






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   lowerCamelCase				:







		'''simple docstring'''



		def __init__(							self    ,		_UpperCamelCase    ,		_UpperCamelCase=sys.maxsize       )     ->							List[str]:
							UpperCAmelCase_   :				List[str]						=						'bilinear'
							UpperCAmelCase_   :				List[str]						=						max_size
							UpperCAmelCase_   :				List[Any]						=						short_edge_length



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

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

							return img_augs





class   lowerCamelCase				:







		'''simple docstring'''



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



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							int:
							UpperCAmelCase_   :				List[Any]						=						tuple(max(_UpperCamelCase       ) for s in zip(*[img.shape for img in images]       )       )
							UpperCAmelCase_   :				Union[str, Any]						=						[im.shape[-2:] for im in images]
							UpperCAmelCase_   :				Union[str, 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    ,		_UpperCamelCase    ,		_UpperCamelCase=False       )     ->							List[Any]:
							with torch.no_grad():
												if not isinstance(_UpperCamelCase    ,		_UpperCamelCase       ):
																	UpperCAmelCase_   :				int						=						[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
												UpperCAmelCase_   :				Optional[int]						=						torch.tensor([im.shape[:2] for im in images]       )
												UpperCAmelCase_   :				List[str]						=						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
												UpperCAmelCase_   :				Tuple						=						[self.normalizer(_UpperCamelCase       ) for x in images]
												# now pad them to do the following operations
												UpperCAmelCase_	,  UpperCAmelCase_   :				str						=						self.pad(_UpperCamelCase       )
												# Normalize

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





def 					lowercase__				(					__snake_case   :							Optional[int]					,	__snake_case   :							Optional[Any]  ):


					'''simple docstring'''


					boxes[:, 0::2] *= scale_yx[:, 1]
					boxes[:, 1::2] *= scale_yx[:, 0]
					return boxes






def 					lowercase__				(					__snake_case   :							Any					,	__snake_case   :							Tuple[int, int]  ):


					'''simple docstring'''


					assert torch.isfinite(__snake_case  ).all(), "Box tensor contains infinite or NaN!"
					UpperCAmelCase_	,  UpperCAmelCase_   :				Optional[int]						=						box_size
					tensor[:, 0].clamp_(min=0					,	max=__snake_case  )
					tensor[:, 1].clamp_(min=0					,	max=__snake_case  )
					tensor[:, 2].clamp_(min=0					,	max=__snake_case  )
					tensor[:, 3].clamp_(min=0					,	max=__snake_case  )














from typing import Mapping

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


__UpperCAmelCase     							=						logging.get_logger(__name__)

__UpperCAmelCase     							=						{
    'google/umt5-small': 'https://huggingface.co/google/umt5-small/resolve/main/config.json',
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}



class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		_snake_case   :   Union[str, Any]							=  '''umt5'''
		_snake_case   :   Union[str, Any]							=  ['''past_key_values''']



		def __init__(							self    ,		_UpperCamelCase=2_5_0_1_1_2    ,		_UpperCamelCase=5_1_2    ,		_UpperCamelCase=6_4    ,		_UpperCamelCase=1_0_2_4    ,		_UpperCamelCase=8    ,		_UpperCamelCase=None    ,		_UpperCamelCase=6    ,		_UpperCamelCase=3_2    ,		_UpperCamelCase=1_2_8    ,		_UpperCamelCase=0.1    ,		_UpperCamelCase=1E-6    ,		_UpperCamelCase=1.0    ,		_UpperCamelCase="gated-gelu"    ,		_UpperCamelCase=True    ,		_UpperCamelCase=True    ,		_UpperCamelCase="T5Tokenizer"    ,		_UpperCamelCase=True    ,		_UpperCamelCase=0    ,		_UpperCamelCase=1    ,		_UpperCamelCase=0    ,		**_UpperCamelCase    ,		)     ->							List[Any]:
							super().__init__(
							    is_encoder_decoder=_UpperCamelCase    ,		tokenizer_class=_UpperCamelCase    ,		tie_word_embeddings=_UpperCamelCase    ,		pad_token_id=_UpperCamelCase    ,		eos_token_id=_UpperCamelCase    ,		decoder_start_token_id=_UpperCamelCase    ,		**_UpperCamelCase    ,		)
							UpperCAmelCase_   :				Union[str, Any]						=						vocab_size
							UpperCAmelCase_   :				List[str]						=						d_model
							UpperCAmelCase_   :				Any						=						d_kv
							UpperCAmelCase_   :				Optional[int]						=						d_ff
							UpperCAmelCase_   :				List[Any]						=						num_layers
							UpperCAmelCase_   :				Optional[Any]						=						(
							    num_decoder_layers if num_decoder_layers is not None else self.num_layers
							)  # default = symmetry
							UpperCAmelCase_   :				Optional[int]						=						num_heads
							UpperCAmelCase_   :				Optional[int]						=						relative_attention_num_buckets
							UpperCAmelCase_   :				Dict						=						relative_attention_max_distance
							UpperCAmelCase_   :				Tuple						=						dropout_rate
							UpperCAmelCase_   :				Union[str, Any]						=						layer_norm_epsilon
							UpperCAmelCase_   :				Optional[int]						=						initializer_factor
							UpperCAmelCase_   :				List[str]						=						feed_forward_proj
							UpperCAmelCase_   :				Any						=						use_cache

							UpperCAmelCase_   :				List[Any]						=						self.feed_forward_proj.split('-'       )
							UpperCAmelCase_   :				List[Any]						=						act_info[-1]
							UpperCAmelCase_   :				Union[str, Any]						=						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\''       )

							if feed_forward_proj == "gated-gelu":
												UpperCAmelCase_   :				Optional[int]						=						'gelu_new'



		@property
		def  __UpperCAmelCase      (							self       )     ->							int:
							return self.d_model



		@property
		def  __UpperCAmelCase      (							self       )     ->							Any:
							return self.num_heads



		@property
		def  __UpperCAmelCase      (							self       )     ->							List[Any]:
							return self.num_layers





class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		@property
		# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
		def  __UpperCAmelCase      (							self       )     ->							Mapping[str, Mapping[int, str]]:
							UpperCAmelCase_   :				str						=						{
							    'input_ids': {0: 'batch', 1: 'encoder_sequence'},
							    'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
							}
							if self.use_past:
												UpperCAmelCase_   :				Optional[int]						=						'past_encoder_sequence + sequence'
												UpperCAmelCase_   :				str						=						{0: 'batch'}
												UpperCAmelCase_   :				Optional[int]						=						{0: 'batch', 1: 'past_decoder_sequence + sequence'}
							else:
												UpperCAmelCase_   :				Optional[int]						=						{0: 'batch', 1: 'decoder_sequence'}
												UpperCAmelCase_   :				Union[str, Any]						=						{0: 'batch', 1: 'decoder_sequence'}

							if self.use_past:
												self.fill_with_past_key_values_(_UpperCamelCase    ,		direction='inputs'       )

							return common_inputs



		@property
		# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
		def  __UpperCAmelCase      (							self       )     ->							int:
							return 1_3



		@property
		def  __UpperCAmelCase      (							self       )     ->							float:
							return 5E-4






