





import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs







class    __snake_case	(							__lowerCamelCase      ):





       '''simple docstring'''







       def __init__(  self			:   Optional[int]							,    A			:   List[str]							,    A			:   List[str]=None							,    A			:   Tuple=True							,    A			:   int=None							,    **A			:   Optional[int]		):
         __snake_case:						Dict			  =						parent
         __snake_case:						List[str]			  =						config_class
         __snake_case:						Optional[int]			  =						has_text_modality
         __snake_case:						int			  =						kwargs
         __snake_case:						Any			  =						common_properties




       def 							UpperCAmelCase__			(  self			:   List[Any]		):
         __snake_case:						int			  =						self.config_class(**self.inputs_dict		)
         __snake_case:						Union[str, Any]			  =						(
             ["""hidden_size""", """num_attention_heads""", """num_hidden_layers"""]
             if self.common_properties is None
             else self.common_properties
         )

         # Add common fields for text models
         if self.has_text_modality:
           common_properties.extend(["""vocab_size"""]		)

         # Test that config has the common properties as getters
         for prop in common_properties:
           self.parent.assertTrue(hasattr(A							,    A		)							,    msg=f'''`{prop}` does not exist'''		)

         # Test that config has the common properties as setter
         for idx, name in enumerate(A		):
           try:
             setattr(A							,    A							,    A		)
             self.parent.assertEqual(
                 getattr(A							,    A		)							,    A							,    msg=f'''`{name} value {idx} expected, but was {getattr(A							,    A		)}'''		)
           except NotImplementedError:
             # Some models might not be able to implement setters for common_properties
             # In that case, a NotImplementedError is raised
             pass

        # Test if config class can be called with Config(prop_name=..)
         for idx, name in enumerate(A		):
           try:
             __snake_case:						List[Any]			  =						self.config_class(**{name: idx}		)
             self.parent.assertEqual(
                 getattr(A							,    A		)							,    A							,    msg=f'''`{name} value {idx} expected, but was {getattr(A							,    A		)}'''		)
           except NotImplementedError:
             # Some models might not be able to implement setters for common_properties
             # In that case, a NotImplementedError is raised
             pass




       def 							UpperCAmelCase__			(  self			:   Tuple		):
         __snake_case:						List[str]			  =						self.config_class(**self.inputs_dict		)
         __snake_case:						Dict			  =						json.loads(config.to_json_string()		)
         for key, value in self.inputs_dict.items():
           self.parent.assertEqual(obj[key]							,    A		)




       def 							UpperCAmelCase__			(  self			:   List[str]		):
         __snake_case:						Optional[Any]			  =						self.config_class(**self.inputs_dict		)

         with tempfile.TemporaryDirectory() as tmpdirname:
           __snake_case:						Optional[Any]			  =						os.path.join(A							,    """config.json"""		)
           config_first.to_json_file(A		)
           __snake_case:						List[str]			  =						self.config_class.from_json_file(A		)

         self.parent.assertEqual(config_second.to_dict()							,    config_first.to_dict()		)




       def 							UpperCAmelCase__			(  self			:   List[str]		):
         __snake_case:						Dict			  =						self.config_class(**self.inputs_dict		)

         with tempfile.TemporaryDirectory() as tmpdirname:
           config_first.save_pretrained(A		)
           __snake_case:						Optional[int]			  =						self.config_class.from_pretrained(A		)

         self.parent.assertEqual(config_second.to_dict()							,    config_first.to_dict()		)




       def 							UpperCAmelCase__			(  self			:   int		):
         __snake_case:						Any			  =						self.config_class(**self.inputs_dict		)

         __snake_case:						Tuple			  =						"""test"""
         with tempfile.TemporaryDirectory() as tmpdirname:
           __snake_case:						Union[str, Any]			  =						os.path.join(A							,    A		)
           config_first.save_pretrained(A		)
           __snake_case:						Union[str, Any]			  =						self.config_class.from_pretrained(A							,    subfolder=A		)

         self.parent.assertEqual(config_second.to_dict()							,    config_first.to_dict()		)




       def 							UpperCAmelCase__			(  self			:   int		):
         __snake_case:						Tuple			  =						self.config_class(**self.inputs_dict							,    num_labels=5		)
         self.parent.assertEqual(len(config.idalabel		)							,    5		)
         self.parent.assertEqual(len(config.labelaid		)							,    5		)

