'''simple docstring'''






import unittest

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

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 (
       LayoutLMForMaskedLM,
       LayoutLMForQuestionAnswering,
       LayoutLMForSequenceClassification,
       LayoutLMForTokenClassification,
       LayoutLMModel,
   )







class lowercase_					:







   """simple docstring"""

   def __init__(			self			: Optional[Any],   UpperCamelCase__			: str,   UpperCamelCase__			: str=13,   UpperCamelCase__			: Union[str, Any]=7,   UpperCamelCase__			: Optional[Any]=True,   UpperCamelCase__			: str=True,   UpperCamelCase__			: Tuple=True,   UpperCamelCase__			: Optional[int]=True,   UpperCamelCase__			: Tuple=99,   UpperCamelCase__			: List[str]=32,   UpperCamelCase__			: Optional[int]=5,   UpperCamelCase__			: str=4,   UpperCamelCase__			: Any=37,   UpperCamelCase__			: Optional[Any]="gelu",   UpperCamelCase__			: Optional[int]=0.1,   UpperCamelCase__			: Union[str, Any]=0.1,   UpperCamelCase__			: int=5_12,   UpperCamelCase__			: int=16,   UpperCamelCase__			: Union[str, Any]=2,   UpperCamelCase__			: Tuple=0.02,   UpperCamelCase__			: Any=3,   UpperCamelCase__			: str=4,   UpperCamelCase__			: Tuple=None,   UpperCamelCase__			: Tuple=10_00,   )     -> List[str]:
        _A								=     parent
        _A								=     batch_size
        _A								=     seq_length
        _A								=     is_training
        _A								=     use_input_mask
        _A								=     use_token_type_ids
        _A								=     use_labels
        _A								=     vocab_size
        _A								=     hidden_size
        _A								=     num_hidden_layers
        _A								=     num_attention_heads
        _A								=     intermediate_size
        _A								=     hidden_act
        _A								=     hidden_dropout_prob
        _A								=     attention_probs_dropout_prob
        _A								=     max_position_embeddings
        _A								=     type_vocab_size
        _A								=     type_sequence_label_size
        _A								=     initializer_range
        _A								=     num_labels
        _A								=     num_choices
        _A								=     scope
        _A								=     range_bbox
   def   __UpperCAmelCase      (			self			: Any    )     -> Optional[int]:
        _A								=     ids_tensor([self.batch_size, self.seq_length],   self.vocab_size    )

        _A								=     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]:
                       _A								=     bbox[i, j, 3]
                       _A								=     bbox[i, j, 1]
                       _A								=     t
                  if bbox[i, j, 2] < bbox[i, j, 0]:
                       _A								=     bbox[i, j, 2]
                       _A								=     bbox[i, j, 0]
                       _A								=     t

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

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

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

        _A								=     self.get_config()

