





import logging
import os
import threading
import time


try:
    import warnings
except ImportError:
    snake_case_				=			None

try:
    import msvcrt
except ImportError:
    snake_case_				=			None

try:
    import fcntl
except ImportError:
    snake_case_				=			None


# Backward compatibility
# ------------------------------------------------
try:
    TimeoutError
except NameError:
    snake_case_				=			OSError


# Data
# ------------------------------------------------
snake_case_				=			[
    'Timeout',
    'BaseFileLock',
    'WindowsFileLock',
    'UnixFileLock',
    'SoftFileLock',
    'FileLock',
]

snake_case_				=			'3.0.12'


snake_case_				=			None






def lowerCamelCase__    (							)      ->				Dict:
   global _logger
   __snake_case							       =   _logger or logging.getLogger(__name__       )
   return _logger

class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase  ):


      def __init__(self       :      Union[str, Any]				,			a__       :      int      ):





         """simple docstring"""



         __snake_case							       =   lock_file
         return None


      def __str__(self       :      List[str]      ):





         """simple docstring"""



         __snake_case							       =   f"""The file lock '{self.lock_file}' could not be acquired."""
         return temp






class 		SCREAMING_SNAKE_CASE__				:


      def __init__(self       :      Union[str, Any]				,			a__       :      List[str]      ):





         """simple docstring"""



         __snake_case							       =   lock
         return None


      def __enter__(self       :      Optional[Any]      ):





         """simple docstring"""



         return self.lock


      def __exit__(self       :      List[str]				,			a__       :      Optional[Any]				,			a__       :      Optional[int]				,			a__       :      Optional[int]      ):





         """simple docstring"""



         self.lock.release()
         return None






class 		SCREAMING_SNAKE_CASE__				:


      def __init__(self       :      Union[str, Any]				,			a__       :      int				,			a__       :      str=-1				,			a__       :      Tuple=None      ):





         """simple docstring"""



         __snake_case							       =   max_filename_length if max_filename_length is not None else 255
         # Hash the filename if it's too long
         __snake_case							       =   self.hash_filename_if_too_long(a__				,			a__      )
         # The path to the lock file.
         __snake_case							       =   lock_file

         # The file descriptor for the *_lock_file* as it is returned by the
         # os.open() function.
         # This file lock is only NOT None, if the object currently holds the
         # lock.
         __snake_case							       =   None

         # The default timeout value.
         __snake_case							       =   timeout

         # We use this lock primarily for the lock counter.
         __snake_case							       =   threading.Lock()

         # The lock counter is used for implementing the nested locking
         # mechanism. Whenever the lock is acquired, the counter is increased and
         # the lock is only released, when this value is 0 again.
         __snake_case							       =   0
         return None


      @property
      def    a (self       :      Any      ):





         """simple docstring"""



         return self._lock_file


      @property
      def    a (self       :      Dict      ):





         """simple docstring"""



         return self._timeout


      @timeout.setter
      def    a (self       :      Optional[Any]				,			a__       :      Optional[Any]      ):





         """simple docstring"""



         __snake_case							       =   float(a__      )
         return None


      def    a (self       :      Optional[int]      ):





         """simple docstring"""



         raise NotImplementedError()


      def    a (self       :      Optional[int]      ):





         """simple docstring"""



         raise NotImplementedError()


      @property
      def    a (self       :      List[str]      ):





         """simple docstring"""



         return self._lock_file_fd is not None


      def    a (self       :      Dict				,			a__       :      Any=None				,			a__       :      Optional[Any]=0.0_5      ):





         """simple docstring"""



         if timeout is None:
            __snake_case							       =   self.timeout

         # Increment the number right at the beginning.
         # We can still undo it, if something fails.
         with self._thread_lock:
            self._lock_counter += 1

