



"""simple docstring"""

import logging

import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.albert.modeling_albert import (
    ALBERT_INPUTS_DOCSTRING,
    ALBERT_START_DOCSTRING,
    AlbertModel,
    AlbertPreTrainedModel,
    AlbertTransformer,
)


A_	:						Tuple				=			logging.getLogger(__name__)
class 						lowerCAmelCase__	(  _lowerCamelCase   ):


	'''simple docstring'''







	def  _lowerCAmelCase    (			self							:					List[str]    ,					_SCREAMING_SNAKE_CASE							:					int    ,					_SCREAMING_SNAKE_CASE							:					Tuple    ,					_SCREAMING_SNAKE_CASE							:					Optional[int]=None    ,					_SCREAMING_SNAKE_CASE							:					List[Any]=None						)	->  List[Any]:





					"""simple docstring"""


					if current_layer == 0:
									SCREAMING_SNAKE_CASE	:       str 							=    self.embedding_hidden_mapping_in(_SCREAMING_SNAKE_CASE						)
					else:
									SCREAMING_SNAKE_CASE	:       int 							=    hidden_states[0]

					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    int(self.config.num_hidden_layers / self.config.num_hidden_groups						)

					# Index of the hidden group
					SCREAMING_SNAKE_CASE	:       str 							=    int(current_layer / (self.config.num_hidden_layers / self.config.num_hidden_groups)						)

					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    self.albert_layer_groups[group_idx](
					    _SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]    ,					)
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    layer_group_output[0]

					return (hidden_states,)




@add_start_docstrings(
    '''The bare ALBERT Model transformer with PABEE outputting raw hidden-states without any specific head on top.'''       ,       _lowerCamelCase       ,       )
class 						lowerCAmelCase__	(  _lowerCamelCase   ):


	'''simple docstring'''







	def __init__(			self							:					Optional[Any]    ,					_SCREAMING_SNAKE_CASE							:					Dict						)	->  List[str]:





					"""simple docstring"""


					super().__init__(_SCREAMING_SNAKE_CASE						)

					SCREAMING_SNAKE_CASE	:       Tuple 							=    AlbertTransformerWithPabee(_SCREAMING_SNAKE_CASE						)

					self.init_weights()
					SCREAMING_SNAKE_CASE	:       Dict 							=    0
					SCREAMING_SNAKE_CASE	:       Any 							=    0
					SCREAMING_SNAKE_CASE	:       Any 							=    0

					SCREAMING_SNAKE_CASE	:       List[str] 							=    0





	def  _lowerCAmelCase    (			self							:					Any    ,					_SCREAMING_SNAKE_CASE							:					Dict						)	->  Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       str 							=    threshold





	def  _lowerCAmelCase    (			self							:					int    ,					_SCREAMING_SNAKE_CASE							:					str						)	->  Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Dict 							=    patience





	def  _lowerCAmelCase    (			self							:					Tuple						)	->  Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       List[Any] 							=    0
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    0





	def  _lowerCAmelCase    (			self							:					Optional[Any]						)	->  str:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       int 							=    self.inference_layers_num / self.inference_instances_num
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    (
					    f"""*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ="""
					    f""" {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***"""
					)
					print(_SCREAMING_SNAKE_CASE						)







	@add_start_docstrings_to_model_forward(_SCREAMING_SNAKE_CASE						)
	def  _lowerCAmelCase    (			self							:					int    ,					_SCREAMING_SNAKE_CASE							:					int=None    ,					_SCREAMING_SNAKE_CASE							:					Tuple=None    ,					_SCREAMING_SNAKE_CASE							:					Any=None    ,					_SCREAMING_SNAKE_CASE							:					int=None    ,					_SCREAMING_SNAKE_CASE							:					List[Any]=None    ,					_SCREAMING_SNAKE_CASE							:					List[Any]=None    ,					_SCREAMING_SNAKE_CASE							:					List[str]=None    ,					_SCREAMING_SNAKE_CASE							:					List[Any]=None    ,					_SCREAMING_SNAKE_CASE							:					str=False    ,					)	->  List[str]:





