
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_						):
	lowerCAmelCase__         = ''
	lowerCAmelCase__         = 'hf-legacy'  # "hf://"" is reserved for hffs



	def __init__(    self						,  lowercase = None						,  lowercase = None						,  **lowercase						,  )				->       Optional[Any]:
					super().__init__(self						,  **lowercase )
					lowerCamelCase_						=			repo_info
					lowerCamelCase_						=			token
					lowerCamelCase_						=			None



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



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase=False						,  **lowercase )				->       Optional[int]:
					self._get_dirs()
					lowerCamelCase_						=			PurePosixPath(path.strip("/" ) )
					lowerCamelCase_						=			{}
					for p, f in self.dir_cache.items():
									lowerCamelCase_						=			PurePosixPath(p.strip("/" ) )
									lowerCamelCase_						=			p.parent
									if root == path:
													lowerCamelCase_						=			f
					lowerCamelCase_						=			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     _SCREAMING_SNAKE_CASE     (      snake_case_						):



	def __init__(    self						,  *lowercase						,  lowercase=None						,  lowercase=None						,  **lowercase )				->       List[str]:
					super().__init__(*lowercase						,  **lowercase )
					lowerCamelCase_						=			eval_examples
					lowerCamelCase_						=			post_process_function



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

					lowerCamelCase_						=			self.eval_dataset if eval_dataset is None else eval_dataset
					lowerCamelCase_						=			self.get_eval_dataloader(lowercase )
					lowerCamelCase_						=			self.eval_examples if eval_examples is None else eval_examples

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

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

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

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

					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() )

					lowerCamelCase_						=			self.callback_handler.on_evaluate(self.args						,  self.state						,  self.control						,  lowercase )
					return metrics



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase=None						,  lowercase = "test"						,  **lowercase )				->       Union[str, Any]:
					lowerCamelCase_						=			gen_kwargs.copy()

					lowerCamelCase_						=			self.get_test_dataloader(lowercase )

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

					lowerCamelCase_						=			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(
					        lowercase						,  lowercase						,  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

					lowerCamelCase_						=			self.post_process_function(lowercase						,  lowercase						,  lowercase						,  "predict" )
					lowerCamelCase_						=			self.compute_metrics(lowercase )

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


