


'''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}							)







'''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