					"""simple docstring"""


					if input_ids is not None and inputs_embeds is not None:
									raise ValueError('You cannot specify both input_ids and inputs_embeds at the same time'						)
					elif input_ids is not None:
									SCREAMING_SNAKE_CASE	:       int 							=    input_ids.size()
					elif inputs_embeds is not None:
									SCREAMING_SNAKE_CASE	:       Any 							=    inputs_embeds.size()[:-1]
					else:
									raise ValueError('You have to specify either input_ids or inputs_embeds'						)

					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    input_ids.device if input_ids is not None else inputs_embeds.device

					if attention_mask is None:
									SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    torch.ones(_SCREAMING_SNAKE_CASE    ,					device=_SCREAMING_SNAKE_CASE						)
					if token_type_ids is None:
									SCREAMING_SNAKE_CASE	:       List[str] 							=    torch.zeros(_SCREAMING_SNAKE_CASE    ,					dtype=torch.long    ,					device=_SCREAMING_SNAKE_CASE						)

					SCREAMING_SNAKE_CASE	:       List[Any] 							=    attention_mask.unsqueeze(1						).unsqueeze(2						)
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    extended_attention_mask.to(dtype=self.dtype						)  # fp16 compatibility
					SCREAMING_SNAKE_CASE	:       List[str] 							=    (1.0 - extended_attention_mask) * -10_000.0
					SCREAMING_SNAKE_CASE	:       Dict 							=    self.get_head_mask(_SCREAMING_SNAKE_CASE    ,					self.config.num_hidden_layers						)

					SCREAMING_SNAKE_CASE	:       List[Any] 							=    self.embeddings(
					    _SCREAMING_SNAKE_CASE    ,					position_ids=_SCREAMING_SNAKE_CASE    ,					token_type_ids=_SCREAMING_SNAKE_CASE    ,					inputs_embeds=_SCREAMING_SNAKE_CASE						)
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    embedding_output

					if self.training:
									SCREAMING_SNAKE_CASE	:       List[str] 							=    []
									for i in range(self.config.num_hidden_layers						):
													SCREAMING_SNAKE_CASE	:       Any 							=    self.encoder.adaptive_forward(
													    _SCREAMING_SNAKE_CASE    ,					current_layer=_SCREAMING_SNAKE_CASE    ,					attention_mask=_SCREAMING_SNAKE_CASE    ,					head_mask=_SCREAMING_SNAKE_CASE    ,					)

													SCREAMING_SNAKE_CASE	:       List[str] 							=    self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]						)						)
													SCREAMING_SNAKE_CASE	:       Optional[int] 							=    output_layers[i](output_dropout(_SCREAMING_SNAKE_CASE						)						)
													res.append(_SCREAMING_SNAKE_CASE						)
					elif self.patience == 0:  # Use all layers for inference
									SCREAMING_SNAKE_CASE	:       Any 							=    self.encoder(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					head_mask=_SCREAMING_SNAKE_CASE						)
									SCREAMING_SNAKE_CASE	:       Optional[int] 							=    self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]						)						)
									SCREAMING_SNAKE_CASE	:       Tuple 							=    [output_layers[self.config.num_hidden_layers - 1](_SCREAMING_SNAKE_CASE						)]
					else:
									SCREAMING_SNAKE_CASE	:       List[Any] 							=    0
									SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    None
									SCREAMING_SNAKE_CASE	:       int 							=    0
									for i in range(self.config.num_hidden_layers						):
													calculated_layer_num += 1
													SCREAMING_SNAKE_CASE	:       Dict 							=    self.encoder.adaptive_forward(
													    _SCREAMING_SNAKE_CASE    ,					current_layer=_SCREAMING_SNAKE_CASE    ,					attention_mask=_SCREAMING_SNAKE_CASE    ,					head_mask=_SCREAMING_SNAKE_CASE    ,					)

