
'''simple docstring'''






import unittest

from transformers import YosoConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

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 (
							    YosoForMaskedLM,
							    YosoForMultipleChoice,
							    YosoForQuestionAnswering,
							    YosoForSequenceClassification,
							    YosoForTokenClassification,
							    YosoModel,
							)
							from transformers.models.yoso.modeling_yoso import YOSO_PRETRAINED_MODEL_ARCHIVE_LIST

class 							_snake_case     :
	"""simple docstring"""





	def __init__(						self				: Optional[int]					,						UpperCamelCase_				: List[str]					,						UpperCamelCase_				: int=13					,						UpperCamelCase_				: Optional[Any]=7					,						UpperCamelCase_				: Tuple=True					,						UpperCamelCase_				: Union[str, Any]=True					,						UpperCamelCase_				: List[str]=True					,						UpperCamelCase_				: Optional[int]=True					,						UpperCamelCase_				: Optional[int]=99					,						UpperCamelCase_				: Optional[Any]=32					,						UpperCamelCase_				: Union[str, Any]=5					,						UpperCamelCase_				: Tuple=4					,						UpperCamelCase_				: Dict=37					,						UpperCamelCase_				: Tuple="gelu"					,						UpperCamelCase_				: Any=0.1					,						UpperCamelCase_				: List[str]=0.1					,						UpperCamelCase_				: int=512					,						UpperCamelCase_				: Any=16					,						UpperCamelCase_				: Any=2					,						UpperCamelCase_				: Optional[Any]=0.0_2					,						UpperCamelCase_				: int=3					,						UpperCamelCase_				: Dict=4					,						UpperCamelCase_				: Optional[int]=None					,						):
			lowerCAmelCase_   :       int    							=parent
			lowerCAmelCase_   :       Tuple    							=batch_size
			lowerCAmelCase_   :       Optional[Any]    							=seq_length
			lowerCAmelCase_   :       int    							=is_training
			lowerCAmelCase_   :       Tuple    							=use_input_mask
			lowerCAmelCase_   :       List[str]    							=use_token_type_ids
			lowerCAmelCase_   :       Optional[int]    							=use_labels
			lowerCAmelCase_   :       int    							=vocab_size
			lowerCAmelCase_   :       int    							=hidden_size
			lowerCAmelCase_   :       List[str]    							=num_hidden_layers
			lowerCAmelCase_   :       Tuple    							=num_attention_heads
			lowerCAmelCase_   :       Tuple    							=intermediate_size
			lowerCAmelCase_   :       Tuple    							=hidden_act
			lowerCAmelCase_   :       List[Any]    							=hidden_dropout_prob
			lowerCAmelCase_   :       str    							=attention_probs_dropout_prob
			lowerCAmelCase_   :       int    							=max_position_embeddings
			lowerCAmelCase_   :       Optional[Any]    							=type_vocab_size
			lowerCAmelCase_   :       int    							=type_sequence_label_size
			lowerCAmelCase_   :       Optional[int]    							=initializer_range
			lowerCAmelCase_   :       Union[str, Any]    							=num_labels
			lowerCAmelCase_   :       int    							=num_choices
			lowerCAmelCase_   :       List[str]    							=scope




	def 	__A      (						self				: List[Any]      ):
			lowerCAmelCase_   :       int    							=ids_tensor([self.batch_size, self.seq_length]					,						self.vocab_size      )

			lowerCAmelCase_   :       List[Any]    							=None
			if self.use_input_mask:
					lowerCAmelCase_   :       Union[str, Any]    							=random_attention_mask([self.batch_size, self.seq_length]      )

			lowerCAmelCase_   :       List[str]    							=None
			if self.use_token_type_ids:
					lowerCAmelCase_   :       Union[str, Any]    							=ids_tensor([self.batch_size, self.seq_length]					,						self.type_vocab_size      )

