






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 		_A						( UpperCAmelCase_ ):

  def __init__(    self						:	Optional[Any]   ,				*lowerCamelCase__						:	Dict   ,				lowerCamelCase__						:	List[str]=None   ,				lowerCamelCase__						:	Dict=None   ,				**lowerCamelCase__						:	int			):
     """simple docstring"""

     super().__init__(*lowerCamelCase__   ,				**lowerCamelCase__			)
     __UpperCamelCase	:			int					=					eval_examples
     __UpperCamelCase	:			int					=					post_process_function

  def    a					(    self						:	Any   ,				lowerCamelCase__						:	Optional[Dataset] = None   ,				lowerCamelCase__						:	Any=None   ,				lowerCamelCase__						:	Optional[List[str]] = None   ,				lowerCamelCase__						:	str = "eval"   ,				**lowerCamelCase__						:	Optional[Any]   ,				):
     """simple docstring"""

     __UpperCamelCase	:			Optional[int]					=					gen_kwargs.copy()
     __UpperCamelCase	:			List[str]					=					(
         gen_kwargs["""max_length"""] if gen_kwargs.get("""max_length"""			) is not None else self.args.generation_max_length
     )
     __UpperCamelCase	:			List[str]					=					(
         gen_kwargs["""num_beams"""] if gen_kwargs.get("""num_beams"""			) is not None else self.args.generation_num_beams
     )
     __UpperCamelCase	:			str					=					gen_kwargs

     __UpperCamelCase	:			Tuple					=					self.eval_dataset if eval_dataset is None else eval_dataset
     __UpperCamelCase	:			Any					=					self.get_eval_dataloader(lowerCamelCase__			)
     __UpperCamelCase	:			Optional[Any]					=					self.eval_examples if eval_examples is None else eval_examples

     # Temporarily disable metric computation, we will do it in the loop here.
     __UpperCamelCase	:			int					=					self.compute_metrics
     __UpperCamelCase	:			int					=					None
     __UpperCamelCase	:			Union[str, Any]					=					time.time()
     __UpperCamelCase	:			int					=					self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
     try:
        __UpperCamelCase	:			Tuple					=					eval_loop(
            lowerCamelCase__   ,				description="""Evaluation"""   ,				prediction_loss_only=True if compute_metrics is None else None   ,				ignore_keys=lowerCamelCase__   ,				metric_key_prefix=lowerCamelCase__   ,				)
     finally:
        __UpperCamelCase	:			Any					=					compute_metrics
     __UpperCamelCase	:			Optional[int]					=					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(
             lowerCamelCase__   ,				lowerCamelCase__   ,				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
        __UpperCamelCase	:			Optional[Any]					=					self.post_process_function(lowerCamelCase__   ,				lowerCamelCase__   ,				lowerCamelCase__			)
        __UpperCamelCase	:			Any					=					self.compute_metrics(lowerCamelCase__			)

        # Prefix all keys with metric_key_prefix + '_'
        for key in list(metrics.keys()			):
           if not key.startswith(f'{metric_key_prefix}_'			):
              __UpperCamelCase	:			Union[str, Any]					=					metrics.pop(lowerCamelCase__			)

        metrics.update(output.metrics			)
     else:
        __UpperCamelCase	:			Any					=					output.metrics

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

     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()			)

     __UpperCamelCase	:			List[Any]					=					self.callback_handler.on_evaluate(self.args   ,				self.state   ,				self.control   ,				lowerCamelCase__			)
     return metrics

  def    a					(    self						:	List[str]   ,				lowerCamelCase__						:	Optional[Any]   ,				lowerCamelCase__						:	Union[str, Any]   ,				lowerCamelCase__						:	List[str]=None   ,				lowerCamelCase__						:	str = "test"   ,				**lowerCamelCase__						:	Optional[Any]			):
     """simple docstring"""

