

'''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        A_ :






     '''simple docstring'''



     def __init__(		self :     Dict     ,			lowercase_ :     str     ,			lowercase_ :     List[str]=13     ,			lowercase_ :     Any=7     ,			lowercase_ :     Dict=True     ,			lowercase_ :     str=True     ,			lowercase_ :     List[Any]=True     ,			lowercase_ :     Any=True     ,			lowercase_ :     Tuple=99     ,			lowercase_ :     str=24     ,			lowercase_ :     str=2     ,			lowercase_ :     Any=6     ,			lowercase_ :     Dict=37     ,			lowercase_ :     List[str]="gelu"     ,			lowercase_ :     Dict=0.1     ,			lowercase_ :     Tuple=0.1     ,			lowercase_ :     Optional[Any]=512     ,			lowercase_ :     List[Any]=16     ,			lowercase_ :     str=2     ,			lowercase_ :     int=0.02     ,			lowercase_ :     List[Any]=3     ,			lowercase_ :     List[Any]=None     ,			lowercase_ :     int=1_000     ,			)						->	Dict:
      UpperCAmelCase       :	Dict											= parent
      UpperCAmelCase       :	List[Any]											= batch_size
      UpperCAmelCase       :	Union[str, Any]											= seq_length
      UpperCAmelCase       :	List[Any]											= is_training
      UpperCAmelCase       :	int											= use_input_mask
      UpperCAmelCase       :	List[Any]											= use_token_type_ids
      UpperCAmelCase       :	Optional[Any]											= use_labels
      UpperCAmelCase       :	Tuple											= vocab_size
      UpperCAmelCase       :	Dict											= hidden_size
      UpperCAmelCase       :	Dict											= num_hidden_layers
      UpperCAmelCase       :	List[Any]											= num_attention_heads
      UpperCAmelCase       :	Dict											= intermediate_size
      UpperCAmelCase       :	Union[str, Any]											= hidden_act
      UpperCAmelCase       :	List[str]											= hidden_dropout_prob
      UpperCAmelCase       :	Tuple											= attention_probs_dropout_prob
      UpperCAmelCase       :	List[str]											= max_position_embeddings
      UpperCAmelCase       :	Optional[int]											= type_vocab_size
      UpperCAmelCase       :	List[str]											= type_sequence_label_size
      UpperCAmelCase       :	Any											= initializer_range
      UpperCAmelCase       :	Optional[Any]											= num_labels
      UpperCAmelCase       :	Optional[Any]											= scope
      UpperCAmelCase       :	Optional[Any]											= range_bbox


     def    UpperCAmelCase_       (		self :     List[Any]       )						->	Any:
      UpperCAmelCase       :	Dict											= ids_tensor([self.batch_size, self.seq_length]     ,			self.vocab_size       )

      UpperCAmelCase       :	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]:
         UpperCAmelCase       :	Union[str, Any]											= bbox[i, j, 3]
         UpperCAmelCase       :	Optional[Any]											= bbox[i, j, 1]
         UpperCAmelCase       :	Dict											= t
        if bbox[i, j, 2] < bbox[i, j, 0]:
         UpperCAmelCase       :	Optional[Any]											= bbox[i, j, 2]
         UpperCAmelCase       :	Dict											= bbox[i, j, 0]
         UpperCAmelCase       :	List[str]											= t

      UpperCAmelCase       :	List[Any]											= None
      if self.use_input_mask:
       UpperCAmelCase       :	Union[str, Any]											= ids_tensor([self.batch_size, self.seq_length]     ,			vocab_size=2       )

      UpperCAmelCase       :	Tuple											= None
      if self.use_token_type_ids:
       UpperCAmelCase       :	List[Any]											= ids_tensor([self.batch_size, self.seq_length]     ,			self.type_vocab_size       )

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

      UpperCAmelCase       :	str											= self.get_config()

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


     def    UpperCAmelCase_       (		self :     List[str]       )						->	List[Any]:
      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     ,			lowercase_ :     Tuple     ,			lowercase_ :     Tuple     ,			lowercase_ :     str     ,			lowercase_ :     Any     ,			lowercase_ :     Union[str, Any]     ,			lowercase_ :     List[str]     ,			lowercase_ :     str     ,			)						->	int:
      UpperCAmelCase       :	Tuple											= LiltModel(config=lowercase_       )
      model.to(lowercase_       )
      model.eval()
      UpperCAmelCase       :	List[str]											= model(lowercase_     ,			bbox=lowercase_     ,			attention_mask=lowercase_     ,			token_type_ids=lowercase_       )
      UpperCAmelCase       :	str											= model(lowercase_     ,			bbox=lowercase_     ,			token_type_ids=lowercase_       )
      UpperCAmelCase       :	List[str]											= model(lowercase_     ,			bbox=lowercase_       )