         __snake_case							       =   id(self      )
         __snake_case							       =   self._lock_file
         __snake_case							       =   time.time()
         try:
            while True:
               with self._thread_lock:
                  if not self.is_locked:
                     logger().debug(f"""Attempting to acquire lock {lock_id} on {lock_filename}"""      )
                     self._acquire()

               if self.is_locked:
                  logger().debug(f"""Lock {lock_id} acquired on {lock_filename}"""      )
                  break
               elif timeout >= 0 and time.time() - start_time > timeout:
                  logger().debug(f"""Timeout on acquiring lock {lock_id} on {lock_filename}"""      )
                  raise Timeout(self._lock_file      )
               else:
                  logger().debug(
                      f"""Lock {lock_id} not acquired on {lock_filename}, waiting {poll_intervall} seconds ..."""      )
                  time.sleep(a__      )
         except:  # noqa
            # Something did go wrong, so decrement the counter.
            with self._thread_lock:
               __snake_case							       =   max(0				,			self._lock_counter - 1      )

            raise
         return _Acquire_ReturnProxy(lock=self      )


      def    a (self       :      List[Any]				,			a__       :      Union[str, Any]=False      ):





         """simple docstring"""



         with self._thread_lock:
            if self.is_locked:
               self._lock_counter -= 1

               if self._lock_counter == 0 or force:
                  __snake_case							       =   id(self      )
                  __snake_case							       =   self._lock_file

                  logger().debug(f"""Attempting to release lock {lock_id} on {lock_filename}"""      )
                  self._release()
                  __snake_case							       =   0
                  logger().debug(f"""Lock {lock_id} released on {lock_filename}"""      )

         return None


      def __enter__(self       :      Union[str, Any]      ):





         """simple docstring"""



         self.acquire()
         return self


      def __exit__(self       :      List[Any]				,			a__       :      Union[str, Any]				,			a__       :      Union[str, Any]				,			a__       :      Optional[int]      ):





         """simple docstring"""



         self.release()
         return None


      def __del__(self       :      str      ):





         """simple docstring"""



         self.release(force=a__      )
         return None


      def    a (self       :      Dict				,			a__       :      str				,			a__       :      int      ):





         """simple docstring"""



         __snake_case							       =   os.path.basename(a__      )
         if len(a__      ) > max_length and max_length > 0:
            __snake_case							       =   os.path.dirname(a__      )
            __snake_case							       =   str(hash(a__      )      )
            __snake_case							       =   filename[: max_length - len(a__      ) - 8] + '''...''' + hashed_filename + '''.lock'''
            return os.path.join(a__				,			a__      )
         else:
            return path






class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase  ):


      def __init__(self       :      Optional[Any]				,			a__       :      List[str]				,			a__       :      List[Any]=-1				,			a__       :      Tuple=None      ):





         """simple docstring"""



         from .file_utils import relative_to_absolute_path

         super().__init__(a__				,			timeout=a__				,			max_filename_length=a__      )
         __snake_case							       =   '''\\\\?\\''' + relative_to_absolute_path(self.lock_file      )


      def    a (self       :      List[str]      ):





         """simple docstring"""



         __snake_case							       =   os.O_RDWR | os.O_CREAT | os.O_TRUNC

         try:
            __snake_case							       =   os.open(self._lock_file				,			a__      )
         except OSError:
            pass
         else:
            try:
               msvcrt.locking(a__				,			msvcrt.LK_NBLCK				,			1      )
            except OSError:
               os.close(a__      )
            else:
               __snake_case							       =   fd
         return None


      def    a (self       :      int      ):





         """simple docstring"""



         __snake_case							       =   self._lock_file_fd
         __snake_case							       =   None
         msvcrt.locking(a__				,			msvcrt.LK_UNLCK				,			1      )
         os.close(a__      )

         try:
            os.remove(self._lock_file      )
         # Probably another instance of the application
         # that acquired the file lock.
         except OSError:
            pass
         return None






class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase  ):


      def __init__(self       :      Union[str, Any]				,			a__       :      Any				,			a__       :      List[Any]=-1				,			a__       :      Tuple=None      ):





         """simple docstring"""



