




import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image






def    snake_case   (				A		,  A=256     )				->				Union[str, Any]:




	'''simple docstring'''


	lowerCamelCase_				:						Optional[Any]				  =				min(img.size     )

	if s < target_image_size:
		raise ValueError(f"min dim for image {s} < {target_image_size}"     )

	lowerCamelCase_				:						Optional[Any]				  =				target_image_size / s
	lowerCamelCase_				:						Optional[int]				  =				(round(r * img.size[1]     ), round(r * img.size[0]     ))
	lowerCamelCase_				:						Any				  =				TF.resize(A		,  A		,  interpolation=PIL.Image.LANCZOS     )
	lowerCamelCase_				:						str				  =				TF.center_crop(A		,  output_size=2 * [target_image_size]     )
	lowerCamelCase_				:						Union[str, Any]				  =				torch.unsqueeze(T.ToTensor()(A     )		,  0     )
	return img






def    snake_case   (				A     )				->				Optional[Any]:




	'''simple docstring'''


	lowerCamelCase_				:						Dict				  =				2.0 * x - 1.0
	return x






def    snake_case   (				A		,  A=True		,  A="RGB"     )				->				Optional[Any]:




	'''simple docstring'''


	lowerCamelCase_				:						List[Any]				  =				x.detach().cpu()
	if process:
		lowerCamelCase_				:						Dict				  =				post_process_tensor(A     )
	lowerCamelCase_				:						Tuple				  =				x.numpy()
	if process:
		lowerCamelCase_				:						Dict				  =				(255 * x).astype(np.uinta     )
	lowerCamelCase_				:						Tuple				  =				Image.fromarray(A     )
	if not x.mode == mode:
		lowerCamelCase_				:						List[str]				  =				x.convert(A     )
	return x






def    snake_case   (				A     )				->				Any:




	'''simple docstring'''


	lowerCamelCase_				:						Optional[int]				  =				torch.clamp(A		,  -1.0		,  1.0     )
	lowerCamelCase_				:						List[str]				  =				(x + 1.0) / 2.0
	lowerCamelCase_				:						Any				  =				x.permute(1		,  2		,  0     )
	return x






def    snake_case   (				A     )				->				Tuple:




	'''simple docstring'''


	lowerCamelCase_				:						Dict				  =				post_process_tensor(x.squeeze()     )
	return x.permute(2		,  0		,  1     ).unsqueeze(0     )


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,
)


snake_case_      :					Dict				      =     {
    'configuration_big_bird': ['BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BigBirdConfig', 'BigBirdOnnxConfig'],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					str				      =     ['BigBirdTokenizer']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					Any				      =     ['BigBirdTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					Optional[Any]				      =     [
	    'BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'BigBirdForCausalLM',
	    'BigBirdForMaskedLM',
	    'BigBirdForMultipleChoice',
	    'BigBirdForPreTraining',
	    'BigBirdForQuestionAnswering',
	    'BigBirdForSequenceClassification',
	    'BigBirdForTokenClassification',
	    'BigBirdLayer',
	    'BigBirdModel',
	    'BigBirdPreTrainedModel',
	    'load_tf_weights_in_big_bird',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					Any				      =     [
	    'FlaxBigBirdForCausalLM',
	    'FlaxBigBirdForMaskedLM',
	    'FlaxBigBirdForMultipleChoice',
	    'FlaxBigBirdForPreTraining',
	    'FlaxBigBirdForQuestionAnswering',
	    'FlaxBigBirdForSequenceClassification',
	    'FlaxBigBirdForTokenClassification',
	    'FlaxBigBirdModel',
	    'FlaxBigBirdPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_big_bird import BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP, BigBirdConfig, BigBirdOnnxConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_big_bird import BigBirdTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_big_bird_fast import BigBirdTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_big_bird import (
		    BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BigBirdForCausalLM,
		    BigBirdForMaskedLM,
		    BigBirdForMultipleChoice,
		    BigBirdForPreTraining,
		    BigBirdForQuestionAnswering,
		    BigBirdForSequenceClassification,
		    BigBirdForTokenClassification,
		    BigBirdLayer,
		    BigBirdModel,
		    BigBirdPreTrainedModel,
		    load_tf_weights_in_big_bird,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_big_bird import (
		    FlaxBigBirdForCausalLM,
		    FlaxBigBirdForMaskedLM,
		    FlaxBigBirdForMultipleChoice,
		    FlaxBigBirdForPreTraining,
		    FlaxBigBirdForQuestionAnswering,
		    FlaxBigBirdForSequenceClassification,
		    FlaxBigBirdForTokenClassification,
		    FlaxBigBirdModel,
		    FlaxBigBirdPreTrainedModel,
		)

else:
	import sys

	snake_case_      :					int				      =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
