


'''simple docstring'''


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_     (						SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__=256  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: str  	=   min(img.size  )

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

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







def     snake_case_     (						SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: str  	=   2.0 * x - 1.0
				return x







def     snake_case_     (						SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__=True							,   SCREAMING_SNAKE_CASE__="RGB"  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   x.detach().cpu()
				if process:
								_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   post_process_tensor(SCREAMING_SNAKE_CASE__  )
				_SCREAMING_SNAKE_CASE				: List[str]  	=   x.numpy()
				if process:
								_SCREAMING_SNAKE_CASE				: Optional[int]  	=   (255 * x).astype(np.uinta  )
				_SCREAMING_SNAKE_CASE				: int  	=   Image.fromarray(SCREAMING_SNAKE_CASE__  )
				if not x.mode == mode:
								_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   x.convert(SCREAMING_SNAKE_CASE__  )
				return x







def     snake_case_     (						SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: Any  	=   torch.clamp(SCREAMING_SNAKE_CASE__							,   -1.0							,   1.0  )
				_SCREAMING_SNAKE_CASE				: int  	=   (x + 1.0) / 2.0
				_SCREAMING_SNAKE_CASE				: Tuple  	=   x.permute(1							,   2							,   0  )
				return x







def     snake_case_     (						SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: List[str]  	=   post_process_tensor(x.squeeze()  )
				return x.permute(2							,   0							,   1  ).unsqueeze(0  )








'''simple docstring'''


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,
)


UpperCAmelCase_	:     Union[str, Any]							     =	{'configuration_pegasus': ['PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusConfig']}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_	:     Dict							     =	['PegasusTokenizer']

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_	:     Optional[int]							     =	['PegasusTokenizerFast']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_	:     Optional[int]							     =	[
				    'PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'PegasusForCausalLM',
				    'PegasusForConditionalGeneration',
				    'PegasusModel',
				    'PegasusPreTrainedModel',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_	:     Optional[Any]							     =	[
				    'TFPegasusForConditionalGeneration',
				    'TFPegasusModel',
				    'TFPegasusPreTrainedModel',
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_	:     Any							     =	[
				    'FlaxPegasusForConditionalGeneration',
				    'FlaxPegasusModel',
				    'FlaxPegasusPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_pegasus import PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_pegasus import PegasusTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_pegasus_fast import PegasusTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_pegasus import (
								    PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
								    PegasusForCausalLM,
								    PegasusForConditionalGeneration,
								    PegasusModel,
								    PegasusPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_pegasus import TFPegasusForConditionalGeneration, TFPegasusModel, TFPegasusPreTrainedModel

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_pegasus import (
								    FlaxPegasusForConditionalGeneration,
								    FlaxPegasusModel,
								    FlaxPegasusPreTrainedModel,
								)

else:
				import sys

				UpperCAmelCase_	:     List[str]							     =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




