

"""simple docstring"""


import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize





class     snake_case	:

				def __init__(	self							:				Optional[Any]     ,  UpperCamelCase__							:				str     ,  UpperCamelCase__							:				Optional[int]=sys.maxsize)->   str:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[Any]          =			"bilinear"
						__lowerCAmelCase:       Dict          =			max_size
						__lowerCAmelCase:       List[Any]          =			short_edge_length




				def __call__(	self							:				Any     ,  UpperCamelCase__							:				int)->   str:



						'''simple docstring'''


						__lowerCAmelCase:       Any          =			[]
						for img in imgs:
								__lowerCAmelCase      , __lowerCAmelCase:       int          =			img.shape[:2]
								# later: provide list and randomly choose index for resize
								__lowerCAmelCase:       Tuple          =			np.random.randint(self.short_edge_length[0]     ,  self.short_edge_length[1] + 1)
								if size == 0:
										return img
								__lowerCAmelCase:       List[Any]          =			size * 1.0 / min(UpperCamelCase__     ,  UpperCamelCase__)
								if h < w:
										__lowerCAmelCase      , __lowerCAmelCase:       Tuple          =			size, scale * w
								else:
										__lowerCAmelCase      , __lowerCAmelCase:       str          =			scale * h, size
								if max(UpperCamelCase__     ,  UpperCamelCase__) > self.max_size:
										__lowerCAmelCase:       Union[str, Any]          =			self.max_size * 1.0 / max(UpperCamelCase__     ,  UpperCamelCase__)
										__lowerCAmelCase:       Optional[int]          =			newh * scale
										__lowerCAmelCase:       str          =			neww * scale
								__lowerCAmelCase:       Union[str, Any]          =			int(neww + 0.5)
								__lowerCAmelCase:       int          =			int(newh + 0.5)

								if img.dtype == np.uinta:
										__lowerCAmelCase:       List[Any]          =			Image.fromarray(UpperCamelCase__)
										__lowerCAmelCase:       str          =			pil_image.resize((neww, newh)     ,  PILImageResampling.BILINEAR)
										__lowerCAmelCase:       List[Any]          =			np.asarray(UpperCamelCase__)
								else:
										__lowerCAmelCase:       int          =			img.permute(2     ,  0     ,  1).unsqueeze(0)  # 3, 0, 1)  # hw(c) -> nchw
										__lowerCAmelCase:       Tuple          =			nn.functional.interpolate(
										    UpperCamelCase__     ,  (newh, neww)     ,  mode=self.interp_method     ,  align_corners=UpperCamelCase__).squeeze(0)
								img_augs.append(UpperCamelCase__)

						return img_augs





class     snake_case	:

				def __init__(	self							:				int     ,  UpperCamelCase__							:				List[str])->   List[str]:



						'''simple docstring'''


						__lowerCAmelCase:       Tuple          =			ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]     ,  cfg.INPUT.MAX_SIZE_TEST)
						__lowerCAmelCase:       Optional[int]          =			cfg.INPUT.FORMAT
						__lowerCAmelCase:       Union[str, Any]          =			cfg.SIZE_DIVISIBILITY
						__lowerCAmelCase:       Dict          =			cfg.PAD_VALUE
						__lowerCAmelCase:       List[str]          =			cfg.INPUT.MAX_SIZE_TEST
						__lowerCAmelCase:       List[Any]          =			cfg.MODEL.DEVICE
						__lowerCAmelCase:       Any          =			torch.tensor(cfg.MODEL.PIXEL_STD).to(self.device).view(len(cfg.MODEL.PIXEL_STD)     ,  1     ,  1)
						__lowerCAmelCase:       Dict          =			torch.tensor(cfg.MODEL.PIXEL_MEAN).to(self.device).view(len(cfg.MODEL.PIXEL_STD)     ,  1     ,  1)
						__lowerCAmelCase:       Union[str, Any]          =			lambda UpperCamelCase__: (x - self.pixel_mean) / self.pixel_std

				def 							lowercase_     (	self							:				Any     ,  UpperCamelCase__							:				Optional[Any])->   List[str]:



						'''simple docstring'''


