



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

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=1_0_2_4  ,	_SCREAMING_SNAKE_CASE=1_0_2_4  ,	_SCREAMING_SNAKE_CASE=False  ,	**_SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			SeqaSeqDataset(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	type_path="train"  ,	**_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			tok.pad_token_id

	def get_lens(_SCREAMING_SNAKE_CASE   ):
		__lowercase		       =			tqdm(
		    DataLoader(_SCREAMING_SNAKE_CASE  ,	batch_size=5_1_2  ,	num_workers=8  ,	shuffle=_SCREAMING_SNAKE_CASE  ,	collate_fn=ds.collate_fn   )  ,	desc=str(ds.len_file   )  ,	)
		__lowercase		       =			[]
		for batch in dl:
			__lowercase		       =			batch["input_ids"].ne(_SCREAMING_SNAKE_CASE   ).sum(1   ).tolist()
			__lowercase		       =			batch["labels"].ne(_SCREAMING_SNAKE_CASE   ).sum(1   ).tolist()
			if consider_target:
				for src, tgt in zip(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
					max_lens.append(max(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )   )
			else:
				max_lens.extend(_SCREAMING_SNAKE_CASE   )
		return max_lens

	__lowercase		       =			get_lens(_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			SeqaSeqDataset(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	type_path="val"  ,	**_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			get_lens(_SCREAMING_SNAKE_CASE   )
	pickle_save(_SCREAMING_SNAKE_CASE  ,	train_ds.len_file   )
	pickle_save(_SCREAMING_SNAKE_CASE  ,	val_ds.len_file   )


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








# 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


snake_case__					:     List[str]    		=   {
    """configuration_mgp_str""": ["""MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MgpstrConfig"""],
    """processing_mgp_str""": ["""MgpstrProcessor"""],
    """tokenization_mgp_str""": ["""MgpstrTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case__					:     Dict    		=   [
	    """MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """MgpstrModel""",
	    """MgpstrPreTrainedModel""",
	    """MgpstrForSceneTextRecognition""",
	]

if TYPE_CHECKING:
	from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
	from .processing_mgp_str import MgpstrProcessor
	from .tokenization_mgp_str import MgpstrTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_mgp_str import (
		    MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MgpstrForSceneTextRecognition,
		    MgpstrModel,
		    MgpstrPreTrainedModel,
		)
else:
	import sys

	snake_case__					:     Union[str, Any]    		=   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



