





import argparse
import logging
import os
import sys
import tempfile
from pathlib import Path

import lightning_base
import pytest
import pytorch_lightning as pl
import torch
from convert_pl_checkpoint_to_hf import convert_pl_to_hf
from distillation import distill_main
from finetune import SummarizationModule, main
from huggingface_hub import list_models
from parameterized import parameterized
from run_eval import generate_summaries_or_translations
from torch import nn

from transformers import AutoConfig, AutoModelForSeqaSeqLM
from transformers.testing_utils import CaptureStderr, CaptureStdout, TestCasePlus, require_torch_gpu, slow
from utils import label_smoothed_nll_loss, lmap, load_json


logging.basicConfig(level=logging.DEBUG)

UpperCamelCase_     =logging.getLogger()
UpperCamelCase_     =torch.cuda.is_available()
UpperCamelCase_     ={
    '''max_tokens_per_batch''': None,
    '''supervise_forward''': True,
    '''normalize_hidden''': True,
    '''label_smoothing''': 0.2,
    '''eval_max_gen_length''': None,
    '''eval_beams''': 1,
    '''val_metric''': '''loss''',
    '''save_top_k''': 1,
    '''adafactor''': True,
    '''early_stopping_patience''': 2,
    '''logger_name''': '''default''',
    '''length_penalty''': 0.5,
    '''cache_dir''': '''''',
    '''task''': '''summarization''',
    '''num_workers''': 2,
    '''alpha_hid''': 0,
    '''freeze_embeds''': True,
    '''enc_only''': False,
    '''tgt_suffix''': '''''',
    '''resume_from_checkpoint''': None,
    '''sortish_sampler''': True,
    '''student_decoder_layers''': 1,
    '''val_check_interval''': 1.0,
    '''output_dir''': '''''',
    '''fp16''': False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    '''no_teacher''': False,
    '''fp16_opt_level''': '''O1''',
    '''gpus''': 1 if CUDA_AVAILABLE else 0,
    '''n_tpu_cores''': 0,
    '''max_grad_norm''': 1.0,
    '''do_train''': True,
    '''do_predict''': True,
    '''accumulate_grad_batches''': 1,
    '''server_ip''': '''''',
    '''server_port''': '''''',
    '''seed''': 42,
    '''model_name_or_path''': '''sshleifer/bart-tiny-random''',
    '''config_name''': '''''',
    '''tokenizer_name''': '''facebook/bart-large''',
    '''do_lower_case''': False,
    '''learning_rate''': 0.3,
    '''lr_scheduler''': '''linear''',
    '''weight_decay''': 0.0,
    '''adam_epsilon''': 1e-08,
    '''warmup_steps''': 0,
    '''max_epochs''': 1,
    '''train_batch_size''': 2,
    '''eval_batch_size''': 2,
    '''max_source_length''': 12,
    '''max_target_length''': 12,
    '''val_max_target_length''': 12,
    '''test_max_target_length''': 12,
    '''fast_dev_run''': False,
    '''no_cache''': False,
    '''n_train''': -1,
    '''n_val''': -1,
    '''n_test''': -1,
    '''student_encoder_layers''': 1,
    '''freeze_encoder''': False,
    '''auto_scale_batch_size''': False,
    '''overwrite_output_dir''': False,
    '''student''': None,
}





def  a_       (							_lowercase ,		_lowercase     ):
						_UpperCamelCase    :      List[str]						     =					'\n'.join(_UpperCAmelCase     )
						Path(_UpperCAmelCase     ).open('''w'''     ).writelines(_UpperCAmelCase     )


UpperCamelCase_     =[''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
UpperCamelCase_     =['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
UpperCamelCase_     ='''patrickvonplaten/t5-tiny-random'''
UpperCamelCase_     ='''sshleifer/t5-tinier-random'''
UpperCamelCase_     ='''sshleifer/bart-tiny-random'''
UpperCamelCase_     ='''sshleifer/tiny-mbart'''
UpperCamelCase_     ='''sshleifer/tiny-marian-en-de'''
UpperCamelCase_     ='''stas/tiny-wmt19-en-de'''


