'''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 					UpperCamelCase_						( snake_case_	:	List[Any]						,		snake_case_	:	List[Any]=2_56				) ->					int:






 '''simple docstring'''

 __lowerCAmelCase    							=						min(img.size				)

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

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







def 					UpperCamelCase_						( snake_case_	:	str				) ->					Tuple:






 '''simple docstring'''

 __lowerCAmelCase    							=						2.0 * x - 1.0
 return x







def 					UpperCamelCase_						( snake_case_	:	Union[str, Any]						,		snake_case_	:	Tuple=True						,		snake_case_	:	Any="RGB"				) ->					Any:






 '''simple docstring'''

 __lowerCAmelCase    							=						x.detach().cpu()
 if process:
  __lowerCAmelCase    							=						post_process_tensor(snake_case_				)
 __lowerCAmelCase    							=						x.numpy()
 if process:
  __lowerCAmelCase    							=						(2_55 * x).astype(np.uinta				)
 __lowerCAmelCase    							=						Image.fromarray(snake_case_				)
 if not x.mode == mode:
  __lowerCAmelCase    							=						x.convert(snake_case_				)
 return x







def 					UpperCamelCase_						( snake_case_	:	Dict				) ->					Tuple:






 '''simple docstring'''

 __lowerCAmelCase    							=						torch.clamp(snake_case_						,		-1.0						,		1.0				)
 __lowerCAmelCase    							=						(x + 1.0) / 2.0
 __lowerCAmelCase    							=						x.permute(1						,		2						,		0				)
 return x







def 					UpperCamelCase_						( snake_case_	:	int				) ->					Any:






 '''simple docstring'''

 __lowerCAmelCase    							=						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_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


_A      :				Optional[int] 				=    {
    '''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      :				Any 				=    [
     '''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      :				Tuple 				=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






