


"""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 				A_      (					unittest.TestCase    ):



						"""simple docstring"""






						def      UpperCAmelCase__					( self			:Any )    ->      List[Any]:
												UpperCAmelCase					=			tempfile.mkdtemp()

												UpperCAmelCase					=			[
												    '[UNK]',
												    '[CLS]',
												    '[SEP]',
												    '[PAD]',
												    '[MASK]',
												    'want',
												    '##want',
												    '##ed',
												    'wa',
												    'un',
												    'runn',
												    '##ing',
												    ',',
												    'low',
												    'lowest',
												]
												UpperCAmelCase					=			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] ) )

												UpperCAmelCase					=			{
												    'do_resize': True,
												    'size': 20,
												    'do_center_crop': True,
												    'crop_size': 18,
												    'do_normalize': True,
												    'image_mean': [0.4814_5466, 0.457_8275, 0.4082_1073],
												    'image_std': [0.2686_2954, 0.2613_0258, 0.2757_7711],
												}
												UpperCAmelCase					=			os.path.join(self.tmpdirname       ,  lowercase_ )
												with open(self.image_processor_file       ,  'w'       ,  encoding='utf-8' ) as fp:
																		json.dump(lowercase_       ,  lowercase_ )






						def      UpperCAmelCase__					( self			:Dict       ,  **lowercase_			:Union[str, Any] )    ->      int:
												return BertTokenizer.from_pretrained(self.tmpdirname       ,  **lowercase_ )






						def      UpperCAmelCase__					( self			:List[str]       ,  **lowercase_			:str )    ->      Optional[Any]:
												return BertTokenizerFast.from_pretrained(self.tmpdirname       ,  **lowercase_ )






						def      UpperCAmelCase__					( self			:Union[str, Any]       ,  **lowercase_			:List[Any] )    ->      Optional[Any]:
												return EfficientNetImageProcessor.from_pretrained(self.tmpdirname       ,  **lowercase_ )






						def      UpperCAmelCase__					( self			:Optional[Any] )    ->      List[str]:
												shutil.rmtree(self.tmpdirname )






						def      UpperCAmelCase__					( self			:str )    ->      List[Any]:
												UpperCAmelCase					=			[np.random.randint(2_55       ,  size=(3, 30, 4_00)       ,  dtype=np.uinta )]
												UpperCAmelCase					=			[Image.fromarray(np.moveaxis(lowercase_       ,  0       ,  -1 ) ) for x in image_inputs]
												return image_inputs






						def      UpperCAmelCase__					( self			:Union[str, Any] )    ->      List[str]:
												UpperCAmelCase					=			self.get_tokenizer()
												UpperCAmelCase					=			self.get_rust_tokenizer()
												UpperCAmelCase					=			self.get_image_processor()

												UpperCAmelCase					=			AlignProcessor(tokenizer=lowercase_       ,  image_processor=lowercase_ )
												processor_slow.save_pretrained(self.tmpdirname )
												UpperCAmelCase					=			AlignProcessor.from_pretrained(self.tmpdirname       ,  use_fast=lowercase_ )

												UpperCAmelCase					=			AlignProcessor(tokenizer=lowercase_       ,  image_processor=lowercase_ )
												processor_fast.save_pretrained(self.tmpdirname )
												UpperCAmelCase					=			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       ,  lowercase_ )
												self.assertIsInstance(processor_fast.tokenizer       ,  lowercase_ )

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






						def      UpperCAmelCase__					( self			:Tuple )    ->      Optional[int]:
												UpperCAmelCase					=			AlignProcessor(tokenizer=self.get_tokenizer()       ,  image_processor=self.get_image_processor() )
												processor.save_pretrained(self.tmpdirname )

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

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

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

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






						def      UpperCAmelCase__					( self			:str )    ->      Optional[int]:
												UpperCAmelCase					=			self.get_image_processor()
												UpperCAmelCase					=			self.get_tokenizer()

												UpperCAmelCase					=			AlignProcessor(tokenizer=lowercase_       ,  image_processor=lowercase_ )

												UpperCAmelCase					=			self.prepare_image_inputs()

