



import random
import shutil
import tempfile
import unittest

import numpy as np

from transformers import TaTokenizer, TaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torch
from transformers.utils.import_utils import is_speech_available, is_torch_available


if is_torch_available():
	pass

if is_speech_available():
	from transformers import EncodecFeatureExtractor, MusicgenProcessor


_lowercase				:					Dict     					=random.Random()




def    UpperCAmelCase					(  lowercase__						:					Tuple				,					lowercase__						:					Dict=1.0				,					lowercase__						:					List[str]=None				,					lowercase__						:					Tuple=None	):



			'''simple docstring'''





			if rng is None:
						a__					=  global_rng

			a__					=  []
			for batch_idx in range(shape[0]	):
						values.append([]	)
						for _ in range(shape[1]	):
									values[-1].append(rng.random() * scale	)

			return values





@require_torch
@require_sentencepiece
class lowerCAmelCase_       (   unittest.TestCase			):







	'''simple docstring'''




	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  """facebook/musicgen-small"""
				a__					=  tempfile.mkdtemp()

	def 				_A			(	self   ,       **lowerCamelCase       ):



				'''simple docstring'''


				return TaTokenizer.from_pretrained(self.checkpoint   ,       **__lowerCAmelCase       )

	def 				_A			(	self   ,       **lowerCamelCase       ):



				'''simple docstring'''


				return EncodecFeatureExtractor.from_pretrained(self.checkpoint   ,       **__lowerCAmelCase       )

	def 				_A			(	self       ):



				'''simple docstring'''


				shutil.rmtree(self.tmpdirname       )

	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  self.get_tokenizer()
				a__					=  self.get_feature_extractor()

				a__					=  MusicgenProcessor(tokenizer=__lowerCAmelCase   ,       feature_extractor=__lowerCAmelCase       )

				processor.save_pretrained(self.tmpdirname       )
				a__					=  MusicgenProcessor.from_pretrained(self.tmpdirname       )

				self.assertEqual(processor.tokenizer.get_vocab()   ,       tokenizer.get_vocab()       )
				self.assertIsInstance(processor.tokenizer   ,       __lowerCAmelCase       )

				self.assertEqual(processor.feature_extractor.to_json_string()   ,       feature_extractor.to_json_string()       )
				self.assertIsInstance(processor.feature_extractor   ,       __lowerCAmelCase       )

	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  MusicgenProcessor(tokenizer=self.get_tokenizer()   ,       feature_extractor=self.get_feature_extractor()       )
				processor.save_pretrained(self.tmpdirname       )

				a__					=  self.get_tokenizer(bos_token="""(BOS)"""   ,       eos_token="""(EOS)"""       )
				a__					=  self.get_feature_extractor(do_normalize=__lowerCAmelCase   ,       padding_value=1.0       )

				a__					=  MusicgenProcessor.from_pretrained(
				    self.tmpdirname   ,       bos_token="""(BOS)"""   ,       eos_token="""(EOS)"""   ,       do_normalize=__lowerCAmelCase   ,       padding_value=1.0       )

				self.assertEqual(processor.tokenizer.get_vocab()   ,       tokenizer_add_kwargs.get_vocab()       )
				self.assertIsInstance(processor.tokenizer   ,       __lowerCAmelCase       )

				self.assertEqual(processor.feature_extractor.to_json_string()   ,       feature_extractor_add_kwargs.to_json_string()       )
				self.assertIsInstance(processor.feature_extractor   ,       __lowerCAmelCase       )

	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  self.get_feature_extractor()
				a__					=  self.get_tokenizer()

				a__					=  MusicgenProcessor(tokenizer=__lowerCAmelCase   ,       feature_extractor=__lowerCAmelCase       )

				a__					=  floats_list((3, 1000)       )

				a__					=  feature_extractor(__lowerCAmelCase   ,       return_tensors="""np"""       )
				a__					=  processor(__lowerCAmelCase   ,       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 				_A			(	self       ):



				'''simple docstring'''


				a__					=  self.get_feature_extractor()
				a__					=  self.get_tokenizer()

				a__					=  MusicgenProcessor(tokenizer=__lowerCAmelCase   ,       feature_extractor=__lowerCAmelCase       )

				a__					=  """This is a test string"""

				a__					=  processor(text=__lowerCAmelCase       )

				a__					=  tokenizer(__lowerCAmelCase       )

