





from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig


__lowercase       :int       =  logging.get_logger(__name__)

# General docstring
__lowercase       :List[Any]       =  "RegNetConfig"

# Base docstring
__lowercase       :Union[str, Any]       =  "facebook/regnet-y-040"
__lowercase       :Optional[int]       =  [1, 1_088, 7, 7]

# Image classification docstring
__lowercase       :Dict       =  "facebook/regnet-y-040"
__lowercase       :List[Any]       =  "tabby, tabby cat"

__lowercase       :List[Any]       =  [
    "facebook/regnet-y-040",
    # See all regnet models at https://huggingface.co/models?filter=regnet
]



class    _a     (				tf.keras.layers.Layer							):






							"""simple docstring"""

							def __init__(      self				:     List[Any]							,	a				:     int							,	a				:     int = 3							,	a				:     int = 1							,	a				:     int = 1							,	a				:     Optional[str] = "relu"							,	**a				:     str							,	)	->Optional[Any]:
													super().__init__(**_UpperCamelCase    )
													# The padding and conv has been verified in
													# https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2    )
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					tf.keras.layers.ConvaD(
													    filters=_UpperCamelCase							,	kernel_size=_UpperCamelCase							,	strides=_UpperCamelCase							,	padding="VALID"							,	groups=_UpperCamelCase							,	use_bias=_UpperCamelCase							,	name="convolution"							,	)
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					tf.keras.layers.BatchNormalization(epsilon=1E-5							,	momentum=0.9							,	name="normalization"    )
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					ACTaFN[activation] if activation is not None else tf.identity






							def      A_  (      self				:     List[Any]							,	a				:     int    )	->int:
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					self.convolution(self.padding(_UpperCamelCase    )    )
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					self.normalization(_UpperCamelCase    )
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					self.activation(_UpperCamelCase    )
													return hidden_state







class    _a     (				tf.keras.layers.Layer							):






							"""simple docstring"""

							def __init__(      self				:     Optional[Any]							,	a				:     RegNetConfig							,	**a				:     Union[str, Any]    )	->Union[str, Any]:
													super().__init__(**_UpperCamelCase    )
													SCREAMING_SNAKE_CASE__     :				List[str]   =					config.num_channels
													SCREAMING_SNAKE_CASE__     :				Any   =					TFRegNetConvLayer(
													    out_channels=config.embedding_size							,	kernel_size=3							,	stride=2							,	activation=config.hidden_act							,	name="embedder"							,	)






							def      A_  (      self				:     List[Any]							,	a				:     Dict    )	->Any:
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					shape_list(_UpperCamelCase    )[1]
													if tf.executing_eagerly() and num_channels != self.num_channels:
																			raise ValueError(
																			    "Make sure that the channel dimension of the pixel values match with the one set in the configuration."    )

													# When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
													# So change the input format from `NCHW` to `NHWC`.
													# shape = (batch_size, in_height, in_width, in_channels=num_channels)
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					tf.transpose(_UpperCamelCase							,	perm=(0, 2, 3, 1)    )
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					self.embedder(_UpperCamelCase    )
													return hidden_state







class    _a     (				tf.keras.layers.Layer							):






							"""simple docstring"""

							def __init__(      self				:     Any							,	a				:     int							,	a				:     int = 2							,	**a				:     List[Any]    )	->Dict:
													super().__init__(**_UpperCamelCase    )
													SCREAMING_SNAKE_CASE__     :				Any   =					tf.keras.layers.ConvaD(
													    filters=_UpperCamelCase							,	kernel_size=1							,	strides=_UpperCamelCase							,	use_bias=_UpperCamelCase							,	name="convolution"    )
													SCREAMING_SNAKE_CASE__     :				Any   =					tf.keras.layers.BatchNormalization(epsilon=1E-5							,	momentum=0.9							,	name="normalization"    )






							def      A_  (      self				:     str							,	a				:     tf.Tensor							,	a				:     bool = False    )	->tf.Tensor:
													return self.normalization(self.convolution(_UpperCamelCase    )							,	training=_UpperCamelCase    )







class    _a     (				tf.keras.layers.Layer							):






