
import warnings
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


lowercase__			  =   logging.get_logger(__name__)

lowercase__			  =   {
    "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/config.json",
    # See all BART models at https://huggingface.co/models?filter=bart
}







class     lowercase		( SCREAMING_SNAKE_CASE__		):
							lowercase_	:	Tuple	 ='''bart'''
							lowercase_	:	int	 =['''past_key_values''']
							lowercase_	:	Any	 ={'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}




							def __init__(    self       ,A__=5_0_2_6_5       ,A__=1_0_2_4       ,A__=1_2       ,A__=4_0_9_6       ,A__=1_6       ,A__=1_2       ,A__=4_0_9_6       ,A__=1_6       ,A__=0.0       ,A__=0.0       ,A__="gelu"       ,A__=1_0_2_4       ,A__=0.1       ,A__=0.0       ,A__=0.0       ,A__=0.02       ,A__=0.0       ,A__=False       ,A__=True       ,A__=3       ,A__=1       ,A__=0       ,A__=2       ,A__=True       ,A__=2       ,A__=2       ,**A__       ,):
												lowercase						     =						vocab_size
												lowercase						     =						max_position_embeddings
												lowercase						     =						d_model
												lowercase						     =						encoder_ffn_dim
												lowercase						     =						encoder_layers
												lowercase						     =						encoder_attention_heads
												lowercase						     =						decoder_ffn_dim
												lowercase						     =						decoder_layers
												lowercase						     =						decoder_attention_heads
												lowercase						     =						dropout
												lowercase						     =						attention_dropout
												lowercase						     =						activation_dropout
												lowercase						     =						activation_function
												lowercase						     =						init_std
												lowercase						     =						encoder_layerdrop
												lowercase						     =						decoder_layerdrop
												lowercase						     =						classifier_dropout
												lowercase						     =						use_cache
												lowercase						     =						encoder_layers
												lowercase						     =						scale_embedding  # scale factor will be sqrt(d_model) if True

												super().__init__(
												    num_labels=A__       ,pad_token_id=A__       ,bos_token_id=A__       ,eos_token_id=A__       ,is_encoder_decoder=A__       ,decoder_start_token_id=A__       ,forced_eos_token_id=A__       ,**A__       ,)

												# ensure backward compatibility for BART CNN models
												if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated'''       ,A__):
																	lowercase						     =						self.bos_token_id
																	warnings.warn(
																	    f'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
																	    '''The config can simply be saved and uploaded again to be fixed.''')







class     lowercase		( SCREAMING_SNAKE_CASE__		):




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

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

																	if self.use_past:
																						self.fill_with_past_key_values_(A__       ,direction='''inputs''')
												elif self.task == "causal-lm":
																	# TODO: figure this case out.
																	lowercase						     =						OrderedDict(
																	    [
																	        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
																	        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
																	    ])
																	if self.use_past:
																						lowercase     ,						lowercase						     =						self.num_layers
																						for i in range(A__):
																											lowercase						     =						{0: '''batch''', 2: '''past_sequence + sequence'''}
																											lowercase						     =						{0: '''batch''', 2: '''past_sequence + sequence'''}
												else:
																	lowercase						     =						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 			A__				(    self):
												if self.task in ["default", "seq2seq-lm"]:
																	lowercase						     =						super().outputs
												else:
																	lowercase						     =						super(A__       ,self).outputs
																	if self.use_past:
																						lowercase     ,						lowercase						     =						self.num_layers
																						for i in range(A__):
																											lowercase						     =						{0: '''batch''', 2: '''past_sequence + sequence'''}
																											lowercase						     =						{0: '''batch''', 2: '''past_sequence + sequence'''}
												return common_outputs




							def 			A__				(    self       ,A__       ,A__ = -1       ,A__ = -1       ,A__ = False       ,A__ = None       ,):
												lowercase						     =						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
												    A__       ,A__       ,A__       ,A__       ,A__)

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

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

																	lowercase						     =						torch.cat(
																	    [common_inputs['''decoder_attention_mask'''], torch.ones(A__       ,A__)]       ,dim=1)

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

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




							def 			A__				(    self       ,A__       ,A__ = -1       ,A__ = -1       ,A__ = False       ,A__ = None       ,):
												lowercase						     =						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
												    A__       ,A__       ,A__       ,A__       ,A__)

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

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




							def 			A__				(    self       ,A__       ,A__ = -1       ,A__ = -1       ,A__ = False       ,A__ = None       ,):
												# Copied from OnnxConfig.generate_dummy_inputs
												# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
												# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
												lowercase						     =						compute_effective_axis_dimension(
												    A__       ,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
												lowercase						     =						tokenizer.num_special_tokens_to_add(A__)
												lowercase						     =						compute_effective_axis_dimension(
												    A__       ,fixed_dimension=OnnxConfig.default_fixed_sequence       ,num_token_to_add=A__)

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




							def 			A__				(    self       ,A__       ,A__ = -1       ,A__ = -1       ,A__ = False       ,A__ = None       ,):
												if self.task in ["default", "seq2seq-lm"]:
																	lowercase						     =						self._generate_dummy_inputs_for_default_and_seqaseq_lm(
																	    A__       ,batch_size=A__       ,seq_length=A__       ,is_pair=A__       ,framework=A__)

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

												return common_inputs





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






from pathlib import Path

import fire
from tqdm import tqdm



def 	UpperCamelCase   (							lowerCAmelCase__="ro"			,      lowerCAmelCase__="en"			,      lowerCAmelCase__="wmt16"			,      lowerCAmelCase__=None		):
					'''simple docstring'''
					try:
										import datasets
					except (ModuleNotFoundError, ImportError):
										raise ImportError('''run pip install datasets'''		)
					lowercase						     =						f'{src_lang}-{tgt_lang}'
					print(f'Converting {dataset}-{pair}'		)
					lowercase						     =						datasets.load_dataset(lowerCAmelCase__			,      lowerCAmelCase__		)
					if save_dir is None:
										lowercase						     =						f'{dataset}-{pair}'
					lowercase						     =						Path(lowerCAmelCase__		)
					save_dir.mkdir(exist_ok=lowerCAmelCase__		)

					for split in ds.keys():
										print(f'Splitting {split} with {ds[split].num_rows} records'		)

										# to save to val.source, val.target like summary datasets
										lowercase						     =						'''val''' if split == '''validation''' else split
										lowercase						     =						save_dir.joinpath(f'{fn}.source'		)
										lowercase						     =						save_dir.joinpath(f'{fn}.target'		)
										lowercase						     =						src_path.open('''w+'''		)
										lowercase						     =						tgt_path.open('''w+'''		)

										# reader is the bottleneck so writing one record at a time doesn't slow things down
										for x in tqdm(ds[split]		):
															lowercase						     =						x['''translation''']
															src_fp.write(ex[src_lang] + '''\n'''		)
															tgt_fp.write(ex[tgt_lang] + '''\n'''		)

					print(f'Saved {dataset} dataset to {save_dir}'		)


if __name__ == "__main__":
							fire.Fire(download_wmt_dataset)