												UpperCAmelCase					=			image_processor(lowercase_       ,  return_tensors='np' )
												UpperCAmelCase					=			processor(images=lowercase_       ,  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      UpperCAmelCase__					( self			:Optional[int] )    ->      Union[str, Any]:
												UpperCAmelCase					=			self.get_image_processor()
												UpperCAmelCase					=			self.get_tokenizer()

												UpperCAmelCase					=			AlignProcessor(tokenizer=lowercase_       ,  image_processor=lowercase_ )

												UpperCAmelCase					=			'lower newer'

												UpperCAmelCase					=			processor(text=lowercase_ )

												UpperCAmelCase					=			tokenizer(lowercase_       ,  padding='max_length'       ,  max_length=64 )

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






						def      UpperCAmelCase__					( self			:str )    ->      Union[str, Any]:
												UpperCAmelCase					=			self.get_image_processor()
												UpperCAmelCase					=			self.get_tokenizer()

												UpperCAmelCase					=			AlignProcessor(tokenizer=lowercase_       ,  image_processor=lowercase_ )

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

												UpperCAmelCase					=			processor(text=lowercase_       ,  images=lowercase_ )

												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(lowercase_ ):
																		processor()






						def      UpperCAmelCase__					( self			:Optional[Any] )    ->      str:
												UpperCAmelCase					=			self.get_image_processor()
												UpperCAmelCase					=			self.get_tokenizer()

												UpperCAmelCase					=			AlignProcessor(tokenizer=lowercase_       ,  image_processor=lowercase_ )

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

												UpperCAmelCase					=			processor.batch_decode(lowercase_ )
												UpperCAmelCase					=			tokenizer.batch_decode(lowercase_ )

												self.assertListEqual(lowercase_       ,  lowercase_ )






						def      UpperCAmelCase__					( self			:Optional[int] )    ->      Union[str, Any]:
												UpperCAmelCase					=			self.get_image_processor()
												UpperCAmelCase					=			self.get_tokenizer()

												UpperCAmelCase					=			AlignProcessor(tokenizer=lowercase_       ,  image_processor=lowercase_ )

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

												UpperCAmelCase					=			processor(text=lowercase_       ,  images=lowercase_ )

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










"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case_    					=	{
    """configuration_rembert""": ["""REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RemBertConfig""", """RemBertOnnxConfig"""]
}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_    					=	["""RemBertTokenizer"""]

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_    					=	["""RemBertTokenizerFast"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_    					=	[
				    """REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """RemBertForCausalLM""",
				    """RemBertForMaskedLM""",
				    """RemBertForMultipleChoice""",
				    """RemBertForQuestionAnswering""",
				    """RemBertForSequenceClassification""",
				    """RemBertForTokenClassification""",
				    """RemBertLayer""",
				    """RemBertModel""",
				    """RemBertPreTrainedModel""",
				    """load_tf_weights_in_rembert""",
				]


try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_    					=	[
				    """TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """TFRemBertForCausalLM""",
				    """TFRemBertForMaskedLM""",
				    """TFRemBertForMultipleChoice""",
				    """TFRemBertForQuestionAnswering""",
				    """TFRemBertForSequenceClassification""",
				    """TFRemBertForTokenClassification""",
				    """TFRemBertLayer""",
				    """TFRemBertModel""",
				    """TFRemBertPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_rembert import RemBertTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_rembert_fast import RemBertTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_rembert import (
								    REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    RemBertForCausalLM,
								    RemBertForMaskedLM,
								    RemBertForMultipleChoice,
								    RemBertForQuestionAnswering,
								    RemBertForSequenceClassification,
								    RemBertForTokenClassification,
								    RemBertLayer,
								    RemBertModel,
								    RemBertPreTrainedModel,
								    load_tf_weights_in_rembert,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_rembert import (
								    TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFRemBertForCausalLM,
								    TFRemBertForMaskedLM,
								    TFRemBertForMultipleChoice,
								    TFRemBertForQuestionAnswering,
								    TFRemBertForSequenceClassification,
								    TFRemBertForTokenClassification,
								    TFRemBertLayer,
								    TFRemBertModel,
								    TFRemBertPreTrainedModel,
								)


else:
				import sys

				snake_case_    					=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





