




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


__UpperCamelCase      : int					      =   logging.get_logger(__name__)

__UpperCamelCase      : Any					      =   {
    "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 	__lowerCAmelCase			(			__magic_name__				):
							UpperCamelCase__        =						'''mbart'''
							UpperCamelCase__        =						['''past_key_values''']
							UpperCamelCase__        =						{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
							def __init__(		self		:int	,   __magic_name__		:List[str]=5_0265	,   __magic_name__		:List[Any]=1024	,   __magic_name__		:str=12	,   __magic_name__		:str=4096	,   __magic_name__		:List[str]=16	,   __magic_name__		:Optional[Any]=12	,   __magic_name__		:List[str]=4096	,   __magic_name__		:Dict=16	,   __magic_name__		:List[str]=0.0	,   __magic_name__		:Optional[int]=0.0	,   __magic_name__		:Any=True	,   __magic_name__		:Dict=True	,   __magic_name__		:int="gelu"	,   __magic_name__		:Tuple=1024	,   __magic_name__		:Any=0.1	,   __magic_name__		:Tuple=0.0	,   __magic_name__		:Any=0.0	,   __magic_name__		:Tuple=0.02	,   __magic_name__		:int=0.0	,   __magic_name__		:Optional[Any]=False	,   __magic_name__		:Optional[Any]=1	,   __magic_name__		:Dict=0	,   __magic_name__		:Optional[Any]=2	,   __magic_name__		:int=2	,   **__magic_name__		:int	,   ):







								'''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=__magic_name__	,   bos_token_id=__magic_name__	,   eos_token_id=__magic_name__	,   is_encoder_decoder=__magic_name__	,   forced_eos_token_id=__magic_name__	,   **__magic_name__	,   )







class 	__lowerCAmelCase			(			__magic_name__				):
							@property
							def    lowerCamelCase__							(		self		:Optional[Any]       ):







								'''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_(__magic_name__	,   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(__magic_name__       ):
											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    lowerCamelCase__							(		self		:Optional[Any]       ):







								'''simple docstring'''







								if self.task in ["default", "seq2seq-lm"]:
									a		       =							super().outputs
								else:
									a		       =							super(__magic_name__	,   self       ).outputs
									if self.use_past:
										a       ,				a		       =							self.num_layers
										for i in range(__magic_name__       ):
											a		       =							{0: """batch""", 2: """past_sequence + sequence"""}
											a		       =							{0: """batch""", 2: """past_sequence + sequence"""}
								return common_outputs
							def    lowerCamelCase__							(		self		:Optional[Any]	,   __magic_name__		:PreTrainedTokenizer	,   __magic_name__		:int = -1	,   __magic_name__		:int = -1	,   __magic_name__		:bool = False	,   __magic_name__		:Optional[TensorType] = None	,   ):







								'''simple docstring'''







								a		       =							self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    __magic_name__	,   __magic_name__	,   __magic_name__	,   __magic_name__	,   __magic_name__       )

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

								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(__magic_name__	,   __magic_name__       )]	,   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(__magic_name__	,   __magic_name__       )
									a		       =							max(__magic_name__	,   __magic_name__       ) - min_num_layers
									a		       =							"""encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

									for _ in range(__magic_name__       ):
										common_inputs["past_key_values"].append(
										    (
										        torch.zeros(__magic_name__       ),
										        torch.zeros(__magic_name__       ),
										        torch.zeros(__magic_name__       ),
										        torch.zeros(__magic_name__       ),
										    )       )
									# TODO: test this.
									a		       =							encoder_shape if remaining_side_name == """encoder""" else decoder_shape
									for _ in range(__magic_name__	,   __magic_name__       ):
										common_inputs["past_key_values"].append((torch.zeros(__magic_name__       ), torch.zeros(__magic_name__       ))       )
								return common_inputs
							def    lowerCamelCase__							(		self		:Any	,   __magic_name__		:PreTrainedTokenizer	,   __magic_name__		:int = -1	,   __magic_name__		:int = -1	,   __magic_name__		:bool = False	,   __magic_name__		:Optional[TensorType] = None	,   ):







