code: 
  
  
  
  
  
  
  from typing import Dict, List, Optional, Union
  
  import numpy as np
  
  from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
  from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
  from ...image_utils import (
      IMAGENET_STANDARD_MEAN,
      IMAGENET_STANDARD_STD,
      ChannelDimension,
      ImageInput,
      PILImageResampling,
      make_list_of_images,
      to_numpy_array,
      valid_images,
  )
  from ...utils import TensorType, logging
  
  
  __snake_case							      =						logging.get_logger(__name__)
  
  
  
  
  
  
  
  class     UpperCAmelCase_		(      lowercase				):
  
  
  
        """simple docstring"""
        UpperCamelCase_       :      List[str]     =['pixel_values']
        def __init__(       self					,       SCREAMING_SNAKE_CASE_ = True					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR					,       SCREAMING_SNAKE_CASE_ = True					,       SCREAMING_SNAKE_CASE_ = 1 / 255					,       SCREAMING_SNAKE_CASE_ = True					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_ = None					,       **SCREAMING_SNAKE_CASE_					,       )       ->   None:
            super().__init__(**SCREAMING_SNAKE_CASE_				)
            UpperCamelCase :List[str]										=     size if size is not None else {'''height''': 224, '''width''': 224}
            UpperCamelCase :List[Any]										=     get_size_dict(SCREAMING_SNAKE_CASE_				)
            UpperCamelCase :Optional[Any]										=     do_resize
            UpperCamelCase :Dict										=     do_rescale
            UpperCamelCase :Optional[int]										=     do_normalize
            UpperCamelCase :Any										=     size
            UpperCamelCase :Tuple										=     resample
            UpperCamelCase :Union[str, Any]										=     rescale_factor
            UpperCamelCase :List[str]										=     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
            UpperCamelCase :str										=     image_std if image_std is not None else IMAGENET_STANDARD_STD
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR					,       SCREAMING_SNAKE_CASE_ = None					,       **SCREAMING_SNAKE_CASE_					,       )       ->   np.ndarray:
            UpperCamelCase :str										=     get_size_dict(SCREAMING_SNAKE_CASE_				)
            if "height" not in size or "width" not in size:
                raise ValueError(F'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'''				)
            return resize(
                SCREAMING_SNAKE_CASE_					,       size=(size['''height'''], size['''width'''])					,       resample=SCREAMING_SNAKE_CASE_					,       data_format=SCREAMING_SNAKE_CASE_					,       **SCREAMING_SNAKE_CASE_				)
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_ = None					,       **SCREAMING_SNAKE_CASE_				)       ->   np.ndarray:
            return rescale(SCREAMING_SNAKE_CASE_					,       scale=SCREAMING_SNAKE_CASE_					,       data_format=SCREAMING_SNAKE_CASE_					,       **SCREAMING_SNAKE_CASE_				)
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_ = None					,       **SCREAMING_SNAKE_CASE_					,       )       ->   np.ndarray:
            return normalize(SCREAMING_SNAKE_CASE_					,       mean=SCREAMING_SNAKE_CASE_					,       std=SCREAMING_SNAKE_CASE_					,       data_format=SCREAMING_SNAKE_CASE_					,       **SCREAMING_SNAKE_CASE_				)
  
  
  
  
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST					,       **SCREAMING_SNAKE_CASE_					,       )       ->   int:
            UpperCamelCase :Any										=     do_resize if do_resize is not None else self.do_resize
            UpperCamelCase :Tuple										=     do_rescale if do_rescale is not None else self.do_rescale
            UpperCamelCase :List[Any]										=     do_normalize if do_normalize is not None else self.do_normalize
            UpperCamelCase :Union[str, Any]										=     resample if resample is not None else self.resample
            UpperCamelCase :List[str]										=     rescale_factor if rescale_factor is not None else self.rescale_factor
            UpperCamelCase :Tuple										=     image_mean if image_mean is not None else self.image_mean
            UpperCamelCase :Any										=     image_std if image_std is not None else self.image_std
  
            UpperCamelCase :int										=     size if size is not None else self.size
            UpperCamelCase :str										=     get_size_dict(SCREAMING_SNAKE_CASE_				)
  
            UpperCamelCase :Union[str, Any]										=     make_list_of_images(SCREAMING_SNAKE_CASE_				)
  
            if not valid_images(SCREAMING_SNAKE_CASE_				):
                raise ValueError(
                    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                    '''torch.Tensor, tf.Tensor or jax.ndarray.'''				)
  
            if do_resize and size is None:
                raise ValueError('''Size must be specified if do_resize is True.'''				)
  
            if do_rescale and rescale_factor is None:
                raise ValueError('''Rescale factor must be specified if do_rescale is True.'''				)
  
            # All transformations expect numpy arrays.
            UpperCamelCase :Tuple										=     [to_numpy_array(SCREAMING_SNAKE_CASE_				) for image in images]
  
            if do_resize:
                UpperCamelCase :Optional[int]										=     [self.resize(image=SCREAMING_SNAKE_CASE_					,       size=SCREAMING_SNAKE_CASE_					,       resample=SCREAMING_SNAKE_CASE_				) for image in images]
  
            if do_rescale:
                UpperCamelCase :int										=     [self.rescale(image=SCREAMING_SNAKE_CASE_					,       scale=SCREAMING_SNAKE_CASE_				) for image in images]
  
            if do_normalize:
                UpperCamelCase :Optional[Any]										=     [self.normalize(image=SCREAMING_SNAKE_CASE_					,       mean=SCREAMING_SNAKE_CASE_					,       std=SCREAMING_SNAKE_CASE_				) for image in images]
  
            UpperCamelCase :List[str]										=     [to_channel_dimension_format(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				) for image in images]
  
            UpperCamelCase :Dict										=     {'''pixel_values''': images}
            return BatchFeature(data=SCREAMING_SNAKE_CASE_					,       tensor_type=SCREAMING_SNAKE_CASE_				)
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_sentencepiece_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  __snake_case							      =						{
      """configuration_open_llama""": ["""OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OpenLlamaConfig"""],
  }
  
  try:
   if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __snake_case							      =						["""LlamaTokenizer"""]
  
  try:
   if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __snake_case							      =						["""LlamaTokenizerFast"""]
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __snake_case							      =						[
       """OpenLlamaForCausalLM""",
       """OpenLlamaModel""",
       """OpenLlamaPreTrainedModel""",
       """OpenLlamaForSequenceClassification""",
   ]
  
  
  if TYPE_CHECKING:
   from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig
  
   try:
    if not is_sentencepiece_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from transformers import LlamaTokenizer
  
   try:
    if not is_tokenizers_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from transformers import LlamaTokenizerFast
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_open_llama import (
        OpenLlamaForCausalLM,
        OpenLlamaForSequenceClassification,
        OpenLlamaModel,
        OpenLlamaPreTrainedModel,
    )
  
  
  else:
   import sys
  
   __snake_case							      =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  
  
  
  
  