

from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


lowerCamelCase 	=   logging.get_logger(__name__)

lowerCamelCase 	=   {
    '''facebook/mbart-large-cc25''': '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json''',
    # See all MBART models at https://huggingface.co/models?filter=mbart
}







class 		__magic_name__     (				lowerCamelCase__				):

   '''simple docstring'''




   lowerCamelCase__					:      Optional[Any]        =  'mbart'
   lowerCamelCase__					:      List[str]        =  ['past_key_values']
   lowerCamelCase__					:      Dict        =  {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}



   def __init__(							self,       lowercase_=50265,       lowercase_=1024,       lowercase_=12,       lowercase_=4096,       lowercase_=16,       lowercase_=12,       lowercase_=4096,       lowercase_=16,       lowercase_=0.0,       lowercase_=0.0,       lowercase_=True,       lowercase_=True,       lowercase_="gelu",       lowercase_=1024,       lowercase_=0.1,       lowercase_=0.0,       lowercase_=0.0,       lowercase_=0.02,       lowercase_=0.0,       lowercase_=False,       lowercase_=1,       lowercase_=0,       lowercase_=2,       lowercase_=2,       **lowercase_,       )				->       Dict:






     """simple docstring"""






     a__    =vocab_size
     a__    =max_position_embeddings
     a__    =d_model
     a__    =encoder_ffn_dim
     a__    =encoder_layers
     a__    =encoder_attention_heads
     a__    =decoder_ffn_dim
     a__    =decoder_layers
     a__    =decoder_attention_heads
     a__    =dropout
     a__    =attention_dropout
     a__    =activation_dropout
     a__    =activation_function
     a__    =init_std
     a__    =encoder_layerdrop
     a__    =decoder_layerdrop
     a__    =classifier_dropout
     a__    =use_cache
     a__    =encoder_layers
     a__    =scale_embedding  # scale factor will be sqrt(d_model) if True
     super().__init__(
         pad_token_id=lowercase_,       bos_token_id=lowercase_,       eos_token_id=lowercase_,       is_encoder_decoder=lowercase_,       forced_eos_token_id=lowercase_,       **lowercase_,       )







class 		__magic_name__     (				lowerCamelCase__				):

   '''simple docstring'''




   @property
   def 					_UpperCAmelCase					(							self			)				->       Mapping[str, Mapping[int, str]]:






     """simple docstring"""






     if self.task in ["default", "seq2seq-lm"]:
       a__    =OrderedDict(
           [
               ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
               ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
           ]			)

       if self.use_past:
         a__    ={0: '''batch'''}
         a__    ={0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
       else:
         a__    ={0: '''batch''', 1: '''decoder_sequence'''}
         a__    ={0: '''batch''', 1: '''decoder_sequence'''}

       if self.use_past:
         self.fill_with_past_key_values_(lowercase_,       direction='''inputs'''			)
     elif self.task == "causal-lm":
       # TODO: figure this case out.
       a__    =OrderedDict(
           [
               ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
               ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
           ]			)
       if self.use_past:
         a__, a__    =self.num_layers
         for i in range(lowercase_			):
           a__    ={0: '''batch''', 2: '''past_sequence + sequence'''}
           a__    ={0: '''batch''', 2: '''past_sequence + sequence'''}
     else:
       a__    =OrderedDict(
           [
               ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
               ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
               ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
               ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
           ]			)

     return common_inputs



   @property
   def 					_UpperCAmelCase					(							self			)				->       Mapping[str, Mapping[int, str]]:






     """simple docstring"""






     if self.task in ["default", "seq2seq-lm"]:
       a__    =super().outputs
     else:
       a__    =super(lowercase_,       self			).outputs
       if self.use_past:
         a__, a__    =self.num_layers
         for i in range(lowercase_			):
           a__    ={0: '''batch''', 2: '''past_sequence + sequence'''}
           a__    ={0: '''batch''', 2: '''past_sequence + sequence'''}
     return common_outputs



   def 					_UpperCAmelCase					(							self,       lowercase_,       lowercase_ = -1,       lowercase_ = -1,       lowercase_ = False,       lowercase_ = None,       )				->       Mapping[str, Any]:






     """simple docstring"""






     a__    =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         lowercase_,       lowercase_,       lowercase_,       lowercase_,       lowercase_			)

