


import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer

import datasets
from datasets import logging


A_								=   '''\

'''

A_								=   '''
Perplexity (PPL) is one of the most common metrics for evaluating language models.
It is defined as the exponentiated average negative log-likelihood of a sequence.

For more information, see https://huggingface.co/docs/transformers/perplexity
'''

A_								=   '''
Args:
    model_id (str): model used for calculating Perplexity
            NOTE: Perplexity can only be calculated for causal language models.
                    This includes models such as gpt2, causal variations of bert,
                    causal versions of t5, and more (the full list can be found
                    in the AutoModelForCausalLM documentation here:
                    https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )

    input_texts (list of str): input text, each separate text snippet
        is one list entry.
    batch_size (int): the batch size to run texts through the model. Defaults to 16.
    add_start_token (bool): whether to add the start token to the texts,
        so the perplexity can include the probability of the first word. Defaults to True.
    device (str): device to run on, defaults to \'cuda\' when available
Returns:
    perplexity: dictionary containing the perplexity scores for the texts
        in the input list, as well as the mean perplexity. If one of the input texts is
        longer than the max input length of the model, then it is truncated to the
        max length for the perplexity computation.
Examples:
    Example 1:
        >>> perplexity = datasets.load_metric("perplexity")
        >>> input_texts = ["lorem ipsum", "Happy Birthday!", "Bienvenue"]
        >>> results = perplexity.compute(model_id=\'gpt2\',
        ...                              add_start_token=False,
        ...                              input_texts=input_texts) # doctest:+ELLIPSIS
        >>> print(list(results.keys()))
        [\'perplexities\', \'mean_perplexity\']
        >>> print(round(results["mean_perplexity"], 2))
        78.22
        >>> print(round(results["perplexities"][0], 2))
        11.11

    Example 2:
        >>> perplexity = datasets.load_metric("perplexity")
        >>> input_texts = datasets.load_dataset("wikitext",
        ...                                     "wikitext-2-raw-v1",
        ...                                     split="test")["text"][:50] # doctest:+ELLIPSIS
        [...]
        >>> input_texts = [s for s in input_texts if s!=\'\']
        >>> results = perplexity.compute(model_id=\'gpt2\',
        ...                              input_texts=input_texts) # doctest:+ELLIPSIS
        >>> print(list(results.keys()))
        [\'perplexities\', \'mean_perplexity\']
        >>> print(round(results["mean_perplexity"], 2))
        60.35
        >>> print(round(results["perplexities"][0], 2))
        81.12
'''

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,			_KWARGS_DESCRIPTION						)
class     snake_case      (							datasets.Metric						):
			'''simple docstring'''

			def _lowercase			(						self  :  str       )       ->       str:




								"""simple docstring"""






								return datasets.MetricInfo(
								    description=_DESCRIPTION				,				citation=_CITATION				,				inputs_description=_KWARGS_DESCRIPTION				,				features=datasets.Features(
								        {
								            '''input_texts''': datasets.Value('''string'''       ),
								        }       )				,				reference_urls=['''https://huggingface.co/docs/transformers/perplexity''']				,				)




			def _lowercase			(						self  :  Optional[Any]				,				lowerCAmelCase_  :  str				,				lowerCAmelCase_  :  Dict				,				lowerCAmelCase_  :  Any = 16				,				lowerCAmelCase_  :  List[Any] = True				,				lowerCAmelCase_  :  List[Any]=None       )       ->       Any:




								"""simple docstring"""






								if device is not None:
													assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
													if device == "gpu":
																		SCREAMING_SNAKE_CASE_						   =							'''cuda'''
								else:
													SCREAMING_SNAKE_CASE_						   =							'''cuda''' if torch.cuda.is_available() else '''cpu'''

								SCREAMING_SNAKE_CASE_						   =							AutoModelForCausalLM.from_pretrained(__SCREAMING_SNAKE_CASE       )
								SCREAMING_SNAKE_CASE_						   =							model.to(__SCREAMING_SNAKE_CASE       )

								SCREAMING_SNAKE_CASE_						   =							AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE       )