								'''simple docstring'''







								a		       =							self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    __magic_name__	,   __magic_name__	,   __magic_name__	,   __magic_name__	,   __magic_name__       )

								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(__magic_name__	,   __magic_name__	,   dtype=__magic_name__       )]	,   dim=1       )
									a		       =							[
									    (torch.zeros(__magic_name__       ), torch.zeros(__magic_name__       )) for _ in range(__magic_name__       )
									]
								return common_inputs
							def    lowerCamelCase__							(		self		:Optional[Any]	,   __magic_name__		:PreTrainedTokenizer	,   __magic_name__		:int = -1	,   __magic_name__		:int = -1	,   __magic_name__		:bool = False	,   __magic_name__		:Optional[TensorType] = None	,   ):







								'''simple docstring'''







								a		       =							compute_effective_axis_dimension(
								    __magic_name__	,   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(__magic_name__       )
								a		       =							compute_effective_axis_dimension(
								    __magic_name__	,   fixed_dimension=OnnxConfig.default_fixed_sequence	,   num_token_to_add=__magic_name__       )

								# Generate dummy inputs according to compute batch and sequence
								a		       =							[""" """.join([tokenizer.unk_token]       ) * seq_length] * batch_size
								a		       =							dict(tokenizer(__magic_name__	,   return_tensors=__magic_name__       )       )
								return common_inputs
							def    lowerCamelCase__							(		self		:int	,   __magic_name__		:PreTrainedTokenizer	,   __magic_name__		:int = -1	,   __magic_name__		:int = -1	,   __magic_name__		:bool = False	,   __magic_name__		:Optional[TensorType] = None	,   ):







								'''simple docstring'''







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

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

								return common_inputs





							def    lowerCamelCase__							(		self		:List[Any]	,   __magic_name__		:Optional[Any]	,   __magic_name__		:str	,   __magic_name__		:List[str]	,   __magic_name__		:Tuple       ):







								'''simple docstring'''







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







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 	__lowerCAmelCase			(			__magic_name__				):
							def    lowerCamelCase__							(		self		:List[str]	,   __magic_name__		:Dict	,   __magic_name__		:Union[str, Any]=False       ):







								'''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(__magic_name__       )	,   """wb"""       ).close()
								a		       =							pa.Table.from_pydict({"""filename""": [Path(__magic_name__       ).name] * 100}       )
								if take == -1:
									a		       =							len(__magic_name__       ) - skip
								if skip is not None and take is not None:
									a		       =							pa_table.slice(__magic_name__	,   __magic_name__       )
								return pa_table







class 	__lowerCAmelCase			(			__magic_name__				):
							def    lowerCamelCase__							(		self		:List[Any]       ):







								'''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(__magic_name__       )
								split_dict.add(__magic_name__       )
								a		       =							DatasetInfo(splits=__magic_name__       )

								with tempfile.TemporaryDirectory() as tmp_dir:
									a		       =							ReaderTest(__magic_name__	,   __magic_name__       )

									a		       =							"""test[:33%]"""
									a		       =							Dataset(**reader.read(__magic_name__	,   __magic_name__	,   __magic_name__       )       )
									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(__magic_name__	,   __magic_name__	,   __magic_name__       ) for instr in instructions]
										a       ,				a		       =							(Dataset(**__magic_name__       ) 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	,   __magic_name__       )
										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	,   __magic_name__       )
										self.assertEqual(str(test_dset.split       )	,   """test[:33%]"""       )
										del train_dset, test_dset
							def    lowerCamelCase__							(		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(__magic_name__       )
								a		       =							DatasetInfo(splits=__magic_name__       )

								with tempfile.TemporaryDirectory() as tmp_dir:
									a		       =							ReaderTest(__magic_name__	,   __magic_name__       )

									a		       =							"""train[:33%]"""
									a		       =							Dataset(**reader.read(__magic_name__	,   __magic_name__	,   __magic_name__       )       )
									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    lowerCamelCase__							(		self		:List[str]       ):







