



"""simple docstring"""






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 (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


_lowercase : List[Any]      						=						logging.get_logger(__name__)


if is_vision_available():
		import PIL

class        __SCREAMING_SNAKE_CASE	(      A_   ):


					'''simple docstring'''





					_a   			=							["pixel_values"]



					def __init__(       self							:  List[str],  lowerCamelCase							:  bool = True,  lowerCamelCase							:  Dict[str, int] = None,  lowerCamelCase							:  PILImageResampling = PILImageResampling.BICUBIC,  lowerCamelCase							:  bool = True,  lowerCamelCase							:  Dict[str, int] = None,  lowerCamelCase							:  bool = True,  lowerCamelCase							:  Union[int, float] = 1 / 255,  lowerCamelCase							:  bool = True,  lowerCamelCase							:  Optional[Union[float, List[float]]] = None,  lowerCamelCase							:  Optional[Union[float, List[float]]] = None,  lowerCamelCase							:  bool = True,  **lowerCamelCase							:  List[Any],  )->					None:
								super().__init__(**snake_case__     )
								lowerCamelCase__		:		Any			=size if size is not None else {"shortest_edge": 224}
								lowerCamelCase__		:		str			=get_size_dict(snake_case__,  default_to_square=snake_case__     )
								lowerCamelCase__		:		Dict			=crop_size if crop_size is not None else {"height": 224, "width": 224}
								lowerCamelCase__		:		Optional[int]			=get_size_dict(snake_case__,  default_to_square=snake_case__,  param_name='''crop_size'''     )

								lowerCamelCase__		:		List[str]			=do_resize
								lowerCamelCase__		:		List[str]			=size
								lowerCamelCase__		:		Optional[int]			=resample
								lowerCamelCase__		:		List[str]			=do_center_crop
								lowerCamelCase__		:		List[Any]			=crop_size
								lowerCamelCase__		:		Any			=do_rescale
								lowerCamelCase__		:		Union[str, Any]			=rescale_factor
								lowerCamelCase__		:		Dict			=do_normalize
								lowerCamelCase__		:		Tuple			=image_mean if image_mean is not None else OPENAI_CLIP_MEAN
								lowerCamelCase__		:		Union[str, Any]			=image_std if image_std is not None else OPENAI_CLIP_STD
								lowerCamelCase__		:		Union[str, Any]			=do_convert_rgb



					def 		snake_case       (       self							:  List[str],  lowerCamelCase							:  np.ndarray,  lowerCamelCase							:  Dict[str, int],  lowerCamelCase							:  PILImageResampling = PILImageResampling.BICUBIC,  lowerCamelCase							:  Optional[Union[str, ChannelDimension]] = None,  **lowerCamelCase							:  Optional[int],  )->					np.ndarray:
								lowerCamelCase__		:		List[str]			=get_size_dict(snake_case__,  default_to_square=snake_case__     )
								if "shortest_edge" not in size:
											raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''     )
								lowerCamelCase__		:		Dict			=get_resize_output_image_size(snake_case__,  size=size['''shortest_edge'''],  default_to_square=snake_case__     )
								return resize(snake_case__,  size=snake_case__,  resample=snake_case__,  data_format=snake_case__,  **snake_case__     )



					def 		snake_case       (       self							:  Tuple,  lowerCamelCase							:  np.ndarray,  lowerCamelCase							:  Dict[str, int],  lowerCamelCase							:  Optional[Union[str, ChannelDimension]] = None,  **lowerCamelCase							:  Optional[Any],  )->					np.ndarray:
								lowerCamelCase__		:		Union[str, Any]			=get_size_dict(snake_case__     )
								if "height" not in size or "width" not in size:
											raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''     )
								return center_crop(snake_case__,  size=(size['''height'''], size['''width''']),  data_format=snake_case__,  **snake_case__     )



					def 		snake_case       (       self							:  Tuple,  lowerCamelCase							:  np.ndarray,  lowerCamelCase							:  Union[int, float],  lowerCamelCase							:  Optional[Union[str, ChannelDimension]] = None,  **lowerCamelCase							:  Optional[int],  )->					Any:
								return rescale(snake_case__,  scale=snake_case__,  data_format=snake_case__,  **snake_case__     )



					def 		snake_case       (       self							:  Dict,  lowerCamelCase							:  np.ndarray,  lowerCamelCase							:  Union[float, List[float]],  lowerCamelCase							:  Union[float, List[float]],  lowerCamelCase							:  Optional[Union[str, ChannelDimension]] = None,  **lowerCamelCase							:  Optional[int],  )->					np.ndarray:
								return normalize(snake_case__,  mean=snake_case__,  std=snake_case__,  data_format=snake_case__,  **snake_case__     )