								# if batch_size > 1 (which generally leads to padding being required), and
								# if there is not an already assigned pad_token, assign an existing
								# special token to also be the padding token
								if tokenizer.pad_token is None and batch_size > 1:
													SCREAMING_SNAKE_CASE_						   =							list(tokenizer.special_tokens_map_extended.values()       )
													# check that the model already has at least one special token defined
													assert (
													    len(__SCREAMING_SNAKE_CASE       ) > 0
													), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
													# assign one of the special tokens to also be the pad token
													tokenizer.add_special_tokens({'''pad_token''': existing_special_tokens[0]}       )

								if add_start_token:
													# leave room for <BOS> token to be added:
													assert (
													    tokenizer.bos_token is not None
													), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
													SCREAMING_SNAKE_CASE_						   =							model.config.max_length - 1
								else:
													SCREAMING_SNAKE_CASE_						   =							model.config.max_length

								SCREAMING_SNAKE_CASE_						   =							tokenizer(
								    __SCREAMING_SNAKE_CASE				,				add_special_tokens=__SCREAMING_SNAKE_CASE				,				padding=__SCREAMING_SNAKE_CASE				,				truncation=__SCREAMING_SNAKE_CASE				,				max_length=__SCREAMING_SNAKE_CASE				,				return_tensors='''pt'''				,				return_attention_mask=__SCREAMING_SNAKE_CASE				,				).to(__SCREAMING_SNAKE_CASE       )

								SCREAMING_SNAKE_CASE_						   =							encodings['''input_ids''']
								SCREAMING_SNAKE_CASE_						   =							encodings['''attention_mask''']

								# check that each input is long enough:
								if add_start_token:
													assert torch.all(torch.ge(attn_masks.sum(1       )				,				1       )       ), "Each input text must be at least one token long."
								else:
													assert torch.all(
													    torch.ge(attn_masks.sum(1       )				,				2       )       ), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."

								SCREAMING_SNAKE_CASE_						   =							[]
								SCREAMING_SNAKE_CASE_						   =							CrossEntropyLoss(reduction='''none'''       )

								for start_index in logging.tqdm(range(0				,				len(__SCREAMING_SNAKE_CASE       )				,				__SCREAMING_SNAKE_CASE       )       ):
													SCREAMING_SNAKE_CASE_						   =							min(start_index + batch_size				,				len(__SCREAMING_SNAKE_CASE       )       )
													SCREAMING_SNAKE_CASE_						   =							encoded_texts[start_index:end_index]
													SCREAMING_SNAKE_CASE_						   =							attn_masks[start_index:end_index]

													if add_start_token:
																		SCREAMING_SNAKE_CASE_						   =							torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0       )       ).to(__SCREAMING_SNAKE_CASE       )
																		SCREAMING_SNAKE_CASE_						   =							torch.cat([bos_tokens_tensor, encoded_batch]				,				dim=1       )
																		SCREAMING_SNAKE_CASE_						   =							torch.cat(
																		    [torch.ones(bos_tokens_tensor.size()				,				dtype=torch.intaa       ).to(__SCREAMING_SNAKE_CASE       ), attn_mask]				,				dim=1       )

													SCREAMING_SNAKE_CASE_						   =							encoded_batch

													with torch.no_grad():
																		SCREAMING_SNAKE_CASE_						   =							model(__SCREAMING_SNAKE_CASE				,				attention_mask=__SCREAMING_SNAKE_CASE       ).logits

													SCREAMING_SNAKE_CASE_						   =							out_logits[..., :-1, :].contiguous()
													SCREAMING_SNAKE_CASE_						   =							labels[..., 1:].contiguous()
													SCREAMING_SNAKE_CASE_						   =							attn_mask[..., 1:].contiguous()

													SCREAMING_SNAKE_CASE_						   =							torch.expa(
													    (loss_fct(shift_logits.transpose(1				,				2       )				,				__SCREAMING_SNAKE_CASE       ) * shift_attention_mask_batch).sum(1       )
													    / shift_attention_mask_batch.sum(1       )       )

													ppls += perplexity_batch.tolist()

								return {"perplexities": ppls, "mean_perplexity": np.mean(__SCREAMING_SNAKE_CASE       )}








