



from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
   import PIL


_lowercase						  =		logging.get_logger(__name__)



def 		lowerCAmelCase__    (					UpperCamelCase_				:       List[Any]	)->  Union[str, Any]:
    if isinstance(_lowerCamelCase ,       (list, tuple)	) and isinstance(videos[0] ,       (list, tuple)	) and is_valid_image(videos[0][0]	):
        return videos

    elif isinstance(_lowerCamelCase ,       (list, tuple)	) and is_valid_image(videos[0]	):
        return [videos]

    elif is_valid_image(_lowerCamelCase	):
        return [[videos]]

    raise ValueError(f"Could not make batched video from {videos}"	)



class     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				["pixel_values"]




     def __init__(				self ,						a__ = True ,						a__ = None ,						a__ = PILImageResampling.BILINEAR ,						a__ = True ,						a__ = None ,						a__ = True ,						a__ = 1 / 2_5_5 ,						a__ = True ,						a__ = None ,						a__ = None ,						**a__ ,						):
         super().__init__(**UpperCamelCase__)
         A__		 =					size if size is not None else {'''shortest_edge''': 2_2_4}
         A__		 =					get_size_dict(UpperCamelCase__ ,						default_to_square=UpperCamelCase__)
         A__		 =					crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
         A__		 =					get_size_dict(UpperCamelCase__ ,						param_name='''crop_size''')

         A__		 =					do_resize
         A__		 =					size
         A__		 =					do_center_crop
         A__		 =					crop_size
         A__		 =					resample
         A__		 =					do_rescale
         A__		 =					rescale_factor
         A__		 =					do_normalize
         A__		 =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         A__		 =					image_std if image_std is not None else IMAGENET_STANDARD_STD




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ = PILImageResampling.BILINEAR ,						a__ = None ,						**a__ ,						):
         A__		 =					get_size_dict(UpperCamelCase__ ,						default_to_square=UpperCamelCase__)
         if "shortest_edge" in size:
             A__		 =					get_resize_output_image_size(UpperCamelCase__ ,						size['''shortest_edge'''] ,						default_to_square=UpperCamelCase__)
         elif "height" in size and "width" in size:
             A__		 =					(size['''height'''], size['''width'''])
         else:
             raise ValueError(F"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}")
         return resize(UpperCamelCase__ ,						size=UpperCamelCase__ ,						resample=UpperCamelCase__ ,						data_format=UpperCamelCase__ ,						**UpperCamelCase__)




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ = None ,						**a__ ,						):
         A__		 =					get_size_dict(UpperCamelCase__)
         if "height" not in size or "width" not in size:
             raise ValueError(F"Size must have 'height' and 'width' as keys. Got {size.keys()}")
         return center_crop(UpperCamelCase__ ,						size=(size['''height'''], size['''width''']) ,						data_format=UpperCamelCase__ ,						**UpperCamelCase__)




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ = None ,						**a__ ,						):
         return rescale(UpperCamelCase__ ,						scale=UpperCamelCase__ ,						data_format=UpperCamelCase__ ,						**UpperCamelCase__)




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ ,						a__ = None ,						**a__ ,						):
         return normalize(UpperCamelCase__ ,						mean=UpperCamelCase__ ,						std=UpperCamelCase__ ,						data_format=UpperCamelCase__ ,						**UpperCamelCase__)




     def 		snake_case_		(				self ,						a__ ,						a__ = None ,						a__ = None ,						a__ = None ,						a__ = None ,						a__ = None ,						a__ = None ,						a__ = None ,						a__ = None ,						a__ = None ,						a__ = None ,						a__ = ChannelDimension.FIRST ,						):





         if do_resize and size is None or resample is None:
             raise ValueError('''Size and resample must be specified if do_resize is True.''')

         if do_center_crop and crop_size is None:
             raise ValueError('''Crop size must be specified if do_center_crop is True.''')

         if do_rescale and rescale_factor is None:
             raise ValueError('''Rescale factor must be specified if do_rescale is True.''')

         if do_normalize and (image_mean is None or image_std is None):
             raise ValueError('''Image mean and std must be specified if do_normalize is True.''')

         # All transformations expect numpy arrays.
         A__		 =					to_numpy_array(UpperCamelCase__)

         if do_resize:
             A__		 =					self.resize(image=UpperCamelCase__ ,						size=UpperCamelCase__ ,						resample=UpperCamelCase__)

         if do_center_crop:
             A__		 =					self.center_crop(UpperCamelCase__ ,						size=UpperCamelCase__)

         if do_rescale:
             A__		 =					self.rescale(image=UpperCamelCase__ ,						scale=UpperCamelCase__)

         if do_normalize:
             A__		 =					self.normalize(image=UpperCamelCase__ ,						mean=UpperCamelCase__ ,						std=UpperCamelCase__)