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

			lowerCAmelCase_   :       Dict    							=self.get_config()

			return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




	def 	__A      (						self				: int      ):
			return YosoConfig(
			    vocab_size=self.vocab_size					,						hidden_size=self.hidden_size					,						num_hidden_layers=self.num_hidden_layers					,						num_attention_heads=self.num_attention_heads					,						intermediate_size=self.intermediate_size					,						hidden_act=self.hidden_act					,						hidden_dropout_prob=self.hidden_dropout_prob					,						attention_probs_dropout_prob=self.attention_probs_dropout_prob					,						max_position_embeddings=self.max_position_embeddings					,						type_vocab_size=self.type_vocab_size					,						is_decoder=UpperCamelCase_					,						initializer_range=self.initializer_range					,						)




	def 	__A      (						self				: str      ):
			lowerCAmelCase_   :       List[str]    							=self.get_config()
			lowerCAmelCase_   :       Dict    							=300
			return config




	def 	__A      (						self				: int      ):
			(
			    (
			    lowerCAmelCase_
			)							,		(
			    lowerCAmelCase_
			)							,		(
			    lowerCAmelCase_
			)							,		(
			    lowerCAmelCase_
			)							,		(
			    lowerCAmelCase_
			)							,		(
			    lowerCAmelCase_
			)							,		(
			    lowerCAmelCase_
			)							,		
			)   :       Any    							=self.prepare_config_and_inputs()

			lowerCAmelCase_   :       Dict    							=True
			lowerCAmelCase_   :       Dict    							=floats_tensor([self.batch_size, self.seq_length, self.hidden_size]      )
			lowerCAmelCase_   :       Optional[Any]    							=ids_tensor([self.batch_size, self.seq_length]					,						vocab_size=2      )

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




	def 	__A      (						self				: Dict					,						UpperCamelCase_				: int					,						UpperCamelCase_				: Dict					,						UpperCamelCase_				: int					,						UpperCamelCase_				: Dict					,						UpperCamelCase_				: Optional[Any]					,						UpperCamelCase_				: int					,						UpperCamelCase_				: int      ):
			lowerCAmelCase_   :       Optional[int]    							=YosoModel(config=UpperCamelCase_      )
			model.to(UpperCamelCase_      )
			model.eval()
			lowerCAmelCase_   :       List[Any]    							=model(UpperCamelCase_					,						attention_mask=UpperCamelCase_					,						token_type_ids=UpperCamelCase_      )
			lowerCAmelCase_   :       List[Any]    							=model(UpperCamelCase_					,						token_type_ids=UpperCamelCase_      )
			lowerCAmelCase_   :       List[Any]    							=model(UpperCamelCase_      )
			self.parent.assertEqual(result.last_hidden_state.shape					,						(self.batch_size, self.seq_length, self.hidden_size)      )




	def 	__A      (						self				: int					,						UpperCamelCase_				: List[Any]					,						UpperCamelCase_				: List[Any]					,						UpperCamelCase_				: Tuple					,						UpperCamelCase_				: List[Any]					,						UpperCamelCase_				: List[str]					,						UpperCamelCase_				: int					,						UpperCamelCase_				: Optional[int]					,						UpperCamelCase_				: Optional[int]					,						UpperCamelCase_				: Optional[Any]					,						):
			lowerCAmelCase_   :       Tuple    							=True
			lowerCAmelCase_   :       str    							=YosoModel(UpperCamelCase_      )
			model.to(UpperCamelCase_      )
			model.eval()
			lowerCAmelCase_   :       Tuple    							=model(
			    UpperCamelCase_					,						attention_mask=UpperCamelCase_					,						token_type_ids=UpperCamelCase_					,						encoder_hidden_states=UpperCamelCase_					,						encoder_attention_mask=UpperCamelCase_					,						)
			lowerCAmelCase_   :       List[Any]    							=model(
			    UpperCamelCase_					,						attention_mask=UpperCamelCase_					,						token_type_ids=UpperCamelCase_					,						encoder_hidden_states=UpperCamelCase_					,						)
			lowerCAmelCase_   :       int    							=model(UpperCamelCase_					,						attention_mask=UpperCamelCase_					,						token_type_ids=UpperCamelCase_      )
			self.parent.assertEqual(result.last_hidden_state.shape					,						(self.batch_size, self.seq_length, self.hidden_size)      )




