



from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFImageClassifierOutputWithNoAttention,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_resnet import ResNetConfig


UpperCAmelCase							=       logging.get_logger(__name__)

# General docstring
UpperCAmelCase							=       """ResNetConfig"""

# Base docstring
UpperCAmelCase							=       """microsoft/resnet-50"""
UpperCAmelCase							=       [1, 2_048, 7, 7]

# Image classification docstring
UpperCAmelCase							=       """microsoft/resnet-50"""
UpperCAmelCase							=       """tiger cat"""

UpperCAmelCase							=       [
    """microsoft/resnet-50""",
    # See all resnet models at https://huggingface.co/models?filter=resnet
]

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







							'''simple docstring'''






							def __init__(				self	,	a_	,	a_ = 3	,	a_ = 1	,	a_ = "relu"	,	**a_		):
											super().__init__(**a_		)
											a__					   =		kernel_size // 2
											a__					   =		tf.keras.layers.ConvaD(
											    a_	,	kernel_size=a_	,	strides=a_	,	padding="""valid"""	,	use_bias=a_	,	name="""convolution"""		)
											# Use same default momentum and epsilon as PyTorch equivalent
											a__					   =		tf.keras.layers.BatchNormalization(epsilon=1E-5	,	momentum=0.9	,	name="""normalization"""		)
											a__					   =		ACTaFN[activation] if activation is not None else tf.keras.layers.Activation("""linear"""		)


							def _a   (				self	,	a_		):
											# Pad to match that done in the PyTorch Conv2D model
											a__					   =		a__					   =		(self.pad_value, self.pad_value)
											a__					   =		tf.pad(a_	,	[(0, 0), height_pad, width_pad, (0, 0)]		)
											a__					   =		self.conv(a_		)
											return hidden_state







							def _a   (				self	,	a_	,	a_ = False		):
											a__					   =		self.convolution(a_		)
											a__					   =		self.normalization(a_	,	training=a_		)
											a__					   =		self.activation(a_		)
											return hidden_state




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







							'''simple docstring'''






							def __init__(				self	,	a_	,	**a_		):
											super().__init__(**a_		)
											a__					   =		TFResNetConvLayer(
											    config.embedding_size	,	kernel_size=7	,	stride=2	,	activation=config.hidden_act	,	name="""embedder"""	,	)
											a__					   =		tf.keras.layers.MaxPoolaD(pool_size=3	,	strides=2	,	padding="""valid"""	,	name="""pooler"""		)
											a__					   =		config.num_channels







							def _a   (				self	,	a_	,	a_ = False		):
											a__   ,      a__   ,      a__   ,      a__					   =		shape_list(a_		)
											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."""		)
											a__					   =		pixel_values
											a__					   =		self.embedder(a_		)
											a__					   =		tf.pad(a_	,	[[0, 0], [1, 1], [1, 1], [0, 0]]		)
											a__					   =		self.pooler(a_		)
											return hidden_state




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







							'''simple docstring'''






							def __init__(				self	,	a_	,	a_ = 2	,	**a_		):
											super().__init__(**a_		)
											a__					   =		tf.keras.layers.ConvaD(
											    a_	,	kernel_size=1	,	strides=a_	,	use_bias=a_	,	name="""convolution"""		)
											# Use same default momentum and epsilon as PyTorch equivalent
											a__					   =		tf.keras.layers.BatchNormalization(epsilon=1E-5	,	momentum=0.9	,	name="""normalization"""		)







							def _a   (				self	,	a_	,	a_ = False		):
											a__					   =		x
											a__					   =		self.convolution(a_		)
											a__					   =		self.normalization(a_	,	training=a_		)
											return hidden_state




