
"""simple docstring"""






from typing import Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__UpperCAmelCase       =  logging.get_logger(__name__)




class 			_SCREAMING_SNAKE_CASE					(			A__							):
			UpperCAmelCase_  :Optional[int]				 =						["pixel_values"]






			def __init__(					self       ,   __A = True       ,   __A = None       ,   __A = PILImageResampling.BICUBIC       ,   __A = True       ,   __A = None       ,   __A = True       ,   __A = 1 / 255       ,   __A = True       ,   __A = IMAGENET_DEFAULT_MEAN       ,   __A = IMAGENET_DEFAULT_STD       ,   **__A       ,   )			->     None:
						super().__init__(**__A       )
						lowerCAmelCase_    :Optional[int]			     =  size if size is not None else {"""shortest_edge""": 224}
						lowerCAmelCase_    :Optional[Any]			     =  get_size_dict(__A       ,   default_to_square=__A       )
						lowerCAmelCase_    :int			     =  crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
						lowerCAmelCase_    :Dict			     =  get_size_dict(__A       ,   param_name="""crop_size"""       )

						lowerCAmelCase_    :int			     =  do_resize
						lowerCAmelCase_    :Optional[Any]			     =  size
						lowerCAmelCase_    :str			     =  resample
						lowerCAmelCase_    :str			     =  do_center_crop
						lowerCAmelCase_    :List[Any]			     =  crop_size
						lowerCAmelCase_    :Optional[Any]			     =  do_rescale
						lowerCAmelCase_    :Optional[int]			     =  rescale_factor
						lowerCAmelCase_    :Any			     =  do_normalize
						lowerCAmelCase_    :List[str]			     =  image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
						lowerCAmelCase_    :Union[str, Any]			     =  image_std if image_std is not None else IMAGENET_DEFAULT_STD






			def  __lowerCAmelCase (					self       ,   __A       ,   __A       ,   __A = PILImageResampling.BICUBIC       ,   __A = None       ,   **__A       ,   )			->     np.ndarray:
						lowerCAmelCase_    :Any			     =  get_size_dict(__A       ,   default_to_square=__A       )
						# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
						if "shortest_edge" in size:
									lowerCAmelCase_    :Optional[Any]			     =  int((256 / 224) * size["""shortest_edge"""]       )
									lowerCAmelCase_    :Tuple			     =  get_resize_output_image_size(__A       ,   size=__A       ,   default_to_square=__A       )
									lowerCAmelCase_    :str			     =  {"""height""": output_size[0], """width""": output_size[1]}
						if "height" not in size_dict or "width" not in size_dict:
									raise ValueError(
									    f"""Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"""       )
						return resize(
						    __A       ,   size=(size_dict["""height"""], size_dict["""width"""])       ,   resample=__A       ,   data_format=__A       ,   **__A       )






			def  __lowerCAmelCase (					self       ,   __A       ,   __A       ,   __A = None       ,   **__A       ,   )			->     np.ndarray:
						lowerCAmelCase_    :Any			     =  get_size_dict(__A       )
						if "height" not in size or "width" not in size:
									raise ValueError(f"""Size dict must have keys 'height' and 'width'. Got {size.keys()}"""       )
						return center_crop(__A       ,   size=(size["""height"""], size["""width"""])       ,   data_format=__A       ,   **__A       )






			def  __lowerCAmelCase (					self       ,   __A       ,   __A       ,   __A = None       ,   **__A       ,   )			->     np.ndarray:
						return rescale(__A       ,   scale=__A       ,   data_format=__A       ,   **__A       )






			def  __lowerCAmelCase (					self       ,   __A       ,   __A       ,   __A       ,   __A = None       ,   **__A       ,   )			->     np.ndarray:
						return normalize(__A       ,   mean=__A       ,   std=__A       ,   data_format=__A       ,   **__A       )






			def  __lowerCAmelCase (					self       ,   __A       ,   __A = None       ,   __A = None       ,   __A = None       ,   __A = None       ,   __A = None       ,   __A = None       ,   __A = None       ,   __A = None       ,   __A = None       ,   __A = None       ,   __A = None       ,   __A = ChannelDimension.FIRST       ,   **__A       ,   )			->     BatchFeature:
						lowerCAmelCase_    :Union[str, Any]			     =  do_resize if do_resize is not None else self.do_resize
						lowerCAmelCase_    :Union[str, Any]			     =  resample if resample is not None else self.resample
						lowerCAmelCase_    :Optional[int]			     =  do_center_crop if do_center_crop is not None else self.do_center_crop
						lowerCAmelCase_    :Optional[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_    :str			     =  do_normalize if do_normalize is not None else self.do_normalize
						lowerCAmelCase_    :Any			     =  image_mean if image_mean is not None else self.image_mean
						lowerCAmelCase_    :List[Any]			     =  image_std if image_std is not None else self.image_std

						lowerCAmelCase_    :List[Any]			     =  size if size is not None else self.size
						lowerCAmelCase_    :str			     =  get_size_dict(__A       ,   default_to_square=__A       )
						lowerCAmelCase_    :Optional[int]			     =  crop_size if crop_size is not None else self.crop_size
						lowerCAmelCase_    :int			     =  get_size_dict(__A       ,   param_name="""crop_size"""       )

						lowerCAmelCase_    :Optional[Any]			     =  make_list_of_images(__A       )

						if not valid_images(__A       ):
									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."""       )

						# All transformations expect numpy arrays.
						lowerCAmelCase_    :Any			     =  [to_numpy_array(__A       ) for image in images]

						if do_resize:
									lowerCAmelCase_    :Tuple			     =  [self.resize(__A       ,   __A       ,   __A       ) for image in images]

						if do_center_crop:
									lowerCAmelCase_    :int			     =  [self.center_crop(__A       ,   __A       ) for image in images]

						if do_rescale:
									lowerCAmelCase_    :Optional[int]			     =  [self.rescale(__A       ,   __A       ) for image in images]

						if do_normalize:
									lowerCAmelCase_    :List[str]			     =  [self.normalize(__A       ,   __A       ,   __A       ) for image in images]

						lowerCAmelCase_    :Tuple			     =  [to_channel_dimension_format(__A       ,   __A       ) for image in images]

						lowerCAmelCase_    :List[str]			     =  {"""pixel_values""": images}
						return BatchFeature(data=__A       ,   tensor_type=__A       )













from typing import TYPE_CHECKING

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


__A				:    Union[str, Any]       =   {
    "configuration_speech_to_text": ["SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "Speech2TextConfig"],
    "processing_speech_to_text": ["Speech2TextProcessor"],
}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A				:    List[Any]       =   ["Speech2TextTokenizer"]

try:
				if not is_speech_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A				:    List[Any]       =   ["Speech2TextFeatureExtractor"]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A				:    int       =   [
				    "TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "TFSpeech2TextForConditionalGeneration",
				    "TFSpeech2TextModel",
				    "TFSpeech2TextPreTrainedModel",
				]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A				:    Tuple       =   [
				    "SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "Speech2TextForConditionalGeneration",
				    "Speech2TextModel",
				    "Speech2TextPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
				from .processing_speech_to_text import SpeechaTextProcessor

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_speech_to_text import SpeechaTextTokenizer

				try:
								if not is_speech_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_speech_to_text import (
								    TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFSpeechaTextForConditionalGeneration,
								    TFSpeechaTextModel,
								    TFSpeechaTextPreTrainedModel,
								)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_speech_to_text import (
								    SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SpeechaTextForConditionalGeneration,
								    SpeechaTextModel,
								    SpeechaTextPreTrainedModel,
								)

else:
				import sys

				__A				:    Dict       =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