     # Generate decoder inputs
     a__    =seq_length if not self.use_past else 1
     a__    =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         lowercase_,       lowercase_,       lowercase_,       lowercase_,       lowercase_			)
     a__    ={F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
     a__    =dict(**lowercase_,       **lowercase_			)

     if self.use_past:
       if not is_torch_available():
         raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''			)
       else:
         import torch
       a__, a__    =common_inputs['''input_ids'''].shape
       a__    =common_inputs['''decoder_input_ids'''].shape[1]
       a__, a__    =self.num_attention_heads
       a__    =(
           batch,
           num_encoder_attention_heads,
           encoder_seq_length,
           self._config.hidden_size // num_encoder_attention_heads,
       )
       a__    =decoder_seq_length + 3
       a__    =(
           batch,
           num_decoder_attention_heads,
           decoder_past_length,
           self._config.hidden_size // num_decoder_attention_heads,
       )

       a__    =torch.cat(
           [common_inputs['''decoder_attention_mask'''], torch.ones(lowercase_,       lowercase_			)],       dim=1			)

       a__    =[]
       # If the number of encoder and decoder layers are present in the model configuration, both are considered
       a__, a__    =self.num_layers
       a__    =min(lowercase_,       lowercase_			)
       a__    =max(lowercase_,       lowercase_			) - min_num_layers
       a__    ='''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

       for _ in range(lowercase_			):
         common_inputs["past_key_values"].append(
             (
                 torch.zeros(lowercase_			),
                 torch.zeros(lowercase_			),
                 torch.zeros(lowercase_			),
                 torch.zeros(lowercase_			),
             )			)
       # TODO: test this.
       a__    =encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
       for _ in range(lowercase_,       lowercase_			):
         common_inputs["past_key_values"].append((torch.zeros(lowercase_			), torch.zeros(lowercase_			))			)
     return common_inputs



   def 					_UpperCAmelCase					(							self,       lowercase_,       lowercase_ = -1,       lowercase_ = -1,       lowercase_ = False,       lowercase_ = None,       )				->       Mapping[str, Any]:






     """simple docstring"""






     a__    =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         lowercase_,       lowercase_,       lowercase_,       lowercase_,       lowercase_			)

     if self.use_past:
       if not is_torch_available():
         raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''			)
       else:
         import torch
       a__, a__    =common_inputs['''input_ids'''].shape
       # Not using the same length for past_key_values
       a__    =seqlen + 2
       a__, a__    =self.num_layers
       a__, a__    =self.num_attention_heads
       a__    =(
           batch,
           num_encoder_attention_heads,
           past_key_values_length,
           self._config.hidden_size // num_encoder_attention_heads,
       )

       a__    =common_inputs['''attention_mask'''].dtype
       a__    =torch.cat(
           [common_inputs['''attention_mask'''], torch.ones(lowercase_,       lowercase_,       dtype=lowercase_			)],       dim=1			)
       a__    =[
           (torch.zeros(lowercase_			), torch.zeros(lowercase_			)) for _ in range(lowercase_			)
       ]
     return common_inputs



   def 					_UpperCAmelCase					(							self,       lowercase_,       lowercase_ = -1,       lowercase_ = -1,       lowercase_ = False,       lowercase_ = None,       )				->       Mapping[str, Any]:






     """simple docstring"""






     a__    =compute_effective_axis_dimension(
         lowercase_,       fixed_dimension=OnnxConfig.default_fixed_batch,       num_token_to_add=0			)

     # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
     a__    =tokenizer.num_special_tokens_to_add(lowercase_			)
     a__    =compute_effective_axis_dimension(
         lowercase_,       fixed_dimension=OnnxConfig.default_fixed_sequence,       num_token_to_add=lowercase_			)

     # Generate dummy inputs according to compute batch and sequence
     a__    =[''' '''.join([tokenizer.unk_token]			) * seq_length] * batch_size
     a__    =dict(tokenizer(lowercase_,       return_tensors=lowercase_			)			)
     return common_inputs



   def 					_UpperCAmelCase					(							self,       lowercase_,       lowercase_ = -1,       lowercase_ = -1,       lowercase_ = False,       lowercase_ = None,       )				->       Mapping[str, Any]:






     """simple docstring"""






     if self.task in ["default", "seq2seq-lm"]:
       a__    =self._generate_dummy_inputs_for_default_and_seqaseq_lm(
           lowercase_,       batch_size=lowercase_,       seq_length=lowercase_,       is_pair=lowercase_,       framework=lowercase_			)

     elif self.task == "causal-lm":
       a__    =self._generate_dummy_inputs_for_causal_lm(
           lowercase_,       batch_size=lowercase_,       seq_length=lowercase_,       is_pair=lowercase_,       framework=lowercase_			)
     else:
       a__    =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           lowercase_,       batch_size=lowercase_,       seq_length=lowercase_,       is_pair=lowercase_,       framework=lowercase_			)

     return common_inputs



   def 					_UpperCAmelCase					(							self,       lowercase_,       lowercase_,       lowercase_,       lowercase_			)				->       Optional[int]:






     """simple docstring"""






     if self.task in ["default", "seq2seq-lm"]:
       a__    =super()._flatten_past_key_values_(lowercase_,       lowercase_,       lowercase_,       lowercase_			)
     else:
       a__    =super(lowercase_,       self			)._flatten_past_key_values_(
           lowercase_,       lowercase_,       lowercase_,       lowercase_			)



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()				)
