
import json
import os
import shutil
import tempfile
import unittest

import numpy as np

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


if is_vision_available():
							from PIL import Image

							from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor





@require_tokenizers
@require_vision
class 							lowerCAmelCase_						(    unittest.TestCase							):




			'''simple docstring'''


			def      __lowerCamelCase    (	self  ):
										SCREAMING_SNAKE_CASE_	:							Optional[int]											=tempfile.mkdtemp()

										# fmt: off
										SCREAMING_SNAKE_CASE_	:							Union[str, Any]											=['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
										# fmt: on
										SCREAMING_SNAKE_CASE_	:							int											=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]  )  )

										SCREAMING_SNAKE_CASE_	:							Tuple											={
										    '''do_resize''': True,
										    '''size''': {'''height''': 18, '''width''': 18},
										    '''do_normalize''': True,
										    '''image_mean''': [0.5, 0.5, 0.5],
										    '''image_std''': [0.5, 0.5, 0.5],
										}
										SCREAMING_SNAKE_CASE_	:							Optional[Any]											=os.path.join(self.tmpdirname    ,							UpperCAmelCase__  )
										with open(self.image_processor_file    ,							'w'    ,							encoding='utf-8'  ) as fp:
																	json.dump(UpperCAmelCase__    ,							UpperCAmelCase__  )







			def      __lowerCamelCase    (	self    ,							**__UpperCAmelCase  ):
										return BertTokenizer.from_pretrained(self.tmpdirname    ,							**UpperCAmelCase__  )







			def      __lowerCamelCase    (	self    ,							**__UpperCAmelCase  ):
										return ViTImageProcessor.from_pretrained(self.tmpdirname    ,							**UpperCAmelCase__  )







			def      __lowerCamelCase    (	self  ):
										shutil.rmtree(self.tmpdirname  )







			def      __lowerCamelCase    (	self  ):
										SCREAMING_SNAKE_CASE_	:							Dict											=[np.random.randint(255    ,							size=(3, 30, 400)    ,							dtype=np.uinta  )]

										SCREAMING_SNAKE_CASE_	:							Optional[Any]											=[Image.fromarray(np.moveaxis(UpperCAmelCase__    ,							0    ,							-1  )  ) for x in image_inputs]

										return image_inputs







			def      __lowerCamelCase    (	self  ):
										SCREAMING_SNAKE_CASE_	:							List[Any]											=self.get_tokenizer()
										SCREAMING_SNAKE_CASE_	:							Tuple											=self.get_image_processor()

										SCREAMING_SNAKE_CASE_	:							Any											=VisionTextDualEncoderProcessor(tokenizer=UpperCAmelCase__    ,							image_processor=UpperCAmelCase__  )

										processor.save_pretrained(self.tmpdirname  )
										SCREAMING_SNAKE_CASE_	:							List[str]											=VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname  )

										self.assertEqual(processor.tokenizer.get_vocab()    ,							tokenizer.get_vocab()  )
										self.assertIsInstance(processor.tokenizer    ,							(BertTokenizer, BertTokenizerFast)  )

										self.assertEqual(processor.image_processor.to_json_string()    ,							image_processor.to_json_string()  )
										self.assertIsInstance(processor.image_processor    ,							UpperCAmelCase__  )







			def      __lowerCamelCase    (	self  ):
										SCREAMING_SNAKE_CASE_	:							str											=VisionTextDualEncoderProcessor(
										    tokenizer=self.get_tokenizer()    ,							image_processor=self.get_image_processor()  )
										processor.save_pretrained(self.tmpdirname  )

										SCREAMING_SNAKE_CASE_	:							List[str]											=self.get_tokenizer(bos_token='(BOS)'    ,							eos_token='(EOS)'  )
										SCREAMING_SNAKE_CASE_	:							Optional[Any]											=self.get_image_processor(do_normalize=UpperCAmelCase__    ,							padding_value=1.0  )

										SCREAMING_SNAKE_CASE_	:							Tuple											=VisionTextDualEncoderProcessor.from_pretrained(
										    self.tmpdirname    ,							bos_token='(BOS)'    ,							eos_token='(EOS)'    ,							do_normalize=UpperCAmelCase__    ,							padding_value=1.0  )

										self.assertEqual(processor.tokenizer.get_vocab()    ,							tokenizer_add_kwargs.get_vocab()  )
										self.assertIsInstance(processor.tokenizer    ,							(BertTokenizer, BertTokenizerFast)  )

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







			def      __lowerCamelCase    (	self  ):
										SCREAMING_SNAKE_CASE_	:							List[str]											=self.get_image_processor()
										SCREAMING_SNAKE_CASE_	:							List[str]											=self.get_tokenizer()

										SCREAMING_SNAKE_CASE_	:							Optional[int]											=VisionTextDualEncoderProcessor(tokenizer=UpperCAmelCase__    ,							image_processor=UpperCAmelCase__  )