         __snake_case							       =   os.statvfs(os.path.dirname(a__      )      ).f_namemax
         super().__init__(a__				,			timeout=a__				,			max_filename_length=a__      )


      def    a (self       :      List[Any]      ):





         """simple docstring"""



         __snake_case							       =   os.O_RDWR | os.O_CREAT | os.O_TRUNC
         __snake_case							       =   os.open(self._lock_file				,			a__      )

         try:
            fcntl.flock(a__				,			fcntl.LOCK_EX | fcntl.LOCK_NB      )
         except OSError:
            os.close(a__      )
         else:
            __snake_case							       =   fd
         return None


      def    a (self       :      List[Any]      ):





         """simple docstring"""



         __snake_case							       =   self._lock_file_fd
         __snake_case							       =   None
         fcntl.flock(a__				,			fcntl.LOCK_UN      )
         os.close(a__      )
         return None






class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase  ):


      def    a (self       :      Union[str, Any]      ):





         """simple docstring"""



         __snake_case							       =   os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC
         try:
            __snake_case							       =   os.open(self._lock_file				,			a__      )
         except OSError:
            pass
         else:
            __snake_case							       =   fd
         return None


      def    a (self       :      Any      ):





         """simple docstring"""



         os.close(self._lock_file_fd      )
         __snake_case							       =   None

         try:
            os.remove(self._lock_file      )
         # The file is already deleted and that's what we want.
         except OSError:
            pass
         return None






snake_case_				=			None

if msvcrt:
    snake_case_				=			WindowsFileLock
elif fcntl:
    snake_case_				=			UnixFileLock
else:
    snake_case_				=			SoftFileLock

    if warnings is not None:
        warnings.warn('only soft file lock is available')








import unittest

from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        LiltForQuestionAnswering,
        LiltForSequenceClassification,
        LiltForTokenClassification,
        LiltModel,
    )
    from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST

class 		SCREAMING_SNAKE_CASE__				:


      def __init__(self       :      Any				,			a__       :      Union[str, Any]				,			a__       :      int=13				,			a__       :      int=7				,			a__       :      Optional[Any]=True				,			a__       :      Optional[int]=True				,			a__       :      Any=True				,			a__       :      str=True				,			a__       :      List[Any]=99				,			a__       :      Any=24				,			a__       :      List[str]=2				,			a__       :      Optional[int]=6				,			a__       :      int=37				,			a__       :      List[str]="gelu"				,			a__       :      List[Any]=0.1				,			a__       :      Optional[int]=0.1				,			a__       :      Union[str, Any]=512				,			a__       :      List[str]=16				,			a__       :      Optional[int]=2				,			a__       :      Union[str, Any]=0.0_2				,			a__       :      str=3				,			a__       :      Optional[Any]=None				,			a__       :      Any=1000				,			):





         """simple docstring"""



         __snake_case							       =   parent
         __snake_case							       =   batch_size
         __snake_case							       =   seq_length
         __snake_case							       =   is_training
         __snake_case							       =   use_input_mask
         __snake_case							       =   use_token_type_ids
         __snake_case							       =   use_labels
         __snake_case							       =   vocab_size
         __snake_case							       =   hidden_size
         __snake_case							       =   num_hidden_layers
         __snake_case							       =   num_attention_heads
         __snake_case							       =   intermediate_size
         __snake_case							       =   hidden_act
         __snake_case							       =   hidden_dropout_prob
         __snake_case							       =   attention_probs_dropout_prob
         __snake_case							       =   max_position_embeddings
         __snake_case							       =   type_vocab_size
         __snake_case							       =   type_sequence_label_size
         __snake_case							       =   initializer_range
         __snake_case							       =   num_labels
         __snake_case							       =   scope
         __snake_case							       =   range_bbox


      def    a (self       :      Optional[int]      ):





         """simple docstring"""



