
'''simple docstring'''



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__ :						Tuple    							=					logging.get_logger(__name__)

# General docstring
UpperCamelCase__ :						int    							=					"ResNetConfig"

# Base docstring
UpperCamelCase__ :						int    							=					"microsoft/resnet-50"
UpperCamelCase__ :						List[str]    							=					[1, 2_048, 7, 7]

# Image classification docstring
UpperCamelCase__ :						List[Any]    							=					"microsoft/resnet-50"
UpperCamelCase__ :						Any    							=					"tiger cat"

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

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





       """simple docstring"""




       def __init__(							self    ,							A__    ,							A__ = 3    ,							A__ = 1    ,							A__ = "relu"    ,							**A__ )      ->     None:
           super().__init__(**A__ )
           _SCREAMING_SNAKE_CASE       	=			kernel_size // 2
           _SCREAMING_SNAKE_CASE       	=			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
           _SCREAMING_SNAKE_CASE       	=			tf.keras.layers.BatchNormalization(epsilon=1E-5    ,							momentum=0.9    ,							name="""normalization""" )
           _SCREAMING_SNAKE_CASE       	=			ACTaFN[activation] if activation is not None else tf.keras.layers.Activation("""linear""" )






       def     UpperCamelCase			(							self    ,							A__ )      ->     tf.Tensor:
           _SCREAMING_SNAKE_CASE       	=			(self.pad_value, self.pad_value)
           _SCREAMING_SNAKE_CASE       	=			tf.pad(A__    ,							[(0, 0), height_pad, width_pad, (0, 0)] )
           _SCREAMING_SNAKE_CASE       	=			self.conv(A__ )
           return hidden_state






       def     UpperCamelCase			(							self    ,							A__    ,							A__ = False )      ->     tf.Tensor:
           _SCREAMING_SNAKE_CASE       	=			self.convolution(A__ )
           _SCREAMING_SNAKE_CASE       	=			self.normalization(A__    ,							training=A__ )
           _SCREAMING_SNAKE_CASE       	=			self.activation(A__ )
           return hidden_state






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





       """simple docstring"""




       def __init__(							self    ,							A__    ,							**A__ )      ->     None:
           super().__init__(**A__ )
           _SCREAMING_SNAKE_CASE       	=			TFResNetConvLayer(
               config.embedding_size    ,							kernel_size=7    ,							stride=2    ,							activation=config.hidden_act    ,							name="""embedder"""    ,							)
           _SCREAMING_SNAKE_CASE       	=			tf.keras.layers.MaxPoolaD(pool_size=3    ,							strides=2    ,							padding="""valid"""    ,							name="""pooler""" )
           _SCREAMING_SNAKE_CASE       	=			config.num_channels






       def     UpperCamelCase			(							self    ,							A__    ,							A__ = False )      ->     tf.Tensor:
           _SCREAMING_SNAKE_CASE       	=			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.""" )
           _SCREAMING_SNAKE_CASE       	=			pixel_values
           _SCREAMING_SNAKE_CASE       	=			self.embedder(A__ )
           _SCREAMING_SNAKE_CASE       	=			tf.pad(A__    ,							[[0, 0], [1, 1], [1, 1], [0, 0]] )
           _SCREAMING_SNAKE_CASE       	=			self.pooler(A__ )
           return hidden_state






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





       """simple docstring"""




       def __init__(							self    ,							A__    ,							A__ = 2    ,							**A__ )      ->     None:
           super().__init__(**A__ )
           _SCREAMING_SNAKE_CASE       	=			tf.keras.layers.ConvaD(
               A__    ,							kernel_size=1    ,							strides=A__    ,							use_bias=A__    ,							name="""convolution""" )
           # Use same default momentum and epsilon as PyTorch equivalent
           _SCREAMING_SNAKE_CASE       	=			tf.keras.layers.BatchNormalization(epsilon=1E-5    ,							momentum=0.9    ,							name="""normalization""" )






       def     UpperCamelCase			(							self    ,							A__    ,							A__ = False )      ->     tf.Tensor:
           _SCREAMING_SNAKE_CASE       	=			x
           _SCREAMING_SNAKE_CASE       	=			self.convolution(A__ )
           _SCREAMING_SNAKE_CASE       	=			self.normalization(A__    ,							training=A__ )
           return hidden_state