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







							'''simple docstring'''






							def __init__(				self	,	a_	,	a_	,	a_ = 1	,	a_ = "relu"	,	**a_		):
											super().__init__(**a_		)
											a__					   =		in_channels != out_channels or stride != 1
											a__					   =		TFResNetConvLayer(a_	,	stride=a_	,	name="""layer.0"""		)
											a__					   =		TFResNetConvLayer(a_	,	activation=a_	,	name="""layer.1"""		)
											a__					   =		(
											    TFResNetShortCut(a_	,	stride=a_	,	name="""shortcut"""		)
											    if should_apply_shortcut
											    else tf.keras.layers.Activation("""linear"""	,	name="""shortcut"""		)
											)
											a__					   =		ACTaFN[activation]







							def _a   (				self	,	a_	,	a_ = False		):
											a__					   =		hidden_state
											a__					   =		self.conva(a_	,	training=a_		)
											a__					   =		self.conva(a_	,	training=a_		)
											a__					   =		self.shortcut(a_	,	training=a_		)
											hidden_state += residual
											a__					   =		self.activation(a_		)
											return hidden_state




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







							'''simple docstring'''






							def __init__(				self	,	a_	,	a_	,	a_ = 1	,	a_ = "relu"	,	a_ = 4	,	**a_	,	):
											super().__init__(**a_		)
											a__					   =		in_channels != out_channels or stride != 1
											a__					   =		out_channels // reduction
											a__					   =		TFResNetConvLayer(a_	,	kernel_size=1	,	name="""layer.0"""		)
											a__					   =		TFResNetConvLayer(a_	,	stride=a_	,	name="""layer.1"""		)
											a__					   =		TFResNetConvLayer(a_	,	kernel_size=1	,	activation=a_	,	name="""layer.2"""		)
											a__					   =		(
											    TFResNetShortCut(a_	,	stride=a_	,	name="""shortcut"""		)
											    if should_apply_shortcut
											    else tf.keras.layers.Activation("""linear"""	,	name="""shortcut"""		)
											)
											a__					   =		ACTaFN[activation]







							def _a   (				self	,	a_	,	a_ = False		):
											a__					   =		hidden_state
											a__					   =		self.conva(a_	,	training=a_		)
											a__					   =		self.conva(a_	,	training=a_		)
											a__					   =		self.conva(a_	,	training=a_		)
											a__					   =		self.shortcut(a_	,	training=a_		)
											hidden_state += residual
											a__					   =		self.activation(a_		)
											return hidden_state




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







							'''simple docstring'''






							def __init__(				self	,	a_	,	a_	,	a_	,	a_ = 2	,	a_ = 2	,	**a_		):
											super().__init__(**a_		)

											a__					   =		TFResNetBottleNeckLayer if config.layer_type == """bottleneck""" else TFResNetBasicLayer

											a__					   =		[layer(a_	,	a_	,	stride=a_	,	activation=config.hidden_act	,	name="""layers.0"""		)]
											layers += [
											    layer(a_	,	a_	,	activation=config.hidden_act	,	name=F'''layers.{i + 1}'''		)
											    for i in range(depth - 1		)
											]
											a__					   =		layers







							def _a   (				self	,	a_	,	a_ = False		):
											for layer in self.stage_layers:
															a__					   =		layer(a_	,	training=a_		)
											return hidden_state




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







							'''simple docstring'''






							def __init__(				self	,	a_	,	**a_		):
											super().__init__(**a_		)
											# based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
											a__					   =		[
											    TFResNetStage(
											        a_	,	config.embedding_size	,	config.hidden_sizes[0]	,	stride=2 if config.downsample_in_first_stage else 1	,	depth=config.depths[0]	,	name="""stages.0"""	,	)
											]
											for i, (in_channels, out_channels, depth) in enumerate(
											    zip(config.hidden_sizes	,	config.hidden_sizes[1:]	,	config.depths[1:]		)		):
															self.stages.append(TFResNetStage(a_	,	a_	,	a_	,	depth=a_	,	name=F'''stages.{i + 1}'''		)		)







							def _a   (				self	,	a_	,	a_ = False	,	a_ = True	,	a_ = False	,	):
											a__					   =		() if output_hidden_states else None

											for stage_module in self.stages:
															if output_hidden_states:
																			a__					   =		hidden_states + (hidden_state,)

															a__					   =		stage_module(a_	,	training=a_		)

											if output_hidden_states:
															a__					   =		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=a_	,	hidden_states=a_		)




class 			__snake_case   ( SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__					:   int       =       ResNetConfig
							UpperCamelCase__					:   Tuple       =       """resnet"""
							UpperCamelCase__					:   Tuple       =       """pixel_values"""