         __snake_case:						str			  =						3
         self.parent.assertEqual(len(config.idalabel		)							,    3		)
         self.parent.assertEqual(len(config.labelaid		)							,    3		)




       def 							UpperCAmelCase__			(  self			:   Any		):
         if self.config_class.is_composition:
           return
         __snake_case:						Tuple			  =						self.config_class()
         self.parent.assertIsNotNone(A		)




       def 							UpperCAmelCase__			(  self			:   str		):
         __snake_case:						Any			  =						copy.deepcopy(A		)
         __snake_case:						List[Any]			  =						self.config_class(**A		)
         __snake_case:						Dict			  =						[]
         for key, value in config_common_kwargs.items():
           if key == "torch_dtype":
             if not is_torch_available():
               continue
             else:
               import torch

               if config.torch_dtype != torch.floataa:
                 wrong_values.append(("""torch_dtype""", config.torch_dtype, torch.floataa)		)
           elif getattr(A							,    A		) != value:
             wrong_values.append((key, getattr(A							,    A		), value)		)

         if len(A		) > 0:
           __snake_case:						Optional[Any]			  =						"""\n""".join([f'''- {v[0]}: got {v[1]} instead of {v[2]}''' for v in wrong_values]		)
           raise ValueError(f'''The following keys were not properly set in the config:\n{errors}'''		)






       def 							UpperCAmelCase__			(  self			:   Union[str, Any]		):
         self.create_and_test_config_common_properties()
         self.create_and_test_config_to_json_string()
         self.create_and_test_config_to_json_file()
         self.create_and_test_config_from_and_save_pretrained()
         self.create_and_test_config_from_and_save_pretrained_subfolder()
         self.create_and_test_config_with_num_labels()
         self.check_config_can_be_init_without_params()
         self.check_config_arguments_init()













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

__UpperCAmelCase	:       Any    	=							random.Random()







def       A__							(   SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=1.0 , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None)       ->   Any:
  if rng is None:
    __snake_case:						Dict			  =						global_rng

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

  return values







class    __snake_case	(							unittest.TestCase      ):





       '''simple docstring'''







       def __init__(  self			:   int							,    A			:   List[str]							,    A			:   List[Any]=7							,    A			:   Optional[int]=400							,    A			:   List[Any]=2_000							,    A			:   Dict=2_048							,    A			:   Tuple=128							,    A			:   List[Any]=1							,    A			:   Tuple=512							,    A			:   str=30							,    A			:   Optional[Any]=44_100							,    ):
         __snake_case:						Dict			  =						parent
         __snake_case:						Optional[Any]			  =						batch_size
         __snake_case:						Optional[int]			  =						min_seq_length
         __snake_case:						Optional[Any]			  =						max_seq_length
         __snake_case:						List[str]			  =						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
         __snake_case:						Any			  =						spectrogram_length
         __snake_case:						Any			  =						feature_size
         __snake_case:						Union[str, Any]			  =						num_audio_channels
         __snake_case:						Any			  =						hop_length
         __snake_case:						List[str]			  =						chunk_length
         __snake_case:						Any			  =						sampling_rate




       def 							UpperCAmelCase__			(  self			:   List[Any]		):
         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 							UpperCAmelCase__			(  self			:   List[str]							,    A			:   str=False							,    A			:   int=False		):
         def _flatten(A			:   Dict		):
           return list(itertools.chain(*A		)		)

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







@require_torch
@require_torchaudio
class    __snake_case	(							__lowerCamelCase       ,      unittest.TestCase      ):