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





       """simple docstring"""




       def __init__(							self    ,							A__    ,							A__    ,							A__ = 1    ,							A__ = "relu"    ,							**A__ )      ->     None:
           super().__init__(**A__ )
           _SCREAMING_SNAKE_CASE       	=			in_channels != out_channels or stride != 1
           _SCREAMING_SNAKE_CASE       	=			TFResNetConvLayer(A__    ,							stride=A__    ,							name="""layer.0""" )
           _SCREAMING_SNAKE_CASE       	=			TFResNetConvLayer(A__    ,							activation=A__    ,							name="""layer.1""" )
           _SCREAMING_SNAKE_CASE       	=			(
               TFResNetShortCut(A__    ,							stride=A__    ,							name="""shortcut""" )
               if should_apply_shortcut
               else tf.keras.layers.Activation("""linear"""    ,							name="""shortcut""" )
           )
           _SCREAMING_SNAKE_CASE       	=			ACTaFN[activation]






       def     UpperCamelCase			(							self    ,							A__    ,							A__ = False )      ->     tf.Tensor:
           _SCREAMING_SNAKE_CASE       	=			hidden_state
           _SCREAMING_SNAKE_CASE       	=			self.conva(A__    ,							training=A__ )
           _SCREAMING_SNAKE_CASE       	=			self.conva(A__    ,							training=A__ )
           _SCREAMING_SNAKE_CASE       	=			self.shortcut(A__    ,							training=A__ )
           hidden_state += residual
           _SCREAMING_SNAKE_CASE       	=			self.activation(A__ )
           return hidden_state






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





       """simple docstring"""




       def __init__(							self    ,							A__    ,							A__    ,							A__ = 1    ,							A__ = "relu"    ,							A__ = 4    ,							**A__    ,							)      ->     None:
           super().__init__(**A__ )
           _SCREAMING_SNAKE_CASE       	=			in_channels != out_channels or stride != 1
           _SCREAMING_SNAKE_CASE       	=			out_channels // reduction
           _SCREAMING_SNAKE_CASE       	=			TFResNetConvLayer(A__    ,							kernel_size=1    ,							name="""layer.0""" )
           _SCREAMING_SNAKE_CASE       	=			TFResNetConvLayer(A__    ,							stride=A__    ,							name="""layer.1""" )
           _SCREAMING_SNAKE_CASE       	=			TFResNetConvLayer(A__    ,							kernel_size=1    ,							activation=A__    ,							name="""layer.2""" )
           _SCREAMING_SNAKE_CASE       	=			(
               TFResNetShortCut(A__    ,							stride=A__    ,							name="""shortcut""" )
               if should_apply_shortcut
               else tf.keras.layers.Activation("""linear"""    ,							name="""shortcut""" )
           )
           _SCREAMING_SNAKE_CASE       	=			ACTaFN[activation]






       def     UpperCamelCase			(							self    ,							A__    ,							A__ = False )      ->     tf.Tensor:
           _SCREAMING_SNAKE_CASE       	=			hidden_state
           _SCREAMING_SNAKE_CASE       	=			self.conva(A__    ,							training=A__ )
           _SCREAMING_SNAKE_CASE       	=			self.conva(A__    ,							training=A__ )
           _SCREAMING_SNAKE_CASE       	=			self.conva(A__    ,							training=A__ )
           _SCREAMING_SNAKE_CASE       	=			self.shortcut(A__    ,							training=A__ )
           hidden_state += residual
           _SCREAMING_SNAKE_CASE       	=			self.activation(A__ )
           return hidden_state