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

	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  self.get_feature_extractor()
				a__					=  self.get_tokenizer()

				a__					=  MusicgenProcessor(tokenizer=__lowerCAmelCase   ,       feature_extractor=__lowerCAmelCase       )

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

				a__					=  processor.batch_decode(sequences=__lowerCAmelCase       )
				a__					=  tokenizer.batch_decode(__lowerCAmelCase       )

				self.assertListEqual(__lowerCAmelCase   ,       __lowerCAmelCase       )

	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  self.get_feature_extractor()
				a__					=  self.get_tokenizer()

				a__					=  MusicgenProcessor(tokenizer=__lowerCAmelCase   ,       feature_extractor=__lowerCAmelCase       )

				self.assertListEqual(
				    processor.model_input_names   ,       feature_extractor.model_input_names   ,       msg="""`processor` and `feature_extractor` model input names do not match"""   ,       )







	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  self.get_feature_extractor(padding_side="""left"""       )
				a__					=  self.get_tokenizer()

				a__					=  MusicgenProcessor(tokenizer=__lowerCAmelCase   ,       feature_extractor=__lowerCAmelCase       )

				a__					=  [floats_list((1, x)       )[0] for x in range(5   ,       20   ,       5       )]
				a__					=  processor(__lowerCAmelCase       ).padding_mask

				a__					=  np.asarray(floats_list((3, 20)       )       )[:, None, :]
				a__					=  processor.batch_decode(__lowerCAmelCase   ,       padding_mask=__lowerCAmelCase       )

				self.assertIsInstance(__lowerCAmelCase   ,       __lowerCAmelCase       )

				for audio in decoded_audios:
							self.assertIsInstance(__lowerCAmelCase   ,       np.ndarray       )

				self.assertTrue(decoded_audios[0].shape == (1, 10)       )
				self.assertTrue(decoded_audios[1].shape == (1, 15)       )
				self.assertTrue(decoded_audios[2].shape == (1, 20)       )


'''simple docstring'''

import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import ConvNextImageProcessor





class 				lowercase    (    unittest.TestCase			):


   def __init__( self					:						str ,		__lowerCAmelCase					:						Union[str, Any] ,		__lowerCAmelCase					:						Optional[int]=7 ,		__lowerCAmelCase					:						Optional[Any]=3 ,		__lowerCAmelCase					:						Optional[Any]=18 ,		__lowerCAmelCase					:						List[str]=30 ,		__lowerCAmelCase					:						List[str]=400 ,		__lowerCAmelCase					:						Tuple=True ,		__lowerCAmelCase					:						Optional[int]=None ,		__lowerCAmelCase					:						List[str]=0.875 ,		__lowerCAmelCase					:						int=True ,		__lowerCAmelCase					:						Optional[int]=[0.5, 0.5, 0.5] ,		__lowerCAmelCase					:						List[str]=[0.5, 0.5, 0.5] ,		)   ->       Optional[Any]:
         lowercase_     			=   size if size is not None else {"shortest_edge": 20}
         lowercase_     			=   parent
         lowercase_     			=   batch_size
         lowercase_     			=   num_channels
         lowercase_     			=   image_size
         lowercase_     			=   min_resolution
         lowercase_     			=   max_resolution
         lowercase_     			=   do_resize
         lowercase_     			=   size
         lowercase_     			=   crop_pct
         lowercase_     			=   do_normalize
         lowercase_     			=   image_mean
         lowercase_     			=   image_std


   def __UpperCAmelCase							( self					:						List[str])   ->       int:
         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
             "crop_pct": self.crop_pct,
         }





@require_torch
@require_vision
class 				lowercase    (    __lowerCamelCase			,					unittest.TestCase			):
   lowerCamelCase_            =ConvNextImageProcessor if is_vision_available() else None


   def __UpperCAmelCase							( self					:						Optional[Any])   ->       Tuple:
         lowercase_     			=   ConvNextImageProcessingTester(self)