'''simple docstring'''



import re
import time
from typing import Optional

import IPython.display as disp

from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length







def       _UpperCamelCase			(_lowerCamelCase				:  Tuple						)-> List[str]:




     '''simple docstring'''





     __snake_case            =	int(_lowerCamelCase						)
     __snake_case       ,     __snake_case       ,     __snake_case            =	t // 36_00, (t // 60) % 60, t % 60
     return f'''{h}:{m:02d}:{s:02d}''' if h != 0 else f'''{m:02d}:{s:02d}'''







def       _UpperCamelCase			(_lowerCamelCase				:  Any ,						_lowerCamelCase				:  Union[str, Any] ,						_lowerCamelCase				:  str ,						_lowerCamelCase				:  int ,						_lowerCamelCase				:  List[Any]=3_00						)-> int:




     '''simple docstring'''





     return f'''
    <div>
      {prefix}
      <progress value=\'{value}\' max=\'{total}\' style=\'width:{width}px; height:20px; vertical-align: middle;\'></progress>
      {label}
    </div>
    '''







def       _UpperCamelCase			(_lowerCamelCase				:  int						)-> List[Any]:




     '''simple docstring'''





     __snake_case            =	'''<table border="1" class="dataframe">\n'''
     html_code += """  <thead>\n <tr style="text-align: left;">\n"""
     for i in items[0]:
          html_code += f'''      <th>{i}</th>\n'''
     html_code += "    </tr>\n  </thead>\n  <tbody>\n"
     for line in items[1:]:
          html_code += "    <tr>\n"
          for elt in line:
               __snake_case            =	f'''{elt:.6f}''' if isinstance(_lowerCamelCase ,						_lowerCamelCase						) else str(_lowerCamelCase						)
               html_code += f'''      <td>{elt}</td>\n'''
          html_code += "    </tr>\n"
     html_code += "  </tbody>\n</table><p>"
     return html_code





class      lowerCAmelCase :
       __lowercase	:   str										=      5
       __lowercase	:   Optional[Any]										=      0.2


       def __init__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = True     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = 300     ,	)   ->	List[Any]:



            '''simple docstring'''




            __snake_case            =	total
            __snake_case            =	'''''' if prefix is None else prefix
            __snake_case            =	leave
            __snake_case            =	parent
            __snake_case            =	width
            __snake_case            =	None
            __snake_case            =	None
            __snake_case            =	None


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = False     ,	__SCREAMING_SNAKE_CASE = None	)   ->	Any:



            '''simple docstring'''




            __snake_case            =	value
            if comment is not None:
                 __snake_case            =	comment
            if self.last_value is None:
                 __snake_case            =	__snake_case            =	time.time()
                 __snake_case            =	__snake_case            =	value
                 __snake_case            =	__snake_case            =	None
                 __snake_case            =	self.warmup
                 __snake_case            =	1
                 self.update_bar(__SCREAMING_SNAKE_CASE	)
            elif value <= self.last_value and not force_update:
                 return
            elif force_update or self.first_calls > 0 or value >= min(self.last_value + self.wait_for     ,	self.total	):
                 if self.first_calls > 0:
                      self.first_calls -= 1
                 __snake_case            =	time.time()
                 __snake_case            =	current_time - self.start_time
                 # We could have value = self.start_value if the update is called twixe with the same start value.
                 if value > self.start_value:
                      __snake_case            =	self.elapsed_time / (value - self.start_value)
                 else:
                      __snake_case            =	None
                 if value >= self.total:
                      __snake_case            =	self.total
                      __snake_case            =	None
                      if not self.leave:
                           self.close()
                 elif self.average_time_per_item is not None:
                      __snake_case            =	self.average_time_per_item * (self.total - value)
                 self.update_bar(__SCREAMING_SNAKE_CASE	)
                 __snake_case            =	value
                 __snake_case            =	current_time
                 if self.average_time_per_item is None:
                      __snake_case            =	1
                 else:
                      __snake_case            =	max(int(self.update_every / self.average_time_per_item	)     ,	1	)


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=None	)   ->	List[str]:



            '''simple docstring'''