        return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
   def   __UpperCAmelCase      (			self			: Optional[int]    )     -> Optional[int]:
        return LayoutLMConfig(
            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   __UpperCAmelCase      (			self			: Any,   UpperCamelCase__			: Union[str, Any],   UpperCamelCase__			: str,   UpperCamelCase__			: List[str],   UpperCamelCase__			: Any,   UpperCamelCase__			: Optional[Any],   UpperCamelCase__			: Union[str, Any],   UpperCamelCase__			: Union[str, Any],   UpperCamelCase__			: Optional[Any]    )     -> Any:
        _A								=     LayoutLMModel(config=UpperCamelCase__    )
        model.to(UpperCamelCase__    )
        model.eval()
        _A								=     model(UpperCamelCase__,   UpperCamelCase__,   attention_mask=UpperCamelCase__,   token_type_ids=UpperCamelCase__    )
        _A								=     model(UpperCamelCase__,   UpperCamelCase__,   token_type_ids=UpperCamelCase__    )
        _A								=     model(UpperCamelCase__,   UpperCamelCase__    )
        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   __UpperCAmelCase      (			self			: Tuple,   UpperCamelCase__			: Any,   UpperCamelCase__			: Optional[int],   UpperCamelCase__			: Optional[Any],   UpperCamelCase__			: Tuple,   UpperCamelCase__			: Union[str, Any],   UpperCamelCase__			: Tuple,   UpperCamelCase__			: Optional[int],   UpperCamelCase__			: int    )     -> Tuple:
        _A								=     LayoutLMForMaskedLM(config=UpperCamelCase__    )
        model.to(UpperCamelCase__    )
        model.eval()
        _A								=     model(UpperCamelCase__,   UpperCamelCase__,   attention_mask=UpperCamelCase__,   token_type_ids=UpperCamelCase__,   labels=UpperCamelCase__    )
        self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.seq_length, self.vocab_size)    )
   def   __UpperCAmelCase      (			self			: Optional[int],   UpperCamelCase__			: int,   UpperCamelCase__			: Union[str, Any],   UpperCamelCase__			: Optional[Any],   UpperCamelCase__			: str,   UpperCamelCase__			: List[Any],   UpperCamelCase__			: Any,   UpperCamelCase__			: Dict,   UpperCamelCase__			: Union[str, Any]    )     -> Dict:
        _A								=     self.num_labels
        _A								=     LayoutLMForSequenceClassification(UpperCamelCase__    )
        model.to(UpperCamelCase__    )
        model.eval()
        _A								=     model(
            UpperCamelCase__,   UpperCamelCase__,   attention_mask=UpperCamelCase__,   token_type_ids=UpperCamelCase__,   labels=UpperCamelCase__    )
        self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.num_labels)    )
   def   __UpperCAmelCase      (			self			: Union[str, Any],   UpperCamelCase__			: str,   UpperCamelCase__			: List[Any],   UpperCamelCase__			: Union[str, Any],   UpperCamelCase__			: int,   UpperCamelCase__			: Dict,   UpperCamelCase__			: str,   UpperCamelCase__			: str,   UpperCamelCase__			: Optional[int]    )     -> Dict:
        _A								=     self.num_labels
        _A								=     LayoutLMForTokenClassification(config=UpperCamelCase__    )
        model.to(UpperCamelCase__    )
        model.eval()
        _A								=     model(UpperCamelCase__,   UpperCamelCase__,   attention_mask=UpperCamelCase__,   token_type_ids=UpperCamelCase__,   labels=UpperCamelCase__    )
        self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.seq_length, self.num_labels)    )
   def   __UpperCAmelCase      (			self			: List[Any],   UpperCamelCase__			: Optional[Any],   UpperCamelCase__			: List[str],   UpperCamelCase__			: str,   UpperCamelCase__			: Union[str, Any],   UpperCamelCase__			: str,   UpperCamelCase__			: str,   UpperCamelCase__			: str,   UpperCamelCase__			: Dict    )     -> List[Any]:
        _A								=     LayoutLMForQuestionAnswering(config=UpperCamelCase__    )
        model.to(UpperCamelCase__    )
        model.eval()
        _A								=     model(
            UpperCamelCase__,   bbox=UpperCamelCase__,   attention_mask=UpperCamelCase__,   token_type_ids=UpperCamelCase__,   start_positions=UpperCamelCase__,   end_positions=UpperCamelCase__,   )
        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   __UpperCAmelCase      (			self			: Optional[Any]    )     -> List[Any]:
        _A								=     self.prepare_config_and_inputs()
        (
            (
            _A
        )		,					(
            _A
        )		,					(
            _A
        )		,					(
            _A
        )		,					(
            _A
        )		,					(
            _A
        )		,					(
            _A
        )		,					(
            _A
        )		,					
        )								=     config_and_inputs
        _A								=     {
            'input_ids': input_ids,
            'bbox': bbox,
            'token_type_ids': token_type_ids,
            'attention_mask': input_mask,
        }
        return config, inputs_dict







@require_torch
class lowercase_					(		_UpperCamelCase						,						_UpperCamelCase						,						unittest.TestCase ):







   """simple docstring"""

   __lowerCAmelCase    						=      (
       (
           LayoutLMModel,
           LayoutLMForMaskedLM,
           LayoutLMForSequenceClassification,
           LayoutLMForTokenClassification,
           LayoutLMForQuestionAnswering,
       )
       if is_torch_available()
       else None
   )
   __lowerCAmelCase    						=      (
       {
           "document-question-answering": LayoutLMForQuestionAnswering,
           "feature-extraction": LayoutLMModel,
           "fill-mask": LayoutLMForMaskedLM,
           "text-classification": LayoutLMForSequenceClassification,
           "token-classification": LayoutLMForTokenClassification,
           "zero-shot": LayoutLMForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   __lowerCAmelCase    						=      True
   def   __UpperCAmelCase      (			self			: str    )     -> Tuple:
        _A								=     LayoutLMModelTester(self    )
        _A								=     ConfigTester(self,   config_class=UpperCamelCase__,   hidden_size=37    )
   def   __UpperCAmelCase      (			self			: str    )     -> Dict:
        self.config_tester.run_common_tests()
   def   __UpperCAmelCase      (			self			: Dict    )     -> Dict:
        _A								=     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*UpperCamelCase__    )
   def   __UpperCAmelCase      (			self			: List[str]    )     -> Union[str, Any]:
        _A								=     self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
             _A								=     type
             self.model_tester.create_and_check_model(*UpperCamelCase__    )
   def   __UpperCAmelCase      (			self			: Any    )     -> List[Any]:
        _A								=     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase__    )
   def   __UpperCAmelCase      (			self			: Optional[Any]    )     -> int:
        _A								=     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase__    )
   def   __UpperCAmelCase      (			self			: Optional[Any]    )     -> Union[str, Any]:
        _A								=     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*UpperCamelCase__    )
   def   __UpperCAmelCase      (			self			: List[str]    )     -> Tuple:
        _A								=     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*UpperCamelCase__    )

