
"""simple docstring"""




import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
					from PIL import Image

					from transformers import (
					    AutoProcessor,
					    BertTokenizerFast,
					    BlipImageProcessor,
					    GPTaTokenizer,
					    InstructBlipProcessor,
					    PreTrainedTokenizerFast,
					)



@require_vision
class 	lowerCAmelCase_						(unittest.TestCase  ):


				"""simple docstring"""



				def 	__magic_name__    (self    )					->		Any:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   tempfile.mkdtemp()

											SCREAMING_SNAKE_CASE__						:       List[Any]           =   BlipImageProcessor()
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model"""    )
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   BertTokenizerFast.from_pretrained("""hf-internal-testing/tiny-random-bert"""    )

											SCREAMING_SNAKE_CASE__						:       str           =   InstructBlipProcessor(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )

											processor.save_pretrained(self.tmpdirname    )



				def 	__magic_name__    (self   ,       **SCREAMING_SNAKE_CASE__    )					->		Any:



											"""simple docstring"""




											return AutoProcessor.from_pretrained(self.tmpdirname   ,       **SCREAMING_SNAKE_CASE__    ).tokenizer



				def 	__magic_name__    (self   ,       **SCREAMING_SNAKE_CASE__    )					->		int:



											"""simple docstring"""




											return AutoProcessor.from_pretrained(self.tmpdirname   ,       **SCREAMING_SNAKE_CASE__    ).image_processor



				def 	__magic_name__    (self   ,       **SCREAMING_SNAKE_CASE__    )					->		Dict:



											"""simple docstring"""




											return AutoProcessor.from_pretrained(self.tmpdirname   ,       **SCREAMING_SNAKE_CASE__    ).qformer_tokenizer



				def 	__magic_name__    (self    )					->		int:



											"""simple docstring"""




											shutil.rmtree(self.tmpdirname    )



				def 	__magic_name__    (self    )					->		int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Any           =   [np.random.randint(2_55   ,       size=(3, 30, 4_00)   ,       dtype=np.uinta    )]

											SCREAMING_SNAKE_CASE__						:       int           =   [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__   ,       0   ,       -1    )    ) for x in image_inputs]

											return image_inputs



				def 	__magic_name__    (self    )					->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   InstructBlipProcessor(
											    tokenizer=self.get_tokenizer()   ,       image_processor=self.get_image_processor()   ,       qformer_tokenizer=self.get_qformer_tokenizer()   ,       )
											processor.save_pretrained(self.tmpdirname    )

											SCREAMING_SNAKE_CASE__						:       str           =   self.get_tokenizer(bos_token="""(BOS)"""   ,       eos_token="""(EOS)"""    )
											SCREAMING_SNAKE_CASE__						:       Any           =   self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE__   ,       padding_value=1.0    )

											SCREAMING_SNAKE_CASE__						:       List[Any]           =   InstructBlipProcessor.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__    )
											self.assertIsInstance(processor.qformer_tokenizer   ,       SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self    )					->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   self.get_image_processor()
											SCREAMING_SNAKE_CASE__						:       Any           =   self.get_tokenizer()
											SCREAMING_SNAKE_CASE__						:       Tuple           =   self.get_qformer_tokenizer()

											SCREAMING_SNAKE_CASE__						:       str           =   InstructBlipProcessor(
											    tokenizer=SCREAMING_SNAKE_CASE__   ,       image_processor=SCREAMING_SNAKE_CASE__   ,       qformer_tokenizer=SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       List[Any]           =   self.prepare_image_inputs()

											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   image_processor(SCREAMING_SNAKE_CASE__   ,       return_tensors="""np"""    )
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   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    )



				def 	__magic_name__    (self    )					->		Tuple:



											"""simple docstring"""




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

											SCREAMING_SNAKE_CASE__						:       str           =   InstructBlipProcessor(
											    tokenizer=SCREAMING_SNAKE_CASE__   ,       image_processor=SCREAMING_SNAKE_CASE__   ,       qformer_tokenizer=SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   """lower newer"""

											SCREAMING_SNAKE_CASE__						:       int           =   processor(text=SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Any           =   tokenizer(SCREAMING_SNAKE_CASE__   ,       return_token_type_ids=SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       int           =   qformer_tokenizer(SCREAMING_SNAKE_CASE__   ,       return_token_type_ids=SCREAMING_SNAKE_CASE__    )

