
"""simple docstring"""


import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
						import torch

if is_vision_available():
						import PIL


A_    							=					logging.get_logger(__name__)






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__						):
			def constraint_to_multiple_of(lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__=0	,lowerCAmelCase__=None						):
						lowerCamelCase_					=  round(val / multiple						) * multiple

						if max_val is not None and x > max_val:
									lowerCamelCase_					=  math.floor(val / multiple						) * multiple

						if x < min_val:
									lowerCamelCase_					=  math.ceil(val / multiple						) * multiple

						return x

			lowerCamelCase_					=  (output_size, output_size) if isinstance(lowerCAmelCase__	,lowerCAmelCase__						) else output_size

			lowerCamelCase_		, lowerCamelCase_					=  get_image_size(lowerCAmelCase__						)
			lowerCamelCase_		, lowerCamelCase_					=  output_size

			# determine new height and width
			lowerCamelCase_					=  output_height / input_height
			lowerCamelCase_					=  output_width / input_width

			if keep_aspect_ratio:
						# scale as little as possible
						if abs(1 - scale_width						) < abs(1 - scale_height						):
									# fit width
									lowerCamelCase_					=  scale_width
						else:
									# fit height
									lowerCamelCase_					=  scale_height

			lowerCamelCase_					=  constraint_to_multiple_of(scale_height * input_height	,multiple=lowerCAmelCase__						)
			lowerCamelCase_					=  constraint_to_multiple_of(scale_width * input_width	,multiple=lowerCAmelCase__						)

			return (new_height, new_width)




class  __lowerCamelCase  ( lowerCAmelCase			):
				a__:      int          =    ['pixel_values']




				def __init__(				self							,						UpperCAmelCase = True							,						UpperCAmelCase = None							,						UpperCAmelCase = PILImageResampling.BILINEAR							,						UpperCAmelCase = False							,						UpperCAmelCase = 1							,						UpperCAmelCase = True							,						UpperCAmelCase = 1 / 255							,						UpperCAmelCase = True							,						UpperCAmelCase = None							,						UpperCAmelCase = None							,						**UpperCAmelCase							,						):
							super().__init__(**UpperCAmelCase   )
							lowerCamelCase_					=  size if size is not None else {'''height''': 384, '''width''': 384}
							lowerCamelCase_					=  get_size_dict(UpperCAmelCase   )
							lowerCamelCase_					=  do_resize
							lowerCamelCase_					=  size
							lowerCamelCase_					=  keep_aspect_ratio
							lowerCamelCase_					=  ensure_multiple_of
							lowerCamelCase_					=  resample
							lowerCamelCase_					=  do_rescale
							lowerCamelCase_					=  rescale_factor
							lowerCamelCase_					=  do_normalize
							lowerCamelCase_					=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							lowerCamelCase_					=  image_std if image_std is not None else IMAGENET_STANDARD_STD




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase = False							,						UpperCAmelCase = 1							,						UpperCAmelCase = PILImageResampling.BICUBIC							,						UpperCAmelCase = None							,						**UpperCAmelCase							,						):
							lowerCamelCase_					=  get_size_dict(UpperCAmelCase   )
							if "height" not in size or "width" not in size:
										raise ValueError(f"The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"   )
							lowerCamelCase_					=  get_resize_output_image_size(
							    UpperCAmelCase							,						output_size=(size['''height'''], size['''width'''])							,						keep_aspect_ratio=UpperCAmelCase							,						multiple=UpperCAmelCase							,						)
							return resize(UpperCAmelCase							,						size=UpperCAmelCase							,						resample=UpperCAmelCase							,						data_format=UpperCAmelCase							,						**UpperCAmelCase   )




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase = None							,						**UpperCAmelCase							,						):
							return rescale(UpperCAmelCase							,						scale=UpperCAmelCase							,						data_format=UpperCAmelCase							,						**UpperCAmelCase   )




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase = None							,						**UpperCAmelCase							,						):
							return normalize(UpperCAmelCase							,						mean=UpperCAmelCase							,						std=UpperCAmelCase							,						data_format=UpperCAmelCase							,						**UpperCAmelCase   )