							"""simple docstring"""

							def __init__(      self				:     List[Any]							,	a				:     int							,	a				:     int							,	**a				:     List[Any]    )	->Dict:
													super().__init__(**_UpperCamelCase    )
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					tf.keras.layers.GlobalAveragePoolingaD(keepdims=_UpperCamelCase							,	name="pooler"    )
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					[
													    tf.keras.layers.ConvaD(filters=_UpperCamelCase							,	kernel_size=1							,	activation="relu"							,	name="attention.0"    ),
													    tf.keras.layers.ConvaD(filters=_UpperCamelCase							,	kernel_size=1							,	activation="sigmoid"							,	name="attention.2"    ),
													]






							def      A_  (      self				:     Dict							,	a				:     int    )	->Dict:
													SCREAMING_SNAKE_CASE__     :				str   =					self.pooler(_UpperCamelCase    )
													for layer_module in self.attention:
																			SCREAMING_SNAKE_CASE__     :				str   =					layer_module(_UpperCamelCase    )
													SCREAMING_SNAKE_CASE__     :				Dict   =					hidden_state * pooled
													return hidden_state







class    _a     (				tf.keras.layers.Layer							):






							"""simple docstring"""

							def __init__(      self				:     int							,	a				:     RegNetConfig							,	a				:     int							,	a				:     int							,	a				:     int = 1							,	**a				:     List[str]    )	->List[Any]:
													super().__init__(**_UpperCamelCase    )
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					in_channels != out_channels or stride != 1
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					max(1							,	out_channels // config.groups_width    )
													SCREAMING_SNAKE_CASE__     :				Dict   =					(
													    TFRegNetShortCut(_UpperCamelCase							,	stride=_UpperCamelCase							,	name="shortcut"    )
													    if should_apply_shortcut
													    else tf.keras.layers.Activation("linear"							,	name="shortcut"    )
													)
													# `self.layers` instead of `self.layer` because that is a reserved argument.
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					[
													    TFRegNetConvLayer(_UpperCamelCase							,	kernel_size=1							,	activation=config.hidden_act							,	name="layer.0"    ),
													    TFRegNetConvLayer(
													        _UpperCamelCase							,	stride=_UpperCamelCase							,	groups=_UpperCamelCase							,	activation=config.hidden_act							,	name="layer.1"    ),
													    TFRegNetConvLayer(_UpperCamelCase							,	kernel_size=1							,	activation=_UpperCamelCase							,	name="layer.2"    ),
													]
													SCREAMING_SNAKE_CASE__     :				Dict   =					ACTaFN[config.hidden_act]






							def      A_  (      self				:     Dict							,	a				:     Any    )	->Optional[Any]:
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					hidden_state
													for layer_module in self.layers:
																			SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					layer_module(_UpperCamelCase    )
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					self.shortcut(_UpperCamelCase    )
													hidden_state += residual
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					self.activation(_UpperCamelCase    )
													return hidden_state







class    _a     (				tf.keras.layers.Layer							):






							"""simple docstring"""

							def __init__(      self				:     Dict							,	a				:     RegNetConfig							,	a				:     int							,	a				:     int							,	a				:     int = 1							,	**a				:     str    )	->Any:
													super().__init__(**_UpperCamelCase    )
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					in_channels != out_channels or stride != 1
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					max(1							,	out_channels // config.groups_width    )
													SCREAMING_SNAKE_CASE__     :				Any   =					(
													    TFRegNetShortCut(_UpperCamelCase							,	stride=_UpperCamelCase							,	name="shortcut"    )
													    if should_apply_shortcut
													    else tf.keras.layers.Activation("linear"							,	name="shortcut"    )
													)
													SCREAMING_SNAKE_CASE__     :				Any   =					[
													    TFRegNetConvLayer(_UpperCamelCase							,	kernel_size=1							,	activation=config.hidden_act							,	name="layer.0"    ),
													    TFRegNetConvLayer(
													        _UpperCamelCase							,	stride=_UpperCamelCase							,	groups=_UpperCamelCase							,	activation=config.hidden_act							,	name="layer.1"    ),
													    TFRegNetSELayer(_UpperCamelCase							,	reduced_channels=int(round(in_channels / 4    )    )							,	name="layer.2"    ),
													    TFRegNetConvLayer(_UpperCamelCase							,	kernel_size=1							,	activation=_UpperCamelCase							,	name="layer.3"    ),
													]
													SCREAMING_SNAKE_CASE__     :				List[str]   =					ACTaFN[config.hidden_act]






							def      A_  (      self				:     List[str]							,	a				:     Optional[Any]    )	->Dict:
													SCREAMING_SNAKE_CASE__     :				str   =					hidden_state
													for layer_module in self.layers:
																			SCREAMING_SNAKE_CASE__     :				Tuple   =					layer_module(_UpperCamelCase    )
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					self.shortcut(_UpperCamelCase    )
													hidden_state += residual
													SCREAMING_SNAKE_CASE__     :				Any   =					self.activation(_UpperCamelCase    )
													return hidden_state







class    _a     (				tf.keras.layers.Layer							):






