




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_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 lowercase_							       =       ""
 lowercase_							       =       "hf-legacy"  # "hf://"" is reserved for hffs



 def __init__(				self    :						List[str]							,							_lowerCAmelCase    :						Optional[DatasetInfo] = None							,							_lowerCAmelCase    :						Optional[str] = None							,							**_lowerCAmelCase    :						Optional[int]							,							):
       super().__init__(self							,							**_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     repo_info
       SCREAMING_SNAKE_CASE_      		=     token
       SCREAMING_SNAKE_CASE_      		=     None



 def  lowerCAmelCase_    (				self    :						Tuple		):
       if self.dir_cache is None:
             SCREAMING_SNAKE_CASE_      		=     {}
             for hf_file in self.repo_info.siblings:
                   # TODO(QL): add sizes
                   SCREAMING_SNAKE_CASE_      		=     {
                       '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  lowerCAmelCase_    (				self    :						Dict							,							_lowerCAmelCase    :						str							,							_lowerCAmelCase    :						str = "rb"							,							**_lowerCAmelCase    :						Optional[Any]							,							):
       if not isinstance(self.repo_info							,							_lowerCAmelCase		):
             raise NotImplementedError(F"Open is only implemented for dataset repositories, but got {self.repo_info}"		)
       SCREAMING_SNAKE_CASE_      		=     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  lowerCAmelCase_    (				self    :						str							,							_lowerCAmelCase    :						Optional[Any]							,							**_lowerCAmelCase    :						Any		):
       self._get_dirs()
       SCREAMING_SNAKE_CASE_      		=     self._strip_protocol(_lowerCAmelCase		)
       if path in self.dir_cache:
             return self.dir_cache[path]
       else:
             raise FileNotFoundError(_lowerCAmelCase		)




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




import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
     import torch_xla.core.xla_model as xm
     import torch_xla.debug.metrics as met
class    __UpperCamelCase			(       _lowercase				):

 """simple docstring"""



 def __init__(	self		,				*SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=None		,				**SCREAMING_SNAKE_CASE      ) ->			Tuple:
  super().__init__(*SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )
  a__												= eval_examples
  a__												= post_process_function

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE = None		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE = None		,				SCREAMING_SNAKE_CASE = "eval"		,				**SCREAMING_SNAKE_CASE		,				) ->			Dict[str, float]:
  a__												= gen_kwargs.copy()
  a__												= (
      gen_kwargs['''max_length'''] if gen_kwargs.get('''max_length'''      ) is not None else self.args.generation_max_length
  )
  a__												= (
      gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams'''      ) is not None else self.args.generation_num_beams
  )
  a__												= gen_kwargs

  a__												= self.eval_dataset if eval_dataset is None else eval_dataset
  a__												= self.get_eval_dataloader(SCREAMING_SNAKE_CASE      )
  a__												= self.eval_examples if eval_examples is None else eval_examples

  # Temporarily disable metric computation, we will do it in the loop here.
  a__												= self.compute_metrics
  a__												= None
  a__												= time.time()
  a__												= self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
  try:
   a__												= eval_loop(
       SCREAMING_SNAKE_CASE		,				description='''Evaluation'''		,				prediction_loss_only=True if compute_metrics is None else None		,				ignore_keys=SCREAMING_SNAKE_CASE		,				metric_key_prefix=SCREAMING_SNAKE_CASE		,				)
  finally:
   a__												= compute_metrics
  a__												= self.args.eval_batch_size * self.args.world_size
  if f"{metric_key_prefix}_jit_compilation_time" in output.metrics:
   start_time += output.metrics[f"{metric_key_prefix}_jit_compilation_time"]
  output.metrics.update(
      speed_metrics(
          SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				num_samples=output.num_samples		,				num_steps=math.ceil(output.num_samples / total_batch_size      )		,				)      )

  if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
   # Only the main node write the results by default
   a__												= self.post_process_function(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )
   a__												= self.compute_metrics(SCREAMING_SNAKE_CASE      )

   # Prefix all keys with metric_key_prefix + '_'
   for key in list(metrics.keys()      ):
    if not key.startswith(f"{metric_key_prefix}_"      ):
     a__												= metrics.pop(SCREAMING_SNAKE_CASE      )

   metrics.update(output.metrics      )
  else:
   a__												= output.metrics

  if self.args.should_log:
   # Only the main node log the results by default
   self.log(SCREAMING_SNAKE_CASE      )

  if self.args.tpu_metrics_debug or self.args.debug:
   # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
   xm.master_print(met.metrics_report()      )

  a__												= self.callback_handler.on_evaluate(self.args		,				self.state		,				self.control		,				SCREAMING_SNAKE_CASE      )
  return metrics



 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE = "test"		,				**SCREAMING_SNAKE_CASE      ) ->			List[str]:
  a__												= gen_kwargs.copy()

  a__												= self.get_test_dataloader(SCREAMING_SNAKE_CASE      )

  # Temporarily disable metric computation, we will do it in the loop here.
  a__												= self.compute_metrics
  a__												= None
  a__												= time.time()
  a__												= self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
  try:
   a__												= eval_loop(
       SCREAMING_SNAKE_CASE		,				description='''Prediction'''		,				prediction_loss_only=True if compute_metrics is None else None		,				ignore_keys=SCREAMING_SNAKE_CASE		,				metric_key_prefix=SCREAMING_SNAKE_CASE		,				)
  finally:
   a__												= compute_metrics

  a__												= self.args.eval_batch_size * self.args.world_size
  if f"{metric_key_prefix}_jit_compilation_time" in output.metrics:
   start_time += output.metrics[f"{metric_key_prefix}_jit_compilation_time"]
  output.metrics.update(
      speed_metrics(
          SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				num_samples=output.num_samples		,				num_steps=math.ceil(output.num_samples / total_batch_size      )		,				)      )
  if self.post_process_function is None or self.compute_metrics is None:
   return output

  a__												= self.post_process_function(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				'''predict'''      )
  a__												= self.compute_metrics(SCREAMING_SNAKE_CASE      )

  # Prefix all keys with metric_key_prefix + '_'
  for key in list(metrics.keys()      ):
   if not key.startswith(f"{metric_key_prefix}_"      ):
    a__												= metrics.pop(SCREAMING_SNAKE_CASE      )
  metrics.update(output.metrics      )
  return PredictionOutput(predictions=predictions.predictions		,				label_ids=predictions.label_ids		,				metrics=SCREAMING_SNAKE_CASE      )





