
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   lowerCamelCase	:





     def __init__(self   :					Tuple       ,					_A   :					List[Any]       ,					_A   :					Any=1_3       ,					_A   :					Union[str, Any]=7       ,					_A   :					Tuple=True       ,					_A   :					Any=True       ,					_A   :					Optional[Any]=True       ,					_A   :					Optional[int]=True       ,					_A   :					Dict=9_9       ,					_A   :					str=2_4       ,					_A   :					Optional[Any]=2       ,					_A   :					Tuple=6       ,					_A   :					str=3_7       ,					_A   :					Dict="gelu"       ,					_A   :					int=0.1       ,					_A   :					Tuple=0.1       ,					_A   :					Dict=5_1_2       ,					_A   :					List[str]=1_6       ,					_A   :					Optional[Any]=2       ,					_A   :					str=0.02       ,					_A   :					Dict=3       ,					_A   :					Tuple=None       ,					_A   :					List[Any]=1_0_0_0       ,					)		->		Union[str, Any]:
         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 	UpperCAmelCase(self   :					Tuple     )		->		List[str]:
         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 	UpperCAmelCase(self   :					str     )		->		str:
         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 	UpperCAmelCase(self   :					str       ,					_A   :					Tuple       ,					_A   :					Optional[Any]       ,					_A   :					Any       ,					_A   :					List[Any]       ,					_A   :					Union[str, Any]       ,					_A   :					str       ,					_A   :					Optional[Any]       ,					)		->		List[Any]:
         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 	UpperCAmelCase(self   :					Tuple       ,					_A   :					Optional[int]       ,					_A   :					Tuple       ,					_A   :					Optional[int]       ,					_A   :					Optional[Any]       ,					_A   :					Dict       ,					_A   :					str       ,					_A   :					Any       ,					)		->		Union[str, Any]:
         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 	UpperCAmelCase(self   :					List[Any]       ,					_A   :					List[Any]       ,					_A   :					Tuple       ,					_A   :					Dict       ,					_A   :					Union[str, Any]       ,					_A   :					Optional[Any]       ,					_A   :					int       ,					_A   :					Any       ,					)		->		Tuple:
         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 	UpperCAmelCase(self   :					Dict     )		->		int:
         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   lowerCamelCase	(						A_							,				A_							,				A_							,				unittest.TestCase						):
     UpperCAmelCase__       :							Dict     =	(
         (
             LiltModel,
             LiltForSequenceClassification,
             LiltForTokenClassification,
             LiltForQuestionAnswering,
         )
         if is_torch_available()
         else ()
     )
     UpperCAmelCase__       :							Union[str, Any]     =	(
         {
             "feature-extraction": LiltModel,
             "question-answering": LiltForQuestionAnswering,
             "text-classification": LiltForSequenceClassification,
             "token-classification": LiltForTokenClassification,
             "zero-shot": LiltForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     UpperCAmelCase__       :							str     =	False
     UpperCAmelCase__       :							Optional[Any]     =	False





     def 	UpperCAmelCase(self   :					List[Any]       ,					_A   :					List[Any]       ,					_A   :					Union[str, Any]       ,					_A   :					Tuple       ,					_A   :					Any       ,					_A   :					List[Any]     )		->		str:
         return True





     def 	UpperCAmelCase(self   :					Dict     )		->		Optional[Any]:
         snake_case				  =      LiltModelTester(self     )
         snake_case				  =      ConfigTester(self       ,					config_class=_A       ,					hidden_size=3_7     )





     def 	UpperCAmelCase(self   :					str     )		->		Optional[int]:
         self.config_tester.run_common_tests()





     def 	UpperCAmelCase(self   :					List[str]     )		->		str:
         snake_case				  =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_A     )





     def 	UpperCAmelCase(self   :					Union[str, Any]     )		->		Optional[Any]:
         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 	UpperCAmelCase(self   :					Tuple     )		->		List[str]:
         snake_case				  =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*_A     )





     def 	UpperCAmelCase(self   :					str     )		->		int:
         snake_case				  =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*_A     )





     @slow
     def 	UpperCAmelCase(self   :					Tuple     )		->		Tuple:
         for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             snake_case				  =      LiltModel.from_pretrained(_A     )
             self.assertIsNotNone(_A     )







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





     def 	UpperCAmelCase(self   :					Optional[int]     )		->		Tuple:
         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, 7_6_8]     )
         snake_case				  =      torch.tensor(
             [[-0.06_53, 0.09_50, -0.00_61], [-0.05_45, 0.09_26, -0.03_24]]       ,					device=_A       ,					)

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



import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
   from transformers import AutoModelForSeqaSeqLM, AutoTokenizer







@require_torch
@require_sentencepiece
@require_tokenizers
class   lowerCamelCase	(						unittest.TestCase						):





     @slow
     def 	UpperCAmelCase(self   :					List[str]     )		->		Optional[int]:
         snake_case				  =      AutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"       ,					return_dict=_A     ).to(_A     )
         snake_case				  =      AutoTokenizer.from_pretrained("google/mt5-small"     )

         snake_case				  =      tokenizer("Hello there"       ,					return_tensors="pt"     ).input_ids
         snake_case				  =      tokenizer("Hi I am"       ,					return_tensors="pt"     ).input_ids

         snake_case				  =      model(input_ids.to(_A     )       ,					labels=labels.to(_A     )     ).loss
         snake_case				  =      -(labels.shape[-1] * loss.item())

         snake_case				  =      -84.91_27
         self.assertTrue(abs(mtf_score - EXPECTED_SCORE     ) < 1E-4     )