	def 	__A      (						self				: Any					,						UpperCamelCase_				: Optional[Any]					,						UpperCamelCase_				: str					,						UpperCamelCase_				: List[Any]					,						UpperCamelCase_				: int					,						UpperCamelCase_				: int					,						UpperCamelCase_				: Tuple					,						UpperCamelCase_				: int      ):
			lowerCAmelCase_   :       List[str]    							=YosoForMaskedLM(config=UpperCamelCase_      )
			model.to(UpperCamelCase_      )
			model.eval()
			lowerCAmelCase_   :       Optional[Any]    							=model(UpperCamelCase_					,						attention_mask=UpperCamelCase_					,						token_type_ids=UpperCamelCase_					,						labels=UpperCamelCase_      )
			self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.seq_length, self.vocab_size)      )




	def 	__A      (						self				: Optional[Any]					,						UpperCamelCase_				: Any					,						UpperCamelCase_				: Dict					,						UpperCamelCase_				: str					,						UpperCamelCase_				: Union[str, Any]					,						UpperCamelCase_				: Optional[Any]					,						UpperCamelCase_				: Union[str, Any]					,						UpperCamelCase_				: Any      ):
			lowerCAmelCase_   :       Any    							=YosoForQuestionAnswering(config=UpperCamelCase_      )
			model.to(UpperCamelCase_      )
			model.eval()
			lowerCAmelCase_   :       int    							=model(
			    UpperCamelCase_					,						attention_mask=UpperCamelCase_					,						token_type_ids=UpperCamelCase_					,						start_positions=UpperCamelCase_					,						end_positions=UpperCamelCase_					,						)
			self.parent.assertEqual(result.start_logits.shape					,						(self.batch_size, self.seq_length)      )
			self.parent.assertEqual(result.end_logits.shape					,						(self.batch_size, self.seq_length)      )




	def 	__A      (						self				: Optional[int]					,						UpperCamelCase_				: Dict					,						UpperCamelCase_				: Tuple					,						UpperCamelCase_				: Optional[Any]					,						UpperCamelCase_				: Union[str, Any]					,						UpperCamelCase_				: Union[str, Any]					,						UpperCamelCase_				: List[str]					,						UpperCamelCase_				: Union[str, Any]      ):
			lowerCAmelCase_   :       List[Any]    							=self.num_labels
			lowerCAmelCase_   :       Any    							=YosoForSequenceClassification(UpperCamelCase_      )
			model.to(UpperCamelCase_      )
			model.eval()
			lowerCAmelCase_   :       List[Any]    							=model(UpperCamelCase_					,						attention_mask=UpperCamelCase_					,						token_type_ids=UpperCamelCase_					,						labels=UpperCamelCase_      )
			self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.num_labels)      )




	def 	__A      (						self				: Dict					,						UpperCamelCase_				: Tuple					,						UpperCamelCase_				: List[Any]					,						UpperCamelCase_				: Any					,						UpperCamelCase_				: Optional[Any]					,						UpperCamelCase_				: int					,						UpperCamelCase_				: List[str]					,						UpperCamelCase_				: Union[str, Any]      ):
			lowerCAmelCase_   :       Optional[Any]    							=self.num_labels
			lowerCAmelCase_   :       int    							=YosoForTokenClassification(config=UpperCamelCase_      )
			model.to(UpperCamelCase_      )
			model.eval()
			lowerCAmelCase_   :       int    							=model(UpperCamelCase_					,						attention_mask=UpperCamelCase_					,						token_type_ids=UpperCamelCase_					,						labels=UpperCamelCase_      )
			self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.seq_length, self.num_labels)      )




