


'''simple docstring'''
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


lowerCAmelCase						=					logging.get_logger(__name__)

# General docstring
lowerCAmelCase						=					"""RegNetConfig"""

# Base docstring
lowerCAmelCase						=					"""facebook/regnet-y-040"""
lowerCAmelCase						=					[1, 1_088, 7, 7]

# Image classification docstring
lowerCAmelCase						=					"""facebook/regnet-y-040"""
lowerCAmelCase						=					"""tabby, tabby cat"""

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






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






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






       def   UpperCamelCase						( self			,     _UpperCamelCase   )->      Optional[int]:
            _A				=							self.convolution(self.padding(snake_case_   )   )
            _A				=							self.normalization(snake_case_   )
            _A				=							self.activation(snake_case_   )
            return hidden_state






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






       def __init__( self			,     _UpperCamelCase			,     **_UpperCamelCase   )->      int:
            super().__init__(**snake_case_   )
            _A				=							config.num_channels
            _A				=							TFRegNetConvLayer(
                out_channels=config.embedding_size			,     kernel_size=3			,     stride=2			,     activation=config.hidden_act			,     name='embedder'			,     )






       def   UpperCamelCase						( self			,     _UpperCamelCase   )->      Any:
            _A				=							shape_list(snake_case_   )[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)
            _A				=							tf.transpose(snake_case_			,     perm=(0, 2, 3, 1)   )
            _A				=							self.embedder(snake_case_   )
            return hidden_state






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






       def __init__( self			,     _UpperCamelCase			,     _UpperCamelCase = 2			,     **_UpperCamelCase   )->      int:
            super().__init__(**snake_case_   )
            _A				=							tf.keras.layers.ConvaD(
                filters=snake_case_			,     kernel_size=1			,     strides=snake_case_			,     use_bias=snake_case_			,     name='convolution'   )
            _A				=							tf.keras.layers.BatchNormalization(epsilon=1e-5			,     momentum=0.9			,     name='normalization'   )






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase = False   )->      tf.Tensor:
            return self.normalization(self.convolution(snake_case_   )			,     training=snake_case_   )






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






       def __init__( self			,     _UpperCamelCase			,     _UpperCamelCase			,     **_UpperCamelCase   )->      Dict:
            super().__init__(**snake_case_   )
            _A				=							tf.keras.layers.GlobalAveragePoolingaD(keepdims=snake_case_			,     name='pooler'   )
            _A				=							[
                tf.keras.layers.ConvaD(filters=snake_case_			,     kernel_size=1			,     activation='relu'			,     name='attention.0'   ),
                tf.keras.layers.ConvaD(filters=snake_case_			,     kernel_size=1			,     activation='sigmoid'			,     name='attention.2'   ),
            ]






       def   UpperCamelCase						( self			,     _UpperCamelCase   )->      int:
            # [batch_size, h, w, num_channels] -> [batch_size, 1, 1, num_channels]
            _A				=							self.pooler(snake_case_   )
            for layer_module in self.attention:
                 _A				=							layer_module(snake_case_   )
            _A				=							hidden_state * pooled
            return hidden_state






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






       def __init__( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase = 1			,     **_UpperCamelCase   )->      Union[str, Any]:
            super().__init__(**snake_case_   )
            _A				=							in_channels != out_channels or stride != 1
            _A				=							max(1			,     out_channels // config.groups_width   )
            _A				=							(
                TFRegNetShortCut(snake_case_			,     stride=snake_case_			,     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.
            _A				=							[
                TFRegNetConvLayer(snake_case_			,     kernel_size=1			,     activation=config.hidden_act			,     name='layer.0'   ),
                TFRegNetConvLayer(
                    snake_case_			,     stride=snake_case_			,     groups=snake_case_			,     activation=config.hidden_act			,     name='layer.1'   ),
                TFRegNetConvLayer(snake_case_			,     kernel_size=1			,     activation=snake_case_			,     name='layer.2'   ),
            ]
            _A				=							ACTaFN[config.hidden_act]






       def   UpperCamelCase						( self			,     _UpperCamelCase   )->      Any:
            _A				=							hidden_state
            for layer_module in self.layers:
                 _A				=							layer_module(snake_case_   )
            _A				=							self.shortcut(snake_case_   )
            hidden_state += residual
            _A				=							self.activation(snake_case_   )
            return hidden_state






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






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






       def   UpperCamelCase						( self			,     _UpperCamelCase   )->      Optional[Any]:
            _A				=							hidden_state
            for layer_module in self.layers:
                 _A				=							layer_module(snake_case_   )
            _A				=							self.shortcut(snake_case_   )
            hidden_state += residual
            _A				=							self.activation(snake_case_   )
            return hidden_state






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






       def __init__( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase = 2			,     _UpperCamelCase = 2			,     **_UpperCamelCase   )->      str:
            super().__init__(**snake_case_   )

