


'''simple docstring'''




import unittest
from unittest.util import safe_repr

from transformers import AutoTokenizer, RwkvConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import (
				    RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RwkvForCausalLM,
				    RwkvModel,
				)
				from transformers.pytorch_utils import is_torch_greater_or_equal_than_2_0
else:
				__lowercase	:   Tuple   		=					False





class _snake_case :
							"""simple docstring"""





							def __init__( self      ,					__A      ,					__A=14      ,					__A=7      ,					__A=True      ,					__A=False      ,					__A=True      ,					__A=True      ,					__A=True      ,					__A=99      ,					__A=32      ,					__A=5      ,					__A=37      ,					__A="gelu"      ,					__A=0.1      ,					__A=0.1      ,					__A=512      ,					__A=16      ,					__A=2      ,					__A=3      ,					__A=4      ,					__A=None      ,					)  ->Union[str, Any]:



											"""simple docstring"""







											__lowerCamelCase		:   Tuple           =       parent
											__lowerCamelCase		:   Union[str, Any]           =       batch_size
											__lowerCamelCase		:   Optional[int]           =       seq_length
											__lowerCamelCase		:   List[str]           =       is_training
											__lowerCamelCase		:   Dict           =       use_token_type_ids
											__lowerCamelCase		:   Dict           =       use_input_mask
											__lowerCamelCase		:   Any           =       use_labels
											__lowerCamelCase		:   Any           =       use_mc_token_ids
											__lowerCamelCase		:   List[Any]           =       vocab_size
											__lowerCamelCase		:   List[str]           =       hidden_size
											__lowerCamelCase		:   List[Any]           =       num_hidden_layers
											__lowerCamelCase		:   str           =       intermediate_size
											__lowerCamelCase		:   Any           =       hidden_act
											__lowerCamelCase		:   str           =       hidden_dropout_prob
											__lowerCamelCase		:   Dict           =       attention_probs_dropout_prob
											__lowerCamelCase		:   Optional[int]           =       max_position_embeddings
											__lowerCamelCase		:   Optional[Any]           =       type_vocab_size
											__lowerCamelCase		:   Tuple           =       type_sequence_label_size
											__lowerCamelCase		:   List[Any]           =       num_labels
											__lowerCamelCase		:   Optional[int]           =       num_choices
											__lowerCamelCase		:   Optional[Any]           =       scope
											__lowerCamelCase		:   int           =       vocab_size - 1
											__lowerCamelCase		:   Dict           =       vocab_size - 1
											__lowerCamelCase		:   List[Any]           =       vocab_size - 1







							def 		__SCREAMING_SNAKE_CASE( self						)  ->List[Any]:



											"""simple docstring"""







											return RwkvConfig.from_pretrained("sgugger/rwkv-4-pile-7b"						)







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A=False      ,					__A=False      ,					__A=False						)  ->List[str]:



											"""simple docstring"""







											__lowerCamelCase		:   Optional[int]           =       ids_tensor([self.batch_size, self.seq_length]      ,					self.vocab_size						)

											__lowerCamelCase		:   Any           =       None
											if self.use_input_mask:
															__lowerCamelCase		:   Tuple           =       random_attention_mask([self.batch_size, self.seq_length]						)

											__lowerCamelCase		:   int           =       None
											if self.use_token_type_ids:
															__lowerCamelCase		:   str           =       ids_tensor([self.batch_size, self.seq_length]      ,					self.type_vocab_size						)

											__lowerCamelCase		:   List[Any]           =       None
											if self.use_mc_token_ids:
															__lowerCamelCase		:   str           =       ids_tensor([self.batch_size, self.num_choices]      ,					self.seq_length						)

											__lowerCamelCase		:   int           =       None
											__lowerCamelCase		:   List[Any]           =       None
											__lowerCamelCase		:   str           =       None
											if self.use_labels:
															__lowerCamelCase		:   Union[str, Any]           =       ids_tensor([self.batch_size]      ,					self.type_sequence_label_size						)
															__lowerCamelCase		:   List[str]           =       ids_tensor([self.batch_size, self.seq_length]      ,					self.num_labels						)
															__lowerCamelCase		:   Dict           =       ids_tensor([self.batch_size]      ,					self.num_choices						)

