


"""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 			SCREAMING_SNAKE_CASE_  :




							'''simple docstring'''





							def __init__(						self	,					lowerCamelCase__	,					lowerCamelCase__=sys.maxsize)   ->	str:







								'''simple docstring'''




								snake_case__  :						Optional[int]		=      "bilinear"
								snake_case__  :						Optional[int]		=      max_size
								snake_case__  :						Optional[int]		=      short_edge_length







							def __call__(						self	,					lowerCamelCase__)   ->	List[Any]:







								'''simple docstring'''




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

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

								return img_augs


class 			SCREAMING_SNAKE_CASE_  :




							'''simple docstring'''





							def __init__(						self	,					lowerCamelCase__)   ->	Tuple:







								'''simple docstring'''




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




							def        UpperCAmelCase (						self	,					lowerCamelCase__)   ->	Optional[Any]:







								'''simple docstring'''




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

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







							def __call__(						self	,					lowerCamelCase__	,					lowerCamelCase__=False)   ->	Optional[Any]:







								'''simple docstring'''




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

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



def 			A__		(					_UpperCAmelCase    :      Union[str, Any]				,    _UpperCAmelCase    :      int	)							->	Union[str, Any]:

	'''simple docstring'''


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



def 			A__		(					_UpperCAmelCase    :      List[Any]				,    _UpperCAmelCase    :      Tuple[int, int]	)							->	Any:

	'''simple docstring'''


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













"""simple docstring"""


from typing import TYPE_CHECKING

from ..utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available


lowercase_								=	{
    'configuration_utils': ['GenerationConfig'],
    'streamers': ['TextIteratorStreamer', 'TextStreamer'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_								=	[
           'Constraint',
           'ConstraintListState',
           'DisjunctiveConstraint',
           'PhrasalConstraint',
       ]
       lowercase_								=	[
           'BeamHypotheses',
           'BeamScorer',
           'BeamSearchScorer',
           'ConstrainedBeamSearchScorer',
       ]
       lowercase_								=	[
           'EpsilonLogitsWarper',
           'EtaLogitsWarper',
           'ForcedBOSTokenLogitsProcessor',
           'ForcedEOSTokenLogitsProcessor',
           'HammingDiversityLogitsProcessor',
           'InfNanRemoveLogitsProcessor',
           'LogitsProcessor',
           'LogitsProcessorList',
           'LogitsWarper',
           'MinLengthLogitsProcessor',
           'MinNewTokensLengthLogitsProcessor',
           'NoBadWordsLogitsProcessor',
           'NoRepeatNGramLogitsProcessor',
           'PrefixConstrainedLogitsProcessor',
           'RepetitionPenaltyLogitsProcessor',
           'SequenceBiasLogitsProcessor',
           'EncoderRepetitionPenaltyLogitsProcessor',
           'TemperatureLogitsWarper',
           'TopKLogitsWarper',
           'TopPLogitsWarper',
           'TypicalLogitsWarper',
           'EncoderNoRepeatNGramLogitsProcessor',
           'ExponentialDecayLengthPenalty',
           'LogitNormalization',
       ]
       lowercase_								=	[
           'MaxNewTokensCriteria',
           'MaxLengthCriteria',
           'MaxTimeCriteria',
           'StoppingCriteria',
           'StoppingCriteriaList',
           'validate_stopping_criteria',
       ]
       lowercase_								=	[
           'GenerationMixin',
           'top_k_top_p_filtering',
           'GreedySearchEncoderDecoderOutput',
           'GreedySearchDecoderOnlyOutput',
           'SampleEncoderDecoderOutput',
           'SampleDecoderOnlyOutput',
           'BeamSearchEncoderDecoderOutput',
           'BeamSearchDecoderOnlyOutput',
           'BeamSampleEncoderDecoderOutput',
           'BeamSampleDecoderOnlyOutput',
           'ContrastiveSearchEncoderDecoderOutput',
           'ContrastiveSearchDecoderOnlyOutput',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_								=	[
           'TFForcedBOSTokenLogitsProcessor',
           'TFForcedEOSTokenLogitsProcessor',
           'TFLogitsProcessor',
           'TFLogitsProcessorList',
           'TFLogitsWarper',
           'TFMinLengthLogitsProcessor',
           'TFNoBadWordsLogitsProcessor',
           'TFNoRepeatNGramLogitsProcessor',
           'TFRepetitionPenaltyLogitsProcessor',
           'TFTemperatureLogitsWarper',
           'TFTopKLogitsWarper',
           'TFTopPLogitsWarper',
           'TFForceTokensLogitsProcessor',
           'TFSuppressTokensAtBeginLogitsProcessor',
           'TFSuppressTokensLogitsProcessor',
       ]
       lowercase_								=	[
           'TFGenerationMixin',
           'tf_top_k_top_p_filtering',
           'TFGreedySearchDecoderOnlyOutput',
           'TFGreedySearchEncoderDecoderOutput',
           'TFSampleEncoderDecoderOutput',
           'TFSampleDecoderOnlyOutput',
           'TFBeamSearchEncoderDecoderOutput',
           'TFBeamSearchDecoderOnlyOutput',
           'TFBeamSampleEncoderDecoderOutput',
           'TFBeamSampleDecoderOnlyOutput',
           'TFContrastiveSearchEncoderDecoderOutput',
           'TFContrastiveSearchDecoderOnlyOutput',
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_								=	[
           'FlaxForcedBOSTokenLogitsProcessor',
           'FlaxForcedEOSTokenLogitsProcessor',
           'FlaxLogitsProcessor',
           'FlaxLogitsProcessorList',
           'FlaxLogitsWarper',
           'FlaxMinLengthLogitsProcessor',
           'FlaxTemperatureLogitsWarper',
           'FlaxTopKLogitsWarper',
           'FlaxTopPLogitsWarper',
       ]
       lowercase_								=	[
           'FlaxGenerationMixin',
           'FlaxGreedySearchOutput',
           'FlaxSampleOutput',
           'FlaxBeamSearchOutput',
       ]