							"""simple docstring"""

							def __init__(      self				:     Any							,	a				:     RegNetConfig							,	a				:     int							,	a				:     int							,	a				:     int = 2							,	a				:     int = 2							,	**a				:     int    )	->Optional[Any]:
													super().__init__(**_UpperCamelCase    )

													SCREAMING_SNAKE_CASE__     :				Any   =					TFRegNetXLayer if config.layer_type == """x""" else TFRegNetYLayer
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					[
													    # downsampling is done in the first layer with stride of 2
													    layer(_UpperCamelCase							,	_UpperCamelCase							,	_UpperCamelCase							,	stride=_UpperCamelCase							,	name="layers.0"    ),
													    *[layer(_UpperCamelCase							,	_UpperCamelCase							,	_UpperCamelCase							,	name=f"""layers.{i+1}"""    ) for i in range(depth - 1    )],
													]






							def      A_  (      self				:     Tuple							,	a				:     str    )	->int:


													for layer_module in self.layers:
																			SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					layer_module(_UpperCamelCase    )
													return hidden_state







class    _a     (				tf.keras.layers.Layer							):






							"""simple docstring"""

							def __init__(      self				:     int							,	a				:     RegNetConfig							,	**a				:     Any    )	->Any:
													super().__init__(**_UpperCamelCase    )
													SCREAMING_SNAKE_CASE__     :				Any   =					[]
													# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
													self.stages.append(
													    TFRegNetStage(
													        _UpperCamelCase							,	config.embedding_size							,	config.hidden_sizes[0]							,	stride=2 if config.downsample_in_first_stage else 1							,	depth=config.depths[0]							,	name="stages.0"							,	)    )
													SCREAMING_SNAKE_CASE__     :				Tuple   =					zip(config.hidden_sizes							,	config.hidden_sizes[1:]    )
													for i, ((in_channels, out_channels), depth) in enumerate(zip(_UpperCamelCase							,	config.depths[1:]    )    ):
																			self.stages.append(TFRegNetStage(_UpperCamelCase							,	_UpperCamelCase							,	_UpperCamelCase							,	depth=_UpperCamelCase							,	name=f"""stages.{i+1}"""    )    )






							def      A_  (      self				:     Union[str, Any]							,	a				:     tf.Tensor							,	a				:     bool = False							,	a				:     bool = True    )	->TFBaseModelOutputWithNoAttention:
													SCREAMING_SNAKE_CASE__     :				Any   =					() if output_hidden_states else None

													for stage_module in self.stages:
																			if output_hidden_states:
																									SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					hidden_states + (hidden_state,)

																			SCREAMING_SNAKE_CASE__     :				Tuple   =					stage_module(_UpperCamelCase    )

													if output_hidden_states:
																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					hidden_states + (hidden_state,)

													if not return_dict:
																			return tuple(v for v in [hidden_state, hidden_states] if v is not None    )

													return TFBaseModelOutputWithNoAttention(last_hidden_state=_UpperCamelCase							,	hidden_states=_UpperCamelCase    )







@keras_serializable
class    _a     (				tf.keras.layers.Layer							):






							"""simple docstring"""

							snake_case_							 =						RegNetConfig

							def __init__(      self				:     Optional[Any]							,	a				:     List[str]							,	**a				:     Any    )	->Tuple:
													super().__init__(**_UpperCamelCase    )
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					config
													SCREAMING_SNAKE_CASE__     :				Dict   =					TFRegNetEmbeddings(_UpperCamelCase							,	name="embedder"    )
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					TFRegNetEncoder(_UpperCamelCase							,	name="encoder"    )
													SCREAMING_SNAKE_CASE__     :				str   =					tf.keras.layers.GlobalAveragePoolingaD(keepdims=_UpperCamelCase							,	name="pooler"    )






