
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,
    get_resize_output_image_size,
    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


lowercase_       :						int     					=	logging.get_logger(__name__)
class 			UpperCamelCase      (							UpperCamelCase__  ):
     A__      =			["""pixel_values"""]
     def __init__(     self  ,     snake_case__ = True  ,     snake_case__ = None  ,     snake_case__ = PILImageResampling.BILINEAR  ,     snake_case__ = True  ,     snake_case__ = None  ,     snake_case__ = True  ,     snake_case__ = 1 / 255  ,     snake_case__ = True  ,     snake_case__ = None  ,     snake_case__ = None  ,     **snake_case__  ,     ):




       """simple docstring"""


       super().__init__(**_a				)
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = size if size is not None else {"""shortest_edge""": 256}
       _SCREAMING_SNAKE_CASE   :				List[str]							    = get_size_dict(_a  ,     default_to_square=_a				)
       _SCREAMING_SNAKE_CASE   :				Tuple							    = crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
       _SCREAMING_SNAKE_CASE   :				int							    = get_size_dict(_a				)
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = do_resize
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = size
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = resample
       _SCREAMING_SNAKE_CASE   :				Dict							    = do_center_crop
       _SCREAMING_SNAKE_CASE   :				str							    = crop_size
       _SCREAMING_SNAKE_CASE   :				Dict							    = do_rescale
       _SCREAMING_SNAKE_CASE   :				Tuple							    = rescale_factor
       _SCREAMING_SNAKE_CASE   :				Any							    = do_normalize
       _SCREAMING_SNAKE_CASE   :				Any							    = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = image_std if image_std is not None else IMAGENET_STANDARD_STD
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__ = PILImageResampling.BICUBIC  ,     snake_case__ = None  ,     **snake_case__  ,     ):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[str]							    = get_size_dict(_a  ,     default_to_square=_a				)
       if "shortest_edge" not in size:
         raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''				)
       _SCREAMING_SNAKE_CASE   :				int							    = get_resize_output_image_size(_a  ,     size=size["shortest_edge"]  ,     default_to_square=_a				)
       return resize(_a  ,     size=_a  ,     resample=_a  ,     data_format=_a  ,     **_a				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__ = None  ,     **snake_case__  ,     ):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = get_size_dict(_a				)
       return center_crop(_a  ,     size=(size["height"], size["width"])  ,     data_format=_a  ,     **_a				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__ = None  ,     **snake_case__				):




       """simple docstring"""


       return rescale(_a  ,     scale=_a  ,     data_format=_a  ,     **_a				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__ = None  ,     **snake_case__  ,     ):




       """simple docstring"""


       return normalize(_a  ,     mean=_a  ,     std=_a  ,     data_format=_a  ,     **_a				)




     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = ChannelDimension.FIRST  ,     **snake_case__  ,     ):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Dict							    = do_resize if do_resize is not None else self.do_resize
       _SCREAMING_SNAKE_CASE   :				Any							    = size if size is not None else self.size
       _SCREAMING_SNAKE_CASE   :				List[str]							    = get_size_dict(_a  ,     default_to_square=_a				)
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = resample if resample is not None else self.resample
       _SCREAMING_SNAKE_CASE   :				int							    = do_center_crop if do_center_crop is not None else self.do_center_crop
       _SCREAMING_SNAKE_CASE   :				str							    = crop_size if crop_size is not None else self.crop_size
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = get_size_dict(_a				)
       _SCREAMING_SNAKE_CASE   :				Tuple							    = do_rescale if do_rescale is not None else self.do_rescale
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = rescale_factor if rescale_factor is not None else self.rescale_factor
       _SCREAMING_SNAKE_CASE   :				Any							    = do_normalize if do_normalize is not None else self.do_normalize
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = image_mean if image_mean is not None else self.image_mean
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = image_std if image_std is not None else self.image_std

