


'''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    lowerCamelCase__   ( A     :    Dict   ,     A     :    Optional[int]   ,     A     :    int   ,     A     :    Optional[int]  ):




						'''simple docstring'''






						if isinstance(A   ,     A  ):
												UpperCAmelCase         =			np.full((len(A  ), sequence_length, 2)   ,     A  )
						else:
												UpperCAmelCase         =			np.full((len(A  ), sequence_length)   ,     A  )

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

						return out_tensor.tolist()


def    lowerCamelCase__   ( A     :    Tuple  ):




						'''simple docstring'''






						UpperCAmelCase         =			ord(A  )
						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
						UpperCAmelCase         =			unicodedata.category(A  )
						if cat.startswith('''P'''  ):
												return True
						return False






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




				def   a__(	self    :				Any   ,					lowerCAmelCase    :				Dict   )->  Tuple:

										"""simple docstring"""




										import torch

										UpperCAmelCase         =			'''label''' if '''label''' in features[0].keys() else '''labels'''
										UpperCAmelCase         =			[feature[label_name] for feature in features] if label_name in features[0].keys() else None
										UpperCAmelCase         =			self.tokenizer.pad(
										    lowerCAmelCase   ,					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

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

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

										return batch









'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_lowercase    :    Any			     =  {
    """configuration_gpt_bigcode""": ["""GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTBigCodeConfig"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase    :    Optional[int]			     =  [
	    """GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """GPTBigCodeForSequenceClassification""",
	    """GPTBigCodeForTokenClassification""",
	    """GPTBigCodeForCausalLM""",
	    """GPTBigCodeModel""",
	    """GPTBigCodePreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_bigcode import (
		    GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTBigCodeForCausalLM,
		    GPTBigCodeForSequenceClassification,
		    GPTBigCodeForTokenClassification,
		    GPTBigCodeModel,
		    GPTBigCodePreTrainedModel,
		)


else:
	import sys

	_lowercase    :    Tuple			     =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





