






"""simple docstring"""







import json
import os
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 AlignProcessor, EfficientNetImageProcessor







@require_vision
class        _SCREAMING_SNAKE_CASE						(  unittest.TestCase							):





							"""simple docstring"""





							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												a__          =  tempfile.mkdtemp()

												a__          =  [
												    '''[UNK]''',
												    '''[CLS]''',
												    '''[SEP]''',
												    '''[PAD]''',
												    '''[MASK]''',
												    '''want''',
												    '''##want''',
												    '''##ed''',
												    '''wa''',
												    '''un''',
												    '''runn''',
												    '''##ing''',
												    ''',''',
												    '''low''',
												    '''lowest''',
												]
												a__          =  os.path.join(self.tmpdirname				,		VOCAB_FILES_NAMES['''vocab_file''']    )
												with open(self.vocab_file				,		'''w'''				,		encoding='''utf-8'''    ) as vocab_writer:
																	vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]    )    )

												a__          =  {
												    '''do_resize''': True,
												    '''size''': 20,
												    '''do_center_crop''': True,
												    '''crop_size''': 18,
												    '''do_normalize''': True,
												    '''image_mean''': [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
												    '''image_std''': [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
												}
												a__          =  os.path.join(self.tmpdirname				,		__A    )
												with open(self.image_processor_file				,		'''w'''				,		encoding='''utf-8'''    ) as fp:
																	json.dump(__A				,		__A    )




							def  lowercase		(   self:     Any				,		**__A:     int    ):

												'''simple docstring'''

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




							def  lowercase		(   self:     List[str]				,		**__A:     Union[str, Any]    ):

												'''simple docstring'''

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




							def  lowercase		(   self:     Union[str, Any]				,		**__A:     Union[str, Any]    ):

												'''simple docstring'''

												return EfficientNetImageProcessor.from_pretrained(self.tmpdirname				,		**__A    )




							def  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

												shutil.rmtree(self.tmpdirname    )




							def  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

												a__          =  [np.random.randint(255				,		size=(3, 30, 400)				,		dtype=np.uinta    )]
												a__          =  [Image.fromarray(np.moveaxis(__A				,		0				,		-1    )    ) for x in image_inputs]
												return image_inputs




							def  lowercase		(   self:     str    ):

												'''simple docstring'''

												a__          =  self.get_tokenizer()
												a__          =  self.get_rust_tokenizer()
												a__          =  self.get_image_processor()

												a__          =  AlignProcessor(tokenizer=__A				,		image_processor=__A    )
												processor_slow.save_pretrained(self.tmpdirname    )
												a__          =  AlignProcessor.from_pretrained(self.tmpdirname				,		use_fast=__A    )

												a__          =  AlignProcessor(tokenizer=__A				,		image_processor=__A    )
												processor_fast.save_pretrained(self.tmpdirname    )
												a__          =  AlignProcessor.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				,		__A    )
												self.assertIsInstance(processor_fast.tokenizer				,		__A    )

												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				,		__A    )
												self.assertIsInstance(processor_fast.image_processor				,		__A    )




							def  lowercase		(   self:     List[Any]    ):

												'''simple docstring'''

												a__          =  AlignProcessor(tokenizer=self.get_tokenizer()				,		image_processor=self.get_image_processor()    )
												processor.save_pretrained(self.tmpdirname    )

												a__          =  self.get_tokenizer(bos_token='''(BOS)'''				,		eos_token='''(EOS)'''    )
												a__          =  self.get_image_processor(do_normalize=__A				,		padding_value=1.0    )

												a__          =  AlignProcessor.from_pretrained(
												    self.tmpdirname				,		bos_token='''(BOS)'''				,		eos_token='''(EOS)'''				,		do_normalize=__A				,		padding_value=1.0    )

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

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




							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												a__          =  self.get_image_processor()
												a__          =  self.get_tokenizer()

												a__          =  AlignProcessor(tokenizer=__A				,		image_processor=__A    )

												a__          =  self.prepare_image_inputs()

												a__          =  image_processor(__A				,		return_tensors='''np'''    )
												a__          =  processor(images=__A				,		return_tensors='''np'''    )

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




							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												a__          =  self.get_image_processor()
												a__          =  self.get_tokenizer()

												a__          =  AlignProcessor(tokenizer=__A				,		image_processor=__A    )

												a__          =  '''lower newer'''

												a__          =  processor(text=__A    )

												a__          =  tokenizer(__A				,		padding='''max_length'''				,		max_length=64    )

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




							def  lowercase		(   self:     List[str]    ):

												'''simple docstring'''

												a__          =  self.get_image_processor()
												a__          =  self.get_tokenizer()

												a__          =  AlignProcessor(tokenizer=__A				,		image_processor=__A    )

												a__          =  '''lower newer'''
												a__          =  self.prepare_image_inputs()

												a__          =  processor(text=__A				,		images=__A    )

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

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




							def  lowercase		(   self:     List[str]    ):

												'''simple docstring'''

												a__          =  self.get_image_processor()
												a__          =  self.get_tokenizer()

												a__          =  AlignProcessor(tokenizer=__A				,		image_processor=__A    )

												a__          =  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

												a__          =  processor.batch_decode(__A    )
												a__          =  tokenizer.batch_decode(__A    )

												self.assertListEqual(__A				,		__A    )





