



"""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

__snake_case				:  Any =       random.Random()


def      _lowercase							(    __snake_case   ,__snake_case=1.0   ,__snake_case=None   ,__snake_case=None      )		->  List[Any]:
			if rng is None:
						__lowerCAmelCase :    List[Any]				=			global_rng

			__lowerCAmelCase :    Union[str, Any]				=			[]
			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				):




	'''simple docstring'''





	def __init__(	self:						Tuple			,     _SCREAMING_SNAKE_CASE:						Optional[int]			,     _SCREAMING_SNAKE_CASE:						Any=7			,     _SCREAMING_SNAKE_CASE:						Optional[int]=400			,     _SCREAMING_SNAKE_CASE:						List[str]=2000			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=2048			,     _SCREAMING_SNAKE_CASE:						str=128			,     _SCREAMING_SNAKE_CASE:						str=1			,     _SCREAMING_SNAKE_CASE:						int=512			,     _SCREAMING_SNAKE_CASE:						Any=30			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=4_4100			,     )							->			Any:

				"""simple docstring"""

				__lowerCAmelCase :    Any				=			parent
				__lowerCAmelCase :    Dict				=			batch_size
				__lowerCAmelCase :    List[str]				=			min_seq_length
				__lowerCAmelCase :    Optional[Any]				=			max_seq_length
				__lowerCAmelCase :    Optional[int]				=			(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
				__lowerCAmelCase :    List[Any]				=			spectrogram_length
				__lowerCAmelCase :    int				=			feature_size
				__lowerCAmelCase :    Dict				=			num_audio_channels
				__lowerCAmelCase :    str				=			hop_length
				__lowerCAmelCase :    Optional[Any]				=			chunk_length
				__lowerCAmelCase :    Optional[Any]				=			sampling_rate

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[Any])							->			int:

				"""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 				_SCREAMING_SNAKE_CASE  (	self:						List[str]			,     _SCREAMING_SNAKE_CASE:						Dict=False			,     _SCREAMING_SNAKE_CASE:						Dict=False)							->			int:

				"""simple docstring"""

				def _flatten(_SCREAMING_SNAKE_CASE:						Optional[Any]):
							return list(itertools.chain(*_SCREAMING_SNAKE_CASE))

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






@require_torch
@require_torchaudio
class      A__  (    __SCREAMING_SNAKE_CASE    ,						unittest.TestCase				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      TvltFeatureExtractor

	def 				_SCREAMING_SNAKE_CASE  (	self:						str)							->			Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase :    str				=			TvltFeatureExtractionTester(self)

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[Any])							->			Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    int				=			self.feature_extraction_class(**self.feat_extract_dict)
				self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,     "spectrogram_length"))
				self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,     "feature_size"))
				self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,     "num_audio_channels"))
				self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,     "hop_length"))
				self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,     "chunk_length"))
				self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,     "sampling_rate"))

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

				"""simple docstring"""

				__lowerCAmelCase :    str				=			self.feature_extraction_class(**self.feat_extract_dict)

				with tempfile.TemporaryDirectory() as tmpdirname:
							__lowerCAmelCase :    Any				=			feat_extract_first.save_pretrained(_SCREAMING_SNAKE_CASE)[0]
							check_json_file_has_correct_format(_SCREAMING_SNAKE_CASE)
							__lowerCAmelCase :    Union[str, Any]				=			self.feature_extraction_class.from_pretrained(_SCREAMING_SNAKE_CASE)

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

	def 				_SCREAMING_SNAKE_CASE  (	self:						int)							->			Dict:

				"""simple docstring"""

				__lowerCAmelCase :    str				=			self.feature_extraction_class(**self.feat_extract_dict)

				with tempfile.TemporaryDirectory() as tmpdirname:
							__lowerCAmelCase :    Union[str, Any]				=			os.path.join(_SCREAMING_SNAKE_CASE			,     "feat_extract.json")
							feat_extract_first.to_json_file(_SCREAMING_SNAKE_CASE)
							__lowerCAmelCase :    Tuple				=			self.feature_extraction_class.from_json_file(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Optional[int]				=			feat_extract_first.to_dict()
				__lowerCAmelCase :    Dict				=			feat_extract_second.to_dict()
				__lowerCAmelCase :    List[str]				=			dict_first.pop("mel_filters")
				__lowerCAmelCase :    Optional[int]				=			dict_second.pop("mel_filters")
				self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE))
				self.assertEqual(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)

	def 				_SCREAMING_SNAKE_CASE  (	self:						Any)							->			List[str]:

				"""simple docstring"""

