

from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch

@dataclass
class    _lowercase				:
   _lowercase     :       torch.Tensor  # [batch_size x 3]
   _lowercase     :       torch.Tensor  # [batch_size x 3]
   _lowercase     :       torch.Tensor  # [batch_size x 3]
   _lowercase     :       torch.Tensor  # [batch_size x 3]
   _lowercase     :       int
   _lowercase     :       int
   _lowercase     :       float
   _lowercase     :       float
   _lowercase     :       Tuple[int]

   def     UpperCamelCase					(				self	:    List[Any]       ) ->			Tuple:
         """simple docstring"""





         assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
         assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
         assert len(self.x.shape       ) == len(self.y.shape       ) == len(self.z.shape       ) == len(self.origin.shape       ) == 2

   def     UpperCamelCase					(				self	:    Any       ) ->			Optional[Any]:
         """simple docstring"""





         return torch.from_numpy(np.array([self.width, self.height]			,						dtype=np.floataa       )       )

   def     UpperCamelCase					(				self	:    Tuple       ) ->			List[str]:
         """simple docstring"""





         return torch.from_numpy(np.array([self.x_fov, self.y_fov]			,						dtype=np.floataa       )       )

   def     UpperCamelCase					(				self	:    int       ) ->			torch.Tensor:
         """simple docstring"""





         A_							       =		torch.arange(self.height * self.width       )
         A_							       =		torch.stack(
             [
                 pixel_indices % self.width,
                 torch.div(lowerCamelCase__			,						self.width			,						rounding_mode='''trunc'''       ),
             ]			,						axis=1			,						)
         return coords

   @property
   def     UpperCamelCase					(				self	:    int       ) ->			int:
         """simple docstring"""





         A_       ,*A_							       =		self.shape
         A_							       =		int(np.prod(lowerCamelCase__       )       )

         A_							       =		self.get_image_coords()
         A_							       =		torch.broadcast_to(coords.unsqueeze(0       )			,						[batch_size * inner_batch_size, *coords.shape]       )
         A_							       =		self.get_camera_rays(lowerCamelCase__       )

         A_							       =		rays.view(lowerCamelCase__			,						inner_batch_size * self.height * self.width			,						2			,						3       )

         return rays

   def     UpperCamelCase					(				self	:    Dict			,						lowerCamelCase__	:    torch.Tensor       ) ->			torch.Tensor:
         """simple docstring"""





         A_       ,*A_       ,A_							       =		coords.shape
         assert n_coords == 2
         assert batch_size == self.origin.shape[0]

         A_							       =		coords.view(lowerCamelCase__			,						-1			,						2       )

         A_							       =		self.resolution()
         A_							       =		self.fov()

         A_							       =		(flat.float() / (res - 1)) * 2 - 1
         A_							       =		fracs * torch.tan(fov / 2       )

         A_							       =		fracs.view(lowerCamelCase__			,						-1			,						2       )
         A_							       =		(
             self.z.view(lowerCamelCase__			,						1			,						3       )
             + self.x.view(lowerCamelCase__			,						1			,						3       ) * fracs[:, :, :1]
             + self.y.view(lowerCamelCase__			,						1			,						3       ) * fracs[:, :, 1:]
         )
         A_							       =		directions / directions.norm(dim=-1			,						keepdim=lowerCamelCase__       )
         A_							       =		torch.stack(
             [
                 torch.broadcast_to(self.origin.view(lowerCamelCase__			,						1			,						3       )			,						[batch_size, directions.shape[1], 3]       ),
                 directions,
             ]			,						dim=2			,						)
         return rays.view(lowerCamelCase__			,						*lowerCamelCase__			,						2			,						3       )





   def     UpperCamelCase					(				self	:    Tuple			,						lowerCamelCase__	:    int			,						lowerCamelCase__	:    int       ) ->			"DifferentiableProjectiveCamera":
         """simple docstring"""





         assert width * self.height == height * self.width, "The aspect ratio should not change."
         return DifferentiableProjectiveCamera(
             origin=self.origin			,						x=self.x			,						y=self.y			,						z=self.z			,						width=lowerCamelCase__			,						height=lowerCamelCase__			,						x_fov=self.x_fov			,						y_fov=self.y_fov			,						)







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE   ):
      '''simple docstring'''







