
"""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  _lowerCAmelCase    :



							def __init__(   self			,    a_			,    a_=13			,    a_=7			,    a_=True			,    a_=True			,    a_=True			,    a_=True			,    a_=99			,    a_=24			,    a_=2			,    a_=6			,    a_=37			,    a_="gelu"			,    a_=0.1			,    a_=0.1			,    a_=512			,    a_=16			,    a_=2			,    a_=0.02			,    a_=3			,    a_=None			,    a_=1000			,    )				->				List[Any]:
								_UpperCAmelCase			=    parent
								_UpperCAmelCase			=    batch_size
								_UpperCAmelCase			=    seq_length
								_UpperCAmelCase			=    is_training
								_UpperCAmelCase			=    use_input_mask
								_UpperCAmelCase			=    use_token_type_ids
								_UpperCAmelCase			=    use_labels
								_UpperCAmelCase			=    vocab_size
								_UpperCAmelCase			=    hidden_size
								_UpperCAmelCase			=    num_hidden_layers
								_UpperCAmelCase			=    num_attention_heads
								_UpperCAmelCase			=    intermediate_size
								_UpperCAmelCase			=    hidden_act
								_UpperCAmelCase			=    hidden_dropout_prob
								_UpperCAmelCase			=    attention_probs_dropout_prob
								_UpperCAmelCase			=    max_position_embeddings
								_UpperCAmelCase			=    type_vocab_size
								_UpperCAmelCase			=    type_sequence_label_size
								_UpperCAmelCase			=    initializer_range
								_UpperCAmelCase			=    num_labels
								_UpperCAmelCase			=    scope
								_UpperCAmelCase			=    range_bbox



							def    _a (   self )				->				Any:
								_UpperCAmelCase			=    ids_tensor([self.batch_size, self.seq_length]			,    self.vocab_size )

								_UpperCAmelCase			=    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			=    bbox[i, j, 3]
											_UpperCAmelCase			=    bbox[i, j, 1]
											_UpperCAmelCase			=    t
										if bbox[i, j, 2] < bbox[i, j, 0]:
											_UpperCAmelCase			=    bbox[i, j, 2]
											_UpperCAmelCase			=    bbox[i, j, 0]
											_UpperCAmelCase			=    t

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

								_UpperCAmelCase			=    None
								if self.use_token_type_ids:
									_UpperCAmelCase			=    ids_tensor([self.batch_size, self.seq_length]			,    self.type_vocab_size )

								_UpperCAmelCase			=    None
								_UpperCAmelCase			=    None
								if self.use_labels:
									_UpperCAmelCase			=    ids_tensor([self.batch_size]			,    self.type_sequence_label_size )
									_UpperCAmelCase			=    ids_tensor([self.batch_size, self.seq_length]			,    self.num_labels )

								_UpperCAmelCase			=    self.get_config()

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



							def    _a (   self )				->				Union[str, 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    _a (   self			,    a_			,    a_			,    a_			,    a_			,    a_			,    a_			,    a_			,    )				->				Any:
								_UpperCAmelCase			=    LiltModel(config=a_ )
								model.to(a_ )
								model.eval()
								_UpperCAmelCase			=    model(a_			,    bbox=a_			,    attention_mask=a_			,    token_type_ids=a_ )
								_UpperCAmelCase			=    model(a_			,    bbox=a_			,    token_type_ids=a_ )
								_UpperCAmelCase			=    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    _a (   self			,    a_			,    a_			,    a_			,    a_			,    a_			,    a_			,    a_			,    )				->				Optional[int]:
								_UpperCAmelCase			=    self.num_labels
								_UpperCAmelCase			=    LiltForTokenClassification(config=a_ )
								model.to(a_ )
								model.eval()
								_UpperCAmelCase			=    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    _a (   self			,    a_			,    a_			,    a_			,    a_			,    a_			,    a_			,    a_			,    )				->				Union[str, Any]:
								_UpperCAmelCase			=    LiltForQuestionAnswering(config=a_ )
								model.to(a_ )
								model.eval()
								_UpperCAmelCase			=    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    _a (   self )				->				Optional[int]:
								_UpperCAmelCase			=    self.prepare_config_and_inputs()
								(
								    (
								    _UpperCAmelCase		
								)			,   (
								    _UpperCAmelCase		
								)			,   (
								    _UpperCAmelCase		
								)			,   (
								    _UpperCAmelCase		
								)			,   (
								    _UpperCAmelCase		
								)			,   (
								    _UpperCAmelCase		
								)			,   (
								    _UpperCAmelCase		
								)			,   		
								)			=    config_and_inputs
								_UpperCAmelCase			=    {
								    "input_ids": input_ids,
								    "bbox": bbox,
								    "token_type_ids": token_type_ids,
								    "attention_mask": input_mask,
								}
								return config, inputs_dict





