



"""simple docstring"""




from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


lowercase_						=       logging.get_logger(__name__)
class 			_snake_case     (   __snake_case):
				UpperCamelCase__			:   List[Any]       						=["pixel_values"]




				def __init__(  self   :  Tuple,   __lowercase   :  bool = True,   __lowercase   :  Union[int, float] = 1 / 255,   __lowercase   :  bool = True,   __lowercase   :  int = 8,   **__lowercase   :  List[str],   ):
							super().__init__(**lowerCamelCase_  )

							lowercase__   					=			do_rescale
							lowercase__   					=			rescale_factor
							lowercase__   					=			do_pad
							lowercase__   					=			pad_size




				def 							A__ (  self   :  Optional[Any],   __lowercase   :  np.ndarray,   __lowercase   :  float,   __lowercase   :  Optional[Union[str, ChannelDimension]] = None,   **__lowercase   :  List[Any]  ):
							return rescale(lowerCamelCase_,   scale=lowerCamelCase_,   data_format=lowerCamelCase_,   **lowerCamelCase_  )




				def 							A__ (  self   :  Optional[int],   __lowercase   :  np.ndarray,   __lowercase   :  int,   __lowercase   :  Optional[Union[str, ChannelDimension]] = None  ):
							lowercase__   					=			get_image_size(lowerCamelCase_  )
							lowercase__   					=			(old_height // size + 1) * size - old_height
							lowercase__   					=			(old_width // size + 1) * size - old_width

							return pad(lowerCamelCase_,   ((0, pad_height), (0, pad_width)),   mode="symmetric",   data_format=lowerCamelCase_  )




				def 							A__ (  self   :  Optional[int],   __lowercase   :  ImageInput,   __lowercase   :  Optional[bool] = None,   __lowercase   :  Optional[float] = None,   __lowercase   :  Optional[bool] = None,   __lowercase   :  Optional[int] = None,   __lowercase   :  Optional[Union[str, TensorType]] = None,   __lowercase   :  Union[str, ChannelDimension] = ChannelDimension.FIRST,   **__lowercase   :  str,   ):
							lowercase__   					=			do_rescale if do_rescale is not None else self.do_rescale
							lowercase__   					=			rescale_factor if rescale_factor is not None else self.rescale_factor
							lowercase__   					=			do_pad if do_pad is not None else self.do_pad
							lowercase__   					=			pad_size if pad_size is not None else self.pad_size

							lowercase__   					=			make_list_of_images(lowerCamelCase_  )

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

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

							# All transformations expect numpy arrays.
							lowercase__   					=			[to_numpy_array(lowerCamelCase_  ) for image in images]

							if do_rescale:
										lowercase__   					=			[self.rescale(image=lowerCamelCase_,   scale=lowerCamelCase_  ) for image in images]

							if do_pad:
										lowercase__   					=			[self.pad(lowerCamelCase_,   size=lowerCamelCase_  ) for image in images]

							lowercase__   					=			[to_channel_dimension_format(lowerCamelCase_,   lowerCamelCase_  ) for image in images]

							lowercase__   					=			{"""pixel_values""": images}
							return BatchFeature(data=lowerCamelCase_,   tensor_type=lowerCamelCase_  )









from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_						=       logging.get_logger(__name__)

lowercase_						=       {
    """transfo-xl-wt103""": """https://huggingface.co/transfo-xl-wt103/resolve/main/config.json""",
}
class 			_snake_case     (   lowercase__):
				UpperCamelCase__			:   Optional[Any]       						="""transfo-xl"""
				UpperCamelCase__			:   Dict       						=["""mems"""]
				UpperCamelCase__			:   Optional[int]       						={
				    """n_token""": """vocab_size""",
				    """hidden_size""": """d_model""",
				    """num_attention_heads""": """n_head""",
				    """num_hidden_layers""": """n_layer""",
				}




				def __init__(  self   :  Optional[Any],   __lowercase   :  Optional[Any]=26_7735,   __lowercase   :  int=[2_0000, 4_0000, 20_0000],   __lowercase   :  Union[str, Any]=1024,   __lowercase   :  Tuple=1024,   __lowercase   :  Tuple=16,   __lowercase   :  Optional[Any]=64,   __lowercase   :  str=4096,   __lowercase   :  Optional[int]=4,   __lowercase   :  Union[str, Any]=False,   __lowercase   :  Union[str, Any]=18,   __lowercase   :  List[str]=1600,   __lowercase   :  List[Any]=1000,   __lowercase   :  Union[str, Any]=True,   __lowercase   :  Tuple=True,   __lowercase   :  Optional[Any]=0,   __lowercase   :  List[str]=-1,   __lowercase   :  int=True,   __lowercase   :  Dict=0.1,   __lowercase   :  Union[str, Any]=0.0,   __lowercase   :  str=True,   __lowercase   :  Optional[Any]="normal",   __lowercase   :  str=0.01,   __lowercase   :  Tuple=0.01,   __lowercase   :  List[Any]=0.02,   __lowercase   :  Any=1e-5,   __lowercase   :  Union[str, Any]=0,   **__lowercase   :  Union[str, Any],   ):
							lowercase__   					=			vocab_size
							lowercase__   					=			[]
							self.cutoffs.extend(__lowercase  )
							if proj_share_all_but_first:
										lowercase__   					=			[False] + [True] * len(self.cutoffs  )
							else:
										lowercase__   					=			[False] + [False] * len(self.cutoffs  )
							lowercase__   					=			d_model
							lowercase__   					=			d_embed
							lowercase__   					=			d_head
							lowercase__   					=			d_inner
							lowercase__   					=			div_val
							lowercase__   					=			pre_lnorm
							lowercase__   					=			n_layer
							lowercase__   					=			n_head
							lowercase__   					=			mem_len
							lowercase__   					=			same_length
							lowercase__   					=			attn_type
							lowercase__   					=			clamp_len
							lowercase__   					=			sample_softmax
							lowercase__   					=			adaptive
							lowercase__   					=			dropout
							lowercase__   					=			dropatt
							lowercase__   					=			untie_r
							lowercase__   					=			init
							lowercase__   					=			init_range
							lowercase__   					=			proj_init_std
							lowercase__   					=			init_std
							lowercase__   					=			layer_norm_epsilon
							super().__init__(eos_token_id=__lowercase,   **__lowercase  )




				@property
				def 							A__ (  self   :  Optional[Any]  ):
							# Message copied from Transformer-XL documentation
							logger.info(F'''The model {self.model_type} is one of the few models that has no sequence length limit.'''  )
							return -1




				@max_position_embeddings.setter
				def 							A__ (  self   :  List[str],   __lowercase   :  Union[str, Any]  ):
							# Message copied from Transformer-XL documentation
							raise NotImplementedError(
							    F'''The model {self.model_type} is one of the few models that has no sequence length limit.'''  )




