


"""simple docstring"""







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__				:
       """simple docstring"""


       def __init__(						self		,     snake_case__		,     snake_case__=13		,     snake_case__=7		,     snake_case__=True		,     snake_case__=True		,     snake_case__=True		,     snake_case__=True		,     snake_case__=99		,     snake_case__=24		,     snake_case__=2		,     snake_case__=6		,     snake_case__=37		,     snake_case__="gelu"		,     snake_case__=0.1		,     snake_case__=0.1		,     snake_case__=512		,     snake_case__=16		,     snake_case__=2		,     snake_case__=0.02		,     snake_case__=3		,     snake_case__=None		,     snake_case__=1_000		,     ):






         """simple docstring"""





         lowerCAmelCase     :     Tuple								=							parent
         lowerCAmelCase     :     Tuple								=							batch_size
         lowerCAmelCase     :     Any								=							seq_length
         lowerCAmelCase     :     Union[str, Any]								=							is_training
         lowerCAmelCase     :     Union[str, Any]								=							use_input_mask
         lowerCAmelCase     :     List[str]								=							use_token_type_ids
         lowerCAmelCase     :     Optional[int]								=							use_labels
         lowerCAmelCase     :     Optional[int]								=							vocab_size
         lowerCAmelCase     :     Any								=							hidden_size
         lowerCAmelCase     :     Any								=							num_hidden_layers
         lowerCAmelCase     :     Optional[Any]								=							num_attention_heads
         lowerCAmelCase     :     List[Any]								=							intermediate_size
         lowerCAmelCase     :     Dict								=							hidden_act
         lowerCAmelCase     :     Any								=							hidden_dropout_prob
         lowerCAmelCase     :     Union[str, Any]								=							attention_probs_dropout_prob
         lowerCAmelCase     :     List[str]								=							max_position_embeddings
         lowerCAmelCase     :     Dict								=							type_vocab_size
         lowerCAmelCase     :     Any								=							type_sequence_label_size
         lowerCAmelCase     :     Optional[int]								=							initializer_range
         lowerCAmelCase     :     int								=							num_labels
         lowerCAmelCase     :     str								=							scope
         lowerCAmelCase     :     Optional[int]								=							range_bbox

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Any								=							ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size						)

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

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

         lowerCAmelCase     :     Optional[Any]								=							None
         if self.use_token_type_ids:
           lowerCAmelCase     :     Union[str, Any]								=							ids_tensor([self.batch_size, self.seq_length]		,     self.type_vocab_size						)

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

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

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

       def        lowercase__							(						self						):






         """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        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__		,     ):






         """simple docstring"""





         lowerCAmelCase     :     int								=							LiltModel(config=snake_case__						)
         model.to(snake_case__						)
         model.eval()
         lowerCAmelCase     :     List[str]								=							model(snake_case__		,     bbox=snake_case__		,     attention_mask=snake_case__		,     token_type_ids=snake_case__						)
         lowerCAmelCase     :     Optional[Any]								=							model(snake_case__		,     bbox=snake_case__		,     token_type_ids=snake_case__						)
         lowerCAmelCase     :     Tuple								=							model(snake_case__		,     bbox=snake_case__						)

         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		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__		,     ):






         """simple docstring"""





         lowerCAmelCase     :     Any								=							self.num_labels
         lowerCAmelCase     :     Dict								=							LiltForTokenClassification(config=snake_case__						)
         model.to(snake_case__						)
         model.eval()
         lowerCAmelCase     :     Tuple								=							model(
             snake_case__		,     bbox=snake_case__		,     attention_mask=snake_case__		,     token_type_ids=snake_case__		,     labels=snake_case__						)
         self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.seq_length, self.num_labels)						)

       def        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__		,     ):






         """simple docstring"""





         lowerCAmelCase     :     List[str]								=							LiltForQuestionAnswering(config=snake_case__						)
         model.to(snake_case__						)
         model.eval()
         lowerCAmelCase     :     List[Any]								=							model(
             snake_case__		,     bbox=snake_case__		,     attention_mask=snake_case__		,     token_type_ids=snake_case__		,     start_positions=snake_case__		,     end_positions=snake_case__		,     )
         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						):






