


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  _lowercase   :
      def __init__(     self				,			a				,			a=1_3				,			a=7				,			a=True				,			a=True				,			a=True				,			a=True				,			a=9_9				,			a=2_4				,			a=2				,			a=6				,			a=3_7				,			a="gelu"				,			a=0.1				,			a=0.1				,			a=5_1_2				,			a=1_6				,			a=2				,			a=0.02				,			a=3				,			a=None				,			a=1_0_0_0				,			):
       snake_case__  :    str							       =parent
       snake_case__  :    Optional[int]							       =batch_size
       snake_case__  :    str							       =seq_length
       snake_case__  :    List[str]							       =is_training
       snake_case__  :    List[str]							       =use_input_mask
       snake_case__  :    Any							       =use_token_type_ids
       snake_case__  :    Any							       =use_labels
       snake_case__  :    List[str]							       =vocab_size
       snake_case__  :    int							       =hidden_size
       snake_case__  :    Union[str, Any]							       =num_hidden_layers
       snake_case__  :    Any							       =num_attention_heads
       snake_case__  :    Optional[int]							       =intermediate_size
       snake_case__  :    int							       =hidden_act
       snake_case__  :    Tuple							       =hidden_dropout_prob
       snake_case__  :    List[Any]							       =attention_probs_dropout_prob
       snake_case__  :    Union[str, Any]							       =max_position_embeddings
       snake_case__  :    str							       =type_vocab_size
       snake_case__  :    Union[str, Any]							       =type_sequence_label_size
       snake_case__  :    int							       =initializer_range
       snake_case__  :    Any							       =num_labels
       snake_case__  :    Optional[Any]							       =scope
       snake_case__  :    Optional[Any]							       =range_bbox
      def 						lowercase__					(     self   ):
       snake_case__  :    Optional[int]							       =ids_tensor([self.batch_size, self.seq_length]				,			self.vocab_size   )

       snake_case__  :    Any							       =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__  :    Tuple							       =bbox[i, j, 3]
          snake_case__  :    int							       =bbox[i, j, 1]
          snake_case__  :    Union[str, Any]							       =t
         if bbox[i, j, 2] < bbox[i, j, 0]:
          snake_case__  :    List[Any]							       =bbox[i, j, 2]
          snake_case__  :    List[str]							       =bbox[i, j, 0]
          snake_case__  :    Dict							       =t

       snake_case__  :    Optional[Any]							       =None
       if self.use_input_mask:
        snake_case__  :    Dict							       =ids_tensor([self.batch_size, self.seq_length]				,			vocab_size=2   )

       snake_case__  :    List[str]							       =None
       if self.use_token_type_ids:
        snake_case__  :    Optional[int]							       =ids_tensor([self.batch_size, self.seq_length]				,			self.type_vocab_size   )

       snake_case__  :    Union[str, Any]							       =None
       snake_case__  :    List[str]							       =None
       if self.use_labels:
        snake_case__  :    str							       =ids_tensor([self.batch_size]				,			self.type_sequence_label_size   )
        snake_case__  :    Optional[int]							       =ids_tensor([self.batch_size, self.seq_length]				,			self.num_labels   )

       snake_case__  :    List[Any]							       =self.get_config()

