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, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast


@require_vision
class 						a__							(       unittest.TestCase		):



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Dict:
								__a		 =       tempfile.mkdtemp()

								__a		 =       BlipImageProcessor()
								__a		 =       BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-BertModel'   )

								__a		 =       BlipProcessor(UpperCAmelCase				,						UpperCAmelCase   )

								processor.save_pretrained(self.tmpdirname   )



			def __SCREAMING_SNAKE_CASE							(						self				,						**UpperCAmelCase   )			->   Dict:
								return AutoProcessor.from_pretrained(self.tmpdirname				,						**UpperCAmelCase   ).tokenizer



			def __SCREAMING_SNAKE_CASE							(						self				,						**UpperCAmelCase   )			->   Tuple:
								return AutoProcessor.from_pretrained(self.tmpdirname				,						**UpperCAmelCase   ).image_processor



			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[Any]:
								shutil.rmtree(self.tmpdirname   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Union[str, Any]:
								__a		 =       [np.random.randint(2_5_5				,						size=(3, 3_0, 4_0_0)				,						dtype=np.uinta   )]

								__a		 =       [Image.fromarray(np.moveaxis(UpperCAmelCase				,						0				,						-1   )   ) for x in image_inputs]

								return image_inputs



			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[str]:
								__a		 =       BlipProcessor(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=UpperCAmelCase				,						padding_value=1.0   )

								__a		 =       BlipProcessor.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				,						UpperCAmelCase   )

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



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Dict:
								__a		 =       self.get_image_processor()
								__a		 =       self.get_tokenizer()

								__a		 =       BlipProcessor(tokenizer=UpperCAmelCase				,						image_processor=UpperCAmelCase   )

								__a		 =       self.prepare_image_inputs()

								__a		 =       image_processor(UpperCAmelCase				,						return_tensors='np'   )
								__a		 =       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 __SCREAMING_SNAKE_CASE							(						self   )			->   Tuple:
								__a		 =       self.get_image_processor()
								__a		 =       self.get_tokenizer()

								__a		 =       BlipProcessor(tokenizer=UpperCAmelCase				,						image_processor=UpperCAmelCase   )

								__a		 =       'lower newer'

								__a		 =       processor(text=UpperCAmelCase   )

								__a		 =       tokenizer(UpperCAmelCase				,						return_token_type_ids=UpperCAmelCase   )

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



			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[str]:
								__a		 =       self.get_image_processor()
								__a		 =       self.get_tokenizer()

								__a		 =       BlipProcessor(tokenizer=UpperCAmelCase				,						image_processor=UpperCAmelCase   )

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

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

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

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



			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[str]:
								__a		 =       self.get_image_processor()
								__a		 =       self.get_tokenizer()

								__a		 =       BlipProcessor(tokenizer=UpperCAmelCase				,						image_processor=UpperCAmelCase   )

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

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

								self.assertListEqual(UpperCAmelCase				,						UpperCAmelCase   )







			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
								__a		 =       self.get_image_processor()
								__a		 =       self.get_tokenizer()

								__a		 =       BlipProcessor(tokenizer=UpperCAmelCase				,						image_processor=UpperCAmelCase   )

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

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

								# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
								self.assertListEqual(list(inputs.keys()   )				,						['pixel_values', 'input_ids', 'attention_mask']   )





import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer


class 						a__							(       __snake_case		):
			A__    :       Any        =      'Wav2Vec2FeatureExtractor'
			A__    :       str        =      'AutoTokenizer'



			def __init__(						self				,						UpperCAmelCase				,						UpperCAmelCase   )			->   Union[str, Any]:
								super().__init__(UpperCAmelCase				,						UpperCAmelCase   )
								__a		 =       self.feature_extractor
								__a		 =       False



			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls				,						UpperCAmelCase				,						**UpperCAmelCase   )			->   Dict:
								try:
													return super().from_pretrained(UpperCAmelCase				,						**UpperCAmelCase   )
								except OSError:
													warnings.warn(
													    f'''Loading a tokenizer inside {cls.__name__} from a config that does not'''
													    ' include a `tokenizer_class` attribute is deprecated and will be '
													    'removed in v5. Please add `\'tokenizer_class\': \'Wav2Vec2CTCTokenizer\'`'
													    ' attribute to either your `config.json` or `tokenizer_config.json` '
													    'file to suppress this warning: '				,						UpperCAmelCase				,						)

													__a		 =       WavaVecaFeatureExtractor.from_pretrained(UpperCAmelCase				,						**UpperCAmelCase   )
													__a		 =       WavaVecaCTCTokenizer.from_pretrained(UpperCAmelCase				,						**UpperCAmelCase   )

													return cls(feature_extractor=UpperCAmelCase				,						tokenizer=UpperCAmelCase   )



			def __call__(						self				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   List[Any]:
								# For backward compatibility
								if self._in_target_context_manager:
													return self.current_processor(*UpperCAmelCase				,						**UpperCAmelCase   )

								if "raw_speech" in kwargs:
													warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'   )
													__a		 =       kwargs.pop('raw_speech'   )
								else:
													__a		 =       kwargs.pop('audio'				,						UpperCAmelCase   )
								__a		 =       kwargs.pop('sampling_rate'				,						UpperCAmelCase   )
								__a		 =       kwargs.pop('text'				,						UpperCAmelCase   )
								if len(UpperCAmelCase   ) > 0:
													__a		 =       args[0]
													__a		 =       args[1:]

								if audio is None and text is None:
													raise ValueError('You need to specify either an `audio` or `text` input to process.'   )

								if audio is not None:
													__a		 =       self.feature_extractor(UpperCAmelCase				,						*UpperCAmelCase				,						sampling_rate=UpperCAmelCase				,						**UpperCAmelCase   )
								if text is not None:
													__a		 =       self.tokenizer(UpperCAmelCase				,						**UpperCAmelCase   )

								if text is None:
													return inputs
								elif audio is None:
													return encodings
								else:
													__a		 =       encodings['input_ids']
													return inputs



			def __SCREAMING_SNAKE_CASE							(						self				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   Tuple:
								# For backward compatibility
								if self._in_target_context_manager:
													return self.current_processor.pad(*UpperCAmelCase				,						**UpperCAmelCase   )

								__a		 =       kwargs.pop('input_features'				,						UpperCAmelCase   )
								__a		 =       kwargs.pop('labels'				,						UpperCAmelCase   )
								if len(UpperCAmelCase   ) > 0:
													__a		 =       args[0]
													__a		 =       args[1:]

								if input_features is not None:
													__a		 =       self.feature_extractor.pad(UpperCAmelCase				,						*UpperCAmelCase				,						**UpperCAmelCase   )
								if labels is not None:
													__a		 =       self.tokenizer.pad(UpperCAmelCase				,						**UpperCAmelCase   )

								if labels is None:
													return input_features
								elif input_features is None:
													return labels
								else:
													__a		 =       labels['input_ids']
													return input_features



			def __SCREAMING_SNAKE_CASE							(						self				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   List[str]:
								return self.tokenizer.batch_decode(*UpperCAmelCase				,						**UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   Union[str, Any]:
								return self.tokenizer.decode(*UpperCAmelCase				,						**UpperCAmelCase   )







			@contextmanager
			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[Any]:
								warnings.warn(
								    '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
								    'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
								    'your audio inputs, or in a separate call.'   )
								__a		 =       True
								__a		 =       self.tokenizer
								yield
								__a		 =       self.feature_extractor
								__a		 =       False