   @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'    )
   def   __UpperCAmelCase      (			self			: Union[str, Any]    )     -> List[str]:
        pass






def    _SCREAMING_SNAKE_CASE		(			):
     # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
     # fmt: off
     _A								=     torch.tensor([[1_0_1,1_0_1_9,1_0_1_4,1_0_1_6,1_0_3_7,1_2_8_4_9,4_7_4_7,1_0_0_4,1_4_2_4_6,2_2_7_8,5_4_3_9,4_5_2_4,5_0_0_2,2_9_3_0,2_1_9_3,2_9_3_0,4_3_4_1,3_2_0_8,1_0_0_5,1_0_5_5,2_1_7_1,2_8_4_8,1_1_3_0_0,3_5_3_1,1_0_2],[1_0_1,4_0_7_0,4_0_3_4,7_0_2_0,1_0_2_4,3_0_5_8,1_0_1_5,1_0_1_3,2_8_6_1,1_0_1_3,6_0_7_0,1_9_2_7_4,2_7_7_2,6_2_0_5,2_7_8_1_4,1_6_1_4_7,1_6_1_4_7,4_3_4_3,2_0_4_7,1_0_2_8_3,1_0_9_6_9,1_4_3_8_9,1_0_1_2,2_3_3_8,1_0_2]]      ,		device=__snake_case							)  # noqa: E231
     _A								=     torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]      ,		device=__snake_case							)  # noqa: E231
     _A								=     torch.tensor([[[0,0,0,0],[4_2_3,2_3_7,4_4_0,2_5_1],[4_2_7,2_7_2,4_4_1,2_8_7],[4_1_9,1_1_5,4_3_7,1_2_9],[9_6_1,8_8_5,9_9_2,9_1_2],[2_5_6,3_8,3_3_0,5_8],[2_5_6,3_8,3_3_0,5_8],[3_3_6,4_2,3_5_3,5_7],[3_6_0,3_9,4_0_1,5_6],[3_6_0,3_9,4_0_1,5_6],[4_1_1,3_9,4_7_1,5_9],[4_7_9,4_1,5_2_8,5_9],[5_3_3,3_9,6_3_0,6_0],[6_7,1_1_3,1_3_4,1_3_1],[1_4_1,1_1_5,2_0_9,1_3_2],[6_8,1_4_9,1_3_3,1_6_6],[1_4_1,1_4_9,1_8_7,1_6_4],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[2_9_5,1_4_8,3_4_9,1_6_5],[4_4_1,1_4_9,4_9_2,1_6_6],[4_9_7,1_4_9,5_4_6,1_6_4],[6_4,2_0_1,1_2_5,2_1_8],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]],[[0,0,0,0],[6_6_2,1_5_0,7_5_4,1_6_6],[6_6_5,1_9_9,7_4_2,2_1_1],[5_1_9,2_1_3,5_5_4,2_2_8],[5_1_9,2_1_3,5_5_4,2_2_8],[1_3_4,4_3_3,1_8_7,4_5_4],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[3_1_4,4_6_9,3_7_6,4_8_2],[5_0_4,6_8_4,5_8_2,7_0_6],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[6_1_0,7_4_9,6_5_2,7_6_5],[1_3_0,6_5_9,1_6_8,6_7_2],[1_7_6,6_5_7,2_3_7,6_7_2],[2_3_8,6_5_7,3_1_2,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[7_1_6,3_0_1,8_2_5,3_1_7],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]]]      ,		device=__snake_case							)  # noqa: E231
     _A								=     torch.tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]      ,		device=__snake_case							)  # noqa: E231
     # these are sequence labels (i.e. at the token level)
     _A								=     torch.tensor([[-1_0_0,1_0,1_0,1_0,9,1,-1_0_0,7,7,-1_0_0,7,7,4,2,5,2,8,8,-1_0_0,-1_0_0,5,0,3,2,-1_0_0],[-1_0_0,1_2,1_2,1_2,-1_0_0,1_2,1_0,-1_0_0,-1_0_0,-1_0_0,-1_0_0,1_0,1_2,9,-1_0_0,-1_0_0,-1_0_0,1_0,1_0,1_0,9,1_2,-1_0_0,1_0,-1_0_0]]      ,		device=__snake_case							)  # noqa: E231
     # fmt: on

     return input_ids, attention_mask, bbox, token_type_ids, labels







@require_torch
class lowercase_					(		unittest.TestCase ):







   """simple docstring"""

   @slow
   def   __UpperCAmelCase      (			self			: str    )     -> Union[str, Any]:
        _A								=     LayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased'    ).to(UpperCamelCase__    )

