


import argparse
import importlib
from pathlib import Path


# Test all the extensions added in the setup
a_ :Optional[int]  							=			[
    'kernels/rwkv/wkv_cuda.cu',
    'kernels/rwkv/wkv_op.cpp',
    'kernels/deformable_detr/ms_deform_attn.h',
    'kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh',
    'models/graphormer/algos_graphormer.pyx',
]




def    a   (							A__							)	-> Union[str, Any]:




      '''simple docstring'''

      for file in FILES_TO_FIND:
            if not (transformers_path / file).exists():
                  return False
      return True


if __name__ == "__main__":
    a_ :List[Any]  							=			argparse.ArgumentParser()
    parser.add_argument('--check_lib', action='store_true', help='Whether to check the build or the actual package.')
    a_ :Dict  							=			parser.parse_args()
    if args.check_lib:
        a_ :List[Any]  							=			importlib.import_module('transformers')
        a_ :List[str]  							=			Path(transformers_module.__file__).parent
    else:
        a_ :Union[str, Any]  							=			Path.cwd() / 'build/lib/transformers'
    if not test_custom_files_are_present(transformers_path):
        raise ValueError('The built release does not contain the custom files. Fix this before going further!')











'''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


__a  			=						logging.getLogger(__name__)







@dataclass
class        UpperCAmelCase_			:
    """simple docstring"""







    lowercase   =						42
    lowercase   =						42
    lowercase   =						42







@dataclass
class        UpperCAmelCase_			:
    """simple docstring"""







    lowercase   =						42
    lowercase   =						42
    lowercase   =						None
    lowercase   =						None







class        UpperCAmelCase_			(			_a      ):
    """simple docstring"""







    lowercase   =						"train"
    lowercase   =						"dev"
    lowercase   =						"test"







class        UpperCAmelCase_			:
    """simple docstring"""







    @staticmethod
    def lowerCamelCase			(       snake_case_   :	Optional[Any]					,	snake_case_   :	Union[Split, str]		):
          raise NotImplementedError




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




    @staticmethod
    def lowerCamelCase			(       snake_case_   :	List[InputExample]					,	snake_case_   :	List[str]					,	snake_case_   :	int					,	snake_case_   :	PreTrainedTokenizer					,	snake_case_   :	Optional[int]=False					,	snake_case_   :	Dict="[CLS]"					,	snake_case_   :	str=1					,	snake_case_   :	Dict="[SEP]"					,	snake_case_   :	List[str]=False					,	snake_case_   :	int=False					,	snake_case_   :	Tuple=0					,	snake_case_   :	Union[str, Any]=0					,	snake_case_   :	List[Any]=-100					,	snake_case_   :	Any=0					,	snake_case_   :	Union[str, Any]=True					,	):
          snake_case__    :       int       		= {label: i for i, label in enumerate(snake_case_		)}

          snake_case__    :       List[Any]       		= []
          for ex_index, example in enumerate(snake_case_		):
                if ex_index % 10_000 == 0:
                      logger.info("""Writing example %d of %d"""					,	snake_case_					,	len(snake_case_		)		)

                snake_case__    :       Tuple       		= []
                snake_case__    :       Dict       		= []
                for word, label in zip(example.words					,	example.labels		):
                      snake_case__    :       Any       		= tokenizer.tokenize(snake_case_		)

                      # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
                      if len(snake_case_		) > 0:
                            tokens.extend(snake_case_		)
                            # 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(snake_case_		) - 1)		)

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
                snake_case__    :       Dict       		= tokenizer.num_special_tokens_to_add()
                if len(snake_case_		) > max_seq_length - special_tokens_count:
                      snake_case__    :       Tuple       		= tokens[: (max_seq_length - special_tokens_count)]
                      snake_case__    :       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]
                snake_case__    :       Dict       		= [sequence_a_segment_id] * len(snake_case_		)

                if cls_token_at_end:
                      tokens += [cls_token]
                      label_ids += [pad_token_label_id]
                      segment_ids += [cls_token_segment_id]
                else:
                      snake_case__    :       str       		= [cls_token] + tokens
                      snake_case__    :       Union[str, Any]       		= [pad_token_label_id] + label_ids
                      snake_case__    :       Any       		= [cls_token_segment_id] + segment_ids

                snake_case__    :       Union[str, Any]       		= tokenizer.convert_tokens_to_ids(snake_case_		)

