





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:
							__lowerCAmelCase			:List[str]	 =     False






class 							_a:







		def __init__(     self			,    __snake_case			,    __snake_case=1_4			,    __snake_case=7			,    __snake_case=True			,    __snake_case=False			,    __snake_case=True			,    __snake_case=True			,    __snake_case=True			,    __snake_case=9_9			,    __snake_case=3_2			,    __snake_case=5			,    __snake_case=3_7			,    __snake_case="gelu"			,    __snake_case=0.1			,    __snake_case=0.1			,    __snake_case=5_1_2			,    __snake_case=1_6			,    __snake_case=2			,    __snake_case=3			,    __snake_case=4			,    __snake_case=None			,    )   ->   Any:


							'''simple docstring'''

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







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


							'''simple docstring'''

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







		def 							lowercase							(     self			,    __snake_case=False			,    __snake_case=False			,    __snake_case=False					)   ->   Optional[Any]:


							'''simple docstring'''

							_snake_case							:       List[str]												= ids_tensor([self.batch_size, self.seq_length]			,    self.vocab_size					)

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

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

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

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

							_snake_case							:       Union[str, Any]												= self.get_config(
							    gradient_checkpointing=__snake_case			,    scale_attn_by_inverse_layer_idx=__snake_case			,    reorder_and_upcast_attn=__snake_case			,    )

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







		def 							lowercase							(     self			,    __snake_case=False			,    __snake_case=False			,    __snake_case=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=__snake_case			,    bos_token_id=self.bos_token_id			,    eos_token_id=self.eos_token_id			,    pad_token_id=self.pad_token_id			,    gradient_checkpointing=__snake_case			,    scale_attn_by_inverse_layer_idx=__snake_case			,    reorder_and_upcast_attn=__snake_case			,    )







		def 							lowercase							(     self					)   ->   Any:


							'''simple docstring'''

							_snake_case							:       Union[str, Any]												= self.get_config()
							_snake_case							:       Dict												= 3_0_0
							return config







		def 							lowercase							(     self					)   ->   str:


							'''simple docstring'''

							(
							    (
							    _snake_case
							)							,			(
							    _snake_case
							)							,			(
							    _snake_case
							)							,			(
							    _snake_case
							)							,			(
							    _snake_case
							)							,			(
							    _snake_case
							)							,			(
							    _snake_case
							)							,			(
							    _snake_case
							)							,			(
							    _snake_case
							)							,			
							)							:       Any												= self.prepare_config_and_inputs()

							_snake_case							:       List[Any]												= floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
							_snake_case							:       Tuple												= 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 							lowercase							(     self			,    __snake_case			,    __snake_case			,    __snake_case			,    __snake_case			,    __snake_case			,    *__snake_case					)   ->   Optional[Any]:


							'''simple docstring'''

							_snake_case							:       Optional[int]												= True
							_snake_case							:       Tuple												= RwkvModel(config=__snake_case					)
							model.to(__snake_case					)
							model.eval()

							_snake_case							:       Tuple												= model(__snake_case					)

							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 							lowercase							(     self			,    __snake_case			,    __snake_case			,    __snake_case			,    __snake_case			,    __snake_case			,    *__snake_case					)   ->   int:


							'''simple docstring'''

							_snake_case							:       str												= RwkvForCausalLM(__snake_case					)
							model.to(__snake_case					)
							model.eval()

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







		def 							lowercase							(     self			,    __snake_case			,    __snake_case			,    __snake_case			,    __snake_case			,    __snake_case			,    *__snake_case					)   ->   Tuple:


							'''simple docstring'''

							_snake_case							:       Any												= RwkvModel(config=__snake_case					)
							model.to(__snake_case					)
							model.eval()

							_snake_case							:       Optional[int]												= model(__snake_case					)
							_snake_case							:       Union[str, Any]												= outputs.last_hidden_state

							_snake_case							:       List[Any]												= model(input_ids[:, :2]					)
							_snake_case							:       Union[str, Any]												= outputs.last_hidden_state

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

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







		def 							lowercase							(     self			,    __snake_case			,    __snake_case			,    __snake_case			,    __snake_case			,    __snake_case			,    *__snake_case			,    __snake_case=False					)   ->   Any:


							'''simple docstring'''

							_snake_case							:       Any												= RwkvForCausalLM(__snake_case					)
							model.to(__snake_case					)
							if gradient_checkpointing:
												model.gradient_checkpointing_enable()

							_snake_case							:       List[Any]												= model(__snake_case			,    labels=__snake_case					)
							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 							lowercase							(     self					)   ->   Optional[int]:


							'''simple docstring'''

							_snake_case							:       Optional[int]												= self.prepare_config_and_inputs()