      A_							       =		[]
      A_							       =		[]
      A_							       =		[]
      A_							       =		[]
      for theta in np.linspace(0		,				2 * np.pi		,				num=20   ):
            A_							       =		np.array([np.sin(SCREAMING_SNAKE_CASE   ), np.cos(SCREAMING_SNAKE_CASE   ), -0.5]   )
            z /= np.sqrt(np.sum(z**2   )   )
            A_							       =		-z * 4
            A_							       =		np.array([np.cos(SCREAMING_SNAKE_CASE   ), -np.sin(SCREAMING_SNAKE_CASE   ), 0.0]   )
            A_							       =		np.cross(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   )
            origins.append(SCREAMING_SNAKE_CASE   )
            xs.append(SCREAMING_SNAKE_CASE   )
            ys.append(SCREAMING_SNAKE_CASE   )
            zs.append(SCREAMING_SNAKE_CASE   )
      return DifferentiableProjectiveCamera(
          origin=torch.from_numpy(np.stack(SCREAMING_SNAKE_CASE		,				axis=0   )   ).float()		,				x=torch.from_numpy(np.stack(SCREAMING_SNAKE_CASE		,				axis=0   )   ).float()		,				y=torch.from_numpy(np.stack(SCREAMING_SNAKE_CASE		,				axis=0   )   ).float()		,				z=torch.from_numpy(np.stack(SCREAMING_SNAKE_CASE		,				axis=0   )   ).float()		,				width=SCREAMING_SNAKE_CASE		,				height=SCREAMING_SNAKE_CASE		,				x_fov=0.7		,				y_fov=0.7		,				shape=(1, len(SCREAMING_SNAKE_CASE   ))		,				)








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	:    Tuple			,						lowerCamelCase__	:    Optional[int]			,						lowerCamelCase__	:    Dict=1_3			,						lowerCamelCase__	:    Any=7			,						lowerCamelCase__	:    Optional[Any]=True			,						lowerCamelCase__	:    Optional[Any]=True			,						lowerCamelCase__	:    List[Any]=True			,						lowerCamelCase__	:    Union[str, Any]=True			,						lowerCamelCase__	:    Optional[int]=9_9			,						lowerCamelCase__	:    Optional[int]=2_4			,						lowerCamelCase__	:    Optional[int]=2			,						lowerCamelCase__	:    Dict=6			,						lowerCamelCase__	:    List[str]=3_7			,						lowerCamelCase__	:    Optional[Any]="gelu"			,						lowerCamelCase__	:    Dict=0.1			,						lowerCamelCase__	:    Optional[int]=0.1			,						lowerCamelCase__	:    Any=5_1_2			,						lowerCamelCase__	:    int=1_6			,						lowerCamelCase__	:    Union[str, Any]=2			,						lowerCamelCase__	:    Optional[int]=0.02			,						lowerCamelCase__	:    Optional[Any]=3			,						lowerCamelCase__	:    Optional[int]=None			,						lowerCamelCase__	:    Any=1_0_0_0			,						) ->			Any:
         """simple docstring"""





         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_							       =		scope
         A_							       =		range_bbox

   def     UpperCamelCase					(				self	:    int       ) ->			int:
         """simple docstring"""





         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
         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_							       =		self.get_config()

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

   def     UpperCamelCase					(				self	:    Optional[int]       ) ->			List[str]:
         """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     UpperCamelCase					(				self	:    Dict			,						lowerCamelCase__	:    Any			,						lowerCamelCase__	:    Union[str, Any]			,						lowerCamelCase__	:    List[Any]			,						lowerCamelCase__	:    Any			,						lowerCamelCase__	:    List[Any]			,						lowerCamelCase__	:    List[str]			,						lowerCamelCase__	:    Any			,						) ->			List[Any]:
         """simple docstring"""





         A_							       =		LiltModel(config=lowerCamelCase__       )
         model.to(lowerCamelCase__       )
         model.eval()
         A_							       =		model(lowerCamelCase__			,						bbox=lowerCamelCase__			,						attention_mask=lowerCamelCase__			,						token_type_ids=lowerCamelCase__       )
         A_							       =		model(lowerCamelCase__			,						bbox=lowerCamelCase__			,						token_type_ids=lowerCamelCase__       )
         A_							       =		model(lowerCamelCase__			,						bbox=lowerCamelCase__       )

         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	:    Optional[int]			,						lowerCamelCase__	:    str			,						lowerCamelCase__	:    Union[str, Any]			,						lowerCamelCase__	:    Union[str, Any]			,						lowerCamelCase__	:    Dict			,						lowerCamelCase__	:    List[Any]			,						lowerCamelCase__	:    Dict			,						lowerCamelCase__	:    Optional[Any]			,						) ->			Union[str, Any]:
         """simple docstring"""





