

import os
import tempfile
from pathlib import Path
from unittest import TestCase

import pyarrow as pa
import pytest

from datasets.arrow_dataset import Dataset
from datasets.arrow_reader import ArrowReader, BaseReader, FileInstructions, ReadInstruction, make_file_instructions
from datasets.info import DatasetInfo
from datasets.splits import NamedSplit, Split, SplitDict, SplitInfo

from .utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases







class 						__magic_name__      (							lowerCamelCase__							):

   '''simple docstring'''




   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_=False	)							->   List[Any]:






     """simple docstring"""






     a__,				a__,				a__  =(
         filename_skip_take['''filename'''],
         filename_skip_take['''skip'''] if '''skip''' in filename_skip_take else None,
         filename_skip_take['''take'''] if '''take''' in filename_skip_take else None,
     )
     open(os.path.join(lowercase_	),   '''wb'''	).close()
     a__  =pa.Table.from_pydict({'''filename''': [Path(lowercase_	).name] * 100}	)
     if take == -1:
       a__  =len(lowercase_	) - skip
     if skip is not None and take is not None:
       a__  =pa_table.slice(lowercase_,   lowercase_	)
     return pa_table







class 						__magic_name__      (							lowerCamelCase__							):

   '''simple docstring'''




   def 			_UpperCAmelCase			(    self	)							->   List[str]:






     """simple docstring"""






     a__  ='''my_name'''
     a__  =SplitInfo(name='''train''',   num_examples=100	)
     a__  =SplitInfo(name='''test''',   num_examples=100	)
     a__  =[train_info, test_info]
     a__  =SplitDict()
     split_dict.add(lowercase_	)
     split_dict.add(lowercase_	)
     a__  =DatasetInfo(splits=lowercase_	)

     with tempfile.TemporaryDirectory() as tmp_dir:
       a__  =ReaderTest(lowercase_,   lowercase_	)

       a__  ='''test[:33%]'''
       a__  =Dataset(**reader.read(lowercase_,   lowercase_,   lowercase_	)	)
       self.assertEqual(dset['''filename'''][0],   F"""{name}-test"""	)
       self.assertEqual(dset.num_rows,   33	)
       self.assertEqual(dset.num_columns,   1	)

       a__  =['''train''', '''test[:33%]''']
       a__  =[Split.TRAIN, ReadInstruction.from_spec('''test[:33%]'''	)]
       for instructions in [instructionsa, instructionsa]:
         a__  =[reader.read(lowercase_,   lowercase_,   lowercase_	) for instr in instructions]
         a__,				a__  =(Dataset(**lowercase_	) for dataset_kwargs in datasets_kwargs)
         self.assertEqual(train_dset['''filename'''][0],   F"""{name}-train"""	)
         self.assertEqual(train_dset.num_rows,   100	)
         self.assertEqual(train_dset.num_columns,   1	)
         self.assertIsInstance(train_dset.split,   lowercase_	)
         self.assertEqual(str(train_dset.split	),   '''train'''	)
         self.assertEqual(test_dset['''filename'''][0],   F"""{name}-test"""	)
         self.assertEqual(test_dset.num_rows,   33	)
         self.assertEqual(test_dset.num_columns,   1	)
         self.assertIsInstance(test_dset.split,   lowercase_	)
         self.assertEqual(str(test_dset.split	),   '''test[:33%]'''	)
         del train_dset, test_dset



   def 			_UpperCAmelCase			(    self	)							->   Any:






     """simple docstring"""






     a__  ='''my_name'''
     a__  =SplitInfo(name='''train''',   num_examples=1000,   shard_lengths=[100] * 10	)
     a__  =[train_info]
     a__  =SplitDict()
     split_dict.add(lowercase_	)
     a__  =DatasetInfo(splits=lowercase_	)

     with tempfile.TemporaryDirectory() as tmp_dir:
       a__  =ReaderTest(lowercase_,   lowercase_	)

       a__  ='''train[:33%]'''
       a__  =Dataset(**reader.read(lowercase_,   lowercase_,   lowercase_	)	)
       self.assertEqual(dset['''filename'''][0],   F"""{name}-train-00000-of-00010"""	)
       self.assertEqual(dset['''filename'''][-1],   F"""{name}-train-00003-of-00010"""	)
       self.assertEqual(dset.num_rows,   330	)
       self.assertEqual(dset.num_columns,   1	)



   def 			_UpperCAmelCase			(    self	)							->   Dict:






