



'''simple docstring'''




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							(      __UpperCAmelCase			):
	__SCREAMING_SNAKE_CASE       :		Optional[Any]		      = ''
	__SCREAMING_SNAKE_CASE       :		Union[str, Any]		      = 'hf-legacy'  # "hf://"" is reserved for hffs


	def __init__(self	,				lowercase = None	,				lowercase = None	,				**lowercase	,				):
				super().__init__(self	,				**lowercase							)
				A_     :						int     =					repo_info
				A_     :						Union[str, Any]     =					token
				A_     :						Union[str, Any]     =					None


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


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


	def    _a     (self	,				lowercase	,				**lowercase							):
				self._get_dirs()
				A_     :						Optional[Any]     =					self._strip_protocol(lowercase							)
				if path in self.dir_cache:
							return self.dir_cache[path]
				else:
							raise FileNotFoundError(lowercase							)


	def    _a     (self	,				lowercase	,				lowercase=False	,				**lowercase							):
				self._get_dirs()
				A_     :						Optional[int]     =					PurePosixPath(path.strip("""/"""							)							)
				A_     :						Any     =					{}
				for p, f in self.dir_cache.items():
							A_     :						str     =					PurePosixPath(p.strip("""/"""							)							)
							A_     :						List[Any]     =					p.parent
							if root == path:
										A_     :						Optional[Any]     =					f
				A_     :						Any     =					list(paths.values()							)
				if detail:
							return out
				else:
							return sorted(f["""name"""] for f in out							)




'''simple docstring'''




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							:


	@property
	def    _a     (self							):
				return self.get_dummy_input()


	@property
	def    _a     (self							):
				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    _a     (self	,				lowercase=True	,				lowercase=False	,				lowercase=False	,				lowercase=False	,				):
				A_     :						List[str]     =					4
				A_     :						int     =					32
				A_     :						Optional[int]     =					(32, 32)

				A_     :						Optional[Any]     =					torch.manual_seed(0							)
				A_     :						int     =					torch.device(lowercase							)
				A_     :						int     =					(batch_size, num_channels) + sizes
				A_     :						Optional[int]     =					randn_tensor(lowercase	,				generator=lowercase	,				device=lowercase							)
				A_     :						Union[str, Any]     =					{"""hidden_states""": hidden_states}

				if include_temb:
							A_     :						str     =					128
							A_     :						List[Any]     =					randn_tensor((batch_size, temb_channels)	,				generator=lowercase	,				device=lowercase							)

				if include_res_hidden_states_tuple:
							A_     :						List[str]     =					torch.manual_seed(1							)
							A_     :						int     =					(randn_tensor(lowercase	,				generator=lowercase	,				device=lowercase							),)

				if include_encoder_hidden_states:
							A_     :						List[str]     =					floats_tensor((batch_size, 32, 32)							).to(lowercase							)

				if include_skip_sample:
							A_     :						Dict     =					randn_tensor(((batch_size, 3) + sizes)	,				generator=lowercase	,				device=lowercase							)

				return dummy_input


	def    _a     (self							):
				A_     :						Tuple     =					{
				    """in_channels""": 32,
				    """out_channels""": 32,
				    """temb_channels""": 128,
				}
				if self.block_type == "up":
							A_     :						Any     =					32

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

				A_     :						Optional[int]     =					self.dummy_input
				return init_dict, inputs_dict


	def    _a     (self	,				lowercase							):
				A_,   A_     :						Optional[Any]     =					self.prepare_init_args_and_inputs_for_common()
				A_     :						int     =					self.block_class(**lowercase							)
				unet_block.to(lowercase							)
				unet_block.eval()

				with torch.no_grad():
							A_     :						List[str]     =					unet_block(**lowercase							)

				if isinstance(lowercase	,				lowercase							):
							A_     :						Union[str, Any]     =					output[0]

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

				A_     :						int     =					output[0, -1, -3:, -3:]
				A_     :						List[Any]     =					torch.tensor(lowercase							).to(lowercase							)
				assert torch_all_close(output_slice.flatten()	,				lowercase	,				atol=5E-3							)


	@unittest.skipIf(torch_device == """mps"""	,				"""Training is not supported in mps"""							)
	def    _a     (self							):
				A_,   A_     :						Tuple     =					self.prepare_init_args_and_inputs_for_common()
				A_     :						List[str]     =					self.block_class(**lowercase							)
				model.to(lowercase							)
				model.train()
				A_     :						Any     =					model(**lowercase							)

				if isinstance(lowercase	,				lowercase							):
							A_     :						str     =					output[0]

				A_     :						Union[str, Any]     =					torch.device(lowercase							)
				A_     :						Tuple     =					randn_tensor(output.shape	,				device=lowercase							)
				A_     :						List[str]     =					torch.nn.functional.mse_loss(lowercase	,				lowercase							)
				loss.backward()