         A_							       =		self.num_labels
         A_							       =		LiltForTokenClassification(config=lowerCamelCase__       )
         model.to(lowerCamelCase__       )
         model.eval()
         A_							       =		model(
             lowerCamelCase__			,						bbox=lowerCamelCase__			,						attention_mask=lowerCamelCase__			,						token_type_ids=lowerCamelCase__			,						labels=lowerCamelCase__       )
         self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.seq_length, self.num_labels)       )

   def     UpperCamelCase					(				self	:    Dict			,						lowerCamelCase__	:    List[Any]			,						lowerCamelCase__	:    str			,						lowerCamelCase__	:    str			,						lowerCamelCase__	:    Dict			,						lowerCamelCase__	:    Any			,						lowerCamelCase__	:    Any			,						lowerCamelCase__	:    List[str]			,						) ->			Any:
         """simple docstring"""





         A_							       =		LiltForQuestionAnswering(config=lowerCamelCase__       )
         model.to(lowerCamelCase__       )
         model.eval()
         A_							       =		model(
             lowerCamelCase__			,						bbox=lowerCamelCase__			,						attention_mask=lowerCamelCase__			,						token_type_ids=lowerCamelCase__			,						start_positions=lowerCamelCase__			,						end_positions=lowerCamelCase__			,						)
         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[int]       ) ->			List[Any]:
         """simple docstring"""





         A_							       =		self.prepare_config_and_inputs()
         (
             (
             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				(					__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,unittest.TestCase			):
   _lowercase     :       Dict  					=    (
       (
           LiltModel,
           LiltForSequenceClassification,
           LiltForTokenClassification,
           LiltForQuestionAnswering,
       )
       if is_torch_available()
       else ()
   )
   _lowercase     :       List[Any]  					=    (
       {
           'feature-extraction': LiltModel,
           'question-answering': LiltForQuestionAnswering,
           'text-classification': LiltForSequenceClassification,
           'token-classification': LiltForTokenClassification,
           'zero-shot': LiltForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   _lowercase     :       Dict  					=    False
   _lowercase     :       Optional[int]  					=    False

   def     UpperCamelCase					(				self	:    Optional[Any]			,						lowerCamelCase__	:    Dict			,						lowerCamelCase__	:    int			,						lowerCamelCase__	:    List[str]			,						lowerCamelCase__	:    List[str]			,						lowerCamelCase__	:    List[str]       ) ->			Any:
         """simple docstring"""





         return True

   def     UpperCamelCase					(				self	:    int       ) ->			Any:
         """simple docstring"""





         A_							       =		LiltModelTester(self       )
         A_							       =		ConfigTester(self			,						config_class=lowerCamelCase__			,						hidden_size=3_7       )

   def     UpperCamelCase					(				self	:    int       ) ->			Optional[Any]:
         """simple docstring"""





         self.config_tester.run_common_tests()

   def     UpperCamelCase					(				self	:    Union[str, Any]       ) ->			Dict:
         """simple docstring"""





         A_							       =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowerCamelCase__       )

   def     UpperCamelCase					(				self	:    Optional[Any]       ) ->			int:
         """simple docstring"""





         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(*lowerCamelCase__       )

   def     UpperCamelCase					(				self	:    List[Any]       ) ->			int:
         """simple docstring"""





         A_							       =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*lowerCamelCase__       )

   def     UpperCamelCase					(				self	:    List[str]       ) ->			Any:
         """simple docstring"""





         A_							       =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*lowerCamelCase__       )





   @slow
   def     UpperCamelCase					(				self	:    Any       ) ->			Dict:
         """simple docstring"""





         for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               A_							       =		LiltModel.from_pretrained(lowerCamelCase__       )
               self.assertIsNotNone(lowerCamelCase__       )



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

   def     UpperCamelCase					(				self	:    List[str]       ) ->			Optional[Any]:
         """simple docstring"""





         A_							       =		LiltModel.from_pretrained('''SCUT-DLVCLab/lilt-roberta-en-base'''       ).to(lowerCamelCase__       )

         A_							       =		torch.tensor([[1, 2]]			,						device=lowerCamelCase__       )
         A_							       =		torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]			,						device=lowerCamelCase__       )

         # forward pass
         with torch.no_grad():
               A_							       =		model(input_ids=lowerCamelCase__			,						bbox=lowerCamelCase__       )

         A_							       =		torch.Size([1, 2, 7_6_8]       )
         A_							       =		torch.tensor(
             [[-0.0653, 0.0950, -0.0061], [-0.0545, 0.0926, -0.0324]]			,						device=lowerCamelCase__			,						)

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





