



"""simple docstring"""




import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


_lowerCAmelCase							:   int				=				logging.getLogger(__name__)






@dataclass
class       UpperCAmelCase_    :
      __SCREAMING_SNAKE_CASE		:  str
      __SCREAMING_SNAKE_CASE		:  List[str]
      __SCREAMING_SNAKE_CASE		:  Optional[List[str]]







@dataclass
class       UpperCAmelCase_    :
      __SCREAMING_SNAKE_CASE		:  List[int]
      __SCREAMING_SNAKE_CASE		:  List[int]
      __SCREAMING_SNAKE_CASE		:  Optional[List[int]]						=					None
      __SCREAMING_SNAKE_CASE		:  Optional[List[int]]						=					None







class       UpperCAmelCase_    (							_UpperCamelCase		):
      __SCREAMING_SNAKE_CASE		:  int						=					'train'
      __SCREAMING_SNAKE_CASE		:  List[str]						=					'dev'
      __SCREAMING_SNAKE_CASE		:  str						=					'test'







class       UpperCAmelCase_    :



      @staticmethod
      def 		snake_case_   (	A	:      str	,      A	:      Union[Split, str]		):
           raise NotImplementedError



      @staticmethod
      def 		snake_case_   (	A	:      str		):
           raise NotImplementedError



      @staticmethod
      def 		snake_case_   (	A	:      List[InputExample]	,      A	:      List[str]	,      A	:      int	,      A	:      PreTrainedTokenizer	,      A	:      Optional[int]=False	,      A	:      int="[CLS]"	,      A	:      Any=1	,      A	:      str="[SEP]"	,      A	:      Any=False	,      A	:      Dict=False	,      A	:      Tuple=0	,      A	:      Dict=0	,      A	:      List[Any]=-1_0_0	,      A	:      Tuple=0	,      A	:      Union[str, Any]=True	,      ):
           _UpperCAmelCase     :      Any														=       {label: i for i, label in enumerate(A		)}

           _UpperCAmelCase     :      str														=       []
           for ex_index, example in enumerate(A		):
                if ex_index % 1_0_0_0_0 == 0:
                     logger.info("Writing example %d of %d"	,      A	,      len(A		)		)

                _UpperCAmelCase     :      Optional[int]														=       []
                _UpperCAmelCase     :      Dict														=       []
                for word, label in zip(example.words	,      example.labels		):
                     _UpperCAmelCase     :      int														=       tokenizer.tokenize(A		)

                     # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
                     if len(A		) > 0:
                          tokens.extend(A		)
                          # Use the real label id for the first token of the word, and padding ids for the remaining tokens
                          label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(A		) - 1)		)

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
                _UpperCAmelCase     :      Optional[int]														=       tokenizer.num_special_tokens_to_add()
                if len(A		) > max_seq_length - special_tokens_count:
                     _UpperCAmelCase     :      Optional[int]														=       tokens[: (max_seq_length - special_tokens_count)]
                     _UpperCAmelCase     :      Tuple														=       label_ids[: (max_seq_length - special_tokens_count)]

                # The convention in BERT is:
                # (a) For sequence pairs:
                #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
                #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
                # (b) For single sequences:
                #  tokens:   [CLS] the dog is hairy . [SEP]
                #  type_ids:   0   0   0   0  0     0   0
                #
                # Where "type_ids" are used to indicate whether this is the first
                # sequence or the second sequence. The embedding vectors for `type=0` and
                # `type=1` were learned during pre-training and are added to the wordpiece
                # embedding vector (and position vector). This is not *strictly* necessary
                # since the [SEP] token unambiguously separates the sequences, but it makes
                # it easier for the model to learn the concept of sequences.
                #
                # For classification tasks, the first vector (corresponding to [CLS]) is
                # used as the "sentence vector". Note that this only makes sense because
                # the entire model is fine-tuned.
                tokens += [sep_token]
                label_ids += [pad_token_label_id]
                if sep_token_extra:
                     # roberta uses an extra separator b/w pairs of sentences
                     tokens += [sep_token]
                     label_ids += [pad_token_label_id]
                _UpperCAmelCase     :      Optional[Any]														=       [sequence_a_segment_id] * len(A		)

