

"""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


__A     :    Optional[int]    =		random.Random()
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__=1.0			,							lowercase__=None			,							lowercase__=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 			__UpperCamelCase (   unittest.TestCase						):


		def 					SCREAMING_SNAKE_CASE__   (self :		Optional[Any]):
							A			=					"facebook/musicgen-small"
							A			=					tempfile.mkdtemp()


		def 					SCREAMING_SNAKE_CASE__   (self :		List[Any]     ,     **__SCREAMING_SNAKE_CASE :		str):
							return TaTokenizer.from_pretrained(self.checkpoint     ,     **__SCREAMING_SNAKE_CASE)


		def 					SCREAMING_SNAKE_CASE__   (self :		Optional[int]     ,     **__SCREAMING_SNAKE_CASE :		List[str]):
							return EncodecFeatureExtractor.from_pretrained(self.checkpoint     ,     **__SCREAMING_SNAKE_CASE)


		def 					SCREAMING_SNAKE_CASE__   (self :		Dict):
							shutil.rmtree(self.tmpdirname)


		def 					SCREAMING_SNAKE_CASE__   (self :		Optional[int]):
							A			=					self.get_tokenizer()
							A			=					self.get_feature_extractor()

							A			=					MusicgenProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,     feature_extractor=__SCREAMING_SNAKE_CASE)

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

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

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


		def 					SCREAMING_SNAKE_CASE__   (self :		Union[str, Any]):
							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=__SCREAMING_SNAKE_CASE     ,     padding_value=1.0)

							A			=					MusicgenProcessor.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.feature_extractor.to_json_string()     ,     feature_extractor_add_kwargs.to_json_string())
							self.assertIsInstance(processor.feature_extractor     ,     __SCREAMING_SNAKE_CASE)


		def 					SCREAMING_SNAKE_CASE__   (self :		Union[str, Any]):
							A			=					self.get_feature_extractor()
							A			=					self.get_tokenizer()

							A			=					MusicgenProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,     feature_extractor=__SCREAMING_SNAKE_CASE)

							A			=					floats_list((3, 1_0_0_0))

							A			=					feature_extractor(__SCREAMING_SNAKE_CASE     ,     return_tensors="np")
							A			=					processor(__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 					SCREAMING_SNAKE_CASE__   (self :		int):
							A			=					self.get_feature_extractor()
							A			=					self.get_tokenizer()

							A			=					MusicgenProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,     feature_extractor=__SCREAMING_SNAKE_CASE)

							A			=					"This is a test string"

							A			=					processor(text=__SCREAMING_SNAKE_CASE)

							A			=					tokenizer(__SCREAMING_SNAKE_CASE)

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


		def 					SCREAMING_SNAKE_CASE__   (self :		Optional[Any]):
							A			=					self.get_feature_extractor()
							A			=					self.get_tokenizer()

							A			=					MusicgenProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,     feature_extractor=__SCREAMING_SNAKE_CASE)

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

							A			=					processor.batch_decode(sequences=__SCREAMING_SNAKE_CASE)
							A			=					tokenizer.batch_decode(__SCREAMING_SNAKE_CASE)

							self.assertListEqual(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)


		def 					SCREAMING_SNAKE_CASE__   (self :		List[str]):
							A			=					self.get_feature_extractor()
							A			=					self.get_tokenizer()

							A			=					MusicgenProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,     feature_extractor=__SCREAMING_SNAKE_CASE)

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


		def 					SCREAMING_SNAKE_CASE__   (self :		Optional[Any]):
							A			=					self.get_feature_extractor(padding_side="left")
							A			=					self.get_tokenizer()

							A			=					MusicgenProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,     feature_extractor=__SCREAMING_SNAKE_CASE)

							A			=					[floats_list((1, x))[0] for x in range(5     ,     2_0     ,     5)]
							A			=					processor(__SCREAMING_SNAKE_CASE).padding_mask

							A			=					np.asarray(floats_list((3, 2_0)))[:, None, :]
							A			=					processor.batch_decode(__SCREAMING_SNAKE_CASE     ,     padding_mask=__SCREAMING_SNAKE_CASE)

							self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)

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

							self.assertTrue(decoded_audios[0].shape == (1, 1_0))
							self.assertTrue(decoded_audios[1].shape == (1, 1_5))
							self.assertTrue(decoded_audios[2].shape == (1, 2_0))








'''simple docstring'''






from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case_       =       logging.get_logger(__name__)

snake_case_       =       {
    'apple/mobilevit-small': 'https://huggingface.co/apple/mobilevit-small/resolve/main/config.json',
    'apple/mobilevit-x-small': 'https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json',
    'apple/mobilevit-xx-small': 'https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json',
    'apple/deeplabv3-mobilevit-small': (
        'https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json'
    ),
    'apple/deeplabv3-mobilevit-x-small': (
        'https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json'
    ),
    'apple/deeplabv3-mobilevit-xx-small': (
        'https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json'
    ),
    # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
}
class 	SCREAMING_SNAKE_CASE__			(				_UpperCAmelCase      ):
    _A			    =							"mobilevit"




    def __init__(					self				,     lowercase__=3				,     lowercase__=256				,     lowercase__=2				,     lowercase__=[144, 192, 240]				,     lowercase__=[16, 32, 64, 96, 128, 160, 640]				,     lowercase__=4				,     lowercase__=2.0				,     lowercase__=4.0				,     lowercase__="silu"				,     lowercase__=3				,     lowercase__=32				,     lowercase__=0.1				,     lowercase__=0.0				,     lowercase__=0.1				,     lowercase__=0.02				,     lowercase__=1e-5				,     lowercase__=True				,     lowercase__=256				,     lowercase__=[6, 12, 18]				,     lowercase__=0.1				,     lowercase__=255				,     **lowercase__				,     ):






          """simple docstring"""






          super().__init__(**lowercase__			)

          SCREAMING_SNAKE_CASE_      :			int     =       num_channels
          SCREAMING_SNAKE_CASE_      :			List[str]     =       image_size
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       patch_size
          SCREAMING_SNAKE_CASE_      :			List[str]     =       hidden_sizes
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       neck_hidden_sizes
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       num_attention_heads
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       mlp_ratio
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       expand_ratio
          SCREAMING_SNAKE_CASE_      :			str     =       hidden_act
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       conv_kernel_size
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       output_stride
          SCREAMING_SNAKE_CASE_      :			Tuple     =       hidden_dropout_prob
          SCREAMING_SNAKE_CASE_      :			int     =       attention_probs_dropout_prob
          SCREAMING_SNAKE_CASE_      :			Tuple     =       classifier_dropout_prob
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       initializer_range
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       layer_norm_eps
          SCREAMING_SNAKE_CASE_      :			str     =       qkv_bias

          # decode head attributes for semantic segmentation
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       aspp_out_channels
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       atrous_rates
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       aspp_dropout_prob
          SCREAMING_SNAKE_CASE_      :			Any     =       semantic_loss_ignore_index






class 	SCREAMING_SNAKE_CASE__			(				_UpperCAmelCase      ):
    _A			    =							version.parse("1.11"      )




    @property
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          return OrderedDict([("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"})]			)




    @property
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          if self.task == "image-classification":
                return OrderedDict([("logits", {0: "batch"})]			)
          else:
                return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]			)







    @property
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          return 1e-4