if TYPE_CHECKING:
       from .configuration_utils import GenerationConfig
       from .streamers import TextIteratorStreamer, TextStreamer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint
              from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
              from .logits_process import (
                  EncoderNoRepeatNGramLogitsProcessor,
                  EncoderRepetitionPenaltyLogitsProcessor,
                  EpsilonLogitsWarper,
                  EtaLogitsWarper,
                  ExponentialDecayLengthPenalty,
                  ForcedBOSTokenLogitsProcessor,
                  ForcedEOSTokenLogitsProcessor,
                  HammingDiversityLogitsProcessor,
                  InfNanRemoveLogitsProcessor,
                  LogitNormalization,
                  LogitsProcessor,
                  LogitsProcessorList,
                  LogitsWarper,
                  MinLengthLogitsProcessor,
                  MinNewTokensLengthLogitsProcessor,
                  NoBadWordsLogitsProcessor,
                  NoRepeatNGramLogitsProcessor,
                  PrefixConstrainedLogitsProcessor,
                  RepetitionPenaltyLogitsProcessor,
                  SequenceBiasLogitsProcessor,
                  TemperatureLogitsWarper,
                  TopKLogitsWarper,
                  TopPLogitsWarper,
                  TypicalLogitsWarper,
              )
              from .stopping_criteria import (
                  MaxLengthCriteria,
                  MaxNewTokensCriteria,
                  MaxTimeCriteria,
                  StoppingCriteria,
                  StoppingCriteriaList,
                  validate_stopping_criteria,
              )
              from .utils import (
                  BeamSampleDecoderOnlyOutput,
                  BeamSampleEncoderDecoderOutput,
                  BeamSearchDecoderOnlyOutput,
                  BeamSearchEncoderDecoderOutput,
                  ContrastiveSearchDecoderOnlyOutput,
                  ContrastiveSearchEncoderDecoderOutput,
                  GenerationMixin,
                  GreedySearchDecoderOnlyOutput,
                  GreedySearchEncoderDecoderOutput,
                  SampleDecoderOnlyOutput,
                  SampleEncoderDecoderOutput,
                  top_k_top_p_filtering,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tf_logits_process import (
                  TFForcedBOSTokenLogitsProcessor,
                  TFForcedEOSTokenLogitsProcessor,
                  TFForceTokensLogitsProcessor,
                  TFLogitsProcessor,
                  TFLogitsProcessorList,
                  TFLogitsWarper,
                  TFMinLengthLogitsProcessor,
                  TFNoBadWordsLogitsProcessor,
                  TFNoRepeatNGramLogitsProcessor,
                  TFRepetitionPenaltyLogitsProcessor,
                  TFSuppressTokensAtBeginLogitsProcessor,
                  TFSuppressTokensLogitsProcessor,
                  TFTemperatureLogitsWarper,
                  TFTopKLogitsWarper,
                  TFTopPLogitsWarper,
              )
              from .tf_utils import (
                  TFBeamSampleDecoderOnlyOutput,
                  TFBeamSampleEncoderDecoderOutput,
                  TFBeamSearchDecoderOnlyOutput,
                  TFBeamSearchEncoderDecoderOutput,
                  TFContrastiveSearchDecoderOnlyOutput,
                  TFContrastiveSearchEncoderDecoderOutput,
                  TFGenerationMixin,
                  TFGreedySearchDecoderOnlyOutput,
                  TFGreedySearchEncoderDecoderOutput,
                  TFSampleDecoderOnlyOutput,
                  TFSampleEncoderDecoderOutput,
                  tf_top_k_top_p_filtering,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .flax_logits_process import (
                  FlaxForcedBOSTokenLogitsProcessor,
                  FlaxForcedEOSTokenLogitsProcessor,
                  FlaxLogitsProcessor,
                  FlaxLogitsProcessorList,
                  FlaxLogitsWarper,
                  FlaxMinLengthLogitsProcessor,
                  FlaxTemperatureLogitsWarper,
                  FlaxTopKLogitsWarper,
                  FlaxTopPLogitsWarper,
              )
              from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput
else:
       import sys

       lowercase_								=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