						__lowerCAmelCase:       str          =			tuple(max(UpperCamelCase__) for s in zip(*[img.shape for img in images]))
						__lowerCAmelCase:       Optional[Any]          =			[im.shape[-2:] for im in images]
						__lowerCAmelCase:       Optional[Any]          =			[
						    nn.functional.pad(
						        UpperCamelCase__     ,  [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]     ,  value=self.pad_value     ,  )
						    for size, im in zip(UpperCamelCase__     ,  UpperCamelCase__)
						]

						return torch.stack(UpperCamelCase__), torch.tensor(UpperCamelCase__)




				def __call__(	self							:				Any     ,  UpperCamelCase__							:				Dict     ,  UpperCamelCase__							:				int=False)->   Optional[Any]:



						'''simple docstring'''


						with torch.no_grad():
								if not isinstance(UpperCamelCase__     ,  UpperCamelCase__):
										__lowerCAmelCase:       List[Any]          =			[images]
								if single_image:
										assert len(UpperCamelCase__) == 1
								for i in range(len(UpperCamelCase__)):
										if isinstance(images[i]     ,  torch.Tensor):
												images.insert(UpperCamelCase__     ,  images.pop(UpperCamelCase__).to(self.device).float())
										elif not isinstance(images[i]     ,  torch.Tensor):
												images.insert(
												    UpperCamelCase__     ,  torch.as_tensor(img_tensorize(images.pop(UpperCamelCase__)     ,  input_format=self.input_format))
												    .to(self.device)
												    .float()     ,  )
            # resize smallest edge
								__lowerCAmelCase:       Tuple          =			torch.tensor([im.shape[:2] for im in images])
								__lowerCAmelCase:       Optional[int]          =			self.aug(UpperCamelCase__)
								# transpose images and convert to torch tensors
								# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
								# now normalize before pad to avoid useless arithmetic
								__lowerCAmelCase:       Optional[Any]          =			[self.normalizer(UpperCamelCase__) for x in images]
								# now pad them to do the following operations
								__lowerCAmelCase      , __lowerCAmelCase:       List[str]          =			self.pad(UpperCamelCase__)
								# Normalize

								if self.size_divisibility > 0:
										raise NotImplementedError()
								# pad
								__lowerCAmelCase:       str          =			torch.true_divide(UpperCamelCase__     ,  UpperCamelCase__)
								if single_image:
										return images[0], sizes[0], scales_yx[0]
								else:
										return images, sizes, scales_yx






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )	->	Dict:
		boxes[:, 0::2] *= scale_yx[:, 1]
		boxes[:, 1::2] *= scale_yx[:, 0]
		return boxes






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )	->	Optional[Any]:
		assert torch.isfinite(__SCREAMING_SNAKE_CASE ).all(), "Box tensor contains infinite or NaN!"
		__lowerCAmelCase      , __lowerCAmelCase:       str          =			box_size
		tensor[:, 0].clamp_(min=0					, max=__SCREAMING_SNAKE_CASE )
		tensor[:, 1].clamp_(min=0					, max=__SCREAMING_SNAKE_CASE )
		tensor[:, 2].clamp_(min=0					, max=__SCREAMING_SNAKE_CASE )
		tensor[:, 3].clamp_(min=0					, max=__SCREAMING_SNAKE_CASE )





"""simple docstring"""


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


__A						   =			logging.get_logger(__name__)

__A						   =			{
    "google/switch-base-8": "https://huggingface.co/google/switch-base-8/blob/main/config.json",
}





class     snake_case	(				__snake_case  ):
				SCREAMING_SNAKE_CASE_   :      Union[str, Any]         =  """switch_transformers"""
				SCREAMING_SNAKE_CASE_   :      Tuple         =  ["""past_key_values"""]
				SCREAMING_SNAKE_CASE_   :      Any         =  {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}

