

import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional

import torch

from ..utils import add_start_docstrings, logging


__UpperCAmelCase    				=     logging.get_logger(__name__)


__UpperCAmelCase    				=     r'\n    Args:\n        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary.\n\n            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n            [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):\n            Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax\n            or scores for each vocabulary token after SoftMax.\n        kwargs (`Dict[str, Any]`, *optional*):\n            Additional stopping criteria specific kwargs.\n\n    Return:\n        `bool`. `False` indicates we should continue, `True` indicates we should stop.\n\n'





class A__	( A ):



 """simple docstring"""







 @add_start_docstrings(A_       )
 def __call__(		self :	Union[str, Any]     ,						A_ :	torch.LongTensor     ,						A_ :	torch.FloatTensor     ,						**A_ :	List[Any]       ):






      '''simple docstring'''




      raise NotImplementedError("StoppingCriteria needs to be subclassed"       )





class A__	( A ):



 """simple docstring"""







 def __init__(		self :	List[str]     ,						A_ :	int     ,						A_ :	Optional[int] = None       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    max_length
      _lowerCAmelCase   :					Dict        =    max_position_embeddings


 @add_start_docstrings(A_       )
 def __call__(		self :	List[str]     ,						A_ :	torch.LongTensor     ,						A_ :	torch.FloatTensor     ,						**A_ :	List[str]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    input_ids.shape[-1]
      _lowerCAmelCase   :					Optional[Any]        =    cur_len >= self.max_length
      if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
           logger.warning_once(
               "This is a friendly reminder - the current text generation call will exceed the model's predefined "
               F'''maximum length ({self.max_position_embeddings}). Depending on the model, you may observe '''
               "exceptions, performance degradation, or nothing at all."       )
      return is_done





class A__	( A ):



 """simple docstring"""







 def __init__(		self :	Any     ,						A_ :	int     ,						A_ :	int       ):






      '''simple docstring'''




      warnings.warn(
          "The class `MaxNewTokensCriteria` is deprecated. "
          F'''Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` '''
          "with `max_length = start_length + max_new_tokens` instead."     ,						A_     ,						)
      _lowerCAmelCase   :					Union[str, Any]        =    start_length
      _lowerCAmelCase   :					List[str]        =    max_new_tokens
      _lowerCAmelCase   :					Optional[int]        =    start_length + max_new_tokens


 @add_start_docstrings(A_       )
 def __call__(		self :	Tuple     ,						A_ :	torch.LongTensor     ,						A_ :	torch.FloatTensor     ,						**A_ :	List[Any]       ):






      '''simple docstring'''




      return input_ids.shape[-1] >= self.max_length





class A__	( A ):



 """simple docstring"""







 def __init__(		self :	Optional[int]     ,						A_ :	float     ,						A_ :	Optional[float] = None       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    max_time
      _lowerCAmelCase   :					List[str]        =    time.time() if initial_timestamp is None else initial_timestamp


 @add_start_docstrings(A_       )
 def __call__(		self :	Union[str, Any]     ,						A_ :	torch.LongTensor     ,						A_ :	torch.FloatTensor     ,						**A_ :	str       ):






      '''simple docstring'''




      return time.time() - self.initial_timestamp > self.max_time





class A__	( A ):



 """simple docstring"""







 @add_start_docstrings(A_       )
 def __call__(		self :	Union[str, Any]     ,						A_ :	torch.LongTensor     ,						A_ :	torch.FloatTensor     ,						**A_ :	Optional[Any]       ):






      '''simple docstring'''




      return any(criteria(A_     ,						A_       ) for criteria in self       )


 @property
 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      for stopping_criterium in self:
           if isinstance(A_     ,						A_       ):
                return stopping_criterium.max_length
           elif isinstance(A_     ,						A_       ):
                return stopping_criterium.max_length
      return None







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      StoppingCriteriaList:

     """simple docstring"""







     _lowerCAmelCase   :					List[Any]        =    stopping_criteria.max_length
     _lowerCAmelCase   :					Optional[int]        =    deepcopy(SCREAMING_SNAKE_CASE				)
     if stopping_max_length is not None and stopping_max_length != max_length:
          warnings.warn("You set different `max_length` for stopping criteria and `max_length` parameter"		,							SCREAMING_SNAKE_CASE				)
     elif stopping_max_length is None:
          new_stopping_criteria.append(MaxLengthCriteria(max_length=SCREAMING_SNAKE_CASE				)				)
     return new_stopping_criteria









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 A__    ( A ):



 """simple docstring"""







 _lowercase     :					List[str]      =						''''''
 _lowercase     :					Optional[Any]      =						'''hf-legacy'''  # "hf://"" is reserved for hffs

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






      '''simple docstring'''




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

 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      if self.dir_cache is None:
           _lowerCAmelCase   :					int        =    {}
           for hf_file in self.repo_info.siblings:
                # TODO(QL): add sizes
                _lowerCAmelCase   :					Optional[int]        =    {
                    "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    __magic_name__       (		self :	Dict     ,						A_ :	str     ,						A_ :	str = "rb"     ,						**A_ :	Optional[int]     ,						):






      '''simple docstring'''




      if not isinstance(self.repo_info     ,						A_       ):
           raise NotImplementedError(F'''Open is only implemented for dataset repositories, but got {self.repo_info}'''       )
      _lowerCAmelCase   :					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    __magic_name__       (		self :	Union[str, Any]     ,						A_ :	List[Any]     ,						**A_ :	str       ):






      '''simple docstring'''




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


 def    __magic_name__       (		self :	Any     ,						A_ :	str     ,						A_ :	Any=False     ,						**A_ :	Dict       ):






      '''simple docstring'''




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






