



'''simple docstring'''





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

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase=1024							,    UpperCamelCase=1024							,    UpperCamelCase=False							,    **UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  str							      =					AutoTokenizer.from_pretrained(UpperCamelCase			)
    lowerCAmelCase__ :  Optional[int]							      =					SeqaSeqDataset(UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    type_path="""train"""							,    **UpperCamelCase			)
    lowerCAmelCase__ :  Optional[Any]							      =					tok.pad_token_id

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

    lowerCAmelCase__ :  int							      =					get_lens(UpperCamelCase			)
    lowerCAmelCase__ :  int							      =					SeqaSeqDataset(UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    type_path="""val"""							,    **UpperCamelCase			)
    lowerCAmelCase__ :  str							      =					get_lens(UpperCamelCase			)
    pickle_save(UpperCamelCase							,    train_ds.len_file			)
    pickle_save(UpperCamelCase							,    val_ds.len_file			)


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







'''simple docstring'''

import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


__lowerCAmelCase									=   logging.get_logger(__name__)

__lowerCAmelCase									=   OrderedDict(
    [
        ('''align''', '''EfficientNetImageProcessor'''),
        ('''beit''', '''BeitImageProcessor'''),
        ('''bit''', '''BitImageProcessor'''),
        ('''blip''', '''BlipImageProcessor'''),
        ('''blip-2''', '''BlipImageProcessor'''),
        ('''bridgetower''', '''BridgeTowerImageProcessor'''),
        ('''chinese_clip''', '''ChineseCLIPImageProcessor'''),
        ('''clip''', '''CLIPImageProcessor'''),
        ('''clipseg''', '''ViTImageProcessor'''),
        ('''conditional_detr''', '''ConditionalDetrImageProcessor'''),
        ('''convnext''', '''ConvNextImageProcessor'''),
        ('''convnextv2''', '''ConvNextImageProcessor'''),
        ('''cvt''', '''ConvNextImageProcessor'''),
        ('''data2vec-vision''', '''BeitImageProcessor'''),
        ('''deformable_detr''', '''DeformableDetrImageProcessor'''),
        ('''deit''', '''DeiTImageProcessor'''),
        ('''deta''', '''DetaImageProcessor'''),
        ('''detr''', '''DetrImageProcessor'''),
        ('''dinat''', '''ViTImageProcessor'''),
        ('''donut-swin''', '''DonutImageProcessor'''),
        ('''dpt''', '''DPTImageProcessor'''),
        ('''efficientformer''', '''EfficientFormerImageProcessor'''),
        ('''efficientnet''', '''EfficientNetImageProcessor'''),
        ('''flava''', '''FlavaImageProcessor'''),
        ('''focalnet''', '''BitImageProcessor'''),
        ('''git''', '''CLIPImageProcessor'''),
        ('''glpn''', '''GLPNImageProcessor'''),
        ('''groupvit''', '''CLIPImageProcessor'''),
        ('''imagegpt''', '''ImageGPTImageProcessor'''),
        ('''instructblip''', '''BlipImageProcessor'''),
        ('''layoutlmv2''', '''LayoutLMv2ImageProcessor'''),
        ('''layoutlmv3''', '''LayoutLMv3ImageProcessor'''),
        ('''levit''', '''LevitImageProcessor'''),
        ('''mask2former''', '''Mask2FormerImageProcessor'''),
        ('''maskformer''', '''MaskFormerImageProcessor'''),
        ('''mgp-str''', '''ViTImageProcessor'''),
        ('''mobilenet_v1''', '''MobileNetV1ImageProcessor'''),
        ('''mobilenet_v2''', '''MobileNetV2ImageProcessor'''),
        ('''mobilevit''', '''MobileViTImageProcessor'''),
        ('''mobilevit''', '''MobileViTImageProcessor'''),
        ('''mobilevitv2''', '''MobileViTImageProcessor'''),
        ('''nat''', '''ViTImageProcessor'''),
        ('''oneformer''', '''OneFormerImageProcessor'''),
        ('''owlvit''', '''OwlViTImageProcessor'''),
        ('''perceiver''', '''PerceiverImageProcessor'''),
        ('''pix2struct''', '''Pix2StructImageProcessor'''),
        ('''poolformer''', '''PoolFormerImageProcessor'''),
        ('''regnet''', '''ConvNextImageProcessor'''),
        ('''resnet''', '''ConvNextImageProcessor'''),
        ('''sam''', '''SamImageProcessor'''),
        ('''segformer''', '''SegformerImageProcessor'''),
        ('''swiftformer''', '''ViTImageProcessor'''),
        ('''swin''', '''ViTImageProcessor'''),
        ('''swin2sr''', '''Swin2SRImageProcessor'''),
        ('''swinv2''', '''ViTImageProcessor'''),
        ('''table-transformer''', '''DetrImageProcessor'''),
        ('''timesformer''', '''VideoMAEImageProcessor'''),
        ('''tvlt''', '''TvltImageProcessor'''),
        ('''upernet''', '''SegformerImageProcessor'''),
        ('''van''', '''ConvNextImageProcessor'''),
        ('''videomae''', '''VideoMAEImageProcessor'''),
        ('''vilt''', '''ViltImageProcessor'''),
        ('''vit''', '''ViTImageProcessor'''),
        ('''vit_hybrid''', '''ViTHybridImageProcessor'''),
        ('''vit_mae''', '''ViTImageProcessor'''),
        ('''vit_msn''', '''ViTImageProcessor'''),
        ('''xclip''', '''CLIPImageProcessor'''),
        ('''yolos''', '''YolosImageProcessor'''),
    ]
)

