


from pathlib import PurePosixPath
from typing import Optional

import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo

from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url



class 	__lowerCamelCase			(_a						):
       _lowercase											=     """"""
       _lowercase											=     """hf-legacy"""  # "hf://"" is reserved for hffs







       def __init__(			self: Tuple,A_: Optional[DatasetInfo] = None,A_: Optional[str] = None,**A_: List[str],):






            '''simple docstring'''




            super().__init__(self,**A_					)
            __UpperCamelCase =			repo_info
            __UpperCamelCase =			token
            __UpperCamelCase =			None







       def 		snake_case_	(			self: Union[str, Any]					):






            '''simple docstring'''




            if self.dir_cache is None:
                 __UpperCamelCase =			{}
                 for hf_file in self.repo_info.siblings:
                      # TODO(QL): add sizes
                      __UpperCamelCase =			{
                          'name': hf_file.rfilename,
                          'size': None,
                          'type': 'file',
                      }
                      self.dir_cache.update(
                          {
                              str(A_					): {'name': str(A_					), 'size': None, 'type': 'directory'}
                              for d in list(PurePosixPath(hf_file.rfilename					).parents					)[:-1]
                          }					)







       def 		snake_case_	(			self: Any,A_: str,A_: str = "rb",**A_: str,):






            '''simple docstring'''




            if not isinstance(self.repo_info,A_					):
                 raise NotImplementedError(F'''Open is only implemented for dataset repositories, but got {self.repo_info}'''					)
            __UpperCamelCase =			hf_hub_url(self.repo_info.id,A_,revision=self.repo_info.sha					)
            return fsspec.open(
                A_,mode=A_,headers=get_authentication_headers_for_url(A_,use_auth_token=self.token					),client_kwargs={'trust_env': True},).open()







       def 		snake_case_	(			self: int,A_: Optional[int],**A_: Any					):






            '''simple docstring'''




            self._get_dirs()
            __UpperCamelCase =			self._strip_protocol(A_					)
            if path in self.dir_cache:
                 return self.dir_cache[path]
            else:
                 raise FileNotFoundError(A_					)







       def 		snake_case_	(			self: List[Any],A_: Union[str, Any],A_: Any=False,**A_: Any					):






            '''simple docstring'''




            self._get_dirs()
            __UpperCamelCase =			PurePosixPath(path.strip('/'					)					)
            __UpperCamelCase =			{}
            for p, f in self.dir_cache.items():
                 __UpperCamelCase =			PurePosixPath(p.strip('/'					)					)
                 __UpperCamelCase =			p.parent
                 if root == path:
                      __UpperCamelCase =			f
            __UpperCamelCase =			list(paths.values()					)
            if detail:
                 return out
            else:
                 return sorted(f['name'] for f in out					)








import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch





@require_torch
class 			lowerCamelCase_				:


 '''simple docstring'''







 @property
 def  lowerCAmelCase_    (				self    :						Optional[Any]		):
       return self.get_dummy_input()



 @property
 def  lowerCAmelCase_    (				self    :						Union[str, Any]		):
       if self.block_type == "down":
             return (4, 32, 16, 16)
       elif self.block_type == "mid":
             return (4, 32, 32, 32)
       elif self.block_type == "up":
             return (4, 32, 64, 64)

       raise ValueError(F"'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'."		)



 def  lowerCAmelCase_    (				self    :						List[str]							,							_lowerCAmelCase    :						Optional[Any]=True							,							_lowerCAmelCase    :						str=False							,							_lowerCAmelCase    :						Optional[int]=False							,							_lowerCAmelCase    :						Dict=False							,							):
       SCREAMING_SNAKE_CASE_      		=     4
       SCREAMING_SNAKE_CASE_      		=     32
       SCREAMING_SNAKE_CASE_      		=     (32, 32)

       SCREAMING_SNAKE_CASE_      		=     torch.manual_seed(0		)
       SCREAMING_SNAKE_CASE_      		=     torch.device(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     (batch_size, num_channels) + sizes
       SCREAMING_SNAKE_CASE_      		=     randn_tensor(_lowerCAmelCase							,							generator=_lowerCAmelCase							,							device=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     {'hidden_states': hidden_states}

       if include_temb:
             SCREAMING_SNAKE_CASE_      		=     128
             SCREAMING_SNAKE_CASE_      		=     randn_tensor((batch_size, temb_channels)							,							generator=_lowerCAmelCase							,							device=_lowerCAmelCase		)

       if include_res_hidden_states_tuple:
             SCREAMING_SNAKE_CASE_      		=     torch.manual_seed(1		)
             SCREAMING_SNAKE_CASE_      		=     (randn_tensor(_lowerCAmelCase							,							generator=_lowerCAmelCase							,							device=_lowerCAmelCase		),)

       if include_encoder_hidden_states:
             SCREAMING_SNAKE_CASE_      		=     floats_tensor((batch_size, 32, 32)		).to(_lowerCAmelCase		)

       if include_skip_sample:
             SCREAMING_SNAKE_CASE_      		=     randn_tensor(((batch_size, 3) + sizes)							,							generator=_lowerCAmelCase							,							device=_lowerCAmelCase		)

       return dummy_input



 def  lowerCAmelCase_    (				self    :						Optional[int]		):
       SCREAMING_SNAKE_CASE_      		=     {
           'in_channels': 32,
           'out_channels': 32,
           'temb_channels': 128,
       }
       if self.block_type == "up":
             SCREAMING_SNAKE_CASE_      		=     32

       if self.block_type == "mid":
             init_dict.pop('out_channels'		)

       SCREAMING_SNAKE_CASE_      		=     self.dummy_input
       return init_dict, inputs_dict



 def  lowerCAmelCase_    (				self    :						Dict							,							_lowerCAmelCase    :						Optional[Any]		):
       SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     self.prepare_init_args_and_inputs_for_common()
       SCREAMING_SNAKE_CASE_      		=     self.block_class(**_lowerCAmelCase		)
       unet_block.to(_lowerCAmelCase		)
       unet_block.eval()

       with torch.no_grad():
             SCREAMING_SNAKE_CASE_      		=     unet_block(**_lowerCAmelCase		)

       if isinstance(_lowerCAmelCase							,							_lowerCAmelCase		):
             SCREAMING_SNAKE_CASE_      		=     output[0]

       self.assertEqual(output.shape							,							self.output_shape		)

       SCREAMING_SNAKE_CASE_      		=     output[0, -1, -3:, -3:]
       SCREAMING_SNAKE_CASE_      		=     torch.tensor(_lowerCAmelCase		).to(_lowerCAmelCase		)
       assert torch_all_close(output_slice.flatten()							,							_lowerCAmelCase							,							atol=5E-3		)




 @unittest.skipIf(torch_device == 'mps'							,							'Training is not supported in mps'		)
 def  lowerCAmelCase_    (				self    :						Any		):
       SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     self.prepare_init_args_and_inputs_for_common()
       SCREAMING_SNAKE_CASE_      		=     self.block_class(**_lowerCAmelCase		)
       model.to(_lowerCAmelCase		)
       model.train()
       SCREAMING_SNAKE_CASE_      		=     model(**_lowerCAmelCase		)

       if isinstance(_lowerCAmelCase							,							_lowerCAmelCase		):
             SCREAMING_SNAKE_CASE_      		=     output[0]

       SCREAMING_SNAKE_CASE_      		=     torch.device(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     randn_tensor(output.shape							,							device=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     torch.nn.functional.mse_loss(_lowerCAmelCase							,							_lowerCAmelCase		)
       loss.backward()