      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 :     List[Any]     ,			lowercase_ :     int     ,			lowercase_ :     int     ,			lowercase_ :     Any     ,			lowercase_ :     Optional[int]     ,			lowercase_ :     Union[str, Any]     ,			lowercase_ :     Optional[Any]     ,			lowercase_ :     Optional[int]     ,			)						->	int:
      UpperCAmelCase       :	str											= self.num_labels
      UpperCAmelCase       :	Optional[int]											= LiltForTokenClassification(config=lowercase_       )
      model.to(lowercase_       )
      model.eval()
      UpperCAmelCase       :	List[str]											= model(
          lowercase_     ,			bbox=lowercase_     ,			attention_mask=lowercase_     ,			token_type_ids=lowercase_     ,			labels=lowercase_       )
      self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.seq_length, self.num_labels)       )


     def    UpperCAmelCase_       (		self :     List[str]     ,			lowercase_ :     Union[str, Any]     ,			lowercase_ :     str     ,			lowercase_ :     Dict     ,			lowercase_ :     Optional[int]     ,			lowercase_ :     List[str]     ,			lowercase_ :     int     ,			lowercase_ :     int     ,			)						->	Optional[Any]:
      UpperCAmelCase       :	int											= LiltForQuestionAnswering(config=lowercase_       )
      model.to(lowercase_       )
      model.eval()
      UpperCAmelCase       :	List[str]											= model(
          lowercase_     ,			bbox=lowercase_     ,			attention_mask=lowercase_     ,			token_type_ids=lowercase_     ,			start_positions=lowercase_     ,			end_positions=lowercase_     ,			)
      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]       )						->	int:
      UpperCAmelCase       :	str											= self.prepare_config_and_inputs()
      (
          (
          UpperCAmelCase					
      )					, (
          UpperCAmelCase					
      )					, (
          UpperCAmelCase					
      )					, (
          UpperCAmelCase					
      )					, (
          UpperCAmelCase					
      )					, (
          UpperCAmelCase					
      )					, (
          UpperCAmelCase					
      )					, 					
      )       :	Optional[Any]											= config_and_inputs
      UpperCAmelCase       :	List[str]											= {
          'input_ids': input_ids,
          'bbox': bbox,
          'token_type_ids': token_type_ids,
          'attention_mask': input_mask,
      }
      return config, inputs_dict




@require_torch
class        A_ (				_snake_case       ,   _snake_case       ,   _snake_case       ,   unittest.TestCase							):






     '''simple docstring'''



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


     def    UpperCAmelCase_       (		self :     Dict     ,			lowercase_ :     Dict     ,			lowercase_ :     Optional[int]     ,			lowercase_ :     Optional[int]     ,			lowercase_ :     List[str]     ,			lowercase_ :     Tuple       )						->	Dict:
      return True


     def    UpperCAmelCase_       (		self :     Optional[Any]       )						->	Tuple:
      UpperCAmelCase       :	Dict											= LiltModelTester(self       )
      UpperCAmelCase       :	Optional[Any]											= ConfigTester(self     ,			config_class=lowercase_     ,			hidden_size=37       )


     def    UpperCAmelCase_       (		self :     Any       )						->	str:
      self.config_tester.run_common_tests()


     def    UpperCAmelCase_       (		self :     Dict       )						->	Any:
      UpperCAmelCase       :	Optional[Any]											= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowercase_       )


     def    UpperCAmelCase_       (		self :     List[Any]       )						->	Optional[int]:
      UpperCAmelCase       :	Tuple											= self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
       UpperCAmelCase       :	Tuple											= type
       self.model_tester.create_and_check_model(*lowercase_       )


     def    UpperCAmelCase_       (		self :     Any       )						->	List[Any]:
      UpperCAmelCase       :	List[Any]											= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*lowercase_       )


     def    UpperCAmelCase_       (		self :     Any       )						->	int:
      UpperCAmelCase       :	Union[str, Any]											= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*lowercase_       )




     @slow
     def    UpperCAmelCase_       (		self :     Union[str, Any]       )						->	Union[str, Any]:


      for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       UpperCAmelCase       :	List[Any]											= LiltModel.from_pretrained(lowercase_       )
       self.assertIsNotNone(lowercase_       )




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






     '''simple docstring'''



     def    UpperCAmelCase_       (		self :     Tuple       )						->	Optional[Any]:
      UpperCAmelCase       :	Union[str, Any]											= LiltModel.from_pretrained('SCUT-DLVCLab/lilt-roberta-en-base'       ).to(lowercase_       )