											__lowerCamelCase		:   Any           =       self.get_config(
											    gradient_checkpointing=__A      ,					scale_attn_by_inverse_layer_idx=__A      ,					reorder_and_upcast_attn=__A      ,					)

											return (
											    config,
											    input_ids,
											    input_mask,
											    None,
											    token_type_ids,
											    mc_token_ids,
											    sequence_labels,
											    token_labels,
											    choice_labels,
											)







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A=False      ,					__A=False      ,					__A=False						)  ->Optional[Any]:



											"""simple docstring"""







											return RwkvConfig(
											    vocab_size=self.vocab_size      ,					hidden_size=self.hidden_size      ,					num_hidden_layers=self.num_hidden_layers      ,					intermediate_size=self.intermediate_size      ,					activation_function=self.hidden_act      ,					resid_pdrop=self.hidden_dropout_prob      ,					attn_pdrop=self.attention_probs_dropout_prob      ,					n_positions=self.max_position_embeddings      ,					type_vocab_size=self.type_vocab_size      ,					use_cache=__A      ,					bos_token_id=self.bos_token_id      ,					eos_token_id=self.eos_token_id      ,					pad_token_id=self.pad_token_id      ,					gradient_checkpointing=__A      ,					scale_attn_by_inverse_layer_idx=__A      ,					reorder_and_upcast_attn=__A      ,					)







							def 		__SCREAMING_SNAKE_CASE( self						)  ->int:



											"""simple docstring"""







											__lowerCamelCase		:   List[str]           =       self.get_config()
											__lowerCamelCase		:   Any           =       300
											return config







							def 		__SCREAMING_SNAKE_CASE( self						)  ->int:



											"""simple docstring"""







											(
											    (
											    __lowerCamelCase
											)    ,       (
											    __lowerCamelCase
											)    ,       (
											    __lowerCamelCase
											)    ,       (
											    __lowerCamelCase
											)    ,       (
											    __lowerCamelCase
											)    ,       (
											    __lowerCamelCase
											)    ,       (
											    __lowerCamelCase
											)    ,       (
											    __lowerCamelCase
											)    ,       (
											    __lowerCamelCase
											)    ,       
											)		:   Optional[Any]           =       self.prepare_config_and_inputs()

											__lowerCamelCase		:   Any           =       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
											__lowerCamelCase		:   Optional[int]           =       ids_tensor([self.batch_size, self.seq_length]      ,					vocab_size=2						)

											return (
											    config,
											    input_ids,
											    input_mask,
											    head_mask,
											    token_type_ids,
											    sequence_labels,
											    token_labels,
											    choice_labels,
											    encoder_hidden_states,
											    encoder_attention_mask,
											)







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A      ,					__A      ,					__A      ,					__A      ,					*__A						)  ->Dict:



											"""simple docstring"""







											__lowerCamelCase		:   Any           =       True
											__lowerCamelCase		:   Any           =       RwkvModel(config=__A						)
											model.to(__A						)
											model.eval()

											__lowerCamelCase		:   Union[str, Any]           =       model(__A						)

											self.parent.assertEqual(result.last_hidden_state.shape      ,					(self.batch_size, self.seq_length, self.hidden_size)						)
											self.parent.assertEqual(len(result.hidden_states						)      ,					config.num_hidden_layers + 1						)







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A      ,					__A      ,					__A      ,					__A      ,					*__A						)  ->Union[str, Any]:



											"""simple docstring"""







											__lowerCamelCase		:   Optional[int]           =       RwkvForCausalLM(__A						)
											model.to(__A						)
											model.eval()

											__lowerCamelCase		:   Optional[Any]           =       model(__A      ,					labels=__A						)
											self.parent.assertEqual(result.loss.shape      ,					()						)
											self.parent.assertEqual(result.logits.shape      ,					(self.batch_size, self.seq_length, self.vocab_size)						)







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A      ,					__A      ,					__A      ,					__A      ,					*__A						)  ->int:



											"""simple docstring"""







											__lowerCamelCase		:   Optional[int]           =       RwkvModel(config=__A						)
											model.to(__A						)
											model.eval()

											__lowerCamelCase		:   Optional[Any]           =       model(__A						)
											__lowerCamelCase		:   int           =       outputs.last_hidden_state

											__lowerCamelCase		:   List[str]           =       model(input_ids[:, :2]						)
											__lowerCamelCase		:   List[str]           =       outputs.last_hidden_state