            _A				=							TFRegNetXLayer if config.layer_type == 'x' else TFRegNetYLayer
            _A				=							[
                # downsampling is done in the first layer with stride of 2
                layer(snake_case_			,     snake_case_			,     snake_case_			,     stride=snake_case_			,     name='layers.0'   ),
                *[layer(snake_case_			,     snake_case_			,     snake_case_			,     name=F'layers.{i+1}'   ) for i in range(depth - 1   )],
            ]






       def   UpperCamelCase						( self			,     _UpperCamelCase   )->      Optional[Any]:
            for layer_module in self.layers:
                 _A				=							layer_module(snake_case_   )
            return hidden_state






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






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






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase = False			,     _UpperCamelCase = True   )->      TFBaseModelOutputWithNoAttention:
            _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(snake_case_   )

            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=snake_case_			,     hidden_states=snake_case_   )






@keras_serializable
class        lowerCAmelCase_  (     tf.keras.layers.Layer	):
       __UpperCAmelCase					=RegNetConfig






       def __init__( self			,     _UpperCamelCase			,     **_UpperCamelCase   )->      int:
            super().__init__(**snake_case_   )
            _A				=							config
            _A				=							TFRegNetEmbeddings(snake_case_			,     name='embedder'   )
            _A				=							TFRegNetEncoder(snake_case_			,     name='encoder'   )
            _A				=							tf.keras.layers.GlobalAveragePoolingaD(keepdims=snake_case_			,     name='pooler'   )






       @unpack_inputs
       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase = None			,     _UpperCamelCase = None			,     _UpperCamelCase = False			,     )->      TFBaseModelOutputWithPoolingAndNoAttention:
            _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.embedder(snake_case_			,     training=snake_case_   )

            _A				=							self.encoder(
                snake_case_			,     output_hidden_states=snake_case_			,     return_dict=snake_case_			,     training=snake_case_   )

            _A				=							encoder_outputs[0]
            _A				=							self.pooler(snake_case_   )

            # Change to NCHW output format have uniformity in the modules
            _A				=							tf.transpose(snake_case_			,     perm=(0, 3, 1, 2)   )
            _A				=							tf.transpose(snake_case_			,     perm=(0, 3, 1, 2)   )

            # Change the other hidden state outputs to NCHW as well
            if output_hidden_states:
                 _A				=							tuple([tf.transpose(snake_case_			,     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=snake_case_			,     pooler_output=snake_case_			,     hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states			,     )






class        lowerCAmelCase_  (     UpperCAmelCase	):
       __UpperCAmelCase					=RegNetConfig
       __UpperCAmelCase					='regnet'
       __UpperCAmelCase					='pixel_values'






       @property
       def   UpperCamelCase						( self   )->      Union[str, Any]:
            return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224)			,     dtype=tf.floataa   )}






lowerCAmelCase						=					R"""
    Parameters:
    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.
        config ([`RegNetConfig`]): 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.
"""

lowerCAmelCase						=					R"""
    Args:
        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConveNextImageProcessor.__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.
"""






@add_start_docstrings(
    'The bare RegNet model outputting raw features without any specific head on top.'      ,							UpperCAmelCase      ,							)
class        lowerCAmelCase_  (     UpperCAmelCase	):






       def __init__( self			,     _UpperCamelCase			,     *_UpperCamelCase			,     **_UpperCamelCase   )->      Optional[int]:
            super().__init__(snake_case_			,     *snake_case_			,     **snake_case_   )
            _A				=							TFRegNetMainLayer(snake_case_			,     name='regnet'   )






       @unpack_inputs
       @add_start_docstrings_to_model_forward(snake_case_   )
       @add_code_sample_docstrings(
           checkpoint=_CHECKPOINT_FOR_DOC			,     output_type=snake_case_			,     config_class=_CONFIG_FOR_DOC			,     modality='vision'			,     expected_output=_EXPECTED_OUTPUT_SHAPE			,     )
       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase = None			,     _UpperCamelCase = None			,     _UpperCamelCase=False			,     )->      Union[TFBaseModelOutputWithPoolingAndNoAttention, Tuple[tf.Tensor]]:
            _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.regnet(
                pixel_values=snake_case_			,     output_hidden_states=snake_case_			,     return_dict=snake_case_			,     training=snake_case_			,     )
            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    '      ,							UpperCAmelCase      ,							)
class        lowerCAmelCase_  (     UpperCAmelCase      ,							UpperCAmelCase	):






       def __init__( self			,     _UpperCamelCase			,     *_UpperCamelCase			,     **_UpperCamelCase   )->      Tuple:
            super().__init__(snake_case_			,     *snake_case_			,     **snake_case_   )
            _A				=							config.num_labels
            _A				=							TFRegNetMainLayer(snake_case_			,     name='regnet'   )
            # classification head
            _A				=							[
                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(snake_case_   )
       @add_code_sample_docstrings(
           checkpoint=_IMAGE_CLASS_CHECKPOINT			,     output_type=snake_case_			,     config_class=_CONFIG_FOR_DOC			,     expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT			,     )
       def   UpperCamelCase						( self			,     _UpperCamelCase = None			,     _UpperCamelCase = None			,     _UpperCamelCase = None			,     _UpperCamelCase = None			,     _UpperCamelCase=False			,     )->      Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:
            _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.regnet(
                snake_case_			,     output_hidden_states=snake_case_			,     return_dict=snake_case_			,     training=snake_case_   )