         __snake_case							       =   ids_tensor([self.batch_size, self.seq_length]				,			self.vocab_size      )

         __snake_case							       =   ids_tensor([self.batch_size, self.seq_length, 4]				,			self.range_bbox      )
         # Ensure that bbox is legal
         for i in range(bbox.shape[0]      ):
            for j in range(bbox.shape[1]      ):
               if bbox[i, j, 3] < bbox[i, j, 1]:
                  __snake_case							       =   bbox[i, j, 3]
                  __snake_case							       =   bbox[i, j, 1]
                  __snake_case							       =   t
               if bbox[i, j, 2] < bbox[i, j, 0]:
                  __snake_case							       =   bbox[i, j, 2]
                  __snake_case							       =   bbox[i, j, 0]
                  __snake_case							       =   t

         __snake_case							       =   None
         if self.use_input_mask:
            __snake_case							       =   ids_tensor([self.batch_size, self.seq_length]				,			vocab_size=2      )

         __snake_case							       =   None
         if self.use_token_type_ids:
            __snake_case							       =   ids_tensor([self.batch_size, self.seq_length]				,			self.type_vocab_size      )

         __snake_case							       =   None
         __snake_case							       =   None
         if self.use_labels:
            __snake_case							       =   ids_tensor([self.batch_size]				,			self.type_sequence_label_size      )
            __snake_case							       =   ids_tensor([self.batch_size, self.seq_length]				,			self.num_labels      )

         __snake_case							       =   self.get_config()

         return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels


      def    a (self       :      List[str]      ):





         """simple docstring"""



         return LiltConfig(
             vocab_size=self.vocab_size				,			hidden_size=self.hidden_size				,			num_hidden_layers=self.num_hidden_layers				,			num_attention_heads=self.num_attention_heads				,			intermediate_size=self.intermediate_size				,			hidden_act=self.hidden_act				,			hidden_dropout_prob=self.hidden_dropout_prob				,			attention_probs_dropout_prob=self.attention_probs_dropout_prob				,			max_position_embeddings=self.max_position_embeddings				,			type_vocab_size=self.type_vocab_size				,			initializer_range=self.initializer_range				,			)


      def    a (self       :      List[Any]				,			a__       :      List[Any]				,			a__       :      Optional[Any]				,			a__       :      List[str]				,			a__       :      int				,			a__       :      Optional[int]				,			a__       :      str				,			a__       :      Optional[int]				,			):





         """simple docstring"""



         __snake_case							       =   LiltModel(config=a__      )
         model.to(a__      )
         model.eval()
         __snake_case							       =   model(a__				,			bbox=a__				,			attention_mask=a__				,			token_type_ids=a__      )
         __snake_case							       =   model(a__				,			bbox=a__				,			token_type_ids=a__      )
         __snake_case							       =   model(a__				,			bbox=a__      )

         self.parent.assertEqual(result.last_hidden_state.shape				,			(self.batch_size, self.seq_length, self.hidden_size)      )
         self.parent.assertEqual(result.pooler_output.shape				,			(self.batch_size, self.hidden_size)      )


      def    a (self       :      Any				,			a__       :      Tuple				,			a__       :      Dict				,			a__       :      Optional[int]				,			a__       :      Dict				,			a__       :      Union[str, Any]				,			a__       :      str				,			a__       :      Tuple				,			):





         """simple docstring"""



         __snake_case							       =   self.num_labels
         __snake_case							       =   LiltForTokenClassification(config=a__      )
         model.to(a__      )
         model.eval()
         __snake_case							       =   model(
             a__				,			bbox=a__				,			attention_mask=a__				,			token_type_ids=a__				,			labels=a__      )
         self.parent.assertEqual(result.logits.shape				,			(self.batch_size, self.seq_length, self.num_labels)      )


      def    a (self       :      int				,			a__       :      Optional[Any]				,			a__       :      int				,			a__       :      int				,			a__       :      Optional[Any]				,			a__       :      Tuple				,			a__       :      Union[str, Any]				,			a__       :      str				,			):





         """simple docstring"""