							@property
							def _a   (				self		):
											return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224)	,	dtype=tf.floataa		)}




UpperCAmelCase							=       R"""
    This model is a TensorFlow
    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a
    regular TensorFlow Module and refer to the TensorFlow documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
"""


UpperCAmelCase							=       R"""
    Args:
        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConvNextImageProcessor.__call__`] for details.

        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""

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







							'''simple docstring'''






							UpperCamelCase__					:   Union[str, Any]       =       ResNetConfig


							def __init__(				self	,	a_	,	**a_		):
											super().__init__(**a_		)
											a__					   =		config
											a__					   =		TFResNetEmbeddings(a_	,	name="""embedder"""		)
											a__					   =		TFResNetEncoder(a_	,	name="""encoder"""		)
											a__					   =		tf.keras.layers.GlobalAveragePoolingaD(keepdims=a_		)







							@unpack_inputs
							def _a   (				self	,	a_	,	a_ = None	,	a_ = None	,	a_ = False	,	):
											a__					   =		(
											    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
											)
											a__					   =		return_dict if return_dict is not None else self.config.use_return_dict

											# TF 2.0 image layers can't use NCHW format when running on CPU.
											# We transpose to NHWC format and then transpose back after the full forward pass.
											# (batch_size, num_channels, height, width) -> (batch_size, height, width, num_channels)
											a__					   =		tf.transpose(a_	,	perm=[0, 2, 3, 1]		)
											a__					   =		self.embedder(a_	,	training=a_		)

											a__					   =		self.encoder(
											    a_	,	output_hidden_states=a_	,	return_dict=a_	,	training=a_		)

											a__					   =		encoder_outputs[0]

											a__					   =		self.pooler(a_		)

											# Transpose all the outputs to the NCHW format
											# (batch_size, height, width, num_channels) -> (batch_size, num_channels, height, width)
											a__					   =		tf.transpose(a_	,	(0, 3, 1, 2)		)
											a__					   =		tf.transpose(a_	,	(0, 3, 1, 2)		)
											a__					   =		()
											for hidden_state in encoder_outputs[1:]:
															a__					   =		hidden_states + tuple(tf.transpose(a_	,	(0, 3, 1, 2)		) for h in hidden_state		)

											if not return_dict:
															return (last_hidden_state, pooled_output) + hidden_states

											a__					   =		hidden_states if output_hidden_states else None

											return TFBaseModelOutputWithPoolingAndNoAttention(
											    last_hidden_state=a_	,	pooler_output=a_	,	hidden_states=a_	,	)




@add_start_docstrings(
    """The bare ResNet model outputting raw features without any specific head on top."""      ,SCREAMING_SNAKE_CASE      ,)
class 			__snake_case   ( SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							def __init__(				self	,	a_	,	**a_		):
											super().__init__(a_	,	**a_		)
											a__					   =		TFResNetMainLayer(config=a_	,	name="""resnet"""		)







							@add_start_docstrings_to_model_forward(a_		)
							@add_code_sample_docstrings(
							    checkpoint=_CHECKPOINT_FOR_DOC	,	output_type=a_	,	config_class=_CONFIG_FOR_DOC	,	modality="""vision"""	,	expected_output=_EXPECTED_OUTPUT_SHAPE	,	)
							@unpack_inputs
							def _a   (				self	,	a_	,	a_ = None	,	a_ = None	,	a_ = False	,	):
											a__					   =		(
											    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
											)
											a__					   =		return_dict if return_dict is not None else self.config.use_return_dict

											a__					   =		self.resnet(
											    pixel_values=a_	,	output_hidden_states=a_	,	return_dict=a_	,	training=a_	,	)
											return resnet_outputs