													SCREAMING_SNAKE_CASE	:       List[Any] 							=    self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]						)						)
													SCREAMING_SNAKE_CASE	:       int 							=    output_layers[i](_SCREAMING_SNAKE_CASE						)
													if regression:
																	SCREAMING_SNAKE_CASE	:       Optional[int] 							=    logits.detach()
																	if patient_result is not None:
																					SCREAMING_SNAKE_CASE	:       List[str] 							=    patient_result.detach()
																	if (patient_result is not None) and torch.abs(patient_result - labels						) < self.regression_threshold:
																					patient_counter += 1
																	else:
																					SCREAMING_SNAKE_CASE	:       List[Any] 							=    0
													else:
																	SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    logits.detach().argmax(dim=1						)
																	if patient_result is not None:
																					SCREAMING_SNAKE_CASE	:       str 							=    patient_result.detach().argmax(dim=1						)
																	if (patient_result is not None) and torch.all(labels.eq(_SCREAMING_SNAKE_CASE						)						):
																					patient_counter += 1
																	else:
																					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    0

													SCREAMING_SNAKE_CASE	:       Any 							=    logits
													if patient_counter == self.patience:
																	break
									SCREAMING_SNAKE_CASE	:       Any 							=    [patient_result]
									self.inference_layers_num += calculated_layer_num
									self.inference_instances_num += 1

					return res




@add_start_docstrings(
    '''Albert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of
    the pooled output) e.g. for GLUE tasks. '''       ,       _lowerCamelCase       ,       )
class 						lowerCAmelCase__	(  _lowerCamelCase   ):


	'''simple docstring'''







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





					"""simple docstring"""


					super().__init__(_SCREAMING_SNAKE_CASE						)
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    config.num_labels

					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    AlbertModelWithPabee(_SCREAMING_SNAKE_CASE						)
					SCREAMING_SNAKE_CASE	:       List[str] 							=    nn.Dropout(config.classifier_dropout_prob						)
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    nn.ModuleList(
					    [nn.Linear(config.hidden_size    ,					self.config.num_labels						) for _ in range(config.num_hidden_layers						)]						)

					self.init_weights()







	@add_start_docstrings_to_model_forward(_SCREAMING_SNAKE_CASE						)
	def  _lowerCAmelCase    (			self							:					Optional[Any]    ,					_SCREAMING_SNAKE_CASE							:					Union[str, Any]=None    ,					_SCREAMING_SNAKE_CASE							:					Tuple=None    ,					_SCREAMING_SNAKE_CASE							:					Optional[int]=None    ,					_SCREAMING_SNAKE_CASE							:					Optional[Any]=None    ,					_SCREAMING_SNAKE_CASE							:					Any=None    ,					_SCREAMING_SNAKE_CASE							:					Optional[int]=None    ,					_SCREAMING_SNAKE_CASE							:					List[Any]=None    ,					)	->  List[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       List[str] 							=    self.albert(
					    input_ids=_SCREAMING_SNAKE_CASE    ,					attention_mask=_SCREAMING_SNAKE_CASE    ,					token_type_ids=_SCREAMING_SNAKE_CASE    ,					position_ids=_SCREAMING_SNAKE_CASE    ,					head_mask=_SCREAMING_SNAKE_CASE    ,					inputs_embeds=_SCREAMING_SNAKE_CASE    ,					output_dropout=self.dropout    ,					output_layers=self.classifiers    ,					regression=self.num_labels == 1    ,					)

					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    (logits[-1],)

					if labels is not None:
									SCREAMING_SNAKE_CASE	:       Any 							=    None
									SCREAMING_SNAKE_CASE	:       Tuple 							=    0
									for ix, logits_item in enumerate(_SCREAMING_SNAKE_CASE						):
													if self.num_labels == 1:
																	#  We are doing regression
																	SCREAMING_SNAKE_CASE	:       List[str] 							=    MSELoss()
																	SCREAMING_SNAKE_CASE	:       List[Any] 							=    loss_fct(logits_item.view(-1						)    ,					labels.view(-1						)						)
													else:
																	SCREAMING_SNAKE_CASE	:       int 							=    CrossEntropyLoss()
																	SCREAMING_SNAKE_CASE	:       str 							=    loss_fct(logits_item.view(-1    ,					self.num_labels						)    ,					labels.view(-1						)						)
													if total_loss is None:
																	SCREAMING_SNAKE_CASE	:       int 							=    loss
													else:
																	total_loss += loss * (ix + 1)
													total_weights += ix + 1
									SCREAMING_SNAKE_CASE	:       Tuple 							=    (total_loss / total_weights,) + outputs