   @property
   def __UpperCAmelCase							( self					:						Optional[int])   ->       int:
         return self.image_processor_tester.prepare_image_processor_dict()


   def __UpperCAmelCase							( self					:						Optional[int])   ->       Any:
         lowercase_     			=   self.image_processing_class(**self.image_processor_dict)
         self.assertTrue(hasattr(__lowerCAmelCase ,		"do_resize"))
         self.assertTrue(hasattr(__lowerCAmelCase ,		"size"))
         self.assertTrue(hasattr(__lowerCAmelCase ,		"crop_pct"))
         self.assertTrue(hasattr(__lowerCAmelCase ,		"do_normalize"))
         self.assertTrue(hasattr(__lowerCAmelCase ,		"image_mean"))
         self.assertTrue(hasattr(__lowerCAmelCase ,		"image_std"))


   def __UpperCAmelCase							( self					:						List[str])   ->       Any:
         lowercase_     			=   self.image_processing_class.from_dict(self.image_processor_dict)
         self.assertEqual(image_processor.size ,		{"shortest_edge": 20})

         lowercase_     			=   self.image_processing_class.from_dict(self.image_processor_dict ,		size=42)
         self.assertEqual(image_processor.size ,		{"shortest_edge": 42})


   def __UpperCAmelCase							( self					:						Any)   ->       Optional[int]:
         pass


   def __UpperCAmelCase							( self					:						List[Any])   ->       Any:
         # Initialize image_processing
         lowercase_     			=   self.image_processing_class(**self.image_processor_dict)
         # create random PIL images
         lowercase_     			=   prepare_image_inputs(self.image_processor_tester ,		equal_resolution=__lowerCAmelCase)
         for image in image_inputs:
               self.assertIsInstance(__lowerCAmelCase ,		Image.Image)

         # Test not batched input
         lowercase_     			=   image_processing(image_inputs[0] ,		return_tensors="pt").pixel_values
         self.assertEqual(
             encoded_images.shape ,		(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["shortest_edge"],
                 self.image_processor_tester.size["shortest_edge"],
             ) ,		)

         # Test batched
         lowercase_     			=   image_processing(__lowerCAmelCase ,		return_tensors="pt").pixel_values
         self.assertEqual(
             encoded_images.shape ,		(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["shortest_edge"],
                 self.image_processor_tester.size["shortest_edge"],
             ) ,		)


   def __UpperCAmelCase							( self					:						Union[str, Any])   ->       str:
         # Initialize image_processing
         lowercase_     			=   self.image_processing_class(**self.image_processor_dict)
         # create random numpy tensors
         lowercase_     			=   prepare_image_inputs(self.image_processor_tester ,		equal_resolution=__lowerCAmelCase ,		numpify=__lowerCAmelCase)
         for image in image_inputs:
               self.assertIsInstance(__lowerCAmelCase ,		np.ndarray)

         # Test not batched input
         lowercase_     			=   image_processing(image_inputs[0] ,		return_tensors="pt").pixel_values
         self.assertEqual(
             encoded_images.shape ,		(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["shortest_edge"],
                 self.image_processor_tester.size["shortest_edge"],
             ) ,		)

         # Test batched
         lowercase_     			=   image_processing(__lowerCAmelCase ,		return_tensors="pt").pixel_values
         self.assertEqual(
             encoded_images.shape ,		(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["shortest_edge"],
                 self.image_processor_tester.size["shortest_edge"],
             ) ,		)


   def __UpperCAmelCase							( self					:						List[str])   ->       List[str]:
         # Initialize image_processing
         lowercase_     			=   self.image_processing_class(**self.image_processor_dict)
         # create random PyTorch tensors
         lowercase_     			=   prepare_image_inputs(self.image_processor_tester ,		equal_resolution=__lowerCAmelCase ,		torchify=__lowerCAmelCase)
         for image in image_inputs:
               self.assertIsInstance(__lowerCAmelCase ,		torch.Tensor)

         # Test not batched input
         lowercase_     			=   image_processing(image_inputs[0] ,		return_tensors="pt").pixel_values
         self.assertEqual(
             encoded_images.shape ,		(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["shortest_edge"],
                 self.image_processor_tester.size["shortest_edge"],
             ) ,		)

         # Test batched
         lowercase_     			=   image_processing(__lowerCAmelCase ,		return_tensors="pt").pixel_values
         self.assertEqual(
             encoded_images.shape ,		(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["shortest_edge"],
                 self.image_processor_tester.size["shortest_edge"],
             ) ,		)