       _SCREAMING_SNAKE_CASE   :				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.
       _SCREAMING_SNAKE_CASE   :				int							    = [to_numpy_array(_a				) for image in images]

       if do_resize:
         _SCREAMING_SNAKE_CASE   :				List[str]							    = [self.resize(image=_a  ,     size=_a  ,     resample=_a				) for image in images]

       if do_center_crop:
         _SCREAMING_SNAKE_CASE   :				str							    = [self.center_crop(image=_a  ,     size=_a				) for image in images]

       if do_rescale:
         _SCREAMING_SNAKE_CASE   :				Optional[int]							    = [self.rescale(image=_a  ,     scale=_a				) for image in images]

       if do_normalize:
         _SCREAMING_SNAKE_CASE   :				Any							    = [self.normalize(image=_a  ,     mean=_a  ,     std=_a				) for image in images]

       _SCREAMING_SNAKE_CASE   :				str							    = [to_channel_dimension_format(_a  ,     _a				) for image in images]

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





"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse

from ...utils.dataclasses import (
    ComputeEnvironment,
    DistributedType,
    DynamoBackend,
    PrecisionType,
    SageMakerDistributedType,
)
from ..menu import BulletMenu


lowercase_       :						Tuple     					=	[
    '''EAGER''',
    '''AOT_EAGER''',
    '''INDUCTOR''',
    '''NVFUSER''',
    '''AOT_NVFUSER''',
    '''AOT_CUDAGRAPHS''',
    '''OFI''',
    '''FX2TRT''',
    '''ONNXRT''',
    '''IPEX''',
]





def    _lowerCAmelCase    (     lowerCamelCase__							:							Dict,						lowerCamelCase__							:							int=None,						lowerCamelCase__							:							Any=None,						lowerCamelCase__							:							Any=None				)  ->    Tuple:
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = True
  while ask_again:
    _SCREAMING_SNAKE_CASE   :				List[str]							    = input(lowerCamelCase__				)
    try:
      if default is not None and len(lowerCamelCase__				) == 0:
        return default
      return convert_value(lowerCamelCase__				) if convert_value is not None else result
    except Exception:
      if error_message is not None:
        print(lowerCamelCase__				)





def    _lowerCAmelCase    (     lowerCamelCase__							:							Any,						lowerCamelCase__							:							Dict=[],						lowerCamelCase__							:							Optional[int]=None,						lowerCamelCase__							:							str=0				)  ->    str:
  _SCREAMING_SNAKE_CASE   :				int							    = BulletMenu(lowerCamelCase__,						lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				str							    = menu.run(default_choice=lowerCamelCase__				)
  return convert_value(lowerCamelCase__				) if convert_value is not None else result





def    _lowerCAmelCase    (     lowerCamelCase__							:							Optional[int]				)  ->    Optional[int]:
  _SCREAMING_SNAKE_CASE   :				str							    = int(lowerCamelCase__				)
  return ComputeEnvironment(["LOCAL_MACHINE", "AMAZON_SAGEMAKER"][value]				)





def    _lowerCAmelCase    (     lowerCamelCase__							:							str				)  ->    List[Any]:
  _SCREAMING_SNAKE_CASE   :				str							    = int(lowerCamelCase__				)
  return DistributedType(["NO", "MULTI_CPU", "MULTI_XPU", "MULTI_GPU", "MULTI_NPU", "TPU"][value]				)





def    _lowerCAmelCase    (     lowerCamelCase__							:							Optional[Any]				)  ->    List[Any]:
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = int(lowerCamelCase__				)
  return DynamoBackend(DYNAMO_BACKENDS[value]				).value





def    _lowerCAmelCase    (     lowerCamelCase__							:							int				)  ->    Dict:
  _SCREAMING_SNAKE_CASE   :				int							    = int(lowerCamelCase__				)
  return PrecisionType(["no", "fp16", "bf16", "fp8"][value]				)





def    _lowerCAmelCase    (     lowerCamelCase__							:							List[Any]				)  ->    Any:
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = int(lowerCamelCase__				)
  return SageMakerDistributedType(["NO", "DATA_PARALLEL", "MODEL_PARALLEL"][value]				)





def    _lowerCAmelCase    (     lowerCamelCase__							:							List[Any]				)  ->    Optional[Any]:
  return {"yes": True, "no": False}[value.lower()]





class 			UpperCamelCase      (							argparse.RawDescriptionHelpFormatter  ):
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Any							    = super()._format_usage(snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Any							    = usage.replace("<command> [<args>] "  ,     ""				)
       return usage