	def 	__A      (						self				: List[Any]					,						UpperCamelCase_				: str					,						UpperCamelCase_				: List[Any]					,						UpperCamelCase_				: Optional[int]					,						UpperCamelCase_				: Optional[int]					,						UpperCamelCase_				: Optional[int]					,						UpperCamelCase_				: List[str]					,						UpperCamelCase_				: Dict      ):
			lowerCAmelCase_   :       int    							=self.num_choices
			lowerCAmelCase_   :       List[str]    							=YosoForMultipleChoice(config=UpperCamelCase_      )
			model.to(UpperCamelCase_      )
			model.eval()
			lowerCAmelCase_   :       Tuple    							=input_ids.unsqueeze(1      ).expand(-1					,						self.num_choices					,						-1      ).contiguous()
			lowerCAmelCase_   :       Dict    							=token_type_ids.unsqueeze(1      ).expand(-1					,						self.num_choices					,						-1      ).contiguous()
			lowerCAmelCase_   :       Tuple    							=input_mask.unsqueeze(1      ).expand(-1					,						self.num_choices					,						-1      ).contiguous()
			lowerCAmelCase_   :       Union[str, Any]    							=model(
			    UpperCamelCase_					,						attention_mask=UpperCamelCase_					,						token_type_ids=UpperCamelCase_					,						labels=UpperCamelCase_					,						)
			self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.num_choices)      )





	def 	__A      (						self				: Union[str, Any]      ):
			lowerCAmelCase_   :       Optional[int]    							=self.prepare_config_and_inputs()
			(
			    (
			    lowerCAmelCase_
			)							,		(
			    lowerCAmelCase_
			)							,		(
			    lowerCAmelCase_
			)							,		(
			    lowerCAmelCase_
			)							,		(
			    lowerCAmelCase_
			)							,		(
			    lowerCAmelCase_
			)							,		(
			    lowerCAmelCase_
			)							,		
			)   :       Any    							=config_and_inputs
			lowerCAmelCase_   :       Union[str, Any]    							={'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
			return config, inputs_dict







@require_torch
class 							_snake_case     (  lowerCAmelCase_						,    lowerCAmelCase_						,    unittest.TestCase ):
	"""simple docstring"""





	_UpperCamelCase							:  Any								=		(
	    (
	        YosoModel,
	        YosoForMaskedLM,
	        YosoForMultipleChoice,
	        YosoForQuestionAnswering,
	        YosoForSequenceClassification,
	        YosoForTokenClassification,
	    )
	    if is_torch_available()
	    else ()
	)
	_UpperCamelCase							:  Tuple								=		False
	_UpperCamelCase							:  Optional[Any]								=		False
	_UpperCamelCase							:  List[Any]								=		False

	_UpperCamelCase							:  List[str]								=		()
	_UpperCamelCase							:  str								=		(
	    {
	        '''feature-extraction''': YosoModel,
	        '''fill-mask''': YosoForMaskedLM,
	        '''question-answering''': YosoForQuestionAnswering,
	        '''text-classification''': YosoForSequenceClassification,
	        '''token-classification''': YosoForTokenClassification,
	        '''zero-shot''': YosoForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)




	def 	__A      (						self				: Tuple      ):
			lowerCAmelCase_   :       Union[str, Any]    							=YosoModelTester(self      )
			lowerCAmelCase_   :       Optional[Any]    							=ConfigTester(self					,						config_class=UpperCamelCase_					,						hidden_size=37      )




	def 	__A      (						self				: Any      ):
			self.config_tester.run_common_tests()




	def 	__A      (						self				: Optional[int]      ):
			lowerCAmelCase_   :       List[str]    							=self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*UpperCamelCase_      )




	def 	__A      (						self				: List[str]      ):
			lowerCAmelCase_   :       str    							=self.model_tester.prepare_config_and_inputs()
			for type in ["absolute", "relative_key", "relative_key_query"]:
					lowerCAmelCase_   :       Optional[Any]    							=type
					self.model_tester.create_and_check_model(*UpperCamelCase_      )




	def 	__A      (						self				: List[str]      ):
			lowerCAmelCase_   :       Any    							=self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase_      )




	def 	__A      (						self				: Optional[Any]      ):
			lowerCAmelCase_   :       Tuple    							=self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_multiple_choice(*UpperCamelCase_      )




	def 	__A      (						self				: List[str]      ):
			lowerCAmelCase_   :       Any    							=self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_question_answering(*UpperCamelCase_      )




	def 	__A      (						self				: List[str]      ):
			lowerCAmelCase_   :       Union[str, Any]    							=self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase_      )




	def 	__A      (						self				: Tuple      ):
			lowerCAmelCase_   :       int    							=self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_token_classification(*UpperCamelCase_      )