					return outputs






"""simple docstring"""

import json
import os
import random
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
							from PIL import Image

							from transformers import FlavaImageProcessor, FlavaProcessor
							from transformers.models.flava.image_processing_flava import (
							    FLAVA_CODEBOOK_MEAN,
							    FLAVA_CODEBOOK_STD,
							    FLAVA_IMAGE_MEAN,
							    FLAVA_IMAGE_STD,
							)
@require_vision
class 						lowerCAmelCase__	(  unittest.TestCase   ):


	'''simple docstring'''







	def  _lowerCAmelCase    (			self							:					int						)	->  Union[str, Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Any 							=    tempfile.mkdtemp()

					# fmt: off
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
					# fmt: on
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    os.path.join(self.tmpdirname    ,					VOCAB_FILES_NAMES['vocab_file']						)

					with open(self.vocab_file    ,					'w'    ,					encoding='utf-8'						) as fp:
									fp.write(''.join([x + '\n' for x in vocab_tokens]						)						)

					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    {
					    'image_mean': FLAVA_IMAGE_MEAN,
					    'image_std': FLAVA_IMAGE_STD,
					    'do_normalize': True,
					    'do_resize': True,
					    'size': 224,
					    'do_center_crop': True,
					    'crop_size': 224,
					    'input_size_patches': 14,
					    'total_mask_patches': 75,
					    'mask_group_max_patches': None,
					    'mask_group_min_patches': 16,
					    'mask_group_min_aspect_ratio': 0.3,
					    'mask_group_max_aspect_ratio': None,
					    'codebook_do_resize': True,
					    'codebook_size': 112,
					    'codebook_do_center_crop': True,
					    'codebook_crop_size': 112,
					    'codebook_do_map_pixels': True,
					    'codebook_do_normalize': True,
					    'codebook_image_mean': FLAVA_CODEBOOK_MEAN,
					    'codebook_image_std': FLAVA_CODEBOOK_STD,
					}

					SCREAMING_SNAKE_CASE	:       List[Any] 							=    os.path.join(self.tmpdirname    ,					_SCREAMING_SNAKE_CASE						)
					with open(self.image_processor_file    ,					'w'    ,					encoding='utf-8'						) as fp:
									json.dump(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					Any    ,					**_SCREAMING_SNAKE_CASE							:					str						)	->  str:





					"""simple docstring"""


					return BertTokenizer.from_pretrained(self.tmpdirname    ,					**_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					int    ,					**_SCREAMING_SNAKE_CASE							:					Any						)	->  Union[str, Any]:





					"""simple docstring"""


					return BertTokenizerFast.from_pretrained(self.tmpdirname    ,					**_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					Union[str, Any]    ,					**_SCREAMING_SNAKE_CASE							:					Dict						)	->  str:





					"""simple docstring"""


					return FlavaImageProcessor.from_pretrained(self.tmpdirname    ,					**_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					Dict						)	->  str:





					"""simple docstring"""


					shutil.rmtree(self.tmpdirname						)





	def  _lowerCAmelCase    (			self							:					int						)	->  Optional[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       int 							=    [np.random.randint(255    ,					size=(3, 30, 400)    ,					dtype=np.uinta						)]

					SCREAMING_SNAKE_CASE	:       str 							=    [Image.fromarray(np.moveaxis(_SCREAMING_SNAKE_CASE    ,					0    ,					-1						)						) for x in image_inputs]

					return image_inputs





	def  _lowerCAmelCase    (			self							:					Tuple						)	->  Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       int 							=    self.get_tokenizer()
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    self.get_rust_tokenizer()
					SCREAMING_SNAKE_CASE	:       str 							=    self.get_image_processor()

