





"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE__					=   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__					=   {
    "junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json",
    "junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json",
    "junnyu/roformer_chinese_char_small": (
        "https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json"
    ),
    "junnyu/roformer_chinese_char_base": (
        "https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json"
    ),
    "junnyu/roformer_small_discriminator": (
        "https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json"
    ),
    "junnyu/roformer_small_generator": (
        "https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json"
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}

class 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      'roformer'





      def __init__(       self      ,						lowercase=50_000      ,						lowercase=None      ,						lowercase=768      ,						lowercase=12      ,						lowercase=12      ,						lowercase=3_072      ,						lowercase="gelu"      ,						lowercase=0.1      ,						lowercase=0.1      ,						lowercase=1_536      ,						lowercase=2      ,						lowercase=0.02      ,						lowercase=1e-12      ,						lowercase=0      ,						lowercase=False      ,						lowercase=True      ,						**lowercase      ,						)		->     Dict:
            super().__init__(pad_token_id=lowercase      ,						**lowercase )

            lowerCAmelCase  						=       vocab_size
            lowerCAmelCase  						=       hidden_size if embedding_size is None else embedding_size
            lowerCAmelCase  						=       hidden_size
            lowerCAmelCase  						=       num_hidden_layers
            lowerCAmelCase  						=       num_attention_heads
            lowerCAmelCase  						=       hidden_act
            lowerCAmelCase  						=       intermediate_size
            lowerCAmelCase  						=       hidden_dropout_prob
            lowerCAmelCase  						=       attention_probs_dropout_prob
            lowerCAmelCase  						=       max_position_embeddings
            lowerCAmelCase  						=       type_vocab_size
            lowerCAmelCase  						=       initializer_range
            lowerCAmelCase  						=       layer_norm_eps
            lowerCAmelCase  						=       rotary_value
            lowerCAmelCase  						=       use_cache







class 		lowercase					(					_UpperCAmelCase					):





      @property
      def     _snake_case			(       self )		->     Mapping[str, Mapping[int, str]]:
            if self.task == "multiple-choice":
                  lowerCAmelCase  						=       {0: """batch""", 1: """choice""", 2: """sequence"""}
            else:
                  lowerCAmelCase  						=       {0: """batch""", 1: """sequence"""}
            lowerCAmelCase  						=       {0: """batch""", 1: """sequence"""}
            return OrderedDict(
                [
                    ("""input_ids""", dynamic_axis),
                    ("""attention_mask""", dynamic_axis),
                    ("""token_type_ids""", dynamic_axis),
                ] )










"""simple docstring"""






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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
							import PIL


UpperCAmelCase    :     Optional[Any]   						=		logging.get_logger(__name__)
def      lowerCamelCase       (     _UpperCamelCase		:      Any       ,			_UpperCamelCase		:      Optional[Any]							)		->       int:

							'''simple docstring'''



							__UpperCAmelCase   :						int      							=					b.T
							__UpperCAmelCase   :						Union[str, Any]      							=					np.sum(np.square(_UpperCamelCase							)       ,			axis=1							)
							__UpperCAmelCase   :						List[Any]      							=					np.sum(np.square(_UpperCamelCase							)       ,			axis=0							)
							__UpperCAmelCase   :						int      							=					np.matmul(_UpperCamelCase       ,			_UpperCamelCase							)
							__UpperCAmelCase   :						Tuple      							=					aa[:, None] - 2 * ab + ba[None, :]
							return d
def      lowerCamelCase       (     _UpperCamelCase		:      Tuple       ,			_UpperCamelCase		:      Optional[Any]							)		->       Optional[int]:

							'''simple docstring'''



							__UpperCAmelCase   :						Tuple      							=					x.reshape(-1       ,			3							)
							__UpperCAmelCase   :						Optional[Any]      							=					squared_euclidean_distance(_UpperCamelCase       ,			_UpperCamelCase							)
							return np.argmin(_UpperCamelCase       ,			axis=1							)





class      lowerCamelCase__   (     A				):







					"""simple docstring"""


					__a	       =							["""pixel_values"""]






					def __init__(       self    : List[str] ,       UpperCamelCase    : Optional[Union[List[List[int]], np.ndarray]] = None ,       UpperCamelCase    : bool = True ,       UpperCamelCase    : Dict[str, int] = None ,       UpperCamelCase    : PILImageResampling = PILImageResampling.BILINEAR ,       UpperCamelCase    : bool = True ,       UpperCamelCase    : bool = True ,       **UpperCamelCase    : int ,       ):
												'''simple docstring'''






												super().__init__(**UpperCamelCase  )
												__UpperCAmelCase   :						Optional[Any]      							=					size if size is not None else {"""height""": 256, """width""": 256}
												__UpperCAmelCase   :						Tuple      							=					get_size_dict(UpperCamelCase  )
												__UpperCAmelCase   :						Union[str, Any]      							=					np.array(UpperCamelCase  ) if clusters is not None else None
												__UpperCAmelCase   :						int      							=					do_resize
												__UpperCAmelCase   :						List[str]      							=					size
												__UpperCAmelCase   :						Dict      							=					resample
												__UpperCAmelCase   :						int      							=					do_normalize
												__UpperCAmelCase   :						List[Any]      							=					do_color_quantize






					def lowerCamelCase__							(       self    : List[str] ,       UpperCamelCase    : np.ndarray ,       UpperCamelCase    : Dict[str, int] ,       UpperCamelCase    : PILImageResampling = PILImageResampling.BILINEAR ,       UpperCamelCase    : Optional[Union[str, ChannelDimension]] = None ,       **UpperCamelCase    : str ,       ):
												'''simple docstring'''