	@slow
	def 	__A      (						self				: Union[str, Any]      ):
			for model_name in YOSO_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					lowerCAmelCase_   :       List[str]    							=YosoModel.from_pretrained(UpperCamelCase_      )
					self.assertIsNotNone(UpperCamelCase_      )





	def 	__A      (						self				: int      ):
			return







@require_torch
class 							_snake_case     (  unittest.TestCase ):
	"""simple docstring"""





	@slow
	def 	__A      (						self				: Optional[Any]      ):
			lowerCAmelCase_   :       List[Any]    							=YosoModel.from_pretrained('''uw-madison/yoso-4096'''      )
			lowerCAmelCase_   :       Union[str, Any]    							=torch.tensor([[0, 1, 2, 3, 4, 5]]      )

			with torch.no_grad():
					lowerCAmelCase_   :       Union[str, Any]    							=model(UpperCamelCase_      )[0]

			lowerCAmelCase_   :       Dict    							=torch.Size((1, 6, 768)      )
			self.assertEqual(output.shape					,						UpperCamelCase_      )

			lowerCAmelCase_   :       Optional[int]    							=torch.tensor(
			    [[[-0.0_6_1_1, 0.1_2_4_2, 0.0_8_4_0], [0.0_2_8_0, -0.0_0_4_8, 0.1_1_2_5], [0.0_1_0_6, 0.0_2_2_6, 0.0_7_5_1]]]      )

			self.assertTrue(torch.allclose(output[:, :3, :3]					,						UpperCamelCase_					,						atol=1E-4      )      )




	@slow
	def 	__A      (						self				: Tuple      ):
			lowerCAmelCase_   :       Union[str, Any]    							=YosoForMaskedLM.from_pretrained('''uw-madison/yoso-4096'''      )
			lowerCAmelCase_   :       Any    							=torch.tensor([[0, 1, 2, 3, 4, 5]]      )

			with torch.no_grad():
					lowerCAmelCase_   :       Optional[Any]    							=model(UpperCamelCase_      )[0]

			lowerCAmelCase_   :       Optional[int]    							=50265

			lowerCAmelCase_   :       Any    							=torch.Size((1, 6, vocab_size)      )
			self.assertEqual(output.shape					,						UpperCamelCase_      )

			lowerCAmelCase_   :       Optional[Any]    							=torch.tensor(
			    [[[-2.1_3_1_3, -3.7_2_8_5, -2.2_4_0_7], [-2.7_0_4_7, -3.3_3_1_4, -2.6_4_0_8], [0.0_6_2_9, -2.5_1_6_6, -0.3_3_5_6]]]      )

			self.assertTrue(torch.allclose(output[:, :3, :3]					,						UpperCamelCase_					,						atol=1E-4      )      )





	@slow
	def 	__A      (						self				: Optional[int]      ):
			lowerCAmelCase_   :       List[Any]    							=YosoForMaskedLM.from_pretrained('''uw-madison/yoso-4096'''      )
			lowerCAmelCase_   :       str    							=torch.arange(4096      ).unsqueeze(0      )

			with torch.no_grad():
					lowerCAmelCase_   :       Dict    							=model(UpperCamelCase_      )[0]

			lowerCAmelCase_   :       Tuple    							=50265

			lowerCAmelCase_   :       Dict    							=torch.Size((1, 4096, vocab_size)      )
			self.assertEqual(output.shape					,						UpperCamelCase_      )

			lowerCAmelCase_   :       Union[str, Any]    							=torch.tensor(
			    [[[-2.3_9_1_4, -4.3_7_4_2, -5.0_9_5_6], [-4.0_9_8_8, -4.2_3_8_4, -7.0_4_0_6], [-3.1_4_2_7, -3.7_1_9_2, -6.6_8_0_0]]]      )

			self.assertTrue(torch.allclose(output[:, :3, :3]					,						UpperCamelCase_					,						atol=1E-4      )      )