							(
							    (
							    _snake_case
							)							,			(
							    _snake_case
							)							,			(
							    _snake_case
							)							,			(
							    _snake_case
							)							,			(
							    _snake_case
							)							,			(
							    _snake_case
							)							,			(
							    _snake_case
							)							,			(
							    _snake_case
							)							,			(
							    _snake_case
							)							,			
							)							:       Any												= config_and_inputs

							_snake_case							:       str												= {"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 							_a(     __A						, __A						, __A						, unittest.TestCase     ):
		lowerCamelCase__  :int 					=		(RwkvModel, RwkvForCausalLM) if is_torch_available() else ()
		lowerCamelCase__  :Optional[Any] 					=		(
		    {'feature-extraction': RwkvModel, 'text-generation': RwkvForCausalLM} if is_torch_available() else {}
		)
		# all_generative_model_classes = (RwkvForCausalLM,) if is_torch_available() else ()
		lowerCamelCase__  :List[Any] 					=		False
		lowerCamelCase__  :Optional[int] 					=		False
		lowerCamelCase__  :Optional[int] 					=		False
		lowerCamelCase__  :List[Any] 					=		False
		lowerCamelCase__  :int 					=		False  # Rwkv does not support head masking







		def 							lowercase							(     self					)   ->   Dict:


							'''simple docstring'''

							_snake_case							:       Union[str, Any]												= RwkvModelTester(self					)
							_snake_case							:       Optional[int]												= ConfigTester(
							    self			,    config_class=__snake_case			,    n_embd=3_7			,    common_properties=["hidden_size", "num_hidden_layers"]					)







		def 							lowercase							(     self			,    __snake_case			,    __snake_case			,    __snake_case=None					)   ->   Any:


							'''simple docstring'''

							if isinstance(__snake_case			,    torch.Tensor					):
												_snake_case							,			_snake_case							:       int												= member.max().item(), member.min().item()
							elif isinstance(__snake_case			,    __snake_case					) or isinstance(__snake_case			,    __snake_case					):
												_snake_case							,			_snake_case							:       int												= max(__snake_case					), min(__snake_case					)

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

							_snake_case							,			_snake_case							:       int												= container

							_snake_case							:       List[str]												= (min_value >= expected_min) and (max_value <= expected_max)

							if not is_inside_interval:
												_snake_case							:       int												= "%s not found in %s" % (safe_repr(__snake_case					), safe_repr(__snake_case					))
												self.fail(self._formatMessage(__snake_case			,    __snake_case					)					)







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


							'''simple docstring'''

							self.config_tester.run_common_tests()







		def 							lowercase							(     self					)   ->   Dict:


							'''simple docstring'''

							_snake_case							:       Union[str, Any]												= self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_rwkv_model(*__snake_case					)







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


							'''simple docstring'''

							_snake_case							:       Union[str, Any]												= self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_causl_lm(*__snake_case					)







		def 							lowercase							(     self					)   ->   int:


							'''simple docstring'''

							_snake_case							:       Optional[Any]												= self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_state_equivalency(*__snake_case					)







		def 							lowercase							(     self					)   ->   Any:


							'''simple docstring'''

							_snake_case							,			_snake_case							:       Optional[int]												= self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
												_snake_case							:       List[str]												= model_class(config=__snake_case					)
												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 							lowercase							(     self					)   ->   List[str]:


							'''simple docstring'''

							_snake_case							,			_snake_case							:       Tuple												= self.model_tester.prepare_config_and_inputs_for_common()
							_snake_case							:       Union[str, Any]												= True

							_snake_case							:       Optional[Any]												= getattr(self.model_tester			,    "seq_length"			,    __snake_case					)

							for model_class in self.all_model_classes:
												_snake_case							:       List[Any]												= True
												_snake_case							:       Dict												= False
												_snake_case							:       int												= True
												_snake_case							:       List[str]												= model_class(__snake_case					)
												model.to(__snake_case					)
												model.eval()

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

												# check that output_attentions also work using config
												del inputs_dict["output_attentions"]
												_snake_case							:       List[str]												= True
												_snake_case							:       List[str]												= model_class(__snake_case					)
												model.to(__snake_case					)
												model.eval()

												_snake_case							:       Optional[int]												= self._prepare_for_class(__snake_case			,    __snake_case					)
												_snake_case							:       Any												= inputs["input_ids"].shape[0]
												with torch.no_grad():
																	_snake_case							:       int												= model(**__snake_case					)
												_snake_case							:       int												= outputs.attentions
												self.assertEqual(len(__snake_case					)			,    self.model_tester.num_hidden_layers					)

												self.assertListEqual(
												    list(attentions[0].shape[-3:]					)			,    [batch_size, seq_len, config.hidden_size]			,    )
												_snake_case							:       List[Any]												= len(__snake_case					)