       return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels
      def 						lowercase__					(     self   ):
       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 						lowercase__					(     self				,			a				,			a				,			a				,			a				,			a				,			a				,			a				,			):
       snake_case__  :    Any							       =LiltModel(config=a   )
       model.to(a   )
       model.eval()
       snake_case__  :    Optional[Any]							       =model(a				,			bbox=a				,			attention_mask=a				,			token_type_ids=a   )
       snake_case__  :    Tuple							       =model(a				,			bbox=a				,			token_type_ids=a   )
       snake_case__  :    List[Any]							       =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 						lowercase__					(     self				,			a				,			a				,			a				,			a				,			a				,			a				,			a				,			):
       snake_case__  :    Tuple							       =self.num_labels
       snake_case__  :    str							       =LiltForTokenClassification(config=a   )
       model.to(a   )
       model.eval()
       snake_case__  :    int							       =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 						lowercase__					(     self				,			a				,			a				,			a				,			a				,			a				,			a				,			a				,			):
       snake_case__  :    int							       =LiltForQuestionAnswering(config=a   )
       model.to(a   )
       model.eval()
       snake_case__  :    str							       =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 						lowercase__					(     self   ):
       snake_case__  :    List[Any]							       =self.prepare_config_and_inputs()
       (
           snake_case__   
       )  :    Optional[int]							       =config_and_inputs
       snake_case__  :    str							       ={
           """input_ids""": input_ids,
           """bbox""": bbox,
           """token_type_ids""": token_type_ids,
           """attention_mask""": input_mask,
       }
       return config, inputs_dict







@require_torch
class  _lowercase   (   _A						,				_A						,				_A						,				unittest.TestCase						):
      _a							:						Dict										=							(
          (
              LiltModel,
              LiltForSequenceClassification,
              LiltForTokenClassification,
              LiltForQuestionAnswering,
          )
          if is_torch_available()
          else ()
      )
      _a							:						int										=							(
          {
              'feature-extraction': LiltModel,
              'question-answering': LiltForQuestionAnswering,
              'text-classification': LiltForSequenceClassification,
              'token-classification': LiltForTokenClassification,
              'zero-shot': LiltForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      _a							:						Union[str, Any]										=							False
      _a							:						Optional[int]										=							False
      def 						lowercase__					(     self				,			a				,			a				,			a				,			a				,			a   ):
       return True
      def 						lowercase__					(     self   ):
       snake_case__  :    List[Any]							       =LiltModelTester(self   )
       snake_case__  :    str							       =ConfigTester(self				,			config_class=a				,			hidden_size=3_7   )
      def 						lowercase__					(     self   ):
       self.config_tester.run_common_tests()
      def 						lowercase__					(     self   ):
       snake_case__  :    Optional[Any]							       =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*a   )
      def 						lowercase__					(     self   ):
       snake_case__  :    Union[str, Any]							       =self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
        snake_case__  :    Optional[int]							       =type
        self.model_tester.create_and_check_model(*a   )
      def 						lowercase__					(     self   ):
       snake_case__  :    List[Any]							       =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*a   )
      def 						lowercase__					(     self   ):
       snake_case__  :    List[str]							       =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*a   )



      @slow
      def 						lowercase__					(     self   ):
       for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        snake_case__  :    Tuple							       =LiltModel.from_pretrained(a   )
        self.assertIsNotNone(a   )







@require_torch
@slow
class  _lowercase   (   unittest.TestCase						):
      def 						lowercase__					(     self   ):
       snake_case__  :    Union[str, Any]							       =LiltModel.from_pretrained("""SCUT-DLVCLab/lilt-roberta-en-base"""   ).to(a   )

       snake_case__  :    Optional[int]							       =torch.tensor([[1, 2]]				,			device=a   )
       snake_case__  :    List[str]							       =torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]				,			device=a   )

       # forward pass
       with torch.no_grad():
        snake_case__  :    List[str]							       =model(input_ids=a				,			bbox=a   )

       snake_case__  :    Tuple							       =torch.Size([1, 2, 7_6_8]   )
       snake_case__  :    int							       =torch.tensor(
           [[-0.0653, 0.0950, -0.0061], [-0.0545, 0.0926, -0.0324]]				,			device=a				,			)

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













from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowerCamelCase	:		Dict										=   logging.get_logger(__name__)