							@unpack_inputs
							def      A_  (      self				:     Optional[int]							,	a				:     tf.Tensor							,	a				:     Optional[bool] = None							,	a				:     Optional[bool] = None							,	a				:     bool = False							,	)	->TFBaseModelOutputWithPoolingAndNoAttention:
													SCREAMING_SNAKE_CASE__     :				Tuple   =					(
													    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
													)
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					return_dict if return_dict is not None else self.config.use_return_dict

													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					self.embedder(_UpperCamelCase							,	training=_UpperCamelCase    )

													SCREAMING_SNAKE_CASE__     :				int   =					self.encoder(
													    _UpperCamelCase							,	output_hidden_states=_UpperCamelCase							,	return_dict=_UpperCamelCase							,	training=_UpperCamelCase    )

													SCREAMING_SNAKE_CASE__     :				Dict   =					encoder_outputs[0]
													SCREAMING_SNAKE_CASE__     :				Any   =					self.pooler(_UpperCamelCase    )

													# Change to NCHW output format have uniformity in the modules
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					tf.transpose(_UpperCamelCase							,	perm=(0, 3, 1, 2)    )
													SCREAMING_SNAKE_CASE__     :				Any   =					tf.transpose(_UpperCamelCase							,	perm=(0, 3, 1, 2)    )

													# Change the other hidden state outputs to NCHW as well
													if output_hidden_states:
																			SCREAMING_SNAKE_CASE__     :				int   =					tuple([tf.transpose(_UpperCamelCase							,	perm=(0, 3, 1, 2)    ) for h in encoder_outputs[1]]    )

													if not return_dict:
																			return (last_hidden_state, pooled_output) + encoder_outputs[1:]

													return TFBaseModelOutputWithPoolingAndNoAttention(
													    last_hidden_state=_UpperCamelCase							,	pooler_output=_UpperCamelCase							,	hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states							,	)







class    _a     (				__lowerCAmelCase							):






							"""simple docstring"""

							snake_case_							 =						RegNetConfig
							snake_case_							 =						"regnet"
							snake_case_							 =						"pixel_values"

							@property
							def      A_  (      self				:     int    )	->Tuple:
													return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_24, 2_24)							,	dtype=tf.floataa    )}







__lowercase       :Optional[int]       =  r"\n    Parameters:\n    This model is a Tensorflow\n    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a\n    regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and\n    behavior.\n        config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n"

__lowercase       :int       =  r"\n    Args:\n        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConveNextImageProcessor.__call__`] for details.\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n"



@add_start_docstrings(
    "The bare RegNet model outputting raw features without any specific head on top."					,							__lowerCAmelCase					,							)
class    _a     (				__lowerCAmelCase							):






							"""simple docstring"""

							def __init__(      self				:     List[Any]							,	a				:     RegNetConfig							,	*a				:     int							,	**a				:     List[Any]    )	->List[str]:
													super().__init__(_UpperCamelCase							,	*_UpperCamelCase							,	**_UpperCamelCase    )
													SCREAMING_SNAKE_CASE__     :				int   =					TFRegNetMainLayer(_UpperCamelCase							,	name="regnet"    )






							@unpack_inputs
							@add_start_docstrings_to_model_forward(_UpperCamelCase    )
							@add_code_sample_docstrings(
							    checkpoint=_CHECKPOINT_FOR_DOC							,	output_type=_UpperCamelCase							,	config_class=_CONFIG_FOR_DOC							,	modality="vision"							,	expected_output=_EXPECTED_OUTPUT_SHAPE							,	)
							def      A_  (      self				:     List[Any]							,	a				:     tf.Tensor							,	a				:     Optional[bool] = None							,	a				:     Optional[bool] = None							,	a				:     Tuple=False							,	)	->Union[TFBaseModelOutputWithPoolingAndNoAttention, Tuple[tf.Tensor]]:
													SCREAMING_SNAKE_CASE__     :				Any   =					(
													    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
													)
													SCREAMING_SNAKE_CASE__     :				str   =					return_dict if return_dict is not None else self.config.use_return_dict

													SCREAMING_SNAKE_CASE__     :				List[str]   =					self.regnet(
													    pixel_values=_UpperCamelCase							,	output_hidden_states=_UpperCamelCase							,	return_dict=_UpperCamelCase							,	training=_UpperCamelCase							,	)
													if not return_dict:
																			return (outputs[0],) + outputs[1:]

													return TFBaseModelOutputWithPoolingAndNoAttention(
													    last_hidden_state=outputs.last_hidden_state							,	pooler_output=outputs.pooler_output							,	hidden_states=outputs.hidden_states							,	)







