






"""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


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


__a							:						Tuple   							=			'lfs-multipart-upload'







class 							_SCREAMING_SNAKE_CASE    (	__snake_case				):





							"""simple docstring"""





							@staticmethod
							def 			lowercase		( __A:  ArgumentParser     ):

												'''simple docstring'''

												a__ 							=	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=__A					,		help='''Local path to repository you want to configure.'''     )
												enable_parser.set_defaults(func=lambda __A     : LfsEnableCommand(__A     )     )

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







class 							_SCREAMING_SNAKE_CASE    :





							"""simple docstring"""





							def __init__( self:  Any					,		__A:  Dict     ):

												'''simple docstring'''

												a__ 							=	args





							def 			lowercase		( self:  str     ):

												'''simple docstring'''

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





def 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_):
					a__ 							=	json.dumps(lowerCamelCase_) + '''\n'''
					sys.stdout.write(lowerCamelCase_)
					sys.stdout.flush()





def 							SCREAMING_SNAKE_CASE							(   ):
					a__ 							=	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 							_SCREAMING_SNAKE_CASE    (	__snake_case				):





							"""simple docstring"""





							def __init__( self:  Optional[Any]					,		__A:  str					,		__A:  int					,		__A:  int     ):

												'''simple docstring'''

												a__ 							=	filepath
												a__ 							=	seek_from
												a__ 							=	read_limit
												a__ 							=	0




							def __enter__( self:  List[Any]     ):

												'''simple docstring'''

												a__ 							=	open(self.filepath					,		'''rb'''     )
												self.f.seek(self.seek_from     )
												return self




							def __len__( self:  int     ):

												'''simple docstring'''

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




							def 			lowercase		( self:  Optional[Any]					,		__A:  List[Any]=-1     ):

												'''simple docstring'''

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




							def __iter__( self:  int     ):

												'''simple docstring'''

												yield self.read(n=4 * 1024 * 1024     )





							def __exit__( self:  Optional[Any]					,		*__A:  Union[str, Any]     ):

												'''simple docstring'''

												self.f.close()







class 							_SCREAMING_SNAKE_CASE    :





							"""simple docstring"""





							def __init__( self:  List[Any]					,		__A:  Optional[int]     ):

												'''simple docstring'''

												a__ 							=	args





							def 			lowercase		( self:  Optional[int]     ):

												'''simple docstring'''

												a__ 							=	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:
																	a__ 							=	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     )

																	a__ 							=	msg['''oid''']
																	a__ 							=	msg['''path''']
																	a__ 							=	msg['''action''']['''href''']
																	a__ 							=	msg['''action''']['''header''']
																	a__ 							=	int(header.pop('''chunk_size'''     )     )
																	a__ 							=	list(header.values()     )

																	a__ 							=	[]
																	for i, presigned_url in enumerate(__A     ):
																						with FileSlice(__A					,		seek_from=i * chunk_size					,		read_limit=__A     ) as data:
																											a__ 							=	requests.put(__A					,		data=__A     )
																											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.

																	a__ 							=	requests.post(
																	    __A					,		json={
																	        '''oid''': oid,
																	        '''parts''': parts,
																	    }					,		)
																	r.raise_for_status()

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










"""simple docstring"""







import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, slow, torch_device

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin







class 							_SCREAMING_SNAKE_CASE    (	__snake_case							,			__snake_case							,			unittest.TestCase				):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE     =UNetaDModel
							_SCREAMING_SNAKE_CASE     ='sample'




							@property
							def 			lowercase		( self:  Any     ):

												'''simple docstring'''

												a__ 							=	4
												a__ 							=	14
												a__ 							=	16

												a__ 							=	floats_tensor((batch_size, num_features, seq_len)     ).to(__A     )
												a__ 							=	torch.tensor([10] * batch_size     ).to(__A     )

												return {"sample": noise, "timestep": time_step}




							@property
							def 			lowercase		( self:  List[Any]     ):

												'''simple docstring'''

												return (4, 14, 16)




							@property
							def 			lowercase		( self:  List[Any]     ):

												'''simple docstring'''

												return (4, 14, 16)




							def 			lowercase		( self:  Optional[Any]     ):

												'''simple docstring'''

												pass




							def 			lowercase		( self:  Any     ):

												'''simple docstring'''

												pass




							@unittest.skipIf(torch_device == '''mps'''					,		'''mish op not supported in MPS'''     )
							def 			lowercase		( self:  Optional[int]     ):

												'''simple docstring'''

												super().test_determinism()




							@unittest.skipIf(torch_device == '''mps'''					,		'''mish op not supported in MPS'''     )
							def 			lowercase		( self:  Optional[Any]     ):

												'''simple docstring'''

												super().test_outputs_equivalence()




							@unittest.skipIf(torch_device == '''mps'''					,		'''mish op not supported in MPS'''     )
							def 			lowercase		( self:  Tuple     ):

												'''simple docstring'''

												super().test_from_save_pretrained()




							@unittest.skipIf(torch_device == '''mps'''					,		'''mish op not supported in MPS'''     )
							def 			lowercase		( self:  int     ):