								'''simple docstring'''







								a		       =							SplitInfo(name="""train"""	,   num_examples=100       )
								a		       =							SplitInfo(name="""test"""	,   num_examples=100       )
								a		       =							SplitDict()
								split_dict.add(__magic_name__       )
								split_dict.add(__magic_name__       )
								a		       =							DatasetInfo(splits=__magic_name__       )

								with tempfile.TemporaryDirectory() as tmp_dir:
									a		       =							ReaderTest(__magic_name__	,   __magic_name__       )

									a		       =							[
									    {"""filename""": os.path.join(__magic_name__	,   """train"""       )},
									    {"""filename""": os.path.join(__magic_name__	,   """test"""       ), """skip""": 10, """take""": 10},
									]
									a		       =							Dataset(**reader.read_files(__magic_name__	,   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 	__A	(			__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase					)   -> Optional[int]:
	a		       =							arrow_file
	with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
		a		       =							ArrowReader.read_table(__lowerCamelCase							,    in_memory=__lowerCamelCase					)
	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 	__A	(			__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase					)   -> str:
	a		       =							arrow_file
	a		       =							ArrowReader(""""""							,    __lowerCamelCase					)
	with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
		a		       =							reader.read_files([{"""filename""": filename}]							,    in_memory=__lowerCamelCase					)
	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 	__A	(			)   -> int:
	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(__lowerCamelCase					).to_spec() == spec_train_test

	a		       =							"""train[2:10]"""
	assert ReadInstruction.from_spec(__lowerCamelCase					).to_spec() == spec_train_abs

	a		       =							"""train[15%:-20%]"""
	assert ReadInstruction.from_spec(__lowerCamelCase					).to_spec() == spec_train_pct

	a		       =							"""train[:10%](closest)"""
	assert ReadInstruction.from_spec(__lowerCamelCase					).to_spec() == "train[:10%]"

	a		       =							"""train[:10%](pct1_dropremainder)"""
	assert ReadInstruction.from_spec(__lowerCamelCase					).to_spec() == spec_train_pct_rounding

	a		       =							"""train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)"""
	assert ReadInstruction.from_spec(__lowerCamelCase					).to_spec() == spec_train_test_pct_rounding





def 	__A	(			)   -> Any:
	a		       =							"""dummy"""
	a		       =							[SplitInfo(name="""train"""							,    num_examples=100					)]
	a		       =							"""train[:33%]"""
	a		       =							"""arrow"""
	a		       =							"""prefix"""

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

	a		       =							[SplitInfo(name="""train"""							,    num_examples=100							,    shard_lengths=[10] * 10					)]
	a		       =							make_file_instructions(__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase					)
	assert isinstance(__lowerCamelCase							,    __lowerCamelCase					)
	assert file_instructions.num_examples == 33
	assert file_instructions.file_instructions == [
	    {"filename": os.path.join(__lowerCamelCase							,    f'{name}-train-00000-of-00010.arrow'					), "skip": 0, "take": -1},
	    {"filename": os.path.join(__lowerCamelCase							,    f'{name}-train-00001-of-00010.arrow'					), "skip": 0, "take": -1},
	    {"filename": os.path.join(__lowerCamelCase							,    f'{name}-train-00002-of-00010.arrow'					), "skip": 0, "take": -1},
	    {"filename": os.path.join(__lowerCamelCase							,    f'{name}-train-00003-of-00010.arrow'					), "skip": 0, "take": 3},
	]





@pytest.mark.parametrize("""name, expected_exception"""							,    [(None, TypeError), ("""""", ValueError)]					)
def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> int:
	a		       =							[SplitInfo(name="""train"""							,    num_examples=100					)]
	a		       =							"""train"""
	a		       =							"""arrow"""
	a		       =							"""prefix_path"""
	with pytest.raises(__lowerCamelCase					):
		a		       =							make_file_instructions(__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase					)