'''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


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


__lowercase	=    '''lfs-multipart-upload'''

class 							_snake_case     (  lowerCAmelCase_ ):
	"""simple docstring"""





	@staticmethod
	def 	__A      (						UpperCamelCase_				: ArgumentParser      ):
			lowerCAmelCase_   :       Dict    							=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=UpperCamelCase_					,						help='''Local path to repository you want to configure.'''      )
			enable_parser.set_defaults(func=lambda UpperCamelCase_      : LfsEnableCommand(UpperCamelCase_      )      )

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







class 							_snake_case     :
	"""simple docstring"""





	def __init__(						self				: Any					,						UpperCamelCase_				: str      ):
			lowerCAmelCase_   :       Any    							=args





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







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ):
		lowerCAmelCase_   :       Optional[int]    							=json.dumps(_SCREAMING_SNAKE_CASE  ) + '''\n'''
		sys.stdout.write(_SCREAMING_SNAKE_CASE  )
		sys.stdout.flush()







def 		SCREAMING_SNAKE_CASE__					(							):
		lowerCAmelCase_   :       List[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 							_snake_case     (  lowerCAmelCase_ ):
	"""simple docstring"""





	def __init__(						self				: Tuple					,						UpperCamelCase_				: str					,						UpperCamelCase_				: int					,						UpperCamelCase_				: int      ):
			lowerCAmelCase_   :       int    							=filepath
			lowerCAmelCase_   :       Optional[Any]    							=seek_from
			lowerCAmelCase_   :       Tuple    							=read_limit
			lowerCAmelCase_   :       Tuple    							=0




	def __enter__(						self				: int      ):
			lowerCAmelCase_   :       str    							=open(self.filepath					,						'''rb'''      )
			self.f.seek(self.seek_from      )
			return self




	def __len__(						self				: Optional[Any]      ):
			lowerCAmelCase_   :       Tuple    							=os.fstat(self.f.fileno()      ).st_size
			return min(self.read_limit					,						total_length - self.seek_from      )




	def 	__A      (						self				: Any					,						UpperCamelCase_				: str=-1      ):
			if self.n_seen >= self.read_limit:
					return b""
			lowerCAmelCase_   :       str    							=self.read_limit - self.n_seen
			lowerCAmelCase_   :       Optional[int]    							=self.f.read(remaining_amount if n < 0 else min(UpperCamelCase_					,						UpperCamelCase_      )      )
			self.n_seen += len(UpperCamelCase_      )
			return data




	def __iter__(						self				: int      ):
			yield self.read(n=4 * 1024 * 1024      )





	def __exit__(						self				: Tuple					,						*UpperCamelCase_				: Tuple      ):
			self.f.close()







class 							_snake_case     :
	"""simple docstring"""





	def __init__(						self				: List[str]					,						UpperCamelCase_				: List[str]      ):
			lowerCAmelCase_   :       int    							=args





	def 	__A      (						self				: List[Any]      ):
			# Immediately after invoking a custom transfer process, git-lfs
			# sends initiation data to the process over stdin.
			# This tells the process useful information about the configuration.
			lowerCAmelCase_   :       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''': 32, '''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:
					lowerCAmelCase_   :       List[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      )

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

					lowerCAmelCase_   :       Dict    							=[]
					for i, presigned_url in enumerate(UpperCamelCase_      ):
							with FileSlice(UpperCamelCase_					,						seek_from=i * chunk_size					,						read_limit=UpperCamelCase_      ) as data:
									lowerCAmelCase_   :       Optional[Any]    							=requests.put(UpperCamelCase_					,						data=UpperCamelCase_      )
									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.

					lowerCAmelCase_   :       Optional[Any]    							=requests.post(
					    UpperCamelCase_					,						json={
					        '''oid''': oid,
					        '''parts''': parts,
					    }					,						)
					r.raise_for_status()

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