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





       """simple docstring"""




       def __init__(							self    ,							A__    ,							A__    ,							A__    ,							A__ = 2    ,							A__ = 2    ,							**A__ )      ->     None:
           super().__init__(**A__ )

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

           _SCREAMING_SNAKE_CASE       	=			[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 )
           ]
           _SCREAMING_SNAKE_CASE       	=			layers






       def     UpperCamelCase			(							self    ,							A__    ,							A__ = False )      ->     tf.Tensor:

           for layer in self.stage_layers:
               _SCREAMING_SNAKE_CASE       	=			layer(A__    ,							training=A__ )
           return hidden_state






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





       """simple docstring"""




       def __init__(							self    ,							A__    ,							**A__ )      ->     None:
           super().__init__(**A__ )
           # based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
           _SCREAMING_SNAKE_CASE       	=			[
               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     UpperCamelCase			(							self    ,							A__    ,							A__ = False    ,							A__ = True    ,							A__ = False    ,							)      ->     TFBaseModelOutputWithNoAttention:
           _SCREAMING_SNAKE_CASE       	=			() if output_hidden_states else None

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

               _SCREAMING_SNAKE_CASE       	=			stage_module(A__    ,							training=A__ )

           if output_hidden_states:
               _SCREAMING_SNAKE_CASE       	=			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 				_a			(__A):





       """simple docstring"""




       SCREAMING_SNAKE_CASE =					ResNetConfig
       SCREAMING_SNAKE_CASE =					'resnet'
       SCREAMING_SNAKE_CASE =					'pixel_values'






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






UpperCamelCase__ :						Optional[int]    							=					R"\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\n    Parameters:\n        config ([`ResNetConfig`]): 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"


UpperCamelCase__ :						str    							=					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            [`ConvNextImageProcessor.__call__`] for details.\n\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"

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





       """simple docstring"""




       SCREAMING_SNAKE_CASE =					ResNetConfig






       def __init__(							self    ,							A__    ,							**A__ )      ->     None:
           super().__init__(**A__ )
           _SCREAMING_SNAKE_CASE       	=			config
           _SCREAMING_SNAKE_CASE       	=			TFResNetEmbeddings(A__    ,							name="""embedder""" )
           _SCREAMING_SNAKE_CASE       	=			TFResNetEncoder(A__    ,							name="""encoder""" )
           _SCREAMING_SNAKE_CASE       	=			tf.keras.layers.GlobalAveragePoolingaD(keepdims=A__ )






       @unpack_inputs
       def     UpperCamelCase			(							self    ,							A__    ,							A__ = None    ,							A__ = None    ,							A__ = False    ,							)      ->     Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:
           _SCREAMING_SNAKE_CASE       	=			(
               output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
           )
           _SCREAMING_SNAKE_CASE       	=			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)
           _SCREAMING_SNAKE_CASE       	=			tf.transpose(A__    ,							perm=[0, 2, 3, 1] )
           _SCREAMING_SNAKE_CASE       	=			self.embedder(A__    ,							training=A__ )

           _SCREAMING_SNAKE_CASE       	=			self.encoder(
               A__    ,							output_hidden_states=A__    ,							return_dict=A__    ,							training=A__ )

           _SCREAMING_SNAKE_CASE       	=			encoder_outputs[0]

           _SCREAMING_SNAKE_CASE       	=			self.pooler(A__ )

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

           _SCREAMING_SNAKE_CASE       	=			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.'					,   __A					,   )
class 				_a			(__A):





       """simple docstring"""




       def __init__(							self    ,							A__    ,							**A__ )      ->     None:
           super().__init__(A__    ,							**A__ )
           _SCREAMING_SNAKE_CASE       	=			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     UpperCamelCase			(							self    ,							A__    ,							A__ = None    ,							A__ = None    ,							A__ = False    ,							)      ->     Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:
           _SCREAMING_SNAKE_CASE       	=			(
               output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
           )
           _SCREAMING_SNAKE_CASE       	=			return_dict if return_dict is not None else self.config.use_return_dict

