idx in range(_A				)]}
  a__    =IterableDataset.from_generator(_A      , gen_kwargs=_A				)
  a__    =len(list(_A				)				)
  assert full_ds.n_shards == world_size * shards_per_node
  a__    =[
      split_dataset_by_node(_A      , rank=_A      , world_size=_A				) for rank in range(_A				)
  ]
  assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
  assert sum(len(list(_A				)				) for ds in datasets_per_rank				) == full_size
  assert len({tuple(x.values()				) for ds in datasets_per_rank for x in ds}				) == full_size




def      UpperCAmelCase__							(     ):





  '''simple docstring'''







  def gen():
    return ({"i": i} for i in range(17				))

  a__    =2
  a__    =IterableDataset.from_generator(_A				)
  a__    =len(list(_A				)				)

  a__    =split_dataset_by_node(_A      , rank=0      , world_size=_A				).shuffle(seed=42				)
  assert len(list(_A				)				) == 1 + full_size // world_size
  with pytest.raises(_A				):
    split_dataset_by_node(_A      , rank=0      , world_size=_A				).shuffle()

  a__    =split_dataset_by_node(full_ds.shuffle(seed=42				)      , rank=0      , world_size=_A				)
  assert len(list(_A				)				) == 1 + full_size // world_size
  with pytest.raises(_A				):
    split_dataset_by_node(full_ds.shuffle()      , rank=0      , world_size=_A				)




@pytest.mark.parametrize('''streaming'''      , [False, True]				)
@require_torch
@pytest.mark.skipif(os.name == '''nt'''      , reason='''execute_subprocess_async doesn\'t support windows'''				)
@pytest.mark.integration
def      UpperCAmelCase__							(     _A :					Dict				):





  '''simple docstring'''







  a__    =2
  a__    =get_torch_dist_unique_port()
  a__    =Path(_A				).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
  a__    =F"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
  a__    =F"""
        --streaming={streaming}
    """.split()
  a__    =[sys.executable] + distributed_args + args
  execute_subprocess_async(_A      , env=os.environ.copy()				)




@pytest.mark.parametrize(
    '''nproc_per_node, num_workers'''      , [
        (2, 2),  # each node has 2 shards and each worker has 1 shards
        (3, 2),  # each node uses all the shards but skips examples, and each worker has 2 shards
    ]      , )
@require_torch
@pytest.mark.skipif(os.name == '''nt'''      , reason='''execute_subprocess_async doesn\'t support windows'''				)
@pytest.mark.integration
def      UpperCAmelCase__							(     _A :					List[str]      , _A :					str				):





  '''simple docstring'''







  a__    =True
  a__    =get_torch_dist_unique_port()
  a__    =Path(_A				).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
  a__    =F"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
  a__    =F"""
        --streaming={streaming}
        --num_workers={num_workers}
    """.split()
  a__    =[sys.executable] + distributed_args + args
  execute_subprocess_async(_A      , env=os.environ.copy()				)