__lowerCamelCase	:		Union[str, Any]										=   {
    """xlm-mlm-en-2048""": """https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json""",
    """xlm-mlm-ende-1024""": """https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json""",
    """xlm-mlm-enfr-1024""": """https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json""",
    """xlm-mlm-enro-1024""": """https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json""",
    """xlm-mlm-tlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json""",
    """xlm-mlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json""",
    """xlm-clm-enfr-1024""": """https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json""",
    """xlm-clm-ende-1024""": """https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json""",
    """xlm-mlm-17-1280""": """https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json""",
    """xlm-mlm-100-1280""": """https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json""",
}







class  _lowercase   (   _A						):
      _a							:						int										=							'xlm'
      _a							:						List[Any]										=							{
          'hidden_size': 'emb_dim',
          'num_attention_heads': 'n_heads',
          'num_hidden_layers': 'n_layers',
          'n_words': 'vocab_size',  # For backward compatibility
      }
      def __init__(     self				,			a=3_0_1_4_5				,			a=2_0_4_8				,			a=1_2				,			a=1_6				,			a=0.1				,			a=0.1				,			a=True				,			a=False				,			a=False				,			a=False				,			a=1				,			a=True				,			a=5_1_2				,			a=2_0_4_8**-0.5				,			a=1e-12				,			a=0.02				,			a=0				,			a=1				,			a=2				,			a=3				,			a=5				,			a=True				,			a="first"				,			a=True				,			a=None				,			a=True				,			a=0.1				,			a=5				,			a=5				,			a=0				,			a=0				,			a=2				,			a=0				,			**a				,			):
       snake_case__  :    str							       =vocab_size
       snake_case__  :    List[Any]							       =emb_dim
       snake_case__  :    List[str]							       =n_layers
       snake_case__  :    Any							       =n_heads
       snake_case__  :    Any							       =dropout
       snake_case__  :    Optional[int]							       =attention_dropout
       snake_case__  :    Dict							       =gelu_activation
       snake_case__  :    Tuple							       =sinusoidal_embeddings
       snake_case__  :    Optional[int]							       =causal
       snake_case__  :    Optional[int]							       =asm
       snake_case__  :    Optional[int]							       =n_langs
       snake_case__  :    Union[str, Any]							       =use_lang_emb
       snake_case__  :    Optional[Any]							       =layer_norm_eps
       snake_case__  :    Union[str, Any]							       =bos_index
       snake_case__  :    List[Any]							       =eos_index
       snake_case__  :    Optional[Any]							       =pad_index
       snake_case__  :    Union[str, Any]							       =unk_index
       snake_case__  :    Dict							       =mask_index
       snake_case__  :    str							       =is_encoder
       snake_case__  :    Optional[Any]							       =max_position_embeddings
       snake_case__  :    Any							       =embed_init_std
       snake_case__  :    Optional[Any]							       =init_std
       snake_case__  :    str							       =summary_type
       snake_case__  :    List[Any]							       =summary_use_proj
       snake_case__  :    str							       =summary_activation
       snake_case__  :    Optional[Any]							       =summary_proj_to_labels
       snake_case__  :    Optional[int]							       =summary_first_dropout
       snake_case__  :    Optional[Any]							       =start_n_top
       snake_case__  :    List[Any]							       =end_n_top
       snake_case__  :    Union[str, Any]							       =mask_token_id
       snake_case__  :    Any							       =lang_id

       if "n_words" in kwargs:
        snake_case__  :    int							       =kwargs["""n_words"""]

       super().__init__(pad_token_id=a				,			bos_token_id=a				,			**a   )







class  _lowercase   (   _A						):
      @property
      def 						lowercase__					(     self   ):
       if self.task == "multiple-choice":
        snake_case__  :    Any							       ={0: """batch""", 1: """choice""", 2: """sequence"""}
       else:
        snake_case__  :    Union[str, Any]							       ={0: """batch""", 1: """sequence"""}
       return OrderedDict(
           [
               ("""input_ids""", dynamic_axis),
               ("""attention_mask""", dynamic_axis),
               ("""token_type_ids""", dynamic_axis),
           ]   )