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase = None							,						UpperCAmelCase = None							,						UpperCAmelCase = None							,						UpperCAmelCase = None							,						UpperCAmelCase = None							,						UpperCAmelCase = None							,						UpperCAmelCase = None							,						UpperCAmelCase = None							,						UpperCAmelCase = None							,						UpperCAmelCase = None							,						UpperCAmelCase = None							,						UpperCAmelCase = ChannelDimension.FIRST							,						**UpperCAmelCase							,						):
							lowerCamelCase_					=  do_resize if do_resize is not None else self.do_resize
							lowerCamelCase_					=  size if size is not None else self.size
							lowerCamelCase_					=  get_size_dict(UpperCAmelCase   )
							lowerCamelCase_					=  keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
							lowerCamelCase_					=  ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
							lowerCamelCase_					=  resample if resample is not None else self.resample
							lowerCamelCase_					=  do_rescale if do_rescale is not None else self.do_rescale
							lowerCamelCase_					=  rescale_factor if rescale_factor is not None else self.rescale_factor
							lowerCamelCase_					=  do_normalize if do_normalize is not None else self.do_normalize
							lowerCamelCase_					=  image_mean if image_mean is not None else self.image_mean
							lowerCamelCase_					=  image_std if image_std is not None else self.image_std

							lowerCamelCase_					=  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_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.
							lowerCamelCase_					=  [to_numpy_array(UpperCAmelCase   ) for image in images]

							if do_resize:
										lowerCamelCase_					=  [self.resize(image=UpperCAmelCase							,						size=UpperCAmelCase							,						resample=UpperCAmelCase   ) for image in images]

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

							if do_normalize:
										lowerCamelCase_					=  [self.normalize(image=UpperCAmelCase							,						mean=UpperCAmelCase							,						std=UpperCAmelCase   ) for image in images]

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

							lowerCamelCase_					=  {'''pixel_values''': images}
							return BatchFeature(data=UpperCAmelCase							,						tensor_type=UpperCAmelCase   )





				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase = None   ):
							lowerCamelCase_					=  outputs.logits

							# Resize logits and compute semantic segmentation maps
							if target_sizes is not None:
										if len(UpperCAmelCase   ) != len(UpperCAmelCase   ):
													raise ValueError(
													    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''   )

										if is_torch_tensor(UpperCAmelCase   ):
													lowerCamelCase_					=  target_sizes.numpy()

										lowerCamelCase_					=  []

										for idx in range(len(UpperCAmelCase   )   ):
													lowerCamelCase_					=  torch.nn.functional.interpolate(
													    logits[idx].unsqueeze(dim=0   )							,						size=target_sizes[idx]							,						mode='''bilinear'''							,						align_corners=UpperCAmelCase   )
													lowerCamelCase_					=  resized_logits[0].argmax(dim=0   )
													semantic_segmentation.append(UpperCAmelCase   )
							else:
										lowerCamelCase_					=  logits.argmax(dim=1   )
										lowerCamelCase_					=  [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]   )]

							return semantic_segmentation










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


SCREAMING_SNAKE_CASE_   :      Dict			=   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_   :      Union[str, Any]			=   {
    '''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-430m-pile''': '''https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-1b5-pile''': '''https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-3b-pile''': '''https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-7b-pile''': '''https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-14b-pile''': '''https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-raven-1b5''': '''https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json''',
    '''RWKV/rwkv-raven-3b''': '''https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json''',
    '''RWKV/rwkv-raven-7b''': '''https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json''',
    '''RWKV/rwkv-raven-14b''': '''https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json''',
}

class 		snake_case_   (							UpperCAmelCase_						):







	'''simple docstring'''
	__UpperCamelCase   =		'''rwkv'''
	__UpperCamelCase   =		{'''max_position_embeddings''': '''context_length'''}




	def __init__(						self							:     List[str]					,      __lowerCamelCase							:     Tuple=50_277					,      __lowerCamelCase							:     List[str]=1_024					,      __lowerCamelCase							:     List[Any]=4_096					,      __lowerCamelCase							:     Tuple=32					,      __lowerCamelCase							:     List[str]=None					,      __lowerCamelCase							:     Optional[int]=None					,      __lowerCamelCase							:     Optional[Any]=1E-5					,      __lowerCamelCase							:     Tuple=0					,      __lowerCamelCase							:     int=0					,      __lowerCamelCase							:     Dict=6					,      __lowerCamelCase							:     List[Any]=False					,      __lowerCamelCase							:     Tuple=True					,      **__lowerCamelCase							:     Optional[int]					,      )       -> List[Any]:

			'''simple docstring'''




			__lowercase											=     vocab_size
			__lowercase											=     context_length
			__lowercase											=     hidden_size
			__lowercase											=     num_hidden_layers
			__lowercase											=     attention_hidden_size if attention_hidden_size is not None else hidden_size
			__lowercase											=     intermediate_size if intermediate_size is not None else 4 * hidden_size
			__lowercase											=     layer_norm_epsilon
			__lowercase											=     rescale_every
			__lowercase											=     use_cache

			__lowercase											=     bos_token_id
			__lowercase											=     eos_token_id

			super().__init__(
			    tie_word_embeddings=__lowerCamelCase					,      bos_token_id=__lowerCamelCase					,      eos_token_id=__lowerCamelCase					,      **__lowerCamelCase   )