     """simple docstring"""






     a__  =SplitInfo(name='''train''',   num_examples=100	)
     a__  =SplitInfo(name='''test''',   num_examples=100	)
     a__  =SplitDict()
     split_dict.add(lowercase_	)
     split_dict.add(lowercase_	)
     a__  =DatasetInfo(splits=lowercase_	)

     with tempfile.TemporaryDirectory() as tmp_dir:
       a__  =ReaderTest(lowercase_,   lowercase_	)

       a__  =[
           {'''filename''': os.path.join(lowercase_,   '''train'''	)},
           {'''filename''': os.path.join(lowercase_,   '''test'''	), '''skip''': 10, '''take''': 10},
       ]
       a__  =Dataset(**reader.read_files(lowercase_,   original_instructions='''train+test[10:20]'''	)	)
       self.assertEqual(dset.num_rows,   110	)
       self.assertEqual(dset.num_columns,   1	)
       del dset





@pytest.mark.parametrize('''in_memory'''     ,				[False, True]							)
def       UpperCAmelCase__    (      _A				:			int     ,				_A				:			Optional[int]     ,				_A				:			Optional[int]							):





  '''simple docstring'''







  a__  =arrow_file
  with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
    a__  =ArrowReader.read_table(_A     ,				in_memory=_A							)
  assert table.shape == dataset.data.shape
  assert set(table.column_names							) == set(dataset.data.column_names							)
  assert dict(table.to_pydict()							) == dict(dataset.data.to_pydict()							)  # to_pydict returns OrderedDict




@pytest.mark.parametrize('''in_memory'''     ,				[False, True]							)
def       UpperCAmelCase__    (      _A				:			Optional[int]     ,				_A				:			Any     ,				_A				:			List[Any]							):





  '''simple docstring'''







  a__  =arrow_file
  a__  =ArrowReader(''''''     ,				_A							)
  with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
    a__  =reader.read_files([{'''filename''': filename}]     ,				in_memory=_A							)
  assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
  a__  =dataset_kwargs['''arrow_table''']
  assert table.shape == dataset.data.shape
  assert set(table.column_names							) == set(dataset.data.column_names							)
  assert dict(table.to_pydict()							) == dict(dataset.data.to_pydict()							)  # to_pydict returns OrderedDict




def       UpperCAmelCase__    (      ):





  '''simple docstring'''







  assert ReadInstruction('''train'''     ,				to=10     ,				unit='''abs'''							).to_spec() == "train[:10]"
  assert ReadInstruction('''train'''     ,				from_=-80     ,				to=10     ,				unit='''%'''							).to_spec() == "train[-80%:10%]"

  a__  ='''train+test'''
  assert ReadInstruction.from_spec(_A							).to_spec() == spec_train_test

  a__  ='''train[2:10]'''
  assert ReadInstruction.from_spec(_A							).to_spec() == spec_train_abs

  a__  ='''train[15%:-20%]'''
  assert ReadInstruction.from_spec(_A							).to_spec() == spec_train_pct

  a__  ='''train[:10%](closest)'''
  assert ReadInstruction.from_spec(_A							).to_spec() == "train[:10%]"

  a__  ='''train[:10%](pct1_dropremainder)'''
  assert ReadInstruction.from_spec(_A							).to_spec() == spec_train_pct_rounding

  a__  ='''train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)'''
  assert ReadInstruction.from_spec(_A							).to_spec() == spec_train_test_pct_rounding




def       UpperCAmelCase__    (      ):





