



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 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase							,     __lowercase       )		->		Any:







						"""simple docstring"""

						if isinstance(__lowercase							,     __lowercase       ):
												A__	:							Tuple						    = np.full((len(__lowercase       ), sequence_length, 2)							,     __lowercase       )
						else:
												A__	:							str						    = np.full((len(__lowercase       ), sequence_length)							,     __lowercase       )

						for i, tensor in enumerate(__lowercase       ):
												if padding_side == "right":
																		if isinstance(__lowercase							,     __lowercase       ):
																								A__	:							Any						    = tensor[:sequence_length]
																		else:
																								A__	:							List[str]						    = tensor[:sequence_length]
												else:
																		if isinstance(__lowercase							,     __lowercase       ):
																								A__	:							Tuple						    = tensor[:sequence_length]
																		else:
																								A__	:							str						    = tensor[:sequence_length]

						return out_tensor.tolist()
def 	snake_case__       (					__lowercase       )		->		int:







						"""simple docstring"""

						A__	:							Optional[Any]						    = ord(__lowercase       )
						if (cp >= 3_3 and cp <= 4_7) or (cp >= 5_8 and cp <= 6_4) or (cp >= 9_1 and cp <= 9_6) or (cp >= 1_2_3 and cp <= 1_2_6):
												return True
						A__	:							Dict						    = unicodedata.category(__lowercase       )
						if cat.startswith("P"       ):
												return True
						return False




@dataclass
class 							lowerCAmelCase__  (	UpperCamelCase						):
					__A     :    PreTrainedTokenizerBase
					__A     :    Union[bool, str, PaddingStrategy]           =						True
					__A     :    Optional[int]           =						None
					__A     :    Optional[int]           =						None
					__A     :    int           =						-1_00
					__A     :    str           =						"pt"







					def  _lowercase			(       self      :     List[str]			,		_A      :     int):
											import torch

											A__	:							List[str]						    = "label" if "label" in features[0].keys() else "labels"
											A__	:							Tuple						    = [feature[label_name] for feature in features] if label_name in features[0].keys() else None
											A__	:							Dict						    = self.tokenizer.pad(
											    _A			,		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

											A__	:							Any						    = torch.tensor(batch["entity_ids"]).shape[1]
											A__	:							Dict						    = self.tokenizer.padding_side
											if padding_side == "right":
																	A__	:							List[str]						    = [
																	    list(_A) + [self.label_pad_token_id] * (sequence_length - len(_A)) for label in labels
																	]
											else:
																	A__	:							List[str]						    = [
																	    [self.label_pad_token_id] * (sequence_length - len(_A)) + list(_A) for label in labels
																	]

											A__	:							Optional[Any]						    = [feature["ner_tags"] for feature in features]
											A__	:							int						    = padding_tensor(_A			,		-1			,		_A			,		_A)
											A__	:							Union[str, Any]						    = [feature["original_entity_spans"] for feature in features]
											A__	:							Optional[Any]						    = padding_tensor(_A			,		(-1, -1)			,		_A			,		_A)
											A__	:							Optional[Any]						    = {k: torch.tensor(_A			,		dtype=torch.intaa) for k, v in batch.items()}

											return batch




import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin




class 							lowerCAmelCase__  (	unittest.TestCase					,							UpperCamelCase						):







					def  _lowercase			(       self      :     List[Any]):
											A__	:							Optional[Any]						    = load_tool("text-classification")
											self.tool.setup()
											A__	:							Any						    = load_tool("text-classification"			,		remote=_A)







					def  _lowercase			(       self      :     List[Any]):
											A__	:							Optional[Any]						    = self.tool("That's quite cool"			,		["positive", "negative"])
											self.assertEqual(_A			,		"positive")







					def  _lowercase			(       self      :     List[str]):
											A__	:							Any						    = self.remote_tool("That's quite cool"			,		["positive", "negative"])
											self.assertEqual(_A			,		"positive")







					def  _lowercase			(       self      :     Optional[Any]):
											A__	:							List[str]						    = self.tool(text="That's quite cool"			,		labels=["positive", "negative"])
											self.assertEqual(_A			,		"positive")







					def  _lowercase			(       self      :     Tuple):
											A__	:							Union[str, Any]						    = self.remote_tool(text="That's quite cool"			,		labels=["positive", "negative"])
											self.assertEqual(_A			,		"positive")