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


def SCREAMING_SNAKE_CASE			(       lowercase_:		Optional[Any]						,  lowercase_:		Any=256			):
       _UpperCamelCase       :    Any					  =					min(img.size			)

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

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


def SCREAMING_SNAKE_CASE			(       lowercase_:		int			):
       _UpperCamelCase       :    List[str]					  =					2.0 * x - 1.0
       return x


def SCREAMING_SNAKE_CASE			(       lowercase_:		Union[str, Any]						,  lowercase_:		Optional[int]=True						,  lowercase_:		Dict="RGB"			):
       _UpperCamelCase       :    int					  =					x.detach().cpu()
       if process:
              _UpperCamelCase       :    Tuple					  =					post_process_tensor(lowercase_			)
       _UpperCamelCase       :    List[str]					  =					x.numpy()
       if process:
              _UpperCamelCase       :    Optional[Any]					  =					(255 * x).astype(np.uinta			)
       _UpperCamelCase       :    List[Any]					  =					Image.fromarray(lowercase_			)
       if not x.mode == mode:
              _UpperCamelCase       :    List[str]					  =					x.convert(lowercase_			)
       return x


def SCREAMING_SNAKE_CASE			(       lowercase_:		Tuple			):
       _UpperCamelCase       :    Optional[Any]					  =					torch.clamp(lowercase_						,  -1.0						,  1.0			)
       _UpperCamelCase       :    Optional[Any]					  =					(x + 1.0) / 2.0
       _UpperCamelCase       :    Optional[Any]					  =					x.permute(1						,  2						,  0			)
       return x





def SCREAMING_SNAKE_CASE			(       lowercase_:		Any			):
       _UpperCamelCase       :    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_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


UpperCAmelCase     						=			{
    '''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:
 UpperCAmelCase     						=			[
     '''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

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