UpperCamelCase_     =logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks





def  a_       (							_lowercase     ):






						for split in ["train", "val", "test"]:
												_dump_articles(os.path.join(_UpperCAmelCase ,		F"""{split}.source"""     ) ,		_UpperCAmelCase     )
												_dump_articles(os.path.join(_UpperCAmelCase ,		F"""{split}.target"""     ) ,		_UpperCAmelCase     )
						return tmp_dir




class 				_a		(   _lowerCAmelCase       ):






				@classmethod
				def        snake_case			(				cls	:					Optional[int]							)			->   int:




										'''simple docstring'''



										logging.disable(logging.CRITICAL							)  # remove noisy download output from tracebacks
										return cls






				@slow
				@require_torch_gpu
				def        snake_case			(				self	:					int							)			->   Any:




										'''simple docstring'''



										_UpperCamelCase    :      Union[str, Any]						     =					list_models()
										_UpperCamelCase    :      Optional[int]						     =					'sshleifer'
										_UpperCamelCase    :      List[str]						     =					[x.modelId for x in model_list if x.modelId.startswith(UpperCAmelCase_							)]
										_UpperCamelCase    :      Optional[int]						     =					['sshleifer/blenderbot-3B', 'sshleifer/blenderbot-90M']
										_UpperCamelCase    :      int						     =					[]
										for m in model_ids:
																if m in allowed_to_be_broken:
																						continue
																try:
																						AutoConfig.from_pretrained(UpperCAmelCase_							)
																except Exception:
																						failures.append(UpperCAmelCase_							)
										assert not failures, f"""The following models could not be loaded through AutoConfig: {failures}"""






				def        snake_case			(				self	:					Optional[int]							)			->   Any:




										'''simple docstring'''



										_UpperCamelCase    :      List[Any]						     =					{'student_encoder_layers': 2, 'student_decoder_layers': 1, 'no_teacher': True}
										self._test_distiller_cli(UpperCAmelCase_							)






				def        snake_case			(				self	:					List[str]							)			->   Dict:




										'''simple docstring'''



										_UpperCamelCase    :      List[str]						     =					{
										    'student_encoder_layers': 2,
										    'student_decoder_layers': 1,
										    'max_epochs': 4,
										    'val_check_interval': 0.25,
										    'alpha_hid': 2.0,
										    'model_name_or_path': 'IGNORE_THIS_IT_DOESNT_GET_USED',
										}
										_UpperCamelCase    :      Dict						     =					self._test_distiller_cli(UpperCAmelCase_,				check_contents=UpperCAmelCase_							)

										_UpperCamelCase    :      int						     =					list(Path(model.output_dir							).glob('''*.ckpt'''							)							)
										self.assertEqual(1,				len(UpperCAmelCase_							)							)
										_UpperCamelCase    :      Any						     =					list(Path(model.output_dir							).glob('''**/*.bin'''							)							)
										self.assertEqual(len(UpperCAmelCase_							),				2							)
										_UpperCamelCase    :      Dict						     =					lmap(str.strip,				Path(model.hparams.data_dir							).joinpath('''test.source'''							).open().readlines()							)
										_UpperCamelCase    :      Optional[Any]						     =					tempfile.mktemp()  # XXX: not being cleaned up
										generate_summaries_or_translations(UpperCAmelCase_,				UpperCAmelCase_,				str(model.output_dir / '''best_tfmr'''							)							)
										self.assertTrue(Path(UpperCAmelCase_							).exists()							)

										_UpperCamelCase    :      Optional[int]						     =					self.get_auto_remove_tmp_dir()
										convert_pl_to_hf(ckpts[0],				transformer_ckpts[0].parent,				UpperCAmelCase_							)
										assert os.path.exists(os.path.join(UpperCAmelCase_,				'''pytorch_model.bin'''							)							)






				def        snake_case			(				self	:					Tuple							)			->   str:




										'''simple docstring'''



										_UpperCamelCase    :      Optional[int]						     =					AutoModelForSeqaSeqLM.from_pretrained(UpperCAmelCase_							)
										_UpperCamelCase    :      Dict						     =					model.dummy_inputs['input_ids'], model.dummy_inputs['attention_mask']
										_UpperCamelCase    :      List[Any]						     =					torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]],				dtype=torch.long,				device=model.device							)
										_UpperCamelCase    :      List[str]						     =					target_ids[:, :-1].contiguous()  # Why this line?
										_UpperCamelCase    :      List[Any]						     =					target_ids[:, 1:].clone()  # why clone?
										_UpperCamelCase    :      Any						     =					model(
										    UpperCAmelCase_,				attention_mask=UpperCAmelCase_,				decoder_input_ids=UpperCAmelCase_,				labels=UpperCAmelCase_,				use_cache=UpperCAmelCase_							).loss

										_UpperCamelCase    :      Union[str, Any]						     =					model(UpperCAmelCase_,				attention_mask=UpperCAmelCase_,				decoder_input_ids=UpperCAmelCase_,				use_cache=UpperCAmelCase_							).logits

										_UpperCamelCase    :      Optional[int]						     =					nn.functional.log_softmax(UpperCAmelCase_,				dim=-1							)
										_UpperCamelCase    :      Any						     =					label_smoothed_nll_loss(
										    UpperCAmelCase_,				UpperCAmelCase_,				0.1,				ignore_index=model.config.pad_token_id							)
										with self.assertRaises(UpperCAmelCase_							):
																# TODO: understand why this breaks
																self.assertEqual(UpperCAmelCase_,				UpperCAmelCase_							)






				def        snake_case			(				self	:					int							)			->   int:




										'''simple docstring'''



										_UpperCamelCase    :      Any						     =					{
										    'student_encoder_layers': 2,
										    'student_decoder_layers': 1,
										    'num_train_epochs': 4,
										    'val_check_interval': 0.25,
										    'alpha_hid': 2.0,
										    'task': 'translation',
										    'model_name_or_path': 'IGNORE_THIS_IT_DOESNT_GET_USED',
										    'tokenizer_name': MBART_TINY,
										    'teacher': MBART_TINY,
										    'src_lang': 'en_XX',
										    'tgt_lang': 'ro_RO',
										}
										_UpperCamelCase    :      Dict						     =					self._test_distiller_cli(UpperCAmelCase_,				check_contents=UpperCAmelCase_							)
										assert model.model.config.model_type == "mbart"

										_UpperCamelCase    :      Any						     =					list(Path(model.output_dir							).glob('''*.ckpt'''							)							)
										self.assertEqual(1,				len(UpperCAmelCase_							)							)
										_UpperCamelCase    :      int						     =					list(Path(model.output_dir							).glob('''**/*.bin'''							)							)
										_UpperCamelCase    :      Dict						     =					list(Path(model.output_dir							).glob('''best_tfmr/*'''							)							)
										assert len(UpperCAmelCase_							) > 2
										self.assertEqual(len(UpperCAmelCase_							),				2							)






				def        snake_case			(				self	:					Any							)			->   Tuple:




										'''simple docstring'''



										_UpperCamelCase    :      Union[str, Any]						     =					{
										    'student_encoder_layers': 1,
										    'student_decoder_layers': 1,
										    'alpha_hid': 2.0,
										    'teacher': T5_TINY,
										    'model_name_or_path': T5_TINY,
										    'tokenizer_name': T5_TINY,
										}
										self._test_distiller_cli(UpperCAmelCase_							)






				def        snake_case			(				self	:					str							)			->   Dict:




										'''simple docstring'''



