





'''simple docstring'''



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


snake_case_   :  Union[str, Any]								=      random.Random()



def  lowercase__(		_UpperCamelCase  : Any ,   _UpperCamelCase  : str=1.0 ,   _UpperCamelCase  : Any=None ,   _UpperCamelCase  : Tuple=None				)->   Dict:






					"""simple docstring"""

					if rng is None:
										_UpperCamelCase											=   global_rng

					_UpperCamelCase											=   []
					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     A_ (  unittest.TestCase  ):

			'''simple docstring'''





			def       a	(					self   ):
								_UpperCamelCase											=   "facebook/musicgen-small"
								_UpperCamelCase											=   tempfile.mkdtemp()




			def       a	(					self			,     **A_   ):
								return TaTokenizer.from_pretrained(self.checkpoint			,     **lowerCamelCase__   )




			def       a	(					self			,     **A_   ):
								return EncodecFeatureExtractor.from_pretrained(self.checkpoint			,     **lowerCamelCase__   )




			def       a	(					self   ):
								shutil.rmtree(self.tmpdirname   )




			def       a	(					self   ):
								_UpperCamelCase											=   self.get_tokenizer()
								_UpperCamelCase											=   self.get_feature_extractor()

								_UpperCamelCase											=   MusicgenProcessor(tokenizer=lowerCamelCase__			,     feature_extractor=lowerCamelCase__   )

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

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

								_UpperCamelCase											=   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   ):
								_UpperCamelCase											=   self.get_feature_extractor()
								_UpperCamelCase											=   self.get_tokenizer()

								_UpperCamelCase											=   MusicgenProcessor(tokenizer=lowerCamelCase__			,     feature_extractor=lowerCamelCase__   )

								_UpperCamelCase											=   floats_list((3, 10_00)   )

								_UpperCamelCase											=   feature_extractor(lowerCamelCase__			,     return_tensors="np"   )
								_UpperCamelCase											=   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   ):
								_UpperCamelCase											=   self.get_feature_extractor()
								_UpperCamelCase											=   self.get_tokenizer()

								_UpperCamelCase											=   MusicgenProcessor(tokenizer=lowerCamelCase__			,     feature_extractor=lowerCamelCase__   )

								_UpperCamelCase											=   "This is a test string"

								_UpperCamelCase											=   processor(text=lowerCamelCase__   )

								_UpperCamelCase											=   tokenizer(lowerCamelCase__   )

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




			def       a	(					self   ):
								_UpperCamelCase											=   self.get_feature_extractor()
								_UpperCamelCase											=   self.get_tokenizer()

								_UpperCamelCase											=   MusicgenProcessor(tokenizer=lowerCamelCase__			,     feature_extractor=lowerCamelCase__   )

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

								_UpperCamelCase											=   processor.batch_decode(sequences=lowerCamelCase__   )
								_UpperCamelCase											=   tokenizer.batch_decode(lowerCamelCase__   )

								self.assertListEqual(lowerCamelCase__			,     lowerCamelCase__   )




			def       a	(					self   ):
								_UpperCamelCase											=   self.get_feature_extractor()
								_UpperCamelCase											=   self.get_tokenizer()

								_UpperCamelCase											=   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   ):
								_UpperCamelCase											=   self.get_feature_extractor(padding_side="left"   )
								_UpperCamelCase											=   self.get_tokenizer()

								_UpperCamelCase											=   MusicgenProcessor(tokenizer=lowerCamelCase__			,     feature_extractor=lowerCamelCase__   )

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

								_UpperCamelCase											=   np.asarray(floats_list((3, 20)   )   )[:, None, :]
								_UpperCamelCase											=   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 datasets import load_dataset

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 SegformerImageProcessor

class     A_ (  unittest.TestCase  ):

			'''simple docstring'''





			def __init__(					self			,     A_			,     A_=7			,     A_=3			,     A_=30			,     A_=4_00			,     A_=True			,     A_=None			,     A_=True			,     A_=[0.5, 0.5, 0.5]			,     A_=[0.5, 0.5, 0.5]			,     A_=False			,     ):
								_UpperCamelCase											=   size if size is not None else {"height": 30, "width": 30}
								_UpperCamelCase											=   parent
								_UpperCamelCase											=   batch_size
								_UpperCamelCase											=   num_channels
								_UpperCamelCase											=   min_resolution
								_UpperCamelCase											=   max_resolution
								_UpperCamelCase											=   do_resize
								_UpperCamelCase											=   size
								_UpperCamelCase											=   do_normalize
								_UpperCamelCase											=   image_mean
								_UpperCamelCase											=   image_std
								_UpperCamelCase											=   do_reduce_labels




			def       a	(					self   ):
								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_reduce_labels": self.do_reduce_labels,
								}




def  lowercase__(		)->   Dict:






					"""simple docstring"""

					_UpperCamelCase											=   load_dataset("hf-internal-testing/fixtures_ade20k" ,   split="test"				)

					_UpperCamelCase											=   Image.open(dataset[0]["file"]				)
					_UpperCamelCase											=   Image.open(dataset[1]["file"]				)

					return image, map



def  lowercase__(		)->   Dict:






					"""simple docstring"""

					_UpperCamelCase											=   load_dataset("hf-internal-testing/fixtures_ade20k" ,   split="test"				)

					_UpperCamelCase											=   Image.open(dataset[0]["file"]				)
					_UpperCamelCase											=   Image.open(dataset[1]["file"]				)
					_UpperCamelCase											=   Image.open(dataset[2]["file"]				)
					_UpperCamelCase											=   Image.open(dataset[3]["file"]				)

					return [imagea, imagea], [mapa, mapa]

@require_torch
@require_vision
class     A_ (  lowerCAmelCase_    ,			unittest.TestCase  ):

			'''simple docstring'''





			_lowerCAmelCase						=    SegformerImageProcessor if is_vision_available() else None




			def       a	(					self   ):
								_UpperCamelCase											=   SegformerImageProcessingTester(self   )




			@property
			def       a	(					self   ):
								return self.image_processor_tester.prepare_image_processor_dict()




			def       a	(					self   ):
								_UpperCamelCase											=   self.image_processing_class(**self.image_processor_dict   )
								self.assertTrue(hasattr(A_			,     "do_resize"   )   )
								self.assertTrue(hasattr(A_			,     "size"   )   )
								self.assertTrue(hasattr(A_			,     "do_normalize"   )   )
								self.assertTrue(hasattr(A_			,     "image_mean"   )   )
								self.assertTrue(hasattr(A_			,     "image_std"   )   )
								self.assertTrue(hasattr(A_			,     "do_reduce_labels"   )   )




			def       a	(					self   ):
								_UpperCamelCase											=   self.image_processing_class.from_dict(self.image_processor_dict   )
								self.assertEqual(image_processor.size			,     {"height": 30, "width": 30}   )
								self.assertEqual(image_processor.do_reduce_labels			,     A_   )

								_UpperCamelCase											=   self.image_processing_class.from_dict(self.image_processor_dict			,     size=42			,     reduce_labels=A_   )
								self.assertEqual(image_processor.size			,     {"height": 42, "width": 42}   )
								self.assertEqual(image_processor.do_reduce_labels			,     A_   )




			def       a	(					self   ):
								pass




			def       a	(					self   ):
								# Initialize image_processing
								_UpperCamelCase											=   self.image_processing_class(**self.image_processor_dict   )
								# create random PIL images
								_UpperCamelCase											=   prepare_image_inputs(self.image_processor_tester			,     equal_resolution=A_   )
								for image in image_inputs:
													self.assertIsInstance(A_			,     Image.Image   )

								# Test not batched input
								_UpperCamelCase											=   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["height"],
								        self.image_processor_tester.size["width"],
								    )			,     )

								# Test batched
								_UpperCamelCase											=   image_processing(A_			,     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["height"],
								        self.image_processor_tester.size["width"],
								    )			,     )




			def       a	(					self   ):
								# Initialize image_processing
								_UpperCamelCase											=   self.image_processing_class(**self.image_processor_dict   )
								# create random numpy tensors
								_UpperCamelCase											=   prepare_image_inputs(self.image_processor_tester			,     equal_resolution=A_			,     numpify=A_   )
								for image in image_inputs:
													self.assertIsInstance(A_			,     np.ndarray   )

								# Test not batched input
								_UpperCamelCase											=   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["height"],
								        self.image_processor_tester.size["width"],
								    )			,     )

