


"""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   _a      :
    def __init__( self							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					Dict=sys.maxsize				):
          lowerCamelCase__           =		'bilinear'
          lowerCamelCase__           =		max_size
          lowerCamelCase__           =		short_edge_length


    def __call__( self							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					Dict				):
          lowerCamelCase__           =		[]
          for img in imgs:
                lowerCamelCase__    ,  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(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)
                if h < w:
                      lowerCamelCase__    ,  lowerCamelCase__           =		size, scale * w
                else:
                      lowerCamelCase__    ,  lowerCamelCase__           =		scale * h, size
                if max(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				) > self.max_size:
                      lowerCamelCase__           =		self.max_size * 1.0 / max(SCREAMING_SNAKE_CASE__							,					SCREAMING_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(SCREAMING_SNAKE_CASE__				)
                      lowerCamelCase__           =		pil_image.resize((neww, newh)							,					PILImageResampling.BILINEAR				)
                      lowerCamelCase__           =		np.asarray(SCREAMING_SNAKE_CASE__				)
                else:
                      lowerCamelCase__           =		img.permute(2							,					0							,					1				).unsqueeze(0				)  # 3, 0, 1)  # hw(c) -> nchw
                      lowerCamelCase__           =		nn.functional.interpolate(
                          SCREAMING_SNAKE_CASE__							,					(newh, neww)							,					mode=self.interp_method							,					align_corners=SCREAMING_SNAKE_CASE__				).squeeze(0				)
                img_augs.append(SCREAMING_SNAKE_CASE__				)

          return img_augs





class   _a      :
    def __init__( self							:					Any							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]				):
          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      _UpperCamelCase       ( self							:					Union[str, Any]							,					SCREAMING_SNAKE_CASE__							:					Optional[int]				):
          lowerCamelCase__           =		tuple(max(SCREAMING_SNAKE_CASE__				) for s in zip(*[img.shape for img in images]				)				)
          lowerCamelCase__           =		[im.shape[-2:] for im in images]
          lowerCamelCase__           =		[
              nn.functional.pad(
                  SCREAMING_SNAKE_CASE__							,					[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]							,					value=self.pad_value							,					)
              for size, im in zip(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)
          ]

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


    def __call__( self							:					Dict							,					SCREAMING_SNAKE_CASE__							:					Dict							,					SCREAMING_SNAKE_CASE__							:					Any=False				):
          with torch.no_grad():
                if not isinstance(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				):
                      lowerCamelCase__           =		[images]
                if single_image:
                      assert len(SCREAMING_SNAKE_CASE__				) == 1
                for i in range(len(SCREAMING_SNAKE_CASE__				)				):
                      if isinstance(images[i]							,					torch.Tensor				):
                            images.insert(SCREAMING_SNAKE_CASE__							,					images.pop(SCREAMING_SNAKE_CASE__				).to(self.device				).float()				)
                      elif not isinstance(images[i]							,					torch.Tensor				):
                            images.insert(
                                SCREAMING_SNAKE_CASE__							,					torch.as_tensor(img_tensorize(images.pop(SCREAMING_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(SCREAMING_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(SCREAMING_SNAKE_CASE__				) for x in images]
                # now pad them to do the following operations
                lowerCamelCase__    ,  lowerCamelCase__           =		self.pad(SCREAMING_SNAKE_CASE__				)
                # Normalize

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





def   snake_case						(				_a:     List[str]		,   _a:     Any							)->		Optional[int]:
      '''simple docstring'''


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






def   snake_case						(				_a:     Dict		,   _a:     Tuple[int, int]							)->		Tuple:
      '''simple docstring'''


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







"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_snake_case          =							{
    "configuration_blenderbot": [
        "BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotConfig",
        "BlenderbotOnnxConfig",
    ],
    "tokenization_blenderbot": ["BlenderbotTokenizer"],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case          =							["BlenderbotTokenizerFast"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case          =							[
       "BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "BlenderbotForCausalLM",
       "BlenderbotForConditionalGeneration",
       "BlenderbotModel",
       "BlenderbotPreTrainedModel",
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case          =							[
       "TFBlenderbotForConditionalGeneration",
       "TFBlenderbotModel",
       "TFBlenderbotPreTrainedModel",
   ]


try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case          =							[
       "FlaxBlenderbotForConditionalGeneration",
       "FlaxBlenderbotModel",
       "FlaxBlenderbotPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_blenderbot import (
       BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
       BlenderbotConfig,
       BlenderbotOnnxConfig,
   )
   from .tokenization_blenderbot import BlenderbotTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_blenderbot import (
          BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
          BlenderbotForCausalLM,
          BlenderbotForConditionalGeneration,
          BlenderbotModel,
          BlenderbotPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_blenderbot import (
          TFBlenderbotForConditionalGeneration,
          TFBlenderbotModel,
          TFBlenderbotPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_blenderbot import (
          FlaxBlenderbotForConditionalGeneration,
          FlaxBlenderbotModel,
          FlaxBlenderbotPreTrainedModel,
      )

else:
   import sys

   _snake_case          =							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