										_UpperCamelCase    :      Union[str, Any]						     =					{
										    'teacher': T5_TINY,
										    'student': T5_TINIER,
										    'model_name_or_path': T5_TINIER,
										    'tokenizer_name': T5_TINIER,
										}
										self._test_distiller_cli(UpperCAmelCase_							)






				def        snake_case			(				self	:					Tuple,				lowerCAmelCase__	:					Union[str, Any],				lowerCAmelCase__	:					Dict=True							)			->   str:




										'''simple docstring'''



										_UpperCamelCase    :      Optional[Any]						     =					{
										    'label_smoothing': 0.0,
										    'early_stopping_patience': -1,
										    'train_batch_size': 1,
										    'eval_batch_size': 2,
										    'max_epochs': 2,
										    'alpha_mlm': 0.2,
										    'alpha_ce': 0.8,
										    'do_predict': True,
										    'model_name_or_path': 'sshleifer/tinier_bart',
										    'teacher': CHEAP_ARGS['model_name_or_path'],
										    'val_check_interval': 0.5,
										}
										default_updates.update(UpperCAmelCase_							)
										_UpperCamelCase    :      dict						     =					CHEAP_ARGS.copy()
										_UpperCamelCase    :      Optional[Any]						     =					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)
										_UpperCamelCase    :      List[str]						     =					self.get_auto_remove_tmp_dir()

										args_d.update(data_dir=UpperCAmelCase_,				output_dir=UpperCAmelCase_,				**UpperCAmelCase_							)
										_UpperCamelCase    :      str						     =					distill_main(argparse.Namespace(**UpperCAmelCase_							)							)
										if not check_contents:
																return model
										_UpperCamelCase    :      Tuple						     =					os.listdir(UpperCAmelCase_							)
										_UpperCamelCase    :      Union[str, Any]						     =					{os.path.basename(UpperCAmelCase_							) for p in contents}
										_UpperCamelCase    :      List[str]						     =					[p for p in contents if p.endswith('''ckpt'''							)]
										assert len(UpperCAmelCase_							) > 0

										self.assertIn('''test_generations.txt''',				UpperCAmelCase_							)
										self.assertIn('''test_results.txt''',				UpperCAmelCase_							)

										_UpperCamelCase    :      Optional[int]						     =					load_json(model.metrics_save_path							)
										_UpperCamelCase    :      Optional[int]						     =					metrics['val'][-1]
										self.assertGreaterEqual(last_step_stats['''val_avg_gen_time'''],				0.01							)
										self.assertGreaterEqual(1.0,				last_step_stats['''val_avg_gen_time''']							)
										self.assertIsInstance(last_step_stats[f"""val_avg_{model.val_metric}"""],				UpperCAmelCase_							)
										_UpperCamelCase    :      Optional[Any]						     =					int(args_d['''max_epochs'''] * (1 / args_d['''val_check_interval''']) + 1							)
										self.assertEqual(len(metrics['''val''']							),				UpperCAmelCase_							)
										self.assertEqual(len(metrics['''test''']							),				1							)
										return model




class 				_a		(   _lowerCAmelCase       ):






				@parameterized.expand(
				    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY],				)
				def        snake_case			(				self	:					int,				lowerCAmelCase__	:					int							)			->   str:




										'''simple docstring'''



										_UpperCamelCase    :      dict						     =					CHEAP_ARGS.copy()
										_UpperCamelCase    :      Any						     =					'translation' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else 'summarization'
										_UpperCamelCase    :      Union[str, Any]						     =					0.1 if task == 'translation' else 0

										_UpperCamelCase    :      Optional[int]						     =					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)
										_UpperCamelCase    :      str						     =					self.get_auto_remove_tmp_dir()
										args_d.update(
										    data_dir=UpperCAmelCase_,				model_name_or_path=UpperCAmelCase_,				tokenizer_name=UpperCAmelCase_,				train_batch_size=2,				eval_batch_size=2,				output_dir=UpperCAmelCase_,				do_predict=UpperCAmelCase_,				task=UpperCAmelCase_,				src_lang='''en_XX''',				tgt_lang='''ro_RO''',				freeze_encoder=UpperCAmelCase_,				freeze_embeds=UpperCAmelCase_,				)
										assert "n_train" in args_d
										_UpperCamelCase    :      List[str]						     =					argparse.Namespace(**UpperCAmelCase_							)
										_UpperCamelCase    :      Any						     =					main(UpperCAmelCase_							)

