"""simple docstring"""


import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
 import torch

if is_datasets_available():
 from datasets import load_dataset

lowerCAmelCase_:					Optional[int]     =					random.Random()







def 					__a   (		A    ,				A=1.0    ,				A=None    ,				A=None      ):






     '''simple docstring'''



     if rng is None:
          lowercase__				 =							global_rng

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

     return values






class    a__    (   unittest.TestCase	):


   def __init__(     self,				_UpperCAmelCase,				_UpperCAmelCase=7,				_UpperCAmelCase=400,				_UpperCAmelCase=2000,				_UpperCAmelCase=2048,				_UpperCAmelCase=128,				_UpperCAmelCase=1,				_UpperCAmelCase=512,				_UpperCAmelCase=30,				_UpperCAmelCase=4_4100,				):
        '''simple docstring'''



        lowercase__				 =							parent
        lowercase__				 =							batch_size
        lowercase__				 =							min_seq_length
        lowercase__				 =							max_seq_length
        lowercase__				 =							(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        lowercase__				 =							spectrogram_length
        lowercase__				 =							feature_size
        lowercase__				 =							num_audio_channels
        lowercase__				 =							hop_length
        lowercase__				 =							chunk_length
        lowercase__				 =							sampling_rate


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        return {
            "spectrogram_length": self.spectrogram_length,
            "feature_size": self.feature_size,
            "num_audio_channels": self.num_audio_channels,
            "hop_length": self.hop_length,
            "chunk_length": self.chunk_length,
            "sampling_rate": self.sampling_rate,
        }


   def 	snake_case__	(     self,				_UpperCAmelCase=False,				_UpperCAmelCase=False  ):
        '''simple docstring'''



        def _flatten(_UpperCAmelCase  ):
             return list(itertools.chain(*_UpperCAmelCase  )  )

        if equal_length:
             lowercase__				 =							[floats_list((self.max_seq_length, self.feature_size)  ) for _ in range(self.batch_size  )]
        else:
             # make sure that inputs increase in size
             lowercase__				 =							[
                 floats_list((x, self.feature_size)  )
                 for x in range(self.min_seq_length,				self.max_seq_length,				self.seq_length_diff  )
             ]
        if numpify:
             lowercase__				 =							[np.asarray(_UpperCAmelCase  ) for x in speech_inputs]
        return speech_inputs






@require_torch
@require_torchaudio
class    a__    (   _a					,					unittest.TestCase	):
   snake_case_							=      TvltFeatureExtractor


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							TvltFeatureExtractionTester(self  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							self.feature_extraction_class(**self.feat_extract_dict  )
        self.assertTrue(hasattr(_UpperCAmelCase,				"spectrogram_length"  )  )
        self.assertTrue(hasattr(_UpperCAmelCase,				"feature_size"  )  )
        self.assertTrue(hasattr(_UpperCAmelCase,				"num_audio_channels"  )  )
        self.assertTrue(hasattr(_UpperCAmelCase,				"hop_length"  )  )
        self.assertTrue(hasattr(_UpperCAmelCase,				"chunk_length"  )  )
        self.assertTrue(hasattr(_UpperCAmelCase,				"sampling_rate"  )  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							self.feature_extraction_class(**self.feat_extract_dict  )

        with tempfile.TemporaryDirectory() as tmpdirname:
             lowercase__				 =							feat_extract_first.save_pretrained(_UpperCAmelCase  )[0]
             check_json_file_has_correct_format(_UpperCAmelCase  )
             lowercase__				 =							self.feature_extraction_class.from_pretrained(_UpperCAmelCase  )

        lowercase__				 =							feat_extract_first.to_dict()
        lowercase__				 =							feat_extract_second.to_dict()
        lowercase__				 =							dict_first.pop("mel_filters"  )
        lowercase__				 =							dict_second.pop("mel_filters"  )
        self.assertTrue(np.allclose(_UpperCAmelCase,				_UpperCAmelCase  )  )
        self.assertEqual(_UpperCAmelCase,				_UpperCAmelCase  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							self.feature_extraction_class(**self.feat_extract_dict  )

        with tempfile.TemporaryDirectory() as tmpdirname:
             lowercase__				 =							os.path.join(_UpperCAmelCase,				"feat_extract.json"  )
             feat_extract_first.to_json_file(_UpperCAmelCase  )
             lowercase__				 =							self.feature_extraction_class.from_json_file(_UpperCAmelCase  )

        lowercase__				 =							feat_extract_first.to_dict()
        lowercase__				 =							feat_extract_second.to_dict()
        lowercase__				 =							dict_first.pop("mel_filters"  )
        lowercase__				 =							dict_second.pop("mel_filters"  )
        self.assertTrue(np.allclose(_UpperCAmelCase,				_UpperCAmelCase  )  )
        self.assertEqual(_UpperCAmelCase,				_UpperCAmelCase  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							self.feature_extraction_class(**self.feat_extract_dict  )

        # create three inputs of length 800, 1000, and 1200
        lowercase__				 =							[floats_list((1, x)  )[0] for x in range(800,				1400,				200  )]
        lowercase__				 =							[np.asarray(_UpperCAmelCase  ) for speech_input in speech_inputs]