				def __init__(	self							:				List[str]     ,  UpperCamelCase__							:				List[str]=3_2_1_2_8     ,  UpperCamelCase__							:				str=7_6_8     ,  UpperCamelCase__							:				Union[str, Any]=6_4     ,  UpperCamelCase__							:				Optional[int]=2_0_4_8     ,  UpperCamelCase__							:				Dict=6_4     ,  UpperCamelCase__							:				List[str]=1_2     ,  UpperCamelCase__							:				Dict=3     ,  UpperCamelCase__							:				Any=1_2     ,  UpperCamelCase__							:				Dict=3     ,  UpperCamelCase__							:				Dict=1_2     ,  UpperCamelCase__							:				List[str]=8     ,  UpperCamelCase__							:				Optional[Any]=False     ,  UpperCamelCase__							:				Optional[int]=0.01     ,  UpperCamelCase__							:				Optional[int]="float32"     ,  UpperCamelCase__							:				Any=False     ,  UpperCamelCase__							:				Dict=3_2     ,  UpperCamelCase__							:				Union[str, Any]=1_2_8     ,  UpperCamelCase__							:				Union[str, Any]=0.1     ,  UpperCamelCase__							:				Optional[int]=1e-6     ,  UpperCamelCase__							:				Optional[Any]=0.001     ,  UpperCamelCase__							:				Dict=0.001     ,  UpperCamelCase__							:				int=1.0     ,  UpperCamelCase__							:				str="relu"     ,  UpperCamelCase__							:				int=True     ,  UpperCamelCase__							:				Any=False     ,  UpperCamelCase__							:				str=True     ,  UpperCamelCase__							:				Tuple=0     ,  UpperCamelCase__							:				str=1     ,  **UpperCamelCase__							:				Tuple     ,  )->   Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       int          =			vocab_size
						__lowerCAmelCase:       str          =			d_model
						__lowerCAmelCase:       str          =			d_kv
						__lowerCAmelCase:       str          =			d_ff

						__lowerCAmelCase:       List[str]          =			num_sparse_encoder_layers

						__lowerCAmelCase:       List[Any]          =			num_layers
						__lowerCAmelCase:       Optional[Any]          =			(
						    num_decoder_layers if num_decoder_layers is not None else self.num_layers
						)  # default = symmetry
						__lowerCAmelCase:       Tuple          =			num_sparse_decoder_layers

						# This tells us, each how many encoder layer we'll have to set a sparse layer.
						if self.num_sparse_encoder_layers > 0:
								__lowerCAmelCase:       int          =			self.num_layers // self.num_sparse_encoder_layers
						else:
								__lowerCAmelCase:       Union[str, Any]          =			self.num_layers  # HACK: this will create 0 sparse layers

						# This tells us, each how many encoder layer we'll have to set a sparse layer.
						if self.num_sparse_decoder_layers > 0:
								__lowerCAmelCase:       Dict          =			self.num_decoder_layers // self.num_sparse_decoder_layers
						else:
								__lowerCAmelCase:       Any          =			self.num_decoder_layers  # HACK: this will create 0 sparse layers

						__lowerCAmelCase:       Dict          =			num_heads
						__lowerCAmelCase:       Dict          =			num_experts
						__lowerCAmelCase:       Any          =			expert_capacity
						__lowerCAmelCase:       List[Any]          =			router_bias
						__lowerCAmelCase:       int          =			router_jitter_noise
						if router_dtype not in ["float32", "float16", "bfloat16"]:
								raise ValueError(f"`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}")
						__lowerCAmelCase:       Dict          =			router_dtype

						__lowerCAmelCase:       Optional[Any]          =			router_ignore_padding_tokens
						__lowerCAmelCase:       Union[str, Any]          =			relative_attention_num_buckets
						__lowerCAmelCase:       str          =			relative_attention_max_distance

						__lowerCAmelCase:       Optional[int]          =			dropout_rate
						__lowerCAmelCase:       Optional[Any]          =			layer_norm_epsilon
						__lowerCAmelCase:       int          =			initializer_factor
						__lowerCAmelCase:       Tuple          =			feed_forward_proj
						__lowerCAmelCase:       int          =			use_cache
						__lowerCAmelCase:       int          =			add_router_probs

						__lowerCAmelCase:       Optional[Any]          =			router_z_loss_coef
						__lowerCAmelCase:       Dict          =			router_aux_loss_coef

						__lowerCAmelCase:       Union[str, Any]          =			self.feed_forward_proj.split("-")
						__lowerCAmelCase:       Tuple          =			act_info[-1]
						__lowerCAmelCase:       str          =			act_info[0] == "gated"

						if len(UpperCamelCase__) > 1 and act_info[0] != "gated" or len(UpperCamelCase__) > 2:
								raise ValueError(
								    f"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
								    "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
								    "'gated-gelu' or 'relu'")

						# for backwards compatibility
						if feed_forward_proj == "gated-gelu":
								__lowerCAmelCase:       List[str]          =			"gelu_new"

						super().__init__(
						    pad_token_id=UpperCamelCase__     ,  eos_token_id=UpperCamelCase__     ,  is_encoder_decoder=UpperCamelCase__     ,  **UpperCamelCase__     ,  )