                if cls_token_at_end:
                     tokens += [cls_token]
                     label_ids += [pad_token_label_id]
                     segment_ids += [cls_token_segment_id]
                else:
                     _UpperCAmelCase     :      Any														=       [cls_token] + tokens
                     _UpperCAmelCase     :      Optional[int]														=       [pad_token_label_id] + label_ids
                     _UpperCAmelCase     :      Dict														=       [cls_token_segment_id] + segment_ids

                _UpperCAmelCase     :      Union[str, Any]														=       tokenizer.convert_tokens_to_ids(A		)

                # The mask has 1 for real tokens and 0 for padding tokens. Only real
                # tokens are attended to.
                _UpperCAmelCase     :      int														=       [1 if mask_padding_with_zero else 0] * len(A		)

                # Zero-pad up to the sequence length.
                _UpperCAmelCase     :      List[Any]														=       max_seq_length - len(A		)
                if pad_on_left:
                     _UpperCAmelCase     :      List[str]														=       ([pad_token] * padding_length) + input_ids
                     _UpperCAmelCase     :      Optional[int]														=       ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
                     _UpperCAmelCase     :      Optional[Any]														=       ([pad_token_segment_id] * padding_length) + segment_ids
                     _UpperCAmelCase     :      int														=       ([pad_token_label_id] * padding_length) + label_ids
                else:
                     input_ids += [pad_token] * padding_length
                     input_mask += [0 if mask_padding_with_zero else 1] * padding_length
                     segment_ids += [pad_token_segment_id] * padding_length
                     label_ids += [pad_token_label_id] * padding_length

                assert len(A		) == max_seq_length
                assert len(A		) == max_seq_length
                assert len(A		) == max_seq_length
                assert len(A		) == max_seq_length

                if ex_index < 5:
                     logger.info("*** Example ***"		)
                     logger.info("guid: %s"	,      example.guid		)
                     logger.info("tokens: %s"	,      " ".join([str(A		) for x in tokens]		)		)
                     logger.info("input_ids: %s"	,      " ".join([str(A		) for x in input_ids]		)		)
                     logger.info("input_mask: %s"	,      " ".join([str(A		) for x in input_mask]		)		)
                     logger.info("segment_ids: %s"	,      " ".join([str(A		) for x in segment_ids]		)		)
                     logger.info("label_ids: %s"	,      " ".join([str(A		) for x in label_ids]		)		)

                if "token_type_ids" not in tokenizer.model_input_names:
                     _UpperCAmelCase     :      Any														=       None

                features.append(
                    InputFeatures(
                        input_ids=A	,      attention_mask=A	,      token_type_ids=A	,      label_ids=A		)		)
           return features


if is_torch_available():
 import torch
 from torch import nn
 from torch.utils.data import Dataset






 class       UpperCAmelCase_    (							_UpperCamelCase		):
       __SCREAMING_SNAKE_CASE		:  List[InputFeatures]
       __SCREAMING_SNAKE_CASE		:  int						=					nn.CrossEntropyLoss().ignore_index



       def __init__(	self	:      Optional[int]	,      A	:      TokenClassificationTask	,      A	:      str	,      A	:      PreTrainedTokenizer	,      A	:      List[str]	,      A	:      str	,      A	:      Optional[int] = None	,      A	:      List[str]=False	,      A	:      Split = Split.train	,      ):
            # Load data features from cache or dataset file
            _UpperCAmelCase     :      Tuple														=       os.path.join(
                A	,      "cached_{}_{}_{}".format(mode.value	,      tokenizer.__class__.__name__	,      str(A		)		)	,      )

