






"""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      UpperCamelCase_			(__A	):
	__magic_name__       =							''''''
	__magic_name__       =							'''hf-legacy'''  # "hf://"" is reserved for hffs





	def __init__( self   :					Any      ,      lowerCAmelCase_   :					Optional[DatasetInfo] = None      ,      lowerCAmelCase_   :					Optional[str] = None      ,      **lowerCAmelCase_   :					List[str]      ,      )		->     str:
				super().__init__(self      ,      **lowerCAmelCase_		)
				UpperCAmelCase_	:					Union[str, Any]     	=   repo_info
				UpperCAmelCase_	:					Optional[int]     	=   token
				UpperCAmelCase_	:					Optional[Any]     	=   None





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





	def 	_SCREAMING_SNAKE_CASE ( self   :					int      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					str = "rb"      ,      **lowerCAmelCase_   :					str      ,      )		->     Dict:
				if not isinstance(self.repo_info      ,      lowerCAmelCase_		):
							raise NotImplementedError(f"""Open is only implemented for dataset repositories, but got {self.repo_info}"""		)
				UpperCAmelCase_	:					List[str]     	=   hf_hub_url(self.repo_info.id      ,      lowerCAmelCase_      ,      revision=self.repo_info.sha		)
				return fsspec.open(
				    lowerCAmelCase_      ,      mode=lowerCAmelCase_      ,      headers=get_authentication_headers_for_url(lowerCAmelCase_      ,      use_auth_token=self.token		)      ,      client_kwargs={"trust_env": True}      ,      ).open()





	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]      ,      lowerCAmelCase_   :					Optional[int]      ,      **lowerCAmelCase_   :					Tuple		)		->     Optional[int]:
				self._get_dirs()
				UpperCAmelCase_	:					Tuple     	=   self._strip_protocol(lowerCAmelCase_		)
				if path in self.dir_cache:
							return self.dir_cache[path]
				else:
							raise FileNotFoundError(lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]      ,      lowerCAmelCase_   :					Optional[Any]      ,      lowerCAmelCase_   :					List[str]=False      ,      **lowerCAmelCase_   :					Union[str, Any]		)		->     Any:
				self._get_dirs()
				UpperCAmelCase_	:					str     	=   PurePosixPath(path.strip("/"		)		)
				UpperCAmelCase_	:					Dict     	=   {}
				for p, f in self.dir_cache.items():
							UpperCAmelCase_	:					str     	=   PurePosixPath(p.strip("/"		)		)
							UpperCAmelCase_	:					Dict     	=   p.parent
							if root == path:
										UpperCAmelCase_	:					Dict     	=   f
				UpperCAmelCase_	:					List[Any]     	=   list(paths.values()		)
				if detail:
							return out
				else:
							return sorted(f["name"] for f in out		)











"""simple docstring"""
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
			from ... import PreTrainedTokenizerBase, TensorType

lowerCamelCase_							      = logging.get_logger(__name__)







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''vision-encoder-decoder'''
	__magic_name__       =							True





	def __init__( self   :					Union[str, Any]      ,      **lowerCAmelCase_   :					List[Any]		)		->     Union[str, Any]:
				super().__init__(**lowerCAmelCase_		)
				if "encoder" not in kwargs or "decoder" not in kwargs:
							raise ValueError(
							    f"""A configuraton of type {self.model_type} cannot be instantiated because """
							    f"""not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}"""		)

				UpperCAmelCase_	:					str     	=   kwargs.pop("encoder"		)
				UpperCAmelCase_	:					Dict     	=   encoder_config.pop("model_type"		)
				UpperCAmelCase_	:					List[Any]     	=   kwargs.pop("decoder"		)
				UpperCAmelCase_	:					Dict     	=   decoder_config.pop("model_type"		)

				UpperCAmelCase_	:					Union[str, Any]     	=   AutoConfig.for_model(lowerCAmelCase_      ,      **lowerCAmelCase_		)
				UpperCAmelCase_	:					List[str]     	=   AutoConfig.for_model(lowerCAmelCase_      ,      **lowerCAmelCase_		)
				UpperCAmelCase_	:					Tuple     	=   True





