from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowercase_  : Dict								=					logging.get_logger(__name__)






class 		_lowerCamelCase		( UpperCamelCase_     ):
    __a						=   ["pixel_values"]
    def __init__(	self			,			lowerCAmelCase = True			,			lowerCAmelCase = 32			,			lowerCAmelCase=PILImageResampling.BILINEAR			,			lowerCAmelCase = True			,			**lowerCAmelCase			,			)							->       None:
     SCREAMING_SNAKE_CASE__: Optional[int]=   do_resize
     SCREAMING_SNAKE_CASE__: Tuple=   do_rescale
     SCREAMING_SNAKE_CASE__: Tuple=   size_divisor
     SCREAMING_SNAKE_CASE__: Dict=   resample
     super().__init__(**lowerCAmelCase )
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase = None			,			**lowerCAmelCase )							->       np.ndarray:
     SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__: Any=   get_image_size(lowerCAmelCase )
     # Rounds the height and width down to the closest multiple of size_divisor
     SCREAMING_SNAKE_CASE__: Optional[Any]=   height // size_divisor * size_divisor
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   width // size_divisor * size_divisor
     SCREAMING_SNAKE_CASE__: Optional[Any]=   resize(lowerCAmelCase			,			(new_h, new_w)			,			resample=lowerCAmelCase			,			data_format=lowerCAmelCase			,			**lowerCAmelCase )
     return image
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase = None			,			**lowerCAmelCase )							->       np.ndarray:
     return rescale(image=lowerCAmelCase			,			scale=lowerCAmelCase			,			data_format=lowerCAmelCase			,			**lowerCAmelCase )



    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase = None			,			lowerCAmelCase = None			,			lowerCAmelCase=None			,			lowerCAmelCase = None			,			lowerCAmelCase = None			,			lowerCAmelCase = ChannelDimension.FIRST			,			**lowerCAmelCase			,			)							->       BatchFeature:
     SCREAMING_SNAKE_CASE__: int=   do_resize if do_resize is not None else self.do_resize
     SCREAMING_SNAKE_CASE__: List[Any]=   do_rescale if do_rescale is not None else self.do_rescale
     SCREAMING_SNAKE_CASE__: str=   size_divisor if size_divisor is not None else self.size_divisor
     SCREAMING_SNAKE_CASE__: Tuple=   resample if resample is not None else self.resample

     if do_resize and size_divisor is None:
      raise ValueError('''size_divisor is required for resizing''' )

     SCREAMING_SNAKE_CASE__: str=   make_list_of_images(lowerCAmelCase )

     if not valid_images(lowerCAmelCase ):
      raise ValueError('''Invalid image(s)''' )

     # All transformations expect numpy arrays.
     SCREAMING_SNAKE_CASE__: int=   [to_numpy_array(lowerCAmelCase ) for img in images]

     if do_resize:
      SCREAMING_SNAKE_CASE__: Optional[Any]=   [self.resize(lowerCAmelCase			,			size_divisor=lowerCAmelCase			,			resample=lowerCAmelCase ) for image in images]

     if do_rescale:
      SCREAMING_SNAKE_CASE__: Optional[Any]=   [self.rescale(lowerCAmelCase			,			scale=1 / 255 ) for image in images]

     SCREAMING_SNAKE_CASE__: List[str]=   [to_channel_dimension_format(lowerCAmelCase			,			lowerCAmelCase ) for image in images]

     SCREAMING_SNAKE_CASE__: Dict=   {'''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 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	lowerCAmelCase						=					'''transfo-xl'''
	lowerCAmelCase						=					['''mems''']
	lowerCAmelCase						=					{
	    '''n_token''': '''vocab_size''',
	    '''hidden_size''': '''d_model''',
	    '''num_attention_heads''': '''n_head''',
	    '''num_hidden_layers''': '''n_layer''',
	}




	def __init__(			self			,     a=26_77_35			,     a=[2_00_00, 4_00_00, 20_00_00]			,     a=10_24			,     a=10_24			,     a=16			,     a=64			,     a=40_96			,     a=4			,     a=False			,     a=18			,     a=16_00			,     a=10_00			,     a=True			,     a=True			,     a=0			,     a=-1			,     a=True			,     a=0.1			,     a=0.0			,     a=True			,     a="normal"			,     a=0.01			,     a=0.01			,     a=0.02			,     a=1E-5			,     a=0			,     **a			,     )			->       Union[str, Any]:
								snake_case_           =				vocab_size
								snake_case_           =				[]
								self.cutoffs.extend(a   )
								if proj_share_all_but_first:
															snake_case_           =				[False] + [True] * len(self.cutoffs   )
								else:
															snake_case_           =				[False] + [False] * len(self.cutoffs   )
								snake_case_           =				d_model
								snake_case_           =				d_embed
								snake_case_           =				d_head
								snake_case_           =				d_inner
								snake_case_           =				div_val
								snake_case_           =				pre_lnorm
								snake_case_           =				n_layer
								snake_case_           =				n_head
								snake_case_           =				mem_len
								snake_case_           =				same_length
								snake_case_           =				attn_type
								snake_case_           =				clamp_len
								snake_case_           =				sample_softmax
								snake_case_           =				adaptive
								snake_case_           =				dropout
								snake_case_           =				dropatt
								snake_case_           =				untie_r
								snake_case_           =				init
								snake_case_           =				init_range
								snake_case_           =				proj_init_std
								snake_case_           =				init_std
								snake_case_           =				layer_norm_epsilon
								super().__init__(eos_token_id=a			,     **a   )




	@property
	def 				_UpperCamelCase     (			self   )			->       int:
								# 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 				_UpperCamelCase     (			self			,     a   )			->       List[str]:
								# 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.'''   )