            # Make sure only the first process in distributed training processes the dataset,
            # and the others will use the cache.
            _UpperCAmelCase     :      List[str]														=       cached_features_file + ".lock"
            with FileLock(A		):
                 if os.path.exists(A		) and not overwrite_cache:
                      logger.info(f'Loading features from cached file {cached_features_file}'		)
                      _UpperCAmelCase     :      int														=       torch.load(A		)
                 else:
                      logger.info(f'Creating features from dataset file at {data_dir}'		)
                      _UpperCAmelCase     :      int														=       token_classification_task.read_examples_from_file(A	,      A		)
                      # TODO clean up all this to leverage built-in features of tokenizers
                      _UpperCAmelCase     :      List[str]														=       token_classification_task.convert_examples_to_features(
                          A	,      A	,      A	,      A	,      cls_token_at_end=bool(model_type in ["xlnet"]		)	,      cls_token=tokenizer.cls_token	,      cls_token_segment_id=2 if model_type in ["xlnet"] else 0	,      sep_token=tokenizer.sep_token	,      sep_token_extra=A	,      pad_on_left=bool(tokenizer.padding_side == "left"		)	,      pad_token=tokenizer.pad_token_id	,      pad_token_segment_id=tokenizer.pad_token_type_id	,      pad_token_label_id=self.pad_token_label_id	,      )
                      logger.info(f'Saving features into cached file {cached_features_file}'		)
                      torch.save(self.features	,      A		)



       def __len__(	self	:      str		):
            return len(self.features		)



       def __getitem__(	self	:      Any	,      A	:      Any		):
            return self.features[i]


if is_tf_available():
 import tensorflow as tf






 class       UpperCAmelCase_    :
       __SCREAMING_SNAKE_CASE		:  List[InputFeatures]
       __SCREAMING_SNAKE_CASE		:  int						=					-1_0_0



       def __init__(	self	:      Any	,      A	:      TokenClassificationTask	,      A	:      str	,      A	:      PreTrainedTokenizer	,      A	:      List[str]	,      A	:      str	,      A	:      Optional[int] = None	,      A	:      List[Any]=False	,      A	:      Split = Split.train	,      ):
            _UpperCAmelCase     :      int														=       token_classification_task.read_examples_from_file(A	,      A		)
            # TODO clean up all this to leverage built-in features of tokenizers
            _UpperCAmelCase     :      Optional[Any]														=       token_classification_task.convert_examples_to_features(
                A	,      A	,      A	,      A	,      cls_token_at_end=bool(model_type in ["xlnet"]		)	,      cls_token=tokenizer.cls_token	,      cls_token_segment_id=2 if model_type in ["xlnet"] else 0	,      sep_token=tokenizer.sep_token	,      sep_token_extra=A	,      pad_on_left=bool(tokenizer.padding_side == "left"		)	,      pad_token=tokenizer.pad_token_id	,      pad_token_segment_id=tokenizer.pad_token_type_id	,      pad_token_label_id=self.pad_token_label_id	,      )

            def gen():
                 for ex in self.features:
                      if ex.token_type_ids is None:
                           yield (
                               {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
                               ex.label_ids,
                           )
                      else:
                           yield (
                               {
                                   "input_ids": ex.input_ids,
                                   "attention_mask": ex.attention_mask,
                                   "token_type_ids": ex.token_type_ids,
                               },
                               ex.label_ids,
                           )

            if "token_type_ids" not in tokenizer.model_input_names:
                 _UpperCAmelCase     :      List[str]														=       tf.data.Dataset.from_generator(
                     A	,      ({"input_ids": tf.intaa, "attention_mask": tf.intaa}, tf.intaa)	,      (
                         {"input_ids": tf.TensorShape([None]		), "attention_mask": tf.TensorShape([None]		)},
                         tf.TensorShape([None]		),
                     )	,      )
            else:
                 _UpperCAmelCase     :      List[str]														=       tf.data.Dataset.from_generator(
                     A	,      ({"input_ids": tf.intaa, "attention_mask": tf.intaa, "token_type_ids": tf.intaa}, tf.intaa)	,      (
                         {
                             "input_ids": tf.TensorShape([None]		),
                             "attention_mask": tf.TensorShape([None]		),
                             "token_type_ids": tf.TensorShape([None]		),
                         },
                         tf.TensorShape([None]		),
                     )	,      )



       def 		snake_case_   (	self	:      Optional[Any]		):
            _UpperCAmelCase     :      Optional[int]														=       self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features		)		)		)

            return self.dataset



       def __len__(	self	:      Optional[Any]		):
            return len(self.features		)



       def __getitem__(	self	:      Optional[Any]	,      A	:      Optional[Any]		):
            return self.features[i]