__lowerCAmelCase									=   _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)



def      __lowerCamelCase     (      lowerCAmelCase_  )			->      Optional[Any]:
		for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
				if class_name in extractors:
						_a     :      List[Any]        =							model_type_to_module_name(lowerCAmelCase_  )

						_a     :      Optional[Any]        =							importlib.import_module(f""".{module_name}"""						,			'transformers.models'  )
						try:
								return getattr(lowerCAmelCase_						,			lowerCAmelCase_  )
						except AttributeError:
								continue

		for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
				if getattr(lowerCAmelCase_						,			'__name__'						,			lowerCAmelCase_  ) == class_name:
						return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
		_a     :      Dict        =							importlib.import_module('transformers'  )
		if hasattr(lowerCAmelCase_						,			lowerCAmelCase_  ):
				return getattr(lowerCAmelCase_						,			lowerCAmelCase_  )

		return None



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_ = None						,			lowerCAmelCase_ = False						,			lowerCAmelCase_ = False						,			lowerCAmelCase_ = None						,			lowerCAmelCase_ = None						,			lowerCAmelCase_ = None						,			lowerCAmelCase_ = False						,			**lowerCAmelCase_						,			)			->      Tuple:
		_a     :      List[str]        =							get_file_from_repo(
		    lowerCAmelCase_						,			lowerCAmelCase_						,			cache_dir=lowerCAmelCase_						,			force_download=lowerCAmelCase_						,			resume_download=lowerCAmelCase_						,			proxies=lowerCAmelCase_						,			use_auth_token=lowerCAmelCase_						,			revision=lowerCAmelCase_						,			local_files_only=lowerCAmelCase_						,			)
		if resolved_config_file is None:
				logger.info(
				    'Could not locate the image processor configuration file, will try to use the model config instead.'  )
				return {}

		with open(lowerCAmelCase_						,			encoding='utf-8'  ) as reader:
				return json.load(lowerCAmelCase_  )

class 	__magic_name__						:







							def __init__(							self					:       List[str]		):
									raise EnvironmentError(
									    'AutoImageProcessor is designed to be instantiated '
									    'using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method.'		)







							@classmethod
							@replace_list_option_in_docstrings(_UpperCAmelCase		)
							def 							__lowercase			(							cls					:       Dict	,_UpperCAmelCase					:       Union[str, Any]	,**_UpperCAmelCase					:       Optional[Any]		):
									_a     :      Any        =							kwargs.pop('config'	,_UpperCAmelCase		)
									_a     :      Dict        =							kwargs.pop('trust_remote_code'	,_UpperCAmelCase		)
									_a     :      Any        =							True