     __UpperCamelCase	:			Dict					=					gen_kwargs.copy()

     __UpperCamelCase	:			str					=					self.get_test_dataloader(lowerCamelCase__			)

     # Temporarily disable metric computation, we will do it in the loop here.
     __UpperCamelCase	:			Dict					=					self.compute_metrics
     __UpperCamelCase	:			Union[str, Any]					=					None
     __UpperCamelCase	:			Union[str, Any]					=					time.time()
     __UpperCamelCase	:			Tuple					=					self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
     try:
        __UpperCamelCase	:			List[str]					=					eval_loop(
            lowerCamelCase__   ,				description="""Prediction"""   ,				prediction_loss_only=True if compute_metrics is None else None   ,				ignore_keys=lowerCamelCase__   ,				metric_key_prefix=lowerCamelCase__   ,				)
     finally:
        __UpperCamelCase	:			Tuple					=					compute_metrics

     __UpperCamelCase	:			Optional[Any]					=					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(
             lowerCamelCase__   ,				lowerCamelCase__   ,				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

     __UpperCamelCase	:			str					=					self.post_process_function(lowerCamelCase__   ,				lowerCamelCase__   ,				lowerCamelCase__   ,				"""predict"""			)
     __UpperCamelCase	:			Any					=					self.compute_metrics(lowerCamelCase__			)

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











import os

import pytest

from transformers.dynamic_module_utils import get_imports


UpperCamelCase    							=      '\nimport os\n'

UpperCamelCase    							=      '\ndef foo():\n    import os\n    return False\n'

UpperCamelCase    							=      '\ndef foo():\n    def bar():\n        if True:\n            import os\n        return False\n    return bar()\n'

UpperCamelCase    							=      '\nimport os\n\ntry:\n    import bar\nexcept ImportError:\n    raise ValueError()\n'

UpperCamelCase    							=      '\nimport os\n\ndef foo():\n    try:\n        import bar\n    except ImportError:\n        raise ValueError()\n'

UpperCamelCase    							=      '\nimport os\n\ntry:\n    import bar\nexcept (ImportError, AttributeError):\n    raise ValueError()\n'

UpperCamelCase    							=      '\nimport os\n\ntry:\n    import bar\nexcept ImportError as e:\n    raise ValueError()\n'

UpperCamelCase    							=      '\nimport os\n\ntry:\n    import bar\nexcept:\n    raise ValueError()\n'

UpperCamelCase    							=      '\nimport os\n\ntry:\n    import bar\n    import baz\nexcept ImportError:\n    raise ValueError()\n'

UpperCamelCase    							=      '\nimport os\n\ntry:\n    import bar\n    import baz\nexcept ImportError:\n    x = 1\n    raise ValueError()\n'

UpperCamelCase    							=      [
    TOP_LEVEL_IMPORT,
    IMPORT_IN_FUNCTION,
    DEEPLY_NESTED_IMPORT,
    TOP_LEVEL_TRY_IMPORT,
    GENERIC_EXCEPT_IMPORT,
    MULTILINE_TRY_IMPORT,
    MULTILINE_BOTH_IMPORT,
    MULTIPLE_EXCEPTS_IMPORT,
    EXCEPT_AS_IMPORT,
    TRY_IMPORT_IN_FUNCTION,
]


@pytest.mark.parametrize("""case"""					,							__lowerCAmelCase							)
def 				__lowerCamelCase     (							__lowerCAmelCase							:					List[Any]					,							__lowerCAmelCase							:					Optional[Any]							)     ->  str:
   __UpperCamelCase	:			str					=					os.path.join(__lowerCAmelCase					,							"""test_file.py"""							)
   with open(__lowerCAmelCase					,							"""w"""							) as _tmp_file:
      _tmp_file.write(__lowerCAmelCase							)

   __UpperCamelCase	:			Optional[int]					=					get_imports(__lowerCAmelCase							)
   assert parsed_imports == ["os"]



