code: 
  
  
  '''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			:		Union[str, Any] =			logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  
  __lowercase			:		str =			'lfs-multipart-upload'
  
  
  
  
  
  class 							__UpperCamelCase       (	lowerCAmelCase__      ):
  
  
  
  
   @staticmethod
   def    __UpperCAmelCase							(     __a							):
  
        '''simple docstring'''
  
  
  
  
  
        __a    : List[str]      					=						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 __a							: LfsEnableCommand(_lowerCamelCase							)							)
  
        __a    : 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 __a							: LfsUploadCommand(_lowerCamelCase							)							)
  
  
  
  
  
  class 							__UpperCamelCase       :
  
  
  
  
   def __init__(     self		,       __a							):
  
        '''simple docstring'''
  
  
  
  
  
        __a    : Dict      					=						args
  
  
  
  
  
   def    __UpperCAmelCase							(     self							):
  
        '''simple docstring'''
  
  
  
  
  
        warnings.warn(
            'Managing repositories through transformers-cli is deprecated. Please use `huggingface-cli` instead.'							)
        __a    : Dict      					=						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 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     List[str]     ):
       __a    : Optional[int]      					=						json.dumps(_SCREAMING_SNAKE_CASE     ) + """\n"""
       sys.stdout.write(_SCREAMING_SNAKE_CASE     )
       sys.stdout.flush()
  
  
  
  
  
  
  def 						lowerCamelCase							():
       __a    : 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 							__UpperCamelCase       (	lowerCAmelCase__      ):
  
  
  
  
   def __init__(     self		,       __a		,       __a		,       __a							):
  
        '''simple docstring'''
  
  
  
  
  
        __a    : Union[str, Any]      					=						filepath
        __a    : str      					=						seek_from
        __a    : List[str]      					=						read_limit
        __a    : List[Any]      					=						0
  
  
  
  
   def __enter__(     self							):
  
        '''simple docstring'''
  
  
  
  
  
        __a    : Tuple      					=						open(self.filepath		,       'rb'							)
        self.f.seek(self.seek_from							)
        return self
  
  
  
  
   def __len__(     self							):
  
        '''simple docstring'''
  
  
  
  
  
        __a    : Union[str, Any]      					=						os.fstat(self.f.fileno()							).st_size
        return min(self.read_limit		,       total_length - self.seek_from							)
  
  
  
  
   def    __UpperCAmelCase							(     self		,       __a=-1							):
  
        '''simple docstring'''
  
  
  
  
  
        if self.n_seen >= self.read_limit:
             return b""
        __a    : List[Any]      					=						self.read_limit - self.n_seen
        __a    : Optional[Any]      					=						self.f.read(remaining_amount if n < 0 else min(_lowerCamelCase		,       _lowerCamelCase							)							)
        self.n_seen += len(_lowerCamelCase							)
        return data
  
  
  
  
   def __iter__(     self							):
  
        '''simple docstring'''
  
  
  
  
  
        yield self.read(n=4 * 1024 * 1024							)
  
  
  
  
  
   def __exit__(     self		,       *__a							):
  
        '''simple docstring'''
  
  
  
  
  
        self.f.close()
  
  
  
  
  
  class 							__UpperCamelCase       :
  
  
  
  
   def __init__(     self		,       __a							):
  
        '''simple docstring'''
  
  
  
  
  
        __a    : str      					=						args
  
  
  
  
  
   def    __UpperCAmelCase							(     self							):
  
        '''simple docstring'''
  
  
  
  
  
        __a    : Tuple      					=						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    : List[str]      					=						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    : List[str]      					=						msg["""oid"""]
             __a    : Dict      					=						msg["""path"""]
             __a    : Dict      					=						msg["""action"""]["""href"""]
             __a    : List[Any]      					=						msg["""action"""]["""header"""]
             __a    : str      					=						int(header.pop('chunk_size'							)							)
             __a    : List[str]      					=						list(header.values()							)
  
             __a    : Tuple      					=						[]
             for i, presigned_url in enumerate(_lowerCamelCase							):
                  with FileSlice(_lowerCamelCase		,       seek_from=i * chunk_size		,       read_limit=_lowerCamelCase							) as data:
                       __a    : List[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.
  
             __a    : List[Any]      					=						requests.post(
                 _lowerCamelCase		,       json={
                     'oid': oid,
                     'parts': parts,
                 }		,       )
             r.raise_for_status()
  
             write_msg({'event': 'complete', 'oid': oid}							)
  
  
  
  
style_context: 
  
  
  '''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 							__UpperCamelCase       (	lowerCAmelCase_	,   lowerCAmelCase_	,   unittest.TestCase      ):
  	A_			    =					UNetaDModel
  	A_			    =					"sample"
  
  
  
  
  	@property
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : List[str]      					=						4
  						__a    : str      					=						14
  						__a    : int      					=						16
  
  						__a    : List[str]      					=						floats_tensor((batch_size, num_features, seq_len)							).to(__a							)
  						__a    : Optional[Any]      					=						torch.tensor([10] * batch_size							).to(__a							)
  
  						return {"sample": noise, "timestep": time_step}
  
  
  
