'''simple docstring'''




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__  (       _snake_case			):






   """simple docstring"""
   def __init__(self       ,				*_a       ,				_a=None       ,				_a=None       ,				**_a				)    ->     str:
        super().__init__(*_a       ,				**_a				)
        lowercase_					:       Optional[Any]     				=					eval_examples
        lowercase_					:       Optional[Any]     				=					post_process_function
   def 		_lowerCamelCase (self       ,				_a = None       ,				_a=None       ,				_a = None       ,				_a = "eval"       ,				**_a       ,				)    ->     Dict[str, float]:
        lowercase_					:       Optional[int]     				=					gen_kwargs.copy()
        lowercase_					:       Any     				=					(
            gen_kwargs['max_length'] if gen_kwargs.get('max_length'				) is not None else self.args.generation_max_length
        )
        lowercase_					:       List[str]     				=					(
            gen_kwargs['num_beams'] if gen_kwargs.get('num_beams'				) is not None else self.args.generation_num_beams
        )
        lowercase_					:       List[str]     				=					gen_kwargs

        lowercase_					:       str     				=					self.eval_dataset if eval_dataset is None else eval_dataset
        lowercase_					:       List[Any]     				=					self.get_eval_dataloader(_a				)
        lowercase_					:       Union[str, Any]     				=					self.eval_examples if eval_examples is None else eval_examples

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

             # Prefix all keys with metric_key_prefix + '_'
             for key in list(metrics.keys()				):
                  if not key.startswith(f'''{metric_key_prefix}_'''				):
                       lowercase_					:       Optional[int]     				=					metrics.pop(_a				)

             metrics.update(output.metrics				)
        else:
             lowercase_					:       int     				=					output.metrics

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

        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()				)

        lowercase_					:       Optional[int]     				=					self.callback_handler.on_evaluate(self.args       ,				self.state       ,				self.control       ,				_a				)
        return metrics





   def 		_lowerCamelCase (self       ,				_a       ,				_a       ,				_a=None       ,				_a = "test"       ,				**_a				)    ->     Union[str, Any]:
        lowercase_					:       Dict     				=					gen_kwargs.copy()

        lowercase_					:       List[str]     				=					self.get_test_dataloader(_a				)

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

        lowercase_					:       Union[str, 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(
                _a       ,				_a       ,				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

        lowercase_					:       int     				=					self.post_process_function(_a       ,				_a       ,				_a       ,				'predict'				)
        lowercase_					:       Tuple     				=					self.compute_metrics(_a				)

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





'''simple docstring'''




import math
import os
import sys

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       List[str]     				=					''
     try:
          with open(SCREAMING_SNAKE_CASE_  ,       'rb'							) as binary_file:
               lowercase_					:       Dict     				=					binary_file.read()
          for dat in data:
               lowercase_					:       Union[str, Any]     				=					f'''{dat:08b}'''
               result += curr_byte
          return result
     except OSError:
          print('File not accessible'							)
          sys.exit()

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     lexicon.pop(SCREAMING_SNAKE_CASE_							)
     lowercase_					:       Any     				=					last_match_id

     if math.loga(SCREAMING_SNAKE_CASE_							).is_integer():
          for curr_key in lexicon:
               lowercase_					:       Tuple     				=					'0' + lexicon[curr_key]

     lowercase_					:       Any     				=					bin(SCREAMING_SNAKE_CASE_							)[2:]

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       Dict     				=					{'0': '0', '1': '1'}
     lowercase_				,lowercase_					:       Any     				=					'', ''
     lowercase_					:       List[Any]     				=					len(SCREAMING_SNAKE_CASE_							)

     for i in range(len(SCREAMING_SNAKE_CASE_							)							):
          curr_string += data_bits[i]
          if curr_string not in lexicon:
               continue

          lowercase_					:       str     				=					lexicon[curr_string]
          result += last_match_id
          add_key_to_lexicon(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)
          index += 1
          lowercase_					:       List[Any]     				=					''

     while curr_string != "" and curr_string not in lexicon:
          curr_string += "0"

     if curr_string != "":
          lowercase_					:       Optional[Any]     				=					lexicon[curr_string]
          result += last_match_id

     return result

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       List[str]     				=					os.path.getsize(SCREAMING_SNAKE_CASE_							)
     lowercase_					:       Any     				=					bin(SCREAMING_SNAKE_CASE_							)[2:]
     lowercase_					:       Optional[Any]     				=					len(SCREAMING_SNAKE_CASE_							)

     return "0" * (length_length - 1) + file_length_binary + compressed

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       List[str]     				=					8
     try:
          with open(SCREAMING_SNAKE_CASE_  ,       'wb'							) as opened_file:
               lowercase_					:       List[Any]     				=					[
                   to_write[i : i + byte_length]
                   for i in range(0  ,       len(SCREAMING_SNAKE_CASE_							)  ,       SCREAMING_SNAKE_CASE_							)
               ]

               if len(result_byte_array[-1]							) % byte_length == 0:
                    result_byte_array.append('10000000'							)
               else:
                    result_byte_array[-1] += "1" + "0" * (
                        byte_length - len(result_byte_array[-1]							) - 1
                    )

               for elem in result_byte_array:
                    opened_file.write(int(SCREAMING_SNAKE_CASE_  ,       2							).to_bytes(1  ,       byteorder='big'							)							)
     except OSError:
          print('File not accessible'							)
          sys.exit()

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       List[str]     				=					read_file_binary(SCREAMING_SNAKE_CASE_							)
     lowercase_					:       List[str]     				=					compress_data(SCREAMING_SNAKE_CASE_							)
     lowercase_					:       Union[str, Any]     				=					add_file_length(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)
     write_file_binary(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)


if __name__ == "__main__":
 compress(sys.argv[1], sys.argv[2])