					def 		snake_case       (       self							:  Optional[int],  lowerCamelCase							:  ImageInput,  lowerCamelCase							:  bool = None,  lowerCamelCase							:  Dict[str, int] = None,  lowerCamelCase							:  PILImageResampling = None,  lowerCamelCase							:  bool = None,  lowerCamelCase							:  int = None,  lowerCamelCase							:  bool = None,  lowerCamelCase							:  float = None,  lowerCamelCase							:  bool = None,  lowerCamelCase							:  Optional[Union[float, List[float]]] = None,  lowerCamelCase							:  Optional[Union[float, List[float]]] = None,  lowerCamelCase							:  bool = None,  lowerCamelCase							:  Optional[Union[str, TensorType]] = None,  lowerCamelCase							:  Optional[ChannelDimension] = ChannelDimension.FIRST,  **lowerCamelCase							:  Any,  )->					PIL.Image.Image:
								lowerCamelCase__		:		Optional[int]			=do_resize if do_resize is not None else self.do_resize
								lowerCamelCase__		:		Union[str, Any]			=size if size is not None else self.size
								lowerCamelCase__		:		List[str]			=get_size_dict(snake_case__,  param_name='''size''',  default_to_square=snake_case__     )
								lowerCamelCase__		:		Any			=resample if resample is not None else self.resample
								lowerCamelCase__		:		Dict			=do_center_crop if do_center_crop is not None else self.do_center_crop
								lowerCamelCase__		:		List[Any]			=crop_size if crop_size is not None else self.crop_size
								lowerCamelCase__		:		Optional[int]			=get_size_dict(snake_case__,  param_name='''crop_size''',  default_to_square=snake_case__     )
								lowerCamelCase__		:		Any			=do_rescale if do_rescale is not None else self.do_rescale
								lowerCamelCase__		:		str			=rescale_factor if rescale_factor is not None else self.rescale_factor
								lowerCamelCase__		:		List[str]			=do_normalize if do_normalize is not None else self.do_normalize
								lowerCamelCase__		:		Union[str, Any]			=image_mean if image_mean is not None else self.image_mean
								lowerCamelCase__		:		str			=image_std if image_std is not None else self.image_std
								lowerCamelCase__		:		Union[str, Any]			=do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

								lowerCamelCase__		:		int			=make_list_of_images(snake_case__     )

								if not valid_images(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_center_crop and crop_size is None:
											raise ValueError('''Crop size must be specified if do_center_crop is True.'''     )

								if do_rescale and rescale_factor is None:
											raise ValueError('''Rescale factor must be specified if do_rescale is True.'''     )

								if do_normalize and (image_mean is None or image_std is None):
											raise ValueError('''Image mean and std must be specified if do_normalize is True.'''     )

								# PIL RGBA images are converted to RGB
								if do_convert_rgb:
											lowerCamelCase__		:		Union[str, Any]			=[convert_to_rgb(snake_case__     ) for image in images]

								# All transformations expect numpy arrays.
								lowerCamelCase__		:		Optional[int]			=[to_numpy_array(snake_case__     ) for image in images]

								if do_resize:
											lowerCamelCase__		:		Optional[Any]			=[self.resize(image=snake_case__,  size=snake_case__,  resample=snake_case__     ) for image in images]

								if do_center_crop:
											lowerCamelCase__		:		Tuple			=[self.center_crop(image=snake_case__,  size=snake_case__     ) for image in images]

								if do_rescale:
											lowerCamelCase__		:		Optional[Any]			=[self.rescale(image=snake_case__,  scale=snake_case__     ) for image in images]

								if do_normalize:
											lowerCamelCase__		:		int			=[self.normalize(image=snake_case__,  mean=snake_case__,  std=snake_case__     ) for image in images]

								lowerCamelCase__		:		str			=[to_channel_dimension_format(snake_case__,  snake_case__     ) for image in images]

								lowerCamelCase__		:		Optional[Any]			={"pixel_values": images}
								return BatchFeature(data=snake_case__,  tensor_type=snake_case__     )






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCamelCase							=       {"""configuration_xglm""": ["""XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XGLMConfig"""]}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase							=       ["""XGLMTokenizer"""]

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase							=       ["""XGLMTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase							=       [
       """XGLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """XGLMForCausalLM""",
       """XGLMModel""",
       """XGLMPreTrainedModel""",
   ]


try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase							=       [
       """FlaxXGLMForCausalLM""",
       """FlaxXGLMModel""",
       """FlaxXGLMPreTrainedModel""",
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase							=       [
       """TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TFXGLMForCausalLM""",
       """TFXGLMModel""",
       """TFXGLMPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_xglm import XGLMTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_xglm_fast import XGLMTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_xglm import (
          TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFXGLMForCausalLM,
          TFXGLMModel,
          TFXGLMPreTrainedModel,
      )


else:
   import sys

   __lowerCamelCase							=       _LazyModule(__name__, globals()["""__file__"""], _import_structure)