        _A		,					_A		,					_A		,					_A		,					_A								=     prepare_layoutlm_batch_inputs()

        # forward pass
        _A								=     model(input_ids=UpperCamelCase__,   bbox=UpperCamelCase__,   attention_mask=UpperCamelCase__,   token_type_ids=UpperCamelCase__    )

        # test the sequence output on [0, :3, :3]
        _A								=     torch.tensor(
            [[0.1_785, -0.1_947, -0.0_425], [-0.3_254, -0.2_807, 0.2_553], [-0.5_391, -0.3_322, 0.3_364]],   device=UpperCamelCase__,   )

        self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3],   UpperCamelCase__,   atol=1e-3    )    )

        # test the pooled output on [1, :3]
        _A								=     torch.tensor([-0.6_580, -0.0_214, 0.8_552],   device=UpperCamelCase__    )

        self.assertTrue(torch.allclose(outputs.pooler_output[1, :3],   UpperCamelCase__,   atol=1e-3    )    )
   @slow
   def   __UpperCAmelCase      (			self			: Tuple    )     -> Dict:
        # initialize model with randomly initialized sequence classification head
        _A								=     LayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased',   num_labels=2    ).to(
            UpperCamelCase__    )

        _A		,					_A		,					_A		,					_A		,					_A								=     prepare_layoutlm_batch_inputs()

        # forward pass
        _A								=     model(
            input_ids=UpperCamelCase__,   bbox=UpperCamelCase__,   attention_mask=UpperCamelCase__,   token_type_ids=UpperCamelCase__,   labels=torch.tensor([1, 1],   device=UpperCamelCase__    ),   )

        # test whether we get a loss as a scalar
        _A								=     outputs.loss
        _A								=     torch.Size([]    )
        self.assertEqual(loss.shape,   UpperCamelCase__    )

        # test the shape of the logits
        _A								=     outputs.logits
        _A								=     torch.Size((2, 2)    )
        self.assertEqual(logits.shape,   UpperCamelCase__    )
   @slow
   def   __UpperCAmelCase      (			self			: List[Any]    )     -> Any:
        # initialize model with randomly initialized token classification head
        _A								=     LayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased',   num_labels=13    ).to(
            UpperCamelCase__    )

        _A		,					_A		,					_A		,					_A		,					_A								=     prepare_layoutlm_batch_inputs()

        # forward pass
        _A								=     model(
            input_ids=UpperCamelCase__,   bbox=UpperCamelCase__,   attention_mask=UpperCamelCase__,   token_type_ids=UpperCamelCase__,   labels=UpperCamelCase__    )

        # test the loss calculation to be around 2.65
        # expected_loss = torch.tensor(2.65, device=torch_device)

        # The loss is currently somewhat random and can vary between 0.1-0.3 atol.
        # self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))

        # test the shape of the logits
        _A								=     outputs.logits
        _A								=     torch.Size((2, 25, 13)    )
        self.assertEqual(logits.shape,   UpperCamelCase__    )

   @slow
   def   __UpperCAmelCase      (			self			: Union[str, Any]    )     -> Tuple:
        # initialize model with randomly initialized token classification head
        _A								=     LayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased'    ).to(UpperCamelCase__    )

        _A		,					_A		,					_A		,					_A		,					_A								=     prepare_layoutlm_batch_inputs()

        # forward pass
        _A								=     model(input_ids=UpperCamelCase__,   bbox=UpperCamelCase__,   attention_mask=UpperCamelCase__,   token_type_ids=UpperCamelCase__    )

        # test the shape of the logits
        _A								=     torch.Size((2, 25)    )
        self.assertEqual(outputs.start_logits.shape,   UpperCamelCase__    )
        self.assertEqual(outputs.end_logits.shape,   UpperCamelCase__    )




'''simple docstring'''






from typing import TYPE_CHECKING

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


_UpperCAmelCase						:    Optional[Any]     					=					{
    '''configuration_unispeech_sat''': ['''UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechSatConfig'''],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase						:    Dict     					=					[
       '''UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''UniSpeechSatForAudioFrameClassification''',
       '''UniSpeechSatForCTC''',
       '''UniSpeechSatForPreTraining''',
       '''UniSpeechSatForSequenceClassification''',
       '''UniSpeechSatForXVector''',
       '''UniSpeechSatModel''',
       '''UniSpeechSatPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_unispeech_sat import (
          UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST,
          UniSpeechSatForAudioFrameClassification,
          UniSpeechSatForCTC,
          UniSpeechSatForPreTraining,
          UniSpeechSatForSequenceClassification,
          UniSpeechSatForXVector,
          UniSpeechSatModel,
          UniSpeechSatPreTrainedModel,
      )

else:
   import sys

   _UpperCAmelCase						:    int     					=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