@require_torch
class  _lowerCAmelCase    (    SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							unittest.TestCase    ):
							lowercase_  :  Tuple      =				(
							    (
							        LiltModel,
							        LiltForSequenceClassification,
							        LiltForTokenClassification,
							        LiltForQuestionAnswering,
							    )
							    if is_torch_available()
							    else ()
							)
							lowercase_  :  List[str]      =				(
							    {
							        '''feature-extraction''': LiltModel,
							        '''question-answering''': LiltForQuestionAnswering,
							        '''text-classification''': LiltForSequenceClassification,
							        '''token-classification''': LiltForTokenClassification,
							        '''zero-shot''': LiltForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							lowercase_  :  Optional[Any]      =				False
							lowercase_  :  Union[str, Any]      =				False



							def    _a (   self			,    a_			,    a_			,    a_			,    a_			,    a_ )				->				List[str]:
								return True



							def    _a (   self )				->				Tuple:
								_UpperCAmelCase			=    LiltModelTester(self )
								_UpperCAmelCase			=    ConfigTester(self			,    config_class=a_			,    hidden_size=37 )



							def    _a (   self )				->				Any:
								self.config_tester.run_common_tests()



							def    _a (   self )				->				List[Any]:
								_UpperCAmelCase			=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*a_ )



							def    _a (   self )				->				Dict:
								_UpperCAmelCase			=    self.model_tester.prepare_config_and_inputs()
								for type in ["absolute", "relative_key", "relative_key_query"]:
									_UpperCAmelCase			=    type
									self.model_tester.create_and_check_model(*a_ )



							def    _a (   self )				->				List[Any]:
								_UpperCAmelCase			=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*a_ )



							def    _a (   self )				->				Dict:
								_UpperCAmelCase			=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*a_ )



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



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





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



							def    _a (   self )				->				str:
								_UpperCAmelCase			=    LiltModel.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base" ).to(a_ )

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

								# forward pass
								with torch.no_grad():
									_UpperCAmelCase			=    model(input_ids=a_			,    bbox=a_ )

								_UpperCAmelCase			=    torch.Size([1, 2, 768] )
								_UpperCAmelCase			=    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 ) )










import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


SCREAMING_SNAKE_CASE__     :     Union[str, Any]		    =       logging.get_logger(__name__)



class       a_       (     SCREAMING_SNAKE_CASE__     ):





	def __init__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					Union[str, Any]:




				"""simple docstring"""



				super().__init__()
				SCREAMING_SNAKE_CASE_								=						nn.ModuleList(SCREAMING_SNAKE_CASE      )





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE = None      ,     SCREAMING_SNAKE_CASE = None      ,     SCREAMING_SNAKE_CASE = None      ,     SCREAMING_SNAKE_CASE = None      ,     SCREAMING_SNAKE_CASE = False      ,     SCREAMING_SNAKE_CASE = True      ,     )  ->					Union[ControlNetOutput, Tuple]:




				"""simple docstring"""



				for i, (image, scale, controlnet) in enumerate(zip(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     self.nets      )      ):
							SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						controlnet(
							    SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     )

							# merge samples
							if i == 0:
										SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						down_samples, mid_sample
							else:
										SCREAMING_SNAKE_CASE_								=						[
										    samples_prev + samples_curr
										    for samples_prev, samples_curr in zip(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )
										]
										mid_block_res_sample += mid_sample

				return down_block_res_samples, mid_block_res_sample





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE = True      ,     SCREAMING_SNAKE_CASE = None      ,     SCREAMING_SNAKE_CASE = False      ,     SCREAMING_SNAKE_CASE = None      ,     )  ->					int:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						0
				SCREAMING_SNAKE_CASE_								=						save_directory
				for controlnet in self.nets:
							controlnet.save_pretrained(
							    SCREAMING_SNAKE_CASE      ,     is_main_process=SCREAMING_SNAKE_CASE      ,     save_function=SCREAMING_SNAKE_CASE      ,     safe_serialization=SCREAMING_SNAKE_CASE      ,     variant=SCREAMING_SNAKE_CASE      ,     )

							idx += 1
							SCREAMING_SNAKE_CASE_								=						model_path_to_save + f'_{idx}'





	@classmethod
	def 						A_(							cls      ,     SCREAMING_SNAKE_CASE      ,     **SCREAMING_SNAKE_CASE      )  ->					Tuple:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						0
				SCREAMING_SNAKE_CASE_								=						[]

				# load controlnet and append to list until no controlnet directory exists anymore
				# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
				# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
				SCREAMING_SNAKE_CASE_								=						pretrained_model_path
				while os.path.isdir(SCREAMING_SNAKE_CASE      ):
							SCREAMING_SNAKE_CASE_								=						ControlNetModel.from_pretrained(SCREAMING_SNAKE_CASE      ,     **SCREAMING_SNAKE_CASE      )
							controlnets.append(SCREAMING_SNAKE_CASE      )

							idx += 1
							SCREAMING_SNAKE_CASE_								=						pretrained_model_path + f'_{idx}'

				logger.info(f'{len(SCREAMING_SNAKE_CASE      )} controlnets loaded from {pretrained_model_path}.'      )

				if len(SCREAMING_SNAKE_CASE      ) == 0:
							raise ValueError(
							    f'No ControlNets found under {os.path.dirname(SCREAMING_SNAKE_CASE      )}. Expected at least {pretrained_model_path + "_0"}.'      )

				return cls(SCREAMING_SNAKE_CASE      )