        # Test not batched input
        lowercase__				 =							feature_extractor(np_speech_inputs[0],				return_tensors="np",				sampling_rate=4_4100  ).audio_values

        self.assertTrue(encoded_audios.ndim == 4  )
        self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size  )
        self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length  )
        self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels  )

        # Test batched
        lowercase__				 =							feature_extractor(_UpperCAmelCase,				return_tensors="np",				sampling_rate=4_4100  ).audio_values

        self.assertTrue(encoded_audios.ndim == 4  )
        self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size  )
        self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length  )
        self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels  )

        # Test audio masking
        lowercase__				 =							feature_extractor(
            _UpperCAmelCase,				return_tensors="np",				sampling_rate=4_4100,				mask_audio=_UpperCAmelCase  ).audio_values

        self.assertTrue(encoded_audios.ndim == 4  )
        self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size  )
        self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length  )
        self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels  )

        # Test 2-D numpy arrays are batched.
        lowercase__				 =							[floats_list((1, x)  )[0] for x in (800, 800, 800)]
        lowercase__				 =							np.asarray(_UpperCAmelCase  )
        lowercase__				 =							feature_extractor(_UpperCAmelCase,				return_tensors="np",				sampling_rate=4_4100  ).audio_values
        self.assertTrue(encoded_audios.ndim == 4  )
        self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size  )
        self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length  )
        self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels  )


   def 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							load_dataset("hf-internal-testing/librispeech_asr_dummy",				"clean",				split="validation"  )
        # automatic decoding with librispeech
        lowercase__				 =							ds.sort("id"  ).select(range(_UpperCAmelCase  )  )[:num_samples]["audio"]

        return [x["array"] for x in speech_samples]


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							self._load_datasamples(1  )
        lowercase__				 =							TvltFeatureExtractor()
        lowercase__				 =							feature_extractor(_UpperCAmelCase,				return_tensors="pt"  ).audio_values

        self.assertEquals(audio_values.shape,				(1, 1, 192, 128)  )

        lowercase__				 =							torch.tensor([[-0.3_032, -0.2_708], [-0.4_434, -0.4_007]]  )
        self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2],				_UpperCAmelCase,				atol=1E-4  )  )





"""simple docstring"""


import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class    a__    (   _a					,					unittest.TestCase	):
   snake_case_							=      MgpstrTokenizer
   snake_case_							=      False
   snake_case_							=      {}
   snake_case_							=      False


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        super().setUp()

        # fmt: off
        lowercase__				 =							["[GO]", "[s]", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z"]
        # fmt: on
        lowercase__				 =							dict(zip(_UpperCAmelCase,				range(len(_UpperCAmelCase  )  )  )  )

        lowercase__				 =							os.path.join(self.tmpdirname,				VOCAB_FILES_NAMES["vocab_file"]  )
        with open(self.vocab_file,				"w",				encoding="utf-8"  ) as fp:
             fp.write(json.dumps(_UpperCAmelCase  ) + "\n"  )


   def 	snake_case__	(     self,				**_UpperCAmelCase  ):
        '''simple docstring'''



        return MgpstrTokenizer.from_pretrained(self.tmpdirname,				**_UpperCAmelCase  )


   def 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							"tester"
        lowercase__				 =							"tester"
        return input_text, output_text


   @unittest.skip("MGP-STR always lower cases letters."  )
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        pass


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							self.get_tokenizers(do_lower_case=_UpperCAmelCase  )
        for tokenizer in tokenizers:
             with self.subTest(F'''{tokenizer.__class__.__name__}'''  ):
                  lowercase__				 =							"[SPECIAL_TOKEN]"

                  tokenizer.add_special_tokens({"cls_token": special_token}  )
                  lowercase__				 =							tokenizer.encode([special_token],				add_special_tokens=_UpperCAmelCase  )
                  self.assertEqual(len(_UpperCAmelCase  ),				1  )

                  lowercase__				 =							tokenizer.decode(_UpperCAmelCase,				skip_special_tokens=_UpperCAmelCase  )
                  self.assertTrue(special_token not in decoded  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							self.get_tokenizers()
        for tokenizer in tokenizers:
             with self.subTest(F'''{tokenizer.__class__.__name__}'''  ):
                  lowercase__ ,						lowercase__				 =							self.get_input_output_texts(_UpperCAmelCase  )

                  lowercase__				 =							tokenizer.tokenize(_UpperCAmelCase  )
                  lowercase__				 =							tokenizer.convert_tokens_to_ids(_UpperCAmelCase  )
                  lowercase__				 =							tokenizer.encode(_UpperCAmelCase,				add_special_tokens=_UpperCAmelCase  )
                  self.assertListEqual(_UpperCAmelCase,				_UpperCAmelCase  )

                  lowercase__				 =							tokenizer.convert_ids_to_tokens(_UpperCAmelCase  )
                  self.assertNotEqual(len(_UpperCAmelCase  ),				0  )
                  lowercase__				 =							tokenizer.decode(_UpperCAmelCase  )
                  self.assertIsInstance(_UpperCAmelCase,				_UpperCAmelCase  )

                  self.assertEqual(text_a.replace(" ",				""  ),				_UpperCAmelCase  )


   @unittest.skip("MGP-STR tokenizer only handles one sequence."  )
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        pass


   @unittest.skip("inputs cannot be pretokenized in MgpstrTokenizer"  )
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        pass