					SCREAMING_SNAKE_CASE	:       int 							=    FlavaProcessor(tokenizer=_SCREAMING_SNAKE_CASE    ,					image_processor=_SCREAMING_SNAKE_CASE						)
					processor_slow.save_pretrained(self.tmpdirname						)
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    FlavaProcessor.from_pretrained(self.tmpdirname    ,					use_fast=_SCREAMING_SNAKE_CASE						)

					SCREAMING_SNAKE_CASE	:       str 							=    FlavaProcessor(tokenizer=_SCREAMING_SNAKE_CASE    ,					image_processor=_SCREAMING_SNAKE_CASE						)
					processor_fast.save_pretrained(self.tmpdirname						)
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    FlavaProcessor.from_pretrained(self.tmpdirname						)

					self.assertEqual(processor_slow.tokenizer.get_vocab()    ,					tokenizer_slow.get_vocab()						)
					self.assertEqual(processor_fast.tokenizer.get_vocab()    ,					tokenizer_fast.get_vocab()						)
					self.assertEqual(tokenizer_slow.get_vocab()    ,					tokenizer_fast.get_vocab()						)
					self.assertIsInstance(processor_slow.tokenizer    ,					_SCREAMING_SNAKE_CASE						)
					self.assertIsInstance(processor_fast.tokenizer    ,					_SCREAMING_SNAKE_CASE						)

					self.assertEqual(processor_slow.image_processor.to_json_string()    ,					image_processor.to_json_string()						)
					self.assertEqual(processor_fast.image_processor.to_json_string()    ,					image_processor.to_json_string()						)
					self.assertIsInstance(processor_slow.image_processor    ,					_SCREAMING_SNAKE_CASE						)
					self.assertIsInstance(processor_fast.image_processor    ,					_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					Optional[Any]						)	->  List[str]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Tuple 							=    FlavaProcessor(tokenizer=self.get_tokenizer()    ,					image_processor=self.get_image_processor()						)
					processor.save_pretrained(self.tmpdirname						)

					SCREAMING_SNAKE_CASE	:       Dict 							=    self.get_tokenizer(bos_token='(BOS)'    ,					eos_token='(EOS)'						)
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    self.get_image_processor(do_normalize=_SCREAMING_SNAKE_CASE    ,					padding_value=1.0						)

					SCREAMING_SNAKE_CASE	:       List[str] 							=    FlavaProcessor.from_pretrained(
					    self.tmpdirname    ,					bos_token='(BOS)'    ,					eos_token='(EOS)'    ,					do_normalize=_SCREAMING_SNAKE_CASE    ,					padding_value=1.0						)

					self.assertEqual(processor.tokenizer.get_vocab()    ,					tokenizer_add_kwargs.get_vocab()						)
					self.assertIsInstance(processor.tokenizer    ,					_SCREAMING_SNAKE_CASE						)

					self.assertEqual(processor.image_processor.to_json_string()    ,					image_processor_add_kwargs.to_json_string()						)
					self.assertIsInstance(processor.image_processor    ,					_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					List[Any]						)	->  Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Any 							=    self.get_image_processor()
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    self.get_tokenizer()

					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    FlavaProcessor(tokenizer=_SCREAMING_SNAKE_CASE    ,					image_processor=_SCREAMING_SNAKE_CASE						)

					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    self.prepare_image_inputs()

					SCREAMING_SNAKE_CASE	:       Any 							=    image_processor(_SCREAMING_SNAKE_CASE    ,					return_tensors='np'						)
					SCREAMING_SNAKE_CASE	:       str 							=    processor(images=_SCREAMING_SNAKE_CASE    ,					return_tensors='np'						)

					for key in input_feat_extract.keys():
									self.assertAlmostEqual(input_feat_extract[key].sum()    ,					input_processor[key].sum()    ,					delta=1E-2						)