				__lowerCAmelCase :    int				=			self.feature_extraction_class(**self.feat_extract_dict)

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

				# Test not batched input
				__lowerCAmelCase :    List[Any]				=			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
				__lowerCAmelCase :    str				=			feature_extractor(_SCREAMING_SNAKE_CASE			,     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
				__lowerCAmelCase :    List[Any]				=			feature_extractor(
				    _SCREAMING_SNAKE_CASE			,     return_tensors="np"			,     sampling_rate=4_4100			,     mask_audio=_SCREAMING_SNAKE_CASE).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.
				__lowerCAmelCase :    List[Any]				=			[floats_list((1, x))[0] for x in (800, 800, 800)]
				__lowerCAmelCase :    List[Any]				=			np.asarray(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Optional[Any]				=			feature_extractor(_SCREAMING_SNAKE_CASE			,     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 				_SCREAMING_SNAKE_CASE  (	self:						List[Any]			,     _SCREAMING_SNAKE_CASE:						Optional[int])							->			Dict:

				"""simple docstring"""

				__lowerCAmelCase :    List[str]				=			load_dataset("hf-internal-testing/librispeech_asr_dummy"			,     "clean"			,     split="validation")
				# automatic decoding with librispeech
				__lowerCAmelCase :    Optional[int]				=			ds.sort("id").select(range(_SCREAMING_SNAKE_CASE))[:num_samples]["audio"]

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







	def 				_SCREAMING_SNAKE_CASE  (	self:						int)							->			List[str]:

				"""simple docstring"""

				__lowerCAmelCase :    int				=			self._load_datasamples(1)
				__lowerCAmelCase :    Any				=			TvltFeatureExtractor()
				__lowerCAmelCase :    Optional[Any]				=			feature_extractor(_SCREAMING_SNAKE_CASE			,     return_tensors="pt").audio_values

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

				__lowerCAmelCase :    Union[str, Any]				=			torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]])
				self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]			,     _SCREAMING_SNAKE_CASE			,     atol=1e-4))




"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case				:  Tuple =       logging.get_logger(__name__)

__snake_case				:  Any =       {
    'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/config.json',
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}



class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      'xglm'
	SCREAMING_SNAKE_CASE        =      ['past_key_values']

	SCREAMING_SNAKE_CASE        =      {
	    'num_attention_heads': 'attention_heads',
	    'hidden_size': 'd_model',
	    'num_hidden_layers': 'num_layers',
	}

	def __init__(	self:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						List[Any]=25_6008			,     _SCREAMING_SNAKE_CASE:						Dict=2048			,     _SCREAMING_SNAKE_CASE:						int=1024			,     _SCREAMING_SNAKE_CASE:						Dict=4096			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=24			,     _SCREAMING_SNAKE_CASE:						int=16			,     _SCREAMING_SNAKE_CASE:						List[str]="gelu"			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=0.1			,     _SCREAMING_SNAKE_CASE:						int=0.1			,     _SCREAMING_SNAKE_CASE:						List[Any]=0.0			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=0.0			,     _SCREAMING_SNAKE_CASE:						Any=0.02			,     _SCREAMING_SNAKE_CASE:						Any=True			,     _SCREAMING_SNAKE_CASE:						Tuple=True			,     _SCREAMING_SNAKE_CASE:						Any=2			,     _SCREAMING_SNAKE_CASE:						str=1			,     _SCREAMING_SNAKE_CASE:						Dict=0			,     _SCREAMING_SNAKE_CASE:						Dict=2			,     **_SCREAMING_SNAKE_CASE:						List[Any]			,     )							->			List[str]:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[int]				=			vocab_size
				__lowerCAmelCase :    int				=			max_position_embeddings
				__lowerCAmelCase :    Optional[Any]				=			d_model
				__lowerCAmelCase :    List[Any]				=			ffn_dim
				__lowerCAmelCase :    int				=			num_layers
				__lowerCAmelCase :    Any				=			attention_heads
				__lowerCAmelCase :    int				=			activation_function
				__lowerCAmelCase :    List[Any]				=			dropout
				__lowerCAmelCase :    Optional[int]				=			attention_dropout
				__lowerCAmelCase :    Optional[int]				=			activation_dropout
				__lowerCAmelCase :    Optional[int]				=			layerdrop
				__lowerCAmelCase :    Optional[int]				=			init_std
				__lowerCAmelCase :    Tuple				=			scale_embedding  # scale factor will be sqrt(d_model) if True
				__lowerCAmelCase :    Dict				=			use_cache

				super().__init__(
				    pad_token_id=_SCREAMING_SNAKE_CASE			,     bos_token_id=_SCREAMING_SNAKE_CASE			,     eos_token_id=_SCREAMING_SNAKE_CASE			,     decoder_start_token_id=_SCREAMING_SNAKE_CASE			,     **_SCREAMING_SNAKE_CASE			,     )