"""simple docstring"""




import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
 from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments






@require_torch
class       UpperCAmelCase_    (							unittest.TestCase		):



      def 		snake_case_   (	self	:      Union[str, Any]	,      A	:      Optional[Any]		):
           for model_result in results.values():
                for batch_size, sequence_length in zip(model_result["bs"]	,      model_result["ss"]		):
                     _UpperCAmelCase     :      List[str]														=       model_result["result"][batch_size][sequence_length]
                     self.assertIsNotNone(A		)



      def 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase     :      Any														=       "sshleifer/tiny-gpt2"
           _UpperCAmelCase     :      int														=       PyTorchBenchmarkArguments(
               models=[MODEL_ID]	,      training=A	,      inference=A	,      sequence_lengths=[8]	,      batch_sizes=[1]	,      multi_process=A	,      )
           _UpperCAmelCase     :      List[Any]														=       PyTorchBenchmark(A		)
           _UpperCAmelCase     :      List[Any]														=       benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result		)
           self.check_results_dict_not_empty(results.memory_inference_result		)



      def 		snake_case_   (	self	:      Dict		):
           _UpperCAmelCase     :      List[str]														=       "sgugger/tiny-distilbert-classification"
           _UpperCAmelCase     :      Optional[int]														=       PyTorchBenchmarkArguments(
               models=[MODEL_ID]	,      training=A	,      inference=A	,      sequence_lengths=[8]	,      batch_sizes=[1]	,      multi_process=A	,      only_pretrain_model=A	,      )
           _UpperCAmelCase     :      Dict														=       PyTorchBenchmark(A		)
           _UpperCAmelCase     :      Optional[Any]														=       benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result		)
           self.check_results_dict_not_empty(results.memory_inference_result		)



      def 		snake_case_   (	self	:      int		):
           _UpperCAmelCase     :      Any														=       "sshleifer/tiny-gpt2"
           _UpperCAmelCase     :      List[Any]														=       PyTorchBenchmarkArguments(
               models=[MODEL_ID]	,      training=A	,      inference=A	,      torchscript=A	,      sequence_lengths=[8]	,      batch_sizes=[1]	,      multi_process=A	,      )
           _UpperCAmelCase     :      Dict														=       PyTorchBenchmark(A		)
           _UpperCAmelCase     :      Optional[Any]														=       benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result		)
           self.check_results_dict_not_empty(results.memory_inference_result		)



      @unittest.skipIf(torch_device == "cpu"	,      "Cant do half precision"		)
      def 		snake_case_   (	self	:      List[str]		):
           _UpperCAmelCase     :      Union[str, Any]														=       "sshleifer/tiny-gpt2"
           _UpperCAmelCase     :      Optional[Any]														=       PyTorchBenchmarkArguments(
               models=[MODEL_ID]	,      training=A	,      inference=A	,      fpaa=A	,      sequence_lengths=[8]	,      batch_sizes=[1]	,      multi_process=A	,      )
           _UpperCAmelCase     :      List[str]														=       PyTorchBenchmark(A		)
           _UpperCAmelCase     :      List[Any]														=       benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result		)
           self.check_results_dict_not_empty(results.memory_inference_result		)



