



'''simple docstring'''
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
					snake_case_			:     int  	=  {
					    'linear': PIL.Image.Resampling.BILINEAR,
					    'bilinear': PIL.Image.Resampling.BILINEAR,
					    'bicubic': PIL.Image.Resampling.BICUBIC,
					    'lanczos': PIL.Image.Resampling.LANCZOS,
					    'nearest': PIL.Image.Resampling.NEAREST,
					}
else:
					snake_case_			:     Optional[Any]  	=  {
					    'linear': PIL.Image.LINEAR,
					    'bilinear': PIL.Image.BILINEAR,
					    'bicubic': PIL.Image.BICUBIC,
					    'lanczos': PIL.Image.LANCZOS,
					    'nearest': PIL.Image.NEAREST,
					}






def A__   (					UpperCAmelCase_							):
						_UpperCamelCase :		int			    =       (images / 2 + 0.5).clamp(0  ,  1							)
						_UpperCamelCase :		Tuple			    =       images.cpu().permute(0  ,  2  ,  3  ,  1							).float().numpy()
						_UpperCamelCase :		List[str]			    =       numpy_to_pil(UpperCAmelCase_							)
						return images






def A__   (					UpperCAmelCase_							):
						if images.ndim == 3:
												_UpperCamelCase :		Optional[int]			    =       images[None, ...]
						_UpperCamelCase :		List[Any]			    =       (images * 2_5_5).round().astype('uint8'							)
						if images.shape[-1] == 1:
												# special case for grayscale (single channel) images
												_UpperCamelCase :		Optional[Any]			    =       [Image.fromarray(image.squeeze()  ,  mode='L'							) for image in images]
						else:
												_UpperCamelCase :		List[str]			    =       [Image.fromarray(UpperCAmelCase_							) for image in images]

						return pil_images







'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


snake_case_			:     Any  	=  {
    'configuration_mvp': ['MVP_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MvpConfig', 'MvpOnnxConfig'],
    'tokenization_mvp': ['MvpTokenizer'],
}

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

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     List[Any]  	=  [
					    'MVP_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'MvpForCausalLM',
					    'MvpForConditionalGeneration',
					    'MvpForQuestionAnswering',
					    'MvpForSequenceClassification',
					    'MvpModel',
					    'MvpPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
					from .tokenization_mvp import MvpTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_mvp_fast import MvpTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mvp import (
										    MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MvpForCausalLM,
										    MvpForConditionalGeneration,
										    MvpForQuestionAnswering,
										    MvpForSequenceClassification,
										    MvpModel,
										    MvpPreTrainedModel,
										)

else:
					import sys

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


