from typing import TYPE_CHECKING

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


A__     =				{"""configuration_mbart""": ["""MBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MBartConfig""", """MBartOnnxConfig"""]}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A__     =				["""MBartTokenizer"""]

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

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A__     =				[
			    """MBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """MBartForCausalLM""",
			    """MBartForConditionalGeneration""",
			    """MBartForQuestionAnswering""",
			    """MBartForSequenceClassification""",
			    """MBartModel""",
			    """MBartPreTrainedModel""",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A__     =				[
			    """TFMBartForConditionalGeneration""",
			    """TFMBartModel""",
			    """TFMBartPreTrainedModel""",
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A__     =				[
			    """FlaxMBartForConditionalGeneration""",
			    """FlaxMBartForQuestionAnswering""",
			    """FlaxMBartForSequenceClassification""",
			    """FlaxMBartModel""",
			    """FlaxMBartPreTrainedModel""",
			]


if TYPE_CHECKING:
			from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_mbart import MBartTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_mbart_fast import MBartTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_mbart import (
						    MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
						    MBartForCausalLM,
						    MBartForConditionalGeneration,
						    MBartForQuestionAnswering,
						    MBartForSequenceClassification,
						    MBartModel,
						    MBartPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_mbart import (
						    FlaxMBartForConditionalGeneration,
						    FlaxMBartForQuestionAnswering,
						    FlaxMBartForSequenceClassification,
						    FlaxMBartModel,
						    FlaxMBartPreTrainedModel,
						)

else:
			import sys

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






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    :
       """simple docstring"""







       def __init__(		self      , _UpperCamelCase      , _UpperCamelCase=sys.maxsize   )      ->							Any:






              """simple docstring"""


              __snake_case       	=							"""bilinear"""
              __snake_case       	=							max_size
              __snake_case       	=							short_edge_length






       def __call__(		self      , _UpperCamelCase   )      ->							Optional[Any]:






              """simple docstring"""


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

                     if img.dtype == np.uinta:
                            __snake_case       	=							Image.fromarray(_UpperCamelCase   )
                            __snake_case       	=							pil_image.resize((neww, newh)      , PILImageResampling.BILINEAR   )
                            __snake_case       	=							np.asarray(_UpperCamelCase   )
                     else:
                            __snake_case       	=							img.permute(2      , 0      , 1   ).unsqueeze(0   )  # 3, 0, 1)  # hw(c) -> nchw
                            __snake_case       	=							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    :
       """simple docstring"""







       def __init__(		self      , _UpperCamelCase   )      ->							Union[str, Any]:






              """simple docstring"""


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


       def 			a		(		self      , _UpperCamelCase   )      ->							int:






              """simple docstring"""


              __snake_case       	=							tuple(max(_UpperCamelCase   ) for s in zip(*[img.shape for img in images]   )   )
              __snake_case       	=							[im.shape[-2:] for im in images]
              __snake_case       	=							[
                  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   )      ->							Union[str, Any]:






              """simple docstring"""


              with torch.no_grad():
                     if not isinstance(_UpperCamelCase      , _UpperCamelCase   ):
                            __snake_case       	=							[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
                     __snake_case       	=							torch.tensor([im.shape[:2] for im in images]   )
                     __snake_case       	=							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
                     __snake_case       	=							[self.normalizer(_UpperCamelCase   ) for x in images]
                     # now pad them to do the following operations
                     __snake_case	,    __snake_case       	=							self.pad(_UpperCamelCase   )
                     # Normalize

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







def 						lowerCamelCase__      ( __A    :Union[str, Any]	,__A    :List[Any]		):


       """simple docstring"""




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







def 						lowerCamelCase__      ( __A    :Tuple	,__A    :Tuple[int, int]		):


       """simple docstring"""




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

