
"""simple docstring"""







import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     List[str]:







							"""simple docstring"""


							if isinstance(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE ):
														__snake_case     			=       np.full((len(SCREAMING_SNAKE_CASE ), sequence_length, 2)							,    SCREAMING_SNAKE_CASE )
							else:
														__snake_case     			=       np.full((len(SCREAMING_SNAKE_CASE ), sequence_length)							,    SCREAMING_SNAKE_CASE )

							for i, tensor in enumerate(SCREAMING_SNAKE_CASE ):
														if padding_side == "right":
																					if isinstance(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE ):
																												__snake_case     			=       tensor[:sequence_length]
																					else:
																												__snake_case     			=       tensor[:sequence_length]
														else:
																					if isinstance(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE ):
																												__snake_case     			=       tensor[:sequence_length]
																					else:
																												__snake_case     			=       tensor[:sequence_length]

							return out_tensor.tolist()
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     Optional[int]:







							"""simple docstring"""


							__snake_case     			=       ord(SCREAMING_SNAKE_CASE )
							if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 1_23 and cp <= 1_26):
														return True
							__snake_case     			=       unicodedata.category(SCREAMING_SNAKE_CASE )
							if cat.startswith("P" ):
														return True
							return False







@dataclass
class 							__magic_name__     (		UpperCamelCase__   ):
					_SCREAMING_SNAKE_CASE       :					PreTrainedTokenizerBase
					_SCREAMING_SNAKE_CASE       :					Union[bool, str, PaddingStrategy]     							=   True
					_SCREAMING_SNAKE_CASE       :					Optional[int]     							=   None
					_SCREAMING_SNAKE_CASE       :					Optional[int]     							=   None
					_SCREAMING_SNAKE_CASE       :					int     							=   -100
					_SCREAMING_SNAKE_CASE       :					str     							=   "pt"





					def 							lowerCAmelCase      (				self   :							Optional[Any]							,						snake_case_   :							int    ):
												import torch

												__snake_case     			=       "label" if "label" in features[0].keys() else "labels"
												__snake_case     			=       [feature[label_name] for feature in features] if label_name in features[0].keys() else None
												__snake_case     			=       self.tokenizer.pad(
												    snake_case_							,						padding=self.padding							,						max_length=self.max_length							,						pad_to_multiple_of=self.pad_to_multiple_of							,						return_tensors="pt" if labels is None else None							,						)

												if labels is None:
																			return batch

												__snake_case     			=       torch.tensor(batch["entity_ids"]    ).shape[1]
												__snake_case     			=       self.tokenizer.padding_side
												if padding_side == "right":
																			__snake_case     			=       [
																			    list(snake_case_    ) + [self.label_pad_token_id] * (sequence_length - len(snake_case_    )) for label in labels
																			]
												else:
																			__snake_case     			=       [
																			    [self.label_pad_token_id] * (sequence_length - len(snake_case_    )) + list(snake_case_    ) for label in labels
																			]

												__snake_case     			=       [feature["ner_tags"] for feature in features]
												__snake_case     			=       padding_tensor(snake_case_							,						-1							,						snake_case_							,						snake_case_    )
												__snake_case     			=       [feature["original_entity_spans"] for feature in features]
												__snake_case     			=       padding_tensor(snake_case_							,						(-1, -1)							,						snake_case_							,						snake_case_    )
												__snake_case     			=       {k: torch.tensor(snake_case_							,						dtype=torch.intaa    ) for k, v in batch.items()}

												return batch


"""simple docstring"""







import inspect
import re
from hashlib import shaaaa
from typing import Dict, List

from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql  # noqa F401
from .text import text
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     str:







							"""simple docstring"""


							__snake_case     			=       []
							for line in lines:
														__snake_case     			=       re.sub(r"#.*"							,    ""							,    SCREAMING_SNAKE_CASE )  # remove comments
														if line:
																					filtered_lines.append(SCREAMING_SNAKE_CASE )
							__snake_case     			=       "\n".join(SCREAMING_SNAKE_CASE )

							# Make a hash from all this code
							__snake_case     			=       full_str.encode("utf-8" )
							return shaaaa(SCREAMING_SNAKE_CASE ).hexdigest()


# get importable module names and hash for caching
_SCREAMING_SNAKE_CASE	       =      {
    """csv""": (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
    """json""": (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
    """pandas""": (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
    """parquet""": (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
    """arrow""": (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
    """text""": (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
    """imagefolder""": (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
    """audiofolder""": (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}

# Used to infer the module to use based on the data files extensions
_SCREAMING_SNAKE_CASE	       =      {
    """.csv""": ("""csv""", {}),
    """.tsv""": ("""csv""", {"""sep""": """\t"""}),
    """.json""": ("""json""", {}),
    """.jsonl""": ("""json""", {}),
    """.parquet""": ("""parquet""", {}),
    """.arrow""": ("""arrow""", {}),
    """.txt""": ("""text""", {}),
}
_EXTENSION_TO_MODULE.update({ext: ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_SCREAMING_SNAKE_CASE	       =      {"""imagefolder""", """audiofolder"""}

# Used to filter data files based on extensions given a module name
_SCREAMING_SNAKE_CASE	       =      {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
		_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)

_MODULE_TO_EXTENSIONS["imagefolder"].append(""".zip""")



_MODULE_TO_EXTENSIONS["audiofolder"].append(""".zip""")