							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												a__          =  self.get_image_processor()
												a__          =  self.get_tokenizer()

												a__          =  AlignProcessor(tokenizer=__A				,		image_processor=__A    )

												a__          =  '''lower newer'''
												a__          =  self.prepare_image_inputs()

												a__          =  processor(text=__A				,		images=__A    )

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










"""simple docstring"""







import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
	import jax.numpy as jnp

	from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
	    FlaxRobertaPreLayerNormForCausalLM,
	    FlaxRobertaPreLayerNormForMaskedLM,
	    FlaxRobertaPreLayerNormForMultipleChoice,
	    FlaxRobertaPreLayerNormForQuestionAnswering,
	    FlaxRobertaPreLayerNormForSequenceClassification,
	    FlaxRobertaPreLayerNormForTokenClassification,
	    FlaxRobertaPreLayerNormModel,
	)







class        _SCREAMING_SNAKE_CASE						(  unittest.TestCase							):





							"""simple docstring"""





							def __init__(   self:     List[str]				,		__A:     Optional[Any]				,		__A:     List[Any]=13				,		__A:     Optional[int]=7				,		__A:     Any=True				,		__A:     str=True				,		__A:     Any=True				,		__A:     str=True				,		__A:     Optional[Any]=99				,		__A:     Union[str, Any]=32				,		__A:     str=5				,		__A:     Any=4				,		__A:     List[str]=37				,		__A:     Union[str, Any]="gelu"				,		__A:     str=0.1				,		__A:     Tuple=0.1				,		__A:     Optional[Any]=512				,		__A:     Union[str, Any]=16				,		__A:     str=2				,		__A:     Any=0.0_2				,		__A:     Tuple=4				,		):

												'''simple docstring'''

												a__          =  parent
												a__          =  batch_size
												a__          =  seq_length
												a__          =  is_training
												a__          =  use_attention_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_choices




							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												a__          =  ids_tensor([self.batch_size, self.seq_length]				,		self.vocab_size    )

												a__          =  None
												if self.use_attention_mask:
																	a__          =  random_attention_mask([self.batch_size, self.seq_length]    )

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

												a__          =  RobertaPreLayerNormConfig(
												    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				,		is_decoder=__A				,		initializer_range=self.initializer_range				,		)

												return config, input_ids, token_type_ids, attention_mask




							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												a__          =  self.prepare_config_and_inputs()
												a__       ,a__       ,a__       ,a__          =  config_and_inputs
												a__          =  {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
												return config, inputs_dict





							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												a__          =  self.prepare_config_and_inputs()
												a__       ,a__       ,a__       ,a__          =  config_and_inputs

												a__          =  True
												a__          =  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
												a__          =  ids_tensor([self.batch_size, self.seq_length]				,		vocab_size=2    )

												return (
												    config,
												    input_ids,
												    token_type_ids,
												    encoder_hidden_states,
												    encoder_attention_mask,
												)







@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class        _SCREAMING_SNAKE_CASE						(  __snake_case       , unittest.TestCase							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =True

							_SCREAMING_SNAKE_CASE    =(
							    (
							        FlaxRobertaPreLayerNormModel,
							        FlaxRobertaPreLayerNormForCausalLM,
							        FlaxRobertaPreLayerNormForMaskedLM,
							        FlaxRobertaPreLayerNormForSequenceClassification,
							        FlaxRobertaPreLayerNormForTokenClassification,
							        FlaxRobertaPreLayerNormForMultipleChoice,
							        FlaxRobertaPreLayerNormForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)




							def  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

												a__          =  FlaxRobertaPreLayerNormModelTester(self    )





							@slow
							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												for model_class_name in self.all_model_classes:
																	a__          =  model_class_name.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''				,		from_pt=__A    )
																	a__          =  model(np.ones((1, 1)    )    )
																	self.assertIsNotNone(__A    )







@require_flax
class        _SCREAMING_SNAKE_CASE						(  unittest.TestCase							):





							"""simple docstring"""





							@slow
							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

												a__          =  FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''				,		from_pt=__A    )

												a__          =  np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]				,		dtype=jnp.intaa    )
												a__          =  model(__A    )[0]
												a__          =  [1, 11, 50265]
												self.assertEqual(list(output.shape    )				,		__A    )
												# compare the actual values for a slice.
												a__          =  np.array(
												    [[[4_0.4_8_8_0, 1_8.0_1_9_9, -5.2_3_6_7], [-1.8_8_7_7, -4.0_8_8_5, 1_0.7_0_8_5], [-2.2_6_1_3, -5.6_1_1_0, 7.2_6_6_5]]]				,		dtype=np.floataa    )
												self.assertTrue(np.allclose(output[:, :3, :3]				,		__A				,		atol=1e-4    )    )





							@slow
							def  lowercase		(   self:     str    ):

												'''simple docstring'''

												a__          =  FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''				,		from_pt=__A    )

												a__          =  np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]				,		dtype=jnp.intaa    )
												a__          =  model(__A    )[0]
												# compare the actual values for a slice.
												a__          =  np.array(
												    [[[0.0_2_0_8, -0.0_3_5_6, 0.0_2_3_7], [-0.1_5_6_9, -0.0_4_1_1, -0.2_6_2_6], [0.1_8_7_9, 0.0_1_2_5, -0.0_0_8_9]]]				,		dtype=np.floataa    )
												self.assertTrue(np.allclose(output[:, :3, :3]				,		__A				,		atol=1e-4    )    )