												'''simple docstring'''

												super().test_from_save_pretrained_variant()




							@unittest.skipIf(torch_device == '''mps'''					,		'''mish op not supported in MPS'''     )
							def 			lowercase		( self:  int     ):

												'''simple docstring'''

												super().test_model_from_pretrained()




							@unittest.skipIf(torch_device == '''mps'''					,		'''mish op not supported in MPS'''     )
							def 			lowercase		( self:  Tuple     ):

												'''simple docstring'''

												super().test_output()




							def 			lowercase		( self:  Optional[Any]     ):

												'''simple docstring'''

												a__ 							=	{
												    '''block_out_channels''': (32, 64, 128, 256),
												    '''in_channels''': 14,
												    '''out_channels''': 14,
												    '''time_embedding_type''': '''positional''',
												    '''use_timestep_embedding''': True,
												    '''flip_sin_to_cos''': False,
												    '''freq_shift''': 1.0,
												    '''out_block_type''': '''OutConv1DBlock''',
												    '''mid_block_type''': '''MidResTemporalBlock1D''',
												    '''down_block_types''': ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D'''),
												    '''up_block_types''': ('''UpResnetBlock1D''', '''UpResnetBlock1D''', '''UpResnetBlock1D'''),
												    '''act_fn''': '''mish''',
												}
												a__ 							=	self.dummy_input
												return init_dict, inputs_dict




							@unittest.skipIf(torch_device == '''mps'''					,		'''mish op not supported in MPS'''     )
							def 			lowercase		( self:  str     ):

												'''simple docstring'''

												a__							,a__ 							=	UNetaDModel.from_pretrained(
												    '''bglick13/hopper-medium-v2-value-function-hor32'''					,		output_loading_info=__A					,		subfolder='''unet'''     )
												self.assertIsNotNone(__A     )
												self.assertEqual(len(loading_info['''missing_keys''']     )					,		0     )

												model.to(__A     )
												a__ 							=	model(**self.dummy_input     )

												assert image is not None, "Make sure output is not None"




							@unittest.skipIf(torch_device == '''mps'''					,		'''mish op not supported in MPS'''     )
							def 			lowercase		( self:  str     ):

												'''simple docstring'''

												a__ 							=	UNetaDModel.from_pretrained('''bglick13/hopper-medium-v2-value-function-hor32'''					,		subfolder='''unet'''     )
												torch.manual_seed(0     )
												if torch.cuda.is_available():
																	torch.cuda.manual_seed_all(0     )

												a__ 							=	model.config.in_channels
												a__ 							=	16
												a__ 							=	torch.randn((1, seq_len, num_features)     ).permute(
												    0					,		2					,		1     )  # match original, we can update values and remove
												a__ 							=	torch.full((num_features,)					,		0     )

												with torch.no_grad():
																	a__ 							=	model(__A					,		__A     ).sample.permute(0					,		2					,		1     )

												a__ 							=	output[0, -3:, -3:].flatten()
												# fmt: off
												a__ 							=	torch.tensor([-2.1_3_7_1_7_2, 1.1_4_2_6_0_1_6, 0.3_6_8_8_6_8_7, -0.7_6_6_9_2_2, 0.7_3_0_3_1_4_6, 0.1_1_0_3_8_8_6_4, -0.4_7_6_0_6_3_3, 0.1_3_2_7_0_1_7_2, 0.0_2_5_9_1_3_4_8]     )
												# fmt: on
												self.assertTrue(torch.allclose(__A					,		__A					,		rtol=1e-3     )     )




							def 			lowercase		( self:  int     ):

												'''simple docstring'''

												pass





							@slow
							def 			lowercase		( self:  Any     ):

												'''simple docstring'''

												a__ 							=	'''harmonai/maestro-150k'''
												a__ 							=	UNetaDModel.from_pretrained(__A					,		subfolder='''unet'''     )
												model.to(__A     )

												a__ 							=	65536
												a__ 							=	torch.sin(torch.arange(__A     )[None, None, :].repeat(1					,		2					,		1     )     ).to(__A     )
												a__ 							=	torch.tensor([1]     ).to(__A     )

												with torch.no_grad():
																	a__ 							=	model(__A					,		__A     ).sample

												a__ 							=	output.abs().sum()
												a__ 							=	output.abs().max()

												assert (output_sum - 2_2_4.0_8_9_6).abs() < 0.5
												assert (output_max - 0.0_6_0_7).abs() < 4e-4







class 							_SCREAMING_SNAKE_CASE    (	__snake_case							,			__snake_case							,			unittest.TestCase				):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE     =UNetaDModel
							_SCREAMING_SNAKE_CASE     ='sample'




							@property
							def 			lowercase		( self:  List[str]     ):

												'''simple docstring'''

												a__ 							=	4
												a__ 							=	14
												a__ 							=	16

												a__ 							=	floats_tensor((batch_size, num_features, seq_len)     ).to(__A     )
												a__ 							=	torch.tensor([10] * batch_size     ).to(__A     )

												return {"sample": noise, "timestep": time_step}




							@property
							def 			lowercase		( self:  Dict     ):

