




'''simple docstring'''


import warnings
from contextlib import contextmanager

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

class      A       (						_a       ):
							lowercase_							     =							'Wav2Vec2FeatureExtractor'
							lowercase_							     =							'AutoTokenizer'







							def __init__(    self			:    Tuple				,     lowerCAmelCase_			:    Dict				,     lowerCAmelCase_			:    List[str]   )     ->				Union[str, Any]:


														"""simple docstring"""


														super().__init__(lowerCAmelCase_				,     lowerCAmelCase_   )
														_a      	=							self.feature_extractor
														_a      	=							False







							@classmethod
							def   __lowerCAmelCase     (    cls			:    Optional[Any]				,     lowerCAmelCase_			:    Optional[Any]				,     **lowerCAmelCase_			:    Optional[Any]   )     ->				Tuple:


														"""simple docstring"""


														try:
																					return super().from_pretrained(lowerCAmelCase_				,     **lowerCAmelCase_   )
														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: '''				,     lowerCAmelCase_				,     )

																					_a      	=							WavaVecaFeatureExtractor.from_pretrained(lowerCAmelCase_				,     **lowerCAmelCase_   )
																					_a      	=							WavaVecaCTCTokenizer.from_pretrained(lowerCAmelCase_				,     **lowerCAmelCase_   )

																					return cls(feature_extractor=lowerCAmelCase_				,     tokenizer=lowerCAmelCase_   )







							def __call__(    self			:    str				,     *lowerCAmelCase_			:    Union[str, Any]				,     **lowerCAmelCase_			:    Any   )     ->				Optional[int]:


														"""simple docstring"""


														if self._in_target_context_manager:
																					return self.current_processor(*lowerCAmelCase_				,     **lowerCAmelCase_   )

														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'''				,     lowerCAmelCase_   )
														_a      	=							kwargs.pop('''sampling_rate'''				,     lowerCAmelCase_   )
														_a      	=							kwargs.pop('''text'''				,     lowerCAmelCase_   )
														if len(lowerCAmelCase_   ) > 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(lowerCAmelCase_				,     *lowerCAmelCase_				,     sampling_rate=lowerCAmelCase_				,     **lowerCAmelCase_   )
														if text is not None:
																					_a      	=							self.tokenizer(lowerCAmelCase_				,     **lowerCAmelCase_   )

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







							def   __lowerCAmelCase     (    self			:    str				,     *lowerCAmelCase_			:    List[str]				,     **lowerCAmelCase_			:    Any   )     ->				Any:


														"""simple docstring"""


														if self._in_target_context_manager:
																					return self.current_processor.pad(*lowerCAmelCase_				,     **lowerCAmelCase_   )

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

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

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







							def   __lowerCAmelCase     (    self			:    Optional[int]				,     *lowerCAmelCase_			:    List[str]				,     **lowerCAmelCase_			:    List[str]   )     ->				Optional[Any]:


														"""simple docstring"""


														return self.tokenizer.batch_decode(*lowerCAmelCase_				,     **lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Tuple				,     *lowerCAmelCase_			:    Tuple				,     **lowerCAmelCase_			:    List[Any]   )     ->				List[str]:


														"""simple docstring"""


														return self.tokenizer.decode(*lowerCAmelCase_				,     **lowerCAmelCase_   )







							@contextmanager
							def   __lowerCAmelCase     (    self			:    Union[str, Any]   )     ->				int:


														"""simple docstring"""


														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







'''simple docstring'''


import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import ImageGPTImageProcessor

class      A       (						unittest.TestCase       ):







							def __init__(    self			:    Tuple				,     lowerCAmelCase_			:    Union[str, Any]				,     lowerCAmelCase_			:    List[str]=7				,     lowerCAmelCase_			:    Dict=3				,     lowerCAmelCase_			:    List[Any]=18				,     lowerCAmelCase_			:    Any=30				,     lowerCAmelCase_			:    Optional[int]=4_00				,     lowerCAmelCase_			:    Union[str, Any]=True				,     lowerCAmelCase_			:    List[str]=None				,     lowerCAmelCase_			:    List[str]=True				,     )     ->				Optional[Any]:


														"""simple docstring"""


														_a      	=							size if size is not None else {'''height''': 18, '''width''': 18}
														_a      	=							parent
														_a      	=							batch_size
														_a      	=							num_channels
														_a      	=							image_size
														_a      	=							min_resolution
														_a      	=							max_resolution
														_a      	=							do_resize
														_a      	=							size
														_a      	=							do_normalize







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				int:


														"""simple docstring"""


														return {
														    # here we create 2 clusters for the sake of simplicity
														    "clusters": np.asarray(
														        [
														            [0.8_8_6_6_4_4_3_6_3_4_0_3_3_2_0_3, 0.6_6_1_8_8_2_9_3_6_9_5_4_4_9_8_3, 0.3_8_9_1_7_4_6_4_0_1_7_8_6_8_0_4],
														            [-0.6_0_4_2_5_5_9_1_4_6_8_8_1_1_0_4, -0.0_2_2_9_5_0_0_8_8_6_0_5_2_8_4_6_9, 0.5_4_2_3_7_9_7_3_6_9_0_0_3_2_9_6],
														        ]   ),
														    "do_resize": self.do_resize,
														    "size": self.size,
														    "do_normalize": self.do_normalize,
														}



