



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,
)


_lowercase						  =		logging.get_logger(__name__)

_lowercase						  =		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"),
    ]
)

_lowercase						  =		_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)



def 		lowerCAmelCase__    (					UpperCamelCase_				:       str	)->  Tuple:
    for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
        if class_name in extractors:
            A__		 =					model_type_to_module_name(UpperCamelCase_	)

            A__		 =					importlib.import_module(f".{module_name}" ,       '''transformers.models'''	)
            try:
                return getattr(UpperCamelCase_ ,       UpperCamelCase_	)
            except AttributeError:
                continue

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

    return None



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



class     _UpperCAmelCase			:




     def __init__(				self):
         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 		snake_case_		(				cls ,						a__ ,						**a__):
         A__		 =					kwargs.pop('''config''' ,						a__)
         A__		 =					kwargs.pop('''trust_remote_code''' ,						a__)
         A__		 =					True

         A__ ,	A__		 =					ImageProcessingMixin.get_image_processor_dict(a__ ,						**a__)
         A__		 =					config_dict.get('''image_processor_type''' ,						a__)
         A__		 =					None
         if "AutoImageProcessor" in config_dict.get('''auto_map''' ,						{}):
             A__		 =					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__		 =					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.''')
                 A__		 =					feature_extractor_class.replace('''FeatureExtractor''' ,						'''ImageProcessor''')
             if "AutoFeatureExtractor" in config_dict.get('''auto_map''' ,						{}):
                 A__		 =					config_dict['''auto_map''']['''AutoFeatureExtractor''']
                 A__		 =					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__):
                 A__		 =					AutoConfig.from_pretrained(a__ ,						**a__)
             # It could be in `config.image_processor_type``
             A__		 =					getattr(a__ ,						'''image_processor_type''' ,						a__)
             if hasattr(a__ ,						'''auto_map''') and "AutoImageProcessor" in config.auto_map:
                 A__		 =					config.auto_map['''AutoImageProcessor''']

         if image_processor_class is not None:
             A__		 =					image_processor_class_from_name(a__)

         A__		 =					image_processor_auto_map is not None
         A__		 =					image_processor_class is not None or type(a__) in IMAGE_PROCESSOR_MAPPING
         A__		 =					resolve_trust_remote_code(
             a__ ,						a__ ,						a__ ,						a__)

         if has_remote_code and trust_remote_code:
             A__		 =					get_class_from_dynamic_module(
                 a__ ,						a__ ,						**a__)
             A__		 =					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:
             A__		 =					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 		snake_case_		(				a__ ,						a__):
         IMAGE_PROCESSOR_MAPPING.register(a__ ,						a__)





import jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


_lowercase						  =		logging.get_logger(__name__)

_lowercase						  =		"T5Config"



def 		lowerCAmelCase__    (					UpperCamelCase_				:       jnp.array ,       UpperCamelCase_				:       int ,       UpperCamelCase_				:       int	)->  jnp.ndarray:
    A__		 =					jnp.zeros_like(UpperCamelCase_	)
    A__		 =					shifted_input_ids.at[:, 1:].set(input_ids[:, :-1]	)
    A__		 =					shifted_input_ids.at[:, 0].set(UpperCamelCase_	)

    A__		 =					jnp.where(shifted_input_ids == -1_0_0 ,       UpperCamelCase_ ,       UpperCamelCase_	)
    return shifted_input_ids



class     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				'''mt5'''
     UpperCamelCase__		    =				MTaConfig







class     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				'''mt5'''
     UpperCamelCase__		    =				MTaConfig







class     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				'''mt5'''
     UpperCamelCase__		    =				MTaConfig