@add_start_docstrings(
    """
    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    """      ,SCREAMING_SNAKE_CASE      ,)
class 			__snake_case   ( SCREAMING_SNAKE_CASE      ,SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							def __init__(				self	,	a_	,	**a_		):
											super().__init__(a_	,	**a_		)
											a__					   =		config.num_labels
											a__					   =		TFResNetMainLayer(a_	,	name="""resnet"""		)
											# classification head
											a__					   =		(
											    tf.keras.layers.Dense(config.num_labels	,	name="""classifier.1"""		)
											    if config.num_labels > 0
											    else tf.keras.layers.Activation("""linear"""	,	name="""classifier.1"""		)
											)


							def _a   (				self	,	a_		):
											a__					   =		tf.keras.layers.Flatten()(a_		)
											a__					   =		self.classifier_layer(a_		)
											return logits







							@add_start_docstrings_to_model_forward(a_		)
							@add_code_sample_docstrings(
							    checkpoint=_IMAGE_CLASS_CHECKPOINT	,	output_type=a_	,	config_class=_CONFIG_FOR_DOC	,	expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT	,	)
							@unpack_inputs
							def _a   (				self	,	a_ = None	,	a_ = None	,	a_ = None	,	a_ = None	,	a_ = False	,	):
											a__					   =		return_dict if return_dict is not None else self.config.use_return_dict

											a__					   =		self.resnet(
											    a_	,	output_hidden_states=a_	,	return_dict=a_	,	training=a_		)

											a__					   =		outputs.pooler_output if return_dict else outputs[1]

											a__					   =		self.classifier(a_		)

											a__					   =		None if labels is None else self.hf_compute_loss(a_	,	a_		)

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

											return TFImageClassifierOutputWithNoAttention(loss=a_	,	logits=a_	,	hidden_states=outputs.hidden_states		)









'''simple docstring'''







from __future__ import annotations


def     _UpperCamelCase				(  snake_case__				):
      __A		 =   [0] * len(snake_case__				)
      for i in range(1				,      len(snake_case__				)				):
            __A		 =   duration_times[i - 1] + waiting_times[i - 1]
      return waiting_times


def     _UpperCamelCase				(  snake_case__				,      snake_case__				):
      return [
          duration_time + waiting_times[i]
          for i, duration_time in enumerate(snake_case__				)
      ]


def     _UpperCamelCase				(  snake_case__				):
      return sum(snake_case__				) / len(snake_case__				)


def     _UpperCamelCase				(  snake_case__				):
      return sum(snake_case__				) / len(snake_case__				)


if __name__ == "__main__":
       # process id's
       __A   :							List[str]     	=						[1, 2, 3]

       # ensure that we actually have processes
       if len(processes) == 0:
              print('Zero amount of processes')
              raise SystemExit(0)

       # duration time of all processes
       __A   :							List[Any]     	=						[1_9, 8, 9]

       # ensure we can match each id to a duration time
       if len(duration_times) != len(processes):
              print('Unable to match all id\'s with their duration time')
              raise SystemExit(0)

       # get the waiting times and the turnaround times
       __A   :							Dict     	=						calculate_waiting_times(duration_times)
       __A   :							Optional[int]     	=						calculate_turnaround_times(duration_times, waiting_times)

       # get the average times
       __A   :							Dict     	=						calculate_average_waiting_time(waiting_times)
       __A   :							Tuple     	=						calculate_average_turnaround_time(turnaround_times)

       # print all the results
       print('Process ID\tDuration Time\tWaiting Time\tTurnaround Time')
       for i, process in enumerate(processes):
              print(
                  F"""{process}\t\t{duration_times[i]}\t\t{waiting_times[i]}\t\t"""
                  F"""{turnaround_times[i]}"""
              )
       print(F"""Average waiting time = {average_waiting_time}""")
       print(F"""Average turn around time = {average_turnaround_time}""")