@add_start_docstrings(
    "\n    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    "					,							__lowerCAmelCase					,							)
class    _a     (				__lowerCAmelCase					,							__lowerCAmelCase							):






							"""simple docstring"""

							def __init__(      self				:     str							,	a				:     RegNetConfig							,	*a				:     Optional[Any]							,	**a				:     List[Any]    )	->Dict:
													super().__init__(_UpperCamelCase							,	*_UpperCamelCase							,	**_UpperCamelCase    )
													SCREAMING_SNAKE_CASE__     :				Any   =					config.num_labels
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					TFRegNetMainLayer(_UpperCamelCase							,	name="regnet"    )
													# classification head
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					[
													    tf.keras.layers.Flatten(),
													    tf.keras.layers.Dense(config.num_labels							,	name="classifier.1"    ) if config.num_labels > 0 else tf.identity,
													]






							@unpack_inputs
							@add_start_docstrings_to_model_forward(_UpperCamelCase    )
							@add_code_sample_docstrings(
							    checkpoint=_IMAGE_CLASS_CHECKPOINT							,	output_type=_UpperCamelCase							,	config_class=_CONFIG_FOR_DOC							,	expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT							,	)
							def      A_  (      self				:     Optional[int]							,	a				:     tf.Tensor = None							,	a				:     tf.Tensor = None							,	a				:     bool = None							,	a				:     bool = None							,	a				:     List[Any]=False							,	)	->Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:
													SCREAMING_SNAKE_CASE__     :				str   =					(
													    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
													)
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					return_dict if return_dict is not None else self.config.use_return_dict

													SCREAMING_SNAKE_CASE__     :				List[str]   =					self.regnet(
													    _UpperCamelCase							,	output_hidden_states=_UpperCamelCase							,	return_dict=_UpperCamelCase							,	training=_UpperCamelCase    )

													SCREAMING_SNAKE_CASE__     :				Dict   =					outputs.pooler_output if return_dict else outputs[1]

													SCREAMING_SNAKE_CASE__     :				Any   =					self.classifier[0](_UpperCamelCase    )
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					self.classifier[1](_UpperCamelCase    )

													SCREAMING_SNAKE_CASE__     :				List[Any]   =					None if labels is None else self.hf_compute_loss(labels=_UpperCamelCase							,	logits=_UpperCamelCase    )

													if not return_dict:
																			SCREAMING_SNAKE_CASE__     :				Any   =					(logits,) + outputs[2:]
																			return ((loss,) + output) if loss is not None else output

													return TFSequenceClassifierOutput(loss=_UpperCamelCase							,	logits=_UpperCamelCase							,	hidden_states=outputs.hidden_states    )






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    _a     (				lowercase__							):






							"""simple docstring"""

							snake_case_							 =						["pixel_values"]

							def __init__(      self				:     int							,	a				:     bool = True							,	a				:     Optional[Dict[str, int]] = None							,	a				:     PILImageResampling = PILImageResampling.BILINEAR							,	a				:     bool = True							,	a				:     Dict[str, int] = None							,	a				:     bool = True							,	a				:     Union[int, float] = 1 / 2_55							,	a				:     bool = True							,	a				:     Optional[Union[float, List[float]]] = None							,	a				:     Optional[Union[float, List[float]]] = None							,	**a				:     List[str]							,	)	->None:
													super().__init__(**a    )
													SCREAMING_SNAKE_CASE__     :				List[str]   =					size if size is not None else {"shortest_edge": 2_56}
													SCREAMING_SNAKE_CASE__     :				Any   =					get_size_dict(a							,	default_to_square=a    )
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
													SCREAMING_SNAKE_CASE__     :				Dict   =					get_size_dict(a    )
													SCREAMING_SNAKE_CASE__     :				List[str]   =					do_resize
													SCREAMING_SNAKE_CASE__     :				List[str]   =					size
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					resample
													SCREAMING_SNAKE_CASE__     :				int   =					do_center_crop
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					crop_size
													SCREAMING_SNAKE_CASE__     :				Any   =					do_rescale
													SCREAMING_SNAKE_CASE__     :				Any   =					rescale_factor
													SCREAMING_SNAKE_CASE__     :				int   =					do_normalize
													SCREAMING_SNAKE_CASE__     :				str   =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
													SCREAMING_SNAKE_CASE__     :				int   =					image_std if image_std is not None else IMAGENET_STANDARD_STD

