





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 SCREAMING_SNAKE_CASE_  (   snake_case_   ):
						__magic_name__:     int	 =    ""
						__magic_name__:     int	 =    "hf-legacy"  # "hf://"" is reserved for hffs






						def __init__( self : str						,				_A : Optional[DatasetInfo] = None						,				_A : Optional[str] = None						,				**_A : Optional[Any]						,				)							->      Tuple:







							"""simple docstring"""


							super().__init__(self						,				**_A		)
							snake_case_						:     List[Any]     =		repo_info
							snake_case_						:     Tuple     =		token
							snake_case_						:     Any     =		None






						def UpperCAmelCase_			( self : List[str]		)							->      List[str]:







							"""simple docstring"""


							if self.dir_cache is None:
								snake_case_						:     Optional[int]     =		{}
								for hf_file in self.repo_info.siblings:
									# TODO(QL): add sizes
									snake_case_						:     Optional[Any]     =		{
									    '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 UpperCAmelCase_			( self : Union[str, Any]						,				_A : str						,				_A : str = "rb"						,				**_A : Tuple						,				)							->      Dict:







							"""simple docstring"""


							if not isinstance(self.repo_info						,				_A		):
								raise NotImplementedError(F"""Open is only implemented for dataset repositories, but got {self.repo_info}"""		)
							snake_case_						:     List[str]     =		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 UpperCAmelCase_			( self : int						,				_A : Tuple						,				**_A : str		)							->      List[str]:







							"""simple docstring"""


							self._get_dirs()
							snake_case_						:     Optional[Any]     =		self._strip_protocol(_A		)
							if path in self.dir_cache:
								return self.dir_cache[path]
							else:
								raise FileNotFoundError(_A		)






						def UpperCAmelCase_			( self : Dict						,				_A : List[str]						,				_A : Union[str, Any]=False						,				**_A : Any		)							->      Tuple:







							"""simple docstring"""


							self._get_dirs()
							snake_case_						:     Dict     =		PurePosixPath(path.strip('/'		)		)
							snake_case_						:     Union[str, Any]     =		{}
							for p, f in self.dir_cache.items():
								snake_case_						:     Optional[int]     =		PurePosixPath(p.strip('/'		)		)
								snake_case_						:     Optional[int]     =		p.parent
								if root == path:
									snake_case_						:     Tuple     =		f
							snake_case_						:     Any     =		list(paths.values()		)
							if detail:
								return out
							else:
								return sorted(f['name'] for f in out		)








import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class SCREAMING_SNAKE_CASE_  (   snake_case_    ,			snake_case_   ):






						@register_to_config
						def __init__( self : Optional[Any]						,				*,
						    _A : int = 4						,				_A : int = 768						,				_A : int						,				_A : Tuple						,				)							->      Dict:







							"""simple docstring"""


							super().__init__()

							snake_case_						:     int     =		nn.Parameter(torch.zeros(_A		)		)

							# parameters for additional clip time embeddings
							snake_case_						:     Tuple     =		nn.Linear(_A						,				_A		)
							snake_case_						:     List[Any]     =		nn.Linear(_A						,				_A		)

							# parameters for encoder hidden states
							snake_case_						:     Union[str, Any]     =		clip_extra_context_tokens
							snake_case_						:     str     =		nn.Linear(
							    _A						,				self.clip_extra_context_tokens * cross_attention_dim		)
							snake_case_						:     Any     =		nn.Linear(_A						,				_A		)
							snake_case_						:     Tuple     =		nn.LayerNorm(_A		)






						def UpperCAmelCase_			( self : List[str]						,				*, _A : Tuple						,				_A : List[Any]						,				_A : str						,				_A : Optional[Any]		)							->      List[Any]:







							"""simple docstring"""


							if do_classifier_free_guidance:
								# Add the classifier free guidance embeddings to the image embeddings
								snake_case_						:     Optional[int]     =		image_embeddings.shape[0]
								snake_case_						:     Optional[Any]     =		self.learned_classifier_free_guidance_embeddings.unsqueeze(0		)
								snake_case_						:     Optional[Any]     =		classifier_free_guidance_embeddings.expand(
								    _A						,				-1		)
								snake_case_						:     Any     =		torch.cat([classifier_free_guidance_embeddings, image_embeddings]						,				dim=0		)

							# The image embeddings batch size and the text embeddings batch size are equal
							assert image_embeddings.shape[0] == prompt_embeds.shape[0]

							snake_case_						:     str     =		prompt_embeds.shape[0]

							# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
							# adding CLIP embeddings to the existing timestep embedding, ...
							snake_case_						:     str     =		self.embedding_proj(_A		)
							snake_case_						:     Dict     =		self.clip_image_embeddings_project_to_time_embeddings(_A		)
							snake_case_						:     Tuple     =		time_projected_image_embeddings + time_projected_prompt_embeds

							# ... and by projecting CLIP embeddings into four
							# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
							snake_case_						:     List[str]     =		self.clip_extra_context_tokens_proj(_A		)
							snake_case_						:     Optional[Any]     =		clip_extra_context_tokens.reshape(_A						,				-1						,				self.clip_extra_context_tokens		)
							snake_case_						:     int     =		clip_extra_context_tokens.permute(0						,				2						,				1		)

							snake_case_						:     Optional[int]     =		self.encoder_hidden_states_proj(_A		)
							snake_case_						:     Any     =		self.text_encoder_hidden_states_norm(_A		)
							snake_case_						:     Dict     =		torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]						,				dim=1		)

							return text_encoder_hidden_states, additive_clip_time_embeddings