										_UpperCamelCase    :      Tuple						     =					module.model.get_input_embeddings()
										assert not input_embeds.weight.requires_grad
										if model == T5_TINY:
																_UpperCamelCase    :      Union[str, Any]						     =					module.model.lm_head
																assert not lm_head.weight.requires_grad
																assert (lm_head.weight == input_embeds.weight).all().item()
										elif model == FSMT_TINY:
																_UpperCamelCase    :      Union[str, Any]						     =					module.model.model
																_UpperCamelCase    :      Any						     =					fsmt.decoder.embed_positions
																assert not embed_pos.weight.requires_grad
																assert not fsmt.decoder.embed_tokens.weight.requires_grad
																# check that embeds are not the same
																assert fsmt.decoder.embed_tokens != fsmt.encoder.embed_tokens
										else:
																_UpperCamelCase    :      Tuple						     =					module.model.model
																_UpperCamelCase    :      Dict						     =					bart.decoder.embed_positions
																assert not embed_pos.weight.requires_grad
																assert not bart.shared.weight.requires_grad
																# check that embeds are the same
																assert bart.decoder.embed_tokens == bart.encoder.embed_tokens
																assert bart.decoder.embed_tokens == bart.shared

										_UpperCamelCase    :      Tuple						     =					load_json(module.output_dir / '''text_batch.json'''							)
										assert isinstance(UpperCAmelCase_,				UpperCAmelCase_							)
										assert len(UpperCAmelCase_							) >= 4






				def        snake_case			(				self	:					List[Any]							)			->   Dict:




										'''simple docstring'''



										_UpperCamelCase    :      dict						     =					CHEAP_ARGS.copy()

										_UpperCamelCase    :      List[Any]						     =					'summarization'
										_UpperCamelCase    :      int						     =					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)

										args_d.update(
										    data_dir=UpperCAmelCase_,				tokenizer_name=UpperCAmelCase_,				train_batch_size=2,				eval_batch_size=2,				do_predict=UpperCAmelCase_,				task=UpperCAmelCase_,				src_lang='''en_XX''',				tgt_lang='''ro_RO''',				freeze_encoder=UpperCAmelCase_,				freeze_embeds=UpperCAmelCase_,				)

										# test models whose config includes the extra_model_args
										_UpperCamelCase    :      Optional[int]						     =					BART_TINY
										_UpperCamelCase    :      int						     =					self.get_auto_remove_tmp_dir()
										_UpperCamelCase    :      int						     =					args_d.copy()
										args_da.update(
										    model_name_or_path=UpperCAmelCase_,				output_dir=UpperCAmelCase_,				)
										_UpperCamelCase    :      List[Any]						     =					('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
										for p in extra_model_params:
																_UpperCamelCase    :      str						     =					0.5
										_UpperCamelCase    :      Any						     =					argparse.Namespace(**UpperCAmelCase_							)
										_UpperCamelCase    :      str						     =					main(UpperCAmelCase_							)
										for p in extra_model_params:
																assert getattr(model.config,				UpperCAmelCase_							) == 0.5, f"""failed to override the model config for param {p}"""

										# test models whose config doesn't include the extra_model_args
										_UpperCamelCase    :      Any						     =					T5_TINY
										_UpperCamelCase    :      Any						     =					self.get_auto_remove_tmp_dir()
										_UpperCamelCase    :      Tuple						     =					args_d.copy()
										args_da.update(
										    model_name_or_path=UpperCAmelCase_,				output_dir=UpperCAmelCase_,				)
										_UpperCamelCase    :      Any						     =					'encoder_layerdrop'
										_UpperCamelCase    :      Dict						     =					0.5
										_UpperCamelCase    :      str						     =					argparse.Namespace(**UpperCAmelCase_							)
										with pytest.raises(UpperCAmelCase_							) as excinfo:
																_UpperCamelCase    :      Optional[Any]						     =					main(UpperCAmelCase_							)
										assert str(excinfo.value							) == f"""model config doesn't have a `{unsupported_param}` attribute"""






				def        snake_case			(				self	:					Tuple							)			->   Dict:




