






"""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,
)


__A		:       str  				=		logging.get_logger(__name__)

__A		:       Union[str, Any]  				=		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'''),
    ]
)

__A		:       List[Any]  				=		_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)



def 						lowercase    (				__snake_case				: str	):
		for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
				if class_name in extractors:
						lowercase_ : Dict							       =   model_type_to_module_name(__snake_case	)

						lowercase_ : Optional[Any]							       =   importlib.import_module(F'''.{module_name}'''		,					'''transformers.models'''	)
						try:
								return getattr(__snake_case		,					__snake_case	)
						except AttributeError:
								continue

		for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
				if getattr(__snake_case		,					'''__name__'''		,					__snake_case	) == 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.
		lowercase_ : str							       =   importlib.import_module('''transformers'''	)
		if hasattr(__snake_case		,					__snake_case	):
				return getattr(__snake_case		,					__snake_case	)

		return None



def 						lowercase    (				__snake_case				: Union[str, os.PathLike]		,					__snake_case				: Optional[Union[str, os.PathLike]] = None		,					__snake_case				: bool = False		,					__snake_case				: bool = False		,					__snake_case				: Optional[Dict[str, str]] = None		,					__snake_case				: Optional[Union[bool, str]] = None		,					__snake_case				: Optional[str] = None		,					__snake_case				: bool = False		,					**__snake_case				: List[str]		,					):
		lowercase_ : Optional[Any]							       =   get_file_from_repo(
		    __snake_case		,					__snake_case		,					cache_dir=__snake_case		,					force_download=__snake_case		,					resume_download=__snake_case		,					proxies=__snake_case		,					use_auth_token=__snake_case		,					revision=__snake_case		,					local_files_only=__snake_case		,					)
		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(__snake_case		,					encoding='''utf-8'''	) as reader:
				return json.load(__snake_case	)






class 					_UpperCAmelCase       :




					def __init__(				self     :						Tuple   )  ->  Union[str, Any]:
							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(A   )
					def   A  (				cls     :						Union[str, Any]    ,						A     :						List[Any]    ,						**A     :						Tuple   )  ->  Dict:
							lowercase_ : Tuple							       =   kwargs.pop('''config'''    ,						A   )
							lowercase_ : Tuple							       =   kwargs.pop('''trust_remote_code'''    ,						A   )
							lowercase_ : Optional[Any]							       =   True

							lowercase_       ,					lowercase_ : Optional[Any]							       =   ImageProcessingMixin.get_image_processor_dict(A    ,						**A   )
							lowercase_ : Union[str, Any]							       =   config_dict.get('''image_processor_type'''    ,						A   )
							lowercase_ : Tuple							       =   None
							if "AutoImageProcessor" in config_dict.get('''auto_map'''    ,						{}   ):
									lowercase_ : Optional[int]							       =   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:
									lowercase_ : Union[str, Any]							       =   config_dict.pop('''feature_extractor_type'''    ,						A   )
									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.'''   )
											lowercase_ : List[Any]							       =   feature_extractor_class.replace('''FeatureExtractor'''    ,						'''ImageProcessor'''   )
									if "AutoFeatureExtractor" in config_dict.get('''auto_map'''    ,						{}   ):
											lowercase_ : str							       =   config_dict['''auto_map''']['''AutoFeatureExtractor''']
											lowercase_ : int							       =   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(A    ,						A   ):
											lowercase_ : List[Any]							       =   AutoConfig.from_pretrained(A    ,						**A   )
									# It could be in `config.image_processor_type``
									lowercase_ : Tuple							       =   getattr(A    ,						'''image_processor_type'''    ,						A   )
									if hasattr(A    ,						'''auto_map'''   ) and "AutoImageProcessor" in config.auto_map:
											lowercase_ : Tuple							       =   config.auto_map['''AutoImageProcessor''']

							if image_processor_class is not None:
									lowercase_ : Tuple							       =   image_processor_class_from_name(A   )

							lowercase_ : Tuple							       =   image_processor_auto_map is not None
							lowercase_ : Any							       =   image_processor_class is not None or type(A   ) in IMAGE_PROCESSOR_MAPPING
							lowercase_ : Tuple							       =   resolve_trust_remote_code(
							    A    ,						A    ,						A    ,						A   )

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

							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   A  (				A     :						int    ,						A     :						Dict   )  ->  Dict:
							IMAGE_PROCESSOR_MAPPING.register(A    ,						A   )






from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


lowercase_       		=  logging.get_logger(__name__)

lowercase_       		=  {
    "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
    "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
    "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
    "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
    "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							't5'
    lowerCamelCase			=							['past_key_values']
    lowerCamelCase			=							{'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}


    def __init__(      self :       Union[str, Any],lowercase_ :       int=3_2_1_2_8,lowercase_ :       int=5_1_2,lowercase_ :       List[str]=6_4,lowercase_ :       Tuple=2_0_4_8,lowercase_ :       Any=6,lowercase_ :       List[str]=None,lowercase_ :       Union[str, Any]=8,lowercase_ :       int=3_2,lowercase_ :       Dict=1_2_8,lowercase_ :       Optional[int]=0.1,lowercase_ :       List[str]=1E-6,lowercase_ :       Tuple=1.0,lowercase_ :       Any="relu",lowercase_ :       Union[str, Any]=True,lowercase_ :       Optional[Any]=True,lowercase_ :       int=0,lowercase_ :       str=1,**lowercase_ :       str,)->    Any:





        '''simple docstring'''







        A__          =   vocab_size
        A__          =   d_model
        A__          =   d_kv
        A__          =   d_ff
        A__          =   num_layers
        A__          =   (
            num_decoder_layers if num_decoder_layers is not None else self.num_layers
        )  # default = symmetry
        A__          =   num_heads
        A__          =   relative_attention_num_buckets
        A__          =   relative_attention_max_distance
        A__          =   dropout_rate
        A__          =   layer_norm_epsilon
        A__          =   initializer_factor
        A__          =   feed_forward_proj
        A__          =   use_cache

        A__          =   self.feed_forward_proj.split('-'		)
        A__          =   act_info[-1]
        A__          =   act_info[0] == 'gated'

        if len(lowercase_		) > 1 and act_info[0] != "gated" or len(lowercase_		) > 2:
            raise ValueError(
                F'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
                'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
                '\'gated-gelu\' or \'relu\''		)

        # for backwards compatibility
        if feed_forward_proj == "gated-gelu":
            A__          =   'gelu_new'

        super().__init__(
            pad_token_id=lowercase_,eos_token_id=lowercase_,is_encoder_decoder=lowercase_,**lowercase_,)




class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    @property
    def 						snake_case__     (      self :       Tuple		)->    Mapping[str, Mapping[int, str]]:





        '''simple docstring'''







        A__          =   {
            'input_ids': {0: 'batch', 1: 'encoder_sequence'},
            'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
        }
        if self.use_past:
            A__          =   'past_encoder_sequence + sequence'
            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_(lowercase_,direction='inputs'		)

        return common_inputs


    @property
    def 						snake_case__     (      self :       Any		)->    int:





        '''simple docstring'''







        return 1_3