					# With rest of the args
					random.seed(1_234						)
					SCREAMING_SNAKE_CASE	:       str 							=    image_processor(
					    _SCREAMING_SNAKE_CASE    ,					return_image_mask=_SCREAMING_SNAKE_CASE    ,					return_codebook_pixels=_SCREAMING_SNAKE_CASE    ,					return_tensors='np'						)
					random.seed(1_234						)
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    processor(
					    images=_SCREAMING_SNAKE_CASE    ,					return_image_mask=_SCREAMING_SNAKE_CASE    ,					return_codebook_pixels=_SCREAMING_SNAKE_CASE    ,					return_tensors='np'						)

					for key in input_feat_extract.keys():
									self.assertAlmostEqual(input_feat_extract[key].sum()    ,					input_processor[key].sum()    ,					delta=1E-2						)





	def  _lowerCAmelCase    (			self							:					Any						)	->  Optional[int]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Dict 							=    self.get_image_processor()
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    self.get_tokenizer()

					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    FlavaProcessor(tokenizer=_SCREAMING_SNAKE_CASE    ,					image_processor=_SCREAMING_SNAKE_CASE						)

					SCREAMING_SNAKE_CASE	:       List[Any] 							=    'lower newer'

					SCREAMING_SNAKE_CASE	:       List[str] 							=    processor(text=_SCREAMING_SNAKE_CASE						)

					SCREAMING_SNAKE_CASE	:       List[str] 							=    tokenizer(_SCREAMING_SNAKE_CASE						)

					for key in encoded_tok.keys():
									self.assertListEqual(encoded_tok[key]    ,					encoded_processor[key]						)





	def  _lowerCAmelCase    (			self							:					Tuple						)	->  int:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    self.get_image_processor()
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    self.get_tokenizer()

					SCREAMING_SNAKE_CASE	:       str 							=    FlavaProcessor(tokenizer=_SCREAMING_SNAKE_CASE    ,					image_processor=_SCREAMING_SNAKE_CASE						)

					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    'lower newer'
					SCREAMING_SNAKE_CASE	:       List[str] 							=    self.prepare_image_inputs()

					SCREAMING_SNAKE_CASE	:       List[str] 							=    processor(text=_SCREAMING_SNAKE_CASE    ,					images=_SCREAMING_SNAKE_CASE						)

					self.assertListEqual(list(inputs.keys()						)    ,					['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values']						)

					# add extra args
					SCREAMING_SNAKE_CASE	:       Any 							=    processor(text=_SCREAMING_SNAKE_CASE    ,					images=_SCREAMING_SNAKE_CASE    ,					return_codebook_pixels=_SCREAMING_SNAKE_CASE    ,					return_image_mask=_SCREAMING_SNAKE_CASE						)

					self.assertListEqual(
					    list(inputs.keys()						)    ,					[
					        'input_ids',
					        'token_type_ids',
					        'attention_mask',
					        'pixel_values',
					        'codebook_pixel_values',
					        'bool_masked_pos',
					    ]    ,					)

					# test if it raises when no input is passed
					with pytest.raises(_SCREAMING_SNAKE_CASE						):
									processor()





	def  _lowerCAmelCase    (			self							:					Optional[Any]						)	->  Union[str, Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       List[str] 							=    self.get_image_processor()
					SCREAMING_SNAKE_CASE	:       Tuple 							=    self.get_tokenizer()

					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    FlavaProcessor(tokenizer=_SCREAMING_SNAKE_CASE    ,					image_processor=_SCREAMING_SNAKE_CASE						)

					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    processor.batch_decode(_SCREAMING_SNAKE_CASE						)
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    tokenizer.batch_decode(_SCREAMING_SNAKE_CASE						)

					self.assertListEqual(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE						)







	def  _lowerCAmelCase    (			self							:					int						)	->  Any:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    self.get_image_processor()
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    self.get_tokenizer()

					SCREAMING_SNAKE_CASE	:       int 							=    FlavaProcessor(tokenizer=_SCREAMING_SNAKE_CASE    ,					image_processor=_SCREAMING_SNAKE_CASE						)

					SCREAMING_SNAKE_CASE	:       Any 							=    'lower newer'
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    self.prepare_image_inputs()

					SCREAMING_SNAKE_CASE	:       Dict 							=    processor(text=_SCREAMING_SNAKE_CASE    ,					images=_SCREAMING_SNAKE_CASE						)

					self.assertListEqual(list(inputs.keys()						)    ,					processor.model_input_names						)