											# Using the state computed on the first inputs, we will get the same output
											__lowerCamelCase		:   Any           =       model(input_ids[:, 2:]      ,					state=outputs.state						)
											__lowerCamelCase		:   str           =       outputs.last_hidden_state

											self.parent.assertTrue(torch.allclose(torch.cat([output_one, output_two]      ,					dim=1						)      ,					__A      ,					atol=1E-5						)						)







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A      ,					__A      ,					__A      ,					__A      ,					*__A      ,					__A=False						)  ->int:



											"""simple docstring"""







											__lowerCamelCase		:   Dict           =       RwkvForCausalLM(__A						)
											model.to(__A						)
											if gradient_checkpointing:
															model.gradient_checkpointing_enable()

											__lowerCamelCase		:   int           =       model(__A      ,					labels=__A						)
											self.parent.assertEqual(result.loss.shape      ,					()						)
											self.parent.assertEqual(result.logits.shape      ,					(self.batch_size, self.seq_length, self.vocab_size)						)
											result.loss.backward()







							def 		__SCREAMING_SNAKE_CASE( self						)  ->List[str]:



											"""simple docstring"""







											__lowerCamelCase		:   int           =       self.prepare_config_and_inputs()

											(
											    (
											    __lowerCamelCase
											)    ,       (
											    __lowerCamelCase
											)    ,       (
											    __lowerCamelCase
											)    ,       (
											    __lowerCamelCase
											)    ,       (
											    __lowerCamelCase
											)    ,       (
											    __lowerCamelCase
											)    ,       (
											    __lowerCamelCase
											)    ,       (
											    __lowerCamelCase
											)    ,       (
											    __lowerCamelCase
											)    ,       
											)		:   str           =       config_and_inputs

											__lowerCamelCase		:   Any           =       {"input_ids": input_ids}

											return config, inputs_dict





@unittest.skipIf(
    not is_torch_greater_or_equal_than_2_0     , reason='''See https://github.com/huggingface/transformers/pull/24204''' )
@require_torch
class _snake_case (			a__     , a__     , a__     , unittest.TestCase ):
							"""simple docstring"""





							__a				       =  (RwkvModel, RwkvForCausalLM) if is_torch_available() else ()
							__a				       =  (
							    {'''feature-extraction''': RwkvModel, '''text-generation''': RwkvForCausalLM} if is_torch_available() else {}
							)
							# all_generative_model_classes = (RwkvForCausalLM,) if is_torch_available() else ()
							__a				       =  False
							__a				       =  False
							__a				       =  False
							__a				       =  False
							__a				       =  False  # Rwkv does not support head masking







							def 		__SCREAMING_SNAKE_CASE( self						)  ->Union[str, Any]:



											"""simple docstring"""







											__lowerCamelCase		:   List[str]           =       RwkvModelTester(self						)
											__lowerCamelCase		:   Tuple           =       ConfigTester(
											    self      ,					config_class=__A      ,					n_embd=37      ,					common_properties=["hidden_size", "num_hidden_layers"]						)







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A      ,					__A=None						)  ->str:



											"""simple docstring"""







											if isinstance(__A      ,					torch.Tensor						):
															__lowerCamelCase    ,       __lowerCamelCase		:   Union[str, Any]           =       member.max().item(), member.min().item()
											elif isinstance(__A      ,					__A						) or isinstance(__A      ,					__A						):
															__lowerCamelCase    ,       __lowerCamelCase		:   Tuple           =       max(__A						), min(__A						)

											if not isinstance(__A      ,					__A						):
															raise TypeError("container should be a list or tuple"						)
											elif len(__A						) != 2:
															raise ValueError("container should have 2 elements"						)

											__lowerCamelCase    ,       __lowerCamelCase		:   Tuple           =       container

											__lowerCamelCase		:   Optional[int]           =       (min_value >= expected_min) and (max_value <= expected_max)

											if not is_inside_interval:
															__lowerCamelCase		:   Optional[Any]           =       "%s not found in %s" % (safe_repr(__A						), safe_repr(__A						))
															self.fail(self._formatMessage(__A      ,					__A						)						)







							def 		__SCREAMING_SNAKE_CASE( self						)  ->int:



											"""simple docstring"""







											self.config_tester.run_common_tests()







							def 		__SCREAMING_SNAKE_CASE( self						)  ->Any:



											"""simple docstring"""







											__lowerCamelCase		:   Optional[int]           =       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_rwkv_model(*__A						)







							def 		__SCREAMING_SNAKE_CASE( self						)  ->Any:



											"""simple docstring"""







											__lowerCamelCase		:   Optional[Any]           =       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_causl_lm(*__A						)







							def 		__SCREAMING_SNAKE_CASE( self						)  ->List[Any]:



											"""simple docstring"""







											__lowerCamelCase		:   Optional[int]           =       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_state_equivalency(*__A						)







							def 		__SCREAMING_SNAKE_CASE( self						)  ->Optional[int]:



											"""simple docstring"""







											__lowerCamelCase    ,       __lowerCamelCase		:   Optional[Any]           =       self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															__lowerCamelCase		:   Dict           =       model_class(config=__A						)
															for name, param in model.named_parameters():
																			if "time_decay" in name:
																							if param.requires_grad:
																											self.assertTrue(param.data.max().item() == 3.0						)
																											self.assertTrue(param.data.min().item() == -5.0						)
																			elif "time_first" in name:
																							if param.requires_grad:
																											# check if it's a ones like
																											self.assertTrue(torch.allclose(param.data      ,					torch.ones_like(param.data						)      ,					atol=1E-5      ,					rtol=1E-5						)						)
																			elif any(x in name for x in ["time_mix_key", "time_mix_receptance"]						):
																							if param.requires_grad:
																											self.assertInterval(
																											    param.data      ,					[0.0, 1.0]      ,					msg=f'Parameter {name} of model {model_class} seems not properly initialized'      ,					)
																			elif "time_mix_value" in name:
																							if param.requires_grad:
																											self.assertInterval(
																											    param.data      ,					[0.0, 1.3]      ,					msg=f'Parameter {name} of model {model_class} seems not properly initialized'      ,					)







							def 		__SCREAMING_SNAKE_CASE( self						)  ->int:



											"""simple docstring"""







											__lowerCamelCase    ,       __lowerCamelCase		:   Tuple           =       self.model_tester.prepare_config_and_inputs_for_common()
											__lowerCamelCase		:   Optional[Any]           =       True

											__lowerCamelCase		:   Any           =       getattr(self.model_tester      ,					"seq_length"      ,					__A						)

											for model_class in self.all_model_classes:
															__lowerCamelCase		:   Optional[Any]           =       True
															__lowerCamelCase		:   List[Any]           =       False
															__lowerCamelCase		:   str           =       True
															__lowerCamelCase		:   Optional[Any]           =       model_class(__A						)
															model.to(__A						)
															model.eval()

															__lowerCamelCase		:   Union[str, Any]           =       self._prepare_for_class(__A      ,					__A						)
															__lowerCamelCase		:   Dict           =       inputs["input_ids"].shape[0]
															with torch.no_grad():
																			__lowerCamelCase		:   int           =       model(**__A						)
															__lowerCamelCase		:   Union[str, Any]           =       outputs.attentions
															self.assertEqual(len(__A						)      ,					self.model_tester.num_hidden_layers						)

															# check that output_attentions also work using config
															del inputs_dict["output_attentions"]
															__lowerCamelCase		:   Dict           =       True
															__lowerCamelCase		:   Optional[Any]           =       model_class(__A						)
															model.to(__A						)
															model.eval()

															__lowerCamelCase		:   int           =       self._prepare_for_class(__A      ,					__A						)
															__lowerCamelCase		:   Dict           =       inputs["input_ids"].shape[0]
															with torch.no_grad():
																			__lowerCamelCase		:   Optional[int]           =       model(**__A						)
															__lowerCamelCase		:   List[str]           =       outputs.attentions
															self.assertEqual(len(__A						)      ,					self.model_tester.num_hidden_layers						)

															self.assertListEqual(
															    list(attentions[0].shape[-3:]						)      ,					[batch_size, seq_len, config.hidden_size]      ,					)
															__lowerCamelCase		:   Union[str, Any]           =       len(__A						)

															# Check attention is always last and order is fine
															__lowerCamelCase		:   Any           =       True
															__lowerCamelCase		:   Any           =       True
															__lowerCamelCase		:   str           =       model_class(__A						)
															model.to(__A						)
															model.eval()