            __snake_case            =	''' ''' * (len(str(self.total	)	) - len(str(__SCREAMING_SNAKE_CASE	)	)) + str(__SCREAMING_SNAKE_CASE	)
            if self.elapsed_time is None:
                 __snake_case            =	F'''[{spaced_value}/{self.total} : < :'''
            elif self.predicted_remaining is None:
                 __snake_case            =	F'''[{spaced_value}/{self.total} {format_time(self.elapsed_time	)}'''
            else:
                 __snake_case            =	(
                     F'''[{spaced_value}/{self.total} {format_time(self.elapsed_time	)} <'''
                     F''' {format_time(self.predicted_remaining	)}'''
                 )
                 self.label += F''', {1/self.average_time_per_item:.2f} it/s'''
            self.label += "]" if self.comment is None or len(self.comment	) == 0 else F''', {self.comment}]'''
            self.display()


       def    lowerCAmelCase   ( self	)   ->	List[Any]:



            '''simple docstring'''




            __snake_case            =	html_progress_bar(self.value     ,	self.total     ,	self.prefix     ,	self.label     ,	self.width	)
            if self.parent is not None:
                 # If this is a child bar, the parent will take care of the display.
                 self.parent.display()
                 return
            if self.output is None:
                 __snake_case            =	disp.display(disp.HTML(self.html_code	)     ,	display_id=__SCREAMING_SNAKE_CASE	)
            else:
                 self.output.update(disp.HTML(self.html_code	)	)




       def    lowerCAmelCase   ( self	)   ->	Dict:



            '''simple docstring'''




            if self.parent is None and self.output is not None:
                 self.output.update(disp.HTML(''''''	)	)





class      lowerCAmelCase (			__lowerCAmelCase):


       def __init__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=None	)   ->	Any:



            '''simple docstring'''




            super().__init__(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	None if column_names is None else [column_names]
            __snake_case            =	None


       def    lowerCAmelCase   ( self	)   ->	Any:



            '''simple docstring'''




            __snake_case            =	html_progress_bar(self.value     ,	self.total     ,	self.prefix     ,	self.label     ,	self.width	)
            if self.inner_table is not None:
                 self.html_code += text_to_html_table(self.inner_table	)
            if self.child_bar is not None:
                 self.html_code += self.child_bar.html_code
            if self.output is None:
                 __snake_case            =	disp.display(disp.HTML(self.html_code	)     ,	display_id=__SCREAMING_SNAKE_CASE	)
            else:
                 self.output.update(disp.HTML(self.html_code	)	)


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE	)   ->	Dict:



            '''simple docstring'''




            if self.inner_table is None:
                 __snake_case            =	[list(values.keys()	), list(values.values()	)]
            else:
                 __snake_case            =	self.inner_table[0]
                 if len(self.inner_table	) == 1:
                      # We give a chance to update the column names at the first iteration
                      for key in values.keys():
                           if key not in columns:
                                columns.append(__SCREAMING_SNAKE_CASE	)
                      __snake_case            =	columns
                 self.inner_table.append([values[c] for c in columns]	)


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=300	)   ->	List[str]:



            '''simple docstring'''




            __snake_case            =	NotebookProgressBar(__SCREAMING_SNAKE_CASE     ,	prefix=__SCREAMING_SNAKE_CASE     ,	parent=self     ,	width=__SCREAMING_SNAKE_CASE	)
            return self.child_bar




       def    lowerCAmelCase   ( self	)   ->	Optional[int]:



            '''simple docstring'''




            __snake_case            =	None
            self.display()





class      lowerCAmelCase (			__lowerCAmelCase):


       def __init__( self	)   ->	str:



            '''simple docstring'''




            __snake_case            =	None
            __snake_case            =	None
            __snake_case            =	False


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)   ->	Optional[int]:



            '''simple docstring'''




            __snake_case            =	'''Epoch''' if args.evaluation_strategy == IntervalStrategy.EPOCH else '''Step'''
            __snake_case            =	0
            __snake_case            =	0
            __snake_case            =	[self.first_column] + ['''Training Loss''']
            if args.evaluation_strategy != IntervalStrategy.NO:
                 column_names.append('''Validation Loss'''	)
            __snake_case            =	NotebookTrainingTracker(state.max_steps     ,	__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)   ->	List[Any]:



            '''simple docstring'''




            __snake_case            =	int(state.epoch	) if int(state.epoch	) == state.epoch else F'''{state.epoch:.2f}'''
            self.training_tracker.update(
                state.global_step + 1     ,	comment=F'''Epoch {epoch}/{state.num_train_epochs}'''     ,	force_update=self._force_next_update     ,	)
            __snake_case            =	False


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=None     ,	**__SCREAMING_SNAKE_CASE	)   ->	Optional[int]:



            '''simple docstring'''




            if not has_length(__SCREAMING_SNAKE_CASE	):
                 return
            if self.prediction_bar is None:
                 if self.training_tracker is not None:
                      __snake_case            =	self.training_tracker.add_child(len(__SCREAMING_SNAKE_CASE	)	)
                 else:
                      __snake_case            =	NotebookProgressBar(len(__SCREAMING_SNAKE_CASE	)	)
                 self.prediction_bar.update(1	)
            else:
                 self.prediction_bar.update(self.prediction_bar.value + 1	)


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)   ->	Optional[int]:



            '''simple docstring'''




            if self.prediction_bar is not None:
                 self.prediction_bar.close()
            __snake_case            =	None


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=None     ,	**__SCREAMING_SNAKE_CASE	)   ->	List[Any]:



            '''simple docstring'''




            if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs:
                 __snake_case            =	{'''Training Loss''': logs['''loss''']}
                 # First column is necessarily Step sine we're not in epoch eval strategy
                 __snake_case            =	state.global_step
                 self.training_tracker.write_line(__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=None     ,	**__SCREAMING_SNAKE_CASE	)   ->	Optional[Any]:



            '''simple docstring'''




            if self.training_tracker is not None:
                 __snake_case            =	{'''Training Loss''': '''No log''', '''Validation Loss''': '''No log'''}
                 for log in reversed(state.log_history	):
                      if "loss" in log:
                           __snake_case            =	log['''loss''']
                           break

                 if self.first_column == "Epoch":
                      __snake_case            =	int(state.epoch	)
                 else:
                      __snake_case            =	state.global_step
                 __snake_case            =	'''eval'''
                 for k in metrics:
                      if k.endswith('''_loss'''	):
                           __snake_case            =	re.sub(r'''\_loss$'''     ,	''''''     ,	__SCREAMING_SNAKE_CASE	)
                 __snake_case            =	metrics.pop('''total_flos'''     ,	__SCREAMING_SNAKE_CASE	)
                 __snake_case            =	metrics.pop('''epoch'''     ,	__SCREAMING_SNAKE_CASE	)
                 __snake_case            =	metrics.pop(F'''{metric_key_prefix}_runtime'''     ,	__SCREAMING_SNAKE_CASE	)
                 __snake_case            =	metrics.pop(F'''{metric_key_prefix}_samples_per_second'''     ,	__SCREAMING_SNAKE_CASE	)
                 __snake_case            =	metrics.pop(F'''{metric_key_prefix}_steps_per_second'''     ,	__SCREAMING_SNAKE_CASE	)
                 __snake_case            =	metrics.pop(F'''{metric_key_prefix}_jit_compilation_time'''     ,	__SCREAMING_SNAKE_CASE	)
                 for k, v in metrics.items():
                      if k == F'''{metric_key_prefix}_loss''':
                           __snake_case            =	v
                      else:
                           __snake_case            =	k.split('''_'''	)
                           __snake_case            =	''' '''.join([part.capitalize() for part in splits[1:]]	)
                           __snake_case            =	v
                 self.training_tracker.write_line(__SCREAMING_SNAKE_CASE	)
                 self.training_tracker.remove_child()
                 __snake_case            =	None
                 # Evaluation takes a long time so we should force the next update.
                 __snake_case            =	True




       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)   ->	Optional[int]:



            '''simple docstring'''




            self.training_tracker.update(
                state.global_step     ,	comment=F'''Epoch {int(state.epoch	)}/{state.num_train_epochs}'''     ,	force_update=__SCREAMING_SNAKE_CASE	)
            __snake_case            =	None