	@classmethod
	def 	_SCREAMING_SNAKE_CASE ( cls   :					List[str]      ,      lowerCAmelCase_   :					PretrainedConfig      ,      lowerCAmelCase_   :					PretrainedConfig      ,      **lowerCAmelCase_   :					Dict		)		->     PretrainedConfig:
				logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"		)
				UpperCAmelCase_	:					Tuple     	=   True
				UpperCAmelCase_	:					str     	=   True

				return cls(encoder=encoder_config.to_dict()      ,      decoder=decoder_config.to_dict()      ,      **lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]		)		->     List[Any]:
				UpperCAmelCase_	:					Union[str, Any]     	=   copy.deepcopy(self.__dict__		)
				UpperCAmelCase_	:					List[str]     	=   self.encoder.to_dict()
				UpperCAmelCase_	:					List[Any]     	=   self.decoder.to_dict()
				UpperCAmelCase_	:					Any     	=   self.__class__.model_type
				return output







class      UpperCamelCase_			(__A	):
	__magic_name__       =							version.parse('''1.11'''	)





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]		)		->     Mapping[str, Mapping[int, str]]:
				return OrderedDict(
				    [
				        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
				    ]		)





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     float:
				return 1e-4





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					Any		)		->     Mapping[str, Mapping[int, str]]:
				return OrderedDict({"last_hidden_state": {0: "batch", 1: "encoder_sequence"}}		)







class      UpperCamelCase_			(__A	):





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     Mapping[str, Mapping[int, str]]:
				UpperCAmelCase_	:					List[Any]     	=   OrderedDict()
				UpperCAmelCase_	:					int     	=   {0: "batch", 1: "past_decoder_sequence + sequence"}
				UpperCAmelCase_	:					Dict     	=   {0: "batch", 1: "past_decoder_sequence + sequence"}
				UpperCAmelCase_	:					Union[str, Any]     	=   {0: "batch", 1: "encoder_sequence"}

				return common_inputs





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]      ,      lowerCAmelCase_   :					"PreTrainedTokenizerBase"      ,      lowerCAmelCase_   :					int = -1      ,      lowerCAmelCase_   :					int = -1      ,      lowerCAmelCase_   :					bool = False      ,      lowerCAmelCase_   :					Optional["TensorType"] = None      ,      )		->     Mapping[str, Any]:
				import torch

				UpperCAmelCase_	:					Optional[int]     	=   OrderedDict()

				UpperCAmelCase_	:					Any     	=   super().generate_dummy_inputs(
				    lowerCAmelCase_      ,      batch_size=lowerCAmelCase_      ,      seq_length=lowerCAmelCase_      ,      is_pair=lowerCAmelCase_      ,      framework=lowerCAmelCase_		)

				UpperCAmelCase_	,      UpperCAmelCase_	:					Optional[Any]     	=   dummy_input["input_ids"].shape
				UpperCAmelCase_	:					List[str]     	=   (batch, encoder_sequence, self._config.encoder_hidden_size)
				UpperCAmelCase_	:					List[str]     	=   dummy_input.pop("input_ids"		)
				UpperCAmelCase_	:					List[str]     	=   dummy_input.pop("attention_mask"		)
				UpperCAmelCase_	:					List[Any]     	=   torch.zeros(lowerCAmelCase_		)

				return common_inputs







class      UpperCamelCase_			(__A	):





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]		)		->     None:
				pass





	def 	_SCREAMING_SNAKE_CASE ( self   :					Any      ,      lowerCAmelCase_   :					PretrainedConfig		)		->     OnnxConfig:
				return VisionEncoderDecoderEncoderOnnxConfig(lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]      ,      lowerCAmelCase_   :					PretrainedConfig      ,      lowerCAmelCase_   :					PretrainedConfig      ,      lowerCAmelCase_   :					str = "default"		)		->     OnnxConfig:
				UpperCAmelCase_	:					Optional[int]     	=   encoder_config.hidden_size
				return VisionEncoderDecoderDecoderOnnxConfig(lowerCAmelCase_      ,      lowerCAmelCase_		)



