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       UpperCamelCase__       (     self)	->     Union[str, Any]:
								__UpperCamelCase					:Dict        =		tempfile.mkdtemp()

								# fmt: off
								__UpperCamelCase					:List[str]        =		["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing""", """,""", """low""", """lowest"""]
								# fmt: on
								__UpperCamelCase					:Optional[Any]        =		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					:str        =		{
								    """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],
								}
								__UpperCamelCase					:Any        =		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			,			**__lowercase)	->     Dict:
								return BertTokenizer.from_pretrained(self.tmpdirname			,			**__lowercase)







				def       UpperCamelCase__       (     self			,			**__lowercase)	->     List[str]:
								return ViTImageProcessor.from_pretrained(self.tmpdirname			,			**__lowercase)







				def       UpperCamelCase__       (     self)	->     Dict:
								shutil.rmtree(self.tmpdirname)







				def       UpperCamelCase__       (     self)	->     Optional[int]:
								__UpperCamelCase					:List[str]        =		[np.random.randint(255			,			size=(3, 30, 400)			,			dtype=np.uinta)]

								__UpperCamelCase					:Tuple        =		[Image.fromarray(np.moveaxis(__lowercase			,			0			,			-1)) for x in image_inputs]

								return image_inputs







				def       UpperCamelCase__       (     self)	->     str:
								__UpperCamelCase					:List[Any]        =		self.get_tokenizer()
								__UpperCamelCase					:str        =		self.get_image_processor()

								__UpperCamelCase					:Any        =		VisionTextDualEncoderProcessor(tokenizer=__lowercase			,			image_processor=__lowercase)

								processor.save_pretrained(self.tmpdirname)
								__UpperCamelCase					:Dict        =		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			,			__lowercase)







				def       UpperCamelCase__       (     self)	->     List[str]:
								__UpperCamelCase					:str        =		VisionTextDualEncoderProcessor(
								    tokenizer=self.get_tokenizer()			,			image_processor=self.get_image_processor())
								processor.save_pretrained(self.tmpdirname)

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

								__UpperCamelCase					:Dict        =		VisionTextDualEncoderProcessor.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			,			(BertTokenizer, BertTokenizerFast))

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







				def       UpperCamelCase__       (     self)	->     Optional[Any]:
								__UpperCamelCase					:Optional[int]        =		self.get_image_processor()
								__UpperCamelCase					:List[Any]        =		self.get_tokenizer()

								__UpperCamelCase					:Any        =		VisionTextDualEncoderProcessor(tokenizer=__lowercase			,			image_processor=__lowercase)

								__UpperCamelCase					:List[str]        =		self.prepare_image_inputs()

								__UpperCamelCase					:Optional[Any]        =		image_processor(__lowercase			,			return_tensors='''np''')
								__UpperCamelCase					:str        =		processor(images=__lowercase			,			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       UpperCamelCase__       (     self)	->     Optional[int]:
								__UpperCamelCase					:Any        =		self.get_image_processor()
								__UpperCamelCase					:Any        =		self.get_tokenizer()

								__UpperCamelCase					:Optional[int]        =		VisionTextDualEncoderProcessor(tokenizer=__lowercase			,			image_processor=__lowercase)

								__UpperCamelCase					:Optional[int]        =		"""lower newer"""

								__UpperCamelCase					:int        =		processor(text=__lowercase)

								__UpperCamelCase					:Optional[Any]        =		tokenizer(__lowercase)

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







				def       UpperCamelCase__       (     self)	->     Tuple:
								__UpperCamelCase					:Optional[int]        =		self.get_image_processor()
								__UpperCamelCase					:List[str]        =		self.get_tokenizer()

								__UpperCamelCase					:str        =		VisionTextDualEncoderProcessor(tokenizer=__lowercase			,			image_processor=__lowercase)

								__UpperCamelCase					:Optional[Any]        =		"""lower newer"""
								__UpperCamelCase					:Optional[Any]        =		self.prepare_image_inputs()

								__UpperCamelCase					:List[str]        =		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 self.assertRaises(__lowercase):
												processor()







				def       UpperCamelCase__       (     self)	->     Tuple:
								__UpperCamelCase					:str        =		self.get_image_processor()
								__UpperCamelCase					:Union[str, Any]        =		self.get_tokenizer()

								__UpperCamelCase					:str        =		VisionTextDualEncoderProcessor(tokenizer=__lowercase			,			image_processor=__lowercase)

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

								__UpperCamelCase					:List[str]        =		processor.batch_decode(__lowercase)
								__UpperCamelCase					:str        =		tokenizer.batch_decode(__lowercase)

								self.assertListEqual(__lowercase			,			__lowercase)







				def       UpperCamelCase__       (     self)	->     int:
								__UpperCamelCase					:Optional[int]        =		self.get_image_processor()
								__UpperCamelCase					:Any        =		self.get_tokenizer()

								__UpperCamelCase					:List[Any]        =		VisionTextDualEncoderProcessor(tokenizer=__lowercase			,			image_processor=__lowercase)

								__UpperCamelCase					:List[str]        =		"""lower newer"""
								__UpperCamelCase					:str        =		self.prepare_image_inputs()

								__UpperCamelCase					:Optional[int]        =		processor(text=__lowercase			,			images=__lowercase)

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






from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


try:
    if not (is_transformers_available() and is_torch_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
    from .notes_encoder import SpectrogramNotesEncoder
    from .continous_encoder import SpectrogramContEncoder
    from .pipeline_spectrogram_diffusion import (
        SpectrogramContEncoder,
        SpectrogramDiffusionPipeline,
        TaFilmDecoder,
    )

try:
    if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ...utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
    from .midi_utils import MidiProcessor