      def 		snake_case_   (	self	:      List[Any]		):
           _UpperCAmelCase     :      Optional[Any]														=       "sshleifer/tiny-gpt2"
           _UpperCAmelCase     :      int														=       AutoConfig.from_pretrained(A		)
           # set architectures equal to `None`
           _UpperCAmelCase     :      Optional[Any]														=       None
           _UpperCAmelCase     :      Optional[Any]														=       PyTorchBenchmarkArguments(
               models=[MODEL_ID]	,      training=A	,      inference=A	,      sequence_lengths=[8]	,      batch_sizes=[1]	,      multi_process=A	,      )
           _UpperCAmelCase     :      List[Any]														=       PyTorchBenchmark(A	,      configs=[config]		)
           _UpperCAmelCase     :      Optional[Any]														=       benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result		)
           self.check_results_dict_not_empty(results.memory_inference_result		)



      def 		snake_case_   (	self	:      int		):
           _UpperCAmelCase     :      Dict														=       "sshleifer/tiny-gpt2"
           _UpperCAmelCase     :      Union[str, Any]														=       PyTorchBenchmarkArguments(
               models=[MODEL_ID]	,      training=A	,      inference=A	,      sequence_lengths=[8]	,      batch_sizes=[1]	,      multi_process=A	,      )
           _UpperCAmelCase     :      Tuple														=       PyTorchBenchmark(A		)
           _UpperCAmelCase     :      Union[str, Any]														=       benchmark.run()
           self.check_results_dict_not_empty(results.time_train_result		)
           self.check_results_dict_not_empty(results.memory_train_result		)



      @unittest.skipIf(torch_device == "cpu"	,      "Can't do half precision"		)
      def 		snake_case_   (	self	:      List[Any]		):
           _UpperCAmelCase     :      Optional[int]														=       "sshleifer/tiny-gpt2"
           _UpperCAmelCase     :      str														=       PyTorchBenchmarkArguments(
               models=[MODEL_ID]	,      training=A	,      inference=A	,      sequence_lengths=[8]	,      batch_sizes=[1]	,      fpaa=A	,      multi_process=A	,      )
           _UpperCAmelCase     :      Tuple														=       PyTorchBenchmark(A		)
           _UpperCAmelCase     :      List[Any]														=       benchmark.run()
           self.check_results_dict_not_empty(results.time_train_result		)
           self.check_results_dict_not_empty(results.memory_train_result		)



      def 		snake_case_   (	self	:      List[Any]		):
           _UpperCAmelCase     :      Optional[Any]														=       "sshleifer/tiny-gpt2"
           _UpperCAmelCase     :      Union[str, Any]														=       AutoConfig.from_pretrained(A		)
           _UpperCAmelCase     :      Dict														=       PyTorchBenchmarkArguments(
               models=[MODEL_ID]	,      training=A	,      inference=A	,      sequence_lengths=[8]	,      batch_sizes=[1]	,      multi_process=A	,      )
           _UpperCAmelCase     :      List[Any]														=       PyTorchBenchmark(A	,      configs=[config]		)
           _UpperCAmelCase     :      List[str]														=       benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result		)
           self.check_results_dict_not_empty(results.memory_inference_result		)



      def 		snake_case_   (	self	:      str		):
           _UpperCAmelCase     :      List[str]														=       "sshleifer/tinier_bart"
           _UpperCAmelCase     :      Any														=       AutoConfig.from_pretrained(A		)
           _UpperCAmelCase     :      Optional[int]														=       PyTorchBenchmarkArguments(
               models=[MODEL_ID]	,      training=A	,      inference=A	,      sequence_lengths=[8]	,      batch_sizes=[1]	,      multi_process=A	,      )
           _UpperCAmelCase     :      int														=       PyTorchBenchmark(A	,      configs=[config]		)
           _UpperCAmelCase     :      Union[str, Any]														=       benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result		)
           self.check_results_dict_not_empty(results.memory_inference_result		)



      def 		snake_case_   (	self	:      Any		):
           _UpperCAmelCase     :      Tuple														=       "sshleifer/tiny-gpt2"
           _UpperCAmelCase     :      Tuple														=       AutoConfig.from_pretrained(A		)
           _UpperCAmelCase     :      List[Any]														=       PyTorchBenchmarkArguments(
               models=[MODEL_ID]	,      training=A	,      inference=A	,      sequence_lengths=[8]	,      batch_sizes=[1]	,      multi_process=A	,      )
           _UpperCAmelCase     :      int														=       PyTorchBenchmark(A	,      configs=[config]		)
           _UpperCAmelCase     :      Optional[int]														=       benchmark.run()
           self.check_results_dict_not_empty(results.time_train_result		)
           self.check_results_dict_not_empty(results.memory_train_result		)