										'''simple docstring'''



										_UpperCamelCase    :      dict						     =					CHEAP_ARGS.copy()

										_UpperCamelCase    :      Dict						     =					'summarization'
										_UpperCamelCase    :      Union[str, Any]						     =					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)

										_UpperCamelCase    :      List[str]						     =					BART_TINY
										_UpperCamelCase    :      Dict						     =					self.get_auto_remove_tmp_dir()

										args_d.update(
										    data_dir=UpperCAmelCase_,				model_name_or_path=UpperCAmelCase_,				output_dir=UpperCAmelCase_,				tokenizer_name=UpperCAmelCase_,				train_batch_size=2,				eval_batch_size=2,				do_predict=UpperCAmelCase_,				task=UpperCAmelCase_,				src_lang='''en_XX''',				tgt_lang='''ro_RO''',				freeze_encoder=UpperCAmelCase_,				freeze_embeds=UpperCAmelCase_,				)

										# emulate finetune.py
										_UpperCamelCase    :      Dict						     =					argparse.ArgumentParser()
										_UpperCamelCase    :      List[str]						     =					pl.Trainer.add_argparse_args(UpperCAmelCase_							)
										_UpperCamelCase    :      Tuple						     =					SummarizationModule.add_model_specific_args(UpperCAmelCase_,				os.getcwd()							)
										_UpperCamelCase    :      str						     =					{'--help': True}

										# --help test
										with pytest.raises(UpperCAmelCase_							) as excinfo:
																with CaptureStdout() as cs:
																						_UpperCamelCase    :      List[str]						     =					parser.parse_args(UpperCAmelCase_							)
																assert False, "--help is expected to sys.exit"
										assert excinfo.type == SystemExit
										_UpperCamelCase    :      Optional[int]						     =					lightning_base.arg_to_scheduler_metavar
										assert expected in cs.out, "--help is expected to list the supported schedulers"

										# --lr_scheduler=non_existing_scheduler test
										_UpperCamelCase    :      int						     =					'non_existing_scheduler'
										_UpperCamelCase    :      Optional[Any]						     =					{f"""--lr_scheduler={unsupported_param}"""}
										with pytest.raises(UpperCAmelCase_							) as excinfo:
																with CaptureStderr() as cs:
																						_UpperCamelCase    :      int						     =					parser.parse_args(UpperCAmelCase_							)
																assert False, "invalid argument is expected to sys.exit"
										assert excinfo.type == SystemExit
										_UpperCamelCase    :      Dict						     =					f"""invalid choice: '{unsupported_param}'"""
										assert expected in cs.err, f"""should have bailed on invalid choice of scheduler {unsupported_param}"""

										# --lr_scheduler=existing_scheduler test
										_UpperCamelCase    :      Optional[int]						     =					'cosine'
										_UpperCamelCase    :      Tuple						     =					args_d.copy()
										_UpperCamelCase    :      str						     =					supported_param
										_UpperCamelCase    :      List[str]						     =					argparse.Namespace(**UpperCAmelCase_							)
										_UpperCamelCase    :      List[str]						     =					main(UpperCAmelCase_							)
										assert (
										    getattr(model.hparams,				'''lr_scheduler'''							) == supported_param
										), f"""lr_scheduler={supported_param} shouldn't fail"""










"""simple docstring"""




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


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


UpperCamelCase_     ="""lfs-multipart-upload"""




class 				_a		(   _lowerCAmelCase       ):






				@staticmethod
				def        snake_case			(				lowerCAmelCase__	:					ArgumentParser							)			->   Any:




										'''simple docstring'''