												'''simple docstring'''

												return (4, 14, 16)




							@property
							def 			lowercase		( self:  int     ):

												'''simple docstring'''

												return (4, 14, 1)




							@unittest.skipIf(torch_device == '''mps'''					,		'''mish op not supported in MPS'''     )
							def 			lowercase		( self:  int     ):

												'''simple docstring'''

												super().test_determinism()




							@unittest.skipIf(torch_device == '''mps'''					,		'''mish op not supported in MPS'''     )
							def 			lowercase		( self:  str     ):

												'''simple docstring'''

												super().test_outputs_equivalence()




							@unittest.skipIf(torch_device == '''mps'''					,		'''mish op not supported in MPS'''     )
							def 			lowercase		( self:  Optional[Any]     ):

												'''simple docstring'''

												super().test_from_save_pretrained()




							@unittest.skipIf(torch_device == '''mps'''					,		'''mish op not supported in MPS'''     )
							def 			lowercase		( self:  List[str]     ):

												'''simple docstring'''

												super().test_from_save_pretrained_variant()




							@unittest.skipIf(torch_device == '''mps'''					,		'''mish op not supported in MPS'''     )
							def 			lowercase		( self:  Optional[int]     ):

												'''simple docstring'''

												super().test_model_from_pretrained()




							@unittest.skipIf(torch_device == '''mps'''					,		'''mish op not supported in MPS'''     )
							def 			lowercase		( self:  int     ):

												'''simple docstring'''

												a__							,a__ 							=	self.prepare_init_args_and_inputs_for_common()
												a__ 							=	self.model_class(**__A     )
												model.to(__A     )
												model.eval()

												with torch.no_grad():
																	a__ 							=	model(**__A     )

																	if isinstance(__A					,		__A     ):
																						a__ 							=	output.sample

												self.assertIsNotNone(__A     )
												a__ 							=	torch.Size((inputs_dict['''sample'''].shape[0], 1)     )
												self.assertEqual(output.shape					,		__A					,		'''Input and output shapes do not match'''     )




							def 			lowercase		( self:  Dict     ):

												'''simple docstring'''

												pass




							def 			lowercase		( self:  Dict     ):

												'''simple docstring'''

												pass




							def 			lowercase		( self:  Any     ):

												'''simple docstring'''

												a__ 							=	{
												    '''in_channels''': 14,
												    '''out_channels''': 14,
												    '''down_block_types''': ['''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D'''],
												    '''up_block_types''': [],
												    '''out_block_type''': '''ValueFunction''',
												    '''mid_block_type''': '''ValueFunctionMidBlock1D''',
												    '''block_out_channels''': [32, 64, 128, 256],
												    '''layers_per_block''': 1,
												    '''downsample_each_block''': True,
												    '''use_timestep_embedding''': True,
												    '''freq_shift''': 1.0,
												    '''flip_sin_to_cos''': False,
												    '''time_embedding_type''': '''positional''',
												    '''act_fn''': '''mish''',
												}
												a__ 							=	self.dummy_input
												return init_dict, inputs_dict




							@unittest.skipIf(torch_device == '''mps'''					,		'''mish op not supported in MPS'''     )
							def 			lowercase		( self:  List[Any]     ):

												'''simple docstring'''

												a__							,a__ 							=	UNetaDModel.from_pretrained(
												    '''bglick13/hopper-medium-v2-value-function-hor32'''					,		output_loading_info=__A					,		subfolder='''value_function'''     )
												self.assertIsNotNone(__A     )
												self.assertEqual(len(vf_loading_info['''missing_keys''']     )					,		0     )

												value_function.to(__A     )
												a__ 							=	value_function(**self.dummy_input     )

												assert image is not None, "Make sure output is not None"




							@unittest.skipIf(torch_device == '''mps'''					,		'''mish op not supported in MPS'''     )
							def 			lowercase		( self:  Union[str, Any]     ):

												'''simple docstring'''

												a__							,a__ 							=	UNetaDModel.from_pretrained(
												    '''bglick13/hopper-medium-v2-value-function-hor32'''					,		output_loading_info=__A					,		subfolder='''value_function'''     )
												torch.manual_seed(0     )
												if torch.cuda.is_available():
																	torch.cuda.manual_seed_all(0     )

												a__ 							=	value_function.config.in_channels
												a__ 							=	14
												a__ 							=	torch.randn((1, seq_len, num_features)     ).permute(
												    0					,		2					,		1     )  # match original, we can update values and remove
												a__ 							=	torch.full((num_features,)					,		0     )

												with torch.no_grad():
																	a__ 							=	value_function(__A					,		__A     ).sample

												# fmt: off
												a__ 							=	torch.tensor([1_6_5.2_5] * seq_len     )
												# fmt: on
												self.assertTrue(torch.allclose(__A					,		__A					,		rtol=1e-3     )     )





							def 			lowercase		( self:  List[str]     ):

												'''simple docstring'''

												pass