  '''simple docstring'''







  a__  ='''dummy'''
  a__  =[SplitInfo(name='''train'''     ,				num_examples=1_00							)]
  a__  ='''train[:33%]'''
  a__  ='''arrow'''
  a__  ='''prefix'''

  a__  =make_file_instructions(_A     ,				_A     ,				_A     ,				_A     ,				_A							)
  assert isinstance(_A     ,				_A							)
  assert file_instructions.num_examples == 33
  assert file_instructions.file_instructions == [
      {"filename": os.path.join(_A     ,				F"""{name}-train.arrow"""							), "skip": 0, "take": 33}
  ]

  a__  =[SplitInfo(name='''train'''     ,				num_examples=1_00     ,				shard_lengths=[10] * 10							)]
  a__  =make_file_instructions(_A     ,				_A     ,				_A     ,				_A     ,				_A							)
  assert isinstance(_A     ,				_A							)
  assert file_instructions.num_examples == 33
  assert file_instructions.file_instructions == [
      {"filename": os.path.join(_A     ,				F"""{name}-train-00000-of-00010.arrow"""							), "skip": 0, "take": -1},
      {"filename": os.path.join(_A     ,				F"""{name}-train-00001-of-00010.arrow"""							), "skip": 0, "take": -1},
      {"filename": os.path.join(_A     ,				F"""{name}-train-00002-of-00010.arrow"""							), "skip": 0, "take": -1},
      {"filename": os.path.join(_A     ,				F"""{name}-train-00003-of-00010.arrow"""							), "skip": 0, "take": 3},
  ]




@pytest.mark.parametrize('''name, expected_exception'''     ,				[(None, TypeError), ('''''', ValueError)]							)
def       UpperCAmelCase__    (      _A				:			int     ,				_A				:			Union[str, Any]							):





  '''simple docstring'''







  a__  =[SplitInfo(name='''train'''     ,				num_examples=1_00							)]
  a__  ='''train'''
  a__  ='''arrow'''
  a__  ='''prefix_path'''
  with pytest.raises(_A							):
    a__  =make_file_instructions(_A     ,				_A     ,				_A     ,				_A     ,				_A							)



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

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


lowerCamelCase									= logging.get_logger(__name__)

lowerCamelCase									= {
    '''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json''',
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}







class 						__magic_name__      (							lowerCamelCase__							):

   '''simple docstring'''




   lowerCamelCase__			:     List[Any]    =       'blenderbot'
   lowerCamelCase__			:     int    =       ['past_key_values']
   lowerCamelCase__			:     Optional[Any]    =       {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}



   def __init__(    self,   lowercase_=8008,   lowercase_=128,   lowercase_=2,   lowercase_=10240,   lowercase_=32,   lowercase_=24,   lowercase_=10240,   lowercase_=32,   lowercase_=0.0,   lowercase_=0.0,   lowercase_=True,   lowercase_=True,   lowercase_="gelu",   lowercase_=2560,   lowercase_=0.1,   lowercase_=0.0,   lowercase_=0.0,   lowercase_=0.02,   lowercase_=1,   lowercase_=False,   lowercase_=0,   lowercase_=1,   lowercase_=2,   lowercase_=3,   lowercase_=2,   **lowercase_,   )							->   Any:






     """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__  =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_,   decoder_start_token_id=lowercase_,   encoder_no_repeat_ngram_size=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":
       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
   # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
   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
       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__  =[]
       a__,				a__  =self.num_layers

       for _ in range(lowercase_	):
         common_inputs["past_key_values"].append(
             (
                 torch.zeros(lowercase_	),
                 torch.zeros(lowercase_	),
                 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
       a__  =seqlen
       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_	)



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_	)							->   Tuple:






     """simple docstring"""






     if direction not in ["inputs", "outputs"]:
       raise ValueError(F"""direction must either be \"inputs\" or \"outputs\", but {direction} was given"""	)

     a__  ='''past_key_values''' if direction == '''inputs''' else '''present'''
     a__,				a__  =self.num_layers

     a__  ='''past_encoder_sequence'''
     a__  ='''past_decoder_sequence''' if direction == '''inputs''' else '''past_decoder_sequence + sequence'''

     for i in range(lowercase_	):
       a__  ={0: '''batch''', 2: decoder_sequence}
       a__  ={0: '''batch''', 2: decoder_sequence}
       a__  ={0: '''batch''', 2: encoder_sequence}
       a__  ={0: '''batch''', 2: encoder_sequence}