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

            _A				=							self.classifier[0](snake_case_   )
            _A				=							self.classifier[1](snake_case_   )

            _A				=							None if labels is None else self.hf_compute_loss(labels=snake_case_			,     logits=snake_case_   )

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

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










from __future__ import annotations

import time

import numpy as np

lowercase_													=      [8, 5, 9, 7]
lowercase_													=      [
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
lowercase_													=      [
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]






class     __a			:



	def __init__(    self				:     Tuple					,						snake_case_				:     list[int]					,						snake_case_				:     list[list[int]]					,						snake_case_				:     list[list[int]]					,						)->				None:
						__lowerCAmelCase						 =claim_vector
						__lowerCAmelCase						 =allocated_resources_table
						__lowerCAmelCase						 =maximum_claim_table



	def 		UpperCamelCase				(    self				:     Optional[int])->				list[int]:
						return [
						    sum(p_item[i] for p_item in self.__allocated_resources_table)
						    for i in range(len(self.__allocated_resources_table[0]))
						]



	def 		UpperCamelCase				(    self				:     Dict)->				list[int]:
						return np.array(self.__claim_vector) - np.array(
						    self.__processes_resource_summation())



	def 		UpperCamelCase				(    self				:     Optional[Any])->				list[list[int]]:
						return [
						    list(np.array(self.__maximum_claim_table[i]) - np.array(snake_case_))
						    for i, allocated_resource in enumerate(self.__allocated_resources_table)
						]



	def 		UpperCamelCase				(    self				:     Dict)->				dict[int, list[int]]:
						return {self.__need().index(snake_case_): i for i in self.__need()}



	def 		UpperCamelCase				(    self				:     Optional[Any]					,						**snake_case_				:     Optional[Any])->				None:
						__lowerCAmelCase						 =self.__need()
						__lowerCAmelCase						 =self.__allocated_resources_table
						__lowerCAmelCase						 =self.__available_resources()
						__lowerCAmelCase						 =self.__need_index_manager()
						for kw, val in kwargs.items():
											if kw and val is True:
																self.__pretty_data()
						print("""_""" * 50 + """\n""")
						while need_list:
											__lowerCAmelCase						 =False
											for each_need in need_list:
																__lowerCAmelCase						 =True
																for index, need in enumerate(snake_case_):
																					if need > available_resources[index]:
																										__lowerCAmelCase						 =False
																										break
																if execution:
																					__lowerCAmelCase						 =True
																					# get the original index of the process from ind_ctrl db
																					for original_need_index, need_clone in need_index_manager.items():
																										if each_need == need_clone:
																															__lowerCAmelCase						 =original_need_index
																					print(F"""Process {process_number + 1} is executing.""")
																					# remove the process run from stack
																					need_list.remove(snake_case_)
																					# update available/freed resources stack
																					__lowerCAmelCase						 =np.array(snake_case_) + np.array(
																					    alloc_resources_table[process_number])
																					print(
																					    """Updated available resource stack for processes: """
																					    + """ """.join([str(snake_case_) for x in available_resources]))
																					break
											if safe:
																print("""The process is in a safe state.\n""")
											else:
																print("""System in unsafe state. Aborting...\n""")
																break



	def 		UpperCamelCase				(    self				:     List[str])->				Optional[Any]:
						print(""" """ * 9 + """Allocated Resource Table""")
						for item in self.__allocated_resources_table:
											print(
											    F"""P{self.__allocated_resources_table.index(snake_case_) + 1}"""
											    + """ """.join(F"""{it:>8}""" for it in item)
											    + """\n""")
						print(""" """ * 9 + """System Resource Table""")
						for item in self.__maximum_claim_table:
											print(
											    F"""P{self.__maximum_claim_table.index(snake_case_) + 1}"""
											    + """ """.join(F"""{it:>8}""" for it in item)
											    + """\n""")
						print(
						    """Current Usage by Active Processes: """
						    + """ """.join(str(snake_case_) for x in self.__claim_vector))
						print(
						    """Initial Available Resources:       """
						    + """ """.join(str(snake_case_) for x in self.__available_resources()))
						time.sleep(1)


if __name__ == "__main__":
				import doctest

				doctest.testmod()