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

												_snake_case							:       str												= self._prepare_for_class(__snake_case			,    __snake_case					)
												_snake_case							:       Union[str, Any]												= inputs["input_ids"].shape[0]
												with torch.no_grad():
																	_snake_case							:       int												= model(**__snake_case					)

												_snake_case							:       int												= 1
												self.assertEqual(out_len + added_hidden_states			,    len(__snake_case					)					)

												_snake_case							:       Any												= outputs.attentions

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







		@slow
		def 							lowercase							(     self					)   ->   Tuple:


							'''simple docstring'''

							for model_name in RWKV_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												_snake_case							:       Optional[Any]												= RwkvModel.from_pretrained(__snake_case					)
												self.assertIsNotNone(__snake_case					)






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







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


							'''simple docstring'''

							_snake_case							:       Optional[int]												= "RWKV/rwkv-4-169m-pile"
							_snake_case							:       int												= AutoTokenizer.from_pretrained(self.model_id					)







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


							'''simple docstring'''

							_snake_case							:       Tuple												= "Hello my name is Jasmine and I am a newbie to the"
							_snake_case							:       Union[str, Any]												= RwkvForCausalLM.from_pretrained(self.model_id					).to(__snake_case					)

							_snake_case							:       Any												= self.tokenizer("Hello my name is"			,    return_tensors="pt"					).input_ids.to(__snake_case					)
							_snake_case							:       Optional[Any]												= model.generate(__snake_case			,    max_new_tokens=1_0					)
							_snake_case							:       Any												= self.tokenizer.decode(output[0].tolist()					)

							self.assertEqual(__snake_case			,    __snake_case					)







		def 							lowercase							(     self					)   ->   Dict:


							'''simple docstring'''

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

							_snake_case							:       Any												= self.tokenizer("Hello my name is"			,    return_tensors="pt"					).input_ids.to(__snake_case					)
							_snake_case							:       int												= RwkvForCausalLM.from_pretrained(self.model_id			,    torch_dtype=torch.bfloataa					).to(__snake_case					)

							_snake_case							:       Tuple												= model.generate(__snake_case			,    max_new_tokens=1_0					)
							_snake_case							:       List[Any]												= self.tokenizer.decode(output[0].tolist()					)

							self.assertEqual(__snake_case			,    __snake_case					)






import json
import os
import subprocess
import sys
import warnings
from argparse import ArgumentParser
from contextlib import AbstractContextManager
from typing import Dict, List, Optional

import requests

from ..utils import logging
from . import BaseTransformersCLICommand


__lowerCAmelCase			:Tuple	 =     logging.get_logger(__name__)  # pylint: disable=invalid-name


__lowerCAmelCase			:Union[str, Any]	 =     'lfs-multipart-upload'






class 							_a(     __A     ):







		@staticmethod
		def 							lowercase							(     __snake_case					)   ->   Tuple:


							'''simple docstring'''

							_snake_case							:       Tuple												= parser.add_parser(
							    "lfs-enable-largefiles"			,    help=(
							        "Deprecated: use `huggingface-cli` instead. Configure your repository to enable upload of files > 5GB."
							    )			,    )
							enable_parser.add_argument("path"			,    type=__snake_case			,    help="Local path to repository you want to configure."					)
							enable_parser.set_defaults(func=lambda __snake_case					: LfsEnableCommand(__snake_case					)					)

							_snake_case							:       Union[str, Any]												= parser.add_parser(
							    __snake_case			,    help=(
							        "Deprecated: use `huggingface-cli` instead. "
							        "Command will get called by git-lfs, do not call it directly."
							    )			,    )
							upload_parser.set_defaults(func=lambda __snake_case					: LfsUploadCommand(__snake_case					)					)






class 							_a:







		def __init__(     self			,    __snake_case					)   ->   Any:


							'''simple docstring'''

							_snake_case							:       List[Any]												= args







		def 							lowercase							(     self					)   ->   Dict:


							'''simple docstring'''

							warnings.warn(
							    "Managing repositories through transformers-cli is deprecated. Please use `huggingface-cli` instead."					)
							_snake_case							:       Dict												= os.path.abspath(self.args.path					)
							if not os.path.isdir(__snake_case					):
												print("This does not look like a valid git repo."					)
												exit(1					)
							subprocess.run(
							    "git config lfs.customtransfer.multipart.path transformers-cli".split()			,    check=__snake_case			,    cwd=__snake_case					)
							subprocess.run(
							    f"""git config lfs.customtransfer.multipart.args {LFS_MULTIPART_UPLOAD_COMMAND}""".split()			,    check=__snake_case			,    cwd=__snake_case			,    )
							print("Local repo set up for largefiles"					)