															__lowerCamelCase		:   Optional[Any]           =       self._prepare_for_class(__A      ,					__A						)
															__lowerCamelCase		:   Any           =       inputs["input_ids"].shape[0]
															with torch.no_grad():
																			__lowerCamelCase		:   int           =       model(**__A						)

															__lowerCamelCase		:   Tuple           =       1
															self.assertEqual(out_len + added_hidden_states      ,					len(__A						)						)

															__lowerCamelCase		:   List[str]           =       outputs.attentions

															self.assertEqual(len(__A						)      ,					self.model_tester.num_hidden_layers						)
															self.assertListEqual(
															    list(self_attentions[0].shape[-3:]						)      ,					[batch_size, seq_len, config.hidden_size]      ,					)







							@slow
							def 		__SCREAMING_SNAKE_CASE( self						)  ->Dict:



											"""simple docstring"""







											for model_name in RWKV_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															__lowerCamelCase		:   int           =       RwkvModel.from_pretrained(__A						)
															self.assertIsNotNone(__A						)





@unittest.skipIf(
    not is_torch_greater_or_equal_than_2_0     , reason='''See https://github.com/huggingface/transformers/pull/24204''' )
@slow
class _snake_case (			unittest.TestCase ):
							"""simple docstring"""





							def 		__SCREAMING_SNAKE_CASE( self						)  ->Optional[Any]:



											"""simple docstring"""







											__lowerCamelCase		:   Tuple           =       "RWKV/rwkv-4-169m-pile"
											__lowerCamelCase		:   int           =       AutoTokenizer.from_pretrained(self.model_id						)







							def 		__SCREAMING_SNAKE_CASE( self						)  ->List[str]:



											"""simple docstring"""







											__lowerCamelCase		:   Dict           =       "Hello my name is Jasmine and I am a newbie to the"
											__lowerCamelCase		:   int           =       RwkvForCausalLM.from_pretrained(self.model_id						).to(__A						)

											__lowerCamelCase		:   Optional[Any]           =       self.tokenizer("Hello my name is"      ,					return_tensors="pt"						).input_ids.to(__A						)
											__lowerCamelCase		:   Tuple           =       model.generate(__A      ,					max_new_tokens=10						)
											__lowerCamelCase		:   Optional[Any]           =       self.tokenizer.decode(output[0].tolist()						)

											self.assertEqual(__A      ,					__A						)







							def 		__SCREAMING_SNAKE_CASE( self						)  ->List[str]:



											"""simple docstring"""







											__lowerCamelCase		:   Optional[int]           =       "Hello my name is Jasmine and I am a newbie to the"

											__lowerCamelCase		:   Union[str, Any]           =       self.tokenizer("Hello my name is"      ,					return_tensors="pt"						).input_ids.to(__A						)
											__lowerCamelCase		:   List[Any]           =       RwkvForCausalLM.from_pretrained(self.model_id      ,					torch_dtype=torch.bfloataa						).to(__A						)

											__lowerCamelCase		:   Union[str, Any]           =       model.generate(__A      ,					max_new_tokens=10						)
											__lowerCamelCase		:   List[str]           =       self.tokenizer.decode(output[0].tolist()						)

											self.assertEqual(__A      ,					__A						)









'''simple docstring'''




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





class _snake_case :
							"""simple docstring"""





							__a				       =  '''block_{0}.dat'''







							def __init__( self      ,					__A						)  ->int:



											"""simple docstring"""







											__lowerCamelCase		:   Dict           =       filename
											__lowerCamelCase		:   Union[str, Any]           =       []







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A						)  ->int:



											"""simple docstring"""







											__lowerCamelCase		:   Optional[int]           =       self.BLOCK_FILENAME_FORMAT.format(__A						)
											with open(__A      ,					"w"						) as file:
															file.write(__A						)
											self.block_filenames.append(__A						)







							def 		__SCREAMING_SNAKE_CASE( self						)  ->int:



											"""simple docstring"""







											return self.block_filenames







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A=None						)  ->Dict:



											"""simple docstring"""







											__lowerCamelCase		:   Optional[int]           =       0
											with open(self.filename						) as file:
															while True:
																			__lowerCamelCase		:   Union[str, Any]           =       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 		__SCREAMING_SNAKE_CASE( self						)  ->List[Any]:



											"""simple docstring"""







											map(os.remove      ,					self.block_filenames						)





class _snake_case :
							"""simple docstring"""





							def 		__SCREAMING_SNAKE_CASE( self      ,					__A						)  ->Optional[int]:



											"""simple docstring"""







											__lowerCamelCase		:   Union[str, Any]           =       -1
											__lowerCamelCase		:   Optional[Any]           =       None

											for i in range(len(__A						)						):
															if min_str is None or choices[i] < min_str:
																			__lowerCamelCase		:   Tuple           =       i

											return min_index





class _snake_case :
							"""simple docstring"""





							def __init__( self      ,					__A						)  ->List[Any]:



											"""simple docstring"""







											__lowerCamelCase		:   Tuple           =       files
											__lowerCamelCase		:   Tuple           =       set()
											__lowerCamelCase		:   Optional[Any]           =       len(__A						)
											__lowerCamelCase		:   List[str]           =       {i: None for i in range(self.num_buffers						)}







							def 		__SCREAMING_SNAKE_CASE( self						)  ->Any:



											"""simple docstring"""







											return {
											    i: self.buffers[i] for i in range(self.num_buffers						) if i not in self.empty
											}







							def 		__SCREAMING_SNAKE_CASE( self						)  ->Dict:



											"""simple docstring"""







											for i in range(self.num_buffers						):
															if self.buffers[i] is None and i not in self.empty:
																			__lowerCamelCase		:   Tuple           =       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 		__SCREAMING_SNAKE_CASE( self      ,					__A						)  ->Any:



											"""simple docstring"""







											__lowerCamelCase		:   Tuple           =       self.buffers[index]
											__lowerCamelCase		:   List[Any]           =       None

											return value





class _snake_case :
							"""simple docstring"""





							def __init__( self      ,					__A						)  ->List[str]:



											"""simple docstring"""







											__lowerCamelCase		:   List[Any]           =       merge_strategy







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A      ,					__A						)  ->Optional[int]:



											"""simple docstring"""







											__lowerCamelCase		:   List[str]           =       FilesArray(self.get_file_handles(__A      ,					__A						)						)
											with open(__A      ,					"w"      ,					__A						) as outfile:
															while buffers.refresh():
																			__lowerCamelCase		:   Optional[Any]           =       self.merge_strategy.select(buffers.get_dict()						)
																			outfile.write(buffers.unshift(__A						)						)







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A						)  ->str:



											"""simple docstring"""







											__lowerCamelCase		:   Union[str, Any]           =       {}

											for i in range(len(__A						)						):
															__lowerCamelCase		:   Tuple           =       open(filenames[i]      ,					"r"      ,					__A						)  # noqa: UP015

											return files





class _snake_case :
							"""simple docstring"""





							def __init__( self      ,					__A						)  ->str:



											"""simple docstring"""







											__lowerCamelCase		:   Tuple           =       block_size







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A=None						)  ->List[str]:



											"""simple docstring"""







											__lowerCamelCase		:   Optional[int]           =       self.get_number_blocks(__A      ,					self.block_size						)
											__lowerCamelCase		:   Optional[int]           =       FileSplitter(__A						)
											splitter.split(self.block_size      ,					__A						)

											__lowerCamelCase		:   List[str]           =       FileMerger(NWayMerge()						)
											__lowerCamelCase		:   str           =       self.block_size / (num_blocks + 1)
											merger.merge(splitter.get_block_filenames()      ,					filename + ".out"      ,					__A						)

											splitter.cleanup()







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A						)  ->List[str]:



											"""simple docstring"""







											return (os.stat(__A						).st_size / block_size) + 1





def 				a_							(    lowercase     )      ->List[Any]:






				'''simple docstring'''

				if string[-1].lower() == "k":
								return int(string[:-1]     ) * 1024
				elif string[-1].lower() == "m":
								return int(string[:-1]     ) * 1024 * 1024
				elif string[-1].lower() == "g":
								return int(string[:-1]     ) * 1024 * 1024 * 1024
				else:
								return int(lowercase     )





def 				a_							(    )      ->Optional[Any]:






				'''simple docstring'''

				__lowerCamelCase		:   List[str]           =       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"     )
				__lowerCamelCase		:   List[str]           =       parser.parse_args()

				__lowerCamelCase		:   Tuple           =       ExternalSort(parse_memory(args.mem     )     )
				sorter.sort(args.filename[0]     )


if __name__ == "__main__":
				main()