@require_torch
@require_vision
class      A       (						_a	,unittest.TestCase       ):
							lowercase_							     =							ImageGPTImageProcessor if is_vision_available() else None







							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				str:


														"""simple docstring"""


														_a      	=							ImageGPTImageProcessingTester(self   )







							@property
							def   __lowerCAmelCase     (    self			:    Tuple   )     ->				int:


														"""simple docstring"""


														return self.image_processor_tester.prepare_image_processor_dict()







							def   __lowerCAmelCase     (    self			:    List[str]   )     ->				Dict:


														"""simple docstring"""


														_a      	=							self.image_processing_class(**self.image_processor_dict   )
														self.assertTrue(hasattr(lowerCAmelCase_				,     '''clusters'''   )   )
														self.assertTrue(hasattr(lowerCAmelCase_				,     '''do_resize'''   )   )
														self.assertTrue(hasattr(lowerCAmelCase_				,     '''size'''   )   )
														self.assertTrue(hasattr(lowerCAmelCase_				,     '''do_normalize'''   )   )







							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				List[str]:


														"""simple docstring"""


														_a      	=							self.image_processing_class.from_dict(self.image_processor_dict   )
														self.assertEqual(image_processor.size				,     {'''height''': 18, '''width''': 18}   )

														_a      	=							self.image_processing_class.from_dict(self.image_processor_dict				,     size=42   )
														self.assertEqual(image_processor.size				,     {'''height''': 42, '''width''': 42}   )







							def   __lowerCAmelCase     (    self			:    str   )     ->				str:


														"""simple docstring"""


														_a      	=							self.image_processing_class(**self.image_processor_dict   )
														_a      	=							json.loads(image_processor.to_json_string()   )
														for key, value in self.image_processor_dict.items():
																					if key == "clusters":
																												self.assertTrue(np.array_equal(lowerCAmelCase_				,     obj[key]   )   )
																					else:
																												self.assertEqual(obj[key]				,     lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    List[str]   )     ->				int:


														"""simple docstring"""


														_a      	=							self.image_processing_class(**self.image_processor_dict   )

														with tempfile.TemporaryDirectory() as tmpdirname:
																					_a      	=							os.path.join(lowerCAmelCase_				,     '''image_processor.json'''   )
																					image_processor_first.to_json_file(lowerCAmelCase_   )
																					_a      	=							self.image_processing_class.from_json_file(lowerCAmelCase_   ).to_dict()

														_a      	=							image_processor_first.to_dict()
														for key, value in image_processor_first.items():
																					if key == "clusters":
																												self.assertTrue(np.array_equal(lowerCAmelCase_				,     image_processor_second[key]   )   )
																					else:
																												self.assertEqual(image_processor_first[key]				,     lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Any   )     ->				List[Any]:


														"""simple docstring"""


														_a      	=							self.image_processing_class(**self.image_processor_dict   )

														with tempfile.TemporaryDirectory() as tmpdirname:
																					image_processor_first.save_pretrained(lowerCAmelCase_   )
																					_a      	=							self.image_processing_class.from_pretrained(lowerCAmelCase_   ).to_dict()

														_a      	=							image_processor_first.to_dict()
														for key, value in image_processor_first.items():
																					if key == "clusters":
																												self.assertTrue(np.array_equal(lowerCAmelCase_				,     image_processor_second[key]   )   )
																					else:
																												self.assertEqual(image_processor_first[key]				,     lowerCAmelCase_   )







							@unittest.skip('''ImageGPT requires clusters at initialization'''   )
							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				Union[str, Any]:


														"""simple docstring"""


														pass



def 						snake_case_       ():





							'''simple docstring'''


							_a      	=							load_dataset('''hf-internal-testing/fixtures_image_utils'''					,		split='''test''' )

							_a      	=							Image.open(dataset[4]['''file'''] )
							_a      	=							Image.open(dataset[5]['''file'''] )

							_a      	=							[imagea, imagea]

							return images

@require_vision
@require_torch
class      A       (						unittest.TestCase       ):







							@slow
							def   __lowerCAmelCase     (    self			:    List[str]   )     ->				int:


														"""simple docstring"""


														_a      	=							ImageGPTImageProcessor.from_pretrained('''openai/imagegpt-small'''   )

														_a      	=							prepare_images()

														# test non-batched
														_a      	=							image_processing(images[0]				,     return_tensors='''pt'''   )

														self.assertIsInstance(encoding.input_ids				,     torch.LongTensor   )
														self.assertEqual(encoding.input_ids.shape				,     (1, 10_24)   )

														_a      	=							[3_06, 1_91, 1_91]
														self.assertEqual(encoding.input_ids[0, :3].tolist()				,     lowerCAmelCase_   )

														# test batched
														_a      	=							image_processing(lowerCAmelCase_				,     return_tensors='''pt'''   )

														self.assertIsInstance(encoding.input_ids				,     torch.LongTensor   )
														self.assertEqual(encoding.input_ids.shape				,     (2, 10_24)   )

														_a      	=							[3_03, 13, 13]
														self.assertEqual(encoding.input_ids[1, -3:].tolist()				,     lowerCAmelCase_   )