         __snake_case							       =   LiltForQuestionAnswering(config=a__      )
         model.to(a__      )
         model.eval()
         __snake_case							       =   model(
             a__				,			bbox=a__				,			attention_mask=a__				,			token_type_ids=a__				,			start_positions=a__				,			end_positions=a__				,			)
         self.parent.assertEqual(result.start_logits.shape				,			(self.batch_size, self.seq_length)      )
         self.parent.assertEqual(result.end_logits.shape				,			(self.batch_size, self.seq_length)      )


      def    a (self       :      Tuple      ):





         """simple docstring"""



         __snake_case							       =   self.prepare_config_and_inputs()
         (
             (
             __snake_case
         )       ,	(
             __snake_case
         )       ,	(
             __snake_case
         )       ,	(
             __snake_case
         )       ,	(
             __snake_case
         )       ,	(
             __snake_case
         )       ,	(
             __snake_case
         )       ,	
         )							       =   config_and_inputs
         __snake_case							       =   {
             '''input_ids''': input_ids,
             '''bbox''': bbox,
             '''token_type_ids''': token_type_ids,
             '''attention_mask''': input_mask,
         }
         return config, inputs_dict






@require_torch
class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase						,      unittest.TestCase  ):
      A_						:				List[Any]							=    (
          (
              LiltModel,
              LiltForSequenceClassification,
              LiltForTokenClassification,
              LiltForQuestionAnswering,
          )
          if is_torch_available()
          else ()
      )
      A_						:				Any							=    (
          {
              'feature-extraction': LiltModel,
              'question-answering': LiltForQuestionAnswering,
              'text-classification': LiltForSequenceClassification,
              'token-classification': LiltForTokenClassification,
              'zero-shot': LiltForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      A_						:				Optional[int]							=    False
      A_						:				List[Any]							=    False


      def    a (self       :      Dict				,			a__       :      Tuple				,			a__       :      Tuple				,			a__       :      Tuple				,			a__       :      Union[str, Any]				,			a__       :      Any      ):





         """simple docstring"""



         return True


      def    a (self       :      Union[str, Any]      ):





         """simple docstring"""



         __snake_case							       =   LiltModelTester(self      )
         __snake_case							       =   ConfigTester(self				,			config_class=a__				,			hidden_size=37      )


      def    a (self       :      Optional[int]      ):





         """simple docstring"""



         self.config_tester.run_common_tests()


      def    a (self       :      int      ):





         """simple docstring"""



         __snake_case							       =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*a__      )


      def    a (self       :      List[Any]      ):





         """simple docstring"""



         __snake_case							       =   self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
            __snake_case							       =   type
            self.model_tester.create_and_check_model(*a__      )


      def    a (self       :      Optional[Any]      ):





         """simple docstring"""



         __snake_case							       =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*a__      )


      def    a (self       :      Union[str, Any]      ):





         """simple docstring"""



         __snake_case							       =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*a__      )


      @slow
      def    a (self       :      Optional[int]      ):





         """simple docstring"""



         for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            __snake_case							       =   LiltModel.from_pretrained(a__      )
            self.assertIsNotNone(a__      )






@require_torch
@slow
class 		SCREAMING_SNAKE_CASE__				(       unittest.TestCase  ):


      def    a (self       :      Tuple      ):





         """simple docstring"""



         __snake_case							       =   LiltModel.from_pretrained('''SCUT-DLVCLab/lilt-roberta-en-base'''      ).to(a__      )

         __snake_case							       =   torch.tensor([[1, 2]]				,			device=a__      )
         __snake_case							       =   torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]				,			device=a__      )

         # forward pass
         with torch.no_grad():
            __snake_case							       =   model(input_ids=a__				,			bbox=a__      )

         __snake_case							       =   torch.Size([1, 2, 768]      )
         __snake_case							       =   torch.tensor(
             [[-0.0_6_5_3, 0.0_9_5_0, -0.0_0_6_1], [-0.0_5_4_5, 0.0_9_2_6, -0.0_3_2_4]]				,			device=a__				,			)

         self.assertTrue(outputs.last_hidden_state.shape				,			a__      )
         self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3]				,			a__				,			atol=1E-3      )      )

