




"""simple docstring"""


import tempfile
import unittest

from make_student import create_student_by_copying_alternating_layers

from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch


A_      =    '''sshleifer/bart-tiny-random'''
A_      =    '''patrickvonplaten/t5-tiny-random'''






@require_torch
class  lowercase(					unittest.TestCase			):







 '''simple docstring'''





 @cached_property
 def 			UpperCamelCase_       (    self:		List[str]		):




   '''simple docstring'''



   return AutoConfig.from_pretrained(a_		)


 def 			UpperCamelCase_       (    self:		int		):




   '''simple docstring'''



   _snake_case							:		List[Any]								=  create_student_by_copying_alternating_layers(a_,				tempfile.mkdtemp(),				e=1,				d=1		)
   self.assertEqual(student.config.num_hidden_layers,				1		)


 def 			UpperCamelCase_       (    self:		List[str]		):




   '''simple docstring'''



   _snake_case							:		Tuple								=  create_student_by_copying_alternating_layers(a_,				tempfile.mkdtemp(),				e=1,				d=a_		)


 def 			UpperCamelCase_       (    self:		Any		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  create_student_by_copying_alternating_layers(a_,				tempfile.mkdtemp(),				e=1,				d=a_		)
   self.assertEqual(student.config.encoder_layers,				1		)
   self.assertEqual(student.config.decoder_layers,				self.teacher_config.encoder_layers		)


 def 			UpperCamelCase_       (    self:		Any		):




   '''simple docstring'''



   _snake_case							:		Tuple								=  create_student_by_copying_alternating_layers(a_,				tempfile.mkdtemp(),				e=1,				d=1		)
   self.assertEqual(student.config.encoder_layers,				1		)
   self.assertEqual(student.config.decoder_layers,				1		)


 def 			UpperCamelCase_       (    self:		Any		):




   '''simple docstring'''



   with self.assertRaises(a_		):
     create_student_by_copying_alternating_layers(a_,				tempfile.mkdtemp(),				e=a_,				d=a_		)









"""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__	(snake_case__							: Optional[int]		,       snake_case__							: Any		,       snake_case__							: List[str]		,       snake_case__							: int   ):
  """simple docstring"""

  if isinstance(snake_case__		,       snake_case__   ):
    _snake_case							:		List[Any]								=  np.full((len(snake_case__   ), sequence_length, 2)		,       snake_case__   )
  else:
    _snake_case							:		Any								=  np.full((len(snake_case__   ), sequence_length)		,       snake_case__   )

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

  return out_tensor.tolist()


def  UpperCAmelCase__	(snake_case__							: Optional[int]   ):
  """simple docstring"""

  _snake_case							:		str								=  ord(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							:		Union[str, Any]								=  unicodedata.category(snake_case__   )
  if cat.startswith("""P"""   ):
    return True
  return False






@dataclass
class  lowercase(					__a			):







 '''simple docstring'''





 lowercase__			 = 42
 lowercase__			 = True
 lowercase__			 = None
 lowercase__			 = None
 lowercase__			 = -1_00
 lowercase__			 = "pt"


 def 			UpperCamelCase_       (    self:		Any,				a_:		Union[str, Any]		):




   '''simple docstring'''



   import torch

   _snake_case							:		Optional[Any]								=  """label""" if """label""" in features[0].keys() else """labels"""
   _snake_case							:		str								=  [feature[label_name] for feature in features] if label_name in features[0].keys() else None
   _snake_case							:		Any								=  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

   _snake_case							:		Optional[int]								=  torch.tensor(batch["""entity_ids"""]		).shape[1]
   _snake_case							:		Any								=  self.tokenizer.padding_side
   if padding_side == "right":
     _snake_case							:		Optional[int]								=  [
         list(a_		) + [self.label_pad_token_id] * (sequence_length - len(a_		)) for label in labels
     ]
   else:
     _snake_case							:		Union[str, Any]								=  [
         [self.label_pad_token_id] * (sequence_length - len(a_		)) + list(a_		) for label in labels
     ]

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

   return batch