      def 		snake_case_   (	self	:      str		):
           _UpperCAmelCase     :      Optional[int]														=       "sshleifer/tinier_bart"
           _UpperCAmelCase     :      int														=       AutoConfig.from_pretrained(A		)
           _UpperCAmelCase     :      List[Any]														=       PyTorchBenchmarkArguments(
               models=[MODEL_ID]	,      training=A	,      inference=A	,      sequence_lengths=[8]	,      batch_sizes=[1]	,      multi_process=A	,      )
           _UpperCAmelCase     :      str														=       PyTorchBenchmark(A	,      configs=[config]		)
           _UpperCAmelCase     :      int														=       benchmark.run()
           self.check_results_dict_not_empty(results.time_train_result		)
           self.check_results_dict_not_empty(results.memory_train_result		)



      def 		snake_case_   (	self	:      Dict		):
           _UpperCAmelCase     :      int														=       "sshleifer/tiny-gpt2"
           with tempfile.TemporaryDirectory() as tmp_dir:
                _UpperCAmelCase     :      Union[str, Any]														=       PyTorchBenchmarkArguments(
                    models=[MODEL_ID]	,      training=A	,      inference=A	,      save_to_csv=A	,      sequence_lengths=[8]	,      batch_sizes=[1]	,      inference_time_csv_file=os.path.join(A	,      "inf_time.csv"		)	,      train_memory_csv_file=os.path.join(A	,      "train_mem.csv"		)	,      inference_memory_csv_file=os.path.join(A	,      "inf_mem.csv"		)	,      train_time_csv_file=os.path.join(A	,      "train_time.csv"		)	,      env_info_csv_file=os.path.join(A	,      "env.csv"		)	,      multi_process=A	,      )
                _UpperCAmelCase     :      int														=       PyTorchBenchmark(A		)
                benchmark.run()
                self.assertTrue(Path(os.path.join(A	,      "inf_time.csv"		)		).exists()		)
                self.assertTrue(Path(os.path.join(A	,      "train_time.csv"		)		).exists()		)
                self.assertTrue(Path(os.path.join(A	,      "inf_mem.csv"		)		).exists()		)
                self.assertTrue(Path(os.path.join(A	,      "train_mem.csv"		)		).exists()		)
                self.assertTrue(Path(os.path.join(A	,      "env.csv"		)		).exists()		)



      def 		snake_case_   (	self	:      List[str]		):
           _UpperCAmelCase     :      List[Any]														=       "sshleifer/tiny-gpt2"

           def _check_summary_is_not_empty(A	:      int		):
                self.assertTrue(hasattr(A	,      "sequential"		)		)
                self.assertTrue(hasattr(A	,      "cumulative"		)		)
                self.assertTrue(hasattr(A	,      "current"		)		)
                self.assertTrue(hasattr(A	,      "total"		)		)

           with tempfile.TemporaryDirectory() as tmp_dir:
                _UpperCAmelCase     :      int														=       PyTorchBenchmarkArguments(
                    models=[MODEL_ID]	,      training=A	,      inference=A	,      sequence_lengths=[8]	,      batch_sizes=[1]	,      log_filename=os.path.join(A	,      "log.txt"		)	,      log_print=A	,      trace_memory_line_by_line=A	,      multi_process=A	,      )
                _UpperCAmelCase     :      int														=       PyTorchBenchmark(A		)
                _UpperCAmelCase     :      Any														=       benchmark.run()
                _check_summary_is_not_empty(result.inference_summary		)
                _check_summary_is_not_empty(result.train_summary		)
                self.assertTrue(Path(os.path.join(A	,      "log.txt"		)		).exists()		)