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

                # Zero-pad up to the sequence length.
                snake_case__    :       Dict       		= max_seq_length - len(snake_case_		)
                if pad_on_left:
                      snake_case__    :       Optional[Any]       		= ([pad_token] * padding_length) + input_ids
                      snake_case__    :       Union[str, Any]       		= ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
                      snake_case__    :       Union[str, Any]       		= ([pad_token_segment_id] * padding_length) + segment_ids
                      snake_case__    :       Union[str, Any]       		= ([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(snake_case_		) == max_seq_length
                assert len(snake_case_		) == max_seq_length
                assert len(snake_case_		) == max_seq_length
                assert len(snake_case_		) == max_seq_length

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

                if "token_type_ids" not in tokenizer.model_input_names:
                      snake_case__    :       Tuple       		= None

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


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







       class        UpperCAmelCase_			(			_a      ):
           """simple docstring"""







           lowercase   =						42
           lowercase   =						nn.CrossEntropyLoss().ignore_index




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

                 # Make sure only the first process in distributed training processes the dataset,
                 # and the others will use the cache.
                 snake_case__    :       Dict       		= cached_features_file + """.lock"""
                 with FileLock(snake_case_		):
                       if os.path.exists(snake_case_		) and not overwrite_cache:
                             logger.info(f"Loading features from cached file {cached_features_file}"		)
                             snake_case__    :       Tuple       		= torch.load(snake_case_		)
                       else:
                             logger.info(f"Creating features from dataset file at {data_dir}"		)
                             snake_case__    :       Any       		= token_classification_task.read_examples_from_file(snake_case_					,	snake_case_		)
                             # TODO clean up all this to leverage built-in features of tokenizers
                             snake_case__    :       Any       		= token_classification_task.convert_examples_to_features(
                                 snake_case_					,	snake_case_					,	snake_case_					,	snake_case_					,	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=snake_case_					,	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					,	snake_case_		)




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




           def __getitem__(       self   :	int					,	snake_case_   :	Dict		):
                 return self.features[i]


if is_tf_available():
       import tensorflow as tf







       class        UpperCAmelCase_			:
           """simple docstring"""







           lowercase   =						42
           lowercase   =						-1_00




           def __init__(       self   :	List[str]					,	snake_case_   :	TokenClassificationTask					,	snake_case_   :	str					,	snake_case_   :	PreTrainedTokenizer					,	snake_case_   :	List[str]					,	snake_case_   :	str					,	snake_case_   :	Optional[int] = None					,	snake_case_   :	Any=False					,	snake_case_   :	Split = Split.train					,	):
                 snake_case__    :       int       		= token_classification_task.read_examples_from_file(snake_case_					,	snake_case_		)
                 # TODO clean up all this to leverage built-in features of tokenizers
                 snake_case__    :       Optional[Any]       		= token_classification_task.convert_examples_to_features(
                     snake_case_					,	snake_case_					,	snake_case_					,	snake_case_					,	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=snake_case_					,	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:
                       snake_case__    :       str       		= tf.data.Dataset.from_generator(
                           snake_case_					,	({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa)					,	(
                               {"""input_ids""": tf.TensorShape([None]		), """attention_mask""": tf.TensorShape([None]		)},
                               tf.TensorShape([None]		),
                           )					,	)
                 else:
                       snake_case__    :       str       		= tf.data.Dataset.from_generator(
                           snake_case_					,	({"""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 lowerCamelCase			(       self   :	Optional[Any]		):
                 snake_case__    :       Optional[Any]       		= 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   :	str					,	snake_case_   :	List[str]		):
                 return self.features[i]