         """simple docstring"""





         lowerCAmelCase     :     Dict								=							self.prepare_config_and_inputs()
         (
             (
             lowerCAmelCase
         )						,  (
             lowerCAmelCase
         )						,  (
             lowerCAmelCase
         )						,  (
             lowerCAmelCase
         )						,  (
             lowerCAmelCase
         )						,  (
             lowerCAmelCase
         )						,  (
             lowerCAmelCase
         )						,  
         )     :     Tuple								=							config_and_inputs
         lowerCAmelCase     :     Any								=							{
             "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__				(		lowercase   ,    lowercase   ,    lowercase   ,    unittest.TestCase				):
       """simple docstring"""


       a  :				int   =(
           (
               LiltModel,
               LiltForSequenceClassification,
               LiltForTokenClassification,
               LiltForQuestionAnswering,
           )
           if is_torch_available()
           else ()
       )
       a  :				Union[str, Any]   =(
           {
               "feature-extraction": LiltModel,
               "question-answering": LiltForQuestionAnswering,
               "text-classification": LiltForSequenceClassification,
               "token-classification": LiltForTokenClassification,
               "zero-shot": LiltForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       a  :				Any   =False
       a  :				Any   =False

       def        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         return True

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Optional[int]								=							LiltModelTester(self						)
         lowerCAmelCase     :     List[str]								=							ConfigTester(self		,     config_class=snake_case__		,     hidden_size=37						)

       def        lowercase__							(						self						):






         """simple docstring"""





         self.config_tester.run_common_tests()

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Union[str, Any]								=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*snake_case__						)

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     str								=							self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
           lowerCAmelCase     :     int								=							type
           self.model_tester.create_and_check_model(*snake_case__						)

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Any								=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*snake_case__						)

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     List[Any]								=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*snake_case__						)







       @slow
       def        lowercase__							(						self						):






         """simple docstring"""





         for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           lowerCAmelCase     :     Dict								=							LiltModel.from_pretrained(snake_case__						)
           self.assertIsNotNone(snake_case__						)


@require_torch
@slow
class       SCREAMING_SNAKE_CASE__				(		unittest.TestCase				):
       """simple docstring"""


       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Any								=							LiltModel.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base"						).to(snake_case__						)

         lowerCAmelCase     :     Tuple								=							torch.tensor([[1, 2]]		,     device=snake_case__						)
         lowerCAmelCase     :     Tuple								=							torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]		,     device=snake_case__						)

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

         lowerCAmelCase     :     Dict								=							torch.Size([1, 2, 768]						)
         lowerCAmelCase     :     Dict								=							torch.tensor(
             [[-0.0653, 0.0950, -0.0061], [-0.0545, 0.0926, -0.0324]]		,     device=snake_case__		,     )

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









"""simple docstring"""







import jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


lowerCAmelCase__      				=   logging.get_logger(__name__)

lowerCAmelCase__      				=   '''T5Config'''
def 					a__			(				SCREAMING_SNAKE_CASE    :    jnp.array				,      SCREAMING_SNAKE_CASE    :    int				,      SCREAMING_SNAKE_CASE    :    int				):


  '''simple docstring'''







  lowerCAmelCase     :     List[str]								=							jnp.zeros_like(SCREAMING_SNAKE_CASE				)
  lowerCAmelCase     :     Optional[int]								=							shifted_input_ids.at[:, 1:].set(input_ids[:, :-1]				)
  lowerCAmelCase     :     List[str]								=							shifted_input_ids.at[:, 0].set(SCREAMING_SNAKE_CASE				)

  lowerCAmelCase     :     str								=							jnp.where(shifted_input_ids == -1_0_0				,      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				)
  return shifted_input_ids


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       a  :				List[Any]   ="mt5"
       a  :				Tuple   =MTaConfig


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       a  :				Union[str, Any]   ="mt5"
       a  :				Optional[Any]   =MTaConfig


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       a  :				str   ="mt5"
       a  :				Dict   =MTaConfig