							def      A_  (      self				:     Tuple							,	a				:     np.ndarray							,	a				:     Dict[str, int]							,	a				:     PILImageResampling = PILImageResampling.BICUBIC							,	a				:     Optional[Union[str, ChannelDimension]] = None							,	**a				:     Optional[int]							,	)	->np.ndarray:
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					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__     :				Optional[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      A_  (      self				:     List[Any]							,	a				:     np.ndarray							,	a				:     Dict[str, int]							,	a				:     Optional[Union[str, ChannelDimension]] = None							,	**a				:     List[Any]							,	)	->np.ndarray:
													SCREAMING_SNAKE_CASE__     :				Tuple   =					get_size_dict(a    )
													return center_crop(a							,	size=(size["height"], size["width"])							,	data_format=a							,	**a    )

							def      A_  (      self				:     Optional[int]							,	a				:     np.ndarray							,	a				:     float							,	a				:     Optional[Union[str, ChannelDimension]] = None							,	**a				:     Dict    )	->np.ndarray:
													return rescale(a							,	scale=a							,	data_format=a							,	**a    )

							def      A_  (      self				:     Union[str, Any]							,	a				:     np.ndarray							,	a				:     Union[float, List[float]]							,	a				:     Union[float, List[float]]							,	a				:     Optional[Union[str, ChannelDimension]] = None							,	**a				:     Union[str, Any]							,	)	->np.ndarray:
													return normalize(a							,	mean=a							,	std=a							,	data_format=a							,	**a    )






							def      A_  (      self				:     Tuple							,	a				:     ImageInput							,	a				:     Optional[bool] = None							,	a				:     Dict[str, int] = None							,	a				:     PILImageResampling = None							,	a				:     bool = None							,	a				:     Dict[str, int] = None							,	a				:     Optional[bool] = None							,	a				:     Optional[float] = None							,	a				:     Optional[bool] = None							,	a				:     Optional[Union[float, List[float]]] = None							,	a				:     Optional[Union[float, List[float]]] = None							,	a				:     Optional[Union[str, TensorType]] = None							,	a				:     Union[str, ChannelDimension] = ChannelDimension.FIRST							,	**a				:     Any							,	)	->Optional[int]:
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					do_resize if do_resize is not None else self.do_resize
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					size if size is not None else self.size
													SCREAMING_SNAKE_CASE__     :				Dict   =					get_size_dict(a							,	default_to_square=a    )
													SCREAMING_SNAKE_CASE__     :				str   =					resample if resample is not None else self.resample
													SCREAMING_SNAKE_CASE__     :				List[str]   =					do_center_crop if do_center_crop is not None else self.do_center_crop
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					crop_size if crop_size is not None else self.crop_size
													SCREAMING_SNAKE_CASE__     :				Dict   =					get_size_dict(a    )
													SCREAMING_SNAKE_CASE__     :				List[str]   =					do_rescale if do_rescale is not None else self.do_rescale
													SCREAMING_SNAKE_CASE__     :				int   =					rescale_factor if rescale_factor is not None else self.rescale_factor
													SCREAMING_SNAKE_CASE__     :				Dict   =					do_normalize if do_normalize is not None else self.do_normalize
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					image_mean if image_mean is not None else self.image_mean
													SCREAMING_SNAKE_CASE__     :				Tuple   =					image_std if image_std is not None else self.image_std

													SCREAMING_SNAKE_CASE__     :				Union[str, 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__     :				List[str]   =					[to_numpy_array(a    ) for image in images]

													if do_resize:
																			SCREAMING_SNAKE_CASE__     :				Tuple   =					[self.resize(image=a							,	size=a							,	resample=a    ) for image in images]

													if do_center_crop:
																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					[self.center_crop(image=a							,	size=a    ) for image in images]

													if do_rescale:
																			SCREAMING_SNAKE_CASE__     :				List[str]   =					[self.rescale(image=a							,	scale=a    ) for image in images]

													if do_normalize:
																			SCREAMING_SNAKE_CASE__     :				Dict   =					[self.normalize(image=a							,	mean=a							,	std=a    ) for image in images]

													SCREAMING_SNAKE_CASE__     :				Dict   =					[to_channel_dimension_format(a							,	a    ) for image in images]

													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					{"pixel_values": images}
													return BatchFeature(data=a							,	tensor_type=a    )