								# Test batched
								_UpperCamelCase											=   image_processing(A_			,     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["height"],
								        self.image_processor_tester.size["width"],
								    )			,     )




			def       a	(					self   ):
								# Initialize image_processing
								_UpperCamelCase											=   self.image_processing_class(**self.image_processor_dict   )
								# create random PyTorch tensors
								_UpperCamelCase											=   prepare_image_inputs(self.image_processor_tester			,     equal_resolution=A_			,     torchify=A_   )
								for image in image_inputs:
													self.assertIsInstance(A_			,     torch.Tensor   )

								# Test not batched input
								_UpperCamelCase											=   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["height"],
								        self.image_processor_tester.size["width"],
								    )			,     )

								# Test batched
								_UpperCamelCase											=   image_processing(A_			,     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["height"],
								        self.image_processor_tester.size["width"],
								    )			,     )




			def       a	(					self   ):
								# Initialize image_processing
								_UpperCamelCase											=   self.image_processing_class(**self.image_processor_dict   )
								# create random PyTorch tensors
								_UpperCamelCase											=   prepare_image_inputs(self.image_processor_tester			,     equal_resolution=A_			,     torchify=A_   )
								_UpperCamelCase											=   []
								for image in image_inputs:
													self.assertIsInstance(A_			,     torch.Tensor   )
													maps.append(torch.zeros(image.shape[-2:]   ).long()   )

								# Test not batched input
								_UpperCamelCase											=   image_processing(image_inputs[0]			,     maps[0]			,     return_tensors="pt"   )
								self.assertEqual(
								    encoding["pixel_values"].shape			,     (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )			,     )
								self.assertEqual(
								    encoding["labels"].shape			,     (
								        1,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )			,     )
								self.assertEqual(encoding["labels"].dtype			,     torch.long   )
								self.assertTrue(encoding["labels"].min().item() >= 0   )
								self.assertTrue(encoding["labels"].max().item() <= 2_55   )

								# Test batched
								_UpperCamelCase											=   image_processing(A_			,     A_			,     return_tensors="pt"   )
								self.assertEqual(
								    encoding["pixel_values"].shape			,     (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )			,     )
								self.assertEqual(
								    encoding["labels"].shape			,     (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )			,     )
								self.assertEqual(encoding["labels"].dtype			,     torch.long   )
								self.assertTrue(encoding["labels"].min().item() >= 0   )
								self.assertTrue(encoding["labels"].max().item() <= 2_55   )

								# Test not batched input (PIL images)
								_UpperCamelCase				,	_UpperCamelCase											=   prepare_semantic_single_inputs()

								_UpperCamelCase											=   image_processing(A_			,     A_			,     return_tensors="pt"   )
								self.assertEqual(
								    encoding["pixel_values"].shape			,     (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )			,     )
								self.assertEqual(
								    encoding["labels"].shape			,     (
								        1,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )			,     )
								self.assertEqual(encoding["labels"].dtype			,     torch.long   )
								self.assertTrue(encoding["labels"].min().item() >= 0   )
								self.assertTrue(encoding["labels"].max().item() <= 2_55   )

								# Test batched input (PIL images)
								_UpperCamelCase				,	_UpperCamelCase											=   prepare_semantic_batch_inputs()

								_UpperCamelCase											=   image_processing(A_			,     A_			,     return_tensors="pt"   )
								self.assertEqual(
								    encoding["pixel_values"].shape			,     (
								        2,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )			,     )
								self.assertEqual(
								    encoding["labels"].shape			,     (
								        2,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )			,     )
								self.assertEqual(encoding["labels"].dtype			,     torch.long   )
								self.assertTrue(encoding["labels"].min().item() >= 0   )
								self.assertTrue(encoding["labels"].max().item() <= 2_55   )




			def       a	(					self   ):
								# Initialize image_processing
								_UpperCamelCase											=   self.image_processing_class(**self.image_processor_dict   )

								# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
								_UpperCamelCase				,	_UpperCamelCase											=   prepare_semantic_single_inputs()
								_UpperCamelCase											=   image_processing(A_			,     A_			,     return_tensors="pt"   )
								self.assertTrue(encoding["labels"].min().item() >= 0   )
								self.assertTrue(encoding["labels"].max().item() <= 1_50   )

								_UpperCamelCase											=   True
								_UpperCamelCase											=   image_processing(A_			,     A_			,     return_tensors="pt"   )
								self.assertTrue(encoding["labels"].min().item() >= 0   )
								self.assertTrue(encoding["labels"].max().item() <= 2_55   )