           _SCREAMING_SNAKE_CASE       	=			self.resnet(
               pixel_values=A__    ,							output_hidden_states=A__    ,							return_dict=A__    ,							training=A__    ,							)
           return resnet_outputs






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





       """simple docstring"""




       def __init__(							self    ,							A__    ,							**A__ )      ->     None:
           super().__init__(A__    ,							**A__ )
           _SCREAMING_SNAKE_CASE       	=			config.num_labels
           _SCREAMING_SNAKE_CASE       	=			TFResNetMainLayer(A__    ,							name="""resnet""" )
           # classification head
           _SCREAMING_SNAKE_CASE       	=			(
               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     UpperCamelCase			(							self    ,							A__ )      ->     tf.Tensor:
           _SCREAMING_SNAKE_CASE       	=			tf.keras.layers.Flatten()(A__ )
           _SCREAMING_SNAKE_CASE       	=			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     UpperCamelCase			(							self    ,							A__ = None    ,							A__ = None    ,							A__ = None    ,							A__ = None    ,							A__ = False    ,							)      ->     Union[Tuple[tf.Tensor], TFImageClassifierOutputWithNoAttention]:
           _SCREAMING_SNAKE_CASE       	=			return_dict if return_dict is not None else self.config.use_return_dict

           _SCREAMING_SNAKE_CASE       	=			self.resnet(
               A__    ,							output_hidden_states=A__    ,							return_dict=A__    ,							training=A__ )

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

           _SCREAMING_SNAKE_CASE       	=			self.classifier(A__ )

           _SCREAMING_SNAKE_CASE       	=			None if labels is None else self.hf_compute_loss(A__    ,							A__ )

           if not return_dict:
               _SCREAMING_SNAKE_CASE       	=			(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'''



#
# Sort large text files in a minimum amount of memory
#
import argparse
import os

class 				_a			:





       """simple docstring"""




       SCREAMING_SNAKE_CASE =					'block_{0}.dat'






       def __init__(							self    ,							A__ )      ->     Union[str, Any]:
          _SCREAMING_SNAKE_CASE       	=			filename
          _SCREAMING_SNAKE_CASE       	=			[]






       def     UpperCamelCase			(							self    ,							A__    ,							A__ )      ->     Optional[Any]:
          _SCREAMING_SNAKE_CASE       	=			self.BLOCK_FILENAME_FORMAT.format(A__ )
          with open(A__    ,							"""w""" ) as file:
             file.write(A__ )
          self.block_filenames.append(A__ )






       def     UpperCamelCase			(							self )      ->     Tuple:
          return self.block_filenames






       def     UpperCamelCase			(							self    ,							A__    ,							A__=None )      ->     Any:
          _SCREAMING_SNAKE_CASE       	=			0
          with open(self.filename ) as file:
             while True:
                _SCREAMING_SNAKE_CASE       	=			file.readlines(A__ )

                if lines == []:
                   break

                if sort_key is None:
                   lines.sort()
                else:
                   lines.sort(key=A__ )

                self.write_block("""""".join(A__ )    ,							A__ )
                i += 1






       def     UpperCamelCase			(							self )      ->     List[Any]:
          map(os.remove    ,							self.block_filenames )






class 				_a			:





       """simple docstring"""




       def     UpperCamelCase			(							self    ,							A__ )      ->     str:
          _SCREAMING_SNAKE_CASE       	=			-1
          _SCREAMING_SNAKE_CASE       	=			None

          for i in range(len(A__ ) ):
             if min_str is None or choices[i] < min_str:
                _SCREAMING_SNAKE_CASE       	=			i

          return min_index






class 				_a			:





       """simple docstring"""




       def __init__(							self    ,							A__ )      ->     Dict:
          _SCREAMING_SNAKE_CASE       	=			files
          _SCREAMING_SNAKE_CASE       	=			set()
          _SCREAMING_SNAKE_CASE       	=			len(A__ )
          _SCREAMING_SNAKE_CASE       	=			{i: None for i in range(self.num_buffers )}






       def     UpperCamelCase			(							self )      ->     Dict:
          return {
              i: self.buffers[i] for i in range(self.num_buffers ) if i not in self.empty
          }






       def     UpperCamelCase			(							self )      ->     Tuple:
          for i in range(self.num_buffers ):
             if self.buffers[i] is None and i not in self.empty:
                _SCREAMING_SNAKE_CASE       	=			self.files[i].readline()

                if self.buffers[i] == "":
                   self.empty.add(A__ )
                   self.files[i].close()

          if len(self.empty ) == self.num_buffers:
             return False

          return True






       def     UpperCamelCase			(							self    ,							A__ )      ->     List[str]:
          _SCREAMING_SNAKE_CASE       	=			self.buffers[index]
          _SCREAMING_SNAKE_CASE       	=			None

          return value






class 				_a			:





       """simple docstring"""




       def __init__(							self    ,							A__ )      ->     str:
          _SCREAMING_SNAKE_CASE       	=			merge_strategy






       def     UpperCamelCase			(							self    ,							A__    ,							A__    ,							A__ )      ->     Union[str, Any]:
          _SCREAMING_SNAKE_CASE       	=			FilesArray(self.get_file_handles(A__    ,							A__ ) )
          with open(A__    ,							"""w"""    ,							A__ ) as outfile:
             while buffers.refresh():
                _SCREAMING_SNAKE_CASE       	=			self.merge_strategy.select(buffers.get_dict() )
                outfile.write(buffers.unshift(A__ ) )






       def     UpperCamelCase			(							self    ,							A__    ,							A__ )      ->     str:
          _SCREAMING_SNAKE_CASE       	=			{}

          for i in range(len(A__ ) ):
             _SCREAMING_SNAKE_CASE       	=			open(filenames[i]    ,							"""r"""    ,							A__ )  # noqa: UP015

          return files






class 				_a			:





       """simple docstring"""




       def __init__(							self    ,							A__ )      ->     Union[str, Any]:
          _SCREAMING_SNAKE_CASE       	=			block_size






       def     UpperCamelCase			(							self    ,							A__    ,							A__=None )      ->     Optional[Any]:
          _SCREAMING_SNAKE_CASE       	=			self.get_number_blocks(A__    ,							self.block_size )
          _SCREAMING_SNAKE_CASE       	=			FileSplitter(A__ )
          splitter.split(self.block_size    ,							A__ )

          _SCREAMING_SNAKE_CASE       	=			FileMerger(NWayMerge() )
          _SCREAMING_SNAKE_CASE       	=			self.block_size / (num_blocks + 1)
          merger.merge(splitter.get_block_filenames()    ,							filename + """.out"""    ,							A__ )

          splitter.cleanup()






       def     UpperCamelCase			(							self    ,							A__    ,							A__ )      ->     Any:
          return (os.stat(A__ ).st_size / block_size) + 1






def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_  )       ->       str:




   """simple docstring"""
   if string[-1].lower() == "k":
      return int(string[:-1]  ) * 10_24
   elif string[-1].lower() == "m":
      return int(string[:-1]  ) * 10_24 * 10_24
   elif string[-1].lower() == "g":
      return int(string[:-1]  ) * 10_24 * 10_24 * 10_24
   else:
      return int(SCREAMING_SNAKE_CASE_  )
def 				lowerCAmelCase_		(			)       ->       List[str]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE       	=			argparse.ArgumentParser()
   parser.add_argument(
       """-m"""      , """--mem"""      , help="""amount of memory to use for sorting"""      , default="""100M"""  )
   parser.add_argument(
       """filename"""      , metavar="""<filename>"""      , nargs=1      , help="""name of file to sort"""  )
   _SCREAMING_SNAKE_CASE       	=			parser.parse_args()

   _SCREAMING_SNAKE_CASE       	=			ExternalSort(parse_memory(args.mem  )  )
   sorter.sort(args.filename[0]  )


if __name__ == "__main__":
     main()