										_UpperCamelCase    :      Optional[int]						     =					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=lowerCAmelCase__,				help='''Local path to repository you want to configure.'''							)
										enable_parser.set_defaults(func=lambda lowerCAmelCase__							: LfsEnableCommand(lowerCAmelCase__							)							)

										_UpperCamelCase    :      Any						     =					parser.add_parser(
										    lowerCAmelCase__,				help=(
										        '''Deprecated: use `huggingface-cli` instead. '''
										        '''Command will get called by git-lfs, do not call it directly.'''
										    ),				)
										upload_parser.set_defaults(func=lambda lowerCAmelCase__							: LfsUploadCommand(lowerCAmelCase__							)							)




class 				_a		:






				def __init__(				self	:					Union[str, Any],				lowerCAmelCase__	:					str							)			->   Dict:




										'''simple docstring'''



										_UpperCamelCase    :      Optional[Any]						     =					args






				def        snake_case			(				self	:					str							)			->   str:




										'''simple docstring'''



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





def  a_       (							_lowercase     ):
						_UpperCamelCase    :      Tuple						     =					json.dumps(_lowercase     ) + '''\n'''
						sys.stdout.write(_lowercase     )
						sys.stdout.flush()





def  a_       (							):
						_UpperCamelCase    :      Optional[Any]						     =					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		(   _lowerCAmelCase       ):






				def __init__(				self	:					Tuple,				lowerCAmelCase__	:					str,				lowerCAmelCase__	:					int,				lowerCAmelCase__	:					int							)			->   Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase    :      int						     =					filepath
										_UpperCamelCase    :      Tuple						     =					seek_from
										_UpperCamelCase    :      Optional[int]						     =					read_limit
										_UpperCamelCase    :      List[str]						     =					0






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




										'''simple docstring'''



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






				def __len__(				self	:					str							)			->   str:




										'''simple docstring'''



										_UpperCamelCase    :      List[str]						     =					os.fstat(self.f.fileno()							).st_size
										return min(self.read_limit,				total_length - self.seek_from							)






				def        snake_case			(				self	:					Dict,				lowerCAmelCase__	:					List[str]=-1							)			->   int:




										'''simple docstring'''



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






				def __iter__(				self	:					int							)			->   List[Any]:




										'''simple docstring'''



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






				def __exit__(				self	:					Optional[Any],				*lowerCAmelCase__	:					List[Any]							)			->   Tuple:




										'''simple docstring'''



										self.f.close()




class 				_a		:






				def __init__(				self	:					List[str],				lowerCAmelCase__	:					Optional[Any]							)			->   Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase    :      Optional[Any]						     =					args






				def        snake_case			(				self	:					List[Any]							)			->   Tuple:




										'''simple docstring'''



										_UpperCamelCase    :      Union[str, 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:
																_UpperCamelCase    :      int						     =					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							)

																_UpperCamelCase    :      Union[str, Any]						     =					msg['''oid''']
																_UpperCamelCase    :      List[str]						     =					msg['''path''']
																_UpperCamelCase    :      Optional[int]						     =					msg['''action''']['''href''']
																_UpperCamelCase    :      Optional[int]						     =					msg['''action''']['''header''']
																_UpperCamelCase    :      Tuple						     =					int(header.pop('''chunk_size'''							)							)
																_UpperCamelCase    :      List[str]						     =					list(header.values()							)

																_UpperCamelCase    :      str						     =					[]
																for i, presigned_url in enumerate(lowerCAmelCase__							):
																						with FileSlice(lowerCAmelCase__,				seek_from=i * chunk_size,				read_limit=lowerCAmelCase__							) as data:
																												_UpperCamelCase    :      Any						     =					requests.put(lowerCAmelCase__,				data=lowerCAmelCase__							)
																												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.

																_UpperCamelCase    :      str						     =					requests.post(
																    lowerCAmelCase__,				json={
																        '''oid''': oid,
																        '''parts''': parts,
																    },				)
																r.raise_for_status()

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