         A__		 =					to_channel_dimension_format(UpperCamelCase__ ,						UpperCamelCase__)
         return image




     def 		snake_case_		(				self ,						a__ ,						a__ = None ,						a__ = None ,						a__ = None ,						a__ = None ,						a__ = None ,						a__ = None ,						a__ = None ,						a__ = None ,						a__ = None ,						a__ = None ,						a__ = None ,						a__ = ChannelDimension.FIRST ,						**a__ ,						):
         A__		 =					do_resize if do_resize is not None else self.do_resize
         A__		 =					resample if resample is not None else self.resample
         A__		 =					do_center_crop if do_center_crop is not None else self.do_center_crop
         A__		 =					do_rescale if do_rescale is not None else self.do_rescale
         A__		 =					rescale_factor if rescale_factor is not None else self.rescale_factor
         A__		 =					do_normalize if do_normalize is not None else self.do_normalize
         A__		 =					image_mean if image_mean is not None else self.image_mean
         A__		 =					image_std if image_std is not None else self.image_std

         A__		 =					size if size is not None else self.size
         A__		 =					get_size_dict(UpperCamelCase__ ,						default_to_square=UpperCamelCase__)
         A__		 =					crop_size if crop_size is not None else self.crop_size
         A__		 =					get_size_dict(UpperCamelCase__ ,						param_name='''crop_size''')

         if not valid_images(UpperCamelCase__):
             raise ValueError(
                 '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                 '''torch.Tensor, tf.Tensor or jax.ndarray.''')

         A__		 =					make_batched(UpperCamelCase__)

         A__		 =					[
             [
                 self._preprocess_image(
                     image=UpperCamelCase__ ,						do_resize=UpperCamelCase__ ,						size=UpperCamelCase__ ,						resample=UpperCamelCase__ ,						do_center_crop=UpperCamelCase__ ,						crop_size=UpperCamelCase__ ,						do_rescale=UpperCamelCase__ ,						rescale_factor=UpperCamelCase__ ,						do_normalize=UpperCamelCase__ ,						image_mean=UpperCamelCase__ ,						image_std=UpperCamelCase__ ,						data_format=UpperCamelCase__ ,						)
                 for img in video
             ]
             for video in videos
         ]

         A__		 =					{'''pixel_values''': videos}
         return BatchFeature(data=UpperCamelCase__ ,						tensor_type=UpperCamelCase__)









from collections import OrderedDict
from typing import Mapping

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


_lowercase						  =		logging.get_logger(__name__)

_lowercase						  =		{
    "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/config.json",
    "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/config.json",
    "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/config.json",
    "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/config.json",
    "bert-base-multilingual-uncased": "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json",
    "bert-base-multilingual-cased": "https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json",
    "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/config.json",
    "bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/config.json",
    "bert-large-uncased-whole-word-masking": (
        "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json"
    ),
    "bert-large-cased-whole-word-masking": (
        "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json"
    ),
    "bert-large-uncased-whole-word-masking-finetuned-squad": (
        "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json"
    ),
    "bert-large-cased-whole-word-masking-finetuned-squad": (
        "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json"
    ),
    "bert-base-cased-finetuned-mrpc": "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json",
    "bert-base-german-dbmdz-cased": "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json",
    "bert-base-german-dbmdz-uncased": "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json",
    "cl-tohoku/bert-base-japanese": "https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json",
    "cl-tohoku/bert-base-japanese-whole-word-masking": (
        "https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json"
    ),
    "cl-tohoku/bert-base-japanese-char": (
        "https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json"
    ),
    "cl-tohoku/bert-base-japanese-char-whole-word-masking": (
        "https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json"
    ),
    "TurkuNLP/bert-base-finnish-cased-v1": (
        "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json"
    ),
    "TurkuNLP/bert-base-finnish-uncased-v1": (
        "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json"
    ),
    "wietsedv/bert-base-dutch-cased": "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json",
    # See all BERT models at https://huggingface.co/models?filter=bert
}



class     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				'''bert'''




     def __init__(				self ,						a__=3_0_5_2_2 ,						a__=7_6_8 ,						a__=1_2 ,						a__=1_2 ,						a__=3_0_7_2 ,						a__="gelu" ,						a__=0.1 ,						a__=0.1 ,						a__=5_1_2 ,						a__=2 ,						a__=0.0_2 ,						a__=1e-12 ,						a__=0 ,						a__="absolute" ,						a__=True ,						a__=None ,						**a__ ,						):
         super().__init__(pad_token_id=a__ ,						**a__)

         A__		 =					vocab_size
         A__		 =					hidden_size
         A__		 =					num_hidden_layers
         A__		 =					num_attention_heads
         A__		 =					hidden_act
         A__		 =					intermediate_size
         A__		 =					hidden_dropout_prob
         A__		 =					attention_probs_dropout_prob
         A__		 =					max_position_embeddings
         A__		 =					type_vocab_size
         A__		 =					initializer_range
         A__		 =					layer_norm_eps
         A__		 =					position_embedding_type
         A__		 =					use_cache
         A__		 =					classifier_dropout







class     _UpperCAmelCase			(       A__     ):




     @property
     def 		snake_case_		(				self):
         if self.task == "multiple-choice":
             A__		 =					{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
         else:
             A__		 =					{0: '''batch''', 1: '''sequence'''}
         return OrderedDict(
             [
                 ('''input_ids''', dynamic_axis),
                 ('''attention_mask''', dynamic_axis),
                 ('''token_type_ids''', dynamic_axis),
             ])