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

										SCREAMING_SNAKE_CASE_	:							Optional[Any]											=image_processor(UpperCAmelCase__    ,							return_tensors='np'  )
										SCREAMING_SNAKE_CASE_	:							List[str]											=processor(images=UpperCAmelCase__    ,							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  ):
										SCREAMING_SNAKE_CASE_	:							Union[str, Any]											=self.get_image_processor()
										SCREAMING_SNAKE_CASE_	:							str											=self.get_tokenizer()

										SCREAMING_SNAKE_CASE_	:							List[str]											=VisionTextDualEncoderProcessor(tokenizer=UpperCAmelCase__    ,							image_processor=UpperCAmelCase__  )

										SCREAMING_SNAKE_CASE_	:							Optional[Any]											='''lower newer'''

										SCREAMING_SNAKE_CASE_	:							List[str]											=processor(text=UpperCAmelCase__  )

										SCREAMING_SNAKE_CASE_	:							List[Any]											=tokenizer(UpperCAmelCase__  )

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







			def      __lowerCamelCase    (	self  ):
										SCREAMING_SNAKE_CASE_	:							List[Any]											=self.get_image_processor()
										SCREAMING_SNAKE_CASE_	:							List[str]											=self.get_tokenizer()

										SCREAMING_SNAKE_CASE_	:							Any											=VisionTextDualEncoderProcessor(tokenizer=UpperCAmelCase__    ,							image_processor=UpperCAmelCase__  )

										SCREAMING_SNAKE_CASE_	:							str											='''lower newer'''
										SCREAMING_SNAKE_CASE_	:							Union[str, Any]											=self.prepare_image_inputs()

										SCREAMING_SNAKE_CASE_	:							Tuple											=processor(text=UpperCAmelCase__    ,							images=UpperCAmelCase__  )

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

										# test if it raises when no input is passed
										with self.assertRaises(UpperCAmelCase__  ):
																	processor()







			def      __lowerCamelCase    (	self  ):
										SCREAMING_SNAKE_CASE_	:							Optional[Any]											=self.get_image_processor()
										SCREAMING_SNAKE_CASE_	:							Union[str, Any]											=self.get_tokenizer()

										SCREAMING_SNAKE_CASE_	:							Dict											=VisionTextDualEncoderProcessor(tokenizer=UpperCAmelCase__    ,							image_processor=UpperCAmelCase__  )

										SCREAMING_SNAKE_CASE_	:							Any											=[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

										SCREAMING_SNAKE_CASE_	:							Union[str, Any]											=processor.batch_decode(UpperCAmelCase__  )
										SCREAMING_SNAKE_CASE_	:							Optional[int]											=tokenizer.batch_decode(UpperCAmelCase__  )

										self.assertListEqual(UpperCAmelCase__    ,							UpperCAmelCase__  )







			def      __lowerCamelCase    (	self  ):
										SCREAMING_SNAKE_CASE_	:							Union[str, Any]											=self.get_image_processor()
										SCREAMING_SNAKE_CASE_	:							Tuple											=self.get_tokenizer()

										SCREAMING_SNAKE_CASE_	:							Union[str, Any]											=VisionTextDualEncoderProcessor(tokenizer=UpperCAmelCase__    ,							image_processor=UpperCAmelCase__  )

										SCREAMING_SNAKE_CASE_	:							Union[str, Any]											='''lower newer'''
										SCREAMING_SNAKE_CASE_	:							List[str]											=self.prepare_image_inputs()

										SCREAMING_SNAKE_CASE_	:							Any											=processor(text=UpperCAmelCase__    ,							images=UpperCAmelCase__  )

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








'''simple docstring'''





from typing import TYPE_CHECKING

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


UpperCamelCase_    						=       {"""configuration_mbart""": ["""MBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MBartConfig""", """MBartOnnxConfig"""]}

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

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

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_    						=       [
							    """MBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """MBartForCausalLM""",
							    """MBartForConditionalGeneration""",
							    """MBartForQuestionAnswering""",
							    """MBartForSequenceClassification""",
							    """MBartModel""",
							    """MBartPreTrainedModel""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_    						=       [
							    """TFMBartForConditionalGeneration""",
							    """TFMBartModel""",
							    """TFMBartPreTrainedModel""",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_    						=       [
							    """FlaxMBartForConditionalGeneration""",
							    """FlaxMBartForQuestionAnswering""",
							    """FlaxMBartForSequenceClassification""",
							    """FlaxMBartModel""",
							    """FlaxMBartPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_mbart import MBartTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_mbart_fast import MBartTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mbart import (
														    MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MBartForCausalLM,
														    MBartForConditionalGeneration,
														    MBartForQuestionAnswering,
														    MBartForSequenceClassification,
														    MBartModel,
														    MBartPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_mbart import (
														    FlaxMBartForConditionalGeneration,
														    FlaxMBartForQuestionAnswering,
														    FlaxMBartForSequenceClassification,
														    FlaxMBartModel,
														    FlaxMBartPreTrainedModel,
														)

else:
							import sys

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