      UpperCAmelCase       :	Optional[Any]											= torch.tensor([[1, 2]]     ,			device=lowercase_       )
      UpperCAmelCase       :	Tuple											= torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]     ,			device=lowercase_       )

      # forward pass
      with torch.no_grad():
       UpperCAmelCase       :	Tuple											= model(input_ids=lowercase_     ,			bbox=lowercase_       )

      UpperCAmelCase       :	Optional[int]											= torch.Size([1, 2, 768]       )
      UpperCAmelCase       :	List[str]											= torch.tensor(
          [[-0.0653, 0.0950, -0.0061], [-0.0545, 0.0926, -0.0324]]     ,			device=lowercase_     ,			)

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






import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch




@require_torch
class    SCREAMING_SNAKE_CASE__     :





	'''simple docstring'''



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


				'''simple docstring'''


				return self.get_dummy_input()



	@property
	def  A							(  self	:    Any	):


				'''simple docstring'''


				if self.block_type == "down":
							return (4, 32, 16, 16)
				elif self.block_type == "mid":
							return (4, 32, 32, 32)
				elif self.block_type == "up":
							return (4, 32, 64, 64)

				raise ValueError(f'''\'{self.block_type}\' is not a supported block_type. Set it to \'up\', \'mid\', or \'down\'.'''	)



	def  A							(  self	:    Union[str, Any]			,  lowercase	:    Any=True			,  lowercase	:    List[Any]=False			,  lowercase	:    List[str]=False			,  lowercase	:    Dict=False			,  ):


				'''simple docstring'''


				_snake_case		=			4
				_snake_case		=			32
				_snake_case		=			(32, 32)

				_snake_case		=			torch.manual_seed(0	)
				_snake_case		=			torch.device(lowercase	)
				_snake_case		=			(batch_size, num_channels) + sizes
				_snake_case		=			randn_tensor(lowercase			,  generator=lowercase			,  device=lowercase	)
				_snake_case		=			{'hidden_states': hidden_states}

				if include_temb:
							_snake_case		=			128
							_snake_case		=			randn_tensor((batch_size, temb_channels)			,  generator=lowercase			,  device=lowercase	)

				if include_res_hidden_states_tuple:
							_snake_case		=			torch.manual_seed(1	)
							_snake_case		=			(randn_tensor(lowercase			,  generator=lowercase			,  device=lowercase	),)

				if include_encoder_hidden_states:
							_snake_case		=			floats_tensor((batch_size, 32, 32)	).to(lowercase	)

				if include_skip_sample:
							_snake_case		=			randn_tensor(((batch_size, 3) + sizes)			,  generator=lowercase			,  device=lowercase	)

				return dummy_input



	def  A							(  self	:    Any	):


				'''simple docstring'''


				_snake_case		=			{
				    'in_channels': 32,
				    'out_channels': 32,
				    'temb_channels': 128,
				}
				if self.block_type == "up":
							_snake_case		=			32

				if self.block_type == "mid":
							init_dict.pop('out_channels'	)

				_snake_case		=			self.dummy_input
				return init_dict, inputs_dict



	def  A							(  self	:    Dict			,  lowercase	:    Optional[int]	):


				'''simple docstring'''


				_snake_case	,					_snake_case		=			self.prepare_init_args_and_inputs_for_common()
				_snake_case		=			self.block_class(**lowercase	)
				unet_block.to(lowercase	)
				unet_block.eval()

				with torch.no_grad():
							_snake_case		=			unet_block(**lowercase	)

				if isinstance(lowercase			,  lowercase	):
							_snake_case		=			output[0]

				self.assertEqual(output.shape			,  self.output_shape	)

				_snake_case		=			output[0, -1, -3:, -3:]
				_snake_case		=			torch.tensor(lowercase	).to(lowercase	)
				assert torch_all_close(output_slice.flatten()			,  lowercase			,  atol=5E-3	)





	@unittest.skipIf(torch_device == 'mps'			,  'Training is not supported in mps'	)
	def  A							(  self	:    Dict	):


				'''simple docstring'''


				_snake_case	,					_snake_case		=			self.prepare_init_args_and_inputs_for_common()
				_snake_case		=			self.block_class(**lowercase	)
				model.to(lowercase	)
				model.train()
				_snake_case		=			model(**lowercase	)

				if isinstance(lowercase			,  lowercase	):
							_snake_case		=			output[0]

				_snake_case		=			torch.device(lowercase	)
				_snake_case		=			randn_tensor(output.shape			,  device=lowercase	)
				_snake_case		=			torch.nn.functional.mse_loss(lowercase			,  lowercase	)
				loss.backward()