



"""simple docstring"""







import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
							from transformers import SpeechaTextFeatureExtractor

lowerCAmelCase_   						=   random.Random()



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase=1.0						,     __lowerCamelCase=None						,     __lowerCamelCase=None	)							->      int:
		if rng is None:
				lowercase__   :    List[Any]				=			global_rng

		lowercase__   :    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
@require_torch
@require_torchaudio
class        __A       (	unittest.TestCase    ):







		'''simple docstring'''





		def __init__(	self						:  Tuple			,_snake_case						:  Dict			,_snake_case						:  Optional[Any]=7			,_snake_case						:  int=400			,_snake_case						:  Dict=2_000			,_snake_case						:  Dict=24			,_snake_case						:  Any=24			,_snake_case						:  str=0.0			,_snake_case						:  Optional[Any]=16_000			,_snake_case						:  List[str]=True			,_snake_case						:  Dict=True			,)					->		Dict:






				"""simple docstring"""



				lowercase__   :    str				=			parent
				lowercase__   :    Union[str, Any]				=			batch_size
				lowercase__   :    Optional[Any]				=			min_seq_length
				lowercase__   :    int				=			max_seq_length
				lowercase__   :    Union[str, Any]				=			(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
				lowercase__   :    Tuple				=			feature_size
				lowercase__   :    str				=			num_mel_bins
				lowercase__   :    List[Any]				=			padding_value
				lowercase__   :    Tuple				=			sampling_rate
				lowercase__   :    List[Any]				=			return_attention_mask
				lowercase__   :    Dict				=			do_normalize







		def 					UpperCAmelCase    (	self						:  Optional[Any]						)					->		str:






				"""simple docstring"""



				return {
				    "feature_size": self.feature_size,
				    "num_mel_bins": self.num_mel_bins,
				    "padding_value": self.padding_value,
				    "sampling_rate": self.sampling_rate,
				    "return_attention_mask": self.return_attention_mask,
				    "do_normalize": self.do_normalize,
				}







		def 					UpperCAmelCase    (	self						:  Any			,_snake_case						:  Any=False			,_snake_case						:  int=False						)					->		Tuple:






				"""simple docstring"""



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

				if equal_length:
						lowercase__   :    Union[str, Any]				=			[floats_list((self.max_seq_length, self.feature_size)						) for _ in range(self.batch_size						)]
				else:
						# make sure that inputs increase in size
						lowercase__   :    Dict				=			[
						    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__   :    Optional[Any]				=			[np.asarray(_snake_case						) for x in speech_inputs]
				return speech_inputs





@require_torch
@require_torchaudio
class        __A       (	A_     ,unittest.TestCase    ):







		'''simple docstring'''





		lowerCAmelCase			:					Tuple   	=				SpeechaTextFeatureExtractor if is_speech_available() else None







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






				"""simple docstring"""



				lowercase__   :    Any				=			SpeechaTextFeatureExtractionTester(self						)







		def 					UpperCAmelCase    (	self						:  Union[str, Any]			,_snake_case						:  List[str]						)					->		List[str]:






				"""simple docstring"""



				self.assertTrue(np.all(np.mean(_snake_case			,axis=0						) < 1e-3						)						)
				self.assertTrue(np.all(np.abs(np.var(_snake_case			,axis=0						) - 1						) < 1e-3						)						)







		def 					UpperCAmelCase    (	self						:  Tuple						)					->		Any:






				"""simple docstring"""



				lowercase__   :    List[str]				=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
				# create three inputs of length 800, 1000, and 1200
				lowercase__   :    Union[str, Any]				=			[floats_list((1, x)						)[0] for x in range(800			,1_400			,200						)]
				lowercase__   :    Any				=			[np.asarray(_snake_case						) for speech_input in speech_inputs]

				# Test feature size
				lowercase__   :    List[str]				=			feature_extractor(_snake_case			,padding=_snake_case			,return_tensors='''np'''						).input_features
				self.assertTrue(input_features.ndim == 3						)
				self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size						)

				# Test not batched input
				lowercase__   :    Dict				=			feature_extractor(speech_inputs[0]			,return_tensors='''np'''						).input_features
				lowercase__   :    str				=			feature_extractor(np_speech_inputs[0]			,return_tensors='''np'''						).input_features
				self.assertTrue(np.allclose(_snake_case			,_snake_case			,atol=1e-3						)						)