def  A					(				UpperCAmelCase					):
					_snake_case							:       str												= json.dumps(UpperCAmelCase					) + "\n"
					sys.stdout.write(UpperCAmelCase					)
					sys.stdout.flush()







def  A					(				):
					_snake_case							:       str												= json.loads(sys.stdin.readline().strip()					)

					if "terminate" in (msg.get("type"					), msg.get("event"					)):
										# terminate message received
										return None

					if msg.get("event"					) not in ("download", "upload"):
										logger.critical("Received unexpected message"					)
										sys.exit(1					)

					return msg






class 							_a(     __A     ):







		def __init__(     self			,    __snake_case			,    __snake_case			,    __snake_case					)   ->   Optional[int]:


							'''simple docstring'''

							_snake_case							:       int												= filepath
							_snake_case							:       Tuple												= seek_from
							_snake_case							:       int												= read_limit
							_snake_case							:       int												= 0







		def __enter__(     self					)   ->   Any:


							'''simple docstring'''

							_snake_case							:       Optional[Any]												= open(self.filepath			,    "rb"					)
							self.f.seek(self.seek_from					)
							return self







		def __len__(     self					)   ->   Dict:


							'''simple docstring'''

							_snake_case							:       Dict												= os.fstat(self.f.fileno()					).st_size
							return min(self.read_limit			,    total_length - self.seek_from					)







		def 							lowercase							(     self			,    __snake_case=-1					)   ->   Union[str, Any]:


							'''simple docstring'''

							if self.n_seen >= self.read_limit:
												return b""
							_snake_case							:       Dict												= self.read_limit - self.n_seen
							_snake_case							:       Tuple												= self.f.read(remaining_amount if n < 0 else min(__snake_case			,    __snake_case					)					)
							self.n_seen += len(__snake_case					)
							return data







		def __iter__(     self					)   ->   int:


							'''simple docstring'''

							yield self.read(n=4 * 1_0_2_4 * 1_0_2_4					)







		def __exit__(     self			,    *__snake_case					)   ->   List[str]:


							'''simple docstring'''

							self.f.close()






class 							_a:







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


							'''simple docstring'''

							_snake_case							:       Any												= args







		def 							lowercase							(     self					)   ->   str:


							'''simple docstring'''

							_snake_case							:       Optional[Any]												= json.loads(sys.stdin.readline().strip()					)
							if not (init_msg.get("event"					) == "init" and init_msg.get("operation"					) == "upload"):
												write_msg({"error": {"code": 3_2, "message": "Wrong lfs init operation"}}					)
												sys.exit(1					)

							# The transfer process should use the information it needs from the
							# initiation structure, and also perform any one-off setup tasks it
							# needs to do. It should then respond on stdout with a simple empty
							# confirmation structure, as follows:
							write_msg({}					)

							# After the initiation exchange, git-lfs will send any number of
							# transfer requests to the stdin of the transfer process, in a serial sequence.
							while True:
												_snake_case							:       Optional[Any]												= read_msg()
												if msg is None:
																	# When all transfers have been processed, git-lfs will send
																	# a terminate event to the stdin of the transfer process.
																	# On receiving this message the transfer process should
																	# clean up and terminate. No response is expected.
																	sys.exit(0					)

												_snake_case							:       Dict												= msg["oid"]
												_snake_case							:       int												= msg["path"]
												_snake_case							:       Any												= msg["action"]["href"]
												_snake_case							:       Union[str, Any]												= msg["action"]["header"]
												_snake_case							:       Any												= int(header.pop("chunk_size"					)					)
												_snake_case							:       List[str]												= list(header.values()					)

												_snake_case							:       Any												= []
												for i, presigned_url in enumerate(__snake_case					):
																	with FileSlice(__snake_case			,    seek_from=i * chunk_size			,    read_limit=__snake_case					) as data:
																						_snake_case							:       List[str]												= requests.put(__snake_case			,    data=__snake_case					)
																						r.raise_for_status()
																						parts.append(
																						    {
																						        "etag": r.headers.get("etag"					),
																						        "partNumber": i + 1,
																						    }					)
																						# In order to support progress reporting while data is uploading / downloading,
																						# the transfer process should post messages to stdout
																						write_msg(
																						    {
																						        "event": "progress",
																						        "oid": oid,
																						        "bytesSoFar": (i + 1) * chunk_size,
																						        "bytesSinceLast": chunk_size,
																						    }					)
																						# Not precise but that's ok.

												_snake_case							:       Optional[Any]												= requests.post(
												    __snake_case			,    json={
												        "oid": oid,
												        "parts": parts,
												    }			,    )
												r.raise_for_status()

												write_msg({"event": "complete", "oid": oid}					)