       '''simple docstring'''







       lowerCAmelCase__						=					TvltFeatureExtractor




       def 							UpperCAmelCase__			(  self			:   Union[str, Any]		):
         __snake_case:						str			  =						TvltFeatureExtractionTester(self		)




       def 							UpperCAmelCase__			(  self			:   int		):
         __snake_case:						Tuple			  =						self.feature_extraction_class(**self.feat_extract_dict		)
         self.assertTrue(hasattr(A							,    """spectrogram_length"""		)		)
         self.assertTrue(hasattr(A							,    """feature_size"""		)		)
         self.assertTrue(hasattr(A							,    """num_audio_channels"""		)		)
         self.assertTrue(hasattr(A							,    """hop_length"""		)		)
         self.assertTrue(hasattr(A							,    """chunk_length"""		)		)
         self.assertTrue(hasattr(A							,    """sampling_rate"""		)		)




       def 							UpperCAmelCase__			(  self			:   Any		):
         __snake_case:						Optional[Any]			  =						self.feature_extraction_class(**self.feat_extract_dict		)

         with tempfile.TemporaryDirectory() as tmpdirname:
           __snake_case:						Tuple			  =						feat_extract_first.save_pretrained(A		)[0]
           check_json_file_has_correct_format(A		)
           __snake_case:						int			  =						self.feature_extraction_class.from_pretrained(A		)

         __snake_case:						List[str]			  =						feat_extract_first.to_dict()
         __snake_case:						str			  =						feat_extract_second.to_dict()
         __snake_case:						List[Any]			  =						dict_first.pop("""mel_filters"""		)
         __snake_case:						str			  =						dict_second.pop("""mel_filters"""		)
         self.assertTrue(np.allclose(A							,    A		)		)
         self.assertEqual(A							,    A		)




       def 							UpperCAmelCase__			(  self			:   Optional[Any]		):
         __snake_case:						str			  =						self.feature_extraction_class(**self.feat_extract_dict		)

         with tempfile.TemporaryDirectory() as tmpdirname:
           __snake_case:						str			  =						os.path.join(A							,    """feat_extract.json"""		)
           feat_extract_first.to_json_file(A		)
           __snake_case:						List[Any]			  =						self.feature_extraction_class.from_json_file(A		)

         __snake_case:						Dict			  =						feat_extract_first.to_dict()
         __snake_case:						Any			  =						feat_extract_second.to_dict()
         __snake_case:						int			  =						dict_first.pop("""mel_filters"""		)
         __snake_case:						int			  =						dict_second.pop("""mel_filters"""		)
         self.assertTrue(np.allclose(A							,    A		)		)
         self.assertEqual(A							,    A		)




       def 							UpperCAmelCase__			(  self			:   Any		):
         # Initialize feature_extractor
         __snake_case:						Union[str, Any]			  =						self.feature_extraction_class(**self.feat_extract_dict		)

         # create three inputs of length 800, 1000, and 1200
         __snake_case:						Dict			  =						[floats_list((1, x)		)[0] for x in range(800							,    1_400							,    200		)]
         __snake_case:						str			  =						[np.asarray(A		) for speech_input in speech_inputs]

         # Test not batched input
         __snake_case:						int			  =						feature_extractor(np_speech_inputs[0]							,    return_tensors="""np"""							,    sampling_rate=44_100		).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
         __snake_case:						Optional[int]			  =						feature_extractor(A							,    return_tensors="""np"""							,    sampling_rate=44_100		).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
         __snake_case:						Union[str, Any]			  =						feature_extractor(
             A							,    return_tensors="""np"""							,    sampling_rate=44_100							,    mask_audio=A		).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.
         __snake_case:						Any			  =						[floats_list((1, x)		)[0] for x in (800, 800, 800)]
         __snake_case:						Union[str, Any]			  =						np.asarray(A		)
         __snake_case:						List[Any]			  =						feature_extractor(A							,    return_tensors="""np"""							,    sampling_rate=44_100		).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 							UpperCAmelCase__			(  self			:   Union[str, Any]							,    A			:   List[str]		):
         __snake_case:						Tuple			  =						load_dataset("""hf-internal-testing/librispeech_asr_dummy"""							,    """clean"""							,    split="""validation"""		)
         # automatic decoding with librispeech
         __snake_case:						List[Any]			  =						ds.sort("""id"""		).select(range(A		)		)[:num_samples]["""audio"""]

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






       def 							UpperCAmelCase__			(  self			:   Optional[int]		):
         __snake_case:						Dict			  =						self._load_datasamples(1		)
         __snake_case:						Optional[int]			  =						TvltFeatureExtractor()
         __snake_case:						Optional[Any]			  =						feature_extractor(A							,    return_tensors="""pt"""		).audio_values

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

         __snake_case:						str			  =						torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]]		)
         self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]							,    A							,    atol=1E-4		)		)






