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




def     a						(							snake_case__: List[str]						,       snake_case__: Tuple=256 ):


				'''simple docstring'''
				lowercase_					  = min(img.size )

				if s < target_image_size:
								raise ValueError(F'''min dim for image {s} < {target_image_size}''' )

				lowercase_					  = target_image_size / s
				lowercase_					  = (round(r * img.size[1] ), round(r * img.size[0] ))
				lowercase_					  = TF.resize(snake_case__						,       snake_case__						,       interpolation=PIL.Image.LANCZOS )
				lowercase_					  = TF.center_crop(snake_case__						,       output_size=2 * [target_image_size] )
				lowercase_					  = torch.unsqueeze(T.ToTensor()(snake_case__ )						,       0 )
				return img




def     a						(							snake_case__: List[Any] ):


				'''simple docstring'''
				lowercase_					  = 2.0 * x - 1.0
				return x




def     a						(							snake_case__: Union[str, Any]						,       snake_case__: Optional[Any]=True						,       snake_case__: Tuple="RGB" ):


				'''simple docstring'''
				lowercase_					  = x.detach().cpu()
				if process:
								lowercase_					  = post_process_tensor(snake_case__ )
				lowercase_					  = x.numpy()
				if process:
								lowercase_					  = (255 * x).astype(np.uinta )
				lowercase_					  = Image.fromarray(snake_case__ )
				if not x.mode == mode:
								lowercase_					  = x.convert(snake_case__ )
				return x




def     a						(							snake_case__: Tuple ):


				'''simple docstring'''
				lowercase_					  = torch.clamp(snake_case__						,       -1.0						,       1.0 )
				lowercase_					  = (x + 1.0) / 2.0
				lowercase_					  = x.permute(1						,       2						,       0 )
				return x




def     a						(							snake_case__: Dict ):


				'''simple docstring'''
				lowercase_					  = post_process_tensor(x.squeeze() )
				return x.permute(2						,       0						,       1 ).unsqueeze(0 )






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


__a        = {
    'configuration_speech_to_text_2': ['SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Speech2Text2Config'],
    'processing_speech_to_text_2': ['Speech2Text2Processor'],
    'tokenization_speech_to_text_2': ['Speech2Text2Tokenizer'],
}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a        = [
						    'SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'Speech2Text2ForCausalLM',
						    'Speech2Text2PreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_speech_to_text_a import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextaConfig
						from .processing_speech_to_text_a import SpeechaTextaProcessor
						from .tokenization_speech_to_text_a import SpeechaTextaTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_speech_to_text_a import (
												    SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SpeechaTextaForCausalLM,
												    SpeechaTextaPreTrainedModel,
												)

else:
						import sys

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




