

'''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      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE=sys.maxsize	):





      '''simple docstring'''

      lowerCAmelCase 						=							"""bilinear"""
      lowerCAmelCase 						=							max_size
      lowerCAmelCase 						=							short_edge_length


  def __call__(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

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

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

      return img_augs



class 				_snake_case					:

  def __init__(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]      , cfg.INPUT.MAX_SIZE_TEST	)
      lowerCAmelCase 						=							cfg.INPUT.FORMAT
      lowerCAmelCase 						=							cfg.SIZE_DIVISIBILITY
      lowerCAmelCase 						=							cfg.PAD_VALUE
      lowerCAmelCase 						=							cfg.INPUT.MAX_SIZE_TEST
      lowerCAmelCase 						=							cfg.MODEL.DEVICE
      lowerCAmelCase 						=							torch.tensor(cfg.MODEL.PIXEL_STD	).to(self.device	).view(len(cfg.MODEL.PIXEL_STD	)      , 1      , 1	)
      lowerCAmelCase 						=							torch.tensor(cfg.MODEL.PIXEL_MEAN	).to(self.device	).view(len(cfg.MODEL.PIXEL_STD	)      , 1      , 1	)
      lowerCAmelCase 						=							lambda _SCREAMING_SNAKE_CASE	: (x - self.pixel_mean) / self.pixel_std

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

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

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


  def __call__(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE=False	):





      '''simple docstring'''

      with torch.no_grad():
          if not isinstance(snake_case_      , snake_case_	):
              lowerCAmelCase 						=							[images]
          if single_image:
              assert len(snake_case_	) == 1
          for i in range(len(snake_case_	)	):
              if isinstance(images[i]      , torch.Tensor	):
                  images.insert(snake_case_      , images.pop(snake_case_	).to(self.device	).float()	)
              elif not isinstance(images[i]      , torch.Tensor	):
                  images.insert(
                      snake_case_      , torch.as_tensor(img_tensorize(images.pop(snake_case_	)      , input_format=self.input_format	)	)
                      .to(self.device	)
                      .float()      , )
            # resize smallest edge
          lowerCAmelCase 						=							torch.tensor([im.shape[:2] for im in images]	)
          lowerCAmelCase 						=							self.aug(snake_case_	)
          # 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 						=							[self.normalizer(snake_case_	) for x in images]
          # now pad them to do the following operations
          lowerCAmelCase 						=							self.pad(snake_case_	)
          # Normalize

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



def       snake_case   (	snake_case      : str					,		snake_case      : Tuple			) ->  int:


    """simple docstring"""


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


def       snake_case   (	snake_case      : Any					,		snake_case      : Union[str, Any]			) ->  Optional[int]:


    """simple docstring"""


    assert torch.isfinite(snake_case			).all(), "Box tensor contains infinite or NaN!"
    lowerCAmelCase 						=							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			)






"""simple docstring"""



from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


_UpperCamelCase								=		logging.get_logger(__name__)

_UpperCamelCase								=		{
    "EleutherAI/gpt-neo-1.3B": "https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json",
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''
			_UpperCamelCase  :						Tuple					     =				'gpt_neo'
			_UpperCamelCase  :						Optional[Any]					     =				['past_key_values']
			_UpperCamelCase  :						int					     =				{'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}





			def __init__(			self   ,      snake_case_=50_257   ,      snake_case_=2_048   ,      snake_case_=2_048   ,      snake_case_=24   ,      snake_case_=[[["global", "local"], 12]]   ,      snake_case_=16   ,      snake_case_=None   ,      snake_case_=256   ,      snake_case_="gelu_new"   ,      snake_case_=0.0   ,      snake_case_=0.0   ,      snake_case_=0.0   ,      snake_case_=0.1   ,      snake_case_=1E-5   ,      snake_case_=0.02   ,      snake_case_=True   ,      snake_case_=50_256   ,      snake_case_=50_256   ,      **snake_case_   ,      ):







										'''simple docstring'''


										A__       :     int								=			vocab_size
										A__       :     Optional[Any]								=			max_position_embeddings
										A__       :     int								=			hidden_size
										A__       :     List[Any]								=			num_layers
										A__       :     Any								=			num_heads
										A__       :     List[str]								=			intermediate_size
										A__       :     Dict								=			window_size
										A__       :     Optional[int]								=			activation_function
										A__       :     Optional[int]								=			resid_dropout
										A__       :     List[str]								=			embed_dropout
										A__       :     str								=			attention_dropout
										A__       :     List[str]								=			classifier_dropout
										A__       :     str								=			layer_norm_epsilon
										A__       :     str								=			initializer_range
										A__       :     Any								=			use_cache