  
  	@property
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						return (4, 14, 16)
  
  
  
  
  	@property
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						return (4, 14, 16)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						pass
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						pass
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						super().test_determinism()
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						super().test_outputs_equivalence()
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						super().test_from_save_pretrained()
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						super().test_from_save_pretrained_variant()
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						super().test_model_from_pretrained()
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						super().test_output()
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Union[str, Any]      					=						{
  						    '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    : str      					=						self.dummy_input
  						return init_dict, inputs_dict
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a					,       __a    : List[str]      					=						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    : Optional[int]      					=						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    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : List[Any]      					=						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    : str      					=						model.config.in_channels
  						__a    : int      					=						16
  						__a    : List[str]      					=						torch.randn((1, seq_len, num_features)							).permute(
  						    0		,       2		,       1							)  # match original, we can update values and remove
  						__a    : List[Any]      					=						torch.full((num_features,)		,       0							)
  
  						with torch.no_grad():
  											__a    : Tuple      					=						model(__a		,       __a							).sample.permute(0		,       2		,       1							)
  
  						__a    : int      					=						output[0, -3:, -3:].flatten()
  						# fmt: off
  						__a    : List[str]      					=						torch.tensor([-2.137172, 1.1426016, 0.3688687, -0.766922, 0.7303146, 0.11038864, -0.4760633, 0.13270172, 0.02591348]							)
  						# fmt: on
  						self.assertTrue(torch.allclose(__a		,       __a		,       rtol=1E-3							)							)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						pass
  
  
  
  
  
  	@slow
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Tuple      					=						'harmonai/maestro-150k'
  						__a    : Any      					=						UNetaDModel.from_pretrained(__a		,       subfolder='unet'							)
  						model.to(__a							)
  
  						__a    : Optional[Any]      					=						6_5536
  						__a    : str      					=						torch.sin(torch.arange(__a							)[None, None, :].repeat(1		,       2		,       1							)							).to(__a							)
  						__a    : int      					=						torch.tensor([1]							).to(__a							)
  
  						with torch.no_grad():
  											__a    : Optional[int]      					=						model(__a		,       __a							).sample
  
  						__a    : Union[str, Any]      					=						output.abs().sum()
  						__a    : Dict      					=						output.abs().max()
  
  						assert (output_sum - 224.0896).abs() < 0.5
  						assert (output_max - 0.0607).abs() < 4E-4
  
  
  
  
  
  class 							__UpperCamelCase       (	lowerCAmelCase_	,   lowerCAmelCase_	,   unittest.TestCase      ):
  	A_			    =					UNetaDModel
  	A_			    =					"sample"
  
  
  
  
  	@property
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : str      					=						4
  						__a    : str      					=						14
  						__a    : Optional[int]      					=						16
  
  						__a    : str      					=						floats_tensor((batch_size, num_features, seq_len)							).to(__a							)
  						__a    : Optional[Any]      					=						torch.tensor([10] * batch_size							).to(__a							)
  
  						return {"sample": noise, "timestep": time_step}
  
  
  
  
  	@property
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						return (4, 14, 16)
  
  
  
  
  	@property
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						return (4, 14, 1)
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						super().test_determinism()
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						super().test_outputs_equivalence()
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						super().test_from_save_pretrained()
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						super().test_from_save_pretrained_variant()
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						super().test_model_from_pretrained()
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a					,       __a    : List[Any]      					=						self.prepare_init_args_and_inputs_for_common()
  						__a    : int      					=						self.model_class(**__a							)
  						model.to(__a							)
  						model.eval()
  
  						with torch.no_grad():
  											__a    : Optional[Any]      					=						model(**__a							)
  
  											if isinstance(__a		,       __a							):
  																__a    : Any      					=						output.sample
  
  						self.assertIsNotNone(__a							)
  						__a    : Tuple      					=						torch.Size((inputs_dict['sample'].shape[0], 1)							)
  						self.assertEqual(output.shape		,       __a		,       'Input and output shapes do not match'							)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						pass
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						pass
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : int      					=						{
  						    '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    : Union[str, Any]      					=						self.dummy_input
  						return init_dict, inputs_dict
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a					,       __a    : List[str]      					=						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    : Optional[Any]      					=						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    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a					,       __a    : Dict      					=						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    : str      					=						value_function.config.in_channels
  						__a    : Dict      					=						14
  						__a    : List[str]      					=						torch.randn((1, seq_len, num_features)							).permute(
  						    0		,       2		,       1							)  # match original, we can update values and remove
  						__a    : List[Any]      					=						torch.full((num_features,)		,       0							)
  
  						with torch.no_grad():
  											__a    : Any      					=						value_function(__a		,       __a							).sample
  
  						# fmt: off
  						__a    : str      					=						torch.tensor([165.25] * seq_len							)
  						# fmt: on
  						self.assertTrue(torch.allclose(__a		,       __a		,       rtol=1E-3							)							)
  
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						pass
  
  
  
  