												__UpperCAmelCase   :						Any      							=					get_size_dict(UpperCamelCase  )
												if "height" not in size or "width" not in size:
																			raise ValueError(f'''Size dictionary must contain both height and width keys. Got {size.keys()}'''  )
												return resize(
												    UpperCamelCase ,       size=(size["""height"""], size["""width"""]) ,       resample=UpperCamelCase ,       data_format=UpperCamelCase ,       **UpperCamelCase  )






					def lowerCamelCase__							(       self    : Dict ,       UpperCamelCase    : np.ndarray ,       UpperCamelCase    : Optional[Union[str, ChannelDimension]] = None ,       ):
												'''simple docstring'''






												__UpperCAmelCase   :						Optional[Any]      							=					rescale(image=UpperCamelCase ,       scale=1 / 127.5 ,       data_format=UpperCamelCase  )
												__UpperCAmelCase   :						Optional[Any]      							=					image - 1
												return image






					def lowerCamelCase__							(       self    : Dict ,       UpperCamelCase    : ImageInput ,       UpperCamelCase    : bool = None ,       UpperCamelCase    : Dict[str, int] = None ,       UpperCamelCase    : PILImageResampling = None ,       UpperCamelCase    : bool = None ,       UpperCamelCase    : Optional[bool] = None ,       UpperCamelCase    : Optional[Union[List[List[int]], np.ndarray]] = None ,       UpperCamelCase    : Optional[Union[str, TensorType]] = None ,       UpperCamelCase    : Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST ,       **UpperCamelCase    : Optional[int] ,       ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[str]      							=					do_resize if do_resize is not None else self.do_resize
												__UpperCAmelCase   :						Any      							=					size if size is not None else self.size
												__UpperCAmelCase   :						Any      							=					get_size_dict(UpperCamelCase  )
												__UpperCAmelCase   :						Optional[Any]      							=					resample if resample is not None else self.resample
												__UpperCAmelCase   :						Any      							=					do_normalize if do_normalize is not None else self.do_normalize
												__UpperCAmelCase   :						Any      							=					do_color_quantize if do_color_quantize is not None else self.do_color_quantize
												__UpperCAmelCase   :						Tuple      							=					clusters if clusters is not None else self.clusters
												__UpperCAmelCase   :						Tuple      							=					np.array(UpperCamelCase  )

												__UpperCAmelCase   :						int      							=					make_list_of_images(UpperCamelCase  )

												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."""  )

												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_color_quantize and clusters is None:
																			raise ValueError("""Clusters must be specified if do_color_quantize is True."""  )

												# All transformations expect numpy arrays.
												__UpperCAmelCase   :						Tuple      							=					[to_numpy_array(UpperCamelCase  ) for image in images]

												if do_resize:
																			__UpperCAmelCase   :						Dict      							=					[self.resize(image=UpperCamelCase ,       size=UpperCamelCase ,       resample=UpperCamelCase  ) for image in images]

												if do_normalize:
																			__UpperCAmelCase   :						Any      							=					[self.normalize(image=UpperCamelCase  ) for image in images]

												if do_color_quantize:
																			__UpperCAmelCase   :						str      							=					[to_channel_dimension_format(UpperCamelCase ,       ChannelDimension.LAST  ) for image in images]
																			# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
																			__UpperCAmelCase   :						Optional[int]      							=					np.array(UpperCamelCase  )
																			__UpperCAmelCase   :						Any      							=					color_quantize(UpperCamelCase ,       UpperCamelCase  ).reshape(images.shape[:-1]  )

																			# flatten to (batch_size, height*width)
																			__UpperCAmelCase   :						Dict      							=					images.shape[0]
																			__UpperCAmelCase   :						Optional[Any]      							=					images.reshape(UpperCamelCase ,       -1  )

																			# We need to convert back to a list of images to keep consistent behaviour across processors.
																			__UpperCAmelCase   :						int      							=					list(UpperCamelCase  )
												else:
																			__UpperCAmelCase   :						List[Any]      							=					[to_channel_dimension_format(UpperCamelCase ,       UpperCamelCase  ) for image in images]

												__UpperCAmelCase   :						List[str]      							=					{"""input_ids""": images}
												return BatchFeature(data=UpperCamelCase ,       tensor_type=UpperCamelCase  )