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

											for key in encoded_tokens_qformer.keys():
																		self.assertListEqual(encoded_tokens_qformer[key]   ,       encoded_processor["""qformer_""" + key]    )



				def 	__magic_name__    (self    )					->		List[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   self.get_image_processor()
											SCREAMING_SNAKE_CASE__						:       Dict           =   self.get_tokenizer()
											SCREAMING_SNAKE_CASE__						:       int           =   self.get_qformer_tokenizer()

											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   InstructBlipProcessor(
											    tokenizer=SCREAMING_SNAKE_CASE__   ,       image_processor=SCREAMING_SNAKE_CASE__   ,       qformer_tokenizer=SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   """lower newer"""
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   self.prepare_image_inputs()

											SCREAMING_SNAKE_CASE__						:       Any           =   processor(text=SCREAMING_SNAKE_CASE__   ,       images=SCREAMING_SNAKE_CASE__    )

											self.assertListEqual(
											    list(inputs.keys()    )   ,       ["""input_ids""", """attention_mask""", """qformer_input_ids""", """qformer_attention_mask""", """pixel_values"""]   ,       )

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



				def 	__magic_name__    (self    )					->		int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Dict           =   self.get_image_processor()
											SCREAMING_SNAKE_CASE__						:       int           =   self.get_tokenizer()
											SCREAMING_SNAKE_CASE__						:       Any           =   self.get_qformer_tokenizer()

											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   InstructBlipProcessor(
											    tokenizer=SCREAMING_SNAKE_CASE__   ,       image_processor=SCREAMING_SNAKE_CASE__   ,       qformer_tokenizer=SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   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 	__magic_name__    (self    )					->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   self.get_image_processor()
											SCREAMING_SNAKE_CASE__						:       Dict           =   self.get_tokenizer()
											SCREAMING_SNAKE_CASE__						:       Tuple           =   self.get_qformer_tokenizer()

											SCREAMING_SNAKE_CASE__						:       List[str]           =   InstructBlipProcessor(
											    tokenizer=SCREAMING_SNAKE_CASE__   ,       image_processor=SCREAMING_SNAKE_CASE__   ,       qformer_tokenizer=SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   """lower newer"""
											SCREAMING_SNAKE_CASE__						:       Tuple           =   self.prepare_image_inputs()

											SCREAMING_SNAKE_CASE__						:       Any           =   processor(text=SCREAMING_SNAKE_CASE__   ,       images=SCREAMING_SNAKE_CASE__    )

											self.assertListEqual(
											    list(inputs.keys()    )   ,       ["""input_ids""", """attention_mask""", """qformer_input_ids""", """qformer_attention_mask""", """pixel_values"""]   ,       )









"""simple docstring"""







from typing import TYPE_CHECKING

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


lowerCAmelCase_   						=   {
    'configuration_speecht5': [
        'SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP',
        'SpeechT5Config',
        'SpeechT5HifiGanConfig',
    ],
    'feature_extraction_speecht5': ['SpeechT5FeatureExtractor'],
    'processing_speecht5': ['SpeechT5Processor'],
}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_   						=   ['SpeechT5Tokenizer']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_   						=   [
							    'SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'SpeechT5ForSpeechToText',
							    'SpeechT5ForSpeechToSpeech',
							    'SpeechT5ForTextToSpeech',
							    'SpeechT5Model',
							    'SpeechT5PreTrainedModel',
							    'SpeechT5HifiGan',
							]

if TYPE_CHECKING:
							from .configuration_speechta import (
							    SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
							    SpeechTaConfig,
							    SpeechTaHifiGanConfig,
							)
							from .feature_extraction_speechta import SpeechTaFeatureExtractor
							from .processing_speechta import SpeechTaProcessor

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_speechta import SpeechTaTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_speechta import (
														    SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
														    SpeechTaForSpeechToSpeech,
														    SpeechTaForSpeechToText,
														    SpeechTaForTextToSpeech,
														    SpeechTaHifiGan,
														    SpeechTaModel,
														    SpeechTaPreTrainedModel,
														)

else:
							import sys

							lowerCAmelCase_   						=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