										A__       :     List[str]								=			bos_token_id
										A__       :     Any								=			eos_token_id

										A__       :     Optional[Any]								=			attention_types
										A__       :     List[Any]								=			self.expand_attention_types_params(snake_case_							)

										if len(self.attention_layers							) != self.num_layers:
																	raise ValueError(
																	    """Configuration for convolutional module is incorrect. """
																	    """It is required that `len(config.attention_layers)` == `config.num_layers` """
																	    F'''but is `len(config.attention_layers) = {len(self.attention_layers							)}`, '''
																	    F'''`config.num_layers = {self.num_layers}`. '''
																	    """`config.attention_layers` is prepared using `config.attention_types`. """
																	    """Please verify the value of `config.attention_types` argument."""							)

										super().__init__(bos_token_id=snake_case_   ,      eos_token_id=snake_case_   ,      **snake_case_							)





			@staticmethod
			def 		lowerCamelCase					(			snake_case_							):







										'''simple docstring'''


										A__       :     Optional[Any]								=			[]
										for item in attention_types:
																	for _ in range(item[1]							):
																								attentions.extend(item[0]							)
										return attentions





def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							):
							import torch

							A__       :     Union[str, Any]								=			input.size()
							A__       :     str								=			len(lowerCAmelCase							)
							A__       :     Union[str, Any]								=			shape[dimension]

							A__       :     Dict								=			torch.arange(0			,  lowerCAmelCase			,  lowerCAmelCase							)
							A__       :     Dict								=			torch.div(sizedim - size			,  lowerCAmelCase			,  rounding_mode="""floor"""							) + 1
							A__       :     str								=			torch.arange(lowerCAmelCase							) + low_indices[:min_length][:, None]

							A__       :     str								=			[slice(lowerCAmelCase							)] * rank
							A__       :     Optional[Any]								=			indices
							A__       :     Tuple								=			input[s]

							A__       :     List[str]								=			list(range(0			,  rank + 1							)							)
							perm.append(perm.pop(dimension + 1							)							)

							return sliced.permute(lowerCAmelCase							)

def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							import torch

							A__       :     Optional[Any]								=			torch.arange(1			,  lowerCAmelCase							)
							A__       :     int								=			torch.remainder(lowerCAmelCase			,  lowerCAmelCase							)
							A__       :     str								=			remainders == 0
							A__       :     Tuple								=			candidates[divisor_indices]
							A__       :     Any								=			torch.max(lowerCAmelCase							)
							return largest_divisor, torch.div(lowerCAmelCase			,  lowerCAmelCase			,  rounding_mode="""floor"""							)



class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''





			@property
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Any								=			OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}							)
										if self.use_past:
																	self.fill_with_past_key_values_(snake_case_   ,      direction="""inputs"""							)
																	A__       :     str								=			{0: """batch""", 1: """past_sequence + sequence"""}
										else:
																	A__       :     Optional[Any]								=			{0: """batch""", 1: """sequence"""}

										return common_inputs





			@property
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										return self._config.num_heads





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_ = -1   ,      snake_case_ = -1   ,      snake_case_ = False   ,      snake_case_ = None   ,      ):







										'''simple docstring'''


										A__       :     Optional[int]								=			super(snake_case_   ,      self							).generate_dummy_inputs(
										    snake_case_   ,      batch_size=snake_case_   ,      seq_length=snake_case_   ,      is_pair=snake_case_   ,      framework=snake_case_							)

										# We need to order the input in the way they appears in the forward()
										A__       :     List[Any]								=			OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}							)

										# Need to add the past_keys
										if self.use_past:
																	if not is_torch_available():
																								raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""							)
																	else:
																								import torch

																								A__				,							A__       :     Dict								=			common_inputs["""input_ids"""].shape
																								# Not using the same length for past_key_values
																								A__       :     Any								=			seqlen + 2
																								A__       :     Any								=			(
																								    batch,
																								    self.num_attention_heads,
																								    past_key_values_length,
																								    self._config.hidden_size // self.num_attention_heads,
																								)
																								A__       :     List[Any]								=			[
																								    (torch.zeros(snake_case_							), torch.zeros(snake_case_							)) for _ in range(self.num_layers							)
																								]

										A__       :     Tuple								=			common_inputs["""attention_mask"""]
										if self.use_past:
																	A__       :     List[Any]								=			ordered_inputs["""attention_mask"""].dtype
																	A__       :     Optional[Any]								=			torch.cat(
																	    [ordered_inputs["""attention_mask"""], torch.ones(snake_case_   ,      snake_case_   ,      dtype=snake_case_							)]   ,      dim=1							)

										return ordered_inputs





			@property
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										return 13