									_a       ,					_a     :      Tuple        =							ImageProcessingMixin.get_image_processor_dict(_UpperCAmelCase	,**_UpperCAmelCase		)
									_a     :      List[Any]        =							config_dict.get('image_processor_type'	,_UpperCAmelCase		)
									_a     :      int        =							None
									if "AutoImageProcessor" in config_dict.get('auto_map'	,{}		):
											_a     :      Any        =							config_dict['auto_map']['AutoImageProcessor']

									# If we still don't have the image processor class, check if we're loading from a previous feature extractor config
									# and if so, infer the image processor class from there.
									if image_processor_class is None and image_processor_auto_map is None:
											_a     :      List[Any]        =							config_dict.pop('feature_extractor_type'	,_UpperCAmelCase		)
											if feature_extractor_class is not None:
													logger.warning(
													    'Could not find image processor class in the image processor config or the model config. Loading'
													    ' based on pattern matching with the model\'s feature extractor configuration.'		)
													_a     :      Optional[int]        =							feature_extractor_class.replace('FeatureExtractor'	,'ImageProcessor'		)
											if "AutoFeatureExtractor" in config_dict.get('auto_map'	,{}		):
													_a     :      List[Any]        =							config_dict['auto_map']['AutoFeatureExtractor']
													_a     :      List[str]        =							feature_extractor_auto_map.replace('FeatureExtractor'	,'ImageProcessor'		)
													logger.warning(
													    'Could not find image processor auto map in the image processor config or the model config.'
													    ' Loading based on pattern matching with the model\'s feature extractor configuration.'		)

        # If we don't find the image processor class in the image processor config, let's try the model config.
									if image_processor_class is None and image_processor_auto_map is None:
											if not isinstance(_UpperCAmelCase	,_UpperCAmelCase		):
													_a     :      Dict        =							AutoConfig.from_pretrained(_UpperCAmelCase	,**_UpperCAmelCase		)
											# It could be in `config.image_processor_type``
											_a     :      Optional[int]        =							getattr(_UpperCAmelCase	,'image_processor_type'	,_UpperCAmelCase		)
											if hasattr(_UpperCAmelCase	,'auto_map'		) and "AutoImageProcessor" in config.auto_map:
													_a     :      Union[str, Any]        =							config.auto_map['AutoImageProcessor']

									if image_processor_class is not None:
											_a     :      Optional[int]        =							image_processor_class_from_name(_UpperCAmelCase		)

									_a     :      List[str]        =							image_processor_auto_map is not None
									_a     :      Optional[int]        =							image_processor_class is not None or type(_UpperCAmelCase		) in IMAGE_PROCESSOR_MAPPING
									_a     :      Optional[int]        =							resolve_trust_remote_code(
									    _UpperCAmelCase	,_UpperCAmelCase	,_UpperCAmelCase	,_UpperCAmelCase		)

									if has_remote_code and trust_remote_code:
											_a     :      Dict        =							get_class_from_dynamic_module(
											    _UpperCAmelCase	,_UpperCAmelCase	,**_UpperCAmelCase		)
											_a     :      int        =							kwargs.pop('code_revision'	,_UpperCAmelCase		)
											if os.path.isdir(_UpperCAmelCase		):
													image_processor_class.register_for_auto_class()
											return image_processor_class.from_dict(_UpperCAmelCase	,**_UpperCAmelCase		)
									elif image_processor_class is not None:
											return image_processor_class.from_dict(_UpperCAmelCase	,**_UpperCAmelCase		)
									# Last try: we use the IMAGE_PROCESSOR_MAPPING.
									elif type(_UpperCAmelCase		) in IMAGE_PROCESSOR_MAPPING:
											_a     :      Dict        =							IMAGE_PROCESSOR_MAPPING[type(_UpperCAmelCase		)]
											return image_processor_class.from_dict(_UpperCAmelCase	,**_UpperCAmelCase		)

									raise ValueError(
									    F"""Unrecognized image processor in {pretrained_model_name_or_path}. Should have a """
									    F"""`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following """
									    F"""`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys()		)}"""		)







							@staticmethod
							def 							__lowercase			(							_UpperCAmelCase					:       Optional[Any]	,_UpperCAmelCase					:       Dict		):
									IMAGE_PROCESSOR_MAPPING.register(_UpperCAmelCase	,_UpperCAmelCase		)