				# Test batched
				lowercase__   :    Optional[int]				=			feature_extractor(_snake_case			,return_tensors='''np'''						).input_features
				lowercase__   :    Union[str, Any]				=			feature_extractor(_snake_case			,return_tensors='''np'''						).input_features
				for enc_seq_a, enc_seq_a in zip(_snake_case			,_snake_case						):
						self.assertTrue(np.allclose(_snake_case			,_snake_case			,atol=1e-3						)						)

				# Test 2-D numpy arrays are batched.
				lowercase__   :    Any				=			[floats_list((1, x)						)[0] for x in (800, 800, 800)]
				lowercase__   :    Dict				=			np.asarray(_snake_case						)
				lowercase__   :    str				=			feature_extractor(_snake_case			,return_tensors='''np'''						).input_features
				lowercase__   :    Tuple				=			feature_extractor(_snake_case			,return_tensors='''np'''						).input_features
				for enc_seq_a, enc_seq_a in zip(_snake_case			,_snake_case						):
						self.assertTrue(np.allclose(_snake_case			,_snake_case			,atol=1e-3						)						)







		def 					UpperCAmelCase    (	self						:  Tuple						)					->		str:






				"""simple docstring"""



				lowercase__   :    Optional[Any]				=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
				lowercase__   :    str				=			[floats_list((1, x)						)[0] for x in range(800			,1_400			,200						)]

				lowercase__   :    str				=			['''longest''', '''max_length''', '''do_not_pad''']
				lowercase__   :    Union[str, Any]				=			[None, 16, None]
				for max_length, padding in zip(_snake_case			,_snake_case						):
						lowercase__   :    Union[str, Any]				=			feature_extractor(
						    _snake_case			,padding=_snake_case			,max_length=_snake_case			,return_attention_mask=_snake_case						)
						lowercase__   :    Any				=			inputs.input_features
						lowercase__   :    Any				=			inputs.attention_mask
						lowercase__   :    Optional[Any]				=			[np.sum(_snake_case						) for x in attention_mask]

						self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]						)
						self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]						)
						self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]						)







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






				"""simple docstring"""



				lowercase__   :    str				=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
				lowercase__   :    List[Any]				=			[floats_list((1, x)						)[0] for x in range(800			,1_400			,200						)]

				lowercase__   :    List[Any]				=			['''longest''', '''max_length''', '''do_not_pad''']
				lowercase__   :    Optional[int]				=			[None, 16, None]
				for max_length, padding in zip(_snake_case			,_snake_case						):
						lowercase__   :    Optional[int]				=			feature_extractor(
						    _snake_case			,max_length=_snake_case			,padding=_snake_case			,return_tensors='''np'''			,return_attention_mask=_snake_case						)
						lowercase__   :    Any				=			inputs.input_features
						lowercase__   :    Any				=			inputs.attention_mask
						lowercase__   :    Tuple				=			[np.sum(_snake_case						) for x in attention_mask]

						self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]						)
						self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1e-6						)
						self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]						)
						self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1e-6						)
						self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]						)







		def 					UpperCAmelCase    (	self						:  Any						)					->		str:






				"""simple docstring"""



				lowercase__   :    Dict				=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
				lowercase__   :    Any				=			[floats_list((1, x)						)[0] for x in range(800			,1_400			,200						)]
				lowercase__   :    List[Any]				=			feature_extractor(
				    _snake_case			,padding='''max_length'''			,max_length=4			,truncation=_snake_case			,return_tensors='''np'''			,return_attention_mask=_snake_case			,)
				lowercase__   :    Optional[int]				=			inputs.input_features
				lowercase__   :    Optional[Any]				=			inputs.attention_mask
				lowercase__   :    str				=			np.sum(attention_mask == 1			,axis=1						)

				self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]						)
				self._check_zero_mean_unit_variance(input_features[1]						)
				self._check_zero_mean_unit_variance(input_features[2]						)







		def 					UpperCAmelCase    (	self						:  Tuple						)					->		Any:






				"""simple docstring"""



				lowercase__   :    Optional[int]				=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
				lowercase__   :    int				=			[floats_list((1, x)						)[0] for x in range(800			,1_400			,200						)]
				lowercase__   :    str				=			feature_extractor(
				    _snake_case			,padding='''longest'''			,max_length=4			,truncation=_snake_case			,return_tensors='''np'''			,return_attention_mask=_snake_case			,)
				lowercase__   :    Optional[Any]				=			inputs.input_features
				lowercase__   :    List[str]				=			inputs.attention_mask
				lowercase__   :    int				=			np.sum(attention_mask == 1			,axis=1						)

				self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]						)
				self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]						)
				self._check_zero_mean_unit_variance(input_features[2]						)

				# make sure that if max_length < longest -> then pad to max_length
				self.assertEqual(input_features.shape			,(3, 4, 24)						)

				lowercase__   :    Dict				=			[floats_list((1, x)						)[0] for x in range(800			,1_400			,200						)]
				lowercase__   :    List[Any]				=			feature_extractor(
				    _snake_case			,padding='''longest'''			,max_length=16			,truncation=_snake_case			,return_tensors='''np'''			,return_attention_mask=_snake_case			,)
				lowercase__   :    List[str]				=			inputs.input_features
				lowercase__   :    List[Any]				=			inputs.attention_mask
				lowercase__   :    Dict				=			np.sum(attention_mask == 1			,axis=1						)

				self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]						)
				self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]						)
				self._check_zero_mean_unit_variance(input_features[2]						)

				# make sure that if max_length < longest -> then pad to max_length
				self.assertEqual(input_features.shape			,(3, 6, 24)						)







		def 					UpperCAmelCase    (	self						:  str						)					->		List[str]:






				"""simple docstring"""



				import torch

				lowercase__   :    List[Any]				=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
				lowercase__   :    int				=			np.random.rand(100			,32						).astype(np.floataa						)
				lowercase__   :    Tuple				=			np_speech_inputs.tolist()

				for inputs in [py_speech_inputs, np_speech_inputs]:
						lowercase__   :    Tuple				=			feature_extractor.pad([{'''input_features''': inputs}]			,return_tensors='''np'''						)
						self.assertTrue(np_processed.input_features.dtype == np.floataa						)
						lowercase__   :    Union[str, Any]				=			feature_extractor.pad([{'''input_features''': inputs}]			,return_tensors='''pt'''						)
						self.assertTrue(pt_processed.input_features.dtype == torch.floataa						)







		def 					UpperCAmelCase    (	self						:  Any			,_snake_case						:  str						)					->		Optional[Any]:






				"""simple docstring"""



				from datasets import load_dataset

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

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







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






				"""simple docstring"""



				lowercase__   :    str				=			np.array([
				    -1.5745, -1.7713, -1.7020, -1.6069, -1.2250, -1.1105, -0.9072, -0.8241,
				    -1.2310, -0.8098, -0.3320, -0.4101, -0.7985, -0.4996, -0.8213, -0.9128,
				    -1.0420, -1.1286, -1.0440, -0.7999, -0.8405, -1.2275, -1.5443, -1.4625,
				]						)
				# fmt: on

				lowercase__   :    List[Any]				=			self._load_datasamples(1						)
				lowercase__   :    Tuple				=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
				lowercase__   :    Optional[Any]				=			feature_extractor(_snake_case			,return_tensors='''pt'''						).input_features
				self.assertEquals(input_features.shape			,(1, 584, 24)						)
				self.assertTrue(np.allclose(input_features[0, 0, :30]			,_snake_case			,atol=1e-4						)						)





"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase_   						=   {'configuration_xglm': ['XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XGLMConfig']}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_   						=   ['XGLMTokenizer']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_   						=   ['XGLMTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_   						=   [
							    'XGLM_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'XGLMForCausalLM',
							    'XGLMModel',
							    'XGLMPreTrainedModel',
							]


try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_   						=   [
							    'FlaxXGLMForCausalLM',
							    'FlaxXGLMModel',
							    'FlaxXGLMPreTrainedModel',
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_   						=   [
							    'TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TFXGLMForCausalLM',
							    'TFXGLMModel',
							    'TFXGLMPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm import XGLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm_fast import XGLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_xglm import (
														    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFXGLMForCausalLM,
														    TFXGLMModel,
														    TFXGLMPreTrainedModel,
														)


else:
							import sys

							lowerCAmelCase_   						=   _LazyModule(__name__, globals()['__file__'], _import_structure)
