
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A     :	Tuple    = {
    'configuration_xmod': [
        'XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XmodConfig',
        'XmodOnnxConfig',
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	int    = [
					    'XMOD_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'XmodForCausalLM',
					    'XmodForMaskedLM',
					    'XmodForMultipleChoice',
					    'XmodForQuestionAnswering',
					    'XmodForSequenceClassification',
					    'XmodForTokenClassification',
					    'XmodModel',
					    'XmodPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_xmod import (
										    XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
										    XmodForCausalLM,
										    XmodForMaskedLM,
										    XmodForMultipleChoice,
										    XmodForQuestionAnswering,
										    XmodForSequenceClassification,
										    XmodForTokenClassification,
										    XmodModel,
										    XmodPreTrainedModel,
										)

else:
					import sys

					A     :	int    = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save


def     __lowerCAmelCase			(			a__						,    a__						,    a__=1024						,    a__=1024						,    a__=False						,    **a__  )       ->  Optional[Any]:
				__a										=    AutoTokenizer.from_pretrained(a__  )
				__a										=    SeqaSeqDataset(a__						,    a__						,    a__						,    a__						,    type_path='''train'''						,    **a__  )
				__a										=    tok.pad_token_id

				def get_lens(a__  ):
								__a										=    tqdm(
								    DataLoader(a__						,    batch_size=512						,    num_workers=8						,    shuffle=a__						,    collate_fn=ds.collate_fn  )						,    desc=str(ds.len_file  )						,    )
								__a										=    []
								for batch in dl:
												__a										=    batch['''input_ids'''].ne(a__  ).sum(1  ).tolist()
												__a										=    batch['''labels'''].ne(a__  ).sum(1  ).tolist()
												if consider_target:
																for src, tgt in zip(a__						,    a__  ):
																				max_lens.append(max(a__						,    a__  )  )
												else:
																max_lens.extend(a__  )
								return max_lens

				__a										=    get_lens(a__  )
				__a										=    SeqaSeqDataset(a__						,    a__						,    a__						,    a__						,    type_path='''val'''						,    **a__  )
				__a										=    get_lens(a__  )
				pickle_save(a__						,    train_ds.len_file  )
				pickle_save(a__						,    val_ds.len